Spatial differentiation and driving factors of ozone concentration in Sichuan Basin
WANG Ke-ke1, KANG Ping1, ZHOU Ming-wei1, ZHANG Xiao-ling1, CHEN Jun-hui2, XIANG Wei-guo1
1. Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China;
2. Sichuan Academy of Eco-Environmental Sciences, Chengdu 610042, China
In order to reveal the spatial distribution of ozone (O3) and its driving factors in Sichuan Basin, the geostatistical methods such as spatial auto-correlation, spatial hot spot detection and geographic detector were used to analyze O3 concentration data collected form 82 national control environmental monitoring sites in 18 cities. The results showed that there was an overall upward trend (from (79.95±18.82)μg/m3 in 2015 to (88.64±11.67)μg/m3 in 2017) of O3 concentration in Sichuan Basin. The O3 concentration reached highest in the midwest of the basin, including Chengdu, Ziyang, Yaan. And the heavy polluted area expanded as the years progressed.A significant annual clustering pattern (spatial positive auto-correlation, Moran's I was greater than 0) of the distribution of O3 concentration was showed in Sichuan Basin, in which the H-H clustering areas in the Midwest and the L-L clustering areas in the Southeast of the basin. Besides, the variation of the annual clustering areas were nearly accordant with the variation of O3 concentration, that is, where the O3 concentration raised (decreased) were transformed into H-H (L-L) clustering areas. The driving force of 20 socio-economic and natural factors to the spatial variation of O3 concentration was quantitatively analyzed by using geographic detector. It was found that the socio-economic factors provided a stronger drive force, especially the proportion of urban construction land (driving value, q=0.5734) and the population density (q=0.5479). In addition, the annual precipitation (q=0.4592) also had a significant effect on the distribution of O3 concentration. The detection of interaction based on geographic detector showed that there was a significant interactions on the spatial variation of O3 concentration, which made the q value of each factor increased by 1.5~2.1times on average. In addition, the average and maximum values of interactive q of each factor both increased year by year. Among the interaction factors that maximized the q value, the population density (7times) and the industrial dust emission (7times) were highest frequency.
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 (in Chinese) [J].Chinese Sci.Bull., 2013,58(13):1178-1187.
[2]
李名升,任晓霞,于洋,等.中国大陆城市PM2.5污染时空分布规律[J].中国环境科学, 2016,36(3):641-650. Li M S, Ren X X, Yu Y, et al.Spatiotemporal pattern of ground-level fine particulate matter PM2.5 pollution in mainland China [J].China Environmental Science, 2016,36(3):641-650.
[3]
程麟钧,王帅,宫正宇,等.中国臭氧浓度的时空变化特征及分区[J].中国环境科学, 2017,37(11):4003-4012. Cheng L J, Wang S, Gong Z Y, et al.Spatial and seasonal variation and regionalization of ozone concentrations in China [J].China Environmental Science, 2017,37(11):4003-4012.
[4]
Li K, Jacob D J, Liao H, et al.Anthropogenic drivers of 2013~2017 trends in summer surface ozone in China.[J].Proceedings of the National Academy of Sciences of the United States of America, 2019,116(2):422-427.
[5]
Wang T, Xue L K, Brimblecombe P, et al.Ozone pollution in China: A review of concentrations, meteorological influences, chemical precursors, and effects [J].Science of the Total Environment, 2017, 575:1582-1596.
[6]
Jin, X M, Holloway, Tracey.Spatial and temporal variability of ozone sensitivity over China observed from the ozone monitoring instrument [J].Journal of Geophysical Research [J].Atmospheres, 2015,120(14): 7229-7246.
[7]
黄小刚,邵天杰,赵景波,等.长三角城市群臭氧浓度的时空分异及驱动因素[J].长江流域资源与环境, 2019,28(6):1434-1445. Huang X G, Cao J J, Zhao J B, et al.Spatiotemporal differentiation of ozone concentration and its driving factors in Yangtze River Delta urban agglomeration [J].Resources and Environment in the Yangtze Basin, 2019,28(6):1434-1445.
[8]
Fu Y, Liao H, Yang Y.Interannual and decadal changes in tropospheric ozone in China and the associated chemistry-climate interactions: A review [J].Advances in Atmospheric Sciences, 2019,36(9):975-993.
[9]
姜蕴聪,杨元建,王泓,等.2015~2018年中国代表性城市PM2.5浓度的城乡差异[J].中国环境科学, 2019,39(11):4552-4560. Jiang Y C, Yang Y J, Wang H, et al.Urban-rural differences in PM2.5 concentrations in the representative cities of China during 2015~2018[J].China Environmental Science, 2019,39(11):4552-4560.
[10]
李珊珊,程念亮,徐峻,等.2014年京津冀地区PM2.5浓度时空分布及来源模拟[J].中国环境科学, 2015,35(10):2908-2916. Li S S, Cheng N L, Xu J, et al.Spatial and temporal distrubions and source simulation of PM2.5 in Beijing-Tianjin-Hebei region in 2014.China Environment Science, 2017,30(5):678-687.
[11]
曹庭伟,吴锴,康平,等.成渝城市群臭氧污染特征及影响因素分析[J].环境科学学报, 2018,38(4):1275-1284. CaoT W, Wu K, Kang P, et al.Study on ozone pollution characteristics and meteorological cause of Chengdu Chongqing urban agglomeration [J].China Environmental Science, 2018,38(4):1275-1284.
[12]
GB 3095—2012环境空气质量标准[S]. GB 3095—2012 Environmental air quality standard [S].
[13]
HJ633—2012环境空气质量指数(AQI)技术规定[S]. HJ633—2012 Technical specification of ambient air quality assessment (Trial) [S].
[14]
国家统计局城市社会经济调查总队.中国城市统计年鉴[M].北京:中国统计出版社, 2015. National bureau of statistics urban socio-economic survey team.China City Statistical Yearbook [M].Beijing: China Statistics Press, 2015.
[15]
国家统计局城市社会经济调查总队.中国城市统计年鉴[M].北京:中国统计出版社, 2016. National bureau of statistics urban socio-economic survey team.China City Statistical Yearbook [M].Beijing: China Statistics Press, 2016.
[16]
国家统计局城市社会经济调查总队.中国城市统计年鉴[M].北京:中国统计出版社, 2017. National bureau of statistics urban socio-economic survey team.China City Statistical Yearbook [M].Beijing: China Statistics Press, 2017.
[17]
王华,郭阳洁,洪松,等.区域气溶胶光学厚度空间格局特征研究[J].武汉大学学报(信息科学版), 2013,38(7):869-874. Wang H, Guo Y J, Hong S, et al.Spatial pattern characteristics of aerosol optical depth in a region based on spatial autocorrelation [J].Geomatics and Information Science of Wuhan University, 2013,38(7): 869-874.
[18]
Anselin L.Local Indicators of Spatial Association—LISA [J].Geographical Analysis, 1995,27(2):93-115.
[19]
周磊,武建军,贾瑞静,等.京津冀PM2.5时空分布特征及其污染风险因素[J].环境科学研究, 2016,29(4):483-493. Zhou L, Wu J J, Jian R J, et al.Investigation of temporal-spatial characteristics and underlying risk of PM2.5 pollution in Bejing-Tianjin-Hebei Area [J].Research of Environmental Sciences, 2016, 29(4):483-493.
[20]
王劲峰,徐成东.地理探测器:原理与展望[J].地理学报, 2017, 72(1):116-134. Wang J F, Xu C D.Geodetector: Principle and prospective [J].Acta Geographica Sinica, 2017,72(1):116-134
[21]
顾莹.上海城市化对臭氧污染影响的数值模拟[D].上海:华东师范大学, 2010. Gu Y.Numerical Simulation of the Impact of Urbanization on Ozone in Shanghai [D].Shanghai: East China Normal University, 2010.
[22]
Hartman R, KwonO-S.Sustainable Growth and the Environment Kuznets Curve [J].Journal of Economic Dynamics and Control, 2005, 29(10):1701-1736
[23]
王杨君,李莉,冯加良,等.基于OSAT方法对上海2010年夏季臭氧源解析的数值模拟研究[J].环境科学学报, 2014,34(3):567-573. Wang Y J, Li L, Feng J L, et al.Source apportionment of ozone in the summer of 2010 in Shanghai using OSAT method [J].Acta Scientiae Circumstantiae, 2014,34(3):567-573.
[24]
陈天增,葛艳丽,刘永春,等.我国机动车排放VOCs及其大气环境影响[J].环境科学, 2018,39(2):478-492. Cheng T Z, Ge Y L, Liu Y C, et al.VOCs Emission from motor vehicles in China and its impact on the atmospheric environment [J].Environmental Science, 2018,39(2):478-492.
[25]
Zhang Y L, Yang W Q, Simpson I, et al.Decadal changes in emissions of volatile organic compounds (VOCs) from on-road vehicles with intensified automobile pollution control: Case study in a busy urban tunnel in south China [J].Environmental Pollution, 2018,233:806-819.
[26]
张可,汪东芳.经济集聚与环境污染的交互影响及空间溢出[J].中国工业经济, 2014,(6):70-82. Zhang K, Wang D F.The interaction and spatial spillover between agglomeration and pollution [J].China Industrial Economics, 2014, (6):70-82.
[27]
王敏,黄滢.中国的环境污染与经济增长[J].经济学季刊, 2015, 14(2):557-578. Wang M, Huang Y.China’s environmental pollution and economic grow [J].China Economic Quarterly, 2015,14(2):557-578.
[28]
黄桂平,曾吉明,王敏,等.工业在全省经济社会发展中的支撑带动作用分析[J].四川冶金, 2018,40(3):1-5+8. Huang G P, Zeng J M, Wang M, et al.Analysis on the supporting and driving role of industry in the province's economic and social development [J].Sichuan Metallugy, 2018,40(3):1-5+8.
[29]
商诗雨.四川省产业结构优化调整对策研究[J].中国集体经济, 2015,(12):29-30. Shang S Y.Study on the countermeasures of industrial structure optimization and adjustment in sichuan province [J].China Collective Economy China Collective Economy China Collective, 2015,(12):29-30.
[30]
杜勤博,吴晓燕,郑素帆,等.气象因素对汕头市大气O3污染的影响[J].气象与环境科学, 2019,42(4):83-89. Du Q B, Wu X Y, Zheng S F, et al.Effects of meteorological factors on atmospheric O3 pollution in Shantou [J].Meteorological and Environmental Sciences, 2019,42(4):83-89.
[31]
赵伟,高博,刘明,等.气象因素对香港地区臭氧污染的影响[J].环境科学, 2019,40(1):55-66. Zhao W, Gao B, Liu M, et al.Impact of meteorological factors on the ozone pollution in Hongkong [J].Environmental Science, 2019, 40(1):55-66.
[32]
梁碧玲,张丽,赖鑫,等.深圳市臭氧污染特征及其与气象条件的关系[J].气象与环境学报, 2017,33(1):66-71. Liang B L, Zhang L, Lai X, et al.Analysis of the characteristics of ozone pollution and its relationship with meteorological conditions in Shenzhen [J].Journal of Meteorology and Environment, 2017,33(1): 66-71.
[33]
蒋维楣,蔡晨霞,李昕.城市低层大气臭氧生成的模拟研究[J].气象科学, 2001,21(2):154-161. Jiang W M, Cai C X, Li X.Simulation of the forming if ozoneinthe low layer air of urban area [J].Scientia Meteorologica Sinca, 2001, 21(2):154-161.
[34]
王艺,朱彬,刘煜,等.中国地区近10年地表反照率变化趋势[J].气象科技, 2011,39(2):147-155. Wang Y, Zhu B, Liu Y, et al.Trend of surface Albedo changes in China in last decade [J].Meteorological Science and Technology, 2011,39(2):147-155.
