A Methodological Comparison on Spatiotemporal Prediction of Criteria Air Pollutants

Abstract

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), PM_2.5 (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, PM_2.5, 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.