Remote sensing estimation of regional PM2.5 based on GTWR model -A case study of southwest China.

Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM2.5 (particulate matter with diameter ≤2.5 µm) being the main contributor. Establishing the spatial distribution of PM2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM2.5 concentrations. However, the R2 values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R2 values between the measured and remote sensed estimated PM2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a "V" -shaped oscillation change.

Assessing the response of vegetation change to drought during 2009-2018 in Yunnan Province, China.

Yunnan Province in southwest China is characterized by a vast area, diverse climate types, rich ecosystem types, and unique biodiversity resources. With consideration of global climate change, there is an urgent need to evaluate the response of vegetation to drought in Yunnan. This study utilized the MOD13A3, MOD17A2, and Tropical Rainfall Measuring Mission (TRMM) 3B43 remote sensing products. The TRMM 3B43 downscaled monthly precipitation data were used to calculate the tropical rainfall condition index (TRCI) for Yunnan. The TRCI was used as a drought index, and the temporal and spatial changes in TRCI, gross primary productivity (GPP), and vegetation condition index (VCI) from 2009 to 2018 were explored. The response of vegetation to drought was evaluated under different time scales and varying land-use types. The results showed that during 2009-2018, (1) at an annual scale, the drought in Yunnan showed a weakening trend, and at a spatial scale, the drought showed a weakening trend from northwest to southeast. This weakening trend was more noticeable for cultivated land than for forest, grassland, and other land-use types. (2) GPP and VCI showed overall increasing trends at an annual scale, indicating gradual improvements in the GPP of vegetation and vegetation status, whereas the summer vegetation index showed a decreasing trend. (3) Although both the GPP and the growth state of vegetation were affected by drought, the responses of GPP and VCI to drought differed under different temporal scales and different land-use types. The responses of GPP and VCI to drought during spring were greater than those over other seasons, and the response of VCI to drought was more sensitive than that of GPP. Drought had a high impact on the GPP and vegetation growth of cultivated land and grassland with shallow root systems, whereas the impact of drought on forest was relatively stable.