武汉市2014—2017年大气污染物分布特征及其潜在来源分析
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摘要
利用武汉市2014—2017年大气污染物(SO_2、NO_2、CO、O_3、PM_(2.5)和PM_(10))和气象要素的观测数据,分析了大气污染物的变化特征及其影响因素.使用HYSPLIT模式计算了影响武汉市的主要气团类型,并利用潜在源区贡献(PSCF)和浓度权重轨迹(CWT)分析方法,揭示了研究期内武汉市不同大气污染物的潜在源区分布及其贡献特性.结果表明,武汉市2014—2017年空气质量逐年好转,SO_2、O_3、PM_(2.5)和PM_(10)的浓度呈逐年下降的趋势,但NO_2和CO的浓度先下降后上升.2017年SO_2、O_3、PM_(2.5)、PM_(10)、NO_2和CO的浓度分别为9.6、50.8、52.7、89.2、47.5μg·m~(-3)和1.1 mg·m~(-3),分别比2014年降低了64.3%、23.0%、24.7%、18.8%、3.5%和5.9%.大气污染物存在显著的季节变化和月变化.大气污染物在四个季节中日变化类似,SO_2和O_3均为单峰型分布,NO_2、CO、PM_(2.5)和PM_(10)均为双峰型分布.武汉市空气污染以PM_(2.5)为主,随着污染程度的加剧PM_(2.5)/PM_(10)的值逐渐增大,在空气质量为严重污染时,PM_(2.5)/PM_(10)高达90%,比空气质量为优时高了31.34%.局地气团(45%)和来自山西、陕西和河南一带的西北气团(12.1%)下大气污染物浓度较高.大气污染物的潜在源区贡献(WPSCF)和浓度权重轨迹(WCWT)的较大值主要集中在武汉市本地及其周边地区,局地污染对武汉市大气污染物的贡献较大,但不同大气污染物受到排放源分布和停留时间等影响其WPSCF和WCWT的分布范围不同.
Air pollutants(SO_2, NO_2, CO, O_3, PM_(2.5) and PM_(10)) and meteorological elements during 2014—2017 in Wuhan were used together to analyze the air pollutants′ variation feature and their influencing factors. The major types of air masses influencing Wuhan were derived by using the Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT) modeling system. Additionally, the distributions of potential source region and their contribution fractions for different air pollutants were derived by using the methods of the potential source contribution function(PSCF) and the concentration weighted trajectory(CWT). Results show that the air quality improved from 2014 to 2017, the concentrations of SO_2, O_3, PM_(2.5), and PM_(10) show an overall decrease gradually while the NO_2 and CO show a short decrease then followed by an increase from 2014 to 2017. The annual concentrations of SO_2, O_3, PM_(2.5), PM_(10), NO_2, and CO were 9.6, 50.8, 52.7, 89.2, 47.5 μg·m~(-3), and 1.1 mg·m~(-3) in 2017, which dropped by 35.7%, 77.0%, 75.3%, 81.2%, 96.5% and 94.1% compared with those in 2014, respectively. The air pollutants exhibited significant seasonal and monthly variations. The diurnal variations of air pollutants were similar in four seasons, with unimodal distributions for SO_2 and O_3 and bimodal distributions for NO_2, CO, PM_(2.5) and PM_(10). The air pollution in Wuhan was still dominated by fine particulate matter. The ratio of PM_(2.5)/PM_(10) enhanced with the deterioration of air quality and can reach as high as 0.9 when the air quality status was seriously polluted(air quality index(AQI) >300), which increased by 31.34% compared with the ratio when the air quality was excellent(0
Air pollutants(SO_2, NO_2, CO, O_3, PM_(2.5) and PM_(10)) and meteorological elements during 2014—2017 in Wuhan were used together to analyze the air pollutants′ variation feature and their influencing factors. The major types of air masses influencing Wuhan were derived by using the Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT) modeling system. Additionally, the distributions of potential source region and their contribution fractions for different air pollutants were derived by using the methods of the potential source contribution function(PSCF) and the concentration weighted trajectory(CWT). Results show that the air quality improved from 2014 to 2017, the concentrations of SO_2, O_3, PM_(2.5), and PM_(10) show an overall decrease gradually while the NO_2 and CO show a short decrease then followed by an increase from 2014 to 2017. The annual concentrations of SO_2, O_3, PM_(2.5), PM_(10), NO_2, and CO were 9.6, 50.8, 52.7, 89.2, 47.5 μg·m~(-3), and 1.1 mg·m~(-3) in 2017, which dropped by 35.7%, 77.0%, 75.3%, 81.2%, 96.5% and 94.1% compared with those in 2014, respectively. The air pollutants exhibited significant seasonal and monthly variations. The diurnal variations of air pollutants were similar in four seasons, with unimodal distributions for SO_2 and O_3 and bimodal distributions for NO_2, CO, PM_(2.5) and PM_(10). The air pollution in Wuhan was still dominated by fine particulate matter. The ratio of PM_(2.5)/PM_(10) enhanced with the deterioration of air quality and can reach as high as 0.9 when the air quality status was seriously polluted(air quality index(AQI) >300), which increased by 31.34% compared with the ratio when the air quality was excellent(0
引文
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Ashbaugh L L, Malm W C, Sadeh W Z. 1985. A residence time probability analysis of sulfur concentrations at Grand Canyon National Park [J]. Atmospheric Environment,19(8): 1263-1270
白永清, 祁海霞, 刘琳,等. 2016. 武汉大气能见度与 PM2.5浓度及相对湿度关系的非线性分析及能见度预报[J]. 气象学报,74(2): 189-199
Bernard S M, Samet J M, Grambsch A, et al. 2001. The potential impacts of climate variability and change on air pollution-related health effects in the United States [J]. Environmental Health Perspectives,109(Suppl 2): 199
蔡坤, 郑泰皓, 陈良富, 等. 2017. 河南省2005～2015年NO2和PM2.5时空变化遥感解析[J]. 中国环境科学,37(12): 4417-4427
曹国良, 安心琴, 周春红, 等. 2010. 中国区域反应性气体排放源清单[J]. 中国环境科学,30(7): 900-906
Chen C, Zhao B. 2011. Review of relationship between indoor and outdoor particles: I/O ratio, infiltration factor and penetration factor [J]. Atmospheric Environment,45(2): 275-288
Chen C, Zhao B, Weschler C J. 2012. Indoor exposure to “outdoor PM10”: assessing its influence on the relationship between PM10 and short-term mortality in US cities [J]. Epidemiology,23(6): 870-878
程麟钧, 王帅, 宫正宇, 等. 2017. 中国臭氧浓度的时空变化特征及分区[J]. 中国环境科学,37(11): 4003-4012
Draxler R R, Hess G D. 1998. An overview of the HYSPLIT_4 modelling system for trajectories [J]. Australian Meteorological Magazine,47(4): 295-308
Fenn M E, Baron J S, Mien E B, et al.2006. Ecological effects of nitrogen deposition in the western United States [J].Bioscience,53: 404-420
He J, Gong S, Yu Y, et al. 2017. Air pollution characteristics and their relation to meteorological conditions during 2014—2015 in major Chinese cities[J]. Environmental Pollution,223: 484-496
Huang R J, Zhang Y, Bozzetti C, et al. 2014. High secondary aerosol contribution to particulate pollution during haze events in China [J]. Nature,514(7521): 218
Hsu Y K, Holsen T M, Hopke P K.2003. Comparison of hybrid receptor models to locate PCB sources in Chicago [J]. Atmospheric Environment,37(4): 545-562
Langrish J P, Li X, Wang S, et al. 2012. Reducing personal exposure to particulate air pollution improves cardiovascular health in patients with coronary heart disease [J]. Environmental Health Perspectives,120(3): 367
Li C, McLinden C, Fioletov V, et al. 2017. India is overtaking China as the world′s largest emitter of anthropogenic sulfur dioxide [J]. Scientific Reports,7(1): 14304
李璇, 聂滕, 齐珺, 等. 2015. 2013年1月北京市PM2.5区域来源解析[J]. 环境科学,36(4): 1148-1153
刘浪, 张文杰, 杜世勇, 等. 2016. 利用SPAMS分析北京市硫酸盐, 硝酸盐和铵盐季节变化特征及潜在源区分布[J]. 环境科学,37(5): 1609-1618
Lucey D, Hadjiiski L, Hopke P K, et al. 2001. Identification of sources of pollutants in precipitation measured at the mid-Atlantic US coast using potential source contribution function (PSCF) [J]. Atmospheric Environment,35(23):3979-3986
马德栗, 李兰, 鞠英芹. 2015. 湖北省霾日数气候特征及夏季典型霾过程气象因子分析[J]. 环境科学与技术,38(11):148-153
Polissar A V, Hopke P K, Harris J M. 2001. Source regions for atmospheric aerosol measured at Barrow, Alaska[J]. Environmental Science & Technology,35(21): 4214-4226
任传斌, 吴立新, 张媛媛, 等. 2016. 北京城区PM2.5输送途径与潜在源区贡献的四季差异分析[J]. 中国环境科学,36(9): 2591-2598
沈利娟, 王红磊, 李莉, 等. 2016. 嘉兴市春季一次持续雾霾过程中气象条件与污染物变化特征分析[J]. 环境科学, 37(8): 2871-2880
沈利娟, 王红磊, 吕升, 等. 2015. 嘉兴市春季PM、主要污染气体和气溶胶粒径分布的周末效应[J]. 环境科学, 36(12): 4348-4357
施双双, 王红磊, 朱彬, 等. 2017. 冬季临安大气本底站气溶胶来源解析及其粒径分布特征[J]. 环境科学,38(10): 4024-4033
Swietlicki E, Puri S, Hansson H C, et al. 1996. Urban air pollution source apportionment using a combination of aerosol and gas monitoring techniques [J]. Atmospheric Environment,30(15): 2795-2809
宋宇, 唐孝炎, 张远航, 等. 2003. 北京市大气能见度规律及下降原因[J]. 环境科学研究,16(2): 10-12
Stein A F, Draxler R R, Rolph G D, et al. 2015. NOAA′s HYSPLIT atmospheric transport and dispersion modeling system [J]. Bulletin of the American Meteorological Society,96(12): 2059-2077
田贺忠, 赵丹, 王艳. 2011. 中国生物质燃烧大气污染物排放清单[J]. 环境科学学报,31(2): 349-357
谭成好, 赵天良, 崔春光,等. 2015. 近50年华中地区霾污染的特征[J]. 中国环境科学,35(8):2272-2280
Tie X, Cao J. 2009a. Aerosol pollution in China: Present and future impact on environment [J]. Particuology, 7(6): 426-431
Tie X, Wu D, Brasseur G. 2009b. Lung cancer mortality and exposure to atmospheric aerosol particles in Guangzhou, China [J]. Atmospheric Environment,43(14): 2375-2377
王红磊, 沈利娟, 唐倩, 等. 2017. 嘉兴市不同天气条件下大气污染物和气溶胶化学组分的分布特征[J]. 环境科学,38(9): 3594-3604
Wang H, Shen L, Zhu B, et al. 2017.Spatial and temporal distributions of air pollutants and size distribution of aerosols over central and eastern China [J]. Archives of Environmental Contamination and Toxicology, 72(4): 481-495
王艳, 柴发合, 王永红, 等. 2008. 长江三角洲地区大气污染物输送规律研究[J]. 环境科学,29(5): 1430-1435
Wang Y, Ying Q, Hu J, et al. 2014. Spatial and temporal variations of six criteria air pollutants in 31 provincial capital cities in China during 2013—2014[J]. Environment International, 73: 413-422
Wang H, Zhu B, Shen L, et al. 2016. Regional characteristics of air pollutants during heavy haze events in the Yangtze River Delta, China [J]. Aerosol and Air Quality Research,16(9): 2159-2171
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
谢雨竹, 潘月鹏, 倪长健, 等. 2015. 成都市区夏季大气污染物浓度时空变化特征分析[J]. 环境科学学报,35(4): 975-983
Xu X, Zhao T, Liu F, et al. 2016. Climate modulation of the Tibetan Plateau on haze in China [J]. Atmospheric Chemistry and Physics,16(3): 1365-1375
徐祥德, 王寅钧, 赵天良,等. 2015. 中国大地形东侧霾空间分布“避风港”效应及其“气候调节”影响下的年代际变异[J]. 科学通报,60(12):1132-1143
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
Yoo J M, Lee Y R, Kim D, et al. 2014. New indices for wet scavenging of air pollutants (O3, CO, NO2, SO2, and PM10) by summertime rain [J]. Atmospheric Environment,82: 226-237
于洲, 刘寿东, 王咏薇, 等. 2016. 杭州市2014年城区大气污染物浓度变化特征观测分析[J]. 科学技术与工程,16(16): 95-104
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