Spatial-temporal correlation effects of CO2-PM2.5-O3 and synergistic control countermeasures in China's provincial area

LI Fei; DONG Long; KONG Shao-jie; QU Zhi-guang; GUO Jin-yuan; ZHOU Yuan-yuan; OU Chang-hong

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China Environmental Science ›› 2023, Vol. 43 ›› Issue (12) : 6246-6260.

Spatial-temporal correlation effects of CO₂-PM_2.5-O₃ and synergistic control countermeasures in China's provincial area

LI Fei, DONG Long, KONG Shao-jie, QU Zhi-guang, GUO Jin-yuan, ZHOU Yuan-yuan, OU Chang-hong

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Abstract

Spatio-temporal characteristics and correlation effects of CO₂ emissions and PM_2.5 and O₃ pollution concentrations in Chinese provinces from 2015 to 2019 were firstly analyzed. Then, the emission factor method was used to compile the emission inventories of CO₂, PM_2.5 and O₃ precursors in each province from 2011 to 2019, and combined the STIRPAT model the synergistic effects of CO₂, PM_2.5 and O₃ precursors were predicted under different scenarios. Moreover, a rating system was established to identify key control areas with their synergistic effects analyzed by sectors, and finally the targeted synergistic control measures were proposed. The results showed that there was no correlation between CO₂ emission reduction and PM_2.5 concentration reduction in 53% of the provinces, and that between CO₂ emission reduction and O₃ concentration reduction in 87% of the provinces. There was a synergistic effect between CO₂ and PM_2.5 in China from 2012 to 2014, while no synergistic effect from 2015 to 2019. Further, there was a synergistic effect between CO₂ and O₃ precursors in China in the most years, but there was no general synergistic effect between pollution reduction and carbon reduction. Based on the analysis of the obtained coefficient of synergy effect, more provinces achieved the synergistic effect in the low carbon scenario than that in the baseline scenario. Further, according to the developed rating system, the studied provinces were divided into four levels of control areas. The sectors in the "Class I Control Area" should give priority to the synergistic emission reduction of CO₂ and PM_2.5, while that in the "Class II Control Area" the synergistic emission reduction of CO₂ and O₃. It was recommended that each sector should take into account the low-carbon scenario, and the "I and II Control Area" should reasonably regulate the population and urbanization rate, and optimize the energy structure, etc.

Key words

hierarchical control / O₃ / PM_2.5 / pollution and carbon reduction / spatio-temporal characteristics / STIRPAT model / synergistic effect

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LI Fei, DONG Long, KONG Shao-jie, QU Zhi-guang, GUO Jin-yuan, ZHOU Yuan-yuan, OU Chang-hong. Spatial-temporal correlation effects of CO₂-PM_2.5-O₃ and synergistic control countermeasures in China's provincial area[J]. China Environmental Science. 2023, 43(12): 6246-6260

References

[1] 中华人民共和国生态环境部.2021中国生态环境状况公报[R]. 北京:生态环境部, 2022. Ministry of Ecology and Environment of the People's Republic of China. 2021 China Ecological and Environment Bulletin[R]. Beijing: Ministry of Ecology and Environment of the People's Republic of China, 2022.
[2] 中央政府网站.在第七十五届联合国大会一般性辩论上的讲话[EB/OL]. www.gov.cn/gongbao/content/2020/content_5549875.htm 2020-09-22. Central government website. Address to the General Debate of the 75th Session of the United Nations General Assembly[EB/OL]. www.gov.cn/gongbao/content/2020/content_5549875.htm 2020-09-22.
[3] Liu F, Klimont Z, Zhang Q, et al. Integrating mitigation of air pollutants and greenhouse gases in Chinese cities: development of GAINS city model for Beijing[J]. Journal of Cleaner Production, 2013,58:25-33.
[4] Wang Y, Wang X, Ning M, et al. The collaborative pollutants and carbon dioxide emission reduction and cost of ultra-low pollutant emission retrofit in China's cement kiln[J]. Journal of Cleaner Production, 2023,405:136939.
[5] Ma L, Cai B F, Wu F, et al. Hourly disaggregation of industrial CO₂ emissions from Shenzhen, China[J]. Environmental Pollution, 2018, 236:396-404.
[6] Gao Y, Zhang L, Huang A S, et al. Unveiling the spatial and sectoral characteristics of a high-resolution emission inventory of CO₂ and air pollutants in China[J]. The Science of the total environment, 2022, 847:157623.
[7] Chen X C, Li J H, Jia M, et al. High spatial resolution emission inventory of air pollutants and carbon in China's independent coking industry[J]. Atmosphere, 2023,14(2):348.
[8] 黄成,陈长虹,李莉,等.长江三角洲地区人为源大气污染物排放特征研究[J]. 环境科学学报, 2011,31(9):1858-1871. Huang C, Chen C H, Li L, et al. Anthropogenic air pollutant emission characteristics in the Yangtze River Delta region, China[J]. Acta Scientiae Circumstantiae, 2011,31(9):1858-1871.
[9] Fu X, Wang S, Zhao B, et al. Emission inventory of primary pollutants and chemical speciation in 2010for the Yangtze River Delta region, China[J]. Atmospheric Environment, 2013,70:39-50.
[10] 罗红成,廖琪,容誉.湖北省能源消费CO₂与大气污染物协同减排研究[J]. 环境污染与防治, 2022,44(2):266-271,277. Luo H C, Liao Q, Rong Y. Research on synergistic emission between CO₂ and air pollutants of energy consumption in Hubei Province[J]. Environmental Pollution & Control, 2022,44(2):266-271,277.
[11] 刘茂辉,刘胜楠,李婧,等.天津市减污降碳协同效应评估与预测[J]. 中国环境科学, 2022,42(8):3940-3949. Liu M H, Liu S N, Li J, et al. Evaluation and prediction of the synergistic effect of pollution reduction and carbon reduction in Tianjin[J]. China Environmental Science, 2022,42(8):3940-3949.
[12] Yu S, Zhang Q, Hao J L, et al. Development of an extended STIRPAT model to assess the driving factors of household carbon dioxide emissions in China[J]. Journal of Environmental Management, 2023, 325:116502.
[13] Zhao L, Zhao T, Yuan R. Scenario simulations for the peak of provincial household CO₂ emissions in China based on the STIRPAT model[J]. Science of the Total Environment, 2022,809:151098.
[14] Liu D, Xiao B. Can China achieve its carbon emission peaking? A scenario analysis based on STIRPAT and system dynamics model[J]. Ecological Indicators, 2018,93:647-657.
[15] Dong B, Xu Y, Li Q, Carbon transfer under China's inter-provincial trade: Evaluation and driving factors[J]. Sustainable Production and Consumption, 2022,32:378-392.
[16] Shan Y, Guan D, Zheng H, et al. China CO₂ emission accounts 1997~2015[J]. Science Data, 2018,5(1):1-14.
[17] HJ 663-2013环境空气质量评价技术规范(试行)[S]. HJ 663-2013 Technical regulation for ambient air quality assessment (on trial)[S].
[18] 吴振信,谢晓晶,王书平.经济增长、产业结构对碳排放的影响分析—基于中国的省际面板数据[J]. 中国管理科学, 2012,20(3):161-166. Wu Z X, Xie X J, Wang S P. The influence of economic development and industrial structure to carbon emission based on China's provincial panel data[J]. Chinese Journal of Management Science, 2012,20(3): 161-166.
[19] 张恪渝,廖明球,杨军.绿色低碳背景下中国产业结构调整分析[J]. 中国人口·资源与环境, 2017,27(3):116-122. Zhang K Y, Liao M Q, Yang J. China's structural adjustment under green and low-carbon development[J]. China Population Resources and Environment, 2017,27(3):116-122.
[20] Wang Y, Hopke P K, Xia X, et al. Source apportionment of airborne particulate matter using inorganic and organic species as tracers[J]. Atmospheric Environment, 2012,55:525-532.
[21] Duan W, Wang X, Cheng S, et al. Regional collaboration to simultaneously mitigate PM_2.5 and O₃ pollution in Beijing-Tianjin- Hebei and the surrounding area: Multi-model synthesis from multiple data sources[J]. Science of The Total Environment, 2022,820:153309.
[22] Liu H, Wu B, Liu S, et al. A regional high-resolution emission inventory of primary air pollutants in 2012for Beijing and the surrounding five provinces of North China[J]. Atmospheric Environment, 2018,181:20-33.
[23] Zheng H, Xu L. Production and consumption-based primary PM_2.5 emissions: Empirical analysis from China's interprovincial trade[J]. Resources, Conservation and Recycling, 2020,155:104661.
[24] Huang Y, Shen H, Chen H, et al. Quantification of global primary emissions of PM_2.5, PM₁₀, and TSP from combustion and industrial process sources[J]. Environmental science & technology, 2014, 48(23):13834-13843.
[25] 环境保护部.城市大气污染物排放清单编制技术手册[R]. 北京, 2015. Ministry of Environmental Protection. Technical manual for urban air pollutant emission inventory preparation[R]. Beijing, 2015.
[26] Liu S, Hua S, Wang K, et al. Spatial-temporal variation characteristics of air pollution in Henan of China: Localized emission inventory, WRF/Chem simulations and potential source contribution analysis[J]. Science of the total environment, 2018,624:396-406.
[27] Bai L, Lu X, Yin S, et al. A recent emission inventory of multiple air pollutant, PM_2.5 chemical species and its spatial-temporal characteristics in central China[J]. Journal of Cleaner Production, 2020,269:122114.
[28] 余宇帆,卢清,郑君瑜,等.珠江三角洲地区重点VOC排放行业的排放清单[J]. 中国环境科学, 2011,31(2):195-201. Yu Y F, Lu Q, Zheng J Y, et al. VOC emission inventory and its uncertainty from the key VOC-related industries in the Pearl River Delta Region[J]. China Environmental Science, 2011,31(2):195-201.
[29] 陈颖,叶代启,刘秀珍,等.我国工业源VOCs排放的源头追踪和行业特征研究[J]. 中国环境科学, 2012,32(1):48-55. Chen Y, Ye D Q, Liu X Z, et al. Source tracing and characteristics of industrial VOCs emissions in China[J]. China Environmental Science, 2012,32(1):48-55.
[30] 毛显强,邢有凯,高玉冰.温室气体与大气污染物协同控制效应评估与规划[J]. 中国环境科学, 2021,41(7):3390-3398. Mao X Q, Xing Y K, Gao Y B. Study on GHGs and air pollutants co-control: assessment and planning[J]. China Environmental Science, 2021,41(7):3390-3398.
[31] York R, Rosa E A, Dietz T. STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts[J]. Ecological Economics, 2003,46(3):351-365.
[32] 潘栋,李楠,李锋,等.基于能源碳排放预测的中国东部地区达峰策略制定[J]. 环境科学学报, 2021,41(3):1142-1152. Pan D, Li N, Li F, et al. Mitigation strategy of Eastern China based on energy-source carbon emission estimation[J]. Acta Scientiae Circumstantiae, 2021,41(3):1142-1152.
[33] 黄莘绒,管卫华,陈明星,等.长三角城市群城镇化与生态环境质量优化研究[J]. 地理科学, 2021,41(1):64-73. Huang X R, Guan W H, Chen M X, et al. Urbanization and optimization of the Yangtze River Delta urban agglomeration[J]. Scientia Geographica Sinica, 2021,41(1):64-73.
[34] 刘晴川,李强,郑旭煦.基于化石能源消耗的重庆市二氧化碳排放峰值预测[J]. 环境科学学报, 2017,37(4):1582-1593. Liu Q C, Li Q, Zheng X X. The prediction of carbon dioxide emissions in Chongqing based on fossil fuel combustion[J]. Acta Scientiae Circumstantiae, 2017,37(4):1582-1593.
[35] 王金营,戈艳霞.全面二孩政策实施下的中国人口发展态势[J]. 人口研究, 2016,40(6):3-21. Wang J Y, Ge Y X. Population trends in China under the universal two-child policy[J]. Population Research, 2016,40(6):3-21.
[36] 《中国能源展望2030》报告发布[J]. 资源节约与环保, 2016,(4):6. China Energy Vision 2030 Report Released[J]. Resources Economization & Environmental Protection, 2016,(4):6.
[37] 袁方成,陈泽华.新时代新型城镇化的要素结构及其优化路径[J]. 华中师范大学报(人文社会科学版), 2020,59(3):20-29. Yuan F C, Chen Z H. New urbanization in the new era: The element structure and path optimization[J]. Journal of Central China Normal University (Humanities and Social Sciences), 2020,59(3):20-29.
[38] 刘彦迪.2030年中国碳排放达峰区域性预测与影响因素分析[D]. 济南:山东大学, 2020. Liu Y D. Regional forecast of China's carbon emission peak in 2030 and analysis of influencing factors[D]. Jinan: Shandong University, 2020.
[39] Lin B, Zhu J. Impact of energy saving and emission reduction policy on urban sustainable development: Empirical evidence from China[J]. Applied Energy, 2019,239:12-22.
[40] Chen W M, Kim H. Energy, economic, and social impacts of a clean energy economic policy: Fuel cells deployment in Delaware[J]. Energy Policy, 2020,144:111617.
[41] 王勇,许子易,张亚新.中国超大城市碳排放达峰的影响因素及组合情景预测—基于门限-STIRPAT模型的研究[J]. 环境科学学报, 2019,39(12):4284-4292. Wang Y, Xu Z Y, Zhang Y X. Influencing factors and combined scenario prediction of carbon emission peaks in megacities in China: Based on Threshold-STIRPAT Model[J]. Acta Scientiae Circumstantiae, 2019,39(12):4284-4292.
[42] 邓小乐,孙慧.基于STIRPAT模型的西北五省区碳排放峰值预测研究[J]. 生态经济, 2016,32(9):36-41. Deng X L, Sun H. Forecast of the northwest five provinces' carbon emissions based on STIRPAT Model[J]. Ecological Economy, 2016, 32(9):36-41.