Publisher(s):China Academic Journals (CD Edition) Electronic Publishing House Co., Ltd.
ISBN:ISBN 978-7-499-00987-5 pdf
First Published: 2020.11.23
Discipline(s): Chemistry/ Metallurgy/ Environment/ Mine Industry
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Air Pollution and Control, as part of China’s S&T Progress Series, has 137 excellent articles concerning China’s air pollution research. It includes two chapters. Chapter 1 systematically analyzes the spatio-temporal changes, pollution characteristics, and sources of air pollutants including PM2.5, volatile organic compounds (VOCs), ozone, sulfur dioxide, and nitrogen oxide in the key areas of China like the Beijing-Tianjin-Hebei and the surrounding areas, the Yangtze River Delta, and the Pearl River Delta, and summarizes the general evolution and scientific law of air pollution in various cities of China. Chapter 2 highlights the effective measures adopted to deal with air pollution in China. These articles fully demonstrate China’s achievements in air pollution prevention and control, which will provide a reference for air pollution control in other countries and regions. The original articles were published in Chinese, and this book is a compilation of English version of selected articles.
HAO Jiming is mainly engaged in the research on energy and environment as well as air pollution control engineering.
1. Spatiotemporal pattern of ground-level fine particulate matter (PM 2.5) pollution in China’s mainland
China Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 01
To investigate the spatiotemporal patterns law of PM 2.5 pollution in China, statistical methods and GIS technology were used to analyze the ground-level PM 2.5 monitoring results in 2014 from 161 cities at or above the prefectural level in national air quality monitoring network. The results showed that only 8.1% of cities met Grade II standard of ambient air quality standards (GB 3095–2012), and about 26.6% of days failed to meet Grade II air quality standards. Diurnal PM 2.5 pollution was the least in summer, late spring and early autumn, and was heavy in winter. Daily PM 2.5 concentration followed an indistinctive bimodal curve with the minimum value around 16:00 and the maximum value around 10:00. The pollution levels were relatively high from midnight to dawn. PM 2.5 pollution was serious in Beijing-Tianjin-Hebei region and its surrounding area, as well as Hubei, Hunan and Anhui. PM 2.5 pollution was light in the southeastern coastal areas, Yunnan, and Tibet. The spatial distribution of PM 2.5 was significantly correlated with wind speed, relative humidity, and land use. The average ratio of PM 2.5 to PM 10 (PM 2.5/PM 10 ratio) was 0.591, which has a spatial pattern of gradually increasing from northwest to southeast, and was higher in the southern region than in the northern region. The monthly average PM 2.5/PM 10 ratio was basically stable (ranged in 0.55–0.6), excluding the higher values in January, February, and lower values in May. The results could benefit a further understanding of the spatiotemporal patterns of PM 2.5 pollution in China macroscopically, so as to promote environmental pollution prevention and control accordingly.
Environmental Science,Part 1: Types of Air Pollution,Vol 39,No. 02
Two decades of PM 2.5 pollution has seriously hindered China’s sustainable development. However, relevant research of PM 2.5 has been hindered because of the lack of long-term historical monitoring data. Therefore, ground observations of PM 2.5 concentration from 2013 to 2016 in four typical regions of China and the MODIS aerosol optical thickness data, boundary layer height, temperature, and other meteorological data from 2000 to 2016 were used as the basic data. A combinatorial simulation model was constructed by combining the two algorithms of backward artificial neural network and support vector regression so as to obtain the PM 2.5 concentration history for the past 20 years using geospatial analysis technology. Results demonstrate that the combination model is better than the single model, with lower error and higher generalization ability. The spatial-temporal analysis results show that the concentration of PM 2.5 continued to increase in the Beijing-Tianjin-Hebei (BTH) region and in three northeastern provinces of China (TNPC); the PM 2.5 concentration decreased slowly in the Pearl River Delta (PRD), the pollution range of PM 2.5 in three of the research areas showed an expanding trend; the PM 2.5 concentration and pollution range remained stable in the Yangtze River Delta (YRD). In 2012, the concentration of PM 2.5 in the four study areas decreased and the pollution range narrowed, but the PM 2.5 concentration rose slightly after that decline and the high pollution range narrowed during 2013–2016, which was related to the country’s governance measures such as PM 2.5 regional defense.
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 03
By simulating vertical stratification data of PM 2.5 with the third-generation air quality model CMAQ and high resolution relative humidity data with mesoscale meteorological model WRF, MODIS AOD data were revised by vertical and humidity correcting method, respectively. A linear regression model between revised AOD and PM 2.5 was established, and the linear correlation coefficient was r = 0.77 ( n = 57, P < 0.01). Based on this model, the average monthly concentrations of PM 2.5 in 10 km resolution in January 2013 were firstly retrieved in the country, and the population exposure level was analyzed. The results showed that the areas where monthly average concentration of PM 2.5 was greater than 100 μg·m ?3 and 200 μg·m ?3 in January 2013 was 10.99% and 1.34% of the national terrestrial area, respectively, and the ratio of exposed population was as high as 45.01% and 6.31%, respectively.
4. Spatial and temporal distrubions and source simulation of PM 2.5 in Beijing-Tianjin-Hebei region in 2014
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 04
The spatial and temporal distributions of PM 2.5, as well as the source characteristic were analyzed in ?Beijing–Tianjin–Hebei region by applying the combination of numerical model CAM x and the direct measurements. The results showed that PM 2.5, which had bimodal daily distributions, was characterized with obvious seasonal pattern with higher concentrations in winter and autumn and lower concentrations in spring and summer. During the heavy polluted days, high concentrations of PM 2.5 (> 150 μg/m 3) normally occurred in the North China Plain (NCP) locating beside the Taihang Mountain, especially to the internal cities of Beijing, Baoding, Shijiazhuang, etc. However, the mountain areas surrounded had lower PM 2.5 concentrations in comparison with the former plain. Totally, 73% of the BTH region had PM 2.5 concentration averaged above 150 μg/m 3, and the contributions from external transport to the particulate mass burden of Beijing, Tianjin and Shijiazhuang were 58%, 54% and 39%, respectively. It was found that regional transportation exerted a significant impact on the local PM 2.5 loadings during a serious pollution weather induced by the southerly incoming pollutants transported via long distance on 6 th–12 th October 2014.
China Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 05
WRF-CAMx modeling system was adopted to examine the regional transmission of PM 2.5 and secondary inorganic aerosol (SIA) in Beijing-Tianjin-Hebei (BTH) and 7 surrounding provinces and cities. Regional emission contribution to PM 2.5 and SIA of BTH was estimated, and the cross-regional transmission matrix among different provinces and cities was also obtained. Results showed that the cross-regional transmission effects of PM 2.5 and SIA components were comparatively significant. PM 2.5 and SIA of BTH contributed by emissions from the other regions were 23.4% and 45.5%, respectively. Local emissions contributed 51.2%–68.8% of the annual average PM 2.5 and 36.7%–56.4% of the SIA in BTH and surrounding provinces and cities. Spatial sources of the four heavy pollution processes in January 2013 of Beijing were analyzed combining backward trajectory model, and it was found that the sources were quite different from each other, including long-distance transmission from northwest direction, short distance transmission from the south and local air mass transmission from southwest and southeast. Regional transmission was obvious during the heavy pollution processes, PM 2.5 and SIA contributed by local emissions were only 35.1%–37.3% and 17.1%–28.4%, respectively. Among that, air mass transmitted from southern part could lead to high pollution level, and the contributions from emissions of BTH were remarkable that reached 83.2% and 76.4% for PM 2.5 and SIA, respectively.
Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 06
By coupling particle source apportionment technology (PSAT) in comprehensive air quality model with extensions (CAMx), the regional transport matrix of PM 2.5 was built in 13 cities of Beijing-Tianjin-Hebei region, 2015. Results showed that the major contributor to PM 2.5 was local source emission (contributed 21.49%–68.74%), while the internal transport from inner-region sources (contributed 13.31%–54.62%) and the external transport from outer-region sources (contributed 13.32%–45.02%) also mattered. The spatio-temporal distribution of PM 2.5 transport matrix was characterized by geographical, meteorological and source patterns, turned out that the local emission exerted the most significant impact to the central part of Beijing-Tianjin-Hebei region in winter, while the regional transport had great contributions to the southern part in other seasons. By assessing the input/output and activity of PM 2.5 transport, Langfang, Hengshui, Chengde, Qinhuangdao and Xingtai played the role of receptor, Tianjin, Cangzhou, Tangshan, Beijing, Shijiazhuang and Handan played the role of source, while Zhangjiakou and Baoding had the similar level of input and output, with balanced transportation. Seasonal matrix of PM 2.5 showed significant transport between Beijing and Langfang, Baoding, Chengde, Tianjin, Cangzhou, while, the city list for Tianjin and Shijiazhuang differed slightly.
7. Air Pollutant Emission Inventory from Iron and Steel Industry in the Beijing-Tianjin-Hebei Region and Its Impact on PM 2.5
Environmental Science,Part 1: Types of Air Pollution,Vol 39,No. 07
The iron and steel industry, which discharges a large amount of pollutants including SO 2, NO x , and PM 2.5, is the main source of atmospheric pollution in the Beijing-Tianjin-Hebei region. Based on the bottom-up method, a high temporal and spatial resolution emission inventory of the iron and steel industry in the Beijing-Tianjin-Hebei region was developed, which took into account the multiple air pollutants released during coking, sintering, pelletizing, ironmaking, steelmaking, and the steel rolling process. As the emission inventory showed, the total emissions of SO 2, NO x , TSP, PM 10, PM 2. 5, CO, and VOC from the iron and steel industry in the Beijing-Tianjin-Hebei region in 2015 were 388.2, 272.3, 791.9, 531.5, 386.8, 8 233.8, and 265.3 kilotons, respectively, among which, sintering and pelletizing were the two processes discharging the most pollutants (17.0%–72.0%), followed by the ironmaking process (4.6%–42.4%) and the steel rolling process (3.5%–35. 7%); the iron and steel industry in Tangshan discharged the most pollutants (39.1%–63.5%) among those in all the 13 cities. The impact of the iron and steel industry on the regional PM 2.5 concentration was simulated by a two-layer nested meteorology-air quality coupling model system (WRF-CMAx) with Particulate Source Apportionment Technology (PSAT). The simulation results showed that the iron and steel industry contributed 14.0%, 15.9%, 12.3%, and 8.7% of the PM 2.5 concentrations of the Beijing-Tianjin-Hebei region in spring, summer, autumn, and winter, respectively, and that the iron and steel industry had the most significant impact on the PM 2.5 concentrations in Tangshan among all the 13 cities, with a contribution rate up to 41.2%, followed by those in Qinhuangdao, Shijiazhuang, and Handan, with contributions of 19.3%, 15.3%, and 15.1%, respectively. The iron and steel industry has an important impact on the PM 2.5 concentration of the Beijing-Tianjin-Hebei region to which the government should pay more attention, and take more effective control measures to address this problem.
China Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 08
Beijing, Shijiazhuang and Tangshan were selected as the typical cities in the Beijing-Tianjin-Hebei region to investigate the seasonal variation characteristics of secondary water-soluble inorganic ions (SNA) and compare the pollution characteristics and physicochemical property of the secondary water-soluble ions between heavy pollution period and other periods. Then CAMx-PSAT model was applied to quantitatively analyze the contribution on PM 2.5 and SNA concentration from pollution sources in BTH region during different seasons. Results showed that PM 2.5 concentration in these cities decreased year by year, and the maximum of SO 4 2?, NO 3 ? and NH 4 + concentration mostly appeared in winter at the same time, illustrating the related correlation of their concentrations. The mass concentrations of SO 4 2?, NO 3 ? and NH 4 + increased significantly during heavy pollution period compared with other periods. The largest concentration ratio of SNA appeared in one to two days before heavy pollution days. The formation of heavy pollution was the combined effects of local pollutant emission and external source region transport. The contribution of external sources to NO 3 ? was higher than that of SO 4 2? and NH 4 +. In addition, the concentrations of PM 2.5, SO 4 2? and NO 3 ? were mostly contributed from traffic sources, resident sources and industrial sources, and the resident sources were the most important contributor for NH 4 +concentration.
Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 08
In order to study the scavenging effect of rime and east wind on the concentrations of PM 2.5 in Beijing during the air heavy pollution, we used the PM 2.5 concentration data of some mountains and plains stations, together with meteorological data, wind profile data, aerosol extinction coefficient radar data collected during December 19–27, 2015, to analyze the clearance mechanisms of rime and east wind on PM 2.5. The results showed that (1) the removal of PM 2.5 by rime was a different mechanism from that by the north wind and rainy weather, in the supercooled conditions, the droplets touched the branches, wires for solid condensation, leading to formation of rime and decreased PM 2.5 concentration. (2) The east wind is a special kind of wind in Beijing area. When easterly wind decreased with height, a strong upward motion could be formed, and the PM 2.5 was uplifted from the surface layer to the top; while with larger westerly wind in top, it was removed into downstream and cleaned. When east wind increased with height, a weak downward movement was easy to form; when this sinking motion could not reach the ground, the surface layer of PM 2.5 capacity became small, which was beneficial to increase the concentration of PM 2.5. (3) The PM 2.5 scavenging ability of east wind depended on two points: one was the strength and development of upward movement of the height formed by the east wind, and the second was the initial height of uplifted motion formed by east wind. The lower the origin of the ascending motion height, the more obvious the removal of PM 2.5. (4) After occurrence of east wind, along with the ascending motion to m·s ?1 magnitude, the mixed layer height increased to 1 200–1 800 meters, and PM 2.5 was uplifted to the top layer and cleaned.
10. Source Apportionment of PM 2.5 in Suburban Area of Beijing-Tianjin-Hebei Region in Autumn and Winter
Environmental Science,Part 1: Types of Air Pollution,Vol 40,No. 09
To identify the main sources of PM 2.5 in Beijing-Tianjin-Hebei (BTH) region, PM 2.5 samples were collected at four suburban sites in BTH region during autumn and winter in 2014–2015. Source apportionment of PM 2.5 was conducted using the chemical mass balance model (CMB). It shows that the main sources of PM 2.5 in autumn and winter were secondary aerosols (36%–58%), traffic (8%–26%), residential coal combustion (8%–16%), and biomass burning (5%–16%). Secondary nitrate was the most important source of PM 2.5 at most sites during autumn and winter (11%–27%). The source apportionment at different pollution levels indicates that the coherence of the increasing trend of different sources among the four sites were much more obvious in autumn than in winter. Also, the increasing contribution of secondary sources (47.2–115.7 μg·m ?3) was much higher than that of primary sources (29.5–43.4 μg·m ?3) in autumn, but such trend was not significant in winter. The total contribution of coal combustion at suburban sites was quite similar to that in urban sites, but in suburban areas residential coal combustion dominates the contribution from coal combustion. Thus, it is very necessary for suburban areas of the BTH region to control emissions from residential coal combustion.
11. Vertical Distribution and Transport of PM 2.5 During Heavy Pollution Events in the Jing-Jin-Ji Region
Environmental Science,Part 1: Types of Air Pollution,Vol 40,No. 10
Based on vehicle-borne tethered balloon measurements, the vertical distribution of particulate matter (PM) concentrations were observed in Gaocun in the Wuqing District of Tianjin from December 17 to 19, 2016, during a period of heavy pollution. Using observational data, the transport flux of PM 2.5 in the Jing-Jin-Ji region was calculated. The results showed that the mixed layer was low at only 200 m during the heavy pollution period. The vertical distribution of PM 2.5 concentrations was closely associated with the heights of mixed layer whereby, below the mixed layer, PM 2.5 concentrations were higher. Vertical variation was insignificant, forming a district pollution layer. Above the mixed layer, PM 2.5 concentrations rapidly decreased and stabilized at low levels. During the observation period, higher concentrations of PM were found with particle sizes of smaller than 1.0 μm, and lower concentrations were observed for particle sizes larger than 2.2 μm. The size profiles of PM tallied with relative humidity and the height of the mixed layer. The size distribution was wider during periods of high humidity and with a lower mixed layer height. The greatest PM 2.5 transport flux was from the southwest, accounting for 63.3% of the total flux; the highest fluxes occurred at the heights of 46–156 m and 156–296 m. The dominant transport direction was southwest below 300 m, while the dominant transport direction was dispersed over 300 m.
12. Indoor Formaldehyde and Benzene Series in Shanghai Residences and Their Associations with Building Characteristics and Lifestyle Behaviors
Environmental Science,Part 1: Types of Air Pollution,Vol 39,No. 100
From March 2013 to December 2014, we on-site inspected indoor concentrations of formaldehyde and a benzene series in 454 children’s bedrooms that were decorated earlier than one year before our inspection. Large differences existed in the formaldehyde and benzene-series concentrations among individual bedrooms. Bedrooms that were inspected in winter had significantly higher concentration of formaldehyde than bedrooms that were inspected in other seasons ( P < 0.001), but the benzene-series concentration had no significant seasonal difference. Among bedrooms that were inspected in spring, those using different materials as wall coverings had significant differences in concentrations of the benzene series. Among bedrooms that were inspected in summer, those using different materials as floor coverings had significant differences in concentrations of the benzene series ( P < 0.01). Among bedrooms that were inspected in autumn, those with > 5 household bonsais had significantly higher concentrations of formaldehyde than other bedrooms. Among bedrooms that were inspected in winter, those with frequent use of air humidifiers and those in which pets were kept had significantly higher concentrations of the benzene series than other bedrooms ( P < 0.05). These results indicate that, after a long time since decoration, the types of household wall and floor covering materials still have certain relationships with indoor benzene-series levels, and compared to decoration materials, household ventilation perhaps has greater effect on indoor formaldehyde levels. The indoor benzene-series level perhaps has associations with indoor humidity level and the keeping of pets in households. Household bonsais may have limited effect on indoor formaldehyde and benzene-series levels in residences that were decorated a long time ago.
13. Spatial-temporal changes of tropospheric HCHO column density and its impact factors over Heilongjiang Province during 2005–2016
China Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 101
The remote sensor data derived from the Ozone Monitoring Instrument (OMI) were used to study the temporal and spatial distribution characteristics of urinary formaldehyde column concentrations in Heilongjiang Province from 2005 to 2016, and the main influencing factors of formaldehyde column concentration were explored. The results showed an upward trend of formaldehyde column concentration in the past 12 years as a whole, with an average growth rate of 0.43 × 10 15 (molec?a)/cm 2. The frequency of formaldehyde column concentration revealed a trend of fluctuations in different years. For instance, from 2005 to 2013, the concentration saw a trend of rapid increase across the board. However, the data from 2013 to 2014 tended to more downward. The concentration from 2014 to 2016 remained to be stable. The concentration of formaldehyde decreased through the seasons with the highest in summer and the lowest in spring (levels: summer > autumn > winter > spring). The average monthly changes of formaldehyde column concentration were in accordance with the distribution of sinusoidal curve. The lowest value of formaldehyde concentration appeared in February to March, and the highest value was in June to July in general. The spatial distribution showed a clear gradient with “high in the south and low in the north”. The high distribution districts were mainly gathered in the southern areas such as Harbin and Daqing, while the low-value areas were located in the Daxing’anling and Heihe. The spatial density of formaldehyde column concentration varied significantly. From 2005 to 2008, the pollution value was within the Grade 1–4 among Heilongjiang Province. However, the pollution value jumped into the Grade 6 for the first time in 2009. Not only the pollution level increased, but also the spatial distribution. From 2009 to 2013, the areas that were labeled Grade 6 pollution expanded. While these areas saw a significant decrease in 2014, the pollution level remained between level 4 to 6 and distributed evenly from 2014 to 2016. The concentration distribution of a formaldehyde column could respond to the change of topography, wind direction, temperature, and precipitation. Energy consumption, industrial production, car ownership, building decoration, and fertilizer application were the important influencing factors for the shift in formaldehyde column.
14. Spatial and temporal distribution and related factors of formaldehyde in China based on satellite remote sensing
China Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 102
The data of this research (national formaldehyde column concentration in 2016) were extracted from OMIHCHO (OMI/Aura Formaldehyde [HCHO] Total Column Daily L2Global Gridded 0.25 degree × 0.25 degree V3). The characteristics of the spatial and temporal distribution of formaldehyde column concentration were analyzed, and then several correlated factors such as temperature, rainfall, vegetation coverage and human activities in various provinces and cities in China were discussed. Results were listed as following. The spatial distribution of formaldehyde column concentration is very unbalance was China. The formaldehyde column concentration was high in the eastern and southeast areas, while the western and northwestern parts of China showed relatively low values. The lowest monthly average formaldehyde concentration was 8.31 × 10 15 molec/cm 2 in October and the highest was 11.87 × 10 15 molec/cm 2 in June. If the average of formaldehyde concentration was arranged by the seasons from high to low, it would be summer, spring, winter and autumn. Concerning correlation between meteorological factors (temperature, rainfall and vegetation) and formaldehyde column concentration, all results showed the spatial difference, but the formaldehyde column concentration was most influenced by the temperature; the rainfall caused a certain degree of formaldehyde elimination; and the vegetation significantly increased the concentration of formaldehyde column in the eastern and southeastern regions. There was also a significant correlation among the concentration of formaldehyde and the regional GDP, industrial value and the increase in motor vehicle ownership of various regions. The industrial added value had the highest correlation with formaldehyde, which confirms that industrial and automobile emissions were the main sources of formaldehyde.
15. The temporal, spatial variation and influencing factor of formaldehyde column concentration in the Yangtze River Delta in the past 10 years
China Environmental Science,Part 1: Types of Air Pollution,Vol 39,No. 103
The study of air pollution in areas with dense economically developed populations was of great significance for ensuring regional environmental security and human settlement health. This article was based on Aura-OMI HCHO data products. It interpreted and analyzed the quantitative distribution, dynamics and influencing factors of formaldehyde column concentration in the Yangtze River Delta region from 2008 to 2017. The results showed that in the past 10 years the average value of formaldehyde column was 14.16 × 10 15 molec/cm 2, the maximum value 15.41 × 10 15 molec/cm 2, the minimum value 12.27 × 10 15 molec/cm 2, the maximum growth rate 17.8%, and the average growth rate 0.17%, and the maximum rate of decline 15.95%. In time, the concentration of formaldehyde has shown a fluctuating upward trend over the past 10 years, mainly in the fourth, third and fifth grades, the highest in summer, followed by spring and autumn, and the smallest in winter. The share ratios in spring, summer, autumn and winter were 25.96%, 34.28%, 22.00%, and 17.76%, respectively. In space, the concentration decreased from the central to the both sides. The coastal area was the lowest, and the high value area gradually shifted from northwest to southeast. The main factors affecting the change of formaldehyde column concentration in the Yangtze River Delta were natural factors and human factors. The natural factors were mainly temperature and precipitation, while the human factors were mainly the total energy consumption, the secondary industry, the tertiary industry, the gross production value, the furniture and building decoration materials. There were similarities and differences in spatial-temporal evolutions and influencing factors of the Yangtze River Delta and Beijing-Tianjin-Hebei region.
Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 104
Pollution characteristic and variation trend of atmospheric carbonyls were investigated in November during the 2014 Beijing APEC. Formaldehyde, acetaldehyde and acetone were the dominant carbonyls, accounting for 82. 66% of total carbonyls, and especially, formaldehyde accounted for 40. 12% of total carbonyls. Atmospheric concentrations of total carbonyls decreased by around 64. 10% after the clean air policy was carried out during the Beijing APEC, and the variation trend of carbonyls showed a similar pattern to those of other pollutants like PM_( 2.5) during the APEC. Strong correlations( R 2 of 0. 67–0. 98) were observed among formaldehyde, acetaldehyde, acetone and total carbonyls during and after the APEC, indicating that they had similar sources; however, poor correlations (R 2 of ?0. 11–0. 42 and 0. 16–0. 94, respectively) were observed before the APEC, implying different emission sources for ambient carbonyls. The calculated ratios of C1/C2, C2/C3 and OC/EC indicated that both vehicles and coal emissions were responsible for atmospheric carbonyls before the APEC, and emissions from coal burning were the major contributor to atmospheric carbonyls during and after the APEC, especially after the APEC.
Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 105
This paper analyzed 17 polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in the flue gas of a certain municipal solid waste incinerator (MSWI) and its surrounding air and other possible sources in Guangdong by using HRGC/HRMS method. It discussed the feature of homologs and main toxic monomers in all samples. It also investigated the relationship among surrounding area, MSWI and possible sources using principle component analysis (PCA) and cluster analysis. The results showed that the concentration of PCDD/Fs was higher in the flue gas than the ambient air, moreover non-effect suffered by prevailing wind direction. The possible sources might be tire factory and open burning based on spot survey. The concentration of PCDD/Fs was lower in tire factory than upwind station, but higher at open burning spot than outer monitoring station. The analysis of homologs showed that OCDD, 1, 2, 3, 4, 6, 7, 8-Hp CDD and 1, 2, 3, 4, 6, 7, 8-Hp CDF were the main materials in the flue gas and air, meanwhile OCDF was also found in atmosphere. There was similar concentration of 17 PCDD / Fs between surrounding monitoring station and tire factory, and the same distribution between flue gas and open burning. The further analysis showed that the linearly dependent coefficients of 1, 2, 3, 7, 8-Pe CDD and 2, 3, 4, 6, 7, 8-Hx CDF were 0. 95 and 0. 75, respectively. It showed the strong correlation of two monomers in all ambient air samples. The PCA and cluster analysis showed that MSWI influenced the surrounding air, tire factory had an impact on upwind station, and open burning had a lower effect on outdrop monitoring station.
18. Variation characteristics and influencing factors of air pollution in Pearl River Delta area from 2006 to 2012
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 106
Observations of Pearl River Delta (PRD) regional air quality monitoring network during 2006–2012 were employed to capture the temporal and spatial variation characteristic of air pollution over PRD and then the causes of that were analyzed. Air quality had improved generally in PRD region as a result of the decrease in SO 2, NO 2 and PM 10 concentrations by 61.7%, 17.4% and 24.3% during 2006–2012, though 12.5% increase in O 3 concentration in the same period. Air quality was better in wet season (April to September) than dry season (October to March), and the monthly-averaged concentrations of various pollutants were all bimodal, with peaks in December and March for SO 2, NO 2 and PM 10 and October and May for O 3. The high concentrations for SO 2, NO 2 and PM 10 were mainly distributed in Foshan and Guangzhou but for O 3 mainly in outside suburbs of PRD. The trends of concentration for different pollutants were not consistent in different regions of PRD. The central region of PRD had more significant decrease in primary pollutants, and those decreases were induced by many factors, especially the economic downturn and environmental protection policies. However, the relatively weaker control over VOCs, as well as climatic change, could intensify the secondary pollution particular in O 3.
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 107
Since there haven't been any research on the explicit analysis of the air pollution of thermal power plants in Beijing-Tianjin-Hebei region, we conducted this research to complement the great need, which was based on the power plants' emission datum consisted of CEMS, EIA, as well as follow-up inspection. With these datum we built the bottom-up emission inventory of all thermal power plants in Beijing-Tianjin-Hebei region. Making use of the WRF output data, we simulated the meso-scale meteorological field by CALMET. Then we simulated the air pollution effect of SO 2, NO x, primary PM 10, sulfates, nitrates under different scenarios. The simulation result showed that: In 2011, the most affected subarea of the air pollution of thermal power plants was the Southwest part of the region, and the highest annual average pollutants emission records was held by Shijiazhuang. After the emission reduction action has been taken, the total amount of SO 2, NO x, PM 10 emitted from the thermal power plants has respectively reduced 33%, 71%, 68% of those in 2011. Another gratifying result was that the annual average concentration of SO 2, NO x, primary PM 10, sulfate, nitrate caused by the power plants has reduced to respectively 46.34%, 78.43%, 76.34%, 39.49%, 73.87%, significantly lower than those before the emission reduction action was taken.
20. Characteristics of Winter Atmospheric Mixing Layer Height in Beijing-Tianjin-Hebei Region and Their Relationship with the Atmospheric Pollution
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 108
Atmospheric mixing layer height (MLH) is one of the main factors affecting the atmospheric diffusion and plays an important role in air quality assessment and distribution of the pollutants. Based on the ceilometers data, this paper has made synchronous observation on MLH in Beijing-Tianjin-Hebei region (Beijing, Tianjin, Shijiazhuang and Qinhuangdao) in heavy polluted February 2014 and analyzed the respective overall change and its regional features. Results show that in February 2014, the average of mixing layer height in Qinhuangdao is the highest, up to 865 ± 268 m, and that in Shijiazhuang is the lowest (568 ± 207 m), Beijing's and Tianjin's are in between, 818 ± 319 m and 834 ± 334 m respectively; combining with the meteorological data, we find that radiation and wind speed are main factors of the mixing layer height; the relationship between the particle concentration and mixing layer height in four sites suggests that mixing layer is less than 800 m and concentration of fine particulate matter in four sites will exceed the national standard (GB 3095-2012, 75 μg·m ?3). During the period of observation, the proportions of days that mixing layer is less than 800 m in Beijing, Tianjin, Shijiazhuang and Qinhuangdao are 50%, 43%, 80% and 36% respectively. Although the concentrations of air pollutants in Shijiazhuang are very high near the ground, the integration of the aerosol load is not high in the mixing layer. Unfavorable atmospheric diffusion conditions are the main causes of heavy pollution in Shijiazhuang for a long time. The results of the study are of great significance for cognitive Beijing-Tianjin-Hebei region pollution distribution, and can provide a scientific reference for reasonable distribution of regional pollution sources.
China Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 109
The temporal and spatial variations of air pollutant concentrations, and types of pollution were investigated during heavy air pollution episodes occurred in Beijing from 2013 to 2014. The results showed that there were 105 heavy pollution days in Beijing during 2013–2014, accounting for 14.4% of the total. And in these heavy air pollution episodes, Beijing suffered the PM 2.5, PM 10 and O 3 as the primary pollutant for 103 days, 1day and 1day, respectively. The heavy pollution days in the winter half year accounted for 76.2%, and pollution episodes could be characterized by calm wind, high relative humidity and low visibility. For the heavy air pollution days, the concentration ratio of PM 2.5 to PM 10 reached to 91.3% which was significantly higher than the annual average level, indicating that PM 2.5 was dominant. Air pollutant concentrations in the southern region of Beijing were higher than those in the northern parts. Areas with higher air pollutant concentrations were mainly located in the plains, and lower values are located in the mountain regions. Moreover, the frequency of heavy air pollution for traffic monitoring sites was higher than other urban sites in Beijing. The heavy air pollution episodes could be grouped into four typical types, namely the sustained-accumulated, the O 3 pollution, the sand-dust caused and the combined type. The sustained-accumulated episodes were always accompanied by enhancements of regional air pollution level for the whole city, and by obvious increase of NO 3 ?, SO 4 2? and NH 4 +concentrations in PM 2.5. It is also found that O 3 pollution became more serious in recent years.
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 11
In January 2013, Beijing area experienced several severe haze weather events. The pollution of fine particles has become an important problem in Beijing. Understanding the sources of PM 2.5 in Beijing is essential for solutions and related policy-formulations. Three-dimensional air quality modelling system was established to analyze the PM 2.5 pollution during 20–24 January in 2013. PSAT technology was used to study the regional sources of Beijing PM 2.5 pollution. The results showed that local emission was the major source of PM 2.5 in Beijing City, with an average contribution rate of 34%. The average contribution rates of Hebei and Tianjin were 26% and 4%, respectively. The neighboring area and the boundary conditions contributed 12% and 24% to PM 2.5 in Beijing. In the heavy pollution period, the influence of regional transportation increased significantly, and became the major source of PM 2.5 pollution in Beijing. Nitrate in PM 2.5 in Beijing mainly came from the surrounding area of Beijing City, while sulfate and secondary organic aerosols showed characteristics of long-distance transportation. Ammonium salt and other components were mainly from Beijing local contribution.
23. Correlation, Seasonal and Temporal Variation of Water-soluble Ions of PM 2.5 in Beijing During 2012–2013
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 12
A total of 486 daily PM 2.5 samples were collected at a background site, 3 urban sites, 4 suburban sites and a boundary transfer site during August 2012–July 2013. Mass concentrations of 9 water-soluble ions were obtained. The average mass concentration of the 9 ions was 60.5 μg·m ?3, and the order of concentration of ions was NO 3 ? > SO 4 2? > NH 4 + > Cl ? > Na + > K + > Ca 2+ > F ? > Mg 2+; Secondary inorganic species SO 4 2?, NO 3 ? and NH 4 + were the major components of water-soluble ions in PM 2.5, with a contribution of 88% to the total ions of PM 2.5. NO 3 ? was the most fluctuated anion during the sampling period. With the increase of pollution level, the accumulation of SNA was obvious, the components that contained nitrogen, NO 3 ? and NH 4 +, occupied the dominant position in the formation of the secondary components. NO 3 ? had a relatively higher contribution to the accumulation of heavy pollution.
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 13
From August 2012 to July 2013, 220 groups of PM 2.5 samples were continuously collected at four locations in the urban area of Beijing (Shijingshan, Chegongzhuang, Dongsi, and Tongzhou), and the primary chemical species of PM 2.5 were analysed by the chemical mass balance method. It was found that the mass of PM 2.5 obtained from chemical mass balance method agreed well with the value from gravimetric measurement, with a good correlation of 0.95 in spring, autumn, and winter. However, the correlation seasonally changed in summer, with a relatively lower correlation coefficient of 0.77. The concentrations of OM, EC, SO 4 2?, NO 3 ?, NH 4 +, Cl ?, crustal matter, and trace elements were 31.4, 3.8, 19.9, 21.6, 14.4, 4.0, 15.4, and 2.9 μg·m ?3, which accounted for 25.1%, 3.0%, 15.9%, 17.2%, 11.5%, 3.2%, 12.3%, and 2.3% of PM 2.5, respectively. Besides crustal matter, concentrations of the primary chemical species in PM 2.5 from the west to the east gradually increased. The most serious PM pollution occurred between 11 and 14 February 2013, during which concentrations of OM, SO 4 2?, NO 3 ?, NH 4 + were 1.9, 5.0, 3.2 and 4.2 times as high as the annual average. SO 4 2?was recognized as the most important species for the pollution in the process. OM was the largest component of urban PM 2.5 during both heating and non-heating periods. Comparing to non-heating period, the concentrations of OM, NH 4 +, NO 3 ?, and SO 4 2? all increased during the heating period, except for the component of crust and EC. The biggest difference between the two periods was the component of Cl ?(4.4 fold), which can be attributed to the burning of coal. For unknown components, the main component was moisture, which accounted for about 6.0% in urban PM 2.5. The highest moisture appeared in summer (6.5%), followed by spring and winter, and the least appeared in fall (3.7%).
25. Spatial and temporal variations of ambient PM 2.5 source contributions using positive matrix factorization
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 14
Ambient PM 2.5 samples were collected simultaneously at 8 monitoring sites (Dingling, Chegongzhuang, Shijingshan, Dongsi, Tongzhou, Liangxiang, Yizhuang and Yufa) in Beijing, from August 2012 to July 2013. And positive matrix factorization (PMF) was used to identify the sources of PM 2.5 based on ambient PM 2.5 compositional data including concentrations of organic carbon (OC), elemental carbon (EC), ions and metal elements. Results from PMF indicated that the six major sources of ambient PM 2.5 were secondary sources, coal combustion, soil dust, vehicle emission, industrial sources and construction dust, with an annual average contribution of 42%, 19%, 19%, 10%, 6% and 4%, respectively. The contributions of the sources to PM 2.5 in Beijing showed significant seasonal variations. Soil dust was the primary source in spring because of the highest frequency of windy weather. Secondary sources became the major contributor in summer, autumn and winter, and even covered 56% in summer. Coal combustion exhibited increased contributions in winter with a value of 25%. The contributions of the PM 2.5 sources also showed some spatial differences. Coal combustion showed significantly higher contributions in suburban areas than in urban areas, whereas the secondary sources were regional. And the secondary sources were dominated during the cumulative pollution events, accounting for more than 50% of the PM 2.5 mass. Strengthening the controls of gaseous precursors (NO x , SO 2 and VOCs) was of great significance for the reduction of PM 2.5 in Beijing.
26. Spatial and temporal distribution and process analysis of PM 2.5 pollution over Beijing during APEC
China Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 15
Models-3/CMAQ modeling system was used to simulate the spatial and temporal distribution of PM 2.5 pollution over Beijing during APEC, 2014 (i.e. November 3 to 11, 2014). IPR, a process analysis tool embedded in CMAQ, was employed to quantify the contributions of different atmospheric processes to the PM 2.5 formation at two typical sites (i.e., Guanyuan and Dingling) during two short-time pollution processes (i.e. Nov. 4 13:00 to Nov. 5 12:00 and Nov. 10 13: 00 to Nov. 11 12:00). The results showed that CMAQ reproduced the temporal variation and magnitude of PM 2.5 reasonably. An adverse synoptic system occurred on Nov. 4 and Nov. 10, resulting in two peak values of PM 2.5 (188 μg/m 3 and 124 μg/m 3). Elevated PM 2.5 levels did not last long because of the pollution control measures and the cold anticyclone. During Nov. 4 13:00 to Nov. 5 12:00, horizontal transport was the primary contributor to the PM 2.5 at both Guanyuan and Dingling, with a contribution rate of 49.6% and 90.9%, respectively, indicating that Beijing was mainly affected by pollution transported from southern areas. During Nov. 10 13:00 to Nov. 11 12:00, PM 2.5 at Guanyuan site mainly came from local emission (78.8%), while PM 2.5 at Dingling site mainly came from relatively weak horizontal transport, demonstrating a local pollution characteristic. Vertical transport played a dominative role in the decrease in PM 2.5 in both pollution processes.
27. Diurnal Variation of PM 2.5 Mass Concentration in Beijing and Influence of Meteorological Factors Based on Long Term Date
Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 16
Diurnal variation of PM 2.5 mass concentration is analyzed based on data of Baolian (representing urban area) and Shangdianzi BAP-Station (representing rural area) from 2005 to 2014. Furthermore, the influence of meteorological factors was also discussed. The results showed that 10-year-average diurnal variation of PM 2.5 mass concentration in urban area had a two-peak pattern being coincident with rush-hour. However, it became clear only after 2007. The monthly (including seasonal) variation presented the change from one-peak pattern to two-peak pattern with the max mass concentration appearing in the morning or late afternoon during rush hour. The mass concentration in the morning rush hour reached its maximum between May and August which was to some degree related with weak wind and high relative humidity as well as great water vapor pressure (indicating the absolute water content in the air). But the smaller variety of mass concentration in the late afternoon attributed to the thicker mixing layer, higher wind speed and more showers. By contraries, it went up greatly after 4 pm in Nov., Dec., Jan. and Feb.. One of the reasons was that the height of mixing layer top decreased sharply. Besides, in some severely and seriously polluted days, the PM 2.5 mass concentration increased after morning rush hour till afternoon which was different from the mean pattern and that in moderately polluted day. The main mechanism attributed to the aerosol from aerial source around brought by south wind to Beijing. The more severe the pollution was,the greater the daily concentration fluctuated. The range of PM 2.5 diurnal variation was determined by the max wind speed and daily change of relative humidity in a day. Besides, it also extended the diurnal variation of concentration when the south wind speed reached 4–6 m·s -1 in the afternoon. In rural area, the mean diurnal variation of PM 2.5 mass concentration showed a one-peak pattern. And the time of concentration reaching its maximum was ahead of that of urban area. Moreover, the values in the day time during May and July were higher than that in winter. These results would be helpful to make policy for finer emission control when the atmosphere is in lower diffusivity situation.
28. Characteristics and transportation pathways and potential sources of a severe PM 2.5 episodes during winter in Beijing
China Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 17
Pollution characteristics processes and potential sources of PM 2.5 were studied in a severe haze episodes inBeijing from November 26 th to December 2 nd, 2015. Pollution characteristics and meteorological parameters were analyzed.Hourly 72-hour backward trajectories in ground (500m) and high altitude (3000m) were classified and the effect ofclusters in ground and high altitude on PM 2.5 were analyzed using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and cluster method. Major potentialsources of PM 2.5 were simulated using Potential Source Contribution Function (PSCF) and Concentration-weightedTrajectory (CWT) methods. The results showed that hourly PM 2.5 concentration in Beijing varied widely and lowtemperature, high humidity and low wind speed provided a suitable condition for the heavily pollution process of PM 2.5. Airflow trajectories from different directions had an significant impact on the spatial distribution of PM 2.5 in Beijing. Airtrajectories from northwestern were the dominant trajectories which had a big influence on PM 2.5 concentration in Beijing. Moreover, air trajectories in ground from southern should not be ignored because air trajectories that passed throughsouthern areas carried anthropogenic pollutants to Beijing. weighted potential source contribution function (WPSCF) and weighted concentration-weighted trajectory (WCWT) values were more than 0.7 and 200 μg/m 3 outside of Beijing,respectively, which revealed that mid-western Mongolia, eastern Xinjiang, mid-western Inner Mongolia, northern Shanxi,Hebei and regions in northern Shandong were the major potential sources of PM2.5 in Beijing.
29. Pollution Characteristics of NH 4 +, NO 3 ?, SO 4 2? in PM 2.5 and Their Precursor Gases During 2015 in Urban Area of Beijing
Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 18
Simultaneous measurements of precursor gases NH 3, NO, NO 2, SO 2 and the main water-soluble ions in PM 2.5 such as sulphate (SO 4 2?), nitrate (NO 3 ?) and ammonium (NH 4 +) (collectively called SNA) were carried out in the urban area of Beijing during 2015-01 to 2015-12, which obtained 325 groups of samples. PTFE membrane filters were used to collect particulate NH 4 +, NO 3 ? and SO 4 2?, followed by the online instruments to collect precursor gases. The pollution characteristics of the precursor gases and SNA were analyzed and their correlation was studied. The mean concentrations of NH 3, NO, NO 2, SO 2, NH 4 +, NO 3 ? and SO 4 2? were 21.5, 17.7, 54.3, 14.2, 8.1, 13.5 and 12.7 μg·m ?3 respectively during the period of monitoring, and SNA accounted for 43.4% of PM 2.5. The concentrations of SO 2, NO x and SNA declined compared to 2014. The concentrations of NO, NO 2 and SO 2 were the highest in winter and the lowest in summer. The concentration of NH 3 was higher in summer and lower in autumn; the concentration and the percentage content of NH 4 + were stable during the four seasons, and both the concentrations and the percentage of NO 3 ? were the lowest in summer. The concentration of SO 4 2? was the highest in winter and the percentage was the lowest in summer. The ratio of ([NO 3 ?] + 2[SO 4 2?]) to [NH 4 +] was 0.97 during the whole year, showing that anions mainly existed in the form of NO 3 ? and SO 4 2?. In summer, the ratio of ([NO 3 ?] + 2[SO 4 2?]) to [NH 4 +] was slightly higher than 1.0, which was the reason why NO 3 ? was bound to Ca 2+, Mg 2+ and Na + besides NH 4 +. With the increase in pollution, the mass concentrations of precursor gases and SNA increased dramatically, among which NO x increased the most rapidly, and SO 2 decreased from severe pollution to serious pollution. The contribution rate of NH 4 + was maintained at a relatively stable level. SO 4 2? had a higher contribution when the pollution level was lower, whereas the concentration of NO 3 ? was higher than others and contributed the most to PM 2.5 in heavy pollution. Heterogeneous transformation on the surface of particulate matters played a more important role in the formation of SO 4 2? and NO 3 ?. The correlations between NO 3 ? and NO 2, between NO 3 ? and NO, between NH 4 + and NH 3, and between SO 4 2? and SO 2 were significant at the confidence level of 0.01. SO 4 2? had negative correlation with SO 2, and NO 3 ? had positive correlation with NO 2. Compared with NH 3, the NH 4 + concentration was more obviously affected by acid gases NO 2 and SO 2.
China Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 19
The variation period of PM 2.5 concentration and its evolution feature in Beijing urban area were investigated using the Morlet wavelet analysis and Cross wavelet transform (XWT) method. The observation data of PM 2.5 concentration and meteorological elements from 2010 to 2015 were applied in this study. The results showed that there were significant periodic variations in PM 2.5 in Beijing urban, with major period of 24h, 8d and 14d. The 14d period was mainly influenced by quasi-two-week atmospheric oscillation. The 8d period was not only related to the synoptic scale of weather system, but also related to the “weekend effect” caused by human activities, in which the synoptic scale system was probably playing the leading role. There were obvious sympathetic vibrations in PM 2.5 and average wind speed in the frequency period of 8d and 14d, and the anti-phase relation between them was also found. Diurnal variations of meteorological condition and anthropogenic emissions might be important factors causing the oscillation period of 24h. These oscillation periods were strongly significant in autumn and winter, because the flow patterns of lower atmosphere were more frequently influenced by strong synoptic systems. Weak oscillation in spring and summer was mainly attributed to low concentration level of PM 2.5, multiple influencing factors and intensive meso-scale thermal circulation induced by topography. The emission reduction measures might be the important factor in weakening Madden-Julian Oscillation after 2014. Although this study achieved some conclusions about oscillation period of PM 2.5 in Beijing urban and its evolution feature more measured data and other analytic methods should be verify in future.
31. Seasonal Variation and Source Analysis for PM 2.5, PM 1 and Their Carbonaceous Components in Beijing
Environmental Science,Part 1: Types of Air Pollution,Vol 39,No. 20
Atmospheric particulate matter is the primary pollutant affecting the ambient air quality in most Chinese cities. In recent years, with the progress of monitoring technology and improvement in sampling equipment, the relevant research objects gradually shift from larger particle sizes (PM 10 and PM 2.5) to smaller particle size (PM 1). The carbonaceous component is an important part of atmospheric particulate matter. Taking Beijing as the research area, sampling for PM 2.5 and PM 1 was conducted in July and October of 2016, and January and April of 2017 as representative months of four seasons. Mass concentrations and seasonal variation characteristics for PM 2.5 and PM 1 were analyzed. The two-layer, nested, meteorology-air quality coupling model system (WRF-CMAQ) was used to model air circulation during the sampling period and thus analyze the source contributions for PM 2.5 and PM 1. The factor analysis method was also used to analyze the source apportionment of carbonaceous components. The results are as followed: the mass concentrations of PM 2.5 and PM 1 showed an increasing trend by spring, summer, autumn and winter. PM 1 was the main part of PM 2.5, and with the increasing frequency of haze in autumn and winter, the mass concentration ratio of PM 1/PM 2.5 became significantly higher. The authors contend that secondary pollution exists in Beijing’s atmosphere, and SOC is more likely to accumulate in smaller particle size. Widespread coal combustion, vehicle emission, residential emission source and biomass combustion emissions are the major contributors to atmospheric particulates, while gasoline engine exhaust, diesel vehicle exhaust, biomass combustion and coal combustion emission are the main source of carbonaceous components in PM 2.5 and PM 1 in Beijing.
32. Simulation and Influencing Factors of Spatial Distribution of PM 2.5 Concentrations in Chongqing
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 21
Land use regression model (LUR model) was used to simulate the spatial distribution of PM 2.5 concentrations in Chongqing with the software of Arc GIS. This research was conducted with a total of 17 PM 2.5 concentrations of monitoring points from 17 air quality monitoring stations recorded in the official website of Chongqing Environmental Protection Bureau. Among them, 16 were chosen as the dependent variables, and the last one was chosen for land use regression model validation test. At each site, we constructed circular buffers with Arc GIS and captured information on roads, population, land use, and DEM. Based on the buffer information, 56 potential geographic predictors were built. Finally, three variables, cropland area within 500 m of the air quality monitoring sites, the site locations’ DEM and primary road length within 1 000 m, of the 56 predictors were left for predicting 84% of the variation of PM 2.5 concentrations and the Pearson coefficients between the three variables and PM 2.5 concentrations were 0.695, ?0.599 and 0.394, respectively. The validation test result showed that the spatial distribution map of PM 2.5 predicted extremely well with an error rate of only 2.7%. And the return map results showed: 1) PM 2.5 concentrations were high in the center of the main city; 2) PM 2.5 concentrations were high along the road; 3) the distribution was closely correlated to the DEM of sampling locations.
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 22
Organic phosphorus esters (OPEs) in atmospheric PM 2.5 in Chengdu city was quantitatively determined by using gas chromatography-mass spectrometry. The distribution characteristic was discussed, back trajectory model and correlation analysis were used to study the sources of OPEs in PM 2.5 in Chengdu city. The results showed that the annual average concentration of Σ7OPEs in atmospheric PM 2.5 in Chengdu city was 6.46 ng·m ?3 for the urban site and was 9.38 ng·m ?3 for the suburb site. Due to the waste material recycling industries in the suburb area and the perennial dominant wind direction in Chengdu, the concentration of Σ7OPEs at suburb site was higher than that at urban site ( P = 0.013). The atmospheric mixed degree influenced the distribution of OPEs in rural and urban area. The source of Σ7OPEs in atmospheric PM 2.5 in Chengdu city was mainly from endogenous pollution which was mainly affected by the local sources around the sampling sites, while the contribution of the exogenous pollution was small.
China Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 23
Filter-based PM 2.5–10 and PM 2.5 samples were collected at an urban site in Urumqi during January–December 2011, and analyzed for the organic carbon(OC) and elemental carbon(EC) mass concentrations by a thermal/optical carbon analyzer. The characteristics of carbonaceous aerosols in Urumqi were preliminarily investigated with the size distribution of OC and EC, OC/EC ratios and their correlations; and the mass concentrations of secondary organic carbon(SOC) were estimated by using the OC/EC ratio or EC-tracer method. The results showed that PM 2.5 and PM 2.5–10 had annual average mass concentrations of 92.8 μg/m 3 and 64.7 μg/m 3, respectively; while OC and EC had annual average mass concentrations of 13.85 μg/m 3 and 2.38 μg/m 3 in PM 2.5, and 2.63 μg/m 3 and 0.57 μg/m 3 in PM 2.5–10, respectively. OC and EC in PM 2.5 and PM 2.–10 demonstrated similar seasonal variations with the highest values observed in winter.Carbonaceous aerosols were mainly concentrated in PM 2.5. OC/EC ratios ranged from 3.62 to 11.21. Good linear correlations were found between OC and EC in summer and autumn( R 2 > 0.65). Estimated SOC in PM 2.5 and PM 2.5–10 ranged 2.31–11.98 μg/m 3 and 0.38–1.49 μg/m 3, respectively.
35. Study on the nonlinear relationship among the visibility, PM 2.5 concentration and relative humidity in Wuhan and the visibility prediction
Acta Meteorologica Sinica,Part 1: Types of Air Pollution,Vol 74,No. 24
Hourly observations of visibility, relative humidity ( RH), and particulate mass concentration in Wuhan for the period from September 2014 to March 2015 have been analyzed in this study to investigate the relationship among these variables. Nonlinear prediction of visibility in Wuhan is explored preliminarily. It is found that the frequent occurrence of haze in Wuhan is largely responsible for the severe reduction in visibility. The formation and accumulation of fine particulates are two important factors inducing haze and low visibility. Both the RH and the particulate mass concentration affect the variation of atmospheric visibility. High RH and large fine particulate mass concentration can significantly reduce the atmospheric visibility. Under wet conditions ( RH ≥ 40%), the visibility deteriorates because the hygroscopic growth of the fine particulate can efficiently enhance light absorption and scattering. When the RH is higher than 90%, the visibility decreases linearly with the increase in RH. Averagely, the visibility decreases by 0.568 km as the RH increases by 1%. Under dry conditions ( RH < 40%), the increase in the PM 2.5 concentration becomes a critical factor for the rapid decrease in visibility. In urban areas where fine particulates in the atmosphere are primary pollutants, the visibility has a nonlinear relationship with RH. This is partly attributed to the influence of PM 2.5 on the visibility and partly attributed to light scattering effects of hygroscopic particles. Results also indicate that there exists a nonlinear relationship between the PM 2.5 concentration and the visibility, which can be described by a power function. The correlation between the PM 2.5 concentration and the visibility is most significant when the RH is less than 90% but larger than 80%. The sensitivity threshold of PM 2.5 concentration for the atmospheric visibility decreases with increasing RH. Under dry conditions, the visibility of 10 km corresponds to a PM 2.5 concentration threshold of 70 μg/m 3, whereas the value is 18–55 μg/m 3 under wet conditions. Decreases in the PM 2.5 concentration can lead to significant improvement in visibility when the PM 2.5 concentration is less than 40 μg/m 3. In addition, results of preliminary experiments have shown that the visibility prediction model, which is developed based on the neural network method, performs well in prediction of visibility in Wuhan. The correlation coefficient between observations and predictions can be up to 0.86, and the root mean square error ( E RMS) is 1.9 km. The TS score is 0.92 for the visibility that is less than 10 km. These results indicate that the model has a crucial skill for haze prediction.
Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 25
In this study, detailed activity level of typical sector in Chengde in 2013 was obtained through a full-coverage investigation. A comprehensive emission inventory with country-level resolution in 2013 was developed based on guide of atmospheric pollutant emission inventory and updated emission factors. Then, the emission inventory within 1 km×1 km grid was generated using sourcebasedspastial surrogates including population, road network and landuse date. Furthemore, meteorology-air quality modeling system (WRF-CMAx) including Particulate Source Apportionment Technology (PSAT) module was established in order to evaluate the impact of topical sector (e.g., electric power,the production of construction materials, the metallurgical industry, etc.) on PM 2.5 concentration in January, April, July and October which were considered as the representative months of winter, spring, summer and autumn. The results showed the total emission of SO 2, NO x , TSP, PM 10, PM 2.5, CO, VOCs and NH 3 in Chengde in 2013 was respectively 81 134 t, 72 556 t, 368 750 t, 119 974 t, 51 152 t, 1 281 371 t, 170 642 t and 81 742 t. Industrial source was the main emission contributor of SO 2, NO x , CO, VOCs, accounting for 89.5%, 51.9%, 82.5% and 45.6% of total emissions, respectively. The major emission source of NO x also included on-road and non-road mobile source, respectively accounting for 26.7% and 10.8%. The major emission source of TSP, PM 10 and PM 2.5 was fugitive dust, accounting for 76.7%, 65.6% and 46.54%, respectively. Ammonium emissions from animals and farm accounted for 67.1% and 15.8% of total emissions, respectively. The numerical simulation result showed that the fugitive dust, the others, the metallurgical industry and boilers industry had relatively higher contributions to PM 2.5 concentration, accounting for 23.1%, 20.6%, 13.3% and 11.2%, respectively. These emission sources should be paid more attention during the decision-making with respect to control strategies.
37. Characterization and regional transmission impact of water-soluble ions in PM 2.5 during winter in typical cities
China Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 26
In this study, PM 2.5 samples were collected from Beijing and Shijiazhuang in winter, 2014. The characteristics of PM 2.5 and water-soluble ions were investigated. The WRF-CAMx modeling system was adopted to simulate the concentration of regional transmission to PM 2.5 and secondary ions during observation. The results showed that the concentration of PM 2.5 in Beijing during sampling was (116.6 ± 87.0) μg/m 3. Mass concentration of water-soluble ions was (45.3 ± 40.6) μg/m 3, and the mass concentrations of SO 4 2–, NO 3 – and NH 4 + were (13.3 ± 13.6) μg/m 3, (14.8 ± 15.1) μg/m 3 and (9.1 ± 7.2) μg/m 3, respectively. The pollution in Shijiazhuang was more serious than that in Beijing, and the PM 2.5 concentration in Shijiazhuang was (267.7 ± 166.7) μg/m 3. The mass concentration of total water-soluble ions, SO 4 2–, NO 3 – and NH 4 + were (111.8 ± 104.3) μg/m 3, (36.6 ± 36.5) μg/m 3, (28.5 ± 29.3) μg/m 3 and (25.5 ± 29.8) μg/m 3, respectively. SOR and NOR were 0.12 and 0.10 in Beijing, 0.11 and 0.14 in Shijiazhuang, respectively. Atmospheric oxidizability in winter was relatively weak. Heterogeneous oxidation is the main reaction mechanism of the secondary conversion in winter. The simulation results showed that the contribution of regional transmission to urban area of Beijing and Shijiazhuang in January was 28.1% and 28.3%, respectively, and the increased regional contribution was found during heavy pollution period. The regional transmission contribution of NO 3 – was much higher than that of SO 4 2– in both sites.
China Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 27
For PM 2.5 pollution investigation in China, the air quality modelling system WRF-CMAQ was applied to calculate the impacts of ammonia emission on PM 2.5 concentration. The results indicated that ammonia emission had the biggest contribution for secondary nitrogen particles, with annual average 99.8% for nitrate, 99.7% for ammonium, while only 4.2%, 29.8% for sulfate and PM 2.5, respectively. Quantification of ammonia emission impacts on PM 2.5 mass concentration were also conducted in January, April, July and October as representative months, counted 20.15 μg/m 3, 12.39 μg/m 3, 13.20 μg/m 3 and 14.20 μg/m 3, respectively, with January ranking the first in monthly average contribution. It is general that ammonia emission had dramatical influence on PM 2.5 in regions where agriculture and animal husbandry well developed, such as Henan, Shandong, Hubei and Hebei Provinces, with annual average contribution all exceeded 20 μg/m 3. In view of this, ammonia emission control will lead to significantly decrease of nitrate and ammonium, therefore reduce the PM 2.5 pollution level.
Acta Meteorologica Sinica,Part 1: Types of Air Pollution,Vol 75,No. 28
The air quality data published by the environmental authorities, the meteorological data collected at Hefei observatory during 2013–2015, together with the vertical extinction coefficient data observed by lidar and water-soluble inorganic ions data of aerosols obtained during scientific field experiments were used to analyze characteristics of severe PM 2.5 pollution (daily average PM 2.5 concentration >150 μg/m 3) in Hefei. The results show that: (1) the PM 2.5 pollution showed evident spatial differences with more severe PM 2.5 pollution days at sites in northeastern Hefei and fewer pollution days at sites in southwestern Hefei. Monthly variations of severe PM 2.5 pollution days were similar at all sites with the largest difference occurring in January. The PM 2.5 concentration showed an evident diurnal variation with two maxima in the morning and evening respectively, and the morning maximum on severe pollution days often occurred later than on light polluted days; (2) on severe PM 2.5 pollution days, the concentrations of other gas pollutants except O 3 increased obviously; (3) severe PM 2.5 pollution days usually corresponded to haze and light fog, accompanied with light winds and stable stratification. The vertical extinction coefficients of aerosols below 600 m at the noon on severe pollution days were much higher than those on other days, while the occurrence height of maximum extinction coefficient decreased; (4) on severe pollution days, the water-soluble inorganic ions in PM 2.5 became more abundant, and the percentage of NO 3 ? increased most, exceeding the percentage of SO 4 2?. The above results advance our understanding of the role of nitrate in the formation of high level PM 2.5 and are helpful for the forecast and control of PM 2.5 pollution.
40. Chemical Characterization, Spatial Distribution, and Source Identification of Organic Matter in PM 2.5 in Summertime Shanghai, China
Environmental Science,Part 1: Types of Air Pollution,Vol 39,No. 29
Particulate organic matter (POM) has attracted increasing attention recently due to its great contribution to fine particles (PM 2.5) and complex components and sources. In the present study, 78 particulate organic compounds in PM 2.5 were quantified at three sites in Shanghai during summer; these sites were located in urban (Xuhui), suburban (Qingpu), and coastal (Lin’gang) areas of the city. Accordingly, the chemical composition and spatial distribution were investigated and sources were explored based on the indicators and diagnostic ratios combined with backward trajectory. The results showed that during the period of observation, the quantified organic matter in the suburban area is about 319 ng·m ?3, close to the urban area but much higher than that of the coastal areas. Fatty acids were the largest contributors, followed by levoglucosan, polycyclic aromatic hydrocarbons (PAHs), n-alkanes, and hopanes. Source analysis based on tracer methods indicates that gasoline vehicle exhausts were the main source of POM in Shanghai. Biomass burning from the northeast impacted somewhat on the urban area and western suburbs during the observation period. Terrestrial plant emissions played an important role in the source of fatty acids at Qingpu and Lin’gang, and emissions of marine phytoplankton and microorganisms were also important for fatty acids at Lin’gang. Coal combustion and motor vehicle exhaust made an important contribution to PAHs according to an analysis of diagnostic ratios. This study presented the characteristics and sources of POM in summertime Shanghai, which facilitates the development of an effective control strategy on PM 2.5 pollution.
41. Seasonal Patterns of PM 2.5 Sources and Chemical Composition from Different Air Mass Directions in Tianjin
China Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 30
PM 2.5 samples were collected simultaneously at inland and coastal sites during four seasons in Tianjin, China. A three-way receptor model and HYSPLIT model were utilized to investigate the sources of PM 2.5 at two sites, and qualitatively determine the major air mass origins, and then the source directional apportionment (SDA) was applied to quantify source contributions from different directions to the ambient PM 2.5. The results showed that PM 2.5 concentration from Bohai Sea direction was relatively low (97.1 μg/m 3), but the proportion of air masses was high (23.7%). The PM 2.5 concentration from Inner Mongolia direction was high compared with the Bohai direction, but the proportion of air masses was low. In the coastal site, the largest contributors to PM 2.5 were crustal dust from SSW for spring (12.8%), sulfate and SOC (secondary organic carbon) from SE for summer (9.8%), coal combustion from WSW for autumn (10.3%), sulfate and SOC from WNW for winter (12.1%). For the inland site, the largest contributors to PM 2.5 during four seasons were crustal dust from SSW (14.5%), sulfate and SOC from S (south direction, 13.5%), vehicle exhaust from SSW (8.9%), sulfate and SOC from WNW respectively (9.5%).
China Environmental Science,Part 1: Types of Air Pollution,Vol 39,No. 31
In order to identify main sources and their characteristics of PM 2. 5 in atmosphere, filter samples of PM 2.5 were collected at five receptors in Shenzhen during March, June, September and December in 2014. Mass concentrations and chemical compositions were analyzed, then the positive matrix factorization (PMF) model was applied for source apportionment. The results showed the annual mean concentration of PM 2.5 reached 35.7μg/m 3 in Shenzhen in 2014, with vehicle emissions, secondary sulfate, secondary organic aerosol (SOA) and secondary nitrate identified as the major sources, contributing 27%, 21%, 12%, and 10% to PM 2.5, respectively. Fugitive dust, biomass combustion, ship emissions, industrial emissions, marine emissions, building dust and coal burning each contributed 2%–6%. The tempo-spatial variations of sources revealed that secondary sulfate, biomass combustion, SOA, industrial emissions, ship emissions and marine emissions had obvious regional pollution characteristics; however, vehicle emissions, secondary nitrate, coal burning, fugitive dust and building dust showed obvious local emission characteristics.
43. Numerical simulations of sources and transport pathways of different PM 2.5 pollution types in Shanghai
China Environmental Science,Part 1: Types of Air Pollution,Vol 39,No. 32
Synoptic weather patterns during the moderate and severe PM 2.5 pollution processes in Shanghai from 2014 to 2017 were analyzed in the paper. The pollution processes were classified into three types, i.e., the transport type, the stagnant type and the combined type. The severe PM 2.5 pollution events in Shanghai were mostly dominated by the transport type, accounting for 45.8% of total severe pollution events. The FLEXPART model driven by the WRF model with MEIC emissions was used to investigate the meteorological attribution and maintenance mechanisms of air pollution under each weather pattern in this paper. Specifically, the transport type showed three main pollution transport pathways affecting Shanghai, i.e., the eastern pathway (the East China Sea), the middle pathway (Jiangsu coastal area) and the western pathway (Anhui-southern Jiangsu), which mainly occurred one day before pollution. The stagnant type showed pollution potential source area in Shanghai and its surrounding regions. The combined type showed both apparent transport pathways and potential source area in and nearby Shanghai.
44. Diurnal Variations and Source Apportionment of Water-soluble Ions in PM 2.5 During Winter in Nanjing Jiangbei New Area
Environmental Science,Part 1: Types of Air Pollution,Vol 41,No. 33
To gain a better understanding of the day-night variation characteristics of water-soluble ions, PM 2.5 samples were continuously collected for two months in the Nanjing Jiangbei New Area during winter. The diurnal variation and sources of watersoluble ions were studied. Results showed that the mass concentration of water-soluble ions ranged from 17.07 μg·m ?3 to 168.43μg·m ?3 with a mean value of (59.01 ± 30.75) μg·m ?3. The average mass concentration of water-soluble ions in daytime was higher than that in the nighttime. The concentration ratio of NO 3 ? and NH 4 + to total ion concentrations was higher at night, while SO 4 2? and Cl ? were higher during daytime. SO 4 2?, NO 3 ?, and NH 4 + (SNA) were the dominant species of water-soluble ions in PM 2.5 in Nanjing. The mass concentration of SNA on polluted days was higher than that on clean days. The ratio of the anion-cation balance ( AE/CE) was larger than 1, indicating that the PM 2.5 was acidic. There was a significant linear correlation between NH 4 + with NO 3 ? and SO 4 2?, indicating that it occurred mainly in the form of NH 4NO 3 and (NH 4) 2SO 4 in PM 2.5. The PMF source apportionment indicated that water-soluble ions of PM 2.5 were mainly derived from motor vehicle emissions, fossil fuel combustion, biomass burning, and dust in the Nanjing Jiangbei New Area.
45. Characteristics and spatial-temporal variation of heavy metals in atmospheric dry and wet deposition of China
China Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 34
This study investigated the characteristics and spatial-temporal variation of heavy metals in atmospheric dry and wet deposition in China based on literature review. Relevant results that were published over the last two decades (i.e., from 1995 to 2015) were searched through databases including CNKI, ISI Web of Science, Wanfang, CQVIP and Google Scholar and comprehensively analyzed and compared. The average or median concentration and annual flux of heavy metals were reported in this study. Our results showed that Cu, Zn, Pb, Cr, Cd, As, Ni and Hg concentrations in atmospheric dust were higher than the Level 1 standards of Environmental Quality Standard for Soils (GB15618-1995) over the last twenty years with multiple of 3.0, 7.4, 7.9, 1.1, 16.5, 1.5, 1.2 and 2.3, respectively. At the same period, Pb and Hg concentrations in rainfalls exceeded the Class I standards of Environmental Quality Standards for Surface Water (GB3838-2002). Compared with 1995–2005, Pb, Cr, Cd, As, Mn and Ni concentrations in atmospheric dust decreased by 32%–50% within 2006–2015. It is noteworthy that Cu, Zn, Pb, Cr, Cd, Ni and Hg concentrations in atmospheric dust of south China were 60.9%, 44.2%, 137.5%, 34.2%, 68.0%, 7.3% and 25.0% higher than those in north China. Nevertheless, As and Mn concentrations were lower in south China. The annual fluxes of Cu, Zn, Pb, Cr, Cd, As, Mn, Ni and Hg in atmospheric dry and wet deposition were (10.99 ± 14.74), (78.87 ± 313.23), (21.81 ± 64.53), (10.38 ± 48.10), (0.37 ± 1.84), (2.54 ± 3.85), (48.00 ± 193.40), (4.79 ± 13.56) and (0.04 ± 0.16) mg/(m 2·a), respectively; the annual fluxes of Cu, Zn, Pb, Cr and Mn in atmospheric dry and wet deposition between 2006 and 2015 were increased by 11.6%, 37.3%, 39.1%, 95.9% and 117.6% when comparing with those between 1995 and 2005; while As, Ni and Hg were reduced by 41.0%, 21.8% and 50.0%, respectively; according to the comparison of different regions of China, the annual fluxes of Cu, Zn, Cr, As, Mn and Ni in atmospheric dry and wet deposition in north were 42.6%, 16.3%, 96.8%, 130.5%, 307.1% and 124.2% higher than those in south respectively. By contrast, Pb and Cd annual fluxes in south were 22.9% and 30.3% higher than those in north. Thus, Cd, Pb and Hg have high priority in preventing heavy metals in soil from atmospheric dry and wet deposition.
46. Distribution Characteristics of Heavy Metals in the Street Dusts in Xuanwei and Their Health Risk Assessment
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 35
Relationship between high lung cancer incidence in Xuanwei residents and environmental pollution has been a hot topic in the field of environmental sciences. Street dusts in Xuanwei power plant area as well as its upwind area (Banqiao town) and downwind area (Laibin town, Tangtang town) were collected. Chemical elements in the street dust samples were investigated using ICP-MS. Health risk assessment of heavy metals in the street dusts was carried out using the US EPA Health Risk Assessment Model. Our results showed that the mass level of Al, V, Ni, Co, Zn and Cd in street dusts followed the order of Xuanwei power plant > Laibin town > Tangtang town. The mean concentrations of V, Cd, Cr, Cu, Mn, Co, Ni, Pb, As and Zn were all higher than the background values in Yunnan soil, indicating that the street dusts of Xuanwei city have been heavily polluted by those metals. The health risk assessment results showed that the non-cancer hazard risks induced by the 10 heavy metals were higher to children compared to adults. The heavy metals in street dust were mainly ingested by human bodies through hand-mouth ingestion. The 5 carcinogenic metals, including Cd, Cr, Ni, Cr and As, had a potential risk of carcinogenicity in human after exposed to the dusts. Cr was the major toxic element to the local children's health.
47. Seasonal Provincial Characteristics of Vertical Distribution of Dust Loadings and Heavy Metals near Surface in City
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 36
With the emergence of urban high-rise building, the vertical space of human daily life gradually extended upward. Seasonal characteristics of vertical distribution of dust loadings and heavy metals near surface are remarkable. In this study, we collected dust deposited on the windowsill at different space height (1 st–8 th floor) from three buildings in Guiyang city during spring, summer, autumn and winter, and analyzed the deposition fluxes of dust and elements including Ca, Fe, Cd, Cr, Cu, Ni, Pb and Zn. The results showed that: the total changing trend of vertical distribution of dust loadings was that the deposition fluxes of dust in winter were the highest, followed by those in spring, and the deposition fluxes of dust in summer were the lowest. The degree of variation on dust loadings dependent on the change of elevation was the highest in winter, followed by that in summer, and was relatively lower in spring and autumn. The effect on dust loadings by seasonal changing was relatively heavier on windowsill on the lower level than on the higher level. The levels of elements were the highest in spring dust, while those in autumn were relatively lower. Among the 8 elements, the variability of Zn in dust related to space time variation was the most obvious, and that of Ca was weaker. The atmospheric inversion condition might be one of the reasons that improved the deposition fluxes of dust and the contents of Pb and Zn in dust during winter and spring.
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 37
Dust was an important pathway of human heavy metal exposure. With the emergence of urban high-rise buildings, the vertical space of human daily life gradually extended upward. Dust and its heavy metal located on the windowsill and other platforms at different height of the building was remarkable. In this study, dust deposition on the windowsill at different heights (1st–8th floors) from three buildings in Guiyang city was collected and the deposition fluxes of dust and elements including Ca, Fe, Cd, Cr, Cu, Ni, Pb and Zn were analyzed. Further, the environmental risk due to heavy metals based on loadings per day was assessed by the single-factor contaminant index and Nemerow multi-factor index method. The results showed that: the deposition fluxes of dust decreased with the increases in the height. The content of elements showed small changes in dust at different height: the contents of Fe, Cd, Cu, Ni, and Pb increased with the height, while the contents of Cr and Zn in dust decreased with the height. In general, the element deposition fluxes decreased as the height of the ground increase, especially Ca, Cr and Zn. The risk assessment based on the deposition fluxes of heavy metals showed that the 1th–3th floor and 7th floor were at higher risk, while the 4th–6th floors were at lower risk, and Cd, Pb, Cu, and Zn were the main risk elements.
49. Sources and risk assessment of heavy metals in ambient PM 2.5 during Youth Asian Game period in Nanjing
China Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 38
In order to evaluate the sources and health risks of heavy metals in PM 2.5 around the Youth Asian Game (YAG) period, thirteen heavy elements including V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Cd, Sn, Sb and Pb in PM 2.5 were analyzed for a site near the Nanjing Olympic Sport Center during 3–28 August, 2013. Results indicated that the concentrations of heavy metals are different among the pre-, during- and after-YAG period, influenced by both the pollution control regulations and meteorological parameters. Their concentrations are higher for the pre-YAG period than those for during-YAG period. Enrichment factor index indicated that Cu, Zn, Cd, Sn, Sb and Pb are highly enriched elements, with their pollution level decreasing as Cd > Cu > Zn > Sb > Sn > Pb. Cluster analysis implied that industrial emission, coal combustion, road dust and vehicle emissions were the major sources of these heavy elements. During the YAG period, the non-carcinogenic risks raised by heavy elements in PM 2.5 through inhalation pathway are less than 1. The risk indexes of five carcinogenic heavy metals are also lower than the thresholds of cancer risk correspondingly.
China Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 39
Daily PM 2.5 samples were collected around the 2014 Spring Festival (SF) at a suburban site of Nanjing and twelve kinds of heavy elements were analyzed. Sources were identified by enrichment factor (EF), cluster analysis and principal component analysis. Human health risks of heavy metals were assessed. Results showed that during SF, the average mass concentration of PM 2.5 was 11.4% higher than that for pre-SF period. After the SF, PM 2.5 concentrations decreased by 31.1%. The mass concentrations of V, Cr, Mn, Ni, Cu, Zn, As, Cd, Sb and Pb decreased by 5.5% (V)–56.7% (Zn), when compared with those for pre-SF period. The variation of PM 2.5 and associated elements reflected the sources variation of industrial plants, vehicle emission and fireworks burning. The concentration of Ba during SF period was 16.2 times of that for pre-SF period, and then decreased by 94%, indicating that firework burning was an important source of it. EF and geoaccumulation index showed that Cd, Sb, Pb, Cu, As, Ni, Ba and Zn are heavily enriched, with the EFs values ranging in 21–2259. Principal component analysis and cluster analysis showed that industrial emissions and coal combustion, fireworks burning and vehicle exhaust, industrial process are the main sources of heavy metals, contributing by 57.5%, 12.4% and 9.9%, respectively. Health risk assessment results indicated that during SF, the risk levels of carcinogenic elements—Cr, Co, Ni, As and Cd were 2.0 × 10 ?6, 8.9 × 10 ?9, 1.3 × 10 ?8, 1.9 × 10 ?7 and 7.7 × 10 ?9, respectively. Except for Cr, the values were below the carcinogenic risk threshold range (10 ? 6–10 ? 4), at an acceptable level.
51. Emission Inventory of Heavy Metals in Fine Particles Emitted from Residential Coal Burning in China
Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 40
Based on a dilution sampling system and domestic burning tests, emission factors (EFs) for eleven heavy metals of V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Sb and Pb in PM 2.5 from raw coal and honeycoal burning were calculated, using their contents in raw coals of different provinces. Then the total emission amounts of heavy metals from residential coals burning in 2012 were calculated and30 km × 30 km grid cell-based emission inventories were established. The results showed that the EFs of Pb, Zn, As and Cu were higher from honeycomb coal burning. They were 27.1, 16. 8, 0.99 and 0.97 mg·kg ?1, which were 56, 6, 10 and 2 times of those for raw coal, respectively. The total emissions of V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Sb and Pb in PM 2.5 from residential coal burning in 2012 were 0.5, 30 1, 59.5, 1.1, 29.3, 20.0, 188.9, 64.9, 1.6, 3.4 and 176.7 t. Hunan, Hebei, Inner Mongolia, Henan, and Shandong held higher emission amounts, which were 12.4%, 12.3%, 10.4%, 9.9% and 9.3% of the total emissions of the whole country. Beijing, Henan, Shandong, Hunan, Jiangxi, Guizhou and Inner Mongolia were the regions with higher emission intensities and emission amounts per capita. The spatial distribution showed that the regions with higher annual emissions of Zn and Pb distributed widely, mainly in Inner Mongolia, Hebei, Beijing, Tianjin, Shandong, Henan, Gansu, Hunan and Jiangxi. The emission inventories for heavy metals in fine particles established here are important for regional air quality modeling and human health risk assessment.
Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 41
Concentrations of 23 metal elements in the dustfall collected from different functional areas of Quanzhou City, China, were determined. Several methods were applied to assess the enrichment degree, pollution level and potential ecological risk of the above elements. The sources of the above elements were analyzed based on the multivariate statistical analysis combining Pb and Sr isotopic tracing technology. The results showed that the concentrations of metals in the dustfall presented significant spatial difference. The results of enrichment factor and geo-accumulation index indicated that Cd, Hg, Zn, Ca, Pb, Cu, Ni and Sr showed relatively higher enrichment degree and pollution level. The results of ecological risk index showed that the comprehensive ecological risk of heavy metals was very high in the dustfall of all functional areas with the sequence of industrial area > heavy traffic area > commercial area > residential area > scenic area > agricultural area. Cd and Hg showed extremely high potential ecological risk, and they contributed 95.56% to the comprehensive potential ecological risk index. The multivariate statistical analysis demonstrated that the elements of V, Fe, Ba, Bi, Ni, Sr, Pb, Cs, Sc, Zn, Cd were mainly from industrial and vehicle emissions; Th, U, Rb, Y, Ti were mainly derived from soil dust; Li, Mn, Cu, Hg, Cr, Co, Ca were mainly from coal combustion. The contribution ranges of parent soil, coal combustion and vehicle emission to the Pb in the dustfall were 29.41%–64. 00%, 22.53%–60.48% and 3.13%–13.47%, respectively, as calculated by a ternary hybrid model. The plots of 87Sr / 86Sr vs 1/Sr showed that Sr in the dustfall was dominated by coal combustion and vehicle emission.
53. Level of Heavy Metals, Influencing Factor, and Risk Assessment in Indoor Dust of City: A Case Study of Guiyang
Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 42
A total of 73 household dust and 6 office dust were collected and the concentrations of Ca, Fe, Cd, Cr, Cu, Ni, Pb and Zn were measured by ICP-OES, in order to study the levels of heavy metals in city indoor dust and assessits risk from indoor and outdoor dust to children. The result showed that: (1) The concentrations of Ca, Fe were 107, 31.9 g·kg ?1 and those of Cd, Cr, Cu, Ni, Pb and Zn were 1.77, 107, 231, 81.9, 199, 721 mg·kg ?1, respectively. (2) The levels of Cu and Zn in office dust were significantly higher than those in household dust, and the levels of other elements had no obvious difference from those in household dust. (3) The levels of Ca and Fe in household dust with different floor numbers were not significantly different, but the levels of Cd, Cu and Pb in household dust with different floor numbers had obvious difference. The levels of elements in household dust from Floor 1 were relatively higher, and the level of Pb in household dust from higher floors was higher than that on lower floors. (4) Outdoor environment, indoor decoration and life styles may cause the difference of elements level in different household dust. (5) There was no obvious risk from heavy metals in dust to children.
Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 43
Heavy metal aerosol particles were first measured with Single Particle Aerosol Mass Spectrometry (SPAMS) in north suburb of Nanjing from January to December, 2013. Using the ART-2a neural network algorithm, we studied the chemical characteristics of aerosol particles and found that the main sources of heavy metal aerosols in Nanjing were industrial emissions, biomass combustion, traffic emissions, fuel combustion and mineral dust, accounting for 35.7%, 34.45%, 13.6%, 11.03% and 4.07%, respectively. Pb, Cd and Cr containing aerosols mainly came from industrial emissions. Cu, Co and Hg-containing aerosols mainly came from biomass burning. V, Zn and Ba-containing aerosols mainly came from traffic emissions. As and Ni-containing aerosols mainly came from fuel combustion.
55. Pollution Characteristics of Heavy Metals in PM 2.5 and Their Human Health Risks Among the Coastal City Group along Western Taiwan Strait Region, China
Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 44
PM 2.5 samples were collected from 11 sampling sites in the coastal city group along western Taiwan Strait region, China, and these heavy metal elements (Zn, Cu, Pb, Ni, Cr, and As) were detected using particle-induced X-ray emission (PIXE) method. The pollution characteristics, enrichment factors and source apportionment of heavy metals in PM 2.5 were analyzed, and furthermore, their human health risks were determined. The result showed concentration distribution was obviously different between PM 2.5 and heavy metals in the city group, for the main sources (e.g., construction dust and ground dust) for PM 2.5 were not the main contribution to these heavy metals. The enrichment factors of Zn, Cu, Pb, Mn, Ni, Cr, and As exceeded 10, which suggested that these metals were enriched and significantly impacted by anthropogenic pollution. Three main groups of heavy metals in PM 2.5 were identified by principal component analysis-multiple linear regression (PCA-MLR), such as coal combustion and traffic emissions (70.59%), multiple sources (coal and oil combustion, pyrometallurgical process, 17.55%) and other industry (11.86%). The risk levels for carcinogenic heavy metals (Ni, Cr, and As) and non-carcinogenic heavy metals (Zn, Cu, Pb, and Mn) were lower than the average level of risk acceptance (10–6), which suggested that these heavy metals did not cause harm to human health in these cities.
56. Concentration Characteristics and Sources of Trace Metals in PM 2.5 During Wintertime in Beijing
Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 45
To study the characteristics and sources of trace metals in PM 2.5 during wintertime in Beijing, PM 2.5 samples were collected from December 2014 to January 2015 by a middle volume sampler in the urban area of Beijing for 30 consecutive days. The mass concentration of PM 2.5 was measured by filter membrane weighting method, and 16 kinds of trace metals were determined by inductively couple plasma-mass spectrometry (ICP-MS). In addition, the pollution characteristics and sources of trace metals were analyzed by enrichment factor (EF) method and factor analysis, respectively. The results showed that the concentrations of five elements (i.e. K, Ca, Fe, Al and Mg) accounted for 90.7% of the total metal elements. The concentrations of the metal elements changed obviously between day and night. Compared with daytime, crustal elements like Mg and Al decreased by more than 30% while anthropogenic elements like Cu and Pb increased by more than 40% in nighttime. Although the concentrations of metal elements increased by nearly one time in heavy pollution days compared with clean days,the mass percent of which in PM 2.5 decreased from 10.9% in clean days to 4.6% in heavy pollution days. This result suggested the accumulation of metal elements in heavy pollution days had a minor contribution to the increased mass concentration of PM 2.5. As the pollution episode progressed, anthropogenic elements (Cu, Zn, As, Se, Ag and Cd) increased faster than crustal elements (Al, Mg, Ca, Mn and Fe), which showed ratios ranging from 2.9 to 5.3 for anthropogenic elements and ratios ranging from 1.2 to 1.8 for crustal elements, when compared between heavy pollution days and clean days. In addition, the EF value of anthropogenic elements was also increased in the pollution days, indicating the concentrations of these elements was further influenced by the anthropogenic sources. Factor analysis showed that metal elements of PM 2.5 during wintertime of Beijing were mainly from coal combustion and biomass burning, motor vehicle and industry emissions, and re-suspension of road dust, with the contributions of 34.2%, 25.5% and 17.1%, respectively.
57. Distribution and Health Risk Assessment of Heavy Metals in Atmospheric Particulate Matter and Dust
Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 46
In order to study the concentration, distribution characteristics, and health risk assessment of toxic heavy metals, Cu, Mn, Pb, Ti, V, Cd, Cr, Co, Mo, and Ni, in atmospheric particulate matter (PM) and dust, the PM and dust samples were collected in all four seasons in 2014 in Beijing using two high volume air samplers (Echo Tecora Inc., Italy) and a dust tank, respectively. Selected metals were quantified by ICP-MS. Annual mean concentrations of PM 2.5and PM 10were 153.40 μg·m ?3and 232.93 μg·m ?3, which were five and three times the limit values in the Ambient Air Quality Standard (GB 3095-2012), respectively. The average PM 2.5/PM 10 was 0.74, implying that PM 2.5 predominated the particulate matter. The results of backward trajectory analysis suggested that exogenous particles originated from the northwest, north-northeast, southeast, and southeast-northwest during winter, spring, summer, and autumn, respectively. The order of annual mean concentrations of selected metals in PM 2.5 and PM 10 was Ti > Mn > Pb > Cu > Cr > Ni > V > Cd > Mo > Co. The sum of the concentrations of Ti, Mn, Pb, Cu, and Cr accounted for 91.93% and 92.49% of the total concentration of target metals in PM 2.5 and PM 10, respectively. The metal content of dust followed the order of Ti > Mn > Pb > Cu > Ni > Cr > V > Co > Mo > Cd and Ti (2 561.48 μg·g ?1) accounted for 72.57% of the total metal content of dust. The geo-accumulation index ( I geo) of Cd, Pb, Cu, and Ni were 4.03, 2.49, 1.33, and 0.43, which represented the states of heavily to extremely contaminated, moderately to heavily contaminated, moderately contaminated and uncontaminated to moderately contaminated, respectively, indicating that dust in the target area included significant amounts of Cd, Pb, and Cu. The health risk assessment suggested that non-carcinogenic and carcinogenic risks of selected metals in PM 10 and dust were within safe limits, but their long-term impact cannot be ignored.
58. Pollution Characteristics and Occupational Exposure Risk of Heavy Metals in Indoor and Outdoor Ambient Particles at a Scaled Electronic Waste Dismantling Plant, Northwest China
Environmental Science,Part 1: Types of Air Pollution,Vol 40,No. 47
Atmospheric particle samples (PM 1.0, PM 2.5, PM 10) were collected from three sampling sites (indoor and outdoor workplaces of a formal e-waste dismantling plant, and upwind area) in an arid area of Northwest China. The contents of six heavy metals (Cd, Cr, Cu, Ni, Pb, and Zn) were analyzed with ICP-OES. Based on this data, the concentration levels, size distribution characteristics, and occupational exposure risks of heavy metals at the indoor and outdoor dismantling workplaces were studied. Particle analysis showed that Zn (4 890 ng·m ?3 indoors, 1 245 ng·m ?3 outdoors) , Pb (indoors 1 201 ng·m ?3, outdoors 240 ng·m ?3), and Cu (1 200 ng·m ?3 indoors, 110 ng·m ?3 outdoors) showed higher pollution levels indoors and outdoors at the dismantling workplace. Moreover, the indoor concentration was much higher than that outdoors, indicating that the dismantling activity was the main cause of the high levels of heavy metal contamination. The indoor and outdoor air pollution characteristics were closely related to the types of electronic waste dismantled. Occupational exposure risk assessments showed that the total non-carcinogenic hazard quotient (HQ) of the indoor and outdoor dismantling workshops was 1.62 × 10 ?3, and 3.60 × 10 ?4, respectively, and the carcinogenic risk values were 2.69 × 10 ?7 and 2.59 × 10 ?9. Cd caused the greatest carcinogenic and non-carcinogenic risks at both indoor and outdoor dismantling workplaces, but it was still far below the limits (1.0) and acceptable ranges (1 × 10 ?6) stipulated by U.S. EPA, indicating that the health risks caused by heavy metals were minor or negligible. Heavy metals in the ambient particulate matter released from an adequately equipped and formally managed e-waste dismantling plant would not lead to any public health risk. The sedimentation characteristics of particulate heavy metals in different organs of the human respiratory system exhibited that the smaller particle size can lead to the greater proportion of deposition in the depths of the respiratory system was. Enterprises should make scientific and effective decisions on the respiratory health risks caused by such fine particles to the health of professional workers.
59. Pollution Characteristics and Potential Ecological Risks of Heavy Metals in Road Dust in Beijing
Environmental Science,Part 1: Types of Air Pollution,Vol 40,No. 48
Based on the concentrations of 21 inorganic elements in particulate matter with diameters less than 10 μm (PM 10) in 2004, and PM 2.5 in 2004 and 2013 of representative road dust in Beijing, the pollution characteristics and potential ecological risks of heavy metals in the dust were analyzed and discussed. The results showed that the six main elements in road dust in Beijing were Si, Ca, Al, Fe, Mg, and K, and the proportions of the total content of the six elements in PM 10 in 2004, PM 2.5 in 2004, and PM 2.5 in 2013 were 96.51%, 96.42%, and 96.53% of the total content of all elements tested, respectively. The elemental enrichment level, pollution degree, and potential ecological risk of heavy metal in road dust in Beijing in 2004 were PM 2.5 > PM 10. Se, a characteristic element of coal dust, was highly enriched in PM 2.5 in 2004, and Cd was high in PM 10 and PM 2.5 in 2004 with enrichment factors of 1024.03, 68.15, and 871.55, respectively. Co, Zn, Ca, and Cu were significantly enriched in PM 10 and PM 2.5 in 2004 with enrichment factors of 12.93, 12.33, 8.30, and 8.07 in PM 10 and 17.41, 21.80, 12.83, and 19.73 in PM 2.5, respectively; Na and Si were not enriched in the road dust. The pollution load index (PLI) of heavy metals was 3.95 in PM 10 and 7.71 in PM 2.5 in 2004. Owing to the implementation of dust, motor vehicles, and combustion source control measures in Beijing and the relocation of the Shougang corporation, the elemental enrichment level, pollution degree, and potential ecological risk of heavy metals in road dust PM 2.5 in 2013 were significantly lower than those in 2004. The enrichment factors of Cd and Se in PM 2.5 in 2013 decreased to 98.47 and 0.95, respectively; those of Cu, Ca, and Zn decreased to 11.90, 8.84, and 8.20, respectively; PLI decreased to 2.56. The results showed that the total potential ecological risk of heavy metals in road dust in Beijing was extremely strong. Heavy metal Cd was the most significant pollution factor and the main potential ecological risk source; its potential ecological risk index (RI) contribution to the total RI of heavy metals was more than 85%. In 2004, the pollution degree of heavy metals in road dust of main roads was significantly higher than that for other road types. The pollution degree of heavy metals in PM 10 was main road > expressway entrance to Beijing > secondary main road > ring road; that for PM 2.5 was main road > ring road > expressway entrance to Beijing > secondary main road. For PM 2.5 in 2013, however, the order was expressway entrance to Beijing > main road > ring road > secondary main road. The pollution degree of heavy metals in road dust of secondary main roads was significantly lower than that for other road types. In 2013, for road dust PM 2.5 in Beijing, the correlation of heavy metals Ti, Zn, V, Cr, Cu, Pb, and Ni was significant owing mainly to traffic-related emissions.
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 49
To describe the characteristics for the content and the distribution of atmospheric lead in urban China, the articles on atmospheric heavy metal contents in Chinese cities publicated from 2000 to 2012 were searched and collected through the search engines of CNKI, Wanfang, CQVIP, Science Direct, Google Scholar and Pub Med etc. Based on the uniform criteria, 69 articles were selected for the further analysis. The atmospheric lead contents were reported in 42 cities of 30 provinces and included more than 5,489 samples. Atmospheric lead concentrations were expressed by the lead concentrations in PM 10. The lead concentrations by those in PM 2.5 and in TSP were transferred, including the limits of the national ambient air quality standards(NAAQS). The weighted mean of atmospheric lead level in urban China was (256.5 ± 192.0) ng/m 3. The atmospheric lead levels in 18 cities (42.9%) were higher than 272.5 ng/m 3, the limits of NAAQS (GB3095-2012) in PM 10, and the average was (551.0 ± 350.3) ng/m 3. The levels of lead in Changsha in Hunan province, Shaoguan in Guangdong province, Shengyang in Liaoning province, Yongxiu in Jiangxi province, Xi'an in Shaanxi province and Hulu island in Liaoning province were 554.4, 637.0, 638.8, 764.1, 1018.6 and 1721.2 ng/m 3, respectively, these were higher than the seasonal limit of NAAQS (545.0ng/m 3). The results also showed that levels of atmospheric lead in developed regions, such as Beijing-Tianjin-Tangshan economic zone, Circum-Bohai-Sea region and Pearl River Delta, were higher than those in other regions. The atmospheric lead contents in winter-spring were 1.2–1.9 times as high as those in autumn-fall in north cities in China, while there were no significant differences in southeast coastal cities. It was showed that lead contents decreased remarkably in Guangzhou, Shanghai, Beijing and Chengdu since 2003. Theseresults suggested that the atmospheric lead contents in urban China were much higher that those in developed countries, although the levels of lead showed the decreased trend.
Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 50
Continuous monitoring of gaseous elemental mercury (GEM), reactive gaseous mercury (RGM) and particulate mercury (PBM) was conducted in the Dongtan wetland park in Chongming Island, Shanghai from March 2014 to February 2015. The average concentrations of GEM, RGM, and PBM were (2.75 ± 1.13) ng·m ?3, (13.39 ± 15.95) pg·m ?3, and (21. 89 ± 40.42) pg·m ?3, respectively, higher than the background concentrations of Northern Hemisphere. The atmospheric mercury showed obvious seasonal variations, with the highest seasonal average GEM concentration in summer (3.65 ng·m ?3), which was mainly influenced by natural sources, while lower GEM concentrations appeared in autumn and winter influenced mainly by anthropogenic sources. The concentration of RGM was the highest in spring and lowest in winter, mainly influenced by the wind direction, while PBM showed high concentrations in autumn and winter, when heavy fine particulate pollution episodes occurred frequently. The concentrations of GEM and PBM were generally elevated in nighttime and lower in daytime caused by the mixing condition of the air masses. Most of the high RGM concentration values occurred in the afternoon of all seasons due to the high atmospheric oxidation. The concentrations of GEM and PBM were high in the west wind due to the emission from anthropogenic sources in Shanghai, Jiangsu, etc. The RGM concentration in southeast wind was obviously higher than those in other wind directions. The RGM was mainly from the anthropogenic sources, and the smaller wind in the southeast direction was against the dispersion of RGM.
62. Distribution Characteristics and Source Analysis of Dustfall Trace Elements during Winter in Beijing
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 51
The dustfall content is one of the evaluation indexes of atmospheric pollution. Trace elements especially heavy metals in dustfall can lead to risks to ecological environment and human health. In order to study the distribution characteristics of trace elements, heavy metals pollution and their sources in winter atmospheric dust, 49 dustfall samples were collected in Beijing City and nearby during November 2013 to March 2014. Then the contents (mass fractions) of 40 trace elements were measured by Elan DRCⅡ type inductively coupled plasma mass (ICP-MS). Test results showed that more than half of the trace elements in the dust were less than 10 mg·kg ?1; about a quarter were between 10–100 mg·kg ?1; while 7 elements (Pb, Zr, Cr, Cu, Zn, Sr and Ba) were more than 100 mg·kg ?1. The contents of Pb, Cu, Zn, Bi, Cd and Mo of winter dustfall in Beijing city were respectively 4.18, 4.66, 5.35, 6.31, 6.62, and 8.62 times as high as those of corresponding elements in the surface soil in the same period, which went beyond the soil background values by more than 300%. The contribution of human activities to dustfall trace heavy metals content in Beijing city was larger than that in the surrounding region. Then sources analysis of dustfall and its 20 main trace elements (Cd, Mo, Nb, Ga, Co, Y, Nd, Li, La, Ni, Rb, V, Ce, Pb, Zr, Cr, Cu, Zn, Sr, Ba) was conducted through a multi-method analysis, including Pearson correlation analysis, Kendall correlation coefficient analysis and principal component analysis. Research results indicated that sources of winter dustfall in Beijing city were mainly composed of the earth's crust sources (including road dust, construction dust and remote transmission of dust) and the burning of fossil fuels (vehicle emissions, coal combustion, biomass combustion and industrial processes).
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 52
Available data of atmospheric nitrogen and phosphorous deposition during Sep, 2013 to Aug, 2014, were collected from three typical monitoring stations of HangJiaHu area, which are located at Hangzhou, Jiaxing andg Huzhou city respectively. The pollution characteristics of this area were discussed. Annual deposition fluxes of nitrogen and phosphorous were significantly high in range of 4 950.74–5 585.80 and 65.25–69.72 kg/ (km 2·a). Atmospheric deposition of nitrogen, phosphorus into the river was 6 038.4 and 77.8 tons, respectively, equivalent to 39.6% and 5.9% of input amount of nitrogen and phosphorus by agricultural. The principal deposition form of nitrogen was wet deposition, while for phosphorous, it was dry deposition. Wet deposition flux of nitrogen and phosphorous was mainly influenced by precipitation, and it increased with the increase of rainfall. Atmospheric deposition of nitrogen and phosphorus had temporal and spatial differences. Atmospheric dry deposition flux of nitrogen was much higher in Hangzhou and Jiaxing; atmospheric dry deposition flux of phosphorous was the highest in Jiaxing; atmospheric wet deposition flux of nitrogen was much higer high in Huzhou and Jiaxing, and atmospheric wet deposition flux of phosphorous was the highest in Huzhou. On the time scale, nitrogen deposition was much higher in summer and autumn, while phosphorus deposition was much higher in autumn and winter.
64. Characteristics and Sources of Water Soluble Inorganic Ions in Fine Particulate Matter During Winter in Xuzhou
Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 53
A total of 32 daily PM 2.5 samples were collected from December 2016 to February 2017 in the urban area of Xuzhou city. Water-soluble inorganic ions (WSIIs) , including F ?, Cl ?, NO 3 ?, SO 4 2?, Na +, Mg 2+, NH 4 +, K + and Ca 2+, were determined by ion chromatography. The average mass concentration of PM 2.5 was (164.8 ± 77.3) μg·m ?3 and the average total mass concentration of the nine ions was (67.5 ± 36.1) μg·m ?3, the contribution of the WSIIs to the PM 2.5 was more than 40.9%. The order of the concentrations of individual ions was NO 3 ? > SO 4 2? > NH 4 + > Cl ? > Ca 2+ > K + > Na + > Mg 2+ > F ?. NH 4 +, NO 3 ?, and SO 4 2- were the major components of the water-soluble ions in the PM 2.5 measurement. The average mass concentration of WSIIs in clean air, mild haze, and severe haze was (12.8 ± 8.8), (59.0 ± 22.8) and (86.3 ± 36.0) μg·m ?3, respectively. The contribution of SNA to WSIIs was 86.4%, 82.8%, and 78.9%, respectively. The correlation between each component of secondary ions with each other was significant. NH 4 +, NO 3 ?, and SO 4 2? were in the form of (NH 4) 2SO 4 and NH 4NO 3. Secondary formation, biomass burning, fossil fuel combustion, and dust were the major sources of the water-soluble ions in PM 2.5.
65. Pollution characteristics and source identification of polycyclic aromatic hydrocarbons in airborne particulates of Beijing-Tianjin-Hebei Region, China
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 54
PM 2.5 and PM 10 were synchronously collected in the ambient air of Beijing, Tianjin, and Shijiazhuang City in four seasons of 2013. The samples were first pretreated by ultrasonic extraction with acetonitrile, and then sixteen polycyclic aromatic hydrocarbons (PAHs) were analyzed by ultrahigh-pressure liquid chromatography. The variation of total PAH concentration was 6.3–251.4 ng/m 3 and 7.0–285.5 ng/m 3 in PM 2.5 and PM 10 of Beijing-Tianjin-Hebei Region, respectively. It was found that the degree of seasonal variation of PAHs pollution was as following: winter > spring > autumn > summer, and the degree of spatial variation of PAHs pollution was as following: Shijiazhuang > Beijing > Tianjin. PAHs were mainly composed of 4, 5, and 6-ring PAHs with percentages of 25.0%–45.1%, 31.7%–40.1%, and 15.1%–28.2%, while the sum percentage of 2 and 3-ring PAHs was below 10.3%. Compared with that in the non-heating season, the percentage of 4-ring PAHs increased in the heating season, while it was obviously deceased for 5 and 6-ring PAHs. PAH diagnostic ratios showed that PAHs sources varied with season, indicating that coal burning and vehicular emission were the two main emission sources of PAHs sources. Coal burning played an important role in the heating season, and vehicular emission was the main emission source in the non-heating season.
66. Pollution of Halogenated Polycyclic Aromatic Hydrocarbons in Atmospheric Particulate Matters of Shenzhen
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 55
Concentrations of halogenated polycyclic aromatic hydrocarbons (HPAHs) in atmospheric PM 10 and PM 2.5 samples collected from Shenzhen were determined using GC-MS. Total concentrations of nine HPAHs in atmospheric PM 10 and PM 2.5 samples ranged from 118 to 1 476 pg·m ? 3 and 89 to 407 pg·m ? 3, respectively. In PM 10 and PM 2.5 samples, the concentration of 9-Br Ant was the highest, followed by 7-BrBaA and 9,10-Br 2Ant. Seasonal levels of total HPAHs in atmospheric PM 10 and PM 2.5 samples in Shenzhen decreased in the following order: winter > autumn > spring > summer, whereas concentrations of individual HPAHs showed different seasonal levels. Meteorological conditions, including temperature, precipitation, and relative humidity,might be important factors affecting the seasonal levels of HPAHs in atmospheric PM 10 and PM 2.5. In addition, there were significant correlations between concentrations of HPAHs and parent PAHs. Finally, the toxic equivalency quotients (TEQs) of HPAHs were estimated. The TEQs of HPAHs in atmospheric PM 10 and PM 2.5 samples ranged from 17.6 to 86.2 pg·m ? 3 and 14.6 to 70.4 pg·m ? 3, respectively. Among individual HPAHs, 7-BrBaA contributed greatly to the total TEQs of HPAHs. Our results indicated that the total TEQs of HPAHs were lower than parent PAHs in atmospheric PM 10 and PM 2.5 samples in Shenzhen.
67. Source apportionment and toxicity quantitation of PM 2.5-associated polycyclic aromatic hydrocarbons obtained from Chengdu, China
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 56
In this study, PM 2.5-associated polycyclic aromatic hydrocarbons (PAHs) were investigated in Chengdu, China, during the winter in 2010–2011. A total of 16 priority PAHs were measured, then the source identification, risk assessment and toxicity quantitation were conducted. The results showed that the total concentrations (ΣPAHs) ranging from 22.79 to 215.82 ng/m 3, with the average concentration 71.38 ng/m 3. The percentage of high ring (4–6 ring) PAHs were in large proportion, ranging from 75.95% to 99.52%. Source apportionment were estimated by EPA PMF5.0 model and five sources were identified, including coal and wood combustion, diesel exhaust emissions and gasoline exhaust emissions. The risk assessment of PAHs was determined by the equivalent factor (TEF), indicating that the toxicity of PAHs presenting in cute level. Finally, the PMF-TEF model was used to apportion the source contributions to toxicity of PAHs, illustrating that the toxicity contribution of PAHs from coal and wood combustion (12.39%), diesel exhaust emissions (24.78%) and gasoline exhaust emissions (62.83%). The conclusion in this work can provide the useful information in the process of the comprehensive environmental control.
68. Pollutional Characteristics and Sources Analysis of Polycyclic Aromatic Hydrocarbons in Atmospheric Fine Particulate Matter in Lanzhou City
Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 57
Polycyclic aromatic hydrocarbons (PAHs) are a group of important toxic compounds. In order to detect the pollutional characteristics of atmospheric PAHs in Fine Particulate Matter (PM 2.5), a total of 60 PM 2.5 samples were collected in Lanzhou City during the winter of 2012 and summer of 2013. The GC/MS measurement results of the samples demonstrated the averagely total mass concentrations of the most significant 16 homologues of PAHs were (191.79 ± 88.29) ng·m ?3 and (8. 94 ± 4.34) ng·m ?3 in winter and summer respectively, indicating a higher pollution level in winter. In winter, the snowfall was the most important meteorological factor for the decrease of PAHs mass concentration in PM 2.5. The percentages of PAHs with 4 rings were the highest in both winter (51.40%) and summer (49.94%) in Lanzhou. The percentage of PAHs with 5-6 rings in summer (41.04%) was higher than that in winter (24.94%). However, the percentage of PAHs with 2-3 rings in summer (9.03%) was lower than that in winter (23.67%). Based on the analysis of characteristic ratios, we concluded that the PAHs in atmospheric PM 2.5 in Lanzhou were mainly sourced from coal and vehicle emissions in winter, especially the diesel vehicles. The absolute contributions of all possible PAHs pollution sources were insignificant in summer, with relatively higher contribution from gasoline vehicles.
69. Atmospheric Pollution Characteristics and Inhalation Exposure Risk of Nitrated Polycyclic Aromatic Hydrocarbons in PM 2.5 at the Ningdong Energy and Chemical Industry Base, Northwest China
Environmental Science,Part 1: Types of Air Pollution,Vol 40,No. 58
Atmospheric PM 2.5 samples were collected by using the active sampling method to investigate the pollution characteristics of nitrated polycyclic aromatic hydrocarbons (NPAHs) at the Ningdong Energy and Chemical Industry Base, Northwest China. Furthermore, the primary sources and the contributions of secondary formation sources as well as the inhalation exposure risks were identified. The main results were as follows. The concentration levels of Σ 12NPAHs in PM 2.5 ranged from 2.06 ng·m ?3 to 37.14 ng·m ?3 at the Ningdong Energy and Chemical Industry Base. The average concentration of Σ 12NPAHs was (25.57 ± 5.76) ng·m ?3 in winter and (6.22 ± 1.74) ng·m ?3 in summer for the Baofeng sampling site associated with the energy industry. The average concentration of Σ 12NPAHs was (7.13 ± 1.44) ng·m ?3 in winter and (2.58 ± 0.39) ng·m ?3 in summer for the Yinglite sampling site associated with chemical and electricity industries. The level of Σ 12NPAHs in PM 2.5 was higher in winter than that in summer because of the increased heating in winter. The atmospheric pollution levels of NPAHs at the Baofeng sampling site were generally higher than those at the Yinglite sampling because of the higher primary NPAHs emissions from coal mining and coke production in Baofeng than those from the chemical industry in Yinglite. The calculated nocturnal/diurnal ratios revealed that the concentration of Σ 12NPAHs in PM 2.5 during the summer season was higher in the daytime than that in the nighttime, but the opposite trend occurred in winter, thus indicating that secondary formation processes made more contributions to NPAHs during summer in the daytime. The congener profiles of NPAHs were mainly composed of primary emission markers such as 2-nitrofluorene (2N-FLO) and 6-nitrochrysene (6N-CHR), which were the predominant ones in winter and summer for both the Baofeng and Yinglite sampling sites. The total proportion of 2N-FLO and 6N-CHR was 46% in winter and 73% in summer for Baofeng and 59% in winter and 55% in summer for Yinglite. Meanwhile, 3N-PHE, which is a marker compound of secondary formation processes, accounted for a higher percentage in summer especially at Yinglite. This finding revealed that the chemical production at Yinglite was associated with higher precursor emissions than that of Baofeng, and thus, more NPAHs were derived from secondary formation processes. Moreover, Σ 12NPAHs/Σ 16PAHs ratios were calculated to identify the potential sources of NPAHs across the city. The results indicated that the higher environmental temperatures in summer promoted the degradation of PAHs and secondary formation of NPAHs, and thus, secondary formation contributed more to NPAHs in summer than in winter. Furthermore, the lung cancer risks induced by inhalation exposures to Σ 5NPAHs were assessed based on the BaP toxicity equivalency factor. The results showed that the lung cancer risk value of Σ 5NPAHs was (3.06 × 10 ?5 ± 1.36 × 10 ?5) in winter and (1.79 × 10 ?5 ± 0.80 × 10 ?5) in summer for the Baofeng sampling site, while the risk value was (2.85 × 10 ?5 ± 1.20 × 10 ?5) in winter and (1.86 × 10 ?5 ± 0.83 × 10 ?5) in summer for the Yinglite sampling site. Notably, the lung cancer risk values in our study for both sampling sites were higher than the standard limit value (1.00 × 10 ?5) of the California Environmental Protection Agency, which indicates that the local population at the Ningdong Energy and Chemical Industry Base was subjected to potentially elevated lung cancer risks due to inhalation exposures to PM 2.5-bound NPAHs.
70. Source Apportionment and Health Risk Assessment of Polycyclic Aromatic Hydrocarbons in PM 2.5 in Changchun City, Autumn of 2017
Environmental Science,Part 1: Types of Air Pollution,Vol 41,No. 59
In this study, 30 PM 2.5 samples were collected from the atmosphere in Changchun City in the autumn of 2017. The concentration and composition characteristics of 17 polycyclic aromatic hydrocarbons (PAHs) in the samples were analyzed by gas chromatography mass spectrometry (GC-MS). The diagnostic ratio and principal component analysis (PCA) method were used to determine the source of PAHs pollution. The health risk assessment was carried out by both calculating the equivalent carcinogenic concentration of benzo(a)pyrene and the lifetime risk of cancer. Results show that the average PM 2.5 concentration in autumn in Changchun is (50.84 ± 12.23) μg·m ?3, and the concentration of organic carbon (OC) and elemental carbon (EC) are (17.07 ± 5.64) μg·m ?3 and (1.33 ± 0.75) μg·m ?3, respectively, accounting for 37% of the total PM 2.5. The total concentration of PAHs is (15.69 ± 5.93) ng·m ?3, which is dominated by medium- to high-ring-number PAHs, accounting for 84.26% of total PAHs. The atmospheric PAHs in Changchun mainly originate from motor vehicle exhaust emissions (44.48%) > coal combustion (29.16%) > biomass burning (26.36%), local transportation (gasoline vehicles) emissions being the main source of pollution. The average carcinogenic concentration of benzo(a)pyrene is in the range of 1.55 ng·m ?3 and 5.38 ng·m ?3, and the average carcinogenic equivalent concentration is (6.44 ± 1.53) ng·m ?3, which is generally considered a slight pollution level. The ingestion of PAHs by breathing is the most harmful to the health of adult women, followed by adult males and children. However, since the lifetime carcinogenic risk value of the entire population did not exceed 1 × 10 –6, their health risks are considered to be at acceptable levels.
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 60
Based on the observational data of haze occurrences from 1962 to 2011, the temporal-spatial variations of haze pollution and the causes over Central China were discussed by using statistical methods, such as linear regression, cluster and correlation analysis. It was found that haze pollution occurred frequently in the areas of northern-central Henan, western-central Hubei and central Hunan, where the population was relatively dense with high aerosol concentrations. The station with the most frequent haze events was Xinxiang in Henan Province, reaching 79.1d/a. The seasonal variations showed that the heaviest haze pollution happened in winter and the slightest haze occurred in summer. Haze pollution was a typical atmospheric environment incident in winter over Central China. These seasonal differences became obscure with the increases of haze frequency in spring, summer and fall. High haze pollution was spatially centered over the urban area. The increasing and decreasing trends in haze occurrences were identified respectively in the polluted region and the relatively clean region over the recent 50 years, revealing the polarization in air environment change over Central China. Haze events in Central China were highly related to the increasing anthropogenic emissions and the decreasing East Asia monsoon over the region during the past 50 years.
72. Spatial distribution and temporal variation of aerosol optical depth over China in the past 15 years
China Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 61
Characteristics of spatial distribution and temporal variation of aerosol optical depth (AOD, on 550 nm) in 2000–2014 in Mainland China were analyzed in this paper with monthly, seasonal and yearly mean data, derived from the NASA MODIS04_ L2 product. Specifically, we used Spearman-test method to investigate the seasonal and yearly variation trends of AOD over China from 2000–2014. The results were as follows: all-year high value of AOD appeared in the Sichuan Basin, the southern Xinjiang basin, the central China region, the Yangtze River Delta, the North China Plain, the Guanzhong Plain and the Pearl River Delta; while all-year low value of AOD appeared in the western Sichuan, the southeast Tibet, the border of Inner Mongolia and northern Hebei Province, and part of the Hetao area. The value of AOD in Northwest China showed a decreasing trend, of which the junction of western Sichuan and southeast Tibet and Shaanxi-Gansu-Ningxia showed a significant downward trend. The AOD value in the eastern part of China mainly showed an upward trend, and there was a marked upward trend in central China, the Yangtze River Delta, the North China Plain and the Guanzhong Plain. AOD value changes significantly with seasons, high in spring and summer and low in autumn and winter. The high value area of AOD and the area of upward trend are basically located in the southeast of Heihe-Tengchong Line, indicating that human activities have a significant impact on the AOD value.
73. Temporal and spatial characteristics of haze days and their relations with climatic factor during 1960–2013 over South China
China Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 62
This study investigates temporal and spatial variations of haze days and the possible climatic factors in South China during recent 54 years. Basic statistical methods are used here based on the observed data from 57 meteorological stations, including linear regression, cluster and correlation analysis. The result shows that more haze days occur in the Pearl River Delta (PRD) region of Guangdong and mid-eastern Guangxi.The haze days increase remarkably during the past 54 years and show a decline trend after 2008. Among the four seasons, the heaviest haze pollution happened in winter, follows spring and autumn, and relatively weak in summer. They are also associated with a decrease trend after 2008 except winter. In addition, all of the haze days in different varied-intensity increased obviously during the past 54 years. It is further found that haze pollution in South China increases not only in the number of days, but also the pollution intensity. The rapidly rise time periods of haze days are different in different regions over South China. It occurs in the 1990s over serious pollution and normally pollution regions,but shows after 2000 for the relatively clean regions. Number of haze days over the serious polluted and normal polluted regions has been decreased during recent 10 years, but it remains a rapid increase for the relatively clean areas. Our further analysis suggests that the decreased trends of precipitation days during recent 54 years would factor in reducing the wet-depositing capacity of atmospheric pollutants. Increase of the breeze days, which connects to the decrease of mean wind speed and strong wind days, on the other hand, would also contribute to the reduction of pollutants diffusion capacity and more haze pollution.
74. Composition and Regional Characteristics of Atmosphere Aerosol and Its Water Soluble Ions over the Yangtze River Delta Region in a Winter Haze Period
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 63
To investigate the pollution characteristics of water-soluble ions in fine atmospheric particles in the Yangtze River Delta during the haze period from 18th to 24th Jan 2013, a joint sampling campaign using Andersen sampler was conducted at five cities (including Nanjing, Suzhou, Hangzhou, Lin’an and Ningbo). The analysis of the size distribution of these ionic species coupled with the local meteorological conditions may shed some insightful light on the haze formation mechanism in this region. The result has shown: firstly, during the observation period, when the Yangtze River Delta located at high pressure or in the front of high pressure and has a large pressure gradient, the lower atmosphere has a significant airflow divergence in favor of pollutant dispersion; while located in weak low pressure and weak high pressure, the equalizing pressure field is not favorable for pollutant dispersion, especially accompanied with lower atmosphere convergence airflow. Secondly, during the hazy period, the concentration of fine particles and total water-soluble inorganic ions (TWSS) has increased dramatically; the increasing proportions of TWSS in fine particles are as follows: Hangzhou 0.9%, Lin’an 4.2%, Nanjing 8.1%. The particle size of secondary ions of SO 4 2?, NO 3 ?, NH 4 +complies fine mode (particle size < 2.1 μm), whose peaks migrate from 0.43–0.65 μm to 0.65–1.1 μm during the observation period, the peak of particle size of Ca 2+, Mg 2+ appears at 4.7–5.8 μm, while the ions of Na +, Cl ?, K + show a bimodal distribution. Moreover, secondary inorganic ions play a significant role in the formation of haze pollution, where the concentrations of secondary inorganic ions of NH 4 +, SO 4 2? and NO 3 ?have higher increasing rates; their relative proportions of increasing from each monitoring points are as follows: Hangzhou 3%, Lin’an 55% and Nanjing 64.9%. Finally, SO 4 2?has the highest mass contribution to SNA, up to 45%; also, the NO 3? / SO 4 2?ratios in each monitoring points are always higher than a fair 0.5, which could indicate the significant contribution of the mobile source towards this particle pollution.
75. Influence of the East Asian winter monsoon and atmospheric humidity on the wintertime haze frequency over central-eastern China
Acta Meteorologica Sinica,Part 1: Types of Air Pollution,Vol 74,No. 64
Based on long-term observations collected at 423 stations in China and the NCEP/NCAR reanalysis data for the period from1961 to 2013, the influence of the East Asian winter monsoon (EAWM) and atmospheric humidity on the wintertime haze frequency over North China, Jianghuai Region and South China has been discussed. Results suggest that: (1) there is a remarkable negative correlation between the EAWM and the wintertime haze frequency. First, a weakening EAWM leads to decreases in surface wind speed, and subsequent increases in the wintertime haze frequency over all the three regions. Decreases in the number of days with daily maximum wind speed of 7–8 m/s in North China and daily maximum wind speed of 6–8 m/s in Jianghuai Region and increases in the number of days with daily maximum wind speed not greater than 2 m/s in South China have the largest impact on the wintertime haze frequency. Second, as the intensity of the EAWM weakens, the surface temperature increases, which induces increases in the wintertime haze frequency, especially in North China; (2) the relative humidity decreases in response to increases in temperature. There is a remarkable negative correlation between the relative humidity and the wintertime haze frequency in Jianghuai Region and South China; (3) the increasing wintertime temperature is conducive to the strengthening of the atmospheric stability. The wintertime haze frequency is negatively correlated with the atmospheric stability over all the three study regions, particularly with the saturation mixing ratio from 850 to 500 hPa; (4) the wintertime haze frequency in North China is significantly positively correlated with the wintertime water vapor net income. In Jianghuai Region, it is significantly positively correlated with the net income of wintertime zonal water vapor transport but significantly negatively correlated with the net income of wintertime meridional water vapor transport. In South China, it has a significantly negative correlation with the net income of wintertime meridional water vapor transport, but has no significant correlation with the net income of wintertime zonal water vapor transport.
76. Seasonal variation of vertical distribution of aerosol types around Shanghai during haze periods
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 65
Using the classification data of different layers aerosols from CALIPSO Satellite Lidar Level 2 aerosol retrieval data during the haze periods from January 2007 to November 2010, the seasonally vertical distribution of different types of aerosols including clean marine, dust, polluted continental, clean continental, polluted dust, smoke and other types around Shanghai during haze periods were analyzed. The results showed as follows. In 0–2 km altitudes the frequency of smoke aerosols' occurrence during haze was significantly higher than during haze-free periods, but in 2–8 km dust, polluted dust and polluted continental aerosols' frequency in haze were higher than in haze-free periods. In 0–2 km, polluted continental aerosols' frequency in spring was higher than in other seasons during haze. In 0–2 km, polluted dust and marine aerosol's frequency in summer were higher than in other seasons during haze, especially polluted dust. In autumn the frequency of smoke aerosols in 0–2 km was higher than in 2–6 km. the frequency of polluted dust, smoke and polluted continental aerosols in winter were higher than in other seasons during haze.
77. Concentration and Particle Size Distribution of Microbiological Aerosol during Haze Days in Beijing
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 66
In this study, we evaluated the bacterial, fungal aerosol concentration, and particle size distribution using microbiological aerosol sampler, and analyzed the particles count concentration of PM 1.0, PM 2.5, PM 5.0 and PM 10.0 using aerodynamic particle sizer during sunny and haze days in Beijing during Jan 8th, 2013 to Feb 4th, 2013. The concentration of bacterial, fungal aerosol, air particulate matter and aerosol distribution were compared between haze days and sunny days. Our results indicated that the proportion of fungal particles smaller than 5 micron, which could deposit in lungs or deeper regions, was much higher than bacterial particles. The biological concentration of bacteria and fungi were higher in sunny days than in haze days, and there was no statistic difference of the microbiological aerosol distribution. The concentration of air particulate matter were higher in haze days than in sunny days, PM 1.0 was the main particulate matters both in sunny days and haze days.
Acta Meteorologica Sinica,Part 1: Types of Air Pollution,Vol 74,No. 67
By analyzing the conventional observation data, sounding data and reanalysis data of NCEP/NCAR, the circulation background, boundary layer characteristics, dynamic conditions, thermal conditions and the airflow trajectory of the heavy haze in Jiangsu Province during autumn and winter of 2014 are discussed. The results show that zonal circulation at 500 hPa was of more straightly west or northwest airflow. The airflow at 500 hPa changed to the northwest can be an indicator for heavy haze that will disappear in 6 to 12 hours. The main surface situations were uniform pattern of pressure, cold front and inverted trough with low pressure. The surface situation changed when heavy haze occurred. Just before cold front coming, the haze was the heaviest. The east path of cold air was more effective for eliminating haze than the middle or west path. In most of the heavy haze days, the bottom of the temperature inversion was near the ground. Lower wind speed of less than 4 m/s was shown to be favorable for the development of haze. The daily variation was obvious with the haze mitigated in the afternoon. As the intensity and duration of temperature inversion were concerned, the inverted trough type was stronger than the cold front west path type, and both of them were stronger than the uniform pressure type. But the three types were almost the same in terms of the thickness of inversion. Different types show different inversion intensity, which may be related to the prefrontal temperature enhancement and wind speed on 925 hPa and near ground, as well as air sources. The height of inversion layer is mostly below 300 m, with the intensity of temperature inversion for every hundred meters between 1 to 5 °C, and higher relative humidity of greater than 40% and less than 90% were shown to be favorable for the development of haze. Underlying sink motion provided a favorable dynamic condition for haze, and weak upward motion at 850 hPa was conducive to the upward development of haze. By using backward trajectory simulation, it was shown that the pollution for the uniform pressure type was mainly from the local emissions, cold front pollutants were from the northwestern or north China and transmitted over a long distance, and the pollution for the type of inverted trough with low pressure mainly from the southern China. By studying the characteristics of heavy haze during autumn and winter of Jiangsu, meaningful results are obtained, which can provide more references for the future forecast.
Chinese Journal of Atmospheric Sciences,Part 1: Types of Air Pollution,Vol 40,No. 68
The spatial and yearly trends of haze during 1980–2013 in Anhui Province and its possible reasons were analyzed based on daily observations from ground-level stations. The effects of urbanization, industrialization and climate change on haze were discussed through an analysis of trends in haze frequency at urban (town) stations and comparison with provincial coal consumption, SO 2 emission, tropospheric NO 2 column content, civil automobile-owned and East Asia monsoon index. The main conclusions were as follows: (1) The number of annual haze days averaged over all stations increased evidently with large fluctuations. The zones with frequent haze were different in different periods. During the 1980s, the number of provincial annual average haze days was 5.5 d, with scattered high values from the zone near the Yangtze River to the Huaihe River. During the 1990s, the number of provincial annual average haze days was 8.5 d, with high values at some county stations and Hefei, the capital city. During the 2000s, the number of provincial annual average haze days was 8.7 d, with three evident high-value zones in the central area between the Yangtze River and Huaihe River, the central area along the Huaihe River, and the central to the eastern area along the Yangtze River, respectively. (2) Based on geographical locations, the province was divided into six sub-regions and the annual haze days averaged over all stations in each sub-region showed different trends. For example, it varied gently in southern Anhui, increased rapidly since 2000 in the region along the Huaihe River, and first increased and then decreased in the region north to the Huaihe River and the region along the Yangtze River. (3) Based on station locations, all stations were divided into two groups: city stations and county stations. The number of annual haze days increased evidently at the city station, while it increased slowly at the county stations until 2008, and then decreased. (4) Urbanization and the rapid increase of civil automobile-owned, which led to a rapid increase of NO x emissions, might be the major impact factors involved in the evident increase of haze days at city stations; whereas, the driving factor of variation of haze days at county stations is likely to be climate change, e.g., the intensity of the monsoon in East Asia.
80. Characteristics of the Size Distribution of Water Soluble Inorganic Ions During a Typical Haze Pollution in the Autumn in Shijiazhuang
Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 69
Abstract: To characterize the size distribution of water soluble inorganic ions (WSII) in haze days, particle samples were collected by an Andersen cascade impactor in Shijiazhuang from October 15 to November 14 in 2013, and the concentrations of eight kinds of WSII (Na +, NH 4 +, K +, Mg 2+, Ca 2+, Cl ?, NO 3 ? and SO 4 2?) during a typical haze episode were analyzed by ion chromatography. Sources and formation mechanism of WSII were analyzed based on their size distributions.The results showed that Shijiazhuang suffers serious air pollution during the autumn season. The daily average concentrations of PM 10 and PM 2.5 were (361.2 ± 138.7) μg·m ?3 and (175.6 ± 87.2) μg·m ?3 and the daily average concentration of PM 2.5 was 2.3 times that of the national secondary standard. The total water soluble inorganic ion concentrations (TWSII) in clean days, light haze days and heavy haze days were (64.4 ± 4.6) μg·m ?3, (109.9 ± 22.0) μg·m ?3 and (212.9 ± 50.1) μg·m ?3 respectively. In addition, the ratio of secondary inorganic ions (SNA: SO 4 2?, NO 3 ? and NH 4 +) in TWSII increased from 44.9% to 77.6% as changed from clean days to the heavy haze days, suggesting the evolution of haze episodes mainly originated from the formation and accumulation of SNA. The size distributions of SO 4 2?, NO 3 ? and NH 4 + were bimodal on clean days, peaking at 0.43–0.65 μm and 4.7–5.8 μm, respectively, which changed to unimodal distribution in both the light and heavy haze days, peaking at 0.65–1.1 μm. Owing to high humidity during the heavy haze days, the aqueous phase reactions of secondary inorganic particles were promoted, which led to the transformation of secondary inorganic ions from condensation mode on clean days to the droplet mode in haze days more obvious. The size distributions of Na +, Mg 2+ and Ca 2+were different with that of SNA, which showed a coarse mode peaking at 4.7–5.8 μm both in clean and haze days, whereas K + and Cl ? showed a bimodal distribution both in clean and haze days, although the modal size was different in clean and haze days.
81. Variation of Size Distribution and the Influencing Factors of Aerosol in Northern Suburbs of Nanjing
Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 70
The size distribution of particulate was analyzed by the FA-3 9 stage sampler in Northern-suburb of Nanjing from January to November in 2014. First,the monitoring result from FA-3 was compared with the results of the same period obtained from a medium flow size grading sampler (KC-120H) and online monitoring instrument of the Environmental Protection Agency. The data correlation coefficients were all greater than 0.95. The fine particle concentration from FA-3 was lower by 13.9% and 16.6%,while PM 10 concentration was higher by 15.2% and 13.3% respectively. However,the deviations were in the acceptable range of atmospheric sampling which could indicate the accurate classification and sampling of particulate for FA-3. Particulate pollution in Northern-suburb Nanjing was serous in which the annual average concentrations of PM 1.1, PM 2.1 and PM 10 were (65.6 ± 37.6), (91.0 ± 54.7) and (168.0 ± 87.0) μg·m -3 respectively; fine particles dominated and most of them had a diameter of less than 1.1 μm. Particle size distribution was bimodal with peaks at 0.43–0.65 and 9–10 μm; the median diameter was 1.83 μm which was in the accumulation mode. In winter, the concentration of fine particle size was higher and in spring the coarse particle size was higher; in summer, the fine particle size concentration was not significantly reduced but coarse particle size was obviously lower than those in other seasons.The differences of particle size distribution in day and at night were very small in coarse segment and in fine segment,the nocturnal concentrations were mostly higher than diurnal concentrations. The precipitation had cleaning effect for each size range of particulateexcept in summer and the effect was more distinct in fine particle size. In haze days,with the aggravation of haze level,the particle concentration in the diameter range of 0.43–2.1 μm increased gradually while in this segment the particle concentration was significantly negatively correlated with visibility. Using relative humidity of 70% as the demarcation,the particle size distribution changed significantly: when humidity was greater than 70%,mass concentration of particle with a diameter of less than 0.43 μm reduced significantly but that with diameter range of 0.43–2.1 μm increased obviously which should be related to the particle hygroscopic growth. The air mass sources could be divided into four categories in northern-suburb of Nanjing. Air mass from the northwest with rapid transport velocity was the cleanest in which the fine particle size concentration was significantly lower than those in other directions; the air mass from local and surrounding was the most severely polluted with high concentrations in both fine and coarse segment,its transmission distance was short and wind speed was small which contributed greatly to air pollution of Nanjing with probability of occurrence of pollution reaching 73.9%.
China Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 71
Remote sensing data, ground monitoring data, meteorological data were used for analyzing effects on the haze pollution from autumn crop residue burning over the Jing-Jin-Ji region during the period from October 12 th to 16 th in 2016. Results indicate that smoke aerosol was found in the atmosphere based on the CALIPSO aerosol subtype products, which means this heavy pollution process was related to the pollutant transmission from the crop residue burning in the surrounding regions. Measurements of AERONET (Aerosol Robotic Network) Beijing site show that aerosol volume size distribution was characterized by bio-modal distribution on October 13 th, and the volume median radii and concentration of fine aerosol mode were 0.33 μm and 0.145 μm 3/μm 2, respectively. Meanwhile, aerosol volume size distribution was characterized by unimodal distribution on October 14 th, and the volume concentration of fine aerosol mode reached 0.34 μm 3/μm 2. According to the ground monitoring data, the concentrations of PM 2.5, CO and SO 2 increased significantly, and the largest values were 339 μg/m 3, 2 mg/m 3and 20 μg/m 3, respectively. Notably, correlation coefficients between the number of crop residue burning spots and CO, PM 10, PM 2.5 reached 0.65, 0.79 and 0.68, respectively, which indicates that the crop residue burning impacted the air quality significantly. The HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model) backward trajectory model was used to simulate the pollutant transport. The backward trajectory results show that the air mass went through crop residue burning area, and then arrived in Jing-Jin-Ji region on 14 th October. The air mass carried large number of polluting gases and particulate matter, and aggravated the haze pollution. In addition, the weak surface wind field with average wind speed of 1 m/s, was not conducive to pollutant dispersion and dilution. The high humidity (mean value of 77.8%) led the hygroscopic growth of aerosol in the air. The stability of the atmosphere is adverse to the pollutant diffusion, and prolongs the process of pollution. Therefore, the heavy haze pollution occurred during the period from October 12 th to 16 th in 2016 accounts for the combination of natural and human factors, namely the local pollutant emission and transmission due to crop residue burning, the local vehicle exhaust, the stability of the atmosphere and the abundant water vapor near the surface.
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 72
Using the method recommended by US EPA, emission of volatile organic compounds (VOCs), sampled from barbecue, Chinese and Western fast-food, Sichuan cuisine and Zhejiang cuisine restaurants in Beijing were investigated. VOCs concentrations and components from different cuisines were studied. The results indicated that based on the calibrated baseline ventilation volume, the VOCs emission level from barbecue was the highest, reaching 12.22 mg·m ?3, while those from fast-food of either Chinese or Western, Sichuan cuisine and Zhejiang cuisine were about 4 mg·m ?3. The components of VOCs from barbecue were different from those in the other cuisines, which were mainly propylene, 1-butylene, n-butane, etc. The non-barbecue cuisines consisted of high concentration of alcohols, and Western fast-food contained relatively high proportion of aldehydes and ketones organic compounds. According to emission concentration of baseline ventilation volume, barbecue released more pollutants than the non-barbecue cuisines at the same scale. So, barbecue should be supervised and controlled with the top priority.
84. Pollution Characteristics of Aldehydes and Ketones in the Exhaust of Beijing Typical Restaurants
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 73
Aldehydes and ketoness, as one of the components in the exhaust of restaurants, are a class of volatile organic compounds (VOCs) with strong chemical reactivity. However, there is no systematic study on aldehydes and ketones in the exhaust of restaurants. To further clarify the food source emission levels of aldehydes and ketones and controlling measures, to access city group catering VOCs emissions control decision-making basis, this study selected 8 Beijing restaurants with different types. The aldehydes and ketones were sampled using DNPH-silica tube, and then ultra performance liquid chromatography was used for quantitative measurement. The aldehydes and ketones concentrations under reference volume condition from 8 restaurants in descending order were roasted duck restaurant, Chinese style barbecue, home dishes, western fast-food, school canteen, Chinese style fast-food, Sichuan cuisine, Huaiyang cuisine. The results showed that the range of aldehydes and ketones (C1–C9) concentrations under reference volume condition in the exhaust of restaurants was 115.47–1,035.99 μg·m ?3. The compositions of aldehyde and ketones in the exhaust of sampled restaurants were obviously different. The percentages of C1–C3 were above 40% in the exhaust from Chinese style restaurants. Fast food might emit more C4–C9 aldehydes and ketones. From the current situation of existing aldehydes and ketones control, the removal efficiency of high voltage electrostatic purifiers widely used in Beijing is limited.
China Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 74
Ambient concentrations of 98 volatile organic compounds (VOCs) species were measured continuously at Dingling (DL, background site), Dongsi (DS, urban site) and Yongledian (YLD, southeast regional transmission site) in Beijing for one year in 2014, in order to better understand the characteristics of VOCs species and their role in chemical reactivity in Beijing. The annual concentration of VOCs in Beijing was (47.36 ± 13.78) × 10 ?9, with alkanes as the most abundant group (39.55%), followed by oxygenated VOCs (OVOCs), and then alkenes and aromatics successively. The VOCs concentrations at DS and YLD sites were much higher than those at DL site. DS site was heavily influenced by vehicular exhausts and the usage of LPG/NG, YLD site had great contributions of vehicular emissions, paint and solvent evaporation, while DL site had more influence of urban pollution transportation. The VOCs concentrations were high in winter and low in summer. Because of the different emission sources, VOCs species at the three sites exhibited different diurnal variations. The ratios of toluene/benzene indicated that coal combustion had a great contribution to VOCs in winter, while contribution of paint and solvent evaporation increased in spring and summer. Alkenes played a predominant role in VOCs chemical reactivity, followed by aromatics and OVOCs. And the key reactive VOCs species in Beijing were ethylene, acetaldehyde, m/p-xylene, methylbenzene, propene, o-xylene, ethylbenzene, n-butane, 1-butene, and propanal.
86. Temporal Variation, Spatial Distribution, and Reactivity Characteristics of Air VOCs in Beijing 2015
Environmental Science,Part 1: Types of Air Pollution,Vol 39,No. 75
In 2015, continuous volatile organic compound (VOC) monitoring was conducted for Dongsi (urban site), the southeast boundary site Yongledian, and Dingling (background site). The average annual mole fraction of atmospheric VOCs in urban areas was (48.93 ± 31.03) × 10 ?9, the average annual mole fraction of the southeast boundary was (54.55 ± 39.64) × 10 ?9, and the average annual mole fraction for the background site was (28.25 ± 21.26) × 10 ?9. Considering VOC components, alkanes occupy the highest proportion, followed by oxygen-containing VOCs, olefins, aromatic hydrocarbons, halogenated hydrocarbons, and acetylene. VOC concentration was higher in winter, lower in summer, higher at night and lower in the daytime. The concentration of acetylene in urban areas was higher in spring, summer and autumn, but higher in winter at the southeast boundary site. However, in the background, a small amount of direct anthropogenic interference was detectable, with the concentration of oxygen VOCs higher at noon and in summer. The species with high mole fractions in the VOCs were identified as mainly ethane, acetylene, ethylene, acetaldehyde, propane, acetone, n-butane, dichloromethane, and other low-carbon substances. The concentrations of benzene and toluene in the high-carbon group were relatively high. From the toluene/benzene ratio, it was found that Beijing VOCs were influenced by many sources other than transportation. However, the ratio of ethane/acetylene has been found to be significantly dependent on the aging of air mass in Beijing, with the southeast boundary particularly affected by movement of the aging air mass. Changes in the ratio of isopentane/TVOC showed that high summer temperature enhanced gasoline volatilization. The southeastern boundary point of OFP was the highest, followed by the urban area and then Dingling. The species with greater contribution to OFP were ethylene, propylene, acetaldehyde, paraxylene and toluene, and alkanes with high mole fraction contributed little to OFP.
87. Potential contribution of secondary organic aerosols and ozone of VOCs in the Northern Suburb of Nanjing
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 76
A continuous observation campaign was carried out with the GC5000 volatile organics online monitoring system from March 1, 2013 to February 28, 2014 in the northern suburb of Nanjing, characteristics of their composition, seasonal variation and diurnal variation were analyzed, PMF, the maximum incremental reactivity coefficient (MIR) and the fractional aerosol coefficients (FAC) were used to estimate the potential formation of secondary organic aerosols (SOA) and O 3 from VOCs and their sources. The results showed that the hourly average fraction volume of the TVOCs was 45.63 × 10 ?9. There was an obvious seasonal cycle of VOCs, with the maximum in winter and autumn and minimum in summer. Diurnal variation of VOC concentration showed a very clear bimodal structure. The SOA concentration values obtained by the VOCs were 2.07 μg/m 3. As the largest contributor, aromatic hydrocarbons accounted for 95.93% and BTEX were the dominant species. Alkenes contributed the largest parts of the ozone formation potential (OFP), closing to 65%. Although alkanes were the most abundant components of VOCs, it is not the main contributor of OFP and SOA. The results from different seasons of receptor model showed that vehicle emissions and industrial emissions were main sources of VOCs in the northern suburb of Nanjing. The sources which contain rich BTEX contributed the largest parts of SOA. Moreover, the sources which contain rich ethylene, propylene and isoprene are the largest contributor of OFP. Vehicle emissions and industrial emissions (including the petrochemical industry) were the main contributor to the concentrations of VOCs, SOA and OFP in spring, autumn and winter. As the influential sources to SOA and OFP, solvent source and plant source should not be overlooked in summer.
88. Sources apportionment of volatile organic compounds VOCs in summertime Nanjing and their potential contribution to secondary organic aerosols (SOA)
China Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 77
In this study, VOCs were continuously measured using an online GC system in Nanjing during August 2013 and 2014, with the mean concentrations of 51.73 × 10 ?9 and 77.47 × 10 ?9. The OH radical loss rate ( L OH) method was applied to assess the chemical reactivity of VOCs. The results showed that alkene and aromatics were the key active components, and dominated the L OH in summertime Nanjing. Fractional aerosol coefficients (FAC) method was used to estimate the formation potentials of secondary organic aerosols (SOA) in Nanjing. The calculated SOA concentrations were 1.95 μg/m 3 in August of 2013 and 1.01 μg/m 3 in August of 2014. Aromatics and alkanes contributed about 95% and 4% to the SOA formation. Positive matrix factorization (PMF) model was deployed to identify the sources of VOCs in Nanjing. In the summer of 2013, fossil fuel evaporation was identified as the largest source and accounted for 22.7% of the measured VOCs, followed by natural gas and liquid gasoline (19.5%) , petroleum chemical industry (13.5%), vehicle emissions (17.7%), natural sources (13.4%) and paint/solvent usages (13.2%). In 2014, the largest VOCs source was natural gas and liquid gasoline (35.2%), followed by oil and chemical industries (20.6%), incomplete combustion (20.5%), fossil fuel evaporation (15.7%) and vehicle emissions (8.1%) .
89. Characteristics of reactive VOCs species during high haze-pollution events in suburban area of Shanghai in winter
Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 78
According to the online gas chromatography continuous observation data of winter hazy pollution at the university town station, we analyzed the characteristics of VOCs in high pollution period and the photochemical reactive activities under different haze pollution. Fifty-five species of VOCs and 735 effective samples were detected during observation period. The volume percentage of ΣVOC ranges from 25.5 × 10 ?9 to 1 320.3 × 10 ?9 (avg ± SD, 240 × 10 ?9 ± 181 × 10 ?9). Toluene and M/p -xylene were the characteristic pollutants for two high pollution period, the volume percentage of which were higher than that in industrial areas. Component features of high pollution period were similar to that at industrial area site, which might be much affected by nearby industrial area. ΣVOCs compounds diurnal variation showed features of higher at night and lower in daytime, however the hourly volume percentage of ozone showed the opposite features. Contribution rate of aromatics to OFP was the highest and reached 70.0%, and next was alkenes and alkynes (contribution rate was 16.7%). OFP values under the northwest wind direction were 2 078. 2 ×10 ?9, which was about 4 times of that under other wind directions. The average MIR value was higher than that in other industrial area sites in this city. Aromatics were dominant contaminant of OFP contribution to different haze pollution and among which the sum of contribution rates of toluene and M/p-xylene were over 50%. Four factors were obtained through PMF5.0 analysis, namely, gasoline pollution sources and vehicle exhaust emissions, petroleum refining and processing, solvent use and organic synthetic materials, with contribution rates being 33.1%, 31.5%, 30.5% and 4.9% respectively.
Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 79
A continuous measurement was conducted in Yixing city urban area from August 24 th to September 15 th using TH-300B continuous online GC-MS instrument during G20 Hangzhou Summit, 2016. The VOCs average mass concentrations of alkane, alkene, aromatic, acetylene, haloalkane hydrocarbons, OVOC and acetonitrile were 11.00 × 10 ?9, 1.93 × 10 ?9, 5.78 × 10 ?9, 1.23 × 10 ?9, 4.16 × 10 ?9, 10.37 × 10 ?9, 0.27 × 10 ?9, respectively. The photochemical reaction activity was calculated by using the maximum potential coefficient of Ozone Formation Potential. Alkene and aromatic hydrocarbons were the most active components of OFP. By applying the positive matrix factorization (PMF) model, five major factors were extracted to identify the sources of NMHCs in Yixing city, including industry (42.2%) , vehicle exhaust (17.9%) , fuel evaporation (20.8%) , paint/solvent usage (7.0%) and plant (12.1%). Combined with the conditional probability function (CPF) analysis, source of anthropogenic pollution was related to the distribution of industrial enterprises in the northwest and southeast, while the plant source was related to the forest hilly region of Southwest Yixing city. The effect of air pollutant emission reduction showed that the primary emission air pollutants had declined significantly during the strict control period from 1 st to 6 th September in G20 summit, 2016, and the industry proportion was reduced to 30.5%, whereas the plant proportion increased to 16.8%.
91. Effect of VOCs on O 3 and SOA Formation Potential During the Combined Pollution Process in Guangzhou Panyu Atmospheric Composition Station
Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 80
An analysis was made by using historical observational data of Guangzhou Panyu Atmospheric Composition Station (GPACS). The results showed that typical haze processes accompanied with high ozone episodes happened during the period from September 2 n d, 2011 to September 5 th, 2011 (P 1) and the period from June 12 th, 2012 to June 15 th, 2012 (P 2), respectively. During the two combined pollution processes (P 1 and P 2), daily visibility ranged from 5.78 km to 6.91 km and from 5.60 km to 9.25 km, and the maximum 8 h O 3 reached 92.14 × 10 ?9 and 91.29 × 10 ?9, respectively. Among the 55 detected volatile organic compounds (VOCs), alkenes and aromatics had the highest reactivity with the 41%, 39% proportions of equivalent propylene concentration and the proportions of 28%, 54% in the aspect of ozone formation potential during P 1. Alkenes and aromatics contributed 35% and 46% of equivalent propylene concentration, as well as 22% and 61% to ozone formation potential during P 2. In terms of SOA formation potential by FAC estimation, alkanes, alkenes and aromatics accounted for 13.2%, 21.4%, 65.4% during P 1 and 4.6%, 13.8%, 81.6% during P 2, respectively. Toluene, isoprene, ethylbenzene and m/p-xylene had large contributions to the ozone and SOA formation. Factors including pollutants transported from the downtown area, continuous gentle wind, high temperature, low humidity and strong radiation gave rise to the occurrence of high ozone episodes in these two haze processes.
92. Emissions, Chemical Composition, and Spatial and Temporal Allocation of the BVOCs in the Yangtze River Delta Region in 2014
Environmental Science,Part 1: Types of Air Pollution,Vol 39,No. 81
Based on the land surface vegetation data interpreted via remote sensing and the meteorological conditions predicted via the WRF model, the MEGAN model was applied to calculate the regional BVOC emissions in the Yangtze River Delta (YRD) in 2014. The chemical components and the temporal and spatial allocations were further analyzed. Results show that the annual BVOC emissions in the YRD were 1 886 kt, in which isoprene emissions were 704.2 kt (accounting for 37.3%), monoterpenes 303 kt (16.1%), and other VOCs 878.8 kt (46.6%). Seasonal variation of the BVOC emissions was very significant. The BVOC emissions had a strong seasonal pattern, with maximum emissions in summer, accounting for 60.9% (1 088 kt) of the total, whereas the minimum emissions occurred in winter, accounting for 3.2% (57 kt). Spatially, the southern YRD produced more BVOC emissions than the northern part did. In Zhejiang, Anhui, Jiangsu, and Shanghai, the BVOC emissions were 842 kt (44.6%), 760 kt (40.3%), 272 kt (14.4%), and 12 kt (0.7%), respectively. This is mainly related to the distribution of vegetation types.
93. Emission Inventory of Atmospheric Pollutants and VOC Species from Crop Residue Burning in Guangdong Province
Environmental Science,Part 1: Types of Air Pollution,Vol 39,No. 82
An emission inventory of atmospheric pollutants from crop residue burning in Guangdong for the period 2008–2016 was developed, based on crop yield data. Emissions of species of volatile organic compounds (VOCs) and corresponding ozone formation potential (OFP) in 2016 were also estimated. Results showed that emissions of atmospheric pollutants from crop residue burning were lower in 2013–2016 than in 2008–2012. This was mainly due to the policy of prohibiting open burning of straw and to improvement of rural living standards, which reduced the proportion of straw burning. In 2016, emissions of SO 2, NO x , NH 3, CH 4, EC, OC, NMVOC, CO, and PM 2.5 were 2 443.7, 16 187.9, 6 943.8, 29 174.4, 3 625.5, 14 830.7, 65 612.6, 591 613.9, and 49 463.0 t, respectively. Rice straw burning was the main source of pollutants, accounting for about 68.55% of total pollutant emissions. The five municipalities with the highest atmospheric pollutant emissions were Zhanjiang, Maoming, Meizhou, Zhaoqing, and Shaoguan, together accounting for about 58.63% of total emissions. The top 10 VOC species for mass-based emissions consisted of ethylene, acetaldehyde, formaldehyde, benzene, ethyne, propylene, ethane, toluene, propane, and propionaldehyde, together contributing 67.91% to total emissions. The top ten OFP-based VOC species were ethylene, formaldehyde, acetaldehyde, propylene, 1-butylene, propionaldehyde, toluene, acrolein, isoprene, and crotonaldehyde, accounting for 80.83% of total OFP.
China Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 83
The spatiotemporal variation characteristics of daily maximum eight-hour average ozone concentrations (O 3-8 h) from 338 cities in China during 2016 were analyzed by rotated empirical orthogonal function (REOF) analysis. Based on the results of REOF analysis focusing on pollution seasons (May to October) in 2016, ten regions were identified in China. The temporal variation patterns of ozone in each region were independent with each other, affected by local meteorological, photochemical or pollution features. A rising trend for annual averaged O 3-8 h was observed during 2014 to 2016 for all regions, except for South China region and the Tibetan Plateau. The regionalization results of ozone were found to be influenced greatly by terrain features, indicating significant terrain and landform effects on ozone spatial correlations. Among the 10 regions, Huanghuai Plain, the North China Plain, Central Yangtze River Plain were realized as priority regions for mitigation strategies, due to their higher ozone concentrations and densely population.
Environmental Science,Part 1: Types of Air Pollution,Vol 40,No. 84
Based on the ozone monitoring data from 2015 to 2017, this study presents the spatial-temporal variation of the ozone concentration and its driving factors in major cities in China via Kriging interpolation, spatial autocorrelation analysis, hot spot analysis, and geographical detector. The results show that: ① The ozone pollution became increasingly heavier from 2015 to 2017, with the number of cities in which the 90th percentile of daily maximum 8 h ozone concentration exceeded the air quality standard (GB 3095-2012) increased from 74 to 121, and the proportion of non-attainment days increased from 5.2 percent to 8.1 percent. ② Ozone pollution mainly happened from April to September, during which the non-attainment days contributed 87.5 percent to 95.3 percent to the yearly total number of ozone polluted days. From May to July, ozone concentrations increased the most dramatically, with the proportion of non-attainment days increasing from 10.6 percent in 2015 to 20.5 percent in 2017. Moreover, in 2017, 83.0 percent of the moderate ozone pollution and 91.0 percent of the severe ozone pollution happened from May to July. ③ With the ever increasing ozone concentration over the North China Plain, the high ozone polluted areas such as the Beijing-Tianjin-Hebei region and Yangtze River Delta urban agglomeration were connected geographically. They formed the most highly polluted area in China, which included the Bohai Rim region, Zhongyuan urban agglomeration, Yangtze River Delta urban agglomeration, Shanxi Province, Guanzhong area, and the middle part of Inner Mongolia. In addition, cities in Pearl River Delta region, Chengdu-and-Chongqing urban agglomeration, and the southern part of East China were also in gathering speed in terms of ozone pollution, among which Chengdu-and-Chongqing urban agglomeration has become a new ozone-polluted center. ④ The spatial agglomeration of ozone concentration has been enhanced year by year with hot spots distributed mainly in the North China Plain and the middle and lower reaches of the Yangtze River. In contrast, there were cold spots in Northeast China, Southwest China, and Southern China. ⑤ The analysis results from geographical detector showed that meteorological factors, industrialization, urbanization, and emissions of ozone precursors all had a significant effect on the distribution of the ozone concentration, but there were also discrepancies in the priority of the driving factors in different regions and seasons.
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 85
A numerical chemical weather forecasting system was established and operationally implemented based on the WRF-Chem Model, an online coupled regional chemical transport model. Performance of the modeling system on daily maximum 1-hour and 8-hour ozone (1-h and 8-h O 3) concentrations was evaluated between May 1st and September 30th, 2013. The results showed that the numerical forecasting has generally good performance. There is no substantial systematic bias in 1-h and 8-h O 3 concentrations and correspondent IAQI in forecasts of 24 h, 48 h, and 72 h. The correlation coefficients (R) are about 0.8, and the mean and median biases are around 1 × 10 ? 9–2 × 10 ? 9. The forecasted O 3 attainment vs. pollution days as well as primary pollutants are also in good agreement with observations. The performance of 48 h forecast is slightly better than that of 24 h and 72 h forecast, and these of the later two are generally close to each other. Meanwhile, further improvement is still needed. For example, model shows substantial biases in O 3 concentrations or IAQI forecasts in some cases, and the accuracy of O 3 IAQI level forecast is substantially lower than that of concentration and IAQI value forecast. In general, the numerical forecasting system shows relatively good performance in O 3 forecasts during May to September, 2013, and it has the capability to support the air quality forecast over Shanghai.
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 86
WRF / Chem model was used to analyze the temporal and spatial distribution characteristics and physical and chemical mechanisms of a typical summer ozone pollution event over the Yangtze River Delta (YRD). The result showed that the model was capable of reproducing the temporal and spatial distribution and evolution characteristics of the typical summer ozone pollution event over YRD. The YRD region was mainly affected by the subtropical high-pressure control, and the weather conditions of sunshine, high temperature and small wind were favorable for the formation of photochemical pollution on August 10-18, 2013. The results of simulation showed that the spatial and temporal distribution of O 3 was obviously affected by the meteorological fields, geographic location, regional transport and chemical formation over YRD. The sensitivity experiment showed that the O 3 concentration affected by maritime airstream was low in Shanghai, but the impact of Shanghai emissions on the spatial and temporal distribution of O 3 concentration over YRD was significant. The main contribution of the high concentration of O 3 in Nanjing surface was chemical generation (alkene and aromatic) and the vertical transport from high-altitude O 3, whereas the main contribution of the high concentration of O 3 in Hangzhou and Suzhou was physics process. The influence of the 15:00 peak concentration of O 3 over YRD was very obvious when O 3 precursor was reduced at the maximum O 3 formation rate (11–13 h).
98. Study on the effects of urban expansion on region climate and ozone pollution over Nanjing in summer
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 87
Based on the meteorological observation data from 1951 to 2010 at the station 58238, as well as air pollutant monitoring data in 2007 from Caochangmen air quality monitoring stations, the variation trend of the meteorological factors in Nanjing and the relationship between air pollution and meteorology was analyzed. With the aid of WRF-CALGRID, impacts of urbanization on local meteorological fields and ozone concentration over Nanjing were discussed. The results showed that the elevated air temperature, the decreased wind speed and the reduced air humidity in Nanjing can be attributed to urban sprawl. And in consideration of the effects of meteorological factors, such as air temperature and wind, on the concentration of ozone, urbanization in Nanjing may evidently impact ozone formation and distribution. The simulated results illustrated that changes of land-use in Nanjing cause an increase in air temperature over 1°C, a decrease in wind speed with 0.4 m/s, a decrease in air humidity with 0.5 g/kg, and an increase in mixing layer height with 100 m. Urbanization reduces near surface NO x concentration due to the increase of PBLH, with the maximum decrease over 6 × 10 ?9. In the north and west of Nanjing, urbanization increases the concentration of O 3 with the value over 2 × 10 ?9, which can be related to the increase of air temperature, the decrease of wind speed and the change of NO. In the south and east of Nanjing, O 3 can be lowered about 1 × 10 ?9–3 × 10 ?9 due to the increase of the height of mixing layer.
Environmental Science,Part 1: Types of Air Pollution,Vol 37,No. 88
Two different pollution situations of O 3 and PM 2.5 during summer in Beijing were analyzed from the perspective of synoptic situations, meteorological elements, precursors, atmospheric oxidation, back-trajectories of air mass and chemical compositions of PM 2.5. The results showed that the synoptic situations in the pollution situation that O 3 reached middle level pollution and PM 2.5 maintained low concentrations (O 3 high-PM 2.5 low) could be characterized as northwest gas flow in 500 hPa height and high-pressure rear in the ground. Whereas the synoptic situations in the pollution situation that O 3 and PM 2.5 both reached middle level pollution (O 3-PM 2.5 high) could be characterized as westerly gas flow in 500 hPa height and low pressure in the ground. Compared with the O 3 high-PM 2.5 low situation, meteorological elements in O 3-PM 2.5 high situation could be characterized as stronger southerly winds and higher relative humidity. In the O 3-PM 2.5 high situation, initial concentrations of O 3 and PM 2.5 were higher and diurnal variations of PM 2.5 were more significant, nevertheless, the average concentrations of O 3 were lower than those in the O 3 high-PM 2.5 low situation, respectively. The analysis of precursors, atmospheric oxidation and chemical compositions of PM 2.5 showed that the accumulation and hygroscopic growth of PM 2.5 under unfavorable meteorological conditions as well as the regional transport caused by strong southerly winds might be the main factors leading to high PM 2.5 concentrations in O 3-PM 2.5 high situation.
Acta Meteorologica Sinica,Part 1: Types of Air Pollution,Vol 75,No. 89
Using a chemistry transport model (SLIMCAT) and reanalysis data, this study investigates the extremely low ozone events in 1997 and 2011 in the Arctic stratosphere. The analysis reveals that, the magnitudes of total column ozone (TCO) anomalies over the Arctic in 1997 and 2011 both could be up to about –80 DU and the ozone decreases between 200 hPa and 30 hPa accounted for about 80% of the TCO anomalies. Our analysis suggests that the two extremely low Arctic TCO events were possibly related to La Ni?a activity, which resulted in a stronger Arctic polar vortex, a lower stratospheric temperature, more polar clouds, and eventually more ozone chemical loss. Furthermore, since the positive sea surface temperature anomalies in the North Pacific in 2011 led to a lower Aleutian low, a weaker troposphere wave forcing, a colder Arctic vortex and more type II PSCs, the ozone chemical loss in the Arctic UTLS region was accelerated in 2011. A comparison of the ozone anomalies in different layers between 1997 and 2011 indicates that the ozone decrease in the Arctic UTLS region in 2011 was much larger than that in 1997.
Environmental Science,Part 1: Types of Air Pollution,Vol 39,No. 90
O 3 continuous monitoring data for the Dingling, Guanyuan, Liulihe, and Qianmen sites from 2006–2015 were analyzed to investigate concentration levels, variation trends, temporal variations, and relationships with precursors and meteorological factors. The results showed that the ten year average concentrations of O 3 at the Dingling site were the highest at 65.2 μg·m ?3, followed by concentrations at Liulihe (53.4 μg·m ?3), Guanyuan (49.6 μg·m ?3) and Qianmen (40.4 μg·m ?3). The O 3 concentrations at Dingling showed a decreasing trend [0.5 μg·(m 3·a) ?1], while O 3 concentrations at Guanyuan [0.9 μg·(m 3·a) ?1], Liulihe [0.3 μg·(m 3·a) ?1], and Qianmen [0.3 μg·(m 3·a) ?1] showed an increasing trend. The highest monthly average concentrations appeared during June and August, and the highest frequency occurred in July (17 times) with average concentrations of 99.8 μg·m ?3. The lowest monthly average concentrations appeared during November and February, and the highest frequency occurred in January (14 times) with an average concentration of 16.6 μg·m ?3. Notably, the time for the peak concentrations of O 3 appeared earlier in the day in recent years. The peak concentrations of O 3 appeared at 15:00–16:00 during 2013–2015, which was 1–2 h earlier than previous years. The heavy air pollution of O 3 occurred on 11 days at the Dingling site in 2015, which was ten days more than in 2013, indicating that O 3 pollution in the downwind suburban regions of Beijing in summer became more and more serious. The concentrations of O 3 and NO 2 at Dingling showed a positive correlation, while the concentrations of O 3 and NO 2 at the other sites showed a negative correlation, indicating that O 3 formation in Dingling was sensitive to NO 2 chemistry, while O 3 formation at the other sites was sensitive to VOC chemistry. The concentrations of O 3 showed a positive correlation with temperature and negative correlations with humidity and surface pressure. Temperature had the greatest influence on O 3 concentration, followed by surface pressure and humidity. For cases when daily maximum temperature exceeded 30 °C and relative humidity was between 30% and 70%, the probability of the O 3 daily maximum 8 h concentration exceeding 200 μg·m ?3 was high, indicating that the air quality level reached levels for light pollution and moderate pollution.
102. Sensitivity analysis of ozone in Beijing-Tianjin-Hebei (BTH) and its surrounding area using OMI satellite remote sensing data
China Environmental Science,Part 1: Types of Air Pollution,Vol 38,No. 91
The chemical sensitivity of O 3 production was assessed using HCHO and NO 2 vertical column densities (VCDs) in BTH and its surrounding areas during the period of June to September from 2005–2016, since HCHO/NO 2 serves as a proxy for the sensitivity. The results showed that VOCs-limited was mainly concentrated in the central areas in the industrial cities, e.g. Beijing, Taiyuan and Shijiazhuang. NO x -limited regime concentrated in north of Beijing, Hebei Province, most areas of Henan Province, and the coastal cities in Shandong Province. The proportion of VOCs-limited conditions of O 3 production increased first and then reduced to 3% in BTH and its surrounding areas in 2016. At the same time, NO x -limited regime showed a trend of increase after the first reduce, and the areas accounted for 65% in 2016. The main reason was that the NO x emission control during “12 th five-year plan” was remarkably effective. The results showed VOCs-limited regimes increased significantly in September, with transitions in regimes of NO x -limited into mixed NO x -VOCs-limited, and mixed NO x -VOCs-limited regimes into VOCs-limited regimes compared with June–August.
Environmental Science,Part 1: Types of Air Pollution,Vol 40,No. 92
The processes affecting photochemical reactions and regional transport of ozone and its precursors in ambient air are very complicated. In this study, statistical analysis of the spatial and temporal distributions of ozone pollution in Zhoushan was carried out based on monitoring data from state monitoring stations in Zhoushan in 2014. Specifically, ozone formation was simulated by community multiscale air quality (CMAQ) model, and the source contribution rate was calculated using the source tracking algorithm of integrated source apportionment method (ISAM). The results showed that ozone pollution was more severe in spring and autumn than in summer and winter, and the highest ozone concentrations mostly appeared during 13:00–15:00 in the afternoon. Putuo Station had the highest ozone concentration while Lincheng Station, located in the downtown area of the city, had the lowest ozone concentration. The overall average ozone concentration was not high. However, peak concentrations that exceeded the standards usually appeared, most often in May. Local ozone formation in Zhoushan City is controlled by the VOC concentration, and source tracking results showed that non-local sources accounted for 69.46% of the total contribution. Among local emission sources, fuel burning boiler sources, industry process sources, on-road mobile sources, and non-road mobile sources made similar contributions to ozone formation. Moreover, they showed significant characteristics of a port city. The contribution rates from shipping sources, petrochemical sources, and storage and transportation sources were 4.45%, 1.01%, and 1.80%, respectively. In conclusion, control of the ozone pollution in Zhoushan City should be based on simultaneous reduction and coordinated prevention involving multiple sources (VOCs as the main one) both locally and in surrounding areas.
104. Impact of meteorological factors and precursors on spatial distribution of O 3 concentration in Eastern China
China Environmental Science,Part 1: Types of Air Pollution,Vol 39,No. 93
Based on the methods of gravity center model, spatial autocorrelation analysis and geographical detector, this paper studied the spatial and temporal distribution of O 3 concentration in Eastern China in 2016 and revealed the impact of meteorological factors and precursors on the spatial distribution and evolution of O 3 concentration. The results are as follows: 1) The average monthly O 3 concentration went through three phases in Eastern China in 2016, i.e., rising gradually from January to March, fluctuating from April to September, and decreasing from October to December. Moreover, O 3 pollution was mainly witnessed in the second phase, which contributed 96% of the yearly ozone pollution. 2) Mainly affected by meteorological factors, namely lower precipitation, lower relative humidity and longer sunshine duration in the northern part of Eastern China, the annual O 3 concentration of northern part was higher than that of the southern part in Eastern China in general. Besides, the pollution centers in the core cities of urban agglomerations generated because of precursors, which was a significant factor of the yearly average ozone concentration distribution. 3) The spatial distribution of O 3 concentration went through a pattern of high in the north and low in the south to the pattern of high in the south and low in the north in 2016. The monthly gravity center of O 3 concentration was moving to the north from January to June, reaching its northernmost point in June. The pattern of high in the north and low in the south was at its most significant phase at that time, and the highest point of pollution level was reached in the Bohai Rim region. Then it moved southward until December when the southernmost point was reached, during which the distribution pattern was transforming into the pattern of high in the south and low in the north. During the rainy season (from March to September), the spatial distribution of ozone concentration was mainly impacted by precipitation and relative humidity compared with a major impact by temperature in the rest of the months. 4) Precursors worked with meteorological factors. A stronger photoreaction, as a result of rising temperature, positively magnified the impact of precursors while the decreasing temperature weakened photoreactions, which, as a result, may promote ozone consumption.
105. Spatio-temporal change and influencing factors of tropospheric NO 2 column density of Yangtze River Delta in the decade
China Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 94
The characteristics of spatial and temporal distribution of tropospheric NO 2 column density over Yangtze River Delta for 2005–2014 were analysed based on satellite derived NO 2 column data from OMI. On this basis, influencing factors of NO 2 changes were analysed from terrain, meteorology, economy, agriculture, life, national major environmental planning, and other aspects. Results demonstrate: a) Tropospheric NO 2 column density increased at the annual rate of 1.04%, with the highest column density 1,184.07 × 10 13 mole/cm 2 in 2011; it raised 20.7% compared with 2005 in 2010 and declined 9.1% compared with 2010 in 2014; b) The spatial distribution of tropospheric NO 2 column density had significant change, high in the middle, low in northern and lower in southern of Yangtze River Delta. c) Precipitation had a highly negative correlation with NO 2 concentrations for the reason of atmospheric wet deposition. The terrain of Yangtze River Delta weakened the unfavorable impact of the high polluted area under the effect of the dominant north wind. d) Tropospheric NO 2 concentration had a correlation coefficient of 0.83 with the second industry output value, and a correlation coefficient of 0.74 with car ownership. Tropospheric NO 2 concentration was closely related to coal consumption and car ownership; in addition, the agricultural straw burning also released a large amount of nitrogen oxides. Series of NO x emission control measures such as controlling amount of coal and denitration applied during the "twelfth five-year" lowered the NO 2 concentration in 2012–2014.
106. Spatial-temporal change of tropospheric NO 2 column density and its impact factors over Shandong province during 2005–2014
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 95
Based on satellite derived NO 2 column data from OMI, we analysed the characteristics of spatial and temporal distribution of tropospheric NO 2 column density and its impact factors over Shandong province for 2005–2014. Results demonstrate: Tropospheric NO 2 column density had a large fluctuation on the temporal scale, increased at the rate of 28.5%, with the highest column density in the year 2011; The spatial distribution of tropospheric NO 2 column density also had significant changes, with variation of the highest value areas, regions with the fourth and fifth level values had extended to a wide range of the central and western part of Shandong province during 2010–2012, which only appeared in developed inland cities during 2005–2009, but narrowed again during 2013–2014; Precipitation had a highly negative correlation with NO 2 concentrations, for the reason of atmospheric wet deposition. Through analyzing the significant increase of regional gross domestic product (GDP), the large and rapid increase of vehicle ownership and the significant straw burning in Shandong province during 2005–2014, the change of NO 2 columns can be attributed to these anthropogenic sources.
107. Research on the spatial/temporal patterns of NO 2 concentration and NO x emissions of Lanzhou by applying satellite data
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 96
Ozone Monitoring Instrument (OMI) on abroad the EOS AURA satellite is widely used to observe the trace gases (O 3, NO 2, SO 2) for its high spatial and temporal resolution. The distribution of NO 2 column concentration over Lanzhou was reconstructed using data from OMI database(2010–2012), following the analysis of variation of spatial and temporal patterns, and the estimation of NO x emissions when considering the distribution of NO 2 concentration under south-western wind. The results have shown that: in the spatial pattern, the NO 2 column concentration at the center of Lanzhou City was the highest, and the concentration of NO 2 decreased with the distance from the center. In the time pattern, the NO 2 column concentration of Lanzhou City reached the highest value in December, and the lowest value in August. The lifetime of NO 2 in Lanzhou during wintertime from 2010 to 2012 was about 10.6, 9.9, 9.1h respectively, and the NO x emission rate was about 175.3, 183.7, 179.9mol/s respectively. The emission rate calculated matched the data provided by the “Lanzhou Environment Bulletin”, which indicated that analyzing the distribution of NO 2 under special wind direction provides a feasible way to estimate the emissions of NO x .
108. Analysis about Spatial and Temporal Distribution of SO 2 and An Ambient SO 2 Pollution Process in Beijing during 2000–2014
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 97
Spatial and temporal distribution of SO 2 during 2000–2014 was all analyzed based on the SO 2 monitoring data that Beijing Municipal Environmental Monitoring Center released and the formation mechanism of a typical air pollution episode in January 2014 was also investigated by combining numerical model CAM x. Analysis results showed that mass concentration of ρ(SO 2) in Beijing in 2014 decreased 69% compared to that in 2000 with an annual gradient from 2000 to 2014 of ?3.5 μg·(m 3·a) ?1. Monthly average concentration of SO 2 changed in a U shape curve and from the lowest to the highest, and seasonal variations of SO 2 concentrations were as follows: winter > spring > autumn > summer; concentration of SO 2 in heating season was significantly higher than that in non-heating season. Annual average concentration of SO 2 was lower in northern and western regions while higher in six city areas and southern area. Concentrations of SO 2 at Shijingshan, Dongsi, Tongzhou monitoring sites were significantly decreased related to SO 2 emission reduction measures. During a heavy air pollution process in January 14–18th 2014 there was obviously SO 2 regional transportation and model simulation analysis based on PAST showed that the contribution of SO 2 regional transport to Beijing was 83% with elevated power plants surrounding Beijing accounting for 21% and the four major Beijing power plants contributing about 3.5% to the SO 2 concentration during this heavy air pollution process.
109. Concentration characteristics and potential sources of polycyclic aromatic hydrocarbons in atmospheric deposition in Shanghai
China Environmental Science,Part 1: Types of Air Pollution,Vol 35,No. 98
To study the atmospheric deposition in Shanghai, we have collected deposition samples in August, September and October in 2014. The concentration, spatial distribution and composition of sixteen polycyclic aromatic hydrocarbons (PAHs) were analyzed. Atmospheric deposition fluxes of ∑15PAHs at eight sampling sites were also calculated. The potential sources of PAHs were apportioned by positive matrix factorization (PMF) model, which could produce a quantitative interpretation. Our results indicated that the total concentrations of PAHs ranged from 0.458 μg/L to 21.013 μg/L in atmospheric deposition. Furthermore, the PAHs concentrations in dissolved phase varied from 0.174 μg/L to 0.625 μg/L, while in particulate phase from 0.275 μg/L to 20.455 μg/L. The atmospheric deposition flux of ∑15PAHs in sampling sites ranged from 0.24 μg/(m 2?d) to 14.74 μg/(m 2?d) and the mean deposition flux of ∑15PAHs was 2.77 μg/(m 2?d). According to the apportionment results using PMF model, the first major sources of PAHs were categorized as mobile vehicle exhausts, such as gasoline car exhausts and diesel car exhausts, which constantly contribute 40.23% to the total PAHs pollution. Another four sources (residential cooking, coal combustion, oil spill and volatilization, coking and coal smelting) identified by PMF model account for 23.73%, 14.75%, 14.35% and 6.92% respectively.
110. Simulation Study of the Emission of Polycyclic Aromatic Hydrocarbons and Sugar Alcohols from Biomass Burning
Environmental Science,Part 1: Types of Air Pollution,Vol 36,No. 99
To measure the emission factors of PM 2.5 and its associated PAHs and sugar alcohols, Chinese red pine stick and four crop straw including rice, wheat, corn and cotton were burned in a chamber. In addition, the kinetics of certain compounds were obtained through the irradiation of the glass filters with PM 2.5 loading by 500 W mercury lamp. The emission factors of PM 2.5 were ranged from (2.26 ± 0.60) g·kg ?1(Chinese red pine stick) to (14.33 ± 5.26) g·kg ?1(corn straw). Although the emission factors of the total 19 PAHs differed from (0.82 ± 0.21) mg·kg ?1(Chinese red pine stick) to (11.14 ± 5.69) mg·kg ?1 (cotton straw), 4-ring PAHs showed predominance over other PAHs accounting for 51%–71% except Chinese red pine in which retene was the predominant compound. The emission factors of 9 sugar alcohols were ranged from (52.34 ± 50.16) mg·kg ?1 (rice straw) to (238.81 ± 33.62) mg·kg ?1 (wheat straw) with levoglucosan accounting for 72%–96% of the total sugar alcohols. Both the selected PAHs and levoglucosan associated with PM 2.5 followed the first order kinetics. The photolysis kinetic coefficient of PAHs (ring number ≥ 4) was decreased with the increase of PAHs loading in filters. Two PAHs source characteristic ratios such as Flua / (Flua + Py) and IP / (IP +BgP) were stable during the irradiation. The photolysis kinetic coefficient of levoglucosan (0.004 5 min ?1) was comparable to benzo[a]anthracene (0.004 1–0.005 0 min ?1).