后G20时期杭州市挥发性有机物和可吸入颗粒物的特征分析
|
摘要
后G20时期(2016年9月至2017年12月),臭氧成为杭州夏季首要大气污染物,可吸入颗粒物(PM_(10))成为杭州冬季首要大气污染物。利用杭州市环境检测朝晖站和环境监测下沙站空气质量数据观测值分析55种挥发性有机化合物(VOC)的臭氧形成潜力,并使用美国国家环境保护局研发的第3代大气质量预估模型(WRF-CMAQ)对杭州市中心PM_(10)的污染数值进行模拟分析,提出了后G20时期PM10和臭氧的最佳管控方案。结果显示:在后G20时期杭州市中心和城郊,芳香烃所占臭氧形成潜力比例最高,其次为烯烃和烷烃,最后为乙炔。在市区和城郊,乙烯、间/对二甲苯、甲苯、丙烯和乙苯依次为生成臭氧最主要的5种挥发性有机化合物,因此管控这5种挥发性有机化合物能最大程度的减少臭氧质量浓度。2017年杭州市中心春夏秋冬四季的PM10中分别有50%, 32%, 48%和45%源于杭州市以外污染源的长距离跨地域传输。其中,春秋冬三季通过长距离传输来到杭州市区的PM_(10)主要源于市区的北方,夏季则主要源于市区的西南方。2017年,杭州市区的交通源、工业源、生活源与农业源对本地排放PM10的贡献率分别为62.6%, 27.8%, 7.3%和2.3%。当杭州本地的工业源和交通源在2016年的基础上分别减排15%和5%时,杭州市中心的PM_(10)年均质量浓度可低于70μg·m~(-3),达到二类区的标准。
In the post-G20 period(September 2016 to December 2017), the dominant atmospheric pollutant in winter was PM_(10) and in summer was tropospheric ozone. For the purpose of better regulating PM_(10) and groundlevel ozone, chemical reactivity of 55 kinds of volatile organic compounds(VOCs) was analyzed at Zhaohui and Xiasha Environmental Monitoring Stations using ozone formation potential(OFP). The third generation atmospheric model WRF-CMAQ developed by the United States Environmental Protection Agency(USEPA) was also used to simulate time-space distributions of PM_(10) in the city center. Results showed that alkenes were the primary precursor in the downtown area, followed by aromatics, alkanes, and alkynes; whereas, aromatics were the primary precursor in the suburbs, followed by alkenes, alkanes, and alkynes. Ethylene, m/p-xylene, toluene,propylene, and ethylbenzene were ranked as the top five VOCs for OFP in both downtown and suburban areas of Hangzhou. For PM_(10) in the city center, regional transport of pollutants accounted for about 50% in spring,32% in summer, 48% in autumn, and 45% in winter. The regional transported PM_(10) mainly came from the north in spring, autumn, and winter but from the southwest in summer. Local Hangzhou PM_(10) levels in 2017 included local industry(62.6%), traffic(27.8%), residence(7.3%), and agriculture(2.3%). Consequently, controlling the top five VOC species was the best way to alleviate ground-level ozone, and based on 2016 statistics, shutting down 15% of the local industry and reducing 5% of the local traffic would reduce the annual PM_(10) level to70 μg·m-3 meeting the second national environmental air quality standard.
In the post-G20 period(September 2016 to December 2017), the dominant atmospheric pollutant in winter was PM_(10) and in summer was tropospheric ozone. For the purpose of better regulating PM_(10) and groundlevel ozone, chemical reactivity of 55 kinds of volatile organic compounds(VOCs) was analyzed at Zhaohui and Xiasha Environmental Monitoring Stations using ozone formation potential(OFP). The third generation atmospheric model WRF-CMAQ developed by the United States Environmental Protection Agency(USEPA) was also used to simulate time-space distributions of PM_(10) in the city center. Results showed that alkenes were the primary precursor in the downtown area, followed by aromatics, alkanes, and alkynes; whereas, aromatics were the primary precursor in the suburbs, followed by alkenes, alkanes, and alkynes. Ethylene, m/p-xylene, toluene,propylene, and ethylbenzene were ranked as the top five VOCs for OFP in both downtown and suburban areas of Hangzhou. For PM_(10) in the city center, regional transport of pollutants accounted for about 50% in spring,32% in summer, 48% in autumn, and 45% in winter. The regional transported PM_(10) mainly came from the north in spring, autumn, and winter but from the southwest in summer. Local Hangzhou PM_(10) levels in 2017 included local industry(62.6%), traffic(27.8%), residence(7.3%), and agriculture(2.3%). Consequently, controlling the top five VOC species was the best way to alleviate ground-level ozone, and based on 2016 statistics, shutting down 15% of the local industry and reducing 5% of the local traffic would reduce the annual PM_(10) level to70 μg·m-3 meeting the second national environmental air quality standard.
引文
[1] XIAO Huadong, SUN Jing, BIAN Xiaofeng, et al. GPU acceleration of the WSM6 cloud microphysics scheme in GRAPES model[J]. Comput Geosci, 2013, 59:156-162.
[2] FOUNTOUKIS C, MART魱N-POMARES L, PEREZ-ASTUDILLO D, et al. Simulating global horizontal irradiance in the Arabian Peninsula:sensitivity to explicit treatment of aerosols[J]. Sol Energ, 2018, 163:347-355.
[3] MOHR C, TAO Weikuo, CHERN J, et al. The NASA goddard multi-scale modeling framework-land information system:global land/atmosphere interaction with resolved convection[J]. Environ Modell Software, 2013, 39:103-115.
[4] DENG Tao, CHEN Yang, WAN Qilin, et al. Comparative evaluation of the impact of GRAPES and MM5 meteorology on CMAQ prediction over Pearl River Delta, China[J]. Particuology, 2018, 40:88-97.
[5] KISHNE A, YIMAN Y, MORGAN C, et al. Evaluation and improvement of the default soil hydraulic parameters for the Noah Land Surface Model[J]. Geoderma, 2017, 285:247-259.
[6] DONG Haotian, CAO Shuyang, TAKEMI T, et al. WRF simulation of surface wind in high latitudes[J]. J Wind Eng Ind Aerodyn, 2018, 179:287-296.
[7] ZEPKA G S, PINTO O, SARAIVA A C V. Lightning forecasting in southeastern Brazil using the WRF model[J]. Atmos Res, 2014, 135/136:344-362.
[8] GUENTHER A, KARL T, HARLEY P, et al. Estimates of global terrestrial isoprene emissions using MEGAN(model of emissions of gases and aerosols from nature)[J]. Atmos Chem Phys, 2006, 6(11):3181-3210.
[9] ZHANG Q, STREETS D G, CARMICHAEL G R, et al. Asian emissions in 2006 for the NASA INTEX-B mission[J].Atmos Chem Phys, 2009, 9(14):5131-5153.
[10] LI Meng, ZHANG Qiang, KUROKAWA J, et al. MIX:a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP[J]. Atmos Chem Phys, 2017, 17:935-963.
[11] HUANG Chuangfeng, CHEN Changhong, LI L, et al. The study of emission inventory of anthropogenic air pollutants and VOC species in the Yangtze River Delta region, China[J]. Atmos Chem Phys, 2011, 11(1):4105-4120.
[12] LI Kangwei, CHEN Linghong, YING Fang, et al. Meteorological and chemical impacts on ozone formation:a case study in Hangzhou, China[J]. Atmos Res, 2017, 196(1):40-52.
[13] CARTER W P L. Development of ozone reactivity scales for volatile organic compounds[J]. Air Waste, 1994, 44(7):881-899.
[14] WANG Qiaoli, LI Sujing, DING Minli, et al. VOC emission characteristics and priority control analysis based on VOC emission inventories and ozone formation potentials in Zhoushan[J]. Atmos Environ, 2018, 182:234-241.
[15] BOYLAN J, RUSSEL A. PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models[J]. Atmos Environ, 2006, 40(26):4946-4959.
[16] CHO S, MORRIS R, MCEACHEM P, et al. Emission sources sensitivity study for ground-level ozone and PM2.5due to oil sands development using air quality modeling system:partⅡ-source apportionment modeling[J]. Atmos Environ, 2012, 55:542-556.
[2] FOUNTOUKIS C, MART魱N-POMARES L, PEREZ-ASTUDILLO D, et al. Simulating global horizontal irradiance in the Arabian Peninsula:sensitivity to explicit treatment of aerosols[J]. Sol Energ, 2018, 163:347-355.
[3] MOHR C, TAO Weikuo, CHERN J, et al. The NASA goddard multi-scale modeling framework-land information system:global land/atmosphere interaction with resolved convection[J]. Environ Modell Software, 2013, 39:103-115.
[4] DENG Tao, CHEN Yang, WAN Qilin, et al. Comparative evaluation of the impact of GRAPES and MM5 meteorology on CMAQ prediction over Pearl River Delta, China[J]. Particuology, 2018, 40:88-97.
[5] KISHNE A, YIMAN Y, MORGAN C, et al. Evaluation and improvement of the default soil hydraulic parameters for the Noah Land Surface Model[J]. Geoderma, 2017, 285:247-259.
[6] DONG Haotian, CAO Shuyang, TAKEMI T, et al. WRF simulation of surface wind in high latitudes[J]. J Wind Eng Ind Aerodyn, 2018, 179:287-296.
[7] ZEPKA G S, PINTO O, SARAIVA A C V. Lightning forecasting in southeastern Brazil using the WRF model[J]. Atmos Res, 2014, 135/136:344-362.
[8] GUENTHER A, KARL T, HARLEY P, et al. Estimates of global terrestrial isoprene emissions using MEGAN(model of emissions of gases and aerosols from nature)[J]. Atmos Chem Phys, 2006, 6(11):3181-3210.
[9] ZHANG Q, STREETS D G, CARMICHAEL G R, et al. Asian emissions in 2006 for the NASA INTEX-B mission[J].Atmos Chem Phys, 2009, 9(14):5131-5153.
[10] LI Meng, ZHANG Qiang, KUROKAWA J, et al. MIX:a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP[J]. Atmos Chem Phys, 2017, 17:935-963.
[11] HUANG Chuangfeng, CHEN Changhong, LI L, et al. The study of emission inventory of anthropogenic air pollutants and VOC species in the Yangtze River Delta region, China[J]. Atmos Chem Phys, 2011, 11(1):4105-4120.
[12] LI Kangwei, CHEN Linghong, YING Fang, et al. Meteorological and chemical impacts on ozone formation:a case study in Hangzhou, China[J]. Atmos Res, 2017, 196(1):40-52.
[13] CARTER W P L. Development of ozone reactivity scales for volatile organic compounds[J]. Air Waste, 1994, 44(7):881-899.
[14] WANG Qiaoli, LI Sujing, DING Minli, et al. VOC emission characteristics and priority control analysis based on VOC emission inventories and ozone formation potentials in Zhoushan[J]. Atmos Environ, 2018, 182:234-241.
[15] BOYLAN J, RUSSEL A. PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models[J]. Atmos Environ, 2006, 40(26):4946-4959.
[16] CHO S, MORRIS R, MCEACHEM P, et al. Emission sources sensitivity study for ground-level ozone and PM2.5due to oil sands development using air quality modeling system:partⅡ-source apportionment modeling[J]. Atmos Environ, 2012, 55:542-556.