Abstract This study investigates the impact of transportation, manufacturing, and energy sectors on air quality in Jakarta using Geographically Weighted Regression (GWR) and remote sensing data. Utilizing datasets from Sentinel-5P for NO2, SO2, and CO concentrations, and predictor variables including road density, industrial activity, and energy production, the research aims to quantify the spatial heterogeneity of air pollution. The findings reveal significant spatial variations in pollutant concentrations across Jakarta and its neighbouring provinces, Banten and West Java, during the dry and wet months of 2023. High NO2 levels were primarily linked to dense traffic in central Jakarta, while elevated SO2 and CO concentrations were associated with industrial and energy activities in the peripheral regions. Seasonal analysis indicated that meteorological factors play a crucial role in pollutant dispersion. The integration of remote sensing data with GWR provided a robust framework for environmental monitoring, addressing gaps in traditional air quality observation methods. This research contributes to the broader understanding of urban air pollution dynamics and offers a replicable model for other regions facing similar challenges.
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