Particulate matter (PM2.5) and ozone (O3) are typical air pollutants that require effective simultaneous control. Current PM2.5 and O3 co-mitigation strategies are primarily focused on the emission optimization of nitrogen oxides (NOx) and volatile organic compounds (VOCs); however, fundamental mechanisms underlying these co-mitigation efforts remain largely unanswered. Here, utilizing an atmospheric chemistry model combined with machine learning technique, we extend the scope of coordinated control efforts beyond just precursors by identifying and controlling common sources of PM2.5 and O3, which are fossil fuel combustion sources including coal combustion, industrial emission, and vehicle exhausts. We conducted further sensitivity simulations and found that reducing fossil fuel combustion emissions significantly lowers PM2.5 levels but has trade-off effects on O3 control, which is attributable to the suppressed aerosol sink of hydroperoxyl radicals and the enhanced atmospheric oxidizing capacity. Furthermore, this unexpected enhancement of O3 can be offset by synergistically controlling certain other specific VOCs sources. For example, during days with high levels of both PM2.5 and O3 pollution, a 40% reduction of VOCs sources at the corresponding level of coal combustion reduction is predicted to lead to an ∼13 ppb (∼14%) reduction in O3 concentration. Our results provide the scientific evidence and theoretical framework for mitigating PM2.5 and O3 pollution through joint control of fossil fuel combustion sources and specific VOCs sources.
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Shi et al. (2024) studied this question.
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