南京江北2014—2016年PM_(2.5)质量浓度分布特征及气象和传输影响因素分析
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摘要
利用2014—2016年南京江北地区PM_(2.5)质量浓度和气象要素的小时数据,并结合HYSPLIT模式后向轨迹聚类分析和PSCF法分析了PM_(2.5)质量浓度的污染特征及其主要影响因素和主要来源特征.结果表明:2014—2016年PM_(2.5)质量浓度呈逐年下降趋势,下降幅度约为17.40%,由2014年的62.1μg·m~(-3)下降至2016年的51.2μg·m~(-3),能见度由2014年的5.8 km上升至2016年6.6 km.PM_(2.5)质量浓度存在显著的月变化和季节变化特征,1月浓度最高,可达93.0μg·m~(-3);8月浓度最低,仅为38.8μg·m~(-3);冬季浓度最高,可达76.8μg·m~(-3),夏季浓度最低,仅为47.1μg·m~(-3).不同季节日变化均为单峰型分布.气象要素对PM_(2.5)质量浓度的影响较大,不同相对湿度下能见度和PM_(2.5)质量浓度具有较好的拟合关系.霾和非霾天PM_(2.5)质量浓度的阈值为15μg·m~(-3).不同季节的主导气团不同,春季主导气团为偏北气流和偏东气流,占比分别为43.50%和30.80%;夏季主导气团以东部气流为主,占比约为68.22%;秋季和冬季主导气团为来自北方的气流,总占比分别为83.52%和100%;偏北内陆气团PM_(2.5)质量浓度较大,偏东海洋性气团PM_(2.5)质量浓度较低.PM_(2.5)质量浓度潜在源区春冬季潜在源区范围较大,夏秋季潜在源区范围较小,季节变化显著.春季潜在来源主要分布在安徽、江西北部、江苏南部和浙江北部等地区,夏秋季分布在安徽东部、浙江北部和江苏南部等地区,冬季分布在安徽、河南东部,山东和江苏等地区.
Based on the hourly monitoring data including meteorological elements and PM_(2.5) mass concentration in the north suburb of Nanjing during 2014 to 2016, PM_(2.5) mass concentration variations and their influencing factors were analyzed by using a cluster analysis derived from Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT) model and Potential Source Contribution Function Analysis(PSCF) method. Results show that the mass concentration of PM_(2.5) has a downward trend from a level of 62.1 μg·m~(-3)(2014) to 51.2 μg·m~(-3)(2016), with a amplitude decline of about 17.4%. The visibility rose from 5.8 km in 2014 to 6.6 km in 2016. There present significantly monthly and seasonal changes for PM_(2.5) concentration, who were the highest in January(93.0 μg·m~(-3)) and the lowest in August(39 μg·m~(-3)), and were the highest in winter(76.8 μg·m~(-3)) and the lowest in summer(47.1 μg·m~(-3)). The diurnal variation of PM_(2.5) in different seasons showed a unimodal distribution. Meteorological factors have a great impact on PM_(2.5) mass concentration, It′s calculated to have a good fitting relationship between visibility and PM_(2.5) under different relative humidity. The threshold value of PM_(2.5) on haze days and non-haze days was 15 μg·m~(-3) uniformly. Dominant air masses varied in different seasons. It′s dominated by northerly and easterly airflows in spring, accounting for 43.50% and 30.80% of the total airflows respectively, by easterly airflows in summer,with a proportion of 68.22%, and by northerly airflow in autumn and winter with a fraction of 83.52% and 100.00%, respectively. The PM_(2.5) concentrations were comparably large under the control of northly airflows from inland and were low under the easterly airflows originated from ocean areas. The range of the potential source areas of PM_(2.5) revealed a significantly seasonal variation with a relatively large area in spring and winter and a small area in summer and autumn. The potential source areas of PM_(2.5) mass concentration are mainly distributed in Anhui, northern Jiangxi, southern Jiangsu and northern Zhejiang in spring; in eastern Anhui, northern Zhejiang and southern Jiangsu in summer and autumn, and in Anhui, eastern Henan, Shandong and Jiangsu in winter.
Based on the hourly monitoring data including meteorological elements and PM_(2.5) mass concentration in the north suburb of Nanjing during 2014 to 2016, PM_(2.5) mass concentration variations and their influencing factors were analyzed by using a cluster analysis derived from Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT) model and Potential Source Contribution Function Analysis(PSCF) method. Results show that the mass concentration of PM_(2.5) has a downward trend from a level of 62.1 μg·m~(-3)(2014) to 51.2 μg·m~(-3)(2016), with a amplitude decline of about 17.4%. The visibility rose from 5.8 km in 2014 to 6.6 km in 2016. There present significantly monthly and seasonal changes for PM_(2.5) concentration, who were the highest in January(93.0 μg·m~(-3)) and the lowest in August(39 μg·m~(-3)), and were the highest in winter(76.8 μg·m~(-3)) and the lowest in summer(47.1 μg·m~(-3)). The diurnal variation of PM_(2.5) in different seasons showed a unimodal distribution. Meteorological factors have a great impact on PM_(2.5) mass concentration, It′s calculated to have a good fitting relationship between visibility and PM_(2.5) under different relative humidity. The threshold value of PM_(2.5) on haze days and non-haze days was 15 μg·m~(-3) uniformly. Dominant air masses varied in different seasons. It′s dominated by northerly and easterly airflows in spring, accounting for 43.50% and 30.80% of the total airflows respectively, by easterly airflows in summer,with a proportion of 68.22%, and by northerly airflow in autumn and winter with a fraction of 83.52% and 100.00%, respectively. The PM_(2.5) concentrations were comparably large under the control of northly airflows from inland and were low under the easterly airflows originated from ocean areas. The range of the potential source areas of PM_(2.5) revealed a significantly seasonal variation with a relatively large area in spring and winter and a small area in summer and autumn. The potential source areas of PM_(2.5) mass concentration are mainly distributed in Anhui, northern Jiangxi, southern Jiangsu and northern Zhejiang in spring; in eastern Anhui, northern Zhejiang and southern Jiangsu in summer and autumn, and in Anhui, eastern Henan, Shandong and Jiangsu in winter.
引文
陈飞, 于洪霞, 柴发合. 2017. 徐州市大气PM2.5污染特征和来源解析[J]. 环境与可持续发展, 42(5):135-138
Draxler R R, Stunder B, Rolph G, et al. 1999. HYSPLIT4 Users′s Guide[M]. US Department of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Air Resources Laboratory
樊曙先,徐建强, 郑有飞,等. 2005. 南京市气溶胶PM2.5一次来源解析[J]. 气象科学, 25(6):587-593
葛跃, 王明新, 白雪,等. 2017. 苏锡常地区PM2.5污染特征及其潜在源区分析[J]. 环境科学学报, 37(3):803-813
Hansen J, Sato M, Ruedy R, et al. 2005. Efficacy of climate forcings[M]. Journal of Geophysical Research: Atmospheres (1984—2012).2571-2592
韩素芹, 冯银厂,边海,等. 2008.天津大气污染物日变化特征的WRF-Chem数值模拟[J]. 中国环境科学, 28(9):828-832
黄辉军,刘红年,蒋维楣,等. 2006. 南京市PM2.5物理化学特性及来源解析[J]. 气候与环境研究, 11(6):713-722
黄鹂鸣, 王格慧, 王荟,等. 2002. 南京市空气中颗粒物PM10、PM2.5污染水平[J]. 中国环境科学, 22(4):334-337
Ji D, Cui Y, Li L, et al. 2018. Characterization and source identification of fine particulate matter in urban Beijing during the 2015 Spring Festival[J]. Science of the Total Environment, 628-629:430-440
贾梦唯,赵天良,张祥志,等. 2016.南京主要大气污染物季节变化及相关气象分析[J]. 中国环境科学, 36(9):2567-2577
Kalaiarasan G, Balakrishnan R M, Sethunath N A, et al. 2018. Source apportionment studies on particulate matter (PM10and PM2.5) in ambient air of urban Mangalore, India[J]. Journal of Environmental Management, 217: 815-824
Kong X, He W, Qin N, et al. 2013. Comparison of transport pathways and potential sources of PM10, in two cities around a large Chinese lake using the modified trajectory analysis[J]. Atmospheric Research, 122(3):284-297
Martini F M S, Hasenkopf C A, Roberts D C. 2015. Statistical analysis of PM2.5 observations from diplomatic facilities in China[J]. Atmospheric Environment, 110:174-185
Ming L, Jin L, Li J, et al. 2017. PM2.5 in the Yangtze River Delta, China: Chemical compositions, seasonal variations, and regional pollution events[J]. Environmental Pollution, 223:200-212
李梓铭, 孙兆彬, 邵勰,等. 2017. 北京城区PM2.5不同时间尺度周期性研究[J]. 中国环境科学, 37(2):407-415
刘晓慧,朱彬,王红磊,等. 2015. 南京市北郊工业区大气能见度消光因子研究[J]. 南京大学学报(自然科学), 51(3):481-489
马佳, 于兴娜, 安俊琳,等. 2016. 南京北郊冬春季大气能见度影响因子贡献研究[J]. 环境科学, 37(1):41-50
沈铁迪, 王体健, 陈璞珑,等. 2015. 南京城区夏秋季能见度与PM2.5化学成分的关系[J]. 中国环境科学, 35(3):652-658
Viana M, Querol X, Alastuey A, et al. 2008. Characterising exposure to PM aerosols for an epidemiological study[J]. Atmospheric Environment, 42(7):1552-1568
Wang H, Zhu B, Zhang Z, et al. 2015. Mixing state of individual carbonaceous particles during a severe haze episode in January 2013, Nanjing, China[J]. Particuology, 20(3):16-23
Wang Y Q, Zhang X Y, Draxler R R. 2009. TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution measurement data[J]. Environmental Modelling & Software, 24(8):938-939
Wessels A, Birmili W, Albrecht C, et al. 2010. Oxidant generation and toxicity of size-fractionated ambient particles in human lung epithelial cells[J]. Environmental Science & Technology, 44(9):3539-3545
王爱平, 朱彬, 银燕,等. 2014. 黄山顶夏季气溶胶数浓度特征及其输送潜在源区[J]. 中国环境科学, 34(4):852-861
王荟, 王格慧, 高士祥,等. 2003.南京市大气颗粒物春季污染的特征[J]. 中国环境科学, 23(1):55-59
王琪,孙巍, 张新宇. 2014.北京地区PM2.5质量浓度分布及其与气象条件影响关系分析[J]. 计算机与应用化学, 31(10): 1193-1196
魏玉香, 杨卫芬, 银燕,等. 2009. 霾天气南京市大气PM2.5中水溶性离子污染特征[J]. 环境科学与技术, 32(11):66-71
Ye W F, Ma Z Y, Ha X Z. 2018. Spatial-temporal patterns of PM2.5 concentrations for 338 Chinese cities[J]. Science of the Total Environment, s 631-632:524-533
杨书申, 邵龙义, 龚铁强,等. 2005. 大气颗粒物浓度检测技术及其发展[J]. 北京工业职业技术学院学报, 4(1):36-39
尹聪, 朱彬, 曹云昌,等. 2011. 秸秆焚烧影响南京空气质量的成因探讨[J]. 中国环境科学, 31(2):207-213
岳毅, 李金娟, 马千里. 2017. 临安本底站2010—2015年PM10污染特征及影响因素分析[J]. 中国环境科学, 37(8):2877-2887
张浩, 石春娥, 吴必文,等. 2017. 合肥市能见度与相对湿度、PM2.5质量浓度的定量关系[J]. 生态环境学报, 26(6):1001-1008
张晓茹, 孔少飞, 银燕,等. 2016.亚青会期间南京大气PM2.5中重金属来源及风险[J]. 中国环境科学, 36(1):1-11
赵晨曦,王云琦,王玉杰,等. 2014.北京地区冬春PM2.5和PM10污染水平时空分布及其与气象条件的关系[J]. 环境科学, 35(2):418-427
赵恒, 王体健, 江飞,等. 2009. 利用后向轨迹模式研究TRACE-P期间香港大气污染物的来源[J]. 热带气象学报, 25(2):181-186
赵阳, 胡恭任, 于瑞莲,等. 2017. 2013年南昌市区PM2.5的浓度水平及时空分布特征与来源[J]. 环境科学研究, 30(6):854-863
钟兆盈, 张耀林. 2018. 蓝天保卫战杭州打得响——2013—2017年优良天数持续上升、PM2.5连续下降[J]. 环境保护, 46(6): 76-78
朱彬,苏继锋,韩志伟,等. 2010. 秸秆焚烧导致南京及周边地区一次严重空气污染过程的分析[J]. 中国环境科学, 30(5):585-592
Draxler R R, Stunder B, Rolph G, et al. 1999. HYSPLIT4 Users′s Guide[M]. US Department of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Air Resources Laboratory
樊曙先,徐建强, 郑有飞,等. 2005. 南京市气溶胶PM2.5一次来源解析[J]. 气象科学, 25(6):587-593
葛跃, 王明新, 白雪,等. 2017. 苏锡常地区PM2.5污染特征及其潜在源区分析[J]. 环境科学学报, 37(3):803-813
Hansen J, Sato M, Ruedy R, et al. 2005. Efficacy of climate forcings[M]. Journal of Geophysical Research: Atmospheres (1984—2012).2571-2592
韩素芹, 冯银厂,边海,等. 2008.天津大气污染物日变化特征的WRF-Chem数值模拟[J]. 中国环境科学, 28(9):828-832
黄辉军,刘红年,蒋维楣,等. 2006. 南京市PM2.5物理化学特性及来源解析[J]. 气候与环境研究, 11(6):713-722
黄鹂鸣, 王格慧, 王荟,等. 2002. 南京市空气中颗粒物PM10、PM2.5污染水平[J]. 中国环境科学, 22(4):334-337
Ji D, Cui Y, Li L, et al. 2018. Characterization and source identification of fine particulate matter in urban Beijing during the 2015 Spring Festival[J]. Science of the Total Environment, 628-629:430-440
贾梦唯,赵天良,张祥志,等. 2016.南京主要大气污染物季节变化及相关气象分析[J]. 中国环境科学, 36(9):2567-2577
Kalaiarasan G, Balakrishnan R M, Sethunath N A, et al. 2018. Source apportionment studies on particulate matter (PM10and PM2.5) in ambient air of urban Mangalore, India[J]. Journal of Environmental Management, 217: 815-824
Kong X, He W, Qin N, et al. 2013. Comparison of transport pathways and potential sources of PM10, in two cities around a large Chinese lake using the modified trajectory analysis[J]. Atmospheric Research, 122(3):284-297
Martini F M S, Hasenkopf C A, Roberts D C. 2015. Statistical analysis of PM2.5 observations from diplomatic facilities in China[J]. Atmospheric Environment, 110:174-185
Ming L, Jin L, Li J, et al. 2017. PM2.5 in the Yangtze River Delta, China: Chemical compositions, seasonal variations, and regional pollution events[J]. Environmental Pollution, 223:200-212
李梓铭, 孙兆彬, 邵勰,等. 2017. 北京城区PM2.5不同时间尺度周期性研究[J]. 中国环境科学, 37(2):407-415
刘晓慧,朱彬,王红磊,等. 2015. 南京市北郊工业区大气能见度消光因子研究[J]. 南京大学学报(自然科学), 51(3):481-489
马佳, 于兴娜, 安俊琳,等. 2016. 南京北郊冬春季大气能见度影响因子贡献研究[J]. 环境科学, 37(1):41-50
沈铁迪, 王体健, 陈璞珑,等. 2015. 南京城区夏秋季能见度与PM2.5化学成分的关系[J]. 中国环境科学, 35(3):652-658
Viana M, Querol X, Alastuey A, et al. 2008. Characterising exposure to PM aerosols for an epidemiological study[J]. Atmospheric Environment, 42(7):1552-1568
Wang H, Zhu B, Zhang Z, et al. 2015. Mixing state of individual carbonaceous particles during a severe haze episode in January 2013, Nanjing, China[J]. Particuology, 20(3):16-23
Wang Y Q, Zhang X Y, Draxler R R. 2009. TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution measurement data[J]. Environmental Modelling & Software, 24(8):938-939
Wessels A, Birmili W, Albrecht C, et al. 2010. Oxidant generation and toxicity of size-fractionated ambient particles in human lung epithelial cells[J]. Environmental Science & Technology, 44(9):3539-3545
王爱平, 朱彬, 银燕,等. 2014. 黄山顶夏季气溶胶数浓度特征及其输送潜在源区[J]. 中国环境科学, 34(4):852-861
王荟, 王格慧, 高士祥,等. 2003.南京市大气颗粒物春季污染的特征[J]. 中国环境科学, 23(1):55-59
王琪,孙巍, 张新宇. 2014.北京地区PM2.5质量浓度分布及其与气象条件影响关系分析[J]. 计算机与应用化学, 31(10): 1193-1196
魏玉香, 杨卫芬, 银燕,等. 2009. 霾天气南京市大气PM2.5中水溶性离子污染特征[J]. 环境科学与技术, 32(11):66-71
Ye W F, Ma Z Y, Ha X Z. 2018. Spatial-temporal patterns of PM2.5 concentrations for 338 Chinese cities[J]. Science of the Total Environment, s 631-632:524-533
杨书申, 邵龙义, 龚铁强,等. 2005. 大气颗粒物浓度检测技术及其发展[J]. 北京工业职业技术学院学报, 4(1):36-39
尹聪, 朱彬, 曹云昌,等. 2011. 秸秆焚烧影响南京空气质量的成因探讨[J]. 中国环境科学, 31(2):207-213
岳毅, 李金娟, 马千里. 2017. 临安本底站2010—2015年PM10污染特征及影响因素分析[J]. 中国环境科学, 37(8):2877-2887
张浩, 石春娥, 吴必文,等. 2017. 合肥市能见度与相对湿度、PM2.5质量浓度的定量关系[J]. 生态环境学报, 26(6):1001-1008
张晓茹, 孔少飞, 银燕,等. 2016.亚青会期间南京大气PM2.5中重金属来源及风险[J]. 中国环境科学, 36(1):1-11
赵晨曦,王云琦,王玉杰,等. 2014.北京地区冬春PM2.5和PM10污染水平时空分布及其与气象条件的关系[J]. 环境科学, 35(2):418-427
赵恒, 王体健, 江飞,等. 2009. 利用后向轨迹模式研究TRACE-P期间香港大气污染物的来源[J]. 热带气象学报, 25(2):181-186
赵阳, 胡恭任, 于瑞莲,等. 2017. 2013年南昌市区PM2.5的浓度水平及时空分布特征与来源[J]. 环境科学研究, 30(6):854-863
钟兆盈, 张耀林. 2018. 蓝天保卫战杭州打得响——2013—2017年优良天数持续上升、PM2.5连续下降[J]. 环境保护, 46(6): 76-78
朱彬,苏继锋,韩志伟,等. 2010. 秸秆焚烧导致南京及周边地区一次严重空气污染过程的分析[J]. 中国环境科学, 30(5):585-592