Analysis of Spatial–Temporal Variability of PM2.5 Concentrations Using Optical Satellite Images and Geographic Information System

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 8; p. 2009
• Main Authors: Heriza, Dewinta, Wu, Chih-Da, Syariz, Muhammad Aldila, Lin, Chao-Hung
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Air monitoring; Air pollution; Air quality; Air quality control; Carbon monoxide; Classification; Datasets; Environmental perception; fine particulate matter; Geographic information systems; Image processing; Information processing; Land cover; Land use; land use regression; Landsat; Landsat satellites; Machine learning; Modelling; National parks; Nitrogen dioxide; optical satellite images; Outdoor air quality; Particulate emissions; Particulate matter; Remote sensing; Roads & highways; Satellite imagery; Satellites; Sensors; Spatial analysis; Spatial discrimination; Spatial resolution; Taiwan
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15082009

Particulate matter less than 2.5 microns in diameter (PM2.5) is an air pollutant that has become a major environmental concern for governments around the world. Management and control require air quality monitoring and prediction. However, previous studies did not fully utilize the spectral information in multispectral satellite images and land use data in geographic datasets. To alleviate these problems, this study proposes the extraction of land use information not only from geographic inventory but also from satellite images with a machine learning-based classification. In this manner, near up-to-date land use data and spectral information from satellite images can be utilized, and the integration of geographic and remote sensing datasets boosts the accuracy of PM2.5 concentration modeling. In the experiments, Landsat-8 imagery with a 30-m spatial resolution was used, and cloud-free image generation was performed prior to the land cover classification. The proposed method, which uses predictors from geographic and multispectral satellite datasets in modeling, was compared with an approach which utilizes geographic and remote sensing datasets, respectively. Quantitative assessments showed that the proposed method and the developed model, with a performance of RMSE = 3.06 µg/m3 and R2 = 0.85 comparatively outperform the models with a performance of RMSE = 3.14 µg/m3 and R2 = 0.68 for remote sensing datasets and a performance of RMSE = 3.47 µg/m3 and R2 = 0.79 for geographic datasets.