Analysis of Trends and Correlation Between Ground-based PM₂․₅ and Satellite AOD in Jakarta (Dec 2022–Mar 2025)

Marthin  Abednego Gultom; Maran Gultom

doi:10.46729/ijstm.v6i3.1216

Marthin Abednego Gultom Geological Engineering Department Cenderawasih University, Papua, Indonesia
Maran Gultom Geology Department Ottow Geissler Papua University, Papua, Indonesia

DOI: https://doi.org/10.46729/ijstm.v6i3.1216

Keywords: PM₂․₅; AOD; Time Series Trends, Jakarta and Air Quality.

Abstract

Monitoring of PM₂․₅ concentrations in urban areas such as Jakarta is crucial given its impact on public health (WHO, 2021) and the urban environment (Zhang et al., 2020), where measuring PM₂․₅ levels is essential for assessing air quality and health risks in metropolitan regions, including Jakarta. However, the limited number of ground-based monitoring stations and variable weather conditions often result in uneven PM₂․₅ data availability (Alim et al., 2023). As an alternative, satellite-derived Aerosol Optical Depth (AOD) can serve as a proxy for particulate pollution monitoring (Liang et al., 2018).This study aims to analyze the temporal trends and quantify the correlation between ground-based PM₂․₅ and satellite AOD in Jakarta from December 2022 through March 2025. PM₂․₅ data were obtained from five Air Quality Monitoring Stations (SPKU) located in Kebon Jeruk, Bundaran HI, Kelapa Gading, Lubang Buaya, and Jagakarsa, while AOD was extracted via Google Earth Engine (MODIS MCD19A2) at the same five locations. Key methods include additive seasonal decomposition of each time series, calculation of Pearson and Spearman correlation coefficients, and cross-correlation analysis to determine the optimal lag. The results indicate that both PM₂․₅ and AOD trends rose from mid-2023, peaked in early 2024, and then gradually declined through late 2024; monthly correlations were very strong (Pearson r = 0.71, p < 0.001; Spearman ρ = 0.76, p < 0.001). Seasonal analysis revealed concentration maxima during the dry season (June–September) and minima in the wet season (December–February). Cross-correlation shows that AOD leads PM₂․₅ fluctuations by one month (lag +1). These findings underscore the potential of satellite AOD as a monthly proxy for estimating PM₂․₅ in Jakarta, supporting more spatially and temporally comprehensive air quality monitoring than ground-based networks alone. In conclusion, satellite AOD can be used as a supplementary indicator for PM₂․₅ air quality monitoring in Jakarta, particularly to fill gaps in ground-based PM₂․₅ data coverage.

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