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Chemical losses of ambient reactive volatile organic compounds (VOCs) is a long-term issue yet to be resolved in VOC source apportionments. These losses substantially reduce the concentrations of highly reactive species in the apportioned factor profiles and result in the underestimation of source contributions. This review assesses the common methods and existing issues in ways to reduce losses and loss impacts in source analyses and suggests research directions for improved VOC source apportionments. Positive matrix factorization (PMF) is now the main VOC source analysis method compared to other mathematical models. The issue in using any apportionment tool is the processing of the data to be analyzed to reduce the impacts of reactive losses. Estimating the initial concentrations of ambient VOCs based on photochemical age has become the primary approach to reduce reactive loss effects in PMF, except for selecting low-reactivity species or nighttime data into the analysis. Currently, the initial concentration method only considers daytime reactions with hydroxyl (⚫ OH) radicals. However, the ⚫ OH rate constants vary with temperature, and that has not been considered. Losses from reactions with O3 and NO3 radicals, especially for alkene species, remain to be included. Thus, the accuracy of the photochemical age estimation is uncertain. Beyond developing accurate quantitative approaches for reactive losses, source analyses methods for the consumed VOCs and the accurate quantification of different source contributions to O3 and secondary organic aerosols are important additional directions for future research. [ABSTRACT FROM AUTHOR]

Chemical losses of ambient reactive volatile organic compounds (VOCs) is a long-term issue yet to be resolved in VOC source apportionments. These losses substantially reduce the concentrations of highly reactive species in the apportioned factor profiles and result in the underestimation of source contributions. This review assesses the common methods and existing issues in ways to reduce losses and loss impacts in source analyses and suggest research directions for improved VOC source apportionments. Positive Matrix Factorization (PMF) is now the main VOC source analysis method compared to other mathematical models. The issue in using any apportionment tool is the processing of the data to be analyzed to reduce the impacts of reactive losses. Estimating the initial concentrations of ambient VOCs based on photochemical age has become the primary approach to reduce reactive loss effects in PMF except for selecting low reactivity species or nighttime data into the analysis. Currently, the initial concentration method only considers daytime reactions with hydroxyl (•OH) radicals. However, the •OH rate constants vary with temperature and that has not been considered. Losses from reactions with O3 and NO3 radicals especially for alkene species remain to be included. Thus, the accuracy of the photochemical-age estimation is uncertain. Beyond developing accurate quantitative approaches for reactive losses, source analyses methods for the consumed VOCs and the accurate quantification of different source contributions to O3 and secondary organic aerosols are important additional directions for future research. [ABSTRACT FROM AUTHOR]

A chemical transport model (CTM) is an essential tool for air quality prediction and management, widely used in air pollution control and health risk assessment. However, the current models do not perform very well in reproducing the observations of some major chemical components, for example, sulfate, nitrate, ammonium and organic carbon. Studies have suggested that the uncertainties in the model chemical mechanism, source emission inventory and meteorological field can cause inaccurate simulation results. Still, the emission source profile (used to create speciated emission inventories for CTMs) of PM 2.5 has not been fully taken into account in current numerical simulation. Based on the characteristics and variation rules of chemical components in typical PM 2.5 sources, different simulation scenarios were designed and the sensitivity of simulated PM 2.5 components to the source chemical profile was explored. Our findings showed that the influence of source profile changes on simulated PM 2.5 components' concentrations cannot be ignored. Simulation results of some components were sensitive to the adopted source profile in CTMs. Moreover, there was a linkage effect: the variation in some components in the source profile would bring changes to the simulated results of other components. These influences are connected to chemical mechanisms of the model since the variation in species allocations in emission sources can affect the potential composition and phase state of aerosols, chemical reaction priority, and multicomponent chemical balance in thermodynamic equilibrium systems. We also found that the perturbation of the PM 2.5 source profile caused variation in simulated gaseous pollutants, which indirectly indicated that the perturbation of source profile would affect the simulation of secondary PM 2.5 components. Our paper highlights the necessity of paying enough attention to the representativeness and timeliness of the source profile when using CTMs for simulation. [ABSTRACT FROM AUTHOR]

Reducing ambient black carbon (BC) relies on the targeted control of anthropogenic emissions. Measuring emission changes in source‐specific BC aerosol is essential to assess the effectiveness of regulatory policies but is difficult due to the presence of meteorology and multiple co‐existing emissions. Herein, we propose a data‐driven approach, combining dispersion‐normalized factor analysis (DN‐PMF) with a machine learning weather adjustment (deweathering) technique, to decompose ambient BC into source emissions and meteorological drivers. Six refined BC sources were extracted from the factor analysis of aethalometer multi‐wavelength BC and concurrent observational datasets. In addition to the widely reported dominant sources, such as vehicular emissions (VE) and coal/biomass burning (BB), a discernible port and shipping emission source were identified with potential impacts on coastal air quality. The source‐specific BC showed abrupt changes in response to interventions (e.g., holidays) after separating weather‐related confounders. Significant reductions in deweathered coal and BB, VE, and local dust verified the effectiveness of policies, such as clean winter‐heating and support for the Clean Air Actions. As revealed by a post‐hoc model explanation technique, the evolution of the boundary layer was the predominant meteorological driver exerting the opposite impact on local sources with respect to distant regional‐wide sources, that is, the port and shipping emissions. Plain Language Summary: Throughout the course of human history, substantial efforts have been made worldwide to alleviate air pollution. Quantifying how a regulatory action (intervention) can impact air pollutant emissions and air quality is crucial, but complicated owing to multiple confounders (e.g., meteorology). Black carbon (BC) is a primary air pollutant that warms the atmosphere, worsens air quality, and impairs human health. Herein, we applied a data‐driven approach, combining factor analysis with machine learning techniques, to attribute ambient BC to its emission sources and meteorological‐related drivers. Using this coupled approach, the impacts of a series of human interventions (e.g., holiday effects, the COVID‐19 lockdown, clean‐heating policy, and the Chinese Clean Air Action) on source‐specific BC can be quantified. Our analyses highlight that this approach is also applicable to other regulated air pollutants, with great potential to facilitate air quality accountability studies. Key Points: Six refined black carbon (BC) sources were resolved from the factor analysis of aethalometer BC combined with concurrent measurementsPositive matrix factorization (PMF) coupled with the deweathering technique enables the detection of emission changes in source‐specific aerosol over timeImpacts of human interventions (holidays and the COVID lockdown, winter heating policy, Clean Air Actions) on BC aerosol were quantified [ABSTRACT FROM AUTHOR]