24 October 2012 Spatially and seasonally non-stationary relationships between PM10 and related factors in Eastern China by geographically weighted regression
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Abstract
The potential of satellite data used for particular matter monitoring is a crucial subject in air quality research. PM10 is influenced by many meteorological factors and has a difference correlation with aerosol optical depth in different place. Geographically weighted regression (GWR) model have been proved to be an effective methods for spatial variation analysis. This paper presented results from a study of PM10 concentration from API in eastern China from 2005 to 2010. Wavelet analysis was used for analyzing the periodicity characteristics of PM10 and AOD. The correlations between PM10 and meteorological factors were also analyzed without AOD and with AOD added, respectively. Obvious spatial and seasonal non-stationary distributions of PM10 concentration were found with spatial auto-correlation analysis. PM10 concentration and AOD have similar periods and discontinuity characteristics in 41 months scale and 70 months scale. Correlation between PM10 concentration and meteorological factors were improved when AOD added as a factor, and the tempo-spatial distributions of the correlations were non-stationary in eastern China because of differences of the regional weather conditions and the pollution sources.
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Yuanyuan Chen, Yuanyuan Chen, Runhe Shi, Runhe Shi, Shijie Shu, Shijie Shu, Wei Gao, Wei Gao, } "Spatially and seasonally non-stationary relationships between PM10 and related factors in Eastern China by geographically weighted regression", Proc. SPIE 8513, Remote Sensing and Modeling of Ecosystems for Sustainability IX, 85130K (24 October 2012); doi: 10.1117/12.928029; https://doi.org/10.1117/12.928029
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