Volatile organic compounds (VOCs) are one of significant contributors to air pollution and have profound effects on human health and the environment. This study introduces a detailed analysis of VOC emissions from various industries within an industrial complex using a high-resolution measurement instrument. This study aimed to identify the VOC profiles and their concentrations across 12 industries. Sampling was conducted across 99 facilities in an industrial complex in South Korea, and VOC analysis was performed based on measurement data using a Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry (PTR-ToF-MS). The results indicated that the emission of oxygenated VOCs (OVOCs) was dominant in most industries. Aromatic hydrocarbons were also dominant in most industries, except in screen printing (SP), lubricating oil and grease manufacturing (LOG), and industrial laundry services (ILS) industries. Chlorinated VOCs (Cl-VOCs) showed a relatively higher level in the metal plating (MP) industry than those in other industries and nitrogen-containing VOCs (N-VOCs) showed high levels in general paints and similar product manufacturing (PNT), MP, and ILS industries, respectively. The gravure printing industry was identified as the highest emitter of VOCs, with the highest daily emissions reaching 5934 mg day⁻¹, primarily consisting of ethyl acetate, toluene, butyl acetate, and propene. The findings suggest that the VOC emissions from the gravure printing and plastic synthetic leather industries should be primarily reduced, and it would be the most cost-effective approach to improving air quality. This study can provide the fundamental data for developing effective reduction technologies and policies of VOC, ultimately contributing to enhanced atmospheric models and regulatory measures.

Air pollution monitoring in Korea has not yet been implemented in agricultural areas. This study compares agricultural air quality with urban air quality during two periods: (1) the entire measurement period and (2) high-PM episodes. To ensure broad spatial cover-age, eight monitoring stations were installed in Yeoju, Nonsan, Naju, Gimhae, Hongcheon, Danyang, Muan, and Sangju. Real-time measurements of PM-10, PM-2.5, SO₂, and NOx were conducted continuously from March 2023 to December 2024. Over the entire meas-urement period, PM concentrations were similar in both agricultural and urban areas, but gaseous pollutants were lower in agricultural areas. PM levels were higher in agricultural areas during summer, whereas urban areas showed higher concentrations in other sea-sons. During high-PM episodes (29 days), all pollutants were significantly higher in urban areas, with PM-2.5 showing a greater difference than PM-10. Diurnal variations revealed that PM-10, PM-2.5, and NO₂ peaked in the morning and reached their lowest levels around 3 PM, with urban levels consistently higher than those in agricultural areas. SO₂ showed a different pattern, reaching its lowest concentration at 6 AM and peaking at noon in urban areas and at 6 PM in agricultural areas. This patter closely followed temperature and wind speed variations.

Air pollution monitoring in Korea has not yet been implemented in agricultural areas. Documenting air quality in purely agricultural areas is inherently valuable. This study compares agricultural air quality with urban air quality during two periods: (1) the entire measurement period and (2) high-PM episodes. To ensure broad spatial coverage, eight monitoring stations were installed in Yeoju, Nonsan, Naju, Gimhae, Hongcheon, Danyang, Muan, and Sangju. Real-time measurements of PM₁₀, PM_2.5, SO₂, and NOx were conducted continuously from March 2023 to December 2024. Over the entire measurement period, PM concentrations were similar in both agricultural and urban areas, but gaseous pollutants were lower in agricultural areas. PM levels were higher in agricultural areas during summer, whereas urban areas showed higher concentrations in other seasons. During high-PM episodes (29 days), all pollutants were significantly higher in urban areas, with PM_2.5 showing a greater difference than PM₁₀. Diurnal variations revealed that PM₁₀, PM_2.5, and NO₂ peaked in the morning and reached their lowest levels around 3 PM, with urban levels consistently higher than those in agricultural areas. SO₂ showed a different pattern, reaching its lowest concentration at 6 AM and peaking at noon in urban areas and at 6 PM in agricultural areas. This pattern closely followed temperature and wind speed variations.

Fine particulate matter (PM_2.5) in agricultural areas forms partly from gaseous precursors (NH₃, NO₂, and SO₂), yet in open fields ambient concentrations are strongly modulated by wind-driven dilution, which can obscure precursor–PM_2.5 relationships. This study compared concentration- and loading-rate-based interpretations of these relationships in Korean agricultural areas, evaluating the loading rate—concentration-scaled by a wind-speed-based air exchange rate—as a complementary indicator of pollutant loading at the monitoring site. Using a 547-day record (January 2024–June 2025) from eight monitoring sites, we examined hourly network-average concentrations and loading rates across four subsets (entire period, summer, non-summer, and high-PM events) with Pearson correlation, principal component analysis, and multiple linear regression. Concentration-based correlations among PM_2.5 and its precursors were weak and inconsistent, whereas loading-rate-based ones were strong and uniform (r ≥ 0.816). The stronger loading-rate correlations may reflect the dominant influence of wind speed. Diurnal patterns reinforced this point: concentrations were bimodal and fell during the windy daytime, while loading rates were unimodal and rose over the same hours, showing that lower concentrations need not imply reduced pollutant loading. NH₃ peaked earlier and higher in summer than the other precursors, suggesting a time-lagged rather than simultaneous link with PM_2.5. Multiple linear regression confirmed strong co-variation (R = 0.977–0.997) but was limited by multicollinearity, so its coefficients reflect relative co-variation rather than source contributions. The loading rate is therefore best used not on its own but as a complementary indicator of pollutant loading alongside concentration.