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A new long-term emission inventory called the Inversed Emission Inventory for Chinese Air Quality (CAQIEI) was developed in this study by assimilating surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS). This inventory contains the constrained monthly emissions of NOx, SO2, CO, primary PM2.5, primary PM10, and NMVOCs in China from 2013 to 2020, with a horizontal resolution of 15 km × 15 km. This paper documents detailed descriptions of the assimilation system and the evaluation results for the emission inventory. The results suggest that CAQIEI can effectively reduce the biases in the a priori emission inventory, with the normalized mean biases ranging from -9.1% to 9.5% in the a posteriori simulation, which are significantly reduced from the biases in the a priori simulations (-45.6% to 93.8%). The calculated RMSEs (0.3 mg/m³ for CO and 9.4-21.1 µg/m³ for other species, on the monthly scale) and correlation coefficients (0.76-0.94) were also improved from the a priori simulations, suggesting that CAQIEI can reasonably reproduce the magnitude and variation of emissions of different air pollutants in China. Based on CAQIEI, we estimated China's total emissions (including both natural and anthropogenic emissions) of the 6 species in 2015 to be as follows: 25.2 Tg of NOx, 17.8 Tg of SO2, 465.4 Tg of CO, 15.0 Tg of PM2.5, 40.1 Tg of PM10, and 46.0 Tg of NMVOCs. From 2015 to 2020, the total emissions reduced by 54.1% for SO2, 44.4% for PM2.5, 33.6% for PM10, 35.7% for CO, and 15.1% for NOx, but increased by 21.0% for NMVOCs. Larger emission reductions were achieved during the 2018-2020 action plan than during the 2013-2017 action plan for most species. In particular, NOx and NMVOC emissions were shown to increase during the 2013-2017 action plain, and there were obvious emission increases in the Fengwei Plain area over the Central China region. However, NOx and NMVOC emissions declined during the 2018-2020 action plan, and the emissions over the Fengwei Plain area also decreased. This suggests that the emission control policies were improved in the 2018-2020 action plan. We also compared CAQIEI with previous inventories, which verified our inversion results in terms of total emissions of NOx, SO2 and NMVOCs, and more importantly identified the potential uncertainties in our current understanding of China's air pollutant emissions. Firstly, CO emissions in China may be substantially underestimated by current inventories, with the CO emissions estimated by CAQIEI (426.8 Tg) being more than twice the amount in previous inventories (120.7-237.7 Tg). Significant underestimations for other air pollutant emissions may also exist over western and northeastern China. In addition, the NMVOC emissions were shown to be substantially underestimated over northern China but overestimated in southern China. Secondly, the emission reduction rates during 2015-2018 estimated by CAQIEI are generally smaller than those estimated by previous inventories, especially for NOx, PM10 and NMVOCs, suggesting that the mitigation effects of the air pollution control may be overestimated currently. In particular, China's NMVOC emissions were shown to have increased by 26.6% from 2015 to 2018, especially over the North China Plain by 38.0%), Northeast China (by 38.3%), and Central China (60.0%). In contrast, the emissions reduction rate of CO may be underestimated. Overall, our emissions inventory sheds new light on the complex variations of air pollutant emissions in China during its two recent clean air action periods, which could significantly improve our understanding of air pollutant emissions and related changes in air quality in China. [ABSTRACT FROM AUTHOR]

In this paper, complex network theory has been applied to reveal the transport patterns and cooperative regions of fine particulate matter (PM 2.5) over China from 2015 to 2019. The results show that the degrees, weighted degrees, and edge lengths of PM 2.5 cities follow power law distributions. We find that the cities in the Beijing–Tianjin–Hebei–Henan–Shandong (BTHHS) region have a strong ability to export PM 2.5 pollution to other cities. By analyzing the transport routes, we show that a mass of links extends southward from the BTHHS to the Yangtze River Delta (YRD) regions with 1 or 2 d time lags. Hence, we conclude that earlier emission reduction in the BTHHS region and early warning measures in the YRD region will provide better air pollution mitigation in both regions. Moreover, significant links are concentrated in wintertime, suggesting the impact of the winter monsoon. In addition, all cities have been divided into nine clusters according to their spatial correlations. We suggest that the cities in the same clusters should be regarded as a whole to control the level of air pollution. This approach is able to characterize the transport and cluster for other air pollutants, such as ozone and NOx. [ABSTRACT FROM AUTHOR]

Satellite remote sensing aerosol optical depth (AOD) and meteorological elements were employed to invert PM2.5 (the fine particulate matter with a diameter below 2.5 µm) in order to control air pollution more effectively. This paper proposes a restricted gradient-descent linear hybrid machine learning model (RGD-LHMLM) by integrating a random forest (RF), a gradient boosting regression tree (GBRT), and a deep neural network (DNN) to estimate the concentration of PM2.5 in China in 2019. The research data included Himawari-8 AOD with high spatiotemporal resolution, ERA5 meteorological data, and geographic information. The results showed that, in the hybrid model developed by linear fitting, the DNN accounted for the largest proportion, and the weight coefficient was 0.62. The R2 values of RF, GBRT, and DNN were reported as 0.79, 0.81, and 0.8, respectively. Preferably, the generalization ability of the mixed model was better than that of each sub-model, and R2 (determination coefficient) reached 0.84, and RMSE (root mean square error) and MAE (mean absolute error) were reported as 12.92 and 8.01 µgm-3 , respectively. For the RGD-LHMLM, R2 was above 0.7 in more than 70 % of the sites and RMSE and MAE were below 20 and 15 µgm-3 , respectively, in more than 70 % of the sites due to the correlation coefficient having a seasonal difference between the meteorological factor and PM2.5. Furthermore, the hybrid model performed best in winter (mean R2 was 0.84) and worst in summer (mean R2 was 0.71). The spatiotemporal distribution characteristics of PM2.5 in China were then estimated and analyzed. According to the results, there was severe pollution in winter with an average concentration of PM2.5 being reported as 62.10 µgm-3. However, there was only slight pollution in summer with an average concentration of PM2.5 being reported as 47.39 µgm-3. The period from 10:00 to 15:00 LT (Beijing time, UTC + 8 every day is the best time for model inversion; at this time the pollution is also high. The findings also indicate that North China and East China are more polluted than other areas, and their average annual concentration of PM2.5 was reported as 82.68 µgm-3. Moreover, there was relatively low pollution in Inner Mongolia, Qinghai, and Tibet, for their average PM2.5 concentrations were reported below 40 µgm-3. [ABSTRACT FROM AUTHOR]

The Huaihai Economic Region (HER, 32.5–36.5°N, 114–121°E) with a land area of 178,000 km2, which contains 20 cities, is one of the earliest regional economic cooperative organizations in China. The huge population (100 million, about 7% of the China's population), local heavy industries, chemical enterprises and vehicle emissions have resulted in serious air pollution. In this paper, a long-term aerosol optical depth (AOD) data set from 2000 to 2018 over HER with 10 km spatial resolution has been produced and analyzed from the latest version of MODIS (MODerate resolution Imaging Spectroradiometer) aerosol products. Validation results show that MODIS AOD has a strong correlation relationship (r = 0.94, slope = 0.92) with Aeronet Robotic Network (AERONET) over HER. The seasonal average AOD maps indicate that the high AOD values mainly occurred with a banding distribution from southeastern to northwestern HER, with AODs larger in summer and spring than in fall and winter. Temporally, the monthly average AOD has increased since 2000 and reached the highest level in 2011 (+0.02/year); then it has obviously declined from 2012 to 2018 (−0.05/year), owing to strict air pollution control implemented since 2012. In general, the annual average AOD over HER is 0.71 for the past nearly 20 years, and in 2018, the annual average AOD firstly lower than that in 2000. The study suggests that there has been a slightly improvement in air quality over HER and long-term and sustainable efforts should be made. [ABSTRACT FROM AUTHOR]

The study and control of air pollution involves measuring the structure of the atmospheric boundary layer (ABL) to understand the mechanisms of the interactions occurring between the atmospheric boundary layer and air pollution. Beijing, the capital of China, experienced heavy haze pollution in December 2016, and the city issued its first red-alert air pollution warning of the year (the highest PM 2.5 concentrations were later found to exceed 450 µgm-3). In this paper, the vertical profiles of wind, temperature, humidity and the extinction coefficient (reflecting aerosol concentrations), as well as ABL heights and turbulence quantities under heavy haze pollution conditions, are analyzed, with data collected from lidar, wind profile radar (WPR), radiosondes, a 325 m meteorological tower (equipped with a 7-layer ultrasonic anemometer and 15-layer low-frequency wind, temperature, and humidity sensors) and ground observations. The ABL heights obtained by three different methods based on lidar extinction coefficient data (Hc) are compared with the heights calculated from radiosonde temperature data (Hθ), and their correlation coefficient can reach 72 %. Our results show that Hθ measured on heavy haze pollution days was generally lower than that measured on clean days without pollution, but Hc increased from clean to heavy pollution days. The time changes in friction velocity (u*) and turbulent kinetic energy (TKE) were clearly inversely correlated with PM 2.5 concentration. Momentum and heat fluxes varied very little with altitude. The nocturnal sensible heat fluxes close to the Earth surface always stay positive. In the daytime of the haze pollution period, sensible heat fluxes were greatly reduced within 300 m of the ground. These findings will deepen our understanding of the boundary layer structure under heavy pollution conditions and improve the boundary layer parameterization in numerical models. [ABSTRACT FROM AUTHOR]

As major chemical components of airborne fine particulate matter (PM 2.5), organic carbon (OC) and elemental carbon (EC) have vital impacts on air quality, climate change, and human health. Because OC and EC are closely associated with fuel combustion, it is helpful for the scientific community and policymakers assessing the efficacy of air pollution control measures to study the impact of control measures and regional transport on OC and EC levels. In this study, hourly mass concentrations of OC and EC associated with PM 2.5 were semi-continuously measured from March 2013 to February 2018. The results showed that annual mean OC and EC concentrations declined from 14.0 to 7.7 µgm-3 and from 4.0 to 2.6 µgm-3 , respectively, from March 2013 to February 2018. In combination with the data of OC and EC in previous studies, an obvious decreasing trend in OC and EC concentrations was found, which was caused by clean energy policies and effective air pollution control measures. However, no obvious change in the ratios of OC and EC to the PM 2.5 mass (on average, 0.164 and 0.049, respectively) was recorded, suggesting that inorganic ions still contributed a lot to PM 2.5. Based on the seasonal variations in OC and EC, it appeared that higher OC and EC concentrations were still observed in the winter months, with the exception of winter of 2017–2018. Traffic policies executed in Beijing resulted in nighttime peaks of OC and EC, caused by heavy-duty vehicles and heavy-duty diesel vehicles being permitted to operate from 00:00 to 06:00 (China standard time, UTC + 8, for all times throughout the paper). In addition, the fact that there was no traffic restriction in weekends led to higher concentrations on weekends compared to weekdays. Significant correlations between OC and EC were observed throughout the study period, suggesting that OC and EC originated from common emission sources, such as exhaust of vehicles and fuel combustion. OC and EC levels increased with enhanced SO2 , CO, and NOx concentrations while the O3 and OC levels were enhanced simultaneously when O3 concentrations were higher than 50 µgm-3. Non-parametric wind regression analysis was performed to examine the sources of OC and EC in the Beijing area. It was found that there were distinct hot spots in the northeast wind sector at wind speeds of approximately 0–6 km h -1 , as well as diffuse signals in the southwestern wind sectors. Source areas further away from Beijing were assessed by potential source contribution function (PSCF) analysis. A high-potential source area was precisely pinpointed, which was located in the northwestern and southern areas of Beijing in 2017 instead of solely in the southern areas of Beijing in 2013. This work shows that improvement of the air quality in Beijing benefits from strict control measures; however, joint prevention and control of regional air pollution in the regions is needed for further improving the air quality. The results provide a reference for controlling air pollution caused by rapid economic development in developing countries. [ABSTRACT FROM AUTHOR]

Since China's clean air action, PM 2.5 (particulate matter with an aerodynamic equivalent diameter of 2.5 µm or less) air quality has improved, while ozone (O 3) pollution has become more severe. Here we apply a coupled meteorology–chemistry model (WRF-Chem: Weather Research and Forecasting model coupled to Chemistry v3.7.1) to quantify the responses of aerosol–radiation interaction (ARI) to anthropogenic emission reductions from 2013 to 2017, including aerosol–photolysis interaction (API) related to photolysis rate change and aerosol–radiation feedback (ARF) related to meteorological field change and their contributions to O 3 increases over eastern China in summer and winter. Sensitivity experiments show that the decreased anthropogenic emissions play a more prominent role in the increased daily maximum 8 h average (MDA8) O 3 in both summer (+ 1.96 ppb vs. + 0.07 ppb) and winter (+ 3.56 ppb vs. - 1.08 ppb) than the impacts of changed meteorological conditions in urban areas. The decreased PM 2.5 caused by emission reductions can result in a weaker impact of ARI on O 3 concentrations, which superimposes its effect on the worsened O 3 air quality. The weakened ARI due to decreased anthropogenic emissions aggravates the summer (winter) O 3 pollution by + 0.81 ppb (+ 0.63 ppb), averaged over eastern China, with weakened API contributing 55.6 % (61.9 %) and ARF contributing 44.4 % (38.1 %), respectively. This superimposed effect is more significant for urban areas during summer (+ 1.77 ppb). Process analysis indicates that the enhanced chemical production is the dominant process for the increased O 3 concentrations caused by weakened ARI in both summer and winter. This study innovatively reveals the adverse effect of weakened aerosol–radiation interaction due to decreased anthropogenic emissions on O 3 air quality, indicating that more stringent coordinated air pollution control strategies should be implemented for significant improvements in future air quality. [ABSTRACT FROM AUTHOR]

Although air quality guidelines generally use the atmospheric concentration of fine particulate matter (PM 2.5) as a metric for air pollution evaluation and management, the fact cannot be ignored that different particle toxicities are unequal and significantly related to their sources and chemical compositions. Therefore, judging the most harmful source and identifying the toxic component would be helpful for optimizing air quality standards and prioritizing targeted PM 2.5 control strategies to protect public health more effectively. Since the combustions of fuels, including oil, coal, and biomass, are the main anthropogenic sources of environmental PM 2.5 , their discrepant contributions to health risks of mixed ambient aerosol pollution dominated by the respective emission intensity and unequal toxicity of chemical components need to be identified. In order to quantify the differences between these combustion primary emissions, 10 types of PM 2.5 from each typical source group, i.e., vehicle exhaust, coal combustion, and plant biomass (domestic biofuel) burning, were collected for comparative study with toxicological mechanisms. In total, 30 types of individual combustion samples were intercompared with representative urban ambient air PM 2.5 samples, whose chemical characteristics and biological effects were investigated by component analysis (carbon, metals, soluble ions) and in vitro toxicity assays (cell viability, oxidative stress, inflammatory response) of human lung adenocarcinoma epithelial cells (A549). Carbonaceous fractions were plenteous in automobile exhaust and biomass burning, while heavy metals were more plentiful in PM 2.5 from coal combustion and automobile exhaust. The overall ranking of mass-normalized cytotoxicity for source-specific PM 2.5 was automobile exhaust > coal combustion > domestic plant biomass burning > ambient urban air, possibly with differential toxicity triggers, and showed that the carbonaceous fractions (organic carbon, OC; elemental carbon, EC) and redox-active transition metals (V, Ni, Cr) assisted by water-soluble ions (Ca 2+ , Mg 2+ , F - , Cl -) might play important roles in inducing cellular reactive organic species (ROS) production, causing oxidative stress and inflammation, resulting in cell injury and apoptosis, and thus damaging human health. Coupled with the source apportionment results of typical urban ambient air PM 2.5 in eastern China, reducing toxic PM 2.5 from these anthropogenic combustions will be greatly beneficial to public health. In addition to the air pollution control measures that have been implemented, like strengthening the vehicle emission standards, switching energy from coal to gas and electricity, and controlling the open incineration of agricultural straws, further methods could be considered, especially by preferentially reducing the diesel exhaust, lessening the coal combustion by replacement with low-ash clean coals, and depressing the rural crop straw biomass burning emissions. [ABSTRACT FROM AUTHOR]

The satellite remote-sensing aerosol optical depth (AOD) and meteorological elements were employed to invert PM2.5 in order to control air pollution more effectively. This paper proposes a restricted gradient-descent linear hybrid machine learning model (RGD–LHMLM) by integrating a random forest (RF), a gradient boosting regression tree (GBRT), and a deep neural network (DNN) to estimate the concentration of PM2.5 in China in 2019. The research data included Himawari-8 AOD with high spatiotemporal resolution, ERA-5 meteorological data, and geographic information. The results showed that, in the hybrid model developed by linear fitting, the DNN accounted for the largest proportion, whereas the weight coefficient was 0.62. The R² values of RF, GBRT, and DNN were reported 0.79, 0.81, and 0.8, respectively. Preferably, the generalization ability of the mixed model was better than that of each sub-model, and R² reached 0.84, whereas RMSE and MAE were reported 12.92 µg/m³ and 8.01 µg/m³, respectively. For the RGD-LHMLM, R² was above 0.7 in more than 70% of the sites, whereas RMSE and MAE were below 20 µg/m³ and 15 µg/m³, respectively, in more than 70% of the sites due to the correlation coefficient having seasonal difference between the meteorological factor and PM2.5. Furthermore, the hybrid model performed best in winter (mean R² was 0.84) and worst in summer (mean R² was 0.71). The spatiotemporal distribution characteristics of PM2.5 in China were then estimated and analyzed. According to the results, there was severe pollution in winter with an average concentration of PM2.5 being reported 62.10 µg/m³. However, there was slight pollution in summer with an average concentration of PM2.5 being reported 47.39 µg/m³. The findings also indicate that North China and East China are more polluted than other areas and that their average annual concentration of PM2.5 was reported 82.68 µg/m³. Moreover, there was relatively low pollution in Inner Mongolia, Qinghai, and Tibet, for their average PM2.5 concentrations were reported below 40 µg/m³. [ABSTRACT FROM AUTHOR]

China has been conducting a series of actions on air quality improvement for the past decades, and air pollutant emissions have been changing swiftly across the country. Provinces are an important administrative unit for air quality management in China; thus a reliable provincial-level emission inventory for multiple years is essential for detecting the varying sources of pollution and evaluating the effectiveness of emission controls. In this study, we selected Jiangsu, one of the most developed provinces in China, and developed a high-resolution emission inventory of nine species for 2015–2019, with improved methodologies for different emission sectors, best available facility-level information on individual sources, and real-world emission measurements. Resulting from implementation of strict emission control measures, the anthropogenic emissions were estimated to have declined 53 %, 20 %, 7 %, 2 %, 10 %, 21 %, 16 %, 6 %, and 18 % for sulfur dioxide (SO 2), nitrogen oxides (NO x), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), ammonia (NH 3), inhalable particulate matter (PM 10), fine particulate matter (PM 2.5), black carbon (BC), and organic carbon (OC) from 2015 to 2019, respectively. Larger abatement of SO 2 , NO x , and PM 2.5 emissions was detected for the more developed region of southern Jiangsu. During the period from 2016 to 2019, the ratio of biogenic volatile organic compounds (BVOCs) to anthropogenic volatile organic compounds (AVOCs) exceeded 50 % in the month of July, indicating the importance of biogenic sources for summer O 3 formation. Our estimates in annual emissions of NO x , NMVOCs, and NH 3 were generally smaller than the national emission inventory, MEIC (the Multi-resolution Emission Inventory for China), but larger for primary particles. The discrepancies between studies resulted mainly from different methods of emission estimation (e.g., the procedure-based approach for AVOC emissions from key industries used in this work) and inconsistent information of emission source operation (e.g., the penetration and removal efficiencies of air pollution control devices). Regarding the different periods, more reduction of SO 2 emissions was found between 2015 and 2017 and of NO x , AVOCs, and PM 2.5 between 2017 and 2019. Among the selected 13 major measures, the ultra-low-emission retrofit in the power sector was the most important contributor to the reduced SO 2 and NO x emissions (accounting for 38 % and 43 % of the emission abatement, respectively) for 2015–2017, but its effect became very limited afterwards as the retrofit had been commonly completed by 2017. Instead, extensive management of coal-fired boilers and the upgrade and renovation of non-electrical industry were the most important measures for 2017–2019, accounting collectively for 61 %, 49 %, and 57 % reduction of SO 2 , NO x , and PM 2.5 , respectively. Controls on key industrial sectors were the most effective for AVOC reduction in the two periods, while measures relating to other sources (transportation and solvent replacement) have become more important in recent years. Our provincial emission inventory was demonstrated to support high-resolution air quality modeling for multiple years. Through scenario setting and modeling, worsened meteorological conditions were found from 2015 to 2019 for PM 2.5 and O 3 pollution alleviation. However, the efforts on emission controls were identified to largely overcome the negative influence of meteorological variation. The changed anthropogenic emissions were estimated to contribute 4.3 and 5.5 µ g m -3 of PM 2.5 concentration reduction for 2015–2017 and 2017–2019, respectively. While O 3 was elevated by 4.9 µ g m -3 for 2015–2017, the changing emissions led to 3.1 µ g m -3 of reduction for 2017–2019, partly (not fully though) offsetting the meteorology-driven growth. The analysis justified the validity of local emission control efforts on air quality improvement and provided a scientific basis to formulate air pollution prevention and control policies for other developed regions in China and worldwide. [ABSTRACT FROM AUTHOR]

Non-methane short-lived climate forcers (SLCFs), including aerosols, ozone, and their precursors, are important climate forcings and primary air pollutants. Future stringent SLCF emissions controls to mitigate air pollution will substantially impact regional climate change. Here, we investigate the changes in future climate extremes and resulting population exposure risks in Asia during 2031–2050 in response to non-methane SLCF emissions reductions using multi-model ensemble (MME) simulations under two shared socioeconomic pathway (SSP) scenarios with the same greenhouse gas (GHG) emissions but weak (SSP3-7.0) and strong (SSP3-7.0-lowNTCF) air quality control measures from the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP), which is endorsed by the Coupled Model Intercomparison Project phase 6 (CMIP6). The MME results show that future reductions in non-methane SLCF emissions lead to an increase of 0.23 ± 0.16 W m -2 in global annual mean effective radiative forcing, thereby magnifying the GHG-induced global surface warming by 0.19 ± 0.1 K during 2031–2050. In terms of the entire study area of Asia, the additional warming caused by the non-methane SLCF reductions increases the temperature of the hottest days (TXx) by 0.3 ± 0.1 K, the percentage of warm days (TX90p) by 4.8 ± 2.2 %, the number of tropical nights (TR) by 1.7 ± 0.8 d, the warm-spell duration (WSDI) by 1.0 ± 0.4 d, the number of heavy precipitation days (R10) by 1.0 ± 0.5 d, the maximum consecutive 5 d precipitation (RX5day) by 1.0 ± 0.3 mm, and the total wet-day precipitation (R95p) by 16.4 ± 7.3 mm during 2031–2050. In terms of sub-regional changes in temperature extremes, the largest regional increases in TXx, TX90p, and WSDI occur in northern India (NIN) and northern China (NC). Relatively large increases in TR are projected in NC and the Sichuan Basin (SCB), reaching 5.1 ± 2.5 d and 4.9 ± 3.3 d, respectively. For precipitation extremes, the regional changes are greatest in southern China (SC), particularly southwestern China (SWC), where reductions in non-methane SLCF emissions increase R10 by 2.5 ± 1.9 d, RX5day by 2.5 ± 1.5 mm, and R95p by 37.5 ± 22.6 mm. Moreover, the populations exposed to temperature and precipitation extremes increase most sharply in NIN, reaching (32.2 ± 11.4) × 10 7 person-days and (4.6 ± 6.1) × 10 6 person-days during 2031–2050, respectively, followed by NC and SCB. Our results highlight the significant impacts of non-methane SLCF reductions on future climate extremes and related exposure risks in eastern and southern Asia, which are comparable to the impact associated with increased GHG forcing in some regions. [ABSTRACT FROM AUTHOR]

Dust is an important aerosol affecting air quality in China in the winter and spring seasons. Dust in China is potentially influenced by the interannual climate variability associated with El Niño. Here, the impacts of El Niño with different temporal and spatial types on dust pollution in boreal winter and spring in China and the potential mechanisms are investigated using a state-of-the-art Earth system model (E3SMv1). We find that the eastern Pacific (EP) and central Pacific (CP) El Niño both increase wintertime dust concentrations by 5–50 µ g m -3 over central-eastern China. Due to a stronger wind and lower relative humidity, which favor dust emissions near sources, and a strengthened northwesterly and reduced precipitation, which are conducive to dust transport, dust concentrations during the CP El Niño are 5–20 µ g m -3 higher in northern China than during the EP El Niño, although the changes are mostly insignificant. El Niño with a short duration (SD) increases boreal winter dust concentrations by 20–100 µ g m -3 over northern China relative to the climatological mean, while there is a decrease of 5–50 µ g m -3 during the long-duration (LD) El Niño, which is also related to the El Niño-induced changes in atmospheric circulation, precipitation, and relative humidity. In the following spring season, all types of El Niño events enhance dust over northern China, but only the increase during the LD El Niño is statistically significant, suggesting that the weaker intensity but longer duration of the LD El Niño events can significantly affect spring dust in China. Our results contribute to the current knowledge of the influence of El Niño on dust pollution, which has profound implications for air pollution control and dust storm prediction. [ABSTRACT FROM AUTHOR]

Aerosol acidity plays a key role in regulating the chemistry and toxicity of atmospheric aerosol particles. The trend of aerosol pH and its drivers is crucial in understanding the multiphase formation pathways of aerosols. Here, we reported the first trend analysis of aerosol pH from 2011 to 2019 in eastern China, calculated with the ISORROPIA model based on observed gas and aerosol compositions. The implementation of the Air Pollution Prevention and Control Action Plan led to -35.8 %, -37.6 %, -9.6 %, -81.0 % and 1.2 % changes of PM 2.5 , SO42- , NHx , non-volatile cations (NVCs) and NO3- in the Yangtze River Delta (YRD) region during this period. Different from the drastic changes of aerosol compositions due to the implementation of the Air Pollution Prevention and Control Action Plan, aerosol pH showed a minor change of -0.24 over the 9 years. Besides the multiphase buffer effect, the opposite effects from the changes of SO42- and non-volatile cations played key roles in determining this minor pH trend, contributing to a change of +0.3 8 and -0.35 , respectively. Seasonal variations in aerosol pH were mainly driven by the temperature, while the diurnal variations were driven by both temperature and relative humidity. In the future, SO2 , NOx and NH3 emissions are expected to be further reduced by 86.9 %, 74.9 % and 41.7 % in 2050 according to the best health effect pollution control scenario (SSP1-26-BHE). The corresponding aerosol pH in eastern China is estimated to increase by ∼0.19 , resulting in 0.04 less NO3- and 0.12 less NH4+ partitioning ratios, which suggests that NH3 and NOx emission controls are effective in mitigating haze pollution in eastern China. [ABSTRACT FROM AUTHOR]

Exposure to high concentration levels of fine particle matter with diameter ≤2.5 µ m (PM 2.5) can lead to great threats to human health in the east of China. Air pollution control has greatly reduced the PM 2.5 concentration and entered a crucial stage that required support like fine seasonal prediction. In this study, we analyzed the contributions of emission predictors and climate variability to seasonal prediction of PM 2.5 concentration. The socioeconomic PM 2.5 , isolated by atmospheric chemical models, could well describe the gradual increasing trend of PM 2.5 during the winters of 2001–2012 and the sharp decreasing trend since 2013. The preceding climate predictors have successfully simulated the interannual variability in winter PM 2.5 concentration. Based on the year-to-year increment approach, a model for seasonal prediction of gridded winter PM 2.5 concentration (10 km × 10 km) in the east of China was trained by integrating emission and climate predictors. The area-averaged percentage of same sign was 81.4 % (relative to the winters of 2001–2019) in the leave-one-out validation. In three densely populated and heavily polluted regions, the correlation coefficients were 0.93 (North China), 0.95 (Yangtze River Delta) and 0.87 (Pearl River Delta) during 2001–2019, and the root-mean-square errors were 6.8, 4.2 and 4.7 µ g m -3. More important, the significant decrease in PM 2.5 concentration, resulting from the implementation of strict emission control measures in recent years, was also reproduced. In the recycling independent tests, the prediction model developed in this study also maintained high accuracy and robustness. Furthermore, the accurate gridded PM 2.5 prediction had the potential to support air pollution control on regional and city scales. [ABSTRACT FROM AUTHOR]

Coordinated control of fine particulate matter (PM 2.5) and ozone (O 3) has become a new and urgent issue for China's air pollution control. Biogenic volatile organic compounds (BVOCs) are important precursors of O 3 and secondary organic aerosol (SOA) formation. China experienced a rapid increase in BVOC emissions as a result of increased vegetation biomass. We applied WRF-Chem3.8 coupling with MEGAN2.1 to conduct long-term simulations for impacts of BVOC emissions on O 3 and SOA during 1981–2018, using the emission factors extrapolated by localized emission rates and annual vegetation biomass. In summer 2018, BVOC emissions were 9.91 Tg (in June), which led to an average increase of 8.6 ppb (16.75 % of the total) in daily maximum 8 h (MDA8) O 3 concentration and 0.84 µgm -3 (73.15 % of the total) in SOA over China. The highest contribution to O 3 is concentrated in the Great Khingan Mountains, Qinling Mountains, and most southern regions while in southern areas for SOA. Isoprene has the greatest contribution to O 3 , while monoterpene has the largest SOA production. BVOC emissions have distinguished impacts in different regions. The Chengdu–Chongqing (CC) region has the highest O 3 and SOA generated by BVOCs, while the Beijing–Tianjin–Hebei (BTH) region has the lowest. From 1981 to 2018, the interannual variation of BVOC emissions caused by increasing leaf biomass resulted in O 3 concentration increasing by 7.38 % at an average rate of 0.11 ppb yr -1 and SOA increasing by 39.30 % at an average rate of 0.008 µgm -3 yr -1. Due to the different changing trends of leaf biomass by region and vegetation type, O 3 and SOA show different interannual variations. The Fenwei Plain (FWP), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions have the most rapid O 3 increment, while the increasing rate of SOA in CC is the highest. BTH has the smallest enhancement in O 3 and SOA concentration. This study will help to recognize the impact of historical BVOC emissions on O 3 and SOA and further provide a reliable scientific basis for the precise prevention and control of air pollution in China. [ABSTRACT FROM AUTHOR]

A multi-model simulation system for street-level circulation and pollutant tracking (S-TRACK) has been developed by integrating the Weather Research and Forecasting (WRF), the STAR-CCM+ (computational fluid dynamics model – CFD), and the Flexible Particle (FLEXPART) models. The winter wind environmental characteristics and the potential contribution of traffic sources to nearby receptor sites in a city district of China are analysed with the system for January 2019. It is found that complex building layouts change the structure of the wind field and thus have an impact on the transport of pollutants. The wind speed inside the building block is lower than the background wind speed due to the dragging effect of dense buildings. Ventilation is better when the dominant airflow is in the same direction as the building layout. Influenced by the building layout, the local circulations show that the windward side of the building is mostly the divergence zone, and the leeward side is mostly the convergence zone, which is more obvious for high buildings. With the hypothesis that the traffic sources are uniformly distributed on each road and with identical traffic intensity, the potential contribution ratios (PCRs) of four traffic sources to certain specific sites under the influence of the street-level circulations are estimated with the method of residence time analysis. It is found that the contribution ratio varies with the height of the receptor site. As a result of the generally upward motion in the airflow, the position with the greatest PCR from the four road traffic sources is located at a certain height which is commonly influenced by the distance of this location from the traffic source and the background wind field (about 15 m in this study). The potential contribution of a road to one of the receptor sites is also investigated under different wind directions. The established system and the results can be used to understand the characteristics of urban wind environment and to help the air pollution control planning in urban areas. [ABSTRACT FROM AUTHOR]

China has made great efforts to monitor and control air pollution in the past decade. Comprehensive characterization and understanding of pollutants in three-dimensions are, however, still lacking. Here, we used data from an observation network consisting of 13 aerosol lidars and more than 1000 ground observation stations combined with a data assimilation technique to conduct a comprehensive analysis of extreme heavy aerosol pollution (HAP) over the North China Plain (NCP) from November–December 2017. During the studied period, the maximum hourly mass concentration of surface PM 2.5 reached ∼390 µ g m -3. After assimilation, the correlation between model results and the independent observation sub-dataset was ∼50 % higher than that without the assimilation, and the root mean square error was reduced by ∼40 %. From pollution development to dissipation, we divided the HAP in the NCP (especially in Beijing) into four phases: an early phase (EP), a transport phase (TP), an accumulation phase (AP), and a removal phase (RP). We then analyzed the evolutionary characteristics of PM 2.5 concentration during different phases on the surface and in 3-D space. We found that the particles were mainly transported from south to north at a height of 1–2 km (during EP and RP) and near the surface (during TP and AP). The amounts of PM 2.5 advected into Beijing with the maximum transport flux intensity (TFI) were through the pathways in the relative order of the southwest > southeast > east pathways. The dissipation of PM 2.5 in the RP stage (with negative TFI) was mainly from north to south with an average transport height of ∼1 km above the surface. Our results quantified the multi-dimensional distribution and evolution of PM 2.5 concentration over the NCP, which may help policymakers develop efficient air pollution control strategies. [ABSTRACT FROM AUTHOR]

In the present work, we performed chassis dynamometer experiments to investigate the emissions and secondary organic aerosol (SOA) formation potential of intermediate-volatility organic compounds (IVOCs) from an on-road Chinese gasoline vehicle. High IVOC emission factors (EFs) and distinct volatility distribution were recognized. The IVOC EFs for the China V vehicle ranged from 12.1 to 226.3 mgperkilogramfuel , with a median value of 83.7 mgperkilogramfuel , which was higher than that from US vehicles. Besides, a large discrepancy in volatility distribution and chemical composition of IVOCs from Chinese gasoline vehicle exhaust was discovered, with larger contributions of B14 – B16 compounds (retention time bins corresponding to C 14 -C 16 n -alkanes) and a higher percentage of n -alkanes. Further we investigated the possible reasons that influence the IVOC EFs and volatility distribution and found that fuel type, starting mode, operating cycles and acceleration rates did have an impact on the IVOC EF. When using E10 (ethanol volume ratio of 10 %, v/v) as fuel, the IVOC EF of the tested vehicle was lower than that using commercial China standard V fuel. The average IVOC-to-THC (total hydrocarbon) ratios for gasoline-fueled and E10-fueled gasoline vehicles were 0.07±0.01 and 0.11±0.02 , respectively. Cold-start operation had higher IVOC EFs than hot-start operation. The China Light-Duty Vehicle Test Cycle (CLTC) produced 70 % higher IVOCs than those from the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). We found that the tested vehicle emitted more IVOCs at lower acceleration rates, which leads to high EFs under CLTC. The only factor that may influence the volatility distribution and compound composition is the engine aftertreatment system, which has compound and volatility selectivity in exhaust purification. These distinct characteristics in EFs and volatility may result in higher SOA formation potential in China. Using published yield data and a surrogate equivalent method, we estimated SOA formation under different OA (organic aerosol) loading and NO x conditions. Results showed that under low- and high-NO x conditions at different OA loadings, IVOCs contributed more than 80 % of the predicted SOA. Furthermore, we built up a parameterization method to simply estimate the vehicular SOA based on our bottom-up measurement of VOCs (volatile organic compounds) and IVOCs, which would provide another dimension of information when considering the vehicular contribution to the ambient OA. Our results indicate that vehicular IVOCs contribute significantly to SOA, implying the importance of reducing IVOCs when making air pollution controlling policies in urban areas of China. [ABSTRACT FROM AUTHOR]

High concentrations of PM 2.5 (particulate matter with an aerodynamic diameter less than 2.5 µ m) in China have caused severe visibility degradation. Accurate simulations of PM 2.5 and its chemical components are essential for evaluating the effectiveness of pollution control strategies and the health and climate impacts of air pollution. In this study, we compared the GEOS-Chem model simulations with comprehensive datasets for organic aerosol (OA), sulfate, nitrate, and ammonium in China. Model results are evaluated spatially and temporally against observations. The new OA scheme with a simplified secondary organic aerosol (SOA) parameterization significantly improves the OA simulations in polluted urban areas, highlighting the important contributions of anthropogenic SOA from semivolatile and intermediate-volatility organic compounds. The model underestimates sulfate and overestimates nitrate for most of the sites throughout the year. More significant underestimation of sulfate occurs in winter, while the overestimation of nitrate is extremely large in summer. The model is unable to capture some of the main features in the diurnal pattern of the PM 2.5 chemical components, suggesting inaccuracies in the presented processes. Potential model adjustments that may lead to a better representation of the boundary layer height, the precursor emissions, hydroxyl radical concentrations, the heterogeneous formation of sulfate and nitrate, and the wet deposition of nitric acid and nitrate have been tested in the sensitivity analysis. The results show that uncertainties in chemistry perhaps dominate the model biases. The proper implementation of heterogeneous sulfate formation and the good estimates of the concentrations of sulfur dioxide, hydroxyl radical, and aerosol liquid water are essential for the improvement of the sulfate simulation. The update of the heterogeneous uptake coefficient of nitrogen dioxide significantly reduces the modeled concentrations of nitrate. However, the large overestimation of nitrate concentrations remains in summer for all tested cases. The possible bias in the chemical production and the wet deposition of nitrate cannot fully explain the model overestimation of nitrate, suggesting issues related to the atmospheric removal of nitric acid and nitrate. A better understanding of the atmospheric nitrogen budget, in particular, the role of the photolysis of particulate nitrate, is needed for future model developments. Moreover, the results suggest that the remaining underestimation of OA in the model is associated with the underrepresented production of SOA. [ABSTRACT FROM AUTHOR]

Future trends in air pollution and greenhouse gas (GHG) emissions for China are of great concern to the community. A set of global scenarios regarding future socio-economic and climate developments, combining shared socio-economic pathways (SSPs) with climate forcing outcomes as described by the Representative Concentration Pathways (RCPs), was created by the Intergovernmental Panel on Climate Change (IPCC). Chinese researchers have also developed various emission scenarios by considering detailed local environmental and climate policies. However, a comprehensive scenario set connecting SSP–RCP scenarios with local policies and representing dynamic emission changes under local policies is still missing. In this work, to fill this gap, we developed a dynamic projection model, the Dynamic Projection model for Emissions in China (DPEC), to explore China's future anthropogenic emission pathways. The DPEC is designed to integrate the energy system model, emission inventory model, dynamic projection model, and parameterized scheme of Chinese policies. The model contains two main modules, an energy-model-driven activity rate projection module and a sector-based emission projection module. The activity rate projection module provides the standardized and unified future energy scenarios after reorganizing and refining the outputs from the energy system model. Here we use a new China-focused version of the Global Change Assessment Model (GCAM-China) to project future energy demand and supply in China under different SSP–RCP scenarios at the provincial level. The emission projection module links a bottom-up emission inventory model, the Multi-resolution Emission Inventory for China (MEIC), to GCAM-China and accurately tracks the evolution of future combustion and production technologies and control measures under different environmental policies. We developed technology-based turnover models for several key emitting sectors (e.g. coal-fired power plants, key industries, and on-road transportation sectors), which can simulate the dynamic changes in the unit/vehicle fleet turnover process by tracking the lifespan of each unit/vehicle on an annual basis. With the integrated modelling framework, we connected five SSP scenarios (SSP1–5), five RCP scenarios (RCP8.5, 7.0, 6.0, 4.5, and 2.6), and three pollution control scenarios (business as usual, BAU; enhanced control policy, ECP; and best health effect, BHE) to produce six combined emission scenarios. With those scenarios, we presented a wide range of China's future emissions to 2050 under different development and policy pathways. We found that, with a combination of strong low-carbon policy and air pollution control policy (i.e. SSP1-26-BHE scenario), emissions of major air pollutants (i.e. SO2 , NOx , PM 2.5 , and non-methane volatile organic compounds – NMVOCs) in China will be reduced by 34 %–66 % in 2030 and 58 %–87 % in 2050 compared to 2015. End-of-pipe control measures are more effective for reducing air pollutant emissions before 2030, while low-carbon policy will play a more important role in continuous emission reduction until 2050. In contrast, China's emissions will remain at a high level until 2050 under a reference scenario without active actions (i.e. SSP3-70-BAU). Compared to similar scenarios set from the CMIP6 (Coupled Model Intercomparison Project Phase 6), our estimates of emission ranges are much lower than the estimates from the harmonized CMIP6 emissions dataset in 2020–2030, but their emission ranges become similar in the year 2050. [ABSTRACT FROM AUTHOR]

Haze pollution caused by PM 2.5 is the largest air quality concern in China in recent years. Long-term measurements of PM 2.5 and the precursors and chemical speciation are crucially important for evaluating the efficiency of emission control, understanding formation and transport of PM 2.5 associated with the change of meteorology, and accessing the impact of human activities on regional climate change. Here we reported long-term continuous measurements of PM 2.5 , chemical components, and their precursors at a regional background station, the Station for Observing Regional Processes of the Earth System (SORPES), in Nanjing, eastern China, since 2011. We found that PM 2.5 at the station has experienced a substantial decrease (-9.1 % yr -1), accompanied by even a very significant reduction of SO2 (-16.7 % yr -1), since the national "Ten Measures of Air" took action in 2013. Control of open biomass burning and fossil-fuel combustion are the two dominant factors that influence the PM 2.5 reduction in early summer and winter, respectively. In the cold season (November–January), the nitrate fraction was significantly increased, especially when air masses were transported from the north. More NH3 available from a substantial reduction of SO2 and increased oxidization capacity are the main factors for the enhanced nitrate formation. The changes of year-to-year meteorology have contributed to 24 % of the PM 2.5 decrease since 2013. This study highlights several important implications on air pollution control policy in China. [ABSTRACT FROM AUTHOR]

Heavy haze usually occurs in winter in eastern China. To control the severe air pollution during the season, comprehensive regional joint-control strategies were implemented throughout a campaign. To evaluate the effectiveness of these strategies and to provide some insights into strengthening the regional joint-control mechanism, the influence of control measures on levels of air pollution was estimated with an integrated measurement-emission-modeling method. To determine the influence of meteorological conditions, and the control measures on the air quality, in a comprehensive study, the 2nd World Internet Conference was held during 16–18 December 2015 in Jiaxing City, Zhejiang province, in the Yangtze River Delta (YRD) region. We first analyzed the air quality changes during four meteorological regimes and then compared the air pollutant concentrations before, during, and after the regulation under static meteorological conditions. Next, we conducted modeling scenarios to quantify the effects caused due to the air pollution control measures. We found that total emissions of SO2 , NOx , PM2.5 , and volatile organic compounds (VOCs) in Jiaxing were reduced by 56 %, 58 %, 64 %, and 80 %, respectively, while total emission reductions of SO2 , NOx , PM2.5 , and VOCs over the YRD region are estimated to be 10 %, 9 %, 10 %, and 11 %, respectively. Modeling results suggest that during the campaign from 8 to 18 December, PM2.5 daily average concentrations decreased by 10 µ g m -3 with an average decrease of 14.6 %. Our implemented optimization analysis compared with previous studies also reveals that local emission reductions play a key role in air quality improvement, although it shall be supplemented by regional linkage. In terms of regional joint control, implementing pollution channel control 48 h before the event is of most benefit in getting similar results. Therefore, it is recommended that a synergistic emission reduction plan between adjacent areas with local pollution emission reductions as the core part should be established and strengthened, and emission reduction plans for different types of pollution through a stronger regional linkage should be reserved. [ABSTRACT FROM AUTHOR]

The implementation of the Air Pollution Prevention and Control Action Plan in China since 2013 has profoundly altered the ambient pollutants in the Beijing–Tianjin–Hebei (BTH) region. Here we show observations of substantially increased O3 concentrations (about 30 %) and a remarkable increase in the ratio of organic carbon (OC) to elemental carbon (EC) in BTH during the autumn from 2013 to 2015, revealing an enhancement in atmospheric oxidizing capacity (AOC) and secondary organic aerosol (SOA) formation. To explore the impacts of increasing AOC on the SOA formation, a severe air pollution episode from 3 to 8 October 2015 with high O3 and PM 2.5 concentrations is simulated using the WRF-Chem model. The model performs reasonably well in simulating the spatial distributions of PM 2.5 and O3 concentrations over BTH and the temporal variations in PM 2.5 , O3 , NO2 , OC, and EC concentrations in Beijing compared to measurements. Sensitivity studies show that the change in AOC substantially influences the SOA formation in BTH. A sensitivity case characterized by a 31 % O3 decrease (or 36 % OH decrease) reduces the SOA level by about 30 % and the SOA fraction in total organic aerosol by 17 % (from 0.52 to 0.43, dimensionless). Spatially, the SOA decrease caused by reduced AOC is ubiquitous in BTH, but the spatial relationship between SOA concentrations and the AOC is dependent on the SOA precursor distribution. Studies on SOA formation pathways further show that when the AOC is reduced, the SOA from oxidation and partitioning of semivolatile primary organic aerosol (POA) and co-emitted intermediate volatile organic compounds (IVOCs) decreases remarkably, followed by those from anthropogenic and biogenic volatile organic compounds (VOCs). Meanwhile, the SOA decrease in the irreversible uptake of glyoxal and methylglyoxal on the aerosol surfaces is negligible. [ABSTRACT FROM AUTHOR]

Understanding the effectiveness of air pollution control policies is important for future policy making. China has implemented strict air pollution control policies since the 11th Five-Year Plan (FYP). There is still a lack of overall evaluation of the effects of air pollution control policies on PM2.5 pollution improvement in China since the 11th FYP. In this study, we aimed to assess the effects of air pollution control policies from 2005 to 2017 on PM2.5 using satellite remote sensing. We used the satellite-derived PM2.5 of 2005–2013 from one of our previous studies. For the data of 2014–2017, we developed a two-stage statistical model to retrieve satellite PM2.5 data using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD), assimilated meteorology, and land use data. The first stage is a day-specific linear mixed effects (LME) model and the second stage is a generalized additive model (GAM). Results show that the Energy Conservation and Emissions Reduction (ECER) policy, implemented in the 11th FYP period and focused on SO2 emissions control, had co-benefits with PM2.5 reductions. The increasing trends of PM2.5 pollution (1.88 and 3.14 µ g m -3 year -1 for all of China and the Jingjinji region in 2004–2007, p<0.005) were suppressed after 2007. The overall PM2.5 trend for all of China was -0.56 µ g m -3 year -1 with marginal significance (p=0.053) and PM2.5 concentrations in the Pearl River Delta region had a big drop (-4.81 µ g m -3 year -1 , p<0.001) in 2007–2010. The ECER policy during the 12th FYP period was basically an extension of the 11th FYP policy. PM2.5 is a kind of composite pollutant which comprises primary particles and secondary particles such as sulfate, nitrate, ammonium, organic carbon, elemental carbon, etc. Since the ECER policy focused on single-pollutant control, it had shown great limitation for PM2.5 reductions. The PM2.5 concentrations did not decrease from 2010 to 2013 in polluted areas (p values of the trends were greater than 0.05). Therefore, China implemented two stricter policies: the 12th FYP on Air Pollution Prevention and Control in Key Regions (APPC-KR) in 2012, and the Action Plan of Air Pollution Prevention and Control (APPC-AP) in 2013. The goal of air quality improvement (especially PM2.5 concentration improvement) and measures for multi-pollutant control were proposed. These policies led to dramatic decreases in PM2.5 after 2013 (-4.27 µ g m -3 year -1 for all of China in 2013–2017, p<0.001). [ABSTRACT FROM AUTHOR]

In the 2015 winter month of December, northern China witnessed the most severe air pollution phenomena since the 2013 winter haze events occurred. This triggered the first-ever red alert in the air pollution control history of Beijing, with an instantaneous fine particulate matter (PM2.5) concentration over 1 mgm-3. Air quality observations reveal large temporal-spatial variations in PM2.5 concentrations over the Beijing-Tianjin-Hebei (Jing-Jin-Ji) area between 2014 and 2015. Compared to 2014, the PM2.5 concentrations over the area decreased significantly in all months except November and December of 2015, with an increase of 36% in December. Analysis shows that the PM2.5 concentrations are significantly correlated with the local meteorological parameters in the Jing-Jin-Ji area such as the stable conditions, relative humidity (RH), and wind field. A comparison of two month simulations (December 2014 and 2015) with the same emission data was performed to explore and quantify the meteorological impacts on the PM2:5 over the Jing-Jin-Ji area. Observation and modeling results show that the worsening meteorological conditions are the main reasons behind this unusual increase of air pollutant concentrations and that the emission control measures taken during this period of time have contributed to mitigate the air pollution (∼9%) in the region. This work provides a scientific insight into the emission control measures vs. the meteorology impacts for the period. [ABSTRACT FROM AUTHOR]