Comparative analysis of seasonal air quality around an industrial area: A case study using air dispersion modelling and pollution index assessment
DOI:
https://doi.org/10.61511/aes.v4i1.2026.3811Keywords:
AERMOD, air quality modelling, industrial emissions, ISPU, PM₂.₅, seasonal variationAbstract
Background: Manufacturing activities can release combustion gases and fine particles whose ambient distribution varies with meteorology and source characteristics. Integrating source oriented dispersion modelling with Indonesia’s Air Pollutant Standard Index (ISPU) can provide complementary spatial and regulatory interpretations. Methods: This secondary-data case study combined facility monitoring records with archived 24 hour AERMOD outputs for rainy and dry season scenarios in Banten, Indonesia. Carbon monoxide (CO), nitrogen oxides/nitrogen dioxide (NOx/NO2), fine particulate matter (PM2,5), and sulfur dioxide (SO2) were assessed. Model output consistency, units, seasonal differences, and ISPU calculation were independently cross checked. Because complete AERMOD input files, receptor level time series, and co-located monitoring series were unavailable, independent calibration and statistical model observation validation were not performed. Findings: Dry-season maximum concentrations were higher for all modelled pollutants: CO increased from 42.745 to 51.226 µg/m³, NOx from 467.8 to 561 µg/m³, PM₂.₅ from 274 to 329 µg/m³, and SO₂ from 483.9 to 580 µg/m³. The relative increase was approximately 20% for each pollutant. ISPU values were good for CO (39.36), NO₂ (22.64), and SO₂ (36.65), while PM₂.₅ reached 68.68 and was classified as moderate. Conclusion: The combined assessment identifies the dry season as the higher-concentration scenario and PM₂.₅ as the principal ambient-air management priority. Facilities should strengthen dry-season surveillance, fugitive-dust control, filtration maintenance, and combustion-efficiency checks. The results are screening-level and comparative because the study relied on secondary model outputs and one monitoring dataset. Novelty/Originality of this article: The article demonstrates how seasonal AERMOD outputs and pollutant specific ISPU values can be used together to prioritize industrial air quality controls while explicitly distinguishing modelled maxima from measured ambient concentrations.
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