Correlation analysis between precursor gases and fine particles in agricultural area

Understanding the interactions between PM_2.5 and its gaseous precursors in agricultural environments is essential for designing effective air quality control strategies. In this study, long-term observations were carried out at eight agricultural monitoring sites across South Korea to investigate the relationships among PM_2.5, its major precursor gases (NH₃, NO₂, and SO₂), and meteorological factors. Both concentration-based metrics and loading-rate approaches, which incorporate wind-driven transport, were applied for comparative analysis. The concentration-based analysis yielded generally weak and unstable correlations, largely attributable to atmospheric dispersion and dilution effects. In contrast, loading rates exhibited consistently strong and statistically significant associations among PM_2.5 and precursor gases (R ≥ 0.816, p < 0.001), indicating their enhanced capability to represent emission–transport interactions. Clear seasonal and diurnal variations were observed for all pollutants, with summer showing distinctly different daily patterns compared to other seasons. Notably, loading-rate maxima systematically lagged behind meteorological peaks by approximately two hours. Ammonia displayed an earlier and more pronounced diurnal signal than other precursors, primarily driven by temperature-dependent volatilization associated with soil–air temperature gradients. Principal component analysis revealed that PM_2.5 loading rates were closely aligned with SO₂ and NO₂, whereas NH₃ formed a separate structure, reflecting its different emission timing. Multiple linear regression further identified SO₂ as the dominant contributor to PM_2.5 formation, followed by NO₂, while NH₃ exhibited a negative relationship due to its temporal offset from PM_2.5 peaks. Overall, this study demonstrates that loading-rate-based analysis provides a more robust framework for elucidating PM_2.5–precursor interactions in agricultural regions and offers improved scientific support for developing targeted mitigation strategies.

Acknowledgments

"This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE)"