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- 저자 : Hemanta Medhi (검색결과 1 건)
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A Methodological Comparison on Spatiotemporal Prediction of Criteria Air Pollutants
Pankaj Singh,Rakesh Chandra Vaishya,Pramod Soni,Hemanta Medhi 한국대기환경학회 2022 Asian Journal of Atmospheric Environment (AJAE) Vol.16 No.1
Air pollution monitoring devices are widely used to quantify at-site air pollution. However, such monitoring sites represent pollution of a limited area, and installing multiple devices for a vast area is costly. This limitation of unavailability of data at non-monitoring sites has necessitated the Spatio-temporal analysis of air pollution and its prediction. Few commonly used methods for Spatio-temporal prediction of pollutants include - ‘Averaging’; ‘Best correlation coefficient method’; ‘Inverse distance weighting method’ and ‘Grid interpolation method.’ Apart from these conventional methods, a new methodology, ‘Weighted average method,’ is proposed and compared for air pollution prediction at non-monitoring sites. The weights in this method are calculated based on both on the distance and directional basis. To compare the proposed method with the existing ones, the air pollution levels of NO2 (Nitrogen dioxide), O3 (Ozone), PM10 (Particulate matter of 10 microns or smaller), PM2.5 (Particulate matter of 2.5 microns or smaller), and SO2 (Sulphur dioxide) were predicted at the non-monitoring site (test stations) by utilizing the available data at monitoring sites in Delhi, India. Preliminary correlation analysis showed that NO2, PM2.5, and SO2 have a directional dependency between different stations. The ‘average’ method performed best with the mode RMSE of 18.85 μg/m3 and R2 value 0.7454 when compared with all the methods. The RMSE value of the new proposed method ‘weighted average method’ was 21.25 μg/m3, resulting in the second-best prediction for the study area. The inverse distance weighting method and the Grid interpolation method were third and fourth, respectively, while the ‘best correlation coefficient’ was the worst with an RMSE value of 41.60 μg/m3. Results also showed that the methods that used dependent stations had performed better when compared to methods that used all station data.
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