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This study explores the potential correlation between income and exposure to air pollution for the city of Madrid, Spain and its neighboring municipalities. Madrid is a well-known European air pollution hotspot with a high mortality burden attributable to nitrogen dioxide (NO2) and fine particulate matter (PM2.5). Statistical analyses were carried out using electoral district level data on gross household income (GHI), and NO2 and PM2.5 concentrations in air obtained from a mesoscale air quality model for the study area. We applied linear regression, bivariate spatial correlation analysis, spatial autoregression and geographically weighted regression to explore the relationship between contaminants and income. Three different strategies were adopted to harmonize data for analysis. While some strategies suggested a link between income and air pollution, others did not, highlighting the need for multiple different approaches where uncertainty is high. Our findings offer important lessons for future spatial geographical studies of air pollution in cities worldwide. In particular we highlight the limitations of census-scale socio-economic data and the lack of non-model derived high-resolution air quality measurement data for many cities and offers lessons for policy makers on improving the integration of these types of essential public information.

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In recent years， regional compound air pollution events caused by fine particles （PM2.5） and ozone （O3） have occurred frequently in economically developed areas of China， in which atmospheric oxidizing capacity （AOC） has played an important role. In this study， the WRF-CMAQ model was used to study the impacts of anthropogenic emission reduction on AOC during the COVID-19 lockdown period. Three representative cities in eastern China （Shijiazhuang， Nanjing， and Guangzhou） were selected for an in-depth analysis to quantify the contribution of meteorology and emissions to the changes in AOC and oxidants and to discuss the impact of AOC changes on the formation of secondary pollutants. The results showed that， compared with that in the same period in 2019， the urban average AOC in Shijiazhuang， Nanjing， and Guangzhou in 2020 increased by 60%， 48.7%， and 12.6%， respectively. The concentrations of O3， hydroxyl radical （·OH）， and nitrogen trioxide （NO3·ï¼‰ increased by 1.6%-26.4%， 14.8%-73.3%， and 37.9%-180%， respectively. The AOC in the three cities increased by 0.06×10-4， 0.12×10-4， and 0.33×10-4 min-1， respectively， due to emission reduction. The meteorological change increased AOC in Shijiazhuang and Nanjing by 20% and 17.9%， respectively， but decreased AOC in Guangzhou by -9.3%. Enhanced AOC led to an increase in the nitrogen oxidation ratio （NOR） and VOCs oxidation ratio （VOR） and promoted the transformation of primary pollutants to secondary pollutants. This offset the effects of primary emission reduction and resulted in a nonlinear decline in secondary pollutants compared to emissions during the COVID-19 lockdown.

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CO2 emissions from power plants are the dominant source of global CO2 emissions, thus in the context of global warming, accurate estimation of CO2 emissions from power plants is essential for the effective control of carbon emissions. Based on the XCO2 retrievals from the Orbiting Carbon Observatory 2 (OCO-2) and the Gaussian Plume Model (GPM), a series of studies have been carried out to estimate CO2 emission from power plants. However, the GPM is an ideal model, and there are a number of assumptions that need to be made when using this model, resulting in large uncertainties in the inverted emissions. Here, based on 6 cases of power plant plumes observed by the OCO-2 satellite over the Yangtze River Delta, China, we use an inline plume rise module coupled in the Community Multi-scale Air Quality model (CMAQ) to simulate the plumes and invert the emissions, and compare the simulated plumes and inverted emissions using the GPM model. We found that CO2 emissions can be significantly overestimated or underestimated based on the GPM simulations, and that the CMAQ inline plume simulation could significantly improve the estimates. However, the simulation bias in wind speed can significantly affect the inversion results. These results indicate that accurate meteorological field and plume simulations are critical for future inversion of point source emissions.

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Based on the ISAM module in the WRF-CMAQ model, this study analyzed the source contribution(both regional and sectoral) of O3 and its precursors(NO2 and VOCs) in Zibo in June 2021. Days with a maximum daily 8-h average(MDA8) O3 higher(lower) than 160 µg·m-3 were defined as polluted(clean) days. Differences in the source contribution between clean days and polluted days were compared, and a typical pollution period was selected for further process analysis. The results showed that NO2 in Zibo mainly came from local emissions in summer, with a relative contribution of 45.1%. Vehicle emissions(33.8%) and natural sources(20.7%) were the primary NO2 sources. VOC contributions from natural sources, solvent usage, and the petrochemical industry were significant, with a total contribution of 78.5%. The MDA8 contribution from local sources was 21.4%, whereas the impact of regional transport(32%) and surrounding cities(26.8%) was also substantial. Among local emission sources, vehicle emissions, the power industry, and the building materials industry contributed 10.9%-18.8% to local MDA8. On O3 pollution days, the MDA8 contribution from local emissions and surrounding cities increased. However, the relative contributions from local sources were similar under different pollution conditions.

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Per- and polyfluoroalkyl substances (PFAS) are a large class of human-made compounds that have contaminated the global environment. One environmental entry point for PFAS is via atmospheric emission. Air releases can impact human health through multiple routes, including direct inhalation and contamination of drinking water following air deposition. In this work, we convert the reference dose (RfD) underlying the United States Environmental Protection Agency's GenX drinking water Health Advisory to an inhalation screening level and compare to predicted PFAS and GenX air concentrations from a fluorochemical manufacturing facility in Eastern North Carolina. We find that the area around the facility experiences ~15 days per year of GenX concentrations above the inhalation screening level we derive. We investigate the sensitivity of model predictions to assumptions regarding model spatial resolution, emissions temporal profiles, and knowledge of air emission chemical composition. Decreasing the chemical specificity of PFAS emissions has the largest impact on deposition predictions with domain-wide total deposition varying by as much as 250 % for total PFAS. However, predicted domain-wide mean and median air concentrations varied by <18 % over all scenarios tested for total PFAS. Other model features like emission temporal variability and model spatial resolution had weaker impacts on predicted PFAS deposition.

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As a typical coastal city, O3 pollution in Rizhao has become increasingly serious in recent years. In order to explore the causes and sources of O3 pollution, IPR process analysis and ISAM source tracking tools based on the CMAQ model were used, respectively, to quantify the contributions of different physicochemical processes and different source tracking areas to O3 in Rizhao. Additionally, by comparing the differences between O3-exceeding days and non-exceeding days, combined with the HYSPLIT model, the regional transportation path of O3 in Rizhao was explored. The results showed that the concentrations of O3, NOx, and VOCs near the coastal areas of Rizhao and Lianyungang were significantly increased on O3 exceedance days compared with those on non-exceedance days. This was mainly because Rizhao was the convergence zone of western, southwestern, and eastern winds on exceedance days, which facilitated the transport and accumulation of pollutants. Process analysis showed that the transport process (TRAN) contribution to the near-surface O3 near the coastal areas of Rizhao and Lianyungang increased significantly on the exceedance days, whereas the contribution to most areas to the west of Linyi decreased. Photochemical reaction (CHEM) had a positive contribution to the O3 concentration in Rizhao during the daytime at all heights, and TRAN had a positive contribution at 0-60 m above the ground, and mainly had a negative contribution above 60 m. The contributions of CHEM and TRAN at 0-60 m above the ground would increase significantly on exceedance days, which was approximately twice that on the non-exceedance days. Source analysis showed that the local sources in Rizhao were the main contribution sources of NOx and VOCs, with the contribution rates of 47.5% and 58.0%, respectively. O3 mainly came from the contribution outside the simulation area (67.5%). The O3 and precursor contributions of the western cities of Rizhao (Weifang, Linyi, etc.) and the southern cities (Lianyungang, etc.) would increase significantly on the days of exceeding the standard. The transportation path analysis showed that the number of exceedances accounted for the largest proportion (11.8%) in the path from the west of Rizhao, which was the main transportation channel of O3 and precursors in Rizhao. This was verified through process analysis and source tracking results, and such trajectories accounted for 13.0% of the total number of trajectories, and their main routes were in the Shaanxi, Shanxi, Hebei, and Shandong regions.

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Bohai Bay, as a significant economic bay area in China, has experienced considerable ecological consequences during its rapid economic development. One of the major environmental challenges is the emission of air pollutants from ships, which has had a severe impact on regional air quality and the health of residents. To assess the influence of pollutants on the air quality around the Bohai Bay area, a Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model was established using a 9 km × 9 km high-resolution ship emission gridded inventory from 2018. The WRF-CMAQ model was employed to compare two scenarios: vessel emissions and non-vessel emissions, in order to evaluate the impact of ship emissions. By analyzing the pollutant concentrations in Bohai Bay and the degree of change in pollutant concentration in six cities under these two scenarios, significant differences were observed. Furthermore, a comparison of the hourly concentration contributions of ship emissions between port cities and inland cities within the same region revealed that inland cities were less affected by ship emissions. The main contributing factors to this disparity were identified as wind direction and wind speed.

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In this study, taking the Beijing-Tianjin-Hebei region as an example, CMAQ and BETR models were constructed to carry out numerical simulation for the pyrene (Pyr) and benzo[a] pyrene (BaP) in 2014. The model results were compared and evaluated for the atmospheric transportation and transformation of PAHs. Additionally, the XGBoost model was used to identify the key atmospheric physicochemical processes and parameters that affect the environmental behavior of PAHs in the CMAQ. The results showed that the ratio of the simulated value of BETR and annual average value of CMAQ to the measured annual average value was between 1/2 and 2, and the seasonal trend of the simulated concentrations of Pyr and BaP from the CMAQ model were basically consistent with the measured values, which verified the reliability of the two types of models. At the same time, the simulated concentration of the CMAQ model averaged from 9 km grid to 27 km grid and was comparable to the BETR concentration. The results showed that the average concentrations of Pyr and BaP in the BETR model were approximately 1.59 and 1.38 times those of the CMAQ simulation concentrations, respectively, indicating that the two models had good comparability in terms of average annual concentration level and spatial distribution. The SHAP-based variable importance on the XGBoost model showed that boundary layer height was the most significant meteorological factor affecting the transportation and transformation of Pyr and BaP, accounting for 22%-35% of all factors, and sometimes even exceeded the emissions for certain cities and pollutants. The boundary layer height was significantly negatively correlated with the concentration of PAHs. Wind speed was a secondary factor affecting the concentration of PAHs and was negatively correlated with the PAHs, whereas the influence of wind direction on the concentration of PAHs varied.

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Wildfire outbreaks can lead to extreme biomass burning (BB) emissions of both oxidized (e.g., nitrogen oxides; NOx = NO+NO2) and reduced form (e.g., ammonia; NH3) nitrogen (N) compounds. High N emissions are major concerns for air quality, atmospheric deposition, and consequential human and ecosystem health impacts. In this study, we use both satellite-based observations and modeling results to quantify the contribution of BB to the total emissions, and approximate the impact on total N deposition in the western U.S. Our results show that during the 2020 wildfire season of August-October, BB contributes significantly to the total emissions, with a satellite-derived fraction of NH3 to the total reactive N emissions (median ~ 40%) in the range of aircraft observations. During the peak of the western August Complex Fires in September, BB contributed to ~55% (for the contiguous U.S.) and ~ 83% (for the western U.S.) of the monthly total NOx and NH3 emissions. Overall, there is good model performance of the George Mason University-Wildfire Forecasting System (GMU-WFS) used in this work. The extreme BB emissions lead to significant contributions to the total N deposition for different ecosystems in California, with an average August - October 2020 relative increase of ~78% (from 7.1 to 12.6 kg ha-1 year-1) in deposition rate to major vegetation types (mixed forests + grasslands/shrublands/savanna) compared to the GMU-WFS simulations without BB emissions. For mixed forest types only, the average N deposition rate increases (from 6.2 to 16.9 kg ha-1 year-1) are even larger at ~173%. Such large N deposition due to extreme BB emissions are much (~6-12 times) larger than low-end critical load thresholds for major vegetation types (e.g., forests at 1.5-3 kg ha-1 year-1), and thus may result in adverse N deposition effects across larger areas of lichen communities found in California's mixed conifer forests.

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Source-tagged source apportionment (SA) has advantages for quantifying the contribution of various source regions and categories to PM2.5; however, it is highly affected by uncertainty in the emission inventory. In this study, we used a Regional multi-Air Pollutant Assimilation System (RAPAS) to optimize daily SO2, NOx and primary PM2.5 (PPM2.5) emissions in the Yangtze River Delta (YRD) in December 2016 by assimilating hourly in-situ measurements. The CMAQ-ISAM model was implemented with prior and posterior emissions respectively to investigate the impacts of optimizing emissions on PM2.5 SA in the YRD megalopolis (YRDM) and three megacities of Shanghai, Nanjing, and Hangzhou in the YRDM. The results showed that RAPAS significantly improved the simulations and reduced the emission uncertainties of the different pollutants. Compared with prior emissions, the posterior emissions in the YRD decreased by 13% and 11% for SO2 and NOx respectively, and increased by 24% for PPM2.5. Compared with SA using prior emissions, the contributions from Hangzhou, northern Zhejiang, and areas outside of the YRD to the YRDM increased. The local contributions from the YRDM, Nanjing and Shanghai decreased by 1.8%, 9.7%, and 2.3%, respectively, whereas that of Hangzhou increased by 5.6%. The changes in the daily local contributions caused by optimizing emissions ranged from -18.0% to 23.6%. Generally, under stable weather conditions, the local contribution changed the most, whereas under unstable weather conditions, the contribution from upwind areas changed significantly. Overall, with optimized emissions, we found in Nanjing, Shanghai, and Hangzhou, local emissions contributed 18.2%, 39.6% and 36.8% of their PM2.5 concentrations, respectively; long-range transport from outside the YRDM contributed 59.2%, 48.1%, and 48.2%, respectively. This study emphasizes the importance of improving emission estimations for source-tagged SA and provides more reliable SA results for the main cities in the YRD, which will contribute to pollution control in these regions.

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We investigate the impact of the COVID-19 outbreak on PM2.5 levels in eleven urban environments across the United States: Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, Phoenix, and Seattle. We estimate daily PM2.5 levels over the contiguous U.S. in March-May 2019 and 2020, and leveraging a deep convolutional neural network, we find a correlation coefficient, an index of agreement, a mean absolute bias, and a root mean square error of 0.90 (0.90), 0.95 (0.95), 1.34 (1.24) µg/m3, and 2.04 (1.87) µg/m3, respectively. Results from Google Community Mobility Reports and estimated PM2.5 concentrations show a greater reduction of PM2.5 in regions with larger decreases in human mobility and those in which individuals remain in their residential areas longer. The relationship between vehicular PM2.5 (i.e., the ratio of vehicular PM2.5 to other sources of PM2.5) emissions and PM2.5 reductions (R = 0.77) in various regions indicates that regions with higher emissions of vehicular PM2.5 generally experience greater decreases in PM2.5. While most of the urban environments ⸺ Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, and Seattle ⸺ show a decrease in PM2.5 levels by 21.1%, 20.7%, 18.5%, 8.05%, 3.29%, 3.63%, 6.71%, 4.82%, 13.5%, and 7.73%, respectively, between March-May of 2020 and 2019, Phoenix shows a 5.5% increase during the same period. Similar to their PM2.5 reductions, Washington DC, New York, and Boston, compared to other cities, exhibit the highest reductions in human mobility and the highest vehicular PM2.5 emissions, highlighting the great impact of human activity on PM2.5 changes in eleven regions. Moreover, compared to changes in meteorological factors, changes in pollutant concentrations, including those of black carbon, organic carbon, SO2, SO4, and especially NO2, appear to have had a significantly greater impact on PM2.5 changes during the study period.

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The spatial layout of the steel industry has an impact on the regional atmospheric environment. In this study, the steel industry evolution model and the driving force analysis model were combined to analyze the evolution of spatial layout of the steel industry in China and the driving factors of this evolution. In addition, the WRF-SMOKE-CMAQ model was used to analyze the spatial dynamics of SO2 emissions from the steel industry. Our analysis presents the evolution of the steel industry in China in four stages: policy-determining, resource-oriented, economic promotion and market-oriented stage. The change in the spatial layout of the Chinese steel industry resulted in a continuously decreasing trend of pollutants in temporal characteristics and a decreasing share of emissions in North China and a continuous growth in East China in spatial characteristics. Our simulation shows that, by 2025, the pollutant SO2 emission concentration will migrate to the southeast, subject to market-oriented factors.

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Wuhan was locked down from 23 January to 8 April 2020 to prevent the spread of the novel coronavirus disease 2019 (COVID-19). Both public and private transportation in Wuhan and its neighboring cities in Hubei Province were suspended or restricted, and the manufacturing industry was partially shut down. This study collected and investigated ground monitoring data to prove that the lockdowns of the cities had significant influences on the air quality in Wuhan. The WRF-CMAQ (Weather Research and Forecasting-Community Multiscale Air Quality) model was used to evaluate the emission reduction from transportation and industry sectors and associated air quality impact. The results indicate that the reduction in traffic emission was nearly 100% immediately after the lockdown between 23 January and 8 February and that the industrial emission tended to decrease by about 50% during the same period. The industrial emission further deceased after 9 February. Emission reduction from transportation and that from industry was not simultaneous. The results imply that the shutdown of industry contributed significantly more to the pollutant reduction than the restricted transportation.

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In this work, we use observations and experimental emissions in a version of NOAA's National Air Quality Forecasting Capability to show that the COVID-19 economic slowdown led to disproportionate impacts on near-surface ozone concentrations across the contiguous U.S. (CONUS). The data-fusion methodology used here includes both U.S. EPA Air Quality System ground and the NASA Aura satellite Ozone Monitoring Instrument (OMI) NO2 observations to infer the representative emissions changes due to the COVID-19 economic slowdown in the U.S. Results show that there were widespread decreases in anthropogenic (e.g., NOx) emissions in the U.S. during March-June 2020, which led to widespread decreases in ozone concentrations in the rural regions that are NOx-limited, but also some localized increases near urban centers that are VOC-limited. Later in June-September, there were smaller decreases, and potentially some relative increases in NOx emissions for many areas of the U.S. (e.g., south-southeast) that led to more extensive increases in ozone concentrations that are partly in agreement with observations. The widespread NOx emissions changes also alters the O3 photochemical formation regimes, most notably the NOx emissions decreases in March-April, which can enhance (mitigate) the NOx-limited (VOC-limited) regimes in different regions of CONUS. The average of all AirNow hourly O3 changes for 2020-2019 range from about +1 to -4 ppb during March-September, and are associated with predominantly urban monitoring sites that demonstrate considerable spatiotemporal variability for the 2020 ozone changes compared to the previous five years individually (2015-2019). The simulated maximum values of the average O3 changes for March-September range from about +8 to -4 ppb (or +40 to -10%). Results of this work have implications for the use of widespread controls of anthropogenic emissions, particularly those from mobile sources, used to curb ozone pollution under the current meteorological and climate conditions in the U.S.

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This study analyzed the impacts of meteorological conditions and changes in air pollutant emissions on PM2.5 across the country during the first quarter of 2020 based on the WRF-CMAQ model. Results showed that the variations in meteorological conditions led to a national PM2.5 concentration decreased of 1.7% from 2020-01 to 2020-03, whereas it increased by 1.6% in January and decreased by 1.3% and 7.9% in February and March, respectively. The reduction of pollutants emissions led to a decrease of 14.1% in national PM2.5 concentration during the first quarter of 2020 and a decrease of 4.0%, 25.7%, and 15.0% in January, February, and March, respectively. Compared to the same period last year, the PM2.5 concentration measured in Wuhan City decreased more than in the entire country. This was caused by improved meteorological conditions and a higher reduction of pollutant emissions in Wuhan City. PM2.5 in Beijing increased annually before the epidemic outbreak and during the strict control period, mainly due to unfavorable meteorological conditions. However, the decrease in PM2.5 in Beijing compared to March 2019 was closely related to the substantial reduction of emissions. The measured PM2.5 in the "2+26" cities, the Fenwei Plain and the Yangtze River Delta (YRD) decreased during the first quarter of 2020, with the largest drop occurring in the Yangtze River Delta due to higher YRD emissions reductions. The meteorological conditions of "2+26" cities and Fenwei Plain were unfavorable before the epidemic outbreak and greatly improved during the strict control period, whereas the Yangtze River Delta had the most favorable meteorological conditions in March. The decrease in PM2.5 concentration caused by the reduction of pollutant emissions in the three key areas was highest during the strict control period.

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In recent years, many surveillance cameras have been installed in the Greater Taipei Area, Taiwan; traffic data obtained from these surveillance cameras could be useful for the development of roadway-based emissions inventories. In this study, web-based traffic information covering the Greater Taipei Area was obtained using a vision-based traffic analysis system. Web-based traffic data were normalized and applied to the Community Multiscale Air Quality (CMAQ) model to study the impact of vehicle emissions on air quality in the Greater Taipei Area. According to an analysis of the obtained traffic data, sedans were the most common vehicles in the Greater Taipei Area, followed by motorcycles. Moderate traffic conditions with an average speed of 30-50 km/h were most prominent during weekdays, whereas traffic flow with an average speed of 50-70 km/h was most common during weekends. The proportion of traffic flows in free-flow conditions (>70 km/h) was higher on weekends than on weekdays. Two peaks of traffic flow were observed during the morning and afternoon peak hours on weekdays. On the weekends, this morning peak was not observed, and the variation in vehicle numbers was lower than on weekdays. The simulation results suggested that the addition of real-time traffic data improved the CMAQ model's performance, especially for the carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations. According to sensitivity tests for total and vehicle emissions in the Greater Taipei Area, vehicle emissions contributed to >90% of CO, 80% of nitrogen oxides (NOx), and approximately 50% of PM2.5 in the downtown areas of Taipei. The vehicle emissions contribution was affected by both vehicle emissions and meteorological conditions. The connection between the surveillance camera data, vehicle emissions, and regional air quality models in this study can also be used to explore the impact of special events (e.g., long weekends and COVID-19 lockdowns) on air quality.

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Continuous on-line observation of particulate matter and PM2.5 chemical composition was conducted from October 15th to November 7th 2019 in East China. During the observation period, a wide range of dust-related processes took place. According to supplementary urban air quality assessment affected by dust (hereafter referred to as supplementary provisions), the observations were divided into four stages including pre-dust event, dust Ⅰ, dust Ⅱ, and post-dust event. The dust Ⅰ stage represented the processes of transportation and retention, while the dust Ⅱ stage represented processes of backflow from the sea and scavenging. The start time of the studied dust event was October 29th 08:00-09:00 based on the supplementary provisions, dust tracers, and air quality models; however, disagreements existed between these data sources with respect to the finishing time. The supplementary provisions could not effectively distinguish backflow dust from sea, and results from different dust tracers were variable. The WRF-CMAQ model simulated dust variation trends well but overestimated short-term suspended dust and backflow dust. PM10, PM2.5, and trace element concentrations were much higher during dust events than during non-dust periods, with highest daily concentrations of (234.8±125.5), (76.8±22.5), and (17.54±10.5) µg·m-3, respectively, which occurred on October 29th. During the dust event, concentration of crustal elements were remarkably high in PM2.5. At the same time, secondary ions (SO42-, NO3-, and NH4+) contributed less to PM2.5 mass concentrations. Four major crustal elements (Al, Si, Ca, and Fe) accounted for 23.5% and 13.7% of the mass concentration of PM2.5 and secondary ions accounted for 24.3% and 41.9% during dust Ⅰ and dust Ⅱ stages, respectively. Based on PMF source apportionment, Ca abundance, PM2.5/PM10 in dust sources, and the reconstruction of crustal material, dust particulates accounted for 43.4%-50.0% of PM2.5 and backflow dust accounted for 19.2%-24.7% of PM2.5.

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Based on the Weather Research and Forecasting model and the Models-3 community multi-scale air quality model (WRF-CMAQ), this study analyzes the impacts of meteorological conditions and changes in air pollutant emissions on the heavy air pollution episode occurred over North China around the 2020 Spring Festival (January to Februray 2020). Regional reductions in air pollutant emissions required to eliminate the PM2.5 heavy pollution episode are also quantified. Our results found that meteorological conditions for the Beijing-Tianjin-Hebei and surrounding "2+26" cities are the worst during the heavy pollution episode around the 2020 Spring Festival as compared with two other typical heavy pollution episodes that occurred after 2015. However, because of the substantial reductions in air pollutant emissions in the "2+26" cities in recent years, and the 32% extra reduction in emissions during January to February 2020 compared with the baseline emission levels of the autumn and winter of 2019 to 2020, the maximum PM2.5 level during this heavy pollution episode around the 2020 Spring Festival was much lower than that in the other two typical episodes. Yet, these emission reductions are still not enough to eliminate regional heavy pollution episodes. Compared with the actual emission levels during January to February 2020, a 20% extra reduction in air pollutant emissions in the "2+26" cities (or a 45% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide severe pollution episodes, and avoid heavy pollution episodes that last three or more consecutive days in Beijing; a 40% extra reduction in emissions (or a 60% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide and continuous heavy pollution episodes. Our analysis finds that during the clean period after the heavy pollution episode around the 2020 Spring Festival, the regionwide heavy pollution episode would only occur with at least a 10-fold increase in air pollutant emissions.

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Cities play a key role in making carbon emission reduction targets achievable and tackling air pollution. Using Guangzhou city as a case, this paper explored the air quality and health co-benefits of peaking carbon dioxide emissions under three scenarios and developed an integrated assessment framework by combining a local air pollutant emission inventory, an atmospheric chemistry transport model, and a health assessment model. The results showed that SO2, PM10, and PM2.5 could achieve larger emission reductions than NH3, VOCs, and NOx among all the scenarios we examined. Under the enhanced peaking scenario with the most stringent mitigation strategies, Guangzhou could meet the local ambient air quality standard for PM2.5 (34 µg/m3), with the most reduction observed in the annual average PM2.5 concentration (28.4%) and related premature deaths (17.08%), compared with the base year 2015. We also identified hotspot grids, which were areas with high concentrations of carbon emissions, high concentrations of air pollution and poor air quality in Guangzhou. Our analysis highlighted the importance of promoting peaking carbon dioxide emission for the improvement of air quality and public health at the city level.

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The COVID-19 epidemic has substantially limited human activities and affected anthropogenic emissions. In this work, daily NO x emissions are inferred using a regional data assimilation system and hourly surface NO2 measurement over China. The results show that because of the coronavirus outbreak, NO x emissions across the whole mainland China dropped sharply after 31 January, began to rise slightly in certain areas after 10 February, and gradually recover across the country after 20 February. Compared with the emissions before the outbreak, NO x emissions fell by more than 60% and ~30% in many large cities and most small to medium cities, respectively. Overall, NO x emissions were reduced by 36% over China, which were mainly contributed by transportation. Evaluations show that the inverted changes over eastern China are credible, whereas those in western China might be underestimated. These findings are of great significance for exploring the reduction potential of NO x emissions in China.