Abstract. In order to study the spatial and temporal variation of characteristics of drought and flood in Dian-Qian-Gui Karst Areas of China, TRMM 3B43 precipitation data from 1998 to 2017 and 72 rain gauge station data from 1998 to 2012 were used to verify the TRMM 3B43 data on monthly scale by correlation coefficient and relative deviation. The TRMM-Z index was taken as an index of drought and flood to quantitatively analyze the drought and flood characteristics. The results show that: (1) there was a significant positive correlation between TRMM 3B43 precipitation data and the measured data of meteorological stations on the monthly scale, with a correlation coefficient of 0.92, passing the significance test of 0.01 level. The calculated result of relative Bias is -0.058, indicating that TRMM 3B43 precipitation is slightly higher than the actual precipitation. (2) From 1998 to 2017, the TRMM-Z index in the research area fluctuated between −1.160 and 1.678, among which the Z index in July 1999 reached the maximum value of 1.678, and in February 2010 the Z index reached the minimum value of −1.160. (3) During the past 20 years, the flood months in the study areas were 47 months, accounting for 19.58% of the study time, and 40 months were drought months, accounting for 16.67% of the study time. Floods mainly occurred in the summer with abundant rainfall, while droughts mainly occurred in the winter with less rainfall. (4) Taking 2008–2009 as typical representative of drought and flood, the spatial variation of drought and flood were researched.
Abstract. This paper was based on Japan's new generation of geostationary satellite Himawari-8 2016 Aerosol Optical Depth (AOD) data and near-ground monitoring station PM2.5 mass concentration data, boundary layer height (BLH), relative humidity (RH), normalized vegetation index (NDVI) data to establish a multivariate linear regression model (MLR) and a geographically weighted regression model (GWR) in Beijing.This provided data and scientific basis for the treatment of air pollution.The results show that: (1) The fitting determination coefficient R2 of the MLR was 0.5244, indicating that there was a significant correlation between PM2.5 and AOD. After GWR model introduced BLH, RH and NDVI in turn, R2 increased from 0.3945 to 0.5403, indicating that the introduction of relevant influencing factors can improve the accuracy of the model, that was, PM2.5 was affected by BLH, RH and NDVI. (2) The regression coefficients of the MLR and GWR of the BLH, RH and NDVI were statistically analyzed. The regression coefficients of the two models were close to each other, but the standard deviation of the GWR regression coefficients was larger than the MLR, indicating that the local information of the GWR model was more abundant. It reflected the difference characteristics of the regression coefficients of each parameter.
Abstract. The paper analyzed the variation characteristics of AQI and its correlation with PM2.5 and PM10 of in Beijing-Tianjin-Hebei region from July 2015 to July 2018 based on hours of pollutants in Beijing-Tianjin-Hebei region, using AQI calculation method and statistical correlation evaluation method. Results showed that:(1) The air quality compliance rate in Beijing-Tianjin-Hebei region was 67%, the average AQI was 97.6577, and the air quality was good. The distribution frequency of primary pollutants was PM2.5, followed by PM10, which accounts for 78.9% of the distribution frequency of the six major pollutants, indicated that PM2.5 and PM10 had a greater impact on the air quality of Beijing-Tianjin-Hebei. (2) The correlation between AQI and PM2.5 and PM10 was significantly positively correlated. R2 was 0.8225 and 0.7749, respectively, P < 0.01, indicated that both showed a greater impact on air quality. (3) AQI and PM2.5 and PM10 showed a gradual decrease trend at 9h–16h, ie 9h highest and 16h lowest. The AQI fluctuated between 94.2816 and 103.3562, indicated that the air quality at 9h–16h was good or slightly polluted. (4) The spatial distribution of AQI, PM2.5 and PM10 was characterized by low northwest and high southeast, and the southeastern part was gradually decreasing from 9h–16h. AQI was negatively correlated with elevation. The higher the elevation, the better the air quality, and the worse the air quality.
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