Significant Changes in Chemistry of Fine Particles in Wintertime Beijing from 2007 to 2017: Impact of Clean Air Actions

Urbanization impacts on evapotranspiration change across seven typical urban agglomerations in China

Evaluating the differences in evapotranspiration between urban and surrounding non-urban areas (i.e., ∆ET) has critical implications for urban ecological planning and water resources management. However, it is unclear how the magnitude of changes in ∆ET caused by urbanization varies under different climatic conditions in China. Here, using the remotely ET estimates at 1 km spatial resolution, we firstly estimated the magnitude of changes in ∆ET and then quantified the main driving factors influencing variations in ∆ET of 7 national-level urban agglomerations (UAs) across China during 2003-2020. Results showed that all annual ETurban values were smaller than ETnon-urban of 7 UAs, and the absolute ∆ET values of cities in South China were generally higher than those in North China. There is an apparent effect of urbanization on ∆ET increase in Guanzhong Plain City Group, Central Plain UA and Guangdong-Hong Kong-Macao Greater Bay Area (GHKMGBA), while ∆ET decrease in Chengdu-Chongqing City Group and Yangtze River Delta (YRDUA) were primarily due to the climate change. The suppressing effects of temperature and NDVI on ∆ET decrease in YRDUA were enhanced, and the promoting effect of GDP on ∆ET increase in GHKMGBA was weakened. Considering nonstationary features, urbanization appears to heighten extreme ∆ET by 0.83 %, 4.83 % and 10.39 % under 5-year, 20-year, and 50-year return periods over all the 7 UAs, respectively. Collectively, our findings confirm that urbanization is a significant factor that leads to ∆ET increase, and the factors affecting the response of urban water circulation system need to be deeply decomposed.

Impacts of the Chengdu 2021 world university games on NO2 pollution: Implications for urban vehicle electrification promotion

Emissions of nitrogen oxides (NOx) are a dominant contributor to ambient nitrogen dioxide (NO2) concentrations, but the quantitative relationship between them at an intracity scale remains elusive. The Chengdu 2021 FISU World University Games (July 22 to August 10, 2023) was the first world-class multisport event in China after the COVID-19 pandemic which led to a substantial decline in NOx emissions in Chengdu. This study evaluated the impact of variations in NOx emissions on NO2 concentrations at a fine spatiotemporal scale by leveraging this event-driven experiment. Based on ground-based and satellite observations, we developed a data-driven approach to estimate full-coverage hourly NO2 concentrations at 1 km resolution. Then, a random-forest-based meteorological normalization method was applied to decouple the impact of meteorological conditions on NO2 concentrations for every grid cell, the resulting data were then compared with the timely bottom-up NOx emissions. The SHapley-Additive-exPlanation (SHAP) method was employed to delineate the individual contributions of meteorological factors and various emission sources to the changes in NO2 concentrations. According to the full-coverage meteorologically normalized NO2 concentrations, a decrease in NOx emissions and favorable meteorological conditions accounted for 80 % and 20 % of the NO2 reduction, respectively, across Chengdu city during the control period. Within the strict control zone, a 30 % decrease in the meteorologically normalized NO2 concentrations was observed during the control period. The normalized NO2 concentrations demonstrated a strong correlation with NOx emissions (R = 0.96). Based on the SHAP analysis, traffic emissions accounted for 73 % of the reduction in NO2 concentrations, underscoring the significance of traffic control measures in improving air quality in urban areas. This study provides insights into the relationship between NO2 concentrations and NOx emissions using real-world data, which implies the substantial benefits of vehicle electrification for sustainable urban development.

Examining trends and variability of PM2.5-associated organic and elemental carbon in the megacity of Beijing, China: Insight from decadal continuous in-situ hourly observations

Organic carbon (OC) and elemental carbon (EC) in fine particulate matter (PM2.5) play pivotal roles in impacting human health, air quality, and climate change dynamics. Long-term monitoring datasets of OC and EC in PM2.5 are indispensable for comprehending their temporal variations, spatial distribution, evolutionary patterns, and trends, as well as for assessing the effectiveness of clean air action plans. This study presents and scrutinizes a comprehensive 10-year hourly dataset of PM2.5-bound OC and EC in the megacity of Beijing, China, spanning from 2013 to 2022. Throughout the entire study period, the average concentrations of OC and EC were recorded at 8.8 ± 8.7 and 2.5 ± 3.0 μg/m3, respectively. Employing the seasonal and trend decomposition methodology, specifically the locally estimated scatter plot smoothing method combined with generalized least squares with the autoregressive moving average method, the study observed a significant decline in OC and EC concentrations, reducing by 5.8 % yr-1 and 9.9 % yr-1 at rates of 0.8 and 0.4 μg/m3 yr-1, respectively. These declining trends were consistently verified using Theil-Sen method. Notably, the winter months exhibited the most substantial declining trends, with rates of 9.3 % yr-1 for OC and 10.9 % yr-1 for EC, aligning with the positive impact of the implemented clean air action plan. Weekend spikes in OC and EC levels were attributed to factors such as traffic regulations and residential emissions. Diurnal variations showcased higher concentrations during nighttime and lower levels during daytime. Although meteorological factors demonstrated an overall positive impact with average reduction in OC and EC concentrations by 8.3 % and 8.7 %, clean air action plans including the Air Pollution Prevention and Control Action Plan (2013-2017) and the Three-Year Action Plan to Win the Blue Sky War (2018-2020) have more contributions in reducing the OC and EC concentrations with mass drop rates of 87.1 % and 89.2 % and 76.7 % and 96.7 %, respectively. Utilizing the non-parametric wind regression method, significant concentration hotspots were identified at wind speeds of ≤2 m/s, with diffuse signals recorded in the southwestern wind sectors at wind speeds of approximately 4-5 m/s. Interannual disparities in potential source regions of OC and EC were evident, with high potential source areas observed in the southern and northwestern provinces of Beijing from 2013 to 2018. In contrast, during 2019-2022, potential source areas with relatively high values of potential source contribution function were predominantly situated in the southern regions of Beijing. This analysis, grounded in observational data, provides insights into the decadal changes in the major atmospheric composition of PM2.5 and facilitates the evaluation of the efficacy of control policies, particularly relevant for developing countries.

Major source categories of PM2.5 oxidative potential in wintertime Beijing and surroundings based on online dithiothreitol-based field measurements

Fine particulate matter (PM2.5) causes millions of premature deaths each year worldwide. Oxidative potential (OP) has been proposed as a better metric for aerosol health effects than PM2.5 mass concentration alone. In this study, we report for the first time online measurements of PM2.5 OP in wintertime Beijing and surroundings based on a dithiothreitol (DTT) assay. These measurements were combined with co-located PM chemical composition measurements to identify the main source categories of aerosol OP. In addition, we highlight the influence of two distinct pollution events on aerosol OP (spring festival celebrations including fireworks and a severe regional dust storm). Source apportionment coupled with multilinear regression revealed that primary PM and oxygenated organic aerosol (OOA) were both important sources of OP, accounting for 41 ± 12 % and 39 ± 10 % of the OPvDTT (OP normalized by the sampled air volume), respectively. The small remainder was attributed to fireworks and dust, mainly resulting from the two distinct pollution events. During the 3.5-day spring festival period, OPvDTT spiked to 4.9 nmol min-1 m-3 with slightly more contribution from OOA (42 ± 11 %) and less from primary PM (31 ± 15 %). During the dust storm, hourly-averaged PM2.5 peaked at a very high value of 548 μg m-3 due to the dominant presence of dust-laden particles (88 % of total PM2.5). In contrast, only mildly elevated OPvDTT values (up to 1.5 nmol min-1 m-3) were observed during this dust event. This observation indicates that variations in OPvDTT cannot be fully explained using PM2.5 alone; one must also consider the chemical composition of PM2.5 when studying aerosol health effects. Our study highlights the need for continued pollution control strategies to reduce primary PM emissions, and more in-depth investigations into the source origins of OOA, to minimize the health risks associated with PM exposure in Beijing.

Dissecting Drivers of Ozone Pollution during the 2022 Multicity Lockdowns in China Sheds Light on Future Control Direction

In 2022, many Chinese cities experienced lockdowns and heatwaves. We analyzed ground and satellite data using machine learning to elucidate chemical and meteorological drivers of changes in O3 pollution in 27 major Chinese cities during lockdowns. We found that there was an increase in O3 concentrations in 23 out of 27 cities compared with the corresponding period in 2021. Random forest modeling indicates that emission reductions in transportation and other sectors, as well as the changes in meteorology, increased the level of O3 in most cities. In cities with over 80% transportation reductions and temperature fluctuations within -2 to 2 °C, the increases in O3 concentrations were mainly attributable to reductions in nitrogen oxide (NOx) emissions. In cities that experienced heatwaves and droughts, increases in the O3 concentrations were primarily driven by increases in temperature and volatile organic compound (VOC) emissions, and reductions in NOx concentrations from ground transport were offset by increases in emissions from coal-fired power generation. Despite 3-99% reduction in passenger volume, most cities remained VOC-limited during lockdowns. These findings demonstrate that to alleviate urban O3 pollution, it will be necessary to further reduce industrial emissions along with transportation sources and to take into account the climate penalty and the impact of heatwaves on O3 pollution.

Elucidating the Mechanism on the Transition-Metal Ion-Synergetic-Catalyzed Oxidation of SO2 with Implications for Sulfate Formation in Beijing Haze

Currently, atmospheric sulfate aerosols cannot be predicted reliably by numerical models because the pathways and kinetics of sulfate formation are unclear. Here, we systematically investigated the synergetic catalyzing role of transition-metal ions (TMIs, Fe3+/Mn2+) in the oxidation of SO2 by O2 on aerosols using chamber experiments. Our results showed that the synergetic effect of TMIs is critically dependent on aerosol pH due to the solubility of Fe(III) species sensitive to the aqueous phase acidity, which is effective only under pH < 3 conditions. The sulfate formation rate on aerosols is 2 orders of magnitude larger than that in bulk solution and increases significantly on smaller aerosols, suggesting that such a synergetic-catalyzed oxidation occurs on the aerosol surface. The kinetic reaction rate can be described as R = k*[H+]-2.95[Mn(II)][Fe(III)][S(IV)] (pH ≤ 3.0). We found that TMI-synergetic-catalyzed oxidation is the dominant pathway of sulfate formation in Beijing when haze particles are very acidic, while heterogeneous oxidation of SO2 by NO2 is the most important pathway when haze particles are weakly acidic. Our work for the first time clarified the role and kinetics of TMI-synergetic-catalyzed oxidation of SO2 by O2 in haze periods, which can be parameterized into models for future studies of sulfate formation.

Source apportionment of PM2.5 using PMF combined online bulk and single-particle measurements: Contribution of fireworks and biomass burning

Fireworks (FW) could significantly worsen air quality in short term during celebrations. Due to similar tracers with biomass burning (BB), the fast and precise qualification of FW and BB is still challenging. In this study, online bulk and single-particle measurements were combined to investigate the contributions of FW and BB to the overall mass concentrations of PM2.5 and specific chemical species by positive matrix factorization (PMF) during the Chinese New Year in Hong Kong in February 2013. With combined information, fresh/aged FW (abundant 140K2NO3+ and 213K3SO4+ formed from 113K2Cl+ discharged by fresh FW) can be extracted from the fresh/aged BB sources, in addition to the Second Aerosol, Vehicles + Road Dust, and Sea Salt factors. The contributions of FW and BB were investigated during three high particle matter episodes influenced by the pollution transported from the Pearl River Delta region. The fresh BB/FW contributed 39.2% and 19.6% to PM2.5 during the Lunar Chinese New Year case. However, the contributions of aged FW/BB enhanced in the last two episodes due to the aging process, evidenced by high contributions from secondary aerosols. Generally, the fresh BB/FW showed more significant contributions to nitrate (35.1% and 15.0%, respectively) compared with sulfate (25.1% and 5.9%, respectively) and OC (14.8% and 11.1%, respectively) on average. In comparison, the aged FW contributed more to sulfate (13.4%). Overall, combining online bulk and single-particle measurement data can combine both instruments' advantages and provide a new perspective for applying source apportionment of aerosols using PMF.

Revisiting the dynamics of gaseous ammonia and ammonium aerosols during the COVID-19 lockdown in urban Beijing using machine learning models

The concentration of atmospheric ammonia (NH3) in urban Beijing substantially decreased during the COVID-19 lockdown (24 January to 3 March 2020), likely due to the reduced human activities. However, quantifying the impact of anthropogenic interventions on NH3 dynamics is challenging, as both meteorology and chemistry mask the real changes in observed NH3 concentrations. Here, we applied machine learning techniques based on random forest models to decouple the impacts of meteorology and emission changes on the gaseous NH3 and ammonium aerosol (NH4+) concentrations in Beijing during the lockdown. Our results showed that the meteorological conditions were unfavorable during the lockdown and tended to cause an increase of 8.4 % in the NH3 concentration. In addition, significant reductions in NOx and SO2 emissions could also elevate NH3 concentrations by favoring NH3 gas-phase partitioning. However, the observed NH3 concentration significantly decreased by 35.9 % during the lockdown, indicating a significant reduction in emissions or enhanced chemical sinks. Rapid gas-to-particle conversion was indeed found during the lockdown. Thus, the observed reduced NH3 concentrations could be partially explained by the enhanced transformation into NH4+. Therefore, the sum of NH3 and NH4+ (collectively, NHx) is a more reliable tracer than NH3 or NH4+ alone to estimate the changes in NH3 emissions. Compared to that under the scenario without lockdowns, the NHx concentration decreased by 26.4 %. We considered that this decrease represents the real decrease in NH3 emissions in Beijing due to the lockdown measures, which was less of a decrease than that based on NH3 only (35.9 %). This study highlights the importance of considering chemical sinks in the atmosphere when applying machine learning techniques to link the concentrations of reactive species with their emissions.

Contrasting effects of clean air actions on surface ozone concentrations in different regions over Beijing from May to September 2013-2020

Due to the nonlinear impacts of meteorology and precursors, the response of ozone (O3) trends to emission changes is very complex over different regions in megacity Beijing. Based on long-term in-situ observations at 35 air quality sites (four categories, i.e., urban, traffic, northern suburban and southern suburban sites) and satellite data, spatiotemporal variability of O3, gaseous precursors, and O3-VOCs-NOx sensitivity were explored through multiple metrics during the warm season from 2013 to 2020. Additionally, the contribution of meteorology and emissions to O3 was separated by a machine-learning-based de-weathered method. The annual averaged MDA8 O3 and O3 increased by 3.7 and 2.9 μg/m3/yr, respectively, with the highest at traffic sites and the lowest in northern suburb, and the rate of Ox (O3 + NO2) was 0.2 μg/m3/yr with the highest in southern suburb, although NO2 declined strongly and HCHO decreased slightly. However, the increment of O3 and Ox in the daytime exhibited decreasing trends to some extent. Additionally, NOx abatements weakened O3 loss through less NO titration, which drove narrowing differences in urban-suburban O3 and Ox. Due to larger decrease of NO2 in urban region and HCHO in northern suburb, the extent of VOCs-limited regime fluctuated over Beijing and northern suburb gradually shifted to transition or NOx-limited regime. Compared with the directly observed trends, the increasing rate of de-weathered O3 was lower, which was attributed to favorable meteorological conditions for O3 generation after 2017, especially in June (the most polluted month); whereas the de-weathered Ox declined except in southern suburb. Overall, clean air actions were effective in reducing the atmospheric oxidation capacity in urban and northern suburban regions, weakening local photochemical production over Beijing and suppressing O3 deterioration in northern suburb. Strengthening VOCs control and keeping NOx abatement, especially in June, will be vital to reverse O3 increase trend in Beijing.

Attribution of Air Quality Benefits to Clean Winter Heating Policies in China: Combining Machine Learning with Causal Inference

Heating is a major source of air pollution. To improve air quality, a range of clean heating policies were implemented in China over the past decade. Here, we evaluated the impacts of winter heating and clean heating policies on air quality in China using a novel, observation-based causal inference approach. During 2015-2021, winter heating causally increased annual PM2.5, daily maximum 8-h average O3, and SO2 by 4.6, 2.5, and 2.3 μg m-3, respectively. From 2015 to 2021, the impacts of winter heating on PM2.5 in Beijing and surrounding cities (i.e., "2 + 26" cities) decreased by 5.9 μg m-3 (41.3%), whereas that in other northern cities only decreased by 1.2 μg m-3 (12.9%). This demonstrates the effectiveness of stricter clean heating policies on PM2.5 in "2 + 26" cities. Overall, clean heating policies caused the annual PM2.5 in mainland China to reduce by 1.9 μg m-3 from 2015 to 2021, potentially avoiding 23,556 premature deaths in 2021.

Significant roles of aged dust aerosols on rapid nitrate formation under dry conditions in a semi-arid city

Mineral dust can accelerate secondary aerosol formation under humid conditions. However, it is unclear whether it can promote secondary aerosol formation under dry conditions. To investigate this issue, two years of comprehensive observations was conducted at a semi-arid site, near the dust source regions. Three types of episodes were selected: dust, anthropogenic-dominated, and mixed (mixed with dust and anthropogenic aerosols). Compared to anthropogenic-dominated episodes under humid conditions, rapid nitrate formation was still observed in mixed episodes under dry conditions, suggesting that active metallic oxides in dust, such as titanium dioxide, could promote photochemical reactions of nitrogen dioxide. The detailed evolutionary processes are further illustrated by a typical dust-to-mixed episode. After the arrival of the dust, titanium sharply increased ten-fold and rapid nitrate formation was observed, together with a rapid increase in the two most important photochemical pollutants, ozone and peroxyacetyl nitrate. The increased secondary organic carbon further illustrated that the suspended dust particles accelerated the atmospheric oxidative capacity, thereby enhancing secondary aerosol formation and eventually leading to haze pollution. These results differ from those in humid regions and therefore expand the scientific understanding of the impact of dust aerosols on haze pollution under dry conditions.

Unmasking air quality: A novel image-based approach to align public perception with pollution levels

In the quest to reconcile public perception of air pollution with scientific measurements, our study introduced a pioneering method involving a gradient boost-regression tree model integrating PM2.5 concentration, visibility, and image-based data. Traditional stationary monitoring often falls short of accurately capturing public air quality perceptions, prompting the need for alternative strategies. Leveraging an extensive dataset of over 20,000 public visibility perception evaluations and over 8,000 stationary images, our models effectively quantify diverse air quality perceptions. The predictive prowess of our models was validated by strong performance metrics for perceived visibility (R = 0.98, RMSE = 0.19), all-day PM2.5 concentrations (R: 0.77-0.78, RMSE: 8.31-9.40), and Central Weather Bureau visibility records (R = 0.82, RMSE = 9.00). Interestingly, image contrast and light intensity hold greater importance than scenery clarity in the visibility perception model. However, clarity is prioritized in PM2.5 and Central Weather Bureau models. Our research also unveiled spatial limitations in stationary monitoring and outlined the variations in predictive image features between near and far stations. Crucially, all models benefit from the characterization of atmospheric light sources through defogging techniques. The image-based insights highlight the disparity between public perception of air pollution and current policy implementation. In other words, policymakers should shift from solely emphasizing the reduction of PM2.5 levels to also incorporating the public's perception of visibility into their strategies. Our findings have broad implications for air quality evaluation, image mining in specific areas, and formulating air quality management strategies that account for public perception.

Impact of COVID-19 restrictions on the concentration and source apportionment of atmospheric ammonia (NH3) across India

The wide spread of the coronavirus disease (COVID-19) has significantly influenced human activities around the world, providing a unique opportunity to investigate the response of air pollution to anthropogenic emission reduction. Compared with numerous studies on conventional air pollutants, atmospheric ammonia (NH₃) that has matched sources from both anthropogenic and natural emissions has been rarely investigated. Here we assess impacts of the COVID-19 lockdown on ambient NH₃ variation across India, one of the most severe NH₃ pollution region in the world. The role of meteorology in shaping emission contribution to NH₃ pollution and respective contribution of each emission source to ambient NH₃ before and after the COVID-19 outbreak were investigated using the XGBoost algorithm coupled with WRF-Chem model. Results showed that ambient NH₃ concentrations in the seven major cities (Hyderabad, Bengaluru, Chennai, Delhi, Lucknow, Kolkata and Mumbai) decreased by 2.1-53.8 % whereas in Ahmedabad increased by 20.3 % during the COVID-19 lockdown period. Obvious decrease in NH₃ in Indo-Gangetic Plain (-17.1 %) was mainly driven by favorable meteorology, whereas the slight decline in NH₃ in South India was mainly driven by epidemic-related emission control (-8.56 %). Source appointment results showed that the contribution of industrial emission (Ind) to ambient NH₃ in most megacities showed a decreasing trend (between 11 % and 26 %) during the lockdown period. However, the reduction effect was mostly compensated by increasing contributions (15-25 %) of residential emission (Res) or agricultural soil emission (Ags). Particularly, in Ahmedabad and Lucknow ambient NH₃ increased by 20.3 % and 12 % during the lockdown period, the reduction effect of Ind on ambient NH₃ (-23 % and -11 %, respectively) was absolutely compensated by enhanced contribution of Res (24 %) and Ags (12 %), respectively. Our results highlight the importance of eliminating residential and agricultural NH₃ emissions especially in North India.

The application of machine learning to air pollution research: A bibliometric analysis

Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.

What controls aerosol δ15N-NO3-? NOx emission sources vs. nitrogen isotope fractionation

Atmospheric δ¹⁵N-NO₃^- has been used to reveal NO_x (NO + NO₂) sources as NO₃^- is the ultimate sink of NO_x. However, it remains questionable whether the nitrogen isotope fractionation among NO_y (NO, NO₂, NO₃, N₂O₅, HNO₃ and NO₃^-) engender the misjudgment of NO_x emission sources by affecting δ¹⁵N-NO_y. To explore this issue, we integrated the dataset of aerosol δ¹⁵N-NO₃^- values and ratios of f_NO2 (f_NO2 = NO₂/(NO₂ + NO)), calculated the nitrogen isotope fractionation factors (Δ_s) among NO_y, compared the total energy consumption in Beijing-Tianjin-Hebei region (BTH) from 2013 to 2018. Results showed that, although the total energy consumption structure changed from 2013 to 2018 in BTH, there were fewer interannual variances of aerosol δ¹⁵N-NO₃^- values. Nitrogen isotope fractionation factors between NO and NO₂ (Δ₀), NO₂ and NO₃ (Δ₂), NO₂ and N₂O₅ (Δ₃), NO₂ and ClONO₂ (Δ₄) also displayed less interannual variations from 2013 to 2018 in BTH. But both aerosol δ¹⁵N-NO₃^- and Δ_s displayed significant seasonal patterns, and there was significant relationship between monthly aerosol δ¹⁵N-NO₃^- and Δ_s, which suggested that Δ_s have important influence on shaping aerosol δ¹⁵N-NO₃^- and further discriminating NO_x emission sources. This study implies that we should refine the Δ_s when employing atmospheric δ¹⁵N-NO₃^- to quantify NO_x source allocation.

Causes of the unexpected slowness in reducing winter PM2.5 for 2014-2018 in Henan Province

Toughest-ever clean air actions in China have been implemented nationwide to improve air quality. However, it was unexpected that from 2014 to 2018, the observed wintertime PM_2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) concentrations showed an insignificant decrease in Henan Province (HNP), a region in the west of the North China Plain. Emission controls seem to have failed to improve winter air quality in HNP, which has caused great confusion in formulating the next air improvement strategy. We employed a deweathering technique to decouple the impact of meteorological conditions. The results showed that the deweathered PM_2.5 trend was -3.3%/yr in winter from 2014 to 2018, which had a larger decrease than the observed concentrations (-0.9%/yr), demonstrating that emission reduction was effective at improving air quality. However, compared with the other two megacity clusters, Beijing-Tianjin-Hebei (BTH) (-8.4%/yr) and Yangtze River Delta (YRD) (-7.4%/yr), the deweathered decreasing trend of PM_2.5 for HNP remained slow. The underlying mechanism driving the changes in PM_2.5 and its chemical components was further explored, using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Model simulations indicated that nitrate dominated the increase of PM_2.5 components in HNP and the proportions of nitrate to total PM_2.5 increased from 22.4% in January 2015 to 39.7% in January 2019. There are two primary reasons for this phenomenon. One is the limited control of nitrogen oxide emissions, which facilitates the conversion of nitric acid to particulate nitrate by ammonia. The other is unfavourable meteorological conditions, particularly increasing humidity, further enhancing nitrate formation through multiphase reactions. This study highly emphasizes the importance of reducing nitrogen oxide emissions owing to their impact on the formation of particulate nitrate in China, especially in the HNP region.

Changes in aerosol loading before, during and after the COVID-19 pandemic outbreak in China: Effects of anthropogenic and natural aerosol

Anthropogenic emissions reduced sharply in the short-term during the coronavirus disease pandemic (COVID-19). As COVID-19 is still ongoing, changes in atmospheric aerosol loading over China and the factors of their variations remain unclear. In this study, we used multi-source satellite observations and reanalysis datasets to synergistically analyze the spring (February-May) evolution of aerosol optical depth (AOD) for multiple aerosol types over Eastern China (EC) before, during and after the COVID-19 lockdown period. Regional meteorological effects and the radiative response were also quantitatively assessed. Compared to the same period before COVID-19 (i.e., in 2019), a total decrease of -14.6 % in tropospheric TROPOMI nitrogen dioxide (NO₂) and a decrease of -6.8 % in MODIS AOD were observed over EC during the lockdown period (i.e., in 2020). After the lockdown period (i.e., in 2021), anthropogenic emissions returned to previous levels and there was a slight increase (+2.3 %) in AOD over EC. Moreover, changes in aerosol loading have spatial differences. AOD decreased significantly in the North China Plain (-14.0 %, NCP) and Yangtze River Delta (-9.4 %) regions, where anthropogenic aerosol dominated the aerosol loading. Impacted by strong wildfires in Southeast Asia during the lockdown period, carbonaceous AOD increased by +9.1 % in South China, which partially offset the emission reductions. Extreme dust storms swept through the northern region in the period after COVID-19, with an increase of +23.5 % in NCP and + 42.9 % in Northeast China (NEC) for dust AOD. However, unfavorable meteorological conditions overwhelmed the benefits of emission reductions, resulting in a +20.1 % increase in AOD in NEC during the lockdown period. Furthermore, the downward shortwave radiative flux showed a positive anomaly due to the reduced aerosol loading in the atmosphere during the lockdown period. This study highlights that we can benefit from short-term controls for the improvement of air pollution, but we also need to seriously considered the cross-regional transport of natural aerosol and meteorological drivers.

Heterogeneous changes of chemical compositions, sources and health risks of PM2.5 with the "Clean Heating" policy at urban/suburban/industrial sites

China has enacted the "Clean Heating" (CH) policy in north China. The domain-specific impacts on PM2.5 constituents and sources in small cities are still lacking, which obstruct the further policy optimization. Here, we performed an intensive observation covering the heating period (HP) and pre-heating period (PHP) in winter of 2017 at urban (UR), industrial (IS), and suburban (SUR) sites in one of the "2 + 26" cities. The mean PM2.5 concentrations at UR and IS decreased by 15.2 % and 4.6 %, while increased by 9.8 % at SUR in the HP compared with the PHP, indicating the heterogeneous responses. The lowest contribution percentages of coal combustion (14.6 %) and industrial emissions (17.1 %) to PM2.5 at UR in the HP implied the CH policy played more effective role. The most increase in NO3-/SO42- ratio by 26.8 % and the highest NO3- concentration at UR in the HP were linked mainly with the thermal-NOx emitted from natural gas (NG) burning in view of NOx emission reductions from other sources. The highest concentrations of OC, SO42-, K+, and Cl-, and contribution percentages of biomass burning (20.0 %) and coal combustion (24.8 %) to PM2.5 at SUR in the HP evidenced the enhanced usage of biomass/coal. Coal banning in the HP at IS and UR led to the obvious decreases in OC, SO42-, As, and Sb. Secondary nitrate became the largest PM2.5 source at IS and UR in the HP. Coal banning, emission control on large-size enterprises and ignored control on small-size enterprises efficiently modified the concentrations and health risks of heavy metals. The lowest carcinogenic risks moved from SUR in the PHP to UR in the HP. The policies on de-NOx of NG-burning related enterprises, reduction of biomass/coal usage in suburban area, and strict regulation of small-size enterprises were urgently need to further improve the air quality.

Incorporation and improvement of a heterogeneous chemistry mechanism in the atmospheric chemistry model GRAPES_Meso5.1/CUACE and its impacts on secondary inorganic aerosol and PM2.5 simulations in Middle-Eastern China

Heterogeneous chemistry is considered one of the critical pathways of secondary inorganic aerosol (SIA) productions. In this study, a heterogeneous chemistry mechanism is incorporated into the atmospheric chemistry model GRAPES_Meso5.1/CUACE. Varying uptake coefficient schemes of SO₂ and NO₂ are compared and the equivalent ratio of inorganic aerosol (ER)-dependent scheme for SO₂ and relative humidity (RH)/ER-dependent scheme for NO₂ are used to form the improved heterogeneous chemistry. Focusing on a severe haze episode in Middle-Eastern China, the impacts of heterogeneous mechanism on SIA and PM_2.5 composition are investigated based on the updated model. Study results show that the differences in RH or ER uptake coefficients result in obvious differences in sulfate and nitrate concentrations, especially during the severe pollution period, because the ER schemes restrict the excessive production of sulfate and nitrate under high RH effectively by including the self-limitation of heterogeneous reactions, which shows better performance in capturing the magnitude and temporal variations of surface SIA and PM_2.5. Normalized mean bias of sulfate, nitrate, ammonium, and PM_2.5 in megacity Beijing decreases from -27.0, -28.3, -58.2, and -26.3 to 1.0, -2.2, -47.2, and -16.5 %, respectively. And the fractions of sulfate, nitrate, ammonium, and organics during the polluted period change from 13.7, 19.3, 6.9, and 60.1 to 16.5, 23.0, 7.6, and 52.9 %, respectively, which is more consistent with the observation (16.0, 23.2, 14.1, and 46.7 %). SIA and PM_2.5 simulations in another megacity Shanghai have the similar improvements. The modeled SIA by heterogeneous processes contributes 11.7 % of total PM_2.5 in Beijing and 22.5 % in Shanghai. That is 13.5 % in the Chinese megalopolis Beijing-Tianjin-Hebei and 19.8 % in Yangtze-River-Delta, indicating a considerable contribution of heterogeneous pathways to haze pollution. This work indicates the importance of detailed and reasonable heterogeneous schemes for better SIA and haze/fog prediction in the atmospheric chemistry model.

Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution

PM_2.5 pollution is a complex process mainly affected by emission sources and meteorological conditions. However, it is hard to accurately assess the effects of emission sources and meteorological conditions on the variation of PM_2.5 concentrations in the complex atmospheric environment. In this study, the Random Forest model with Shapley Additive exPlanations (RF-SHAP) and Partial Dependence Plot (RF-PDP) was combined with Positive Matrix Factorization (PMF) to evaluate the impacts of various factors on PM_2.5 pollution. The results show that anthropogenic emissions and meteorological conditions contributed about 67% (40.5 μg/m³) and 33% (19.7 μg/m³) to variation in PM_2.5 concentrations, respectively. Specifically, secondary nitrate (SN) had the greatest impact among all sources (about 45%). Hence, we further explore the impacts of the primary sources and meteorological conditions on SN formation. Coal combustion and vehicle emissions significantly contribute to the formation of SN by providing a large number of precursor NO_X. Additionally, the RF-PDP method was further employed to estimate the synergistic effects of primary sources and meteorological conditions on SN formation. The results help reveal strategies to simultaneously reduce SN by controlling primary emissions under suitable meteorological conditions. This work also suggests that the machine learning model can utilize online datasets well and provide a reliable approach for analyzing the causes of PM_2.5 pollution.

Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions

Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM2.5 and O3, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.

Evaluating the real changes of air quality due to clean air actions using a machine learning technique: Results from 12 Chinese mega-cities during 2013-2020

China has implemented two national clean air actions in 2013-2017 and 2018-2020, respectively, with the aim of reducing primary emissions and hence improving air quality at a national level. It is important to examine the effectiveness of such emission reductions and assess the resulting changes in air quality. However, such evaluation is difficult as meteorological factors can amplify, or obscure the changes of air pollutants, in addition to the emission reduction. In this study, we applied the random forest machine learning technique to decouple meteorological influences from emissions changes, and examined the deweathered trends of air pollutants in 12 Chinese mega-cities during 2013-2020. The observed concentrations of all criteria pollutants except O₃ showed significant declines from 2013 to 2020, with PM_2.5 annual decline rates of 6-9% in most cities. In contrast, O₃ concentrations increased with annual growth rates of 1-9%. Compared with the observed results, all the pollutants showed smoothed but similar variation in trend and annual rate-of-change after weather normalization. The response of O₃ to NO₂ concentrations indicated significant regional differences in photochemical regimes, and the differences between observed and deweathered results provided implications for volatile organic compound emission reductions in O₃ pollution mitigation. We further evaluated the effectiveness of first and second clean air actions by removing the meteorological influence. We found that the meteorology can make negative or positive contribution in reducing pollutant concentrations from emission reduction, depending on type of pollutants, locations, and time period. Among the 12 mega-cities, only Beijing showed a positive meteorological contribution in amplifying reductions in main pollutants except O₃ during both clean air action periods. Considering the large and variable impact of meteorological effects in changing air quality, we suggest that similar deweathered analysis is needed as a routine policy evaluation tool on a regional basis.

Decade-long trends in chemical component properties of PM2.5 in Beijing, China (2011-2020)

A 10-year-long measurement of water-soluble inorganic ions in PM_2.5 was made in Beijing from June 2011 to December 2020, to investigate the interannual trends of chemical characteristics of PM_2.5 and to provide insights into the future prevention and control of PM_2.5 pollution. From 2011 to 2020, with the implementation of strict air pollution control strategies, significant changes of PM_2.5 have been observed in Beijing, with NO₃^-, SO₄^2- and NH₄⁺ decreasing at rates of 5.10, 8.80 and 7.64% yr^-1 respectively. The percentages of NO₃^- and SO₄^2- under elevated pollution levels were investigated. When PM_2.5 values fell in the range of 0-400 μg m^-3, NO₃^-/ SO₄^2- values were mostly higher than 1 and showed upward trends from 2011 to 2020. However, under extremely heavy haze conditions, SO₄^2- dominated PM_2.5 formation. This result was closely related to the change characteristics of the oxidation ratio of sulfate (SOR), the oxidation ratio of nitrate (NOR) and gaseous precursors under different pollution levels. The change characteristics of NOR and SOR under elevated PM_2.5 levels indicated that the aqueous phase oxidation was the key process driving SO₄^2- formation; while as for NO₃^-, in addition to the availability of NH₄⁺, the atmospheric oxidation capacity made crucial roles. The analysis of typical haze episodes during the past decade indicated that the emission reduction of gaseous pollutants, especially SO₂, made great contributions to the improved PM_2.5 air quality in Beijing. We highlighted that future efforts should focus on enhanced reduction of NO₂ emission and control of atmospheric oxidation capacity to further reduce particulate nitrate formation.

Chang impact analysis of level 3 COVID-19 alert on air pollution indicators using artificial neural network

In this study, mean monthly and diurnal variations in fine particulate matters (PM_2.5), nitrate, sulfate, and gaseous precursors were investigated during the Level 3 COVID-19 alert from May 19 to July 27 in 2021. For comparison, the historical data during the identical period in 2019 and 2020 were also provided to determine the effect of the Level 3 COVID-19 alert on aerosols and gaseous pollutants concentrations in Taichung City. A machine learning model using the artificial neural network technique coupled with a kinetic model was applied to predict NO_x, O₃, nitrate (NO₃ ^-), and sulfate (SO₄ ^2-) to investigate potential emission sources and chemical reaction mechanism. D during the Level 3 COVID-19 alert, a decrease in NO_x concentration due to a decrease in traffic flow under the NO_x-saturated regime was observed to enhance the secondary NO₃ ^- and O₃ formation. The present models were shown to predict 80.1, 77.0, 72.6, and 67.2% concentrations of NO_x, O₃, NO₃ ^-, and SO₄ ^2-, respectively, which could help decision-makers for pollutant emissions reduction policies development and air pollution control strategies. It is recommended that more long-term datasets, including water soluble inorganic salts (WIS), precursors including OH radicals, NH₃, HNO₃, and H₂SO₄, be provided by regulatory air quality monitoring stations to further improve the prediction model accuracy.

Rapid narrowing of the urban-suburban gap in air pollutant concentrations in Beijing from 2014 to 2019

Understanding the spatial patterns of atmospheric pollutants in urban and suburban areas is important for evaluating their effects on regional air quality, climate, and human health. The analyses of pollutant monitoring data of the China National Environmental Monitoring Center revealed that the differences in the concentrations of ambient O₃, PM_2.5, NO₂, SO₂, and CO between urban and suburban areas rapidly decreased from 2014 to 2019 in Beijing. Considering the negligible urbanization and interannual meteorological changes during the study period, the results reveal a quick response of the urban-to-suburban difference (Δ_{Urban-Suburban}) in the ambient pollutants concentrations to emission reduction measures implemented in China in 2013. However, owing to the efficient O₃ formation in summer in urban areas in recent years, we observed a more rapid decrease in the Δ_{Urban-Suburban} in O₃ concentration in summer (64.8%) than in winter (16.1%). In addition, the Δ_{Urban-Suburban} in daytime summer O₃ changed from negative in 2014-2018 to positive in 2019, indicating that the daytime O₃ concentration in urban areas exceeded that in suburban areas. Furthermore, instantaneous changes in Δ_{Urban-Suburban} in air pollutants were more sensitive to meteorological variations in 2014 than in 2019. The results indicate a less significant role of regional air mass transport in the spatial variability of pollutants under a future scenario of strong emission reduction in China.

Response of aerosol composition to the clean air actions in Baoji city of Fen-Wei River Basin

The implementation of air pollution control measures could alter the compositions of submicron aerosols. Identifying the changes can evaluate the atmospheric responses of the implemented control measures and provide more scientific basis for the formulation of new measures. The Fen-Wei River Basin is the most air polluted region in China, and thereby is a key area for the reduction of emissions. Only limited studies determine the changes in the chemical compositions of submicron aerosols. In this study, Baoji was selected as a representative city in the Fen-Wei River Basin. The compositions of submicron aerosols were determined between 2014 and 2019. Organic fractions were determined through an online instrument (Quadrupole Aerosol Chemical Speciation Monitor, Q-ACSM) and source recognition was performed by the Multilinear Engine (ME-2). The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was also employed to evaluate the contributions of emissions reduction and meteorological conditions to the changes of submicron aerosol compositions. The results indicate that the mass concentrations of submicron aerosols have been substantially decreased after implementation of air pollution control measures. This was mainly attributed to the emission reductions of sulfur dioxide (SO₂) and primary organic aerosol (POA). In addition, the main components that drove the pollution episodes swapped from POA, sulfate, nitrate and less-oxidized organic (LO-OOA) in 2014 to nitrate and more-oxidized OOA (MO-OOA) in 2019. Due to the changes of chemical compositions of both precursors and secondary pollutants, the pollution control measures should be modernized to focus on the emissions of ammonia (NH₃), nitrogen oxides (NO_x) and volatile organic compounds (VOC_s) in this region.

Characteristics of aerosol chemistry and acidity in Shanghai after PM2.5 satisfied national guideline: Insight into future emission control

With continuous endeavors to control air pollutant emissions, the average concentration of PM_2.5 in Shanghai in 2019-2020 satisfied the national secondary standard (35 μg m^-3) for the first time. In this study, the two-year dataset of hourly resolution PM_2.5 compositions observed in downtown Shanghai was used to investigate the relative contribution of sulfate and nitrate as well as particulate acidity. The average concentration of SO₂ was reduced to 7.7 μg m^-3, while the concentration of NO_x remained above 40 μg m^-3, indicating that the control of SO₂ was more effective than that of NO_x during the 13th Five-Year Plan period. Thus, the sulfate pollution was significantly reduced whereas the nitrate loading remained almost constant. The monthly N/S ratio varied from below 0.6 to above 2.0, indicating that the contribution of automobile exhaust to PM_2.5 is seasonally dependent. Contrary to sulfate, the nitrate fraction increased rapidly with the increase of PM_2.5 mass, suggesting that the explosive growth of nitrate has become a major driver of haze formation. ISORROPIA simulations show that PM_2.5 was moderately acidic with pH values following the trend of winter > spring > autumn > summer. The diurnal variation of nitrate was related to the changes in aerosol water content, indicating the effect of heterogeneous aqueous reactions on secondary aerosol formation. The effectiveness of emission control for reducing inorganic PM_2.5 varied with different gas precursors and seasons. The abatement of NH₃ emissions will increase particle acidity and acid rain pollution, although it is more effective than that of NO_x when the emission reduction is larger than 60%. This study suggests that the control of vehicle exhaust should be given priority in the Yangtze River Delta for coordinately mitigating PM_2.5 and acid rain pollution.

Impact of the ‘Coal-to-Natural Gas’ Policy on Criteria Air Pollutants in Northern China

During the last decades, China had issued a series of stringent control measures, resulting in a large decline in air pollutant concentrations. To quantify the net change in air pollutant concentrations driven by emissions, we developed an approach of determining the closed interval of the deweathered percentage change (DPC) in the concentration of air pollutants on an annual scale, as well as the closed intervals of cumulative DPC in a year compared with that in the base year. Thus, the hourly mean mass concentrations of criteria air pollutants to determine their interannual variations and the closed intervals of their DPCs during the heating seasons from 2013 to 2019 in Qingdao (a coastal megacity) were analyzed. The seasonal mean SO2 concentration decreased from 2013 to 2019. The seasonal mean CO, NO2, and PM2.5 concentrations also generally decreased from 2013 to 2017, but increased unexpectedly in 2018 (from 0.9 mg m−3 (CO), 42 µg m−3 (NO2), and 51 µg m−3 (PM2.5) in 2017 to 1.1 mg m−3, 48 µg m−3, and 64 µg m−3 in 2018, respectively). The closed intervals of DPC in concentrations of CO, NO2, and PM2.5 from the 2017 heating season (2017/2018) to the 2018 heating season (2018/2019) were obtained at (27%, 30%), (15%, 18%), and (30%, 33%), respectively. Such high positive endpoint values of the closed intervals, in contrast to their small interval lengths, indicate increased emissions of these pollutants and/or their precursors in 2018/2019 compared with 2017/2018, by minimizing the meteorological influences. The rebounds of CO, NO2, and PM2.5 in 2018/2019 were likely associated with a doubled increase in natural gas (NG) consumption implemented by the “coal-to-NG” project, as the total energy consumption showed little difference. Our results suggested an important role of the “coal-to-NG” project in driving concentrations of air pollutant increases in China in 2018/2019, which need integrated assessments.

Evaluation of NOx emissions before, during, and after the COVID-19 lockdowns in China: A comparison of meteorological normalization methods

Meteorological normalization refers to the removal of meteorological effects on air pollutant concentrations for evaluating emission changes. There currently exist various meteorological normalization methods, yielding inconsistent results. This study aims to identify the state-of-the-art method of meteorological normalization for characterizing the spatiotemporal variation of NOx emissions caused by the COVID-19 pandemic in China. We obtained the hourly data of NO2 concentrations and meteorological conditions for 337 cities in China from January 1, 2019, to December 31, 2020. Three random-forest based meteorological normalization methods were compared, including (1) the method that only resamples meteorological variables, (2) the method that resamples meteorological and temporal variables, and (3) the method that does not need resampling, denoted as Resample-M, Resample-M&T, and Resample-None, respectively. The comparison results show that Resample-M&T considerably underestimated the emission reduction of NOx during the lockdowns, Resample-None generates widely fluctuating estimates that blur the emission recovery trend during work resumption, and Resample-M clearly delineates the emission changes over the entire period. Based on the Resample-M results, the maximum emission reduction occurred during January to February 2020, for most cities, with an average decrease of 19.1 ± 9.4% compared to 2019. During April of 2020 when work resumption initiated to the end of 2020, the emissions rapidly bounced back for most cities, with an average increase of 12.6 ± 15.8% relative to those during the strict lockdowns. Consequently, we recommend using Resample-M for meteorological normalization, and the normalized NO2 concentration dynamics for each city provide important implications for future emission reduction.

Smog chamber simulation on heterogeneous reaction of O3 and NO2 on black carbon under various relative humidity conditions

In this study, heterogeneous formation of nitrate from O₃ reaction with NO₂ on black carbon (BC) and KCl-treated BC surface in the presence of NH₃ was simulated under 30-90% RH conditions by using a laboratory smog chamber. We found that O₃ and NO₂ in the chamber quickly reacted into N₂O₅ in the gas phase_, which subsequently hydrolyzed into HNO₃ and further neutralized with NH₃ into NH₄NO₃ on the BC surface, along with a small amount of N₂O₅ decomposed into NO and NO₂ through a reaction with the BC surface active site. Meanwhile, the fractal BC aggregates restructured and condensed to spherical particles during the NH₄NO₃ coating process. Compared to that during the exposure to NO₂ or O₃ alone, the presence of strong signals of CH₂O⁺, CH₂O₂⁺ and CH₄NO⁺ during the simultaneous exposure to both NO₂ and O₃ suggested a synergetic oxidizing effect of NO₂ and O₃, which significantly activated the BC surface by forming carbonyl, carboxylic and nitro groups, promoted the adsorption of water vapor onto the BC surface and enhanced the NH₄NO₃ formation. Under <75 ± 2% RH conditions the coating process of NH₄NO₃ on the BC surface consisted of a diffusion of N₂O₅ onto the surface and a subsequent hydrolysis, due to the limited number of water molecules adsorbed. However, under 90 ± 2% RH conditions N₂O₅ directly hydrolyzed on the aqueous phase of the BC surface due to the multilayer water molecules adsorbed, which caused an instant NH₄NO₃ formation on the surface without any delay. The coating rate of NH₄NO₃ on KCl-treated BC particles was 3-4 times faster than that on the pure BC particles at the initial stage, indicating an increasing formation of NH₄NO₃, mainly due to an enhanced hygroscopicity of BC by KCl salts.

Meteorology-normalized variations of air quality during the COVID-19 lockdown in three Chinese megacities

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in air quality. Here, we applied a machine learning algorithm (random forest model) to eliminate meteorological effects and characterize the high-resolution variation characteristics of air quality induced by COVID-19 in Beijing, Wuhan, and Urumqi. Our RF model estimates showed that the highest decrease in deweathered PM2.5 in Wuhan (-43.6%) and Beijing (-14.0%) was at traffic stations during lockdown period (February 1- March 15, 2020), while it was at industry stations in Urumqi (-54.2%). Deweathered NO2 decreased significantly in each city (∼30%-50%), whereas accompanied by a notable increase in O3. The diurnal patterns show that the morning peaks of traffic-related NO2 and CO almost disappeared. Additionally, our results suggested that meteorological effects offset some of the reduction in pollutant concentrations. Adverse meteorological conditions played a leading role in the variation in PM2.5 concentration in Beijing, which contributed to +33.5%. The true effect of lockdown reduced the PM2.5 concentrations in Wuhan, Beijing, and Urumqi by approximately 14.6%, 17.0%, and 34.0%, respectively. In summary, lockdown is the most important driver of the decline in pollutant concentrations, but the reduction of SO2 and CO is limited and they are mainly influenced by changing trends. This study provides insights into quantifying variations in air quality due to the lockdown by considering meteorological variability, which varies greatly from city to city, and provides a reference for changes in city scale pollutant concentrations during the lockdown.

On the fossil and non-fossil fuel sources of carbonaceous aerosol with radiocarbon and AMS-PMF methods during winter hazy days in a rural area of North China plain

Regional transport is a key source of carbonaceous aerosol in many Chinese megacities including Beijing. The sources of carbonaceous aerosol in urban areas have been studied extensively but are poorly known in upwind rural areas. This work aims to quantify the contributions of fossil and non-fossil fuel emissions to carbonaceous aerosols at a rural site in North China Plain in winter 2016. We integrated online high resolution-time of flight-aerosol mass spectrometer (HR-TOF-AMS) observations and radiocarbon (¹⁴C) measurements of fine particles with Positive Matrix Factorization (PMF) analysis as well as Extended Gelencsér (EG) method. We found that fine particle concentration is much higher at the rural site than in Beijing during the campaign (Dec 7, 2016 to Jan 8, 2017). PMF analysis of the AMS data showed that coal-combustion related organic aerosol (CCOA + Oxidized CCOA) and more oxidized oxygenated organic aerosol (MO-OOA) contributed 48% and 30% of organic matter to non-refractory PM₁ (NR-PM₁) mass. About 2/3 of the OC and EC were from fossil-fuel combustion. The EG method, combining AMS-PMF and ¹⁴C data, showed that primary and secondary OC from fossil fuel contribute 35% and 22% to total carbon (TC), coal combustion emission dominates the fossil fuel sources, and biomass burning accounted for 21% of carbonaceous aerosol. In summary, our results confirm that fossil fuel combustion was the dominant source of carbonaceous aerosol during heavy pollution events in the rural areas. Significant emissions of solid fuel carbonaceous aerosols at rural areas can affect air quality in downwind cities such as Beijing and Tianjin, highlighting the benefits of energy transition from solid fuels to cleaner energy in rural areas.

Decisive role of ozone formation control in winter PM2.5 mitigation in Shenzhen, China

During the COVID-19 lockdown, atmospheric PM_2.5 in the Pearl River Delta (PRD) showed the highest reduction in China, but the reasons, being a critical question for future air quality policy design, are not yet clear. In this study, we analyzed the relationships among gaseous precursors, secondary aerosols and atmospheric oxidation capacity in Shenzhen, a megacity in the PRD, during the lockdown period in 2020 and the same period in 2021. The comprehensive observational datasets showed large lockdown declines in all primary and secondary pollutants (including O₃). We found that, however, the daytime concentrations of secondary aerosols during the lockdown period and normal period were rather similar when the corresponding odd oxygen (O_x≡O₃+NO₂, an indicator of photochemical processing avoiding the titration effect of O₃ by freshly emitted NO) were at similar levels. Therefore, reduced O_x, rather than the large reduction in precursors, was a direct driver to achieve the decline in secondary aerosols. Moreover, O_x was also found to determine the spatial distribution of intercity PM_2.5 levels in winter PRD. Thus, an effective strategy for winter PM_2.5 mitigation should emphasize on control of winter O₃ formation in the PRD and other regions with similar conditions.

Machine learning and theoretical analysis release the non-linear relationship among ozone, secondary organic aerosol and volatile organic compounds

Fine particulate matter (PM_2.5) and ozone (O₃) pollutions are prevalent air quality issues in China. Volatile organic compounds (VOCs) have significant impact on the formation of O₃ and secondary organic aerosols (SOA) contributing PM_2.5. Herein, we investigated 54 VOCs, O₃ and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O₃, SOA and VOCs. The monthly patterns of VOCs and SOA concentrations were characterized by peak values during October to March and reached a minimum from April to September, but the observed O₃ was exactly the opposite. Machine learning methods resolved the importance of individual VOCs on O₃ and SOA that alkenes (mainly ethylene, propylene, and isoprene) have the highest importance to O₃ formation; alkanes (C_n, n ≥ 6) and aromatics were the main source of SOA formation. Machine learning methods revealed and emphasized the importance of photochemical consumptions of VOCs to O₃ and SOA formation. Ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) calculated by consumed VOCs quantitatively indicated that more than 80% of the consumed VOCs were alkenes which dominated the O₃ formation, and the importance of consumed aromatics and alkenes to SOAFP were 40.84% and 56.65%, respectively. Therein, isoprene contributed the most to OFP at 41.45% regardless of the season, while aromatics (58.27%) contributed the most to SOAFP in winter. Collectively, our findings can provide scientific evidence on policymaking for VOCs controls on seasonal scales to achieve effective reduction in both SOA and O₃.

Long-term trend of new particle formation events in the Yangtze River Delta, China and its influencing factors: 7-year dataset analysis

To evaluate the influence of anthropogenic emission reductions since 2013 in China, a long-term trend analysis of the particle number size distribution (PNSD) and new particle formation (NPF) events in the Yangtze River Delta (YRD) region was conducted based on the PNSD measurement (diameter ranging from 3 to 850 nm) at the Lin'an (LAN) regional background station from 2013 to 2019. A modified Mann-Kendall test and a Theil-Sen estimator were used to calculate the overall trend of particle number concentrations in different modes and the relevant influencing factors. We observed a significant decreasing trend in the Aitken and accumulation mode number concentrations, with annual decrease rates of approximately 5.6% and 8.2%, respectively, resulting in an approximately 6.0% decline in total particles annually. However, the nucleation mode particle number concentration showed no significant trend from 2013 to 2016, but an increasing trend from 2016 to 2019, which was related to the NPF events occurrence frequency. The regional NPF events of "banana shape" accounted for an increasing fraction of all NPF events. As a key parameter influencing the NPF event, the condensation sink decreased by approximately 63% from 2013 to 2019. Moreover, the estimated sulfuric acid concentration decreased by approximately 50%, with a higher reduction rate occurring during 2013-2016 as result of the effective SO₂ reduction. Surface meteorological factors (including the air temperature, relative humidity, air pressure, and wind) and the air masses origin were found to played minor roles in the long-term trend of NPF events. As PNSD and NPF events are closely related to changes in the particle emissions and regional air pollution levels, studies concerning PNSD and NPF are necessary to provide important information regarding air quality improvements and evaluating the efficacy of climate change mitigation strategies.

Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions

Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM_2.5 and O₃, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.

Impacts of COVID-19 on air quality in mid-eastern China: An insight into meteorology and emissions

The coronavirus disease (COVID-19) spread rapidly worldwide in the first half of 2020. Stringent national lockdown policies imposed by China to prevent the spread of the virus reduced anthropogenic emissions and improved air quality. A weather research and forecasting model coupled with chemistry was applied to evaluate the impact of meteorology and emissions on air quality during the COVID-19 outbreak (from January 23 to February 29, 2020) in mid-eastern China. The results show that air pollution episodes still occurred on polluted days and accounted for 31.6%-60.5% of the total number of outbreak days in mid-eastern China from January 23 to February 29, 2020. However, anthropogenic emissions decreased significantly, indicating that anthropogenic emission reduction cannot completely offset the impact of unfavorable meteorological conditions on air quality. Favorable meteorological conditions in 2019 improved the overall air quality for a COVID-19 outbreak in 2019 instead of 2020. PM_2.5 concentrations decreased by 4.2%-29.2% in Beijing, Tianjin, Shijiazhuang, and Taiyuan, and increased by 6.1%-11.5% in Jinan and Zhengzhou. PM_2.5 concentrations increased by 10.9%-20.5% without the COVID-19 outbreak of 2020 in mid-eastern China, and the frequency of polluted days increased by 5.3%-18.4%. Source apportionment of PM_2.5 during the COVID-19 outbreak showed that industry and residential emissions were the dominant PM_2.5 contributors (32.7%-49.6% and 26.0%-44.5%, respectively) followed by agriculture (18.7%-24.0%), transportation (7.7%-15.5%), and power (4.1%-5.9%). In Beijing, industrial and residential contributions to PM_2.5 concentrations were lower (32.7%) and higher (44.5%), respectively, than in other cities (38.7%-49.6% for industry and 26.0%-36.2% for residential). Therefore, enhancing regional cooperation and implementing a united air pollution control are effective emission mitigation measures for future air quality improvement, especially the development of new technologies for industrial and cooking fumes.

Aerosol water content enhancement leads to changes in the major formation mechanisms of nitrate and secondary organic aerosols in winter over the North China Plain

In recent years, severe air pollution still frequently occurs in winter despite the effective implementation of clean air actions in China. Therefore, field measurements of particle composition and gas precursors were collected from December 1, 2018 to January 15, 2019 at an urban site in a central Chinese city to investigate the existing mechanisms of pollution. The hourly averaged PM_2.5 concentration during the campaign was 92.7 μg m^-3, with nitrate and organic aerosol (OA) demonstrated as the principal components. Generally, NO₂ oxidation in the daytime was observed as the major mechanism for nitrate generation, and aerosol water content (AWC) showed its influential role with the associated increases in the nitrogen oxidation and nitrate partitioning ratios. When AWC increased from dozens to hundreds of μg m^-3 after the afternoon, nocturnal N₂O₅ hydrolysis was demonstrated as the overriding mechanism and provoked extreme contamination of nitrates. Five sources of organic aerosols (OAs) were identified: hydrocarbon-like OAs (HOAs, 16.5%), coal combustion OAs (CCOAs, 19.2%), biomass burning OAs (BBOAs, 9.9%), semi-volatile oxygenated OAs (SV-OOAs, 29.4%), and low-volatile oxygenated OAs (LV-OOAs, 25.0%). SV-OOAs and LV-OOAs were identified as gasSOAs and aqSOAs according to their sensitivities to the atmospheric oxidation capacity and AWC. In addition, aqueous-phase processing was found to be the dominant pathway for SOA formation when the AWC concentration was higher than 80 μg m^-3. As an influential factor for nitrate and SOA formation, AWC could be greatly affected by RH and the concentrations of inorganic species. Sulfate, which was mainly contributed by anthropogenic emissions, was demonstrated to be a significant factor for active aqueous phase reactions, although SO₂ has been dramatically reduced in recent years. Above all, this study revealed the significant role of AWC in current pollution episode in winter, and will assist in establishing future measures for pollution mitigation.

Changes in air pollutants during the COVID-19 lockdown in Beijing: Insights from a machine-learning technique and implications for future control policy

The COVID-19 lockdowns led to abrupt reductions in human-related emissions worldwide and had an unintended impact on air quality improvement. However, quantifying this impact is difficult as meteorological conditions may mask the real effect of changes in emissions on the observed concentrations of pollutants. Based on the air quality and meteorological data at 35 sites in Beijing from 2015 to 2020, a machine learning technique was applied to decouple the impacts of meteorology and emissions on the concentrations of air pollutants. The results showed that the real (“deweathered”) concentrations of air pollutants (expect for O₃) dropped significantly due to lockdown measures. Compared with the scenario without lockdowns (predicted concentrations), the observed values of PM_2.5, PM₁₀, SO₂, NO₂, and CO during lockdowns decreased by 39.4%, 50.1%, 51.8%, 43.1%, and 35.1%, respectively. In addition, a significant decline for NO₂ and CO was found at the background sites (51% and 37.8%) rather than the traffic sites (37.1% and 35.5%), which is different from the common belief. While the primary emissions reduced during the lockdown period, episodic haze events still occurred due to unfavorable meteorological conditions. Thus, developing an optimized strategy to tackle air pollution in Beijing is essential in the future.

摘要

基于2015–2020年北京35个环境空气站和20个气象站观测资料, 应用机器学习方法 (随机森林算法) 分离了气象条件和源排放对大气污染物浓度的影响. 结果发现, 为应对疫情采取的隔离措施使北京2020年春节期间大气污染物浓度降低了35.1%–51.8%; 其中, 背景站氮氧化物和一氧化碳浓度的降幅最大, 超过了以往报道较多的交通站点. 同时, 2020年春节期间的气象条件不利于污染物扩散, 导致多次霾污染事件发生.为进一步改善北京空气质量, 未来需要优化减排策略.

Spring Festival and COVID-19 Lockdown: Disentangling PM Sources in Major Chinese Cities

Responding to the 2020 COVID-19 outbreak, China imposed an unprecedented lockdown producing reductions in air pollutant emissions. However, the lockdown driven air pollution changes have not been fully quantified. We applied machine learning to quantify the effects of meteorology on surface air quality data in 31 major Chinese cities. The meteorologically normalized NO2, O3, and PM2.5 concentrations changed by -29.5%, +31.2%, and -7.0%, respectively, after the lockdown began. However, part of this effect was also associated with emission changes due to the Chinese Spring Festival, which led to ∼14.1% decrease in NO2, ∼6.6% increase in O3 and a mixed effect on PM2.5 in the studied cities that largely resulted from festival associated fireworks. After decoupling the weather and Spring Festival effects, changes in air quality attributable to the lockdown were much smaller: -15.4%, +24.6%, and -9.7% for NO2, O3, and PM2.5, respectively.

Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China

Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM_2.5) concentrations across China. The emulators were optimized based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM_2.5 concentrations for a given input configuration of emission changes. PM_2.5 concentrations are primarily sensitive to residential (51%-94% of first-order sensitivity index), industrial (7%-31%), and agricultural emissions (0%-24%). Sensitivities of PM_2.5 concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM_2.5 exposure for changes in the five emission sectors is by 68%-81%, down to 15.3-25.9 μg m^-3, remaining above the World Health Organization annual guideline of 10 μg m^-3. The greatest reductions in PM_2.5 exposure are driven by reducing residential and industrial emissions, emphasizing the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35 μg m^-3 is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors.

Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China

Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM2.5) concentrations across China. The emulators were optimized based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM2.5 concentrations for a given input configuration of emission changes. PM2.5 concentrations are primarily sensitive to residential (51%-94% of first-order sensitivity index), industrial (7%-31%), and agricultural emissions (0%-24%). Sensitivities of PM2.5 concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM2.5 exposure for changes in the five emission sectors is by 68%-81%, down to 15.3-25.9 μg m-3, remaining above the World Health Organization annual guideline of 10 μg m-3. The greatest reductions in PM2.5 exposure are driven by reducing residential and industrial emissions, emphasizing the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35 μg m-3 is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors.

Review of online source apportionment research based on observation for ambient particulate matter

Particulate matter source apportionment (SA) is the basis and premise for preventing and controlling haze pollution scientifically and effectively. Traditional offline SA methods lack the capability of handling the rapid changing pollution sources during heavy air pollution periods. With the development of multiple online observation techniques, online SA of particulate matter can now be realized with high temporal resolution, stable and reliable continuous observation data on particle compositions. Here, we start with a summary of online measuring instruments for monitoring particulate matters that contains both online mass concentration (online MC) measurement, and online mass spectrometric (online MS) techniques. The former technique collects ambient particulate matter onto filter membrane and measures the concentrations of chemical components in the particulate matter subsequently. The latter technique could be further divided into two categories: bulk measurement and single particle measurement. Aerosol Mass Spectrometers (AMS) could provide mass spectral information of chemical components of non-refractory aerosols, especially organic aerosols. While the emergence of single-particle aerosol mass spectrometer (SPAMS) technology can provide large number of high time resolution data for online source resolution. This is closely followed by an overview of the methods and results of SA. However, online instruments are still facing challenges, such as abnormal or missing measurements, that could impact the accuracy of online dataset. Machine leaning algorithm are suited for processing the large amount of online observation data, which could be further considered. In addition, the key research challenges and future directions are presented including the integration of online dataset from different online instruments, the ensemble-trained source apportionment approach, and the quantification of source-category-specific human health risk based on online instrumentation and SA methods.

Significant changes in autumn and winter aerosol composition and sources in Beijing from 2012 to 2018: Effects of clean air actions

A seven-year long-term comprehensive measurement of non-refractory submicron particles (NR-PM₁) in autumn and winter in Beijing from 2012 to 2018 was conducted to evaluate the effectiveness of the clean air actions implemented by the Chinese government in September 2013 on aerosols from different sources and chemical processes. Results showed that the NR-PM₁ concentrations decreased by 44.1% in autumn and 73.2% in winter from 2012 to 2018. Sulfate showed a much larger reduction than nitrate and ammonium in both autumn (55%) and winter (86%) and that nitrate even slightly increased by 15.8% in autumn. As a result, aerosol pollution in winter gradually changed from sulfate-rich to nitrate-rich with a sudden change after 2016 and the dominant role of nitrate in autumn was also strengthened after 2016. Among primary organic aerosol (OA) types, biomass burning OA and coal combustion OA exhibited the largest decline in autumn and winter, with reductions of 87.5% and 77.3%, respectively, while hydrocarbon-like OA (HOA) exhibited the smallest decline in both autumn (24.4%) and winter (37.1%). These significant changes in aerosol compositions were highly consistent with the much faster reduction of SO₂ (75-85%) than NOx (36-59%) and were mainly due to the clean air actions rather than the impact of meteorological conditions. What's more, the enhanced atmospheric oxidizing capacity, which was indicated by increased O₃, altered the chemical processes of oxygenated OA (OOA), especially in autumn. Both of less-oxidized OOA (LO-OOA) and more-oxidized OOA showed elevated contributions in OA by 4% in autumn. The increased oxygen-to-carbon ratios of LO-OOA in autumn (from 0.42 to 0.58) and winter (from 0.44 to 0.52) indicated the enhanced atmospheric oxidizing capacity strengthened photochemical reactions and resulted in the increased oxidation degree of LO-OOA. This study demonstrates the effectiveness of the clean air actions for air quality improvement in Beijing.

Air pollutant emissions from coal-fired power plants in China over the past two decades

China is the largest coal producer and consumer in the world, and coal-fired power plants are among its major sources of air pollutants. The Chinese government has implemented various stringent measures to reduce air pollutant emissions over the past two decades. National statistical data, emission inventories, and satellite observations indicate that air pollutant emissions from coal-fired power plants have been effectively controlled. Field measurements at coal-fired power plants can provide valuable information about the long-term trend of air pollutant emissions and the driving factors. In this study, we evaluated air pollutant emissions from 401 units at 308 coal-fired power plants. An appreciable reduction in air pollutant concentrations and emission factors from coal-fired power plants in China is observed over the past two decades. The drivers for this trend from the perspective of policy making, application of removal technologies, tightening of emission standards, technological improvement, monitoring systems, and economic measures are discussed. Currently, concentrations of typical air pollutants from coal-fired power plants in China are lower than those in Japan, Germany, and the US. This can be attributed to the policies and lenient emission standards for power plants in these countries. The technological improvement of air pollution control devices is the key factor that has led to reductions in air pollutant emissions in China. China has built the largest system of clean coal-fired power plants in the world.

Understanding sources of fine particulate matter in China

Fine particulate matter has been a major concern in China as it is closely linked to issues such as haze, health and climate impacts. Since China released its new national air quality standard for fine particulate matter (PM2.5) in 2012, great efforts have been put into reducing its concentration and meeting the standard. Significant improvement has been seen in recent years, especially in Beijing, the capital city of China. This paper reviews how China understands its sources of fine particulate matter, the major contributor to haze, and the most recent findings by researchers. It covers the characteristics of PM2.5 in China, the major methods to understand its sources such as emission inventory and measurement networks, the major research programmes in air quality research, and the major measures that lead to successful control of fine particulate matter pollution. A great example of linking scientific findings to policy is the control of coal combustion from the residential sector in northern China. This review not only provides an overview of the fine particulate matter pollution problem in China, but also its experience of air quality management, which may benefit other countries facing similar issues. This article is part of a discussion meeting issue 'Air quality, past present and future'.

Significant changes in the chemical compositions and sources of PM2.5 in Wuhan since the city lockdown as COVID-19

Wuhan was the first city to adopt the lockdown measures to prevent COVID-19 spreading, which improved the air quality accordingly. This study investigated the variations in chemical compositions, source contributions, and regional transport of fine particles (PM2.5) during January 23-February 22 of 2020, compared with the same period in 2019. The average mass concentration of PM2.5 decreased from 72.9 μg m-3 (2019) to 45.9 μg m-3 (2020), by 27.0 μg m-3. It was predominantly contributed by the emission reduction (92.0%), retrieved from a random forest tree approach. The main chemical species of PM2.5 all decreased with the reductions ranging from 0.85 μg m-3 (chloride) to 9.86 μg m-3 (nitrate) (p < 0.01). Positive matrix factorization model indicated that the mass contributions of seven PM2.5 sources all decreased. However, their contribution percentages varied from -11.0% (industrial processes) to 8.70% (secondary inorganic aerosol). Source contributions of PM2.5 transported from potential geographical regions showed reductions with mean values ranging from 0.22 to 4.36 μg m-3. However, increased contributions of firework burning, secondary inorganic aerosol, road dust, and vehicle emissions from transboundary transport were observed. This study highlighted the complex and nonlinear response of chemical compositions and sources of PM2.5 to air pollution control measures, suggesting the importance of regional-joint control.

Multiphase Photochemistry of Iron-Chloride Containing Particles as a Source of Aqueous Chlorine Radicals and Its Effect on Sulfate Production

Photolysis of iron chlorides is a well-known photolytic source of Cl^• in environmental waters. However, the role of particulate chlorine radicals (Cl^• and Cl₂^•-) in their multiphase oxidative potential has been much less explored. Herein, we examine the effect of Cl^•/Cl₂^•- produced from photolysis of particulate iron chlorides on atmospheric multiphase oxidation. As a model system, experiments on multiphase oxidation of SO₂ by Cl^•/Cl₂^•- were performed. Fast sulfate production from SO₂ oxidation was observed with reactive uptake coefficients of ∼10^-5, comparable to the values necessary for explaining the observations in the haze events in China. The experimental and modeling results found a good positive correlation between the uptake coefficient, γ_SO2, and the Cl^• production rate, d[Cl^•]/dt, as γ_SO2 = 5.3 × 10^-6 × log(d[Cl^•]/dt) + 4.9 × 10^-5. When commonly found particulate dicarboxylic acids (oxalic acid or malonic acid) were added, sulfate production was delayed due to the competition of Fe³⁺ between chloride and the dicarboxylic acid for its complexation at the initial stage. After the delay, comparable sulfate production was observed. The present study highlights the importance of photochemistry of particulate iron chlorides in multiphase oxidation processes in the atmosphere.

Chemical composition and sources of submicron aerosols in winter at a regional site in Beijing-Tianjin-Hebei region: Implications for the Joint Action Plan

The Ministry of Environmental Protection released a Joint Action Plan for Control of Air Pollution (Hereafter, Joint Action Plan, JAP), to reduce PM_2.5 concentrations in the Beijing-Tianjin-Hebei region (BTH) during the winter of 2017. To investigate the effectiveness of the controls, we deployed an aerosol chemical speciation monitor and collected filter samples at Xianghe, a representative site for the BTH, to characterize the aerosol composition during the implementation of the JAP. Those results were compared with earlier data obtained from a literature survey and reanalysis of studies in the BTH. During several pollution episodes in the control period, the major aerosol types changed relative to the earlier studies from sulfate, oxygenated organic aerosol, and coal combustion organic aerosol to nitrate and biomass burning organic aerosol. The dominant secondary inorganic aerosol species during the JAP changed from sulfate to nitrate, and the main source for primary organic aerosol switched from coal combustion to biomass burning. These changes can be explained by the fact that the JAP controls targeted coal combustion and SO₂ but not biomass burning or NO_x emissions. Our evaluation of the control measures provides a scientific basis for developing new policies in the future.