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Air quality time series exhibit significant periodic patterns, which are linked to a diverse array of emission sources and atmospheric processes. To discern and distinguish these periodic drivers, we have devised a two‐step decomposition approach that integrates a machine learning‐based model for weather normalization with Morlet wavelet analysis. This approach was applied to a 7‐year data set encompassing six regulated air pollutants across eight Chinese cities. Our analysis revealed distinct periodicities in weather‐normalized concentrations of primary air pollutants: a dominant annual cycle with periodicity around 365 days, which accounts for over 50% of the variance on average and is primarily driven by recurrent winter heating activities; daily cycles characterized by regular diurnal patterns attributable to combustion sources such as traffic; and periodicities exceeding 512 days that associated with long‐term regulatory policies targeting SO2. Particularly notable was the significant drop in the strength of the annual cycle in northern cities following the implementation of China's clean heating policies in 2017/2018, affirming the success of these initiatives. Additionally, diurnal dispersion and photochemistry, large‐scale atmospheric circulation, and synoptical weather patterns are likely responsible for the observed daily cycle (accounting for over 40% of the variance), annual periodicities, and intra‐monthly variations in the meteorologically driven concentrations, respectively. The varying power of these periodic drivers across time and locations implies the heterogeneity of emission rates and region‐specific climates. This work highlights the efficacy of this decomposition approach in air quality research, warranting attention for its potential value for enhancing our understanding of air pollution dynamics. Plain Language Summary: Periodic patterns can be widely observed in Earth's environment, particularly in the atmosphere, lead to repeating fluctuations in air pollution that correspond to regular time intervals. These changes are driven by a combination of emissions from various sources and weather patterns. Understanding what causes air quality changes is crucial for effective intervention. This study introduces a refined method to analyze air pollution, using machine learning to distinguish between the effects of human activities and natural weather variations on pollution levels. Additionally, Morlet wavelet analysis is used to identify the specific periodic forces driving these changes. Tested on several years of air quality data from eight Chinese cities, the method effectively pinpointed recurring pollution patterns linked to both emissions and weather on different time scales. The study reveals varying impacts of these patterns by region and time, due to differences in emission rates and region‐specific climates. The proposed approach is expected to enhance our understanding of air pollution dynamics and inform policy decisions for better air quality management. Key Points: Drivers of air quality variation can be differentiated and interpreted based on their periodic patterns and powersYearly recurring winter heating‐related activities account for the most significant variation in primary air pollutants in northern Chinese citiesThe varying power of periodic drivers over time and locations emphasizes the heterogeneity of emission rates and region‐specific climates [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]

Poor air quality is often experienced in densely populated areas located alongside large rivers. The presence of a river valley can modify local meteorology, potentially interfering with the formation of air pollutants such as PM2.5. However, the mechanisms behind PM2.5 formation in river valleys and its impacts on surrounding regions remain poorly understood. This study investigates the formation mechanisms of PM2.5 in the Nanjing reach of the Yangtze River, considering its complex terrain and anthropogenic emissions, through numerical simulation. Simulated results revealed higher PM2.5 concentrations along the Yangtze River compared to the average concentrations in Nanjing, primarily driven by elevated nitrate level. Factors such as higher humidity, wind speed, ageostrophic wind field, and shallow boundary layer were identified as key contributors to the increased PM2.5 concentrations along the river. Our study demonstrates significant impact of local thermal circulations on PM2.5 formation, resulting from the interaction between the large water body of the Yangtze River and the urban area. This phenomenon was confirmed through sensitivity experiments, which showed attenuated local thermal circulations in the absence of the Yangtze River. Source apportionment and process analysis results confirm the dominant roles of local thermal circulation related horizontal and vertical advections, with diurnal fluctuations, in shaping PM2.5 variations along the river. Inorganic chemistry processes also played a non‐negligible role, particularly during polluted conditions. Local thermal circulations in river valleys are crucial for PM2.5 formation, highlighting the need for comprehensive strategies to tackle air pollution in similar regions. Plain Language Summary: Throughout history, civilizations have thrived along major rivers across the globe. However, these densely populated areas often suffer from poor air quality due to high anthropogenic emissions. Notably, fine particulate matter (PM2.5) tends to be higher along rivers than in surrounding urban areas, raising questions about the mechanisms behind PM2.5 formation in river valleys and its impacts on neighboring regions. Using numerical experiments, this study focuses on the Nanjing reach of the Yangtze River as an example to model PM2.5 air quality. We observed a noticeable diurnal pattern of source regions for PM2.5 along the Yangtze River. The findings uncover that the topography of river valleys, coupled with urban terrain, can induce a unique small‐scale thermal circulation embedded within the conventional urban heat island circulation. This local thermal circulation acts as a physical barrier, trapping air pollutants within the valley during the daytime, and aggravating PM2.5 air quality along the river. Understanding the role of local thermal circulation is essential for developing comprehensive strategies for combatting air pollution in urban areas near major rivers. Key Points: The PM2.5 concentration along the Yangtze River is higher than in the neighboring Nanjing urban areasThe contribution from regions outside the Yangtze River to PM2.5 along the river shows a pronounced diurnal patternLocal thermal circulations, induced by the Yangtze River's water body and the urban terrains, interfere with PM2.5 formation [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]