Abstract. This study utilizes remote sensing technologies and the Google Earth Engine (GEE) platform in assessing the impacts of urban sprawl on air quality in Cavite for 2020-2023. Sentinel-2 L2A images were used to generate 10 m resolution land cover classification map using supervised classification technique applied on training data for built-up, bare soil, vegetation water, and clouds for all the years. Accuracy assessment showed an overall accuracy of at least 99.90% across the years, with the κ ranging from 0.98 to 0.99. The rate of urbanization was found to increase at least 1% every year, from 25.04% in 2020 to 28.92% in 2023. For air quality, NO2, CO, and O3 derived from Sentinel-5P data for the same period were aggregated to quarterly values to address data gaps. Time-series analysis of the air quality parameters showed that NO2 peaked in the fourth quarter due to cooler weather, CO spiked in the second quarter from energy used during dry season, while O3 peaked in the second and third quarters due to higher temperature. Single-factor ANOVA test (α = 0.05) showed that the NO2 and CO concentrations are significantly different between built-up and non-built-up areas, while O3 showed no significant difference. Regression analysis also showed that NO2 and CO concentrations increase with the expansion of built-up areas, whereas O3 was not directly affected.
Colendres et al. (Tue,) studied this question.