Regression analysis of MODIS aerosol optical thickness and air quality index in Xiamen City
Yiqiang SHI1,2,Qiuqin DENG1,Jun WU1,Jian WANG3
1. Department of Geographic Sciences of School of Science, Jimei University, Xiamen 361021, China 2. Research Center of Remote Sensing and Geo-Information, Jimei University, Xiamen 361021, China 3. Xiamen Environmental Monitoring Central Station, Xiamen 361012, China
Based on MODIS product and the pollution concentration measured near the ground, the authors analyzed the spatio-temporal characteristics of aerosol optical thickness (AOT) and the regression on AOT and air quality index (AQI) by different lengths and seasons in Xiamen City, by using geographic information system (GIS) technology and statistical regression method. The results showed that there was a distinct change in the spatio-temporal characteristics of AOT from 2000 to 2015; for example, the AOT highest monthly average 1.13 appeared in April and the lowest 0.64 appeared in January, AOT seasonal average tended to decrease from spring through summer and autumn to winter, and its yearly average showed a steady trend of slow rise. The higher values of monthly and annual average AOT were almost distributed in the coastal areas and the lower values occur in northwest and northeast regions. R2 of regression model of power function for AQI and AOT was the highest in the five regression models with its value being 0.388 3. AQI was divided into groups with a certain step length, and the regression model with AQI and AOT was built up, which exhibited larger step length and higher R2. According to AQI grading length 50, the precision of the forecasting AQI value and the actual value could reach 77.35%, which could on the whole meet the demand of air quality level forecast. R2 and the precision of the four-season regression models were a little higher than those of the full year regression models, and the R2 was the lowest in spring season, R2in other three seasons is almost the same, with the precision up to 83.33%. With the present remote sensing technology for air pollution monitoring, the utilization of the correlation models to estimate the level of air quality seems to be feasible.
施益强,邓秋琴,吴君,王坚. 厦门市MODIS气溶胶光学厚度与空气质量指数的回归分析[J]. 国土资源遥感, 2020, 32(1): 106-114.
Yiqiang SHI,Qiuqin DENG,Jun WU,Jian WANG. Regression analysis of MODIS aerosol optical thickness and air quality index in Xiamen City. Remote Sensing for Land & Resources, 2020, 32(1): 106-114.
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