Volatile organic compounds (VOCs) emitted from industrial processes, which are major emission sources of air pollutants, could cause significant impacts on air quality. However, studies on the comprehensive analysis from sources contributing to the health risk perspective regarding ambient VOCs in industrial cities are limited. In this study, VOC samples were collected from 15 April 2018 to 19 October 2018 in Changzhi, a typical industrial city in northern China, and a total of 57 VOCs were measured for analysis. The average VOC concentrations were 54.4 µg·m⁻³, with the highest concentrations in autumn (58.4 µg·m⁻³). Ambient VOCs in spring, summer and autumn were all dominated by alkanes (66.8%), with contributions of 70.3%, 66.3% and 63.8%, respectively. The top five concentrations of total VOCs were isopentane (19.0%), ethane (9.5%), n-butane (8.1%), benzene (7.9%) and propane (5.2%), indicating that vehicle exhaust and coal combustion are the main sources of VOCs. Source apportionment by principal component analysis showed that vehicle exhaust (27.5%) and coal combustion (23.5%) were the main sources of VOCs in Changzhi, followed by industrial production (17.4%), solvent evaporation (13.5%), liquefied petroleum gas/natural gas leaking (9.5%), and biogenic emissions (8.7%). Sources of coal combustion and vehicle exhaust contributed more VOCs than industrial production. The carcinogenic risks of benzene (3.4 × 10⁻⁵) and ethylbenzene (2.2 × 10⁻⁶) were higher than the limit levels (1 × 10⁻⁶). Coal combustion contributed most (25.3%) to the carcinogenic risks because of its high VOC emissions. In an industrial city such as Changzhi, vehicle exhaust and coal combustion have become major sources of ambient air VOCs owing to the increasingly stringent industrial standards. Therefore, VOCs from vehicle exhaust and coal combustion also need to take into account mitigation measures for VOCs from the perspective of source contribution to health risk.

Atmospheric particulate nitrate (pNO₃^-), a crucial component of fine particulate matter, significantly contributes to haze pollution. The formation of pNO₃^- is driven by multiple factors including meteorology, emissions, and atmospheric chemistry. Understanding the key drivers of pNO₃^- formation and developing an accurate and physically meaningful method for the timely assessment of the direct causes of pNO₃^- pollution are essential. In this study, we propose a multi-module data-driven integrated framework that incorporates and improves four distinct machine learning modules. This framework enhances the physical interpretability of the statistical outcomes of the driving factors of pNO₃^-, quantifies the impacts of multiple factors on pNO₃^-, and reveals emission reduction trends. Our findings show that meteorology and emissions affect pNO₃^- by 35.3 % and 64.7 %, respectively, while atmospheric chemistry (48.0 %) and humidity (17.1 %) are the key drivers of its formation. Photochemistry promotes the formation of pNO₃^- in summer, whereas liquid-phase reactions dominate in winter at higher humidity levels (>60 %). The industry source (IS) (14.3 %), combustion source (CS) (12.8 %), and transportation source (TS) (11.8 %) are the main emission sources. The formation of pNO₃^- by the primary emissions and the transformation of NOx emitted from CS and TS is more sensitive to the changes of meteorological conditions, and controlling CS has the greater benefits to reduce pNO₃^-. The proposed framework could provide a reliable method for identifying drivers of pNO₃^- pollution at different haze events, supporting the formulation of control measures.