| Abstract： |
The problem of air pollution in China is serious, and the air pollution characteristics and influencing factors are varied in different regions. Based on the environmental monitoring data of atmospheric pollutants (PM2.(5,) PM10, SO2, NO2, CO, and O-3) in Henan province, a major grain-producing area of China, we conducted the hot spot analysis and regression analysis and used a geographically weighted regression (GWR) model to explore the spatial and temporal distribution of atmospheric pollutants, and we investigated the relationship between atmospheric pollutants and normalized differential vegetation index (NDVI). According to the spatial distribution pattern, the high value areas of atmospheric pollutants were primarily concentrated in the northern, which was consistent with the distribution results of hot spots. From the perspective of temporal variation, the concentrations of PM2.5, PM10, SO2, NO2, and CO were highest in winter, followed by spring and autumn, and lowest in summer, while O-3 showed the opposite variation characteristics. The correlation analysis results showed that PM2.(5), PM10, SO2, NO2, and CO had significantly negative correlations with NDVI, while O-3 had a significantly positive correlation with NDVI. Results of the GWR model, which was used to detect the spatial relationship between atmospheric pollutants and NDVI, indicated that the woodland vegetation had a more positive influence compared with the cultivated land vegetation on reducing PM2.5 and PM10. Therefore, the increase in woodland vegetation coverage in farming areas can reduce atmospheric pollutants to an extent. |
