Quantitative estimation of air pollutant emission rate based on urban atmospheric load index

MEI Mei1,2,3 XU Da-hai4 ZHU Rong3 WANG Zong-shuang5

(1.Chinese Academy of Meteorological Sciences, Beijing 100081)
(2.University of Chinese Academy of Sciences, Beijing 100049)
(3.National Climate Center, Laboratory for Climate Studies of China Meteorological Administration, Beijing 100081)
(4.State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of China Meteorological Administration, Chinese Academy of Meteorological Sciences, Beijing 100081)
(5.Environmental Standard Institute, Chinese Research Academy of Environmental Sciences, Beijing 100012)
【Knowledge Link】solar elevation angle

【Abstract】Based on the observed PM2.5 concentration data and atmospheric self-cleaning ability index (ASI) calculated by meteorological observation data, the change in the pollutant emission rate per capita during two periods by applying the urban atmospheric load index was analyzed. Meanwhile, the effects of the meteorological condition and emission reduction on the change in the air pollutant concentration from September 2013 to February 2019 were investigated. The emission reduction in autumn and winter was more obvious than that in spring and summer. The effect initially appeared in the autumn and winter of 2014 due to emission reductions occurring in 74.5% of cities, and the average emission reduction was 12.6% in this area. The emission reduction was substantially in the autumn and winter of 2017 and 2018 in major cities of Beijing-Tianjin-Hebei and its surrounding areas, with an emission reduction rate being 54.0% and 47.7% respectively relative to the base year. Emission in Changzhi during autumn and winter of 2014–2017 was more than that in the base year and started to decline in 2018. The change in the emission rate in Shijiazhuang presented a large fluctuation, and in the winter of 2016, it was 68.2% more than that in 2014. Hence, special attention should be paid to these two cities. The urban atmospheric load index can objectively and quantitatively reflect the direction and magnitude of the change in the emission rate in typical emission reduction periods, and thus it is an effective method to evaluate the effects of meteorological conditions and emission control measures on pollutant concentration changes.

【Keywords】 quantitative evaluation of emission reduction effect; urban atmospheric load index; atmospheric self-cleaning ability index; air pollutant emission rate;

【DOI】

【Funds】 Heavy Air Pollution Causes and Control Governance Projects (DQGG00302) Strategic Leading Sci-tech Program of the Chinese Academy of Sciences (XDA20100304) Youth Fund Project of Laboratory for Climate Studies, National Climate Center, China Meteorological Administration

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    References

    [1] The State Council. 国务院关于印发大气污染防治行动计划的通知 [EB/OL]. http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm (in Chinese).

    [2] Xiong Y J, Xu J, Sun Z B, et al. Air pollution reduction effect evaluation based on data mining algorithm and numerical simulation technology [J]. Acta Scientiae Circumstantiae, 2019, 39 (1): 116–125 (in Chinese).

    [3] Xu J M, Qu Y H, Chang L Y, et al. Practice on forecast and evaluation technique of meteorology for air pollution in Shanghai [J]. Advances in Meteorology Science and Technology, 2017, 7 (6): 150–156 (in Chinese).

    [4] Pan J X, Yan P Z, Li Y T, et al. Air pollution numerical simulation during the same period of Beijing Winter Olympic Games [J]. Research of Environmental Sciences, 2017, 30 (9): 1325–1334 (in Chinese).

    [5] He K B. 基于“大气国十条”的京津冀地区细颗粒物污染防治政策效果评估 [R]. Beijing: Tsinghua University, 2014 (in Chinese).

    [6] Zhai S X, An X Q, Liu J, et al. Effects of emission-sources reduction at different time points on PM2.5 concentration over Beijing Municipality [J]. China Environmental Science, 2014, 34 (6): 1369–1379 (in Chinese).

    [7] Liu J, An X Q, Zhu T, et al. Evaluation of PM2.5 decrease in Beijing after emission restrictions in the Beijing-Tianjin-Hebei and surrounding regions [J]. China Environmental Science, 2014, 34 (11): 2726–2733 (in Chinese).

    [8] Cao T H, Wang Z H, Zhang J, et al. Impact of coordinated emission controls on concentrations and sources of PM2.5 in Beijing during “9·3” military parade: A numerical model [J]. Journal of Beijing Normal University (Natural Science), 2017, 53 (2): 201–207 (in Chinese).

    [9] Wang L H, Zeng F G, Xiang W L, et al. A model evaluation of the effect of implementing heavy air pollution emergency plan to PM2.5 reduction in Beijing [J]. China Environmental Science, 2015, 35 (8): 2546–2553 (in Chinese).

    [10] Zhang R H, Li Q, Zhang R N. Meteorological conditions for the persistent severe fog and haze event over eastern China in January 2013 [J]. Science China: Earth Sciences, 2014, 44 (1): 27–36 (in Chinese).

    [11] Jiang Y R, Zhu R, Zhu K Y, et al. Numerical simulation on the air pollution potential in the severe air pollution episodes in Beijing-Tianjin-Hebei Region [J]. Acta Scientiae Circumstantiae, 2015, 35 (9): 2681–2692 (in Chinese).

    [12] Lv M Y, Zhang H D, Wang J K, et al. Analysis of meteorological causes of two heavily polluted weather processes in Beijing-Tianjin-Hebei Region in winter of 2015 [J]. China Environmental Science, 2019, 39 (7): 2748–2757 (in Chinese).

    [13] Wu P, Ding Y H, Liu Y J, et al. Influence of the East Asian winter monsoon and atmospheric humidity on the wintertime haze frequency over central-eastern China [J]. Acta Meteorologica Sinica, 2016, 74 (3): 352–366 (in Chinese).

    [14] Yin Z C, Wang H J, Yuan D M. Interdecadal increase of haze in winter over North China and the Huang-huai Area and the weakening of the East Asia Winter Monsoon [J]. Chinese Science Bulletin, 2015, 60 (15): 1395–1400 (in Chinese).

    [15] Yin Z C, Wang H J, Chen H P. Understanding severe winter haze events in the North China Plain in 2014: Roles of climate anomalies [J]. Atmospheric Chemistry and Physics, 2017, 17 (3): 1641–1651.

    [16] Wang H J, Chen H P, Liu J P, et al. Arctic Sea Ice Decline Intensified Haze Pollution in Eastern China [J]. Atmospheric and Oceanic Science Letters, 2015, 8 (1): 1–9.

    [17] Zhao S, Li J, Cheng S. Decadal variability in the occurrence of wintertime haze in central eastern China tied to the Pacific Decadal Oscillation [J]. Scientific Reports, 2016, 6 (1): 27424.

    [18] Xu X, Zhao T, Liu F, et al. Climate modulation of the Tibetan Plateau on haze in China [J]. Atmospheric Chemistry and Physics Discussions, 2016, 15 (3): 28915–28937.

    [19] Xu D H, Zhu R. A study on the distribution of ventilation and rainout capacity in mainland China [J]. China Environmental Science, 1989, 9 (5): 367–374 (in Chinese).

    [20] Xu D H, Zhu R, Pan Z T. The studies on standard of emission of SO2 and dispersion model in cities [J]. China Environmental Science, 1990, 10 (4): 309–313 (in Chinese).

    [21] Xu D H, Wang Y, Zhu R. The atmospheric environmental capacity coefficient cumulative frequency curve fitting and its application [J]. China Environmental Science, 2016, 36 (10): 2913–2922 (in Chinese).

    [22] Zhu R, Zhang C J, Mei M. The climate characteristics of atmospheric self-cleaning ability index and its application in China [J]. China Environmental Science, 2018, 38 (10): 3601–3610 (in Chinese).

    [23] Wang J Z, Gong S L, Zhang X Y, et al. A parameterized method for air-quality diagnosis and its applications [J]. Advances in Meteorology. 2012, doi: 10.1155/2012/238589

    [24] Zhang H D, Zhang B H, L M Y, et al. Development and application of stable weather index of Beijing in environmental meteorology [J]. Meteorological Monthly, 2017, 43 (8): 998–1004 (in Chinese).

    [25] Mei M, Zhu R, Sun C Y. Study on meteorological conditions for air pollution and its climatic characteristics in “2 + 26” cities around Beijing-Tianjin-Hebei region in autumn and winter [J]. Climate Change Research, 2019, 15 (3): 270–281 (in Chinese).

    [26] GB/T 3840-91 Technical methods for making local emission standards of air pollutants [S] (in Chinese).

    [27] Xu D H, Wang Y, Zhu R. Atmospheric environmental capacity and urban atmospheric load in mainland China [J]. Science China Earth Sciences, 2018, 48 (7): 924–937 (in Chinese).

    [28] Wu W J, Chang X, Xing J, et al. Assessment of PM2.5 pollution mitigation due to emission reduction from main emission sources in the Beijing-Tianjin-Hebei Region [J]. Environmental Science, 2017, 38 (3): 868–875 (in Chinese).

    [29] Ministry of Environmental Protection, National Development and Reform Commission, Ministry of Industry and Information Technology, et al. 关于印发京津冀及周边地区2017–2018年秋冬季大气污染综合治理攻坚行动方案》的通知 [EB/OL]. http://www.mee.gov.cn/gkml/hbb/bwj/201708/t20170824_420330.htm (in Chinese).

    [30] Jia J, Guo X R, Cheng S Y. Numerical study on the characteristics of PM2.5 in Beijing and the assessment of pollution control measures during APEC [J]. China Environmental Science, 2016, 36 (8): 2337–2346 (in Chinese).

    [31] Liu H L, Gong S L, He J J, et al. The theory and application of evaluation on meteorological condition index for air pollution [Z]. The 35th Annual Meeting of China Meteorological Society, 2018 (in Chinese).

    [32] Zhang X, Xu X, Ding Y, et al. The impact of meteorological changes from 2013 to 2017 on PM2.5 mass reduction in key regions in China [J]. Science China: Earth Sciences, 2019, 62: 1885–1902.

    [33] QX/T 479-2019 Evaluation on meteorological condition index of PM2.5 pollution [S].

    [34] Zhang X Y, Wang Y Q, Lin W L, et al. Changes of atmospheric composition and optical properties over Beijing-2008 Olympic monitoring campaign [J]. Bulletin of the American Meteorological Society, 2009, 90 (11): 1633–1651.

    [35] Xu D, Chen J. The macroscopic mechanisms and associated atmospheric precursor environmental capacities that lead to secondary fine particle pollution [J]. Science China: Earth Sciences, 2019, 49: 2051–2063 (in Chinese).

This Article

ISSN:1000-6923

CN: 11-2201/X

Vol 40, No. 02, Pages 465-474

February 2020

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Abstract

  • 1 Data and methods
  • 2 Result analysis
  • 3 Conclusions
  • References