Atmospheric aerosol pollution across China: a spatiotemporal analysis of satellite-based aerosol optical depth during 2000–2016

ABSTRACT Increasing attention has been paid to the deterioration of air quality in China during the past decade. This study presents the spatiotemporal variations of aerosol concentration across China during 2000–2016 using aerosol optical depth (AOD) from the atmospheric product of Moderate Resolution Imaging Spectroradiometer. Percentile thresholds are applied to define AOD days with different loadings. Temporally, aerosol concentration has increased since 2000 and reached the highest level in 2011; then it has declined from 2011 to 2016. Seasonally, aerosol concentration is the highest in summer and the lowest in winter. Spatially, North China and Sichuan Basin are featured by high aerosol concentration with increasing trends in North China and decreasing trends in Sichuan Basin. North, Southeast and Southwest China have been through increasing days with low AOD loading; however, Northeast China has experienced increasing days with high AOD loading. It is likely that air quality influenced by aerosols has notably improved over North China in spring and summer, over Southwest and Southeast China in autumn, but has degraded over Northeast China in autumn.

[1]  Yi Li,et al.  Estimating ground-level PM2.5 concentrations in Beijing, China using aerosol optical depth and parameters of the temperature inversion layer. , 2017, The Science of the total environment.

[2]  Zhongmin Zhu,et al.  A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth , 2016 .

[3]  Jinyuan Xin,et al.  The empirical correlations between PM2.5, PM10 and AOD in the Beijing metropolitan region and the PM2.5, PM10 distributions retrieved by MODIS. , 2016, Environmental pollution.

[4]  Eleni Marinou,et al.  Nine-year spatial and temporal evolution of desert dust aerosols over South and East Asia as revealed by CALIOP , 2017 .

[5]  A. Cracknell,et al.  Long‐range persistence in global Aerosol Index dynamics , 2006 .

[6]  Yuxiang Luo,et al.  A climatology of aerosol optical depth over China from recent 10 years of MODIS remote sensing data , 2014 .

[7]  Muhammad Bilal,et al.  A New Approach for Estimation of Fine Particulate Concentrations Using Satellite Aerosol Optical Depth and Binning of Meteorological Variables , 2017 .

[8]  Liangfu Chen,et al.  Comparison and evaluation of the MODIS Collection 6 aerosol data in China , 2015 .

[9]  Wenhai Shen,et al.  Spatial and temporal variation of haze in China from 1961 to 2012. , 2016, Journal of environmental sciences.

[10]  Wang Hongqiang.  Analysis of a haze event by micro-pulse light laser detection and ranging measurements in Shanghai , 2010 .

[11]  Yuqi Bai,et al.  Daily Estimation of Ground-Level PM2.5 Concentrations over Beijing Using 3 km Resolution MODIS AOD. , 2015, Environmental science & technology.

[12]  Manish Soni,et al.  Particulate matter estimation over a semi arid region Jaipur, India using satellite AOD and meteorological parameters , 2018, Atmospheric Pollution Research.

[13]  Huadong Guo,et al.  Analysis of aerosol effects on warm clouds over the Yangtze River Delta from multi-sensor satellite observations , 2017 .

[14]  Ke Gui,et al.  Spatial distribution and temporal variation of aerosol optical depth in the Sichuan basin, China, the recent ten years , 2016 .

[15]  Soon-Chang Yoon,et al.  Seasonal and monthly variations of columnar aerosol optical properties over East Asia determined from multi-year MODIS, LIDAR, and AERONET Sun/sky radiometer measurements , 2007 .

[16]  Anthony W. Strawa,et al.  Enhancing the Applicability of Satellite Remote Sensing for PM2.5 Estimation Using MODIS Deep Blue AOD and Land Use Regression in California, United States. , 2016, Environmental science & technology.

[17]  Min Hu,et al.  Mixing state of atmospheric particles over the North China Plain , 2016 .

[18]  Jun Tao,et al.  Chemical and optical characteristics of atmospheric aerosols in Beijing during the Asia-Pacific Economic Cooperation China 2014 , 2016 .

[19]  Xiaoxiong Xiong,et al.  Validation of MODIS aerosol optical depth product over China using CARSNET measurements , 2011 .

[20]  Yong Xue,et al.  Long-time series aerosol optical depth retrieval from AVHRR data over land in North China and Central Europe , 2017 .

[21]  Ke Li,et al.  Quantifying atmospheric nitrogen deposition through a nationwide monitoring network across China , 2015 .

[22]  Jen-Ping Chen,et al.  Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008 , 2011 .

[23]  Jens Redemann,et al.  The comparison of MODIS-Aqua (C5) and CALIOP (V2 & V3) aerosol optical depth , 2011 .

[24]  Jimy Dudhia,et al.  Assessment of the Level-3 MODIS daily aerosol optical depth in the context of surface solar radiation and numerical weather modeling , 2012 .

[25]  Kulwinder Singh Parmar,et al.  Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling. , 2016, The Science of the total environment.

[26]  Nektarios Chrysoulakis,et al.  Estimation of urban PM10 concentration, based on MODIS and MERIS/AATSR synergistic observations , 2013 .

[27]  Qingyang Xiao,et al.  Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia , 2015 .

[28]  Jie Tian,et al.  A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements , 2010 .

[29]  Tsun-Jen Cheng,et al.  Chemical composition and bioreactivity of PM2.5 during 2013 haze events in China , 2016 .

[30]  Kafeel Ahmad,et al.  Time series analysis of aerosol optical depth over New Delhi using Box–Jenkins ARIMA modeling approach , 2016 .

[31]  A. Lacis,et al.  How well do satellite AOD observations represent the spatial and temporal variability of PM2.5 concentration for the United States , 2015 .

[32]  Dongmei Chen,et al.  Comparison of variability and change rate in tropospheric NO2 column obtained from satellite products across China during 1997–2015 , 2017, Int. J. Digit. Earth.