An integrated approach for multi-species VOC impact assessment from multiple organized point sources in industrial parks using dynamic dispersion modeling

Abstract

Volatile organic compound (VOC) impacts in chemical industrial parks are shaped by dynamic emissions, diverse source configurations, and meteorological variability, complicating source attribution and exposure assessment. Existing methods often lack source specificity, overlook episodic events, or use static emission assumptions, limiting their effectiveness in complex environments. In this study, we developed a high-resolution, species-resolved framework combining continuous online monitoring and dispersion modeling to quantify VOC source–receptor relationships. We constructed a two-year, hourly emission inventory covering 40 VOC species from 34 stationary sources and integrated it with AERMOD to simulate source-attributable concentration increments from organized point-source emissions across receptors. Results reveal strong spatial heterogeneity, with a persistent south-southwest transport pattern driven by prevailing northerlies and receptor concentrations varying by over an order of magnitude. Characteristic species—including toluene, acetone, and methylene chloride—were identified based on emission magnitude and receptor-level influence, each displaying distinct dispersion patterns. Time-series analyses show that exposure is dominated by intermittent peaks driven by a few source–species combinations. A representative high-emission event caused receptor concentrations to exceed long-term means by over two orders of magnitude. Paired-case comparisons under comparable emissions and wind directions demonstrate that meteorological dispersion capacity alone can substantially modulate receptor exposure. Notably, the most elevated impacts often occurred under moderate wind conditions aligned with transport pathways rather than extreme stagnation. These results highlight the importance of simultaneously considering compound identity, source variability, and meteorological dynamics. The proposed framework supports refined source apportionment and targeted emission control under both routine and episodic conditions.