A multiscale geographically weighted regression kriging method for spatial downscaling of satellite-based ozone datasets

• Published in: Frontiers in environmental science 2024-01, Vol.11
• Main Authors: Cheng, Shuang, Zhang, Guoqiao, Yang, Xuexi, Lei, Bingfeng
• Format: Article
• Language: English
• Subjects: Agglomeration; Air pollution; Chang-Zhu-Tan urban agglomeration; Coefficient of variation; Correlation; Datasets; Deep learning; Economic development; Emission inventories; Heterogeneity; Humidity; Methods; Model accuracy; multiscale geographically weighted regression kriging; Nitrogen oxides; Normalized difference vegetative index; Optical thickness; Organic compounds; Outdoor air quality; Ozone; Photochemicals; Pollutants; Pollution control; Regression; Regression analysis; Remote sensing; Satellites; Spatial data; Spatial discrimination; Spatial distribution; spatial downscaling; Spatial heterogeneity; Spatial resolution; Urban areas; Vegetation; VOCs; Volatile organic compounds; Wind speed
• ISSN: 2296-665X; 2296-665X
• DOI: 10.3389/fenvs.2023.1267752

Accurate monitoring of ozone (O 3 ) concentrations by remote sensing is essential for achieving pollution control and ecological protection. However, the existing O 3 remote sensing data with a low spatial resolution do not facilitate fine-grained studies of small-scale urban clusters. In this study, the multiscale geographically weighted regression kriging (MGWRK) method was used to spatially downscale O 3 remote sensing products (10 km × 10 km). Downscaling factors were selected from meteorological factors and vegetation, aerosol optical thickness (AOD), and air pollutant emission inventory data. Spatial heterogeneity and scale differences among the factors were considered and compared via multiple regression kriging (MLRK) and geographically weighted regression kriging (GWRK) to generate 1-km annual and seasonal O 3 remote sensing products. The results showed that I) the downscaling accuracy of each model can be expressed as MGWRK > GWRK > MLRK; the local downscaling model yields data that are more consistent with the actual spatial distribution of O 3 after considering the spatial heterogeneity of the influencing factors; and the downscaled annual and seasonal data exhibit satisfactory spatial texture characteristics and consistency with the original spatial distribution of O 3 , while the distribution boundary problem of image elements is eliminated. II) Nitrogen oxide (NOx) and volatile organic compound emissions and temperature exhibit strong positive correlations with O 3 , while wind speed, humidity, the normalized difference vegetation index, and AOD indicate weak positive correlations with O 3 . Moreover, precipitation exhibits a weak negative correlation with O 3 . III) The coefficient of determination (R 2 ) of the 1-km resolution annual O 3 concentration data after downscaling based on the MGWRK model reaches 0.93, while the RRMSE and MAE values are only 3% and 1.86, respectively, with a coefficient of variation of 9.55%; the downscaling accuracy of the seasonal O 3 concentration data is higher in summer and winter than during the other seasons, with R 2 greater than 0.85, further confirming the spatial and temporal downscaling advantages of the MGWRK model for O 3 in the Chang-Zhu-Tan city cluster. This further corroborates the feasibility of the MGWRK model for spatial and temporal O 3 downscaling in the Chang-Zhu-Tan urban area.