基于达标约束的南京市环境空气质量情景模拟
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摘要
以2030年南京市6项污染物达标为约束,在2015年大气污染物排放清单基础上,利用CMAQ模型分析了PM_(2.5)对南京本地不同前体物排放的敏感性,通过情景分析预测排放清单,模拟了4种减排情景的空气质量变化,最终获得达标约束下大气污染物总量控制指标.模拟结果显示,减少一次颗粒物PPM (primary particulate matter)排放对降低大气中的PM_(2.5)浓度最为有效;在周边地区减排的基础上,本地减少PPM排放对PM_(2.5)年均浓度下降的相对贡献可达88%,其次为NH_3、NOx、SO_2与VOCs减排,其相对贡献分别为10. 3%、5. 5%、3. 2%与0. 5%;相比2015年,4种情景下南京市主要大气污染物减排比例在22%～53%,未来控制活动水平对减排SO_2、NH_3与CO较有效,而NOx和VOCs末端治理方面还有较大空间;将SO_2、NOx、PM10、PM_(2.5)、BC、OC、CO、VOCs及NH_3的排放量分别控制在2. 43×104、8. 47×10~4、9. 42×10~4、3. 74×10~4、0. 19×10~4、0. 30×10~4、26. 56×10~4、13. 08×10~4及1. 50×10~4t以内时,预计南京市6项污染指标可以达到国家环境空气质量二级标准.
With the constraint that all six major pollutants in Nanjing must meet the air quality standards by 2030,on the basis of the2015 emission inventory,the CMAQ air quality model was used to conduct PM_(2.5) sensitivity tests,and scenario analysis was used to predict the emission inventory and the air quality of four emission reduction scenarios were simulated. Finally,the total control index under the constraint of meeting the standards was obtained. The results show that primary particulate matter( PPM) reduction is the most effective at reducing the concentration of PM_(2.5) in the atmosphere,on the basis of emission reduction in surrounding areas,PPM emission reduction accounts for 88% of the total reduction of the annual average concentration of PM_(2.5),followed by NH_3,NOx,SO_2,and VOCs,which contribute to 10. 3%,5. 5%,3. 2%,and 0. 5%,respectively. Compared to 2015,the reduction ratios of the major pollutants are between 22% and 53%. Controlling the activity level is more effective for SO_2,NH_3 and CO emissions reduction,while there is still more opportunity for NOxand VOCs end treatment. When the emissions of SO_2,NOx,PM10,PM_(2.5),BC,OC,CO,VOCs,and NH_3 are controlled to 2. 43 × 10~4,8. 47 × 10~4,9. 42 × 10~4,3. 74 × 10~4,0. 19 × 10~4,0. 30 × 10~4,26. 56 × 10~4,13. 08 ×10~4,and 1. 50 × 10~4 t,respectively,it is expected that the levels of the six pollutants in Nanjing can meet the national ambient air quality level 2 standards.
With the constraint that all six major pollutants in Nanjing must meet the air quality standards by 2030,on the basis of the2015 emission inventory,the CMAQ air quality model was used to conduct PM_(2.5) sensitivity tests,and scenario analysis was used to predict the emission inventory and the air quality of four emission reduction scenarios were simulated. Finally,the total control index under the constraint of meeting the standards was obtained. The results show that primary particulate matter( PPM) reduction is the most effective at reducing the concentration of PM_(2.5) in the atmosphere,on the basis of emission reduction in surrounding areas,PPM emission reduction accounts for 88% of the total reduction of the annual average concentration of PM_(2.5),followed by NH_3,NOx,SO_2,and VOCs,which contribute to 10. 3%,5. 5%,3. 2%,and 0. 5%,respectively. Compared to 2015,the reduction ratios of the major pollutants are between 22% and 53%. Controlling the activity level is more effective for SO_2,NH_3 and CO emissions reduction,while there is still more opportunity for NOxand VOCs end treatment. When the emissions of SO_2,NOx,PM10,PM_(2.5),BC,OC,CO,VOCs,and NH_3 are controlled to 2. 43 × 10~4,8. 47 × 10~4,9. 42 × 10~4,3. 74 × 10~4,0. 19 × 10~4,0. 30 × 10~4,26. 56 × 10~4,13. 08 ×10~4,and 1. 50 × 10~4 t,respectively,it is expected that the levels of the six pollutants in Nanjing can meet the national ambient air quality level 2 standards.
引文
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[25]庞杨.京津冀地区大气污染物分布和演变特征的模拟研究[D].南京:南京信息工程大学,2012.Pang Y. Distribution and evolution of atmospheric pollutants over Beijing-Tianjin-Hebei region[D]. Nanjing:Nanjing University of Information Science&Technology,2012.
[26]王天义.钢铁企业烧结、焦化烟气超低排放治理技术的调研及建议[N].世界金属导报,2018-10-16(B12).
[27]彭长寿.水泥行业烟气多种污染物“超低排放”整体解决方案[J].中国水泥,2017,(3):88-97.
[28]严茹莎,李莉,安静宇,等.上海市夏季臭氧生成与其前体物控制模拟研究[J].环境污染与防治,2016,38(1):30-35,40.Yan R S,Li L,An J Y,et al. Simulation study of ozone generation and its precursors control in Shanghai during summer time[J]. Environmental Pollution&Control,2016,38(1):30-35,40.
[2]杨笑笑,汤莉莉,张运江,等.南京夏季市区VOCs特征及O3生成潜势的相关性分析[J].环境科学,2016,37(2):443-451.Yang X X,Tang L L,Zhang Y J,et al. Correlation analysis between characteristics of VOCs and ozone formation potential in summer in Nanjing urban district[J]. Environmental Science,2016,37(2):443-451.
[3]康晖,朱彬,王红磊,等.长三角典型站点冬季大气PM2. 5中OC、EC污染特征[J].环境科学,2018,39(3):961-971.Kang H,Zhu B,Wang H L,et al. Characterization and variation of organic carbon(OC)and elemental carbon(EC)in PM2. 5during the winter in the Yangtze River Delta region,China[J].Environmental Science,2018,39(3):961-971.
[4]王博妮,濮梅娟,陈鹏,等.南京城区冬季大气污染特征[J].环境科学研究,2017,30(9):1335-1345.Wang B N,Pu M J,Chen P,et al. Characteristic analysis of atmospheric pollutants in Nanjing urban area in winter[J].Research of Environmental Sciences,2017,30(9):1335-1345.
[5]南京市统计局去产能与产业转型升级课题组.去产能背景下南京市钢铁、化工等传统产业转型升级路径研究[J].统计科学与实践,2017,(3):37-40.
[6]张予燕,芮冬梅,周灵辉.南京钟山风景区对主城环境空气质量的影响[J].环境监控与预警,2010,2(1):47-50.Zhang Y Y,Rui D M,Zhou L H. Nanjing Zhongshan Mountain Scenic's influence on the air quality in main urban area[J].Environmental Monitoring and Forewarning,2012,2(1):47-50.
[7]娄伟.情景分析方法研究[J].未来与发展,2012,35(9):17-26.Lou W. The study on scenario analysis method[J]. Future and Development,2012,35(9):17-26.
[8]鱼智霞,曹国良,安文辉,等.中国区域2020和2030年的SO2和NOx排放总量情景预测[J].环境工程学报,2017,11(4):2355-2362.Yu Z X,Cao G L,An W H,et al. Scenarios prediction for emissions of SO2,NOxin the year of 2020 and 2030 in China[J]. Journal of Environmental Engineering,2017,11(4):2355-2362.
[9]魏巍.中国人为源挥发性有机化合物的排放现状及未来趋势[D].北京:清华大学,2009.Wei W. Study on current and future anthropogenic emissions of volatile organic compounds in China[D]. Beijing:Tsinghua University,2009.
[10]安文辉,曹国良,蒙小波,等.情景分析法预测西安市大气污染物排放总量[J].环境工程,2016,34(8):99-103.An W H,Cao G L,Meng X B,et al. Scenario analysis to predict the total emissions of air pollutants in Xi'an[J].Environmental Engineering,2016,34(8):99-103.
[11]常嘉成,赵天良,谭成好,等.基于WRF-Chem模拟的玉溪市大气环境容量精细估算[J].环境科学学报,2017,37(10):3876-3884.Chang J C,Zhao T L,Tan C H,et al. An elaborative assessment of atmospheric environmental capacity in Yuxi based on WRFChem modeling[J]. Acta Scientiae Circumstantiae,2017,37(10):3876-3884.
[12]薛文博,付飞,王金南,等.基于全国城市PM2. 5达标约束的大气环境容量模拟[J].中国环境科学,2014,34(10):2490-2496.Xue W B, Fu F, Wang J N, et al. Modeling study on atmospheric environmental capacity of major pollutants constrained by PM2. 5compliance of Chinese cities[J]. Chinese Environmental Science,2014,34(10):2490-2496.
[13]中国清洁空气联盟.京津冀如何实现空气质量达标[EB/OL]. http://www. cleanairchina. org/product/7532. html,2016-02-23.
[14]中国清洁空气联盟.长三角如何实现空气质量达标?[EB/OL]. http://www. cleanairchina. org/product/7751. html,2016-06-17.
[15]南京市环境保护局.南京市大气污染物排放清单更新(2015年)[R].南京:南京市环境保护局,2016.
[16]环境保护部.城市大气污染物排放清单编制工作手册[M].北京:环境保护部,2015.
[17] Boylan J W,Russell A G. PM and light extinction model performance metrics,goals,and criteria for three-dimensional air quality models[J]. Atmospheric Environment,2006,40(26):4946-4959.
[18] Li M,Zhang Q,Kurokawa J I,et al. MIX:a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP[J].Atmospheric Chemistry and Physics,2017,17(2):935-963.
[19]吴文景,常兴,邢佳,等.京津冀地区主要排放源减排对PM2. 5污染改善贡献评估[J].环境科学,2017,38(3):867-875.Wu W J,Chang X,Xin J,et al. Assessment of PM2. 5pollution mitigation due to emission reduction from main emission sources in the Bejing-Tianjin-Hebei region[J]. Environmental Science,2017,38(3):867-875.
[20] Itahashi S,Hayami H,Yumimoto K,et al. Chinese provincescale source apportionments for sulfate aerosol in 2005 evaluated by the tagged tracer method[J]. Environmental Pollution,2017,220:1366-1375.
[21]刘童,王晓军,陈倩,等.烟台市环境受体PM2. 5四季污染特征与来源解析[J].环境科学,2019,40(3):1082-1090.Liu T,Wang X J,Chen Q,et al. Characteristic and source analysis of environmental receptor PM2. 5in four seasons in Yantai city[J]. Environmental Science,2019,40(3):1082-1090.
[22]刘素,马彤,杨艳,等.太原市冬季PM2. 5化学组分特征与来源解析[J].环境科学,2019,40(4):1537-1544.Liu S, Ma T, Yang Y, et al. Characterization and source analysis of PM2. 5chemical components in winter in Taiyuan city[J]. Environmental Science,2019,40(4):1537-1544.
[23]吴明,吴丹,夏俊荣,等.成都冬季PM2. 5化学组分污染特征及来源解析[J].环境科学,2019,40(1):76-85.Wu M, Wu D, Xia J R, et al. Analysis of pollution characteristics and sources of PM2. 5chemical components in Chengdu in Winter[J]. Environmental Science,2019,40(1):76-85.
[24]王苏蓉,喻义勇,王勤耕,等.基于PMF模式的南京市大气细颗粒物源解析[J].中国环境科学,2015,35(12):3535-3542.Wang S R,Yu Y Y,Wang Q G,et al. Source apportionment of PM2. 5in Nanjing by PMF[J]. China Environmental Science,2015,35(12):3535-3542.
[25]庞杨.京津冀地区大气污染物分布和演变特征的模拟研究[D].南京:南京信息工程大学,2012.Pang Y. Distribution and evolution of atmospheric pollutants over Beijing-Tianjin-Hebei region[D]. Nanjing:Nanjing University of Information Science&Technology,2012.
[26]王天义.钢铁企业烧结、焦化烟气超低排放治理技术的调研及建议[N].世界金属导报,2018-10-16(B12).
[27]彭长寿.水泥行业烟气多种污染物“超低排放”整体解决方案[J].中国水泥,2017,(3):88-97.
[28]严茹莎,李莉,安静宇,等.上海市夏季臭氧生成与其前体物控制模拟研究[J].环境污染与防治,2016,38(1):30-35,40.Yan R S,Li L,An J Y,et al. Simulation study of ozone generation and its precursors control in Shanghai during summer time[J]. Environmental Pollution&Control,2016,38(1):30-35,40.
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