Analyses on the spatial-temporal distribution features and causing factors of atmospheric haze in the southern city-group of Sichuan
HE Mu-quan1,2, LIU Zhi-hong1, ZHANG Ying1, ZHANG Yang3, YAN Yan4, HUANG Guan1, ZHANG Juan1, CHEN Jun-hui2, HE Min2, FAN Wu-bo2
1. School of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China; 2. Sichuan Academy of Environmental Sciences, Chengdu, 610041, China; 3. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; 4. School of Earth and Space Science, University of Science and Technology of China, Hefei 230001, China
Abstract:This study checked the quality of MODIS 3km AOD daily data using the local CE318 data in southern Sichuan. After ensuring the availability of MODIS AOD product, the relationship model of MODIS AOD and PM2.5/10 was set up. And the temporal-spatial characteristics of atmospheric haze in the city group of Southern Sichuan were analyzed. The correlation coefficient between CE318AOD data and MODIS AOD product was 0.779, and the optimal correlation coefficient between PM2.5, PM10 and MODIS AOD was 0.894, 0.83 respectively. Spatially, the value of average AOD in the northern region was higher than the southern region of southern Sichuan, and the cities with highest AOD were Neijiang and Zigong. Temporally, the averaged AOD had no significant change from 2006 to 2013, but decreased obviously after 2013. Also the averaged AOD was higher in spring and winter, but lower in summer and autumn, and the largest proportion of high value was in spring. The monthly variation features showed that the AOD value was high from February to April and September and low in other months. The temporal and spatial distribution of AOD in Southern Sichuan were affected by terrain, industrial, wind speed and direction, flow field, PBL and other factors, among which, the terrain and flow field were the most prominent factors.
何沐全, 刘志红, 张颖, 张洋, 颜妍, 黄观, 张娟, 陈军辉, 何敏, 范武波. 川南城市群大气灰霾时空分布特征及成因分析[J]. 中国环境科学, 2017, 37(2): 432-442.
HE Mu-quan, LIU Zhi-hong, ZHANG Ying, ZHANG Yang, YAN Yan, HUANG Guan, ZHANG Juan, CHEN Jun-hui, HE Min, FAN Wu-bo. Analyses on the spatial-temporal distribution features and causing factors of atmospheric haze in the southern city-group of Sichuan. CHINA ENVIRONMENTAL SCIENCECE, 2017, 37(2): 432-442.
Zhang X Y, Wang Y Q, Niu T, et al. Atmospheric aerosol compositions in China:spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols[J]. Atmospheric Chemistry and Physics, 2012,12(2):779-799.
Kaskaoutis D G, Kambezidis H D, Hatzianastassiou N, et al. Aerosol climatology:dependence of the Angstrom exponent on wavelength over four AERONET sites[J]. Atmospheric chemistry and physics discussions. 2007,7(3):7347-7397.
Lin C, Li Y, Yuan Z, et al. Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5[J]. Remote Sensing of Environment, 2015,156:117-128.
Song W, Jia H, Huang J, et al. A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China[J]. Remote Sensing of Environment, 2014,154:1-7.
Wang J. Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass:Implications for air quality studies[J]. Geophysical Research Letters, 2003,30(21):2095-2098.
[17]
Holben B N, Eck T F, Slutsker I, et al. AERONET——A Federated Instrument Network and Data Archive for Aerosol Characterization[J]. Remote Sensing of Environment, 1998, 66(1):1-16.
[18]
Zhou C Y, Liu Q H, Tang Y, et al. Comparison between MODIS aerosol product C004 and C005 and evaluation of their applicability in the north of China[J]. Journal of Remote Sensing, 2009,13(5):854-862.