基于多源遥感数据的中国PM_(2.5)变化趋势与影响因素分析
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摘要
基于长时间序列的遥感反演PM_(2.5)数据,采用Theil-Sen median趋势分析、Mann-Kendall、R/S和相关系数分析法,分析了1998～2014年我国PM_(2.5)时空格局、空间变化特征以及污染来源。结果表明:1998～2014年期间,最高只有24.67%的国土面积上,PM_(2.5)浓度达到世界卫生组织(WHO)的年平均准则值10μg/m~3的要求;PM_(2.5)浓度小于10μg/m~3地区主要是青藏高原、台湾、北疆,内蒙古北部和黑龙江西北部,年均PM_(2.5)浓度大于95μg/m~3的地区主要是南疆和华北平原。1998～2014年期间,全国61.84%的国土面积PM_(2.5)浓度呈上升趋势,平均上升了3.91μg/m~3,上升最大值为39.1μg/m~3。其中,呈显著上升的地区主要分布在中西部地区和华北平原,且未来部分地区仍呈增长的趋势。PM_(2.5)浓度上升的驱动因素包括自然因素与人类活动排放,其中,南疆的PM_(2.5)主要来自塔克拉玛干沙漠的沙尘气溶胶,而其他地区PM_(2.5)主要来自人类活动排放。
Based on the remote sensing retrieval of PM_(2.5) data in the long-time series, the temporal-spatial pattern of PM_(2.5) as well as its variation and sources of pollution in China from 1998 to 2014 were revealed by using the Theil-Sen median trend analysis, Mann-Kendall test and correlation analysis methods. The results showed that PM_(2.5) concentration reached the annual average criterion value of 10 μg/m~3 specified by the World Health Organization(WHO) in the land area of only 24.67% during the period from 1998 to 2014. PM_(2.5) concentrations were less than 10 μg/m~3 mainly in the Qinghai-Tibet Plateau, Taiwan, northern Xinjiang, north of Inner Mongolia and northwest of Heilongjiang, while the average annual PM_(2.5) concentrations were greater than 95 μg/m~3 mainly in southern Xinjiang and North China Plain. During the period from 1998 to 2014, the concentration of PM_(2.5) increased by an average of 3.91 μg/m~3 and the maximum value was 39.1 μg/m~3 in 61.84% of the national land area. Among them, the central and western regions and the North China Plain exhibited a significant rising trend and some regions still show an increasing trend in the future. The driving factors of increase in PM_(2.5) concentration included natural factors and emissions from human activities, wherein it mainly came from the sand dust aerosol in the Taklimakan Desert in the southern Xinjiang, while those in the other areas mainly came from emissions in the human activities.
Based on the remote sensing retrieval of PM_(2.5) data in the long-time series, the temporal-spatial pattern of PM_(2.5) as well as its variation and sources of pollution in China from 1998 to 2014 were revealed by using the Theil-Sen median trend analysis, Mann-Kendall test and correlation analysis methods. The results showed that PM_(2.5) concentration reached the annual average criterion value of 10 μg/m~3 specified by the World Health Organization(WHO) in the land area of only 24.67% during the period from 1998 to 2014. PM_(2.5) concentrations were less than 10 μg/m~3 mainly in the Qinghai-Tibet Plateau, Taiwan, northern Xinjiang, north of Inner Mongolia and northwest of Heilongjiang, while the average annual PM_(2.5) concentrations were greater than 95 μg/m~3 mainly in southern Xinjiang and North China Plain. During the period from 1998 to 2014, the concentration of PM_(2.5) increased by an average of 3.91 μg/m~3 and the maximum value was 39.1 μg/m~3 in 61.84% of the national land area. Among them, the central and western regions and the North China Plain exhibited a significant rising trend and some regions still show an increasing trend in the future. The driving factors of increase in PM_(2.5) concentration included natural factors and emissions from human activities, wherein it mainly came from the sand dust aerosol in the Taklimakan Desert in the southern Xinjiang, while those in the other areas mainly came from emissions in the human activities.
引文
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[31] JIANG W, YUAN L, WANG W, et al. Spatio-temporal analysis of vegetation variation in the Yellow River Basin[J]. Ecological Indicators, 2015(51): 117-126.
[32] FAN N, XIE G D, ZHANG C S, et al. Spatial-temporal dynamic changes of vegetation cover in Lancang river basin during 2001-2010[J]. Resource Science, 2012, 34(7): 1222-1231.
[33] HOU X Y, YING L L, GAO M, et al. Character of vegetation cover change in China's Eastern coastal areas 1998-2008[J]. Seientia Geographica Siniea, 2010, 30(5): 735-741.
[34] WANG G G, ZHOU K F, SUN L, et al. Study on the vegetation dynamic change and R/S analysis in the past ten years in Xinjiang[J]. Remote Sensing Technology and Application, 2010, 25(1): 84-90.
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[37] WANG G, ZHANG R, GOMEZ M E, et al. Persistent sulfate formation from London Fog to Chinese haze[J]. Proceedings of the National Academy of Sciences, 2016, 113(48): 13630-13635.
[38] CHENG Y, ZHANG G, WEI C, et al. Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China[J]. Science Advances, 2016, 12(2):e1601530.
[39] GU B, SUTTON M A, CHANG S X, et al. Agricultural ammonia emissions contribute to China’s urban air pollution[J]. Frontiers in Ecology and the Environment, 2014, 12(5): 265-266.
[2] TIAN S, PAN Y, LIU Z, et al. Size-resolved aerosol chemical analysis of extreme haze pollution events during early 2013in urban Beijing, China. Journal of Hazardous Materials, 2014(279): 452-460.
[3] WANG Y, YAO L, WANG L, et al. Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China. Science China Earth Sciences, 2014, 57: 14-25.
[4] GUAN D, SU X, ZHANG Q, et al. The socioeconomic drivers of China’s primary PM2.5 emissions[J]. Environmental Research Letters, 2014, 9(2): 1-9.
[5] XIE Y, DAI H, DONG H, et al. Economic impacts from PM2.5 pollution-related health effects in China: A provincial-level analysis[J]. Environmental Science & Technology, 2016, 50(9): 4836-4843.
[6] 王振波, 方创琳, 许光, 等. 2014 年中国城市 PM2.5 浓度的时空变化规律[J]. 地理学报, 2015, 70(11): 1720-1734.WANG Z B, FANG C L, XU G, et al. Spatial-temporal characteristics of the PM2.5 in China in 2014[J]. Acta Geographica Sinica, 2015, 70(11): 1720-1734.
[7] WANG Y, YING Q, HU J, et al. Spatial and temporal variations of six criteria air pollutants in 31 provincial capital cities in China during 2013-2014[J]. Environment International, 2014(73): 413-422.
[8] ZHANG Y L, CAO F. Fine particulate matter (PM2.5) in China at a city level[J]. Scientific Reports, 2015(5): 14884.
[9] SUN Y, WANG Z, WILD O, et al. “APEC blue”: secondary aerosol reductions from emission controls in Beijing[J]. Scientific Reports, 2016(6): 20668.
[10] HAN L, ZHOU W, LI W. Increasing impact of urban fine particles (PM2.5) on areas surrounding Chinese cities[J]. Scientific Reports, 2015(5): 12467.
[11] ZHENG Y, ZHANG Q, LIU Y, et al. Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements[J]. Atmospheric Environment, 2016(124): 232-242.
[12] LIN G, FU J, JIANG D, et al. Spatio-temporal variation of PM2.5 concentrations and their relationship with geographic and socioeconomic factors in China[J]. International Journal of Environmental Research and Public Health, 2013, 11(1): 173-186.
[13] PENG J, CHEN S, LU H, et al. Spatiotemporal patterns of remotely sensed PM2.5 concentration in China from 1999 to 2011[J]. Remote Sensing of Environment, 2016(174): 109-121.
[14] LIN C, LI Y, LAU A K H, et al. Estimation of long-term population exposure to PM2.5 for dense urban areas using 1-km MODIS data[J]. Remote Sensing of Environment, 2016(179): 13-22.
[15] MA Z, HU X, SAYER A M, et al. Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004-2013[J]. Environmental Health Perspectives, 2015, 124(2): 184-192.
[16] SHALTOUT A A, BOMAN J, AL-MALAWI D R, et al. Elemental composition of PM2.5 particles sampled in industrial and residential areas of Taif, Saudi Arabia[J]. Aerosol and Air Quality Research, 2013, 13(4): 1356-1364.
[17] HUANG R J, ZHANG Y, BOZZETTI C, et al. High secondary aerosol contribution to particulate pollution during haze events in China[J]. Nature, 2014, 514(7521): 218-222.
[18] JANSEN R C, SHI Y, CHEN J, et al. Using hourly measurements to explore the role of secondary inorganic aerosol in PM2.5 during haze and fog in Hangzhou, China[J]. Advances in Atmospheric Sciences, 2014, 31(6): 1427-1434.
[19] WANG G, ZHANG R, GOMEZ M E, et al. Persistent sulfate formation from London Fog to Chinese haze[J]. Proceedings of the National Academy of Sciences, 2016, 113(48): 13630-13635.
[20] SONG Y, WANG X, MAHER B A, et al. The spatial-temporal characteristics and health impacts of ambient fine particulate matter in China[J]. Journal of Cleaner Production, 2016(112): 1312-1318.
[21] 张小曳, 孙俊英, 王亚强, 等. 我国雾-霾成因及其治理的思考[J].科学通报, 2013, 58(13): 1178-1187.ZHANG X Y, SUN J Y, WANG Y Q, et al. Factors contributing to haze and fog in China[J]. Chinese Science Bulletin, 2013, 58(13): 1178-1187.
[22] YANG L, WANG D, CHENG S, et al. Influence of meteorological conditions and particulate matter on visual range impairment in Jinan, China[J]. Science of the Total Environment, 2007, 383(1): 164-173.
[23] 毛婉柳,徐建华,卢德彬,等. 2015 年长三角地区城PM2.5时空格局及影响因素分析[J]. 长江流域资源与环境,2017,26( 2): 264-272.MAO W L, XU J H, LU D B, et al. An analysis of the spatial-temporal pattern and influencing factors of PM2.5 in the YangzeRiver Delta in 2015[J].Resources and Environment in the Yangtze Basin, 2017, 26(2): 264-272.
[24] 王嫣然, 张学霞, 赵静瑶, 等. 2013-2014 年北京地区 PM2.5 时空分布规律及其与植被覆盖度关系的研究[J]. 生态环境学报, 2016, 25(1): 103-111.WANG Y R, ZHANG X X, ZHAO J Y. Temporal and spatial distribution of PM2.5 and its relationship with vegetation coverage in Beijing during the period of 2013-2014[J]. Ecology and Environmental Sciences, 2016, 25(1): 103-111.
[25] WANG S, ZHOU C, WANG Z, et al. The characteristics and drivers of fine particulate matter (PM2. 5) distribution in China[J]. Journal of Cleaner Production, 2017(142): 1800-1809.
[26] VAN DONKELAA A, MARTINR V, PARK R J. Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing[J]. Journal of Geophysical Research: Atmospheres, 2006, 111(D21).
[27] VAN DONKELAAR A, MARTIN R V, BRAUER M, et al. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application[J]. Environmental Health Perspectives, 2010, 118(6): 847.
[28] VAN DONKELAARA, MARTIN R V, BRAUER M, et al. Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter[J]. Environmental Health Perspectives, 2014, 123(2): 135-143.
[29] LEE S J, SERRE M L, VAN DONKELAARA A, et al. Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the Continental United States[J]. Environmental Health Perspectives, 2012, 120(12): 1727.
[30] DE SHERBININ A, LEVY M A, ZELL E, et al. Using satellite data to develop environmental indicators[J]. Environmental Research Letters, 2014, 9(8): 084013.
[31] JIANG W, YUAN L, WANG W, et al. Spatio-temporal analysis of vegetation variation in the Yellow River Basin[J]. Ecological Indicators, 2015(51): 117-126.
[32] FAN N, XIE G D, ZHANG C S, et al. Spatial-temporal dynamic changes of vegetation cover in Lancang river basin during 2001-2010[J]. Resource Science, 2012, 34(7): 1222-1231.
[33] HOU X Y, YING L L, GAO M, et al. Character of vegetation cover change in China's Eastern coastal areas 1998-2008[J]. Seientia Geographica Siniea, 2010, 30(5): 735-741.
[34] WANG G G, ZHOU K F, SUN L, et al. Study on the vegetation dynamic change and R/S analysis in the past ten years in Xinjiang[J]. Remote Sensing Technology and Application, 2010, 25(1): 84-90.
[35] 徐建华. 计量地理学( 第二版) [M].北京: 高等教育出版社,2014.XU J H. Quantitative Geography ( Second Edition) [M].Beijing: Higher Education Press,2014.
[36] LU D, XU J, YANG D, et al. Spatio-temporal variation and influence factors of PM2.5 concentrations in China from 1998 to 2014[J]. Atmospheric Pollution Research, 2017, 8(6): 1151-1159.
[37] WANG G, ZHANG R, GOMEZ M E, et al. Persistent sulfate formation from London Fog to Chinese haze[J]. Proceedings of the National Academy of Sciences, 2016, 113(48): 13630-13635.
[38] CHENG Y, ZHANG G, WEI C, et al. Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China[J]. Science Advances, 2016, 12(2):e1601530.
[39] GU B, SUTTON M A, CHANG S X, et al. Agricultural ammonia emissions contribute to China’s urban air pollution[J]. Frontiers in Ecology and the Environment, 2014, 12(5): 265-266.
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