Air Quality Prediction Modeling System Research Articles

Development of an integrated machine learning model to improve the secondary inorganic aerosol simulation over the Beijing–Tianjin–Hebei region

Secondary inorganic aerosols (sulfate, nitrate, and ammonium, SNA) are the key components of PM2.5 in China. Accurate and seamless SNA concentration data therefore are important for pollution controls and environmental health risk studies. However, the spatiotemporal characters and changes of SNA are still difficult to accurately estimate due to the shortages of SNA datasets with high accuracy and resolution, as well as the uncertainties in SNA simulations arising from air-quality numerical models. In this study, we developed an integrated model to improve hourly SNA simulations with 5 km spatial resolution over the Beijing–Tianjin–Hebei (BTH) region by integrating the random forest (RF) and Light Gradient Boost Machine (LGBM) using Stacked Generalization. Our model fuses Three-dimensional (3D) numerical simulations from the Weather Research and Forecast model (WRF) and Nested Air Quality Prediction Model System (NAQPMS) with observations of 16 sites from the BTH monitoring network. Three months’ data from January to March 2020 were employed to evaluate the model performance using the cross-validation (CV) method. The results showed that the integrated model provide more accurate simulations of SNA than the 3D numerical model does, with root mean square errors (RMSE) decreased by 33%, 45%, and 35%; correlation coefficient (R) increased by 61%, 28%, and 34%; and Taylor skill score (TSS) increased by 331%, 85%, and 65% for sulfate, nitrate, and ammonium respectively. Moreover, the integrated model showed higher evaluation criteria and more accurate spatiotemporal characteristic compared with the single machine learning (ML) model, especially in heavily polluted area. This study provides a new approach to improve SNA simulations and reveals the potential of ML models for improving aerosol modeling when observational data are scarce.

A two-way coupled regional urban–street network air quality model system for Beijing, China

Abstract. Owing to the substantial traffic emissions in urban areas, especially near road areas, the concentrations of pollutants, such as ozone (O3) and its precursors, have a large difference compared to regional averages, and their distributions cannot be captured accurately by traditional single-scale air quality models. In this study, a new version of a regional urban–street network model (an Integrated Air Quality Modeling System coupling regional urban–street: IAQMS-street v2.0) is presented. An upscaling module is implemented in IAQMS-street v2.0 to calculate the impact of mass transfer to regional scale from street network. The influence of pollutants in the street network is considered in the concentration calculation on the regional scale, which is not considered in a previous version (IAQMS-street v1.0). In this study, the simulated results in Beijing during August 2021, using IAQMS-street v2.0, IAQMS-street v1.0, and the regional model (Nested Air Quality Prediction Modeling System, NAQPMS), are compared. On-road traffic emissions in Beijing, as the key model input data, were established using intelligent image-recognition technology and real-time traffic big data from navigation applications. The simulated results showed that the O3 and nitrogen oxide (NOx) concentrations in Beijing were reproduced by using IAQMS-street v2.0 on both the regional scale and street scale. The prediction fractions within a factor of 2 (FAC2s) between simulations and observations of NO and NO2 increased from 0.11 and 0.34 in NAQPMS to 0.78 and 1.00 in IAQMS-street v2.0, respectively. The normalized mean biases (NMBs) of NO and NO2 decreased from 2.67 and 1.33 to −0.25 and 0.08. In the coupled model, the concentration of NOx at the street scale is higher than that at the regional scale, and the simulated distribution of pollutants on a regional scale was improved in IAQMS-street v2.0 when compared with that in IAQMS-street v1.0. We further used IAQMS-street v2.0 to quantify the contribution of local on-road traffic emissions to the O3 and NOx emissions and analyze the effect of traffic regulation policies in Beijing. Results showed that heavy-duty trucks are the major source of on-road traffic emissions of NOx. The relative contributions of local traffic emissions to NO2, NO, and O3 concentrations were 53.41 %, 57.45 %, and 8.49 %, respectively. We found that traffic regulation policies in Beijing largely decreased the concentrations of NOx and hydrocarbons (HC); however, the O3 concentration near the road increased due to the decrease consumption of O3 by NO. To decrease the O3 concentration in urban areas, controlling the local emissions of HC and NOx from other sources requires consideration.

Exploring the Influence of 34S Fractionation From Emission Sources and SO2 Atmospheric Oxidation on Sulfate Source Apportionment Based on Hourly Resolution δ34S‐SO2/SO42−

AbstractSulfate (SO42−) sources are unclear leading to the underestimation of its concentration in the model. Hourly resolution δ34S‐SO2 and δ34S‐SO42− values of three haze episodes (EP1‐EP3) were synchronously collected for the first time to quantify the influence of 34S fractionation from emission sources (e.g., coal combustion) and SO2 atmospheric oxidation on SO2/SO42− source apportionment. After considering the 34S fractionation from coal combustion and atmospheric oxidation, the reasonable and logical source contributions of SO42− were obtained, showing highly consistent with that of Positive Matrix Factorization model results. Considering the 34S fraction from atmospheric oxidation, the source apportionment of SO2/SO42− obtained by hourly resolution δ34S‐SO2 and δ34S‐SO42− can more accurately reflect the dynamic changes of emission sources. Traffic emissions (49%) and coal combustion (46%–65%) were the major contributors to SO2/SO42− in EP1 and EP2‐EP3, respectively. However, obvious deviations of coal combustion contribution were found without considering 34S fractionation from coal combustion. Especially for the northwest transmission channels in EP2‐EP3, the deviation values accounted for 17.1%–38.5% of secondary SO42−. Moreover, δ34S was considered as a more sensitive source indicator than SO2 concentration by the results comparison of 34S technique and air quality model (Nested Air Quality Prediction Model System), which can provide more reliable evidence for SO2 emission control.

Simulation Evaluation of the Contribution of Typical PM2.5 Pollution and Trans-regional Transport to Ningbo Pollution in the Yangtze River Delta

The weather research and forecasting model (WRF) and the nested air quality prediction model system (NAQPMS) were used to simulate a typical heavy PM2.5 pollution event in the Yangtze River Delta (YRD) on January 10, 2015. The results showed that NAQPMS simulation accurately reproduced the development process of heavy PM2.5 pollution in the YRD. External regional pollution transport was a significant factor, primarily through three channels:the inland channel (Henan-Anhui-Jiangsu), coastal channel (Hebei-Shandong-Jiangsu), and ocean channel (Hebei-Shandong-Huanghai Sea-Jiangsu). The source apportionment of estimating the contribution across regions with Ningbo as the receptor showed that the coastal channel was the main channel in the pollution occurrence stage, and the large transport contribution was from Jiangsu (36.51%). The inland and ocean channels were more important in the heavy pollution stage, and the main external regional contributions were from Zhejiang North (25.55%), Anhui (12.33%), and the ocean (16.92%). Pollution dissipation was mainly affected by the ocean channel, and the main external contribution regions were Jiangsu (14.74%) and the sea (31.43%).

Unexpected high contribution of in-cloud wet scavenging to nitrogen deposition induced by pumping effect of typhoon landfall in China

Atmospheric nitrogen deposition has large eco-environmental effects such as ocean acidification, eutrophication in coastal areas. However, knowledge of the source and the pathway of N deposition in coastal areas is limited, especially during tropical storms, hindering the accurate quantification of how anthropogenic activities influence the ocean ecosystem. In this study, the Nested Air Quality Prediction Modeling System was used to investigate the wet deposition of N induced by typhoon Hagupit over eastern coastal China from an in- and below-cloud process perspective. Our results reveal for the first time an enhancement mechanism of N deposition related to the ‘pumping effect’ of the typhoon. Different from the non-typhoon conditions, air pollutants in the typhoon-affected regions were pumped into the higher altitudes and deposited via the in-cloud scavenging process in the moving path of the typhoon-affected regions. This study updates our understanding of the source–receptor relationship on atmospheric wet deposition caused by tropical cyclones.

Quantifying the Source Contributions to Poor Atmospheric Visibility in Winter over the Central Plains Economic Region in China

The Central Plains Economic Region (CPER) is one of the most polluted regions in China. Air pollution has caused visibility degradation due to the light extinction of fine particles (PM2.5). However, the source of light extinction and visibility degradation is still unclear. In this study, the nested air quality prediction model system coupled with an online tracer-tagging module has been used to quantify the contribution of emission sectors and regions to visibility degradation. The light extinction coefficients were well reproduced over CPER. The results showed that resident-related emissions, traffic and industry were the main sectors of visibility degradation over CPER, contributing 55~62%, 10~28%, and 9~19%, respectively. The contribution of local emissions and regional transport was also investigated, and the results showed that regional transport dominated the light extinction (56~68%), among which transport within Henan province contributes significantly (12~45%). Sensitivity tests showed that the reduction in the resident-related sector was more effective than that of the industry sector. Emission control of 40% in resident-related, industry, and traffic sectors over the whole region can achieve the goal of good visibility. This study will provide scientific suggestions for the control strategies development to mitigate visibility degradation over CPER.

Uncertainties in the simulated intercontinental transport of air pollutants in the springtime from emission and meteorological inputs

The uncertainty in the quantitative estimates of long-range transport of pollutants in air quality models is large. In this study, based on the Global Nested Air Quality Prediction Modeling System (GNAQPMS-SM), using Latin hypercube sampling (LHS) method, we conducted a base simulation and an ensemble of 1° × 1° simulations in April 2017 to assess the uncertainty in model estimates of O3 and PM2.5 intercontinental transport from uncertainties in input parameters (emissions, temperature, wind field). The results show that the base simulation has biases in some regions, and when the uncertainty of input parameters is taken into account, the observations mostly lie within the uncertainty range of the 50 simulated group samples. The uncertainties of input parameters are reasonably quantified in terms of their propagation during computation in GNAQPMS-SM. The best performance occurs with the multiple linear regression (MLR) method, which is used to fit ensemble simulation results. The uncertainty range of monthly O3 transport from North America (NAM) to Europe (EUR) at a 1.2 km altitude is 4.91–5.44 ppb, from EUR to China is 0.78–0.96 ppb, and from China to NAM is 0.76–0.85 ppb. Correspondingly, the MLR result of each is 5.12 ppb, 0.80 ppb, and 0.77 ppb, respectively. The MLR result is thought to be closer to the actual transport amounts, and most of them lie within the uncertainty range. The appreciable differences between base simulation and MLR results reflect the uncertainties in the input parameters. Finally, taking O3 transport from China to Japan as an example, the maximum absolute value of the daily average Spearman correlation coefficient |ρ‾|max between input parameters and contributions is calculated to identify the key uncertainty sources in several regions of O3 transport to Japan. Meteorological and air pollutant observations and the performance of meteorological simulations should be strengthened.

Deep learning-based gas-phase chemical kinetics kernel emulator: Application in a global air quality simulation case

The global atmospheric chemical transport model has become a key technology for air quality forecast and management. However, precise and rapid air quality simulations and forecast are frequently limited by the model’s computational performance. The gas-phase chemistry module is the most time-consuming module in air quality models because its traditional solution method is dynamically stiff. To reduce the solving time of the gas phase chemical module, we built an emulator based on a deep residual neural network emulator (NN) for Carbon Bond Mechanism Z (CBM-Z) mechanism implemented in Global Nested Air Quality Prediction Modeling System. A global high resolution cross-life multi-species dataset was built and trained to evaluate multi-species concentration changes at a single time step of CBM-Z. The results showed that the emulator could accelerate to approximately 300–750 times while maintaining an accuracy similar to that of CBM-Z module (the average correlation coefficient squared was 0.97) at the global scale. This deep learning-based emulator could adequately represent the stiff kinetics of CBM-Z, which involves 47 species and 132 reactions. The emulated ozone (O3), nitrogen oxides (NOx), and hydroxyl radical (OH) were consistent with those of the original CBM-Z module in different global regions, heights, and time. Our results suggest that data-driven emulations have great potential in the construction of hybrid models with process-based air quality models, particularly at larger scales.

Estimating Full-Coverage PM2.5 Concentrations Based on Himawari-8 and NAQPMS Data over Sichuan-Chongqing

Fine particulate matter (PM2.5) has attracted extensive attention due to its harmful effects on humans and the environment. The sparse ground-based air monitoring stations limit their application for scientific research, while aerosol optical depth (AOD) by remote sensing satellite technology retrieval can reflect air quality on a large scale and thus compensate for the shortcomings of ground-based measurements. In this study, the elaborate vertical-humidity method was used to estimate PM2.5 with the spatial resolution 1 km and the temporal resolution 1 hour. For vertical correction, the scale height of aerosols (Ha) was introduced based on the relationship between the visibility data and extinction coefficient of meteorological observations to correct the AOD of the Advance Himawari Imager (AHI) onboard the Himawari-8 satellite. The hygroscopic growth factor (f(RH)) was fitted site-by-site and month by month (1–12 months). Meanwhile, the spatial distribution of the fitted coefficients can be obtained by interpolation assuming that the aerosol properties vary smoothly on a regional scale. The inverse distance weighted (IDW) method was performed to construct the hygroscopic correction factor grid for humidity correction so as to estimate the PM2.5 concentrations in Sichuan and Chongqing from 09:00 to 16:00 in 2017–2018. The results indicate that the correlation between “dry” extinction coefficient and PM2.5 is slightly improved compared to the correlation between AOD and PM2.5, with r coefficient values increasing from 0.12–0.45 to 0.32–0.69. The r of hour-by-hour verification is between 0.69 and 0.85, and the accuracy of the afternoon is higher than that of the morning. Due to the missing rate of AOD in the southwest is very high, this study utilized inverse variance weighting (IVW) gap-filling method combine satellite estimation PM2.5 and the nested air-quality prediction modeling system (NAQPMS) simulation data to obtain the full-coverage hourly PM2.5 concentration and analyze a pollution process in the fall and winter.

Spatio‐Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks

AbstractEfficient and accurate real‐time forecasting of national spatial ozone distribution is critical to the provision of effective early warning. Traditional numerical air quality models require a high computational cost associated with running large‐scale numerical simulations. In this work, we introduce a hybrid model (VAE‐GAN) combining a generative adversarial network (GAN) with a variational autoencoder (VAE) to learn the dynamic ozone distributions in spatial and temporal spaces. The VAE‐GAN model can not only decipher the complex nonlinear relationship between the inputs (the past states/ozone and meteorological factors) and outputs (ozone), but also provide ozone forecasts for a long lead‐time beyond the training period. The performance of VAE‐GAN is demonstrated in hourly and daily spatio‐temporal ozone forecasts over China. The training datasets from 2013 to 2017 and validation datasets from 2018 to 2019 are the collection of data from the air quality reanalysis datasets. With the use of VAE, large dataset sizes are decreased by three orders of magnitude, enabling hourly and daily forecasts to be computed in seconds. Results show that the VAE‐GAN achieves a reasonable accuracy in the prediction of both the spatial and temporal evolution patterns of hourly and daily ozone fields, as compared to the Nested Air Quality Prediction Modeling System (commonly used in China), the reanalysis data and observations during the validation period. Thus, the VAE‐GAN is a cost‐effective tool for large data‐driven predictions, which can potentially reinforce air pollution prediction efforts in providing risk assessment and management in a timely manner.

Quantitative attribution of wintertime haze in coastal east China to local emission and regional intrusion under a stagnant internal boundary layer

A thermal internal boundary layer (TIBL) can cause the fumigation effect and reduce the dispersion capacity of air pollutants. The paper's goal is to quantify the interactions of pollutants between cities subjected to enhanced fumigation effects. To that end, an observational campaign was launched at the Shandong Peninsula of China in the winter of 2020. The campaign focused on monitoring vertical profiles of aerosols, wind field, temperature, and humidity for the boundary-layer dynamic-thermodynamic structure. The Nested Air Quality Prediction Modeling System (NAQPMS) coupled with online source-tagging model was also employed to delve into the source-receptor relationships of the cities involved in the fumigation effect. The results indicated that the presence of a TIBL structure triggered the prolonged and widespread pollution episodes. This resulted in a temperature inversion (250–1000 m in altitude) near the convergence line on surface and rendered the atmospheric boundary layer more stably stratified, i.e., a situation that was unfavorable for the diffusion of pollutants against a persistently sinking flow (with a mean velocity of roughly 0.2 m s−1) for a prolonged time. Meanwhile, TIBLs prevented the fumigation areas from interacting with adjacent inland areas (Shandong Peninsula's local contribution accounting for >90% of the PM2.5) and facilitated inter-city transport of pollutants inside this peninsula (the transport contribution between cities reached up to 55–75%). Such distinct behaviors as observed over this coastal area render air pollution control more of an elusive issue, as is referenced to several inland cities.

Numerical Simulation of Topography Impact on Transport and Source Apportionment on PM2.5 in a Polluted City in Fenwei Plain

The unique energy structure, high intensity of coal production, and complex terrain, make Fenwei Plain a highly polluted region in China. In this study, we characterized the transport characteristic and sources of PM2.5 (the fraction of particulate matter ≤ 2.5 μm) in Sanmenxia, a polluted city in canyon terrain. The results showed that special topography in Sanmenxia had an important role in the transport of particulates. Sanmenxia is located between two northeast-southwest facing mountains, showing a special local circulation. The local circulation was dominated by a downslope wind at nighttime, while the cross−mountain airflow and zonal wind were dominant during the daytime in the canyon terrain. PM2.5 accumulated near Sanmenxia with the influence of downslope, zonal wind, and topography. The main regional transport paths could be summarized into an eastern path, a northern path, and a western path during the severe haze episodes. The PM2.5 source apportionment revealed by an on-line tracer-tagged of the Nested Air Quality Prediction Model System (NAQPMS) showed that the main regional sources of Sanmenxia were Yuncheng, Sanmenxia, and Weinan. The contribution to PM2.5 concentration in Sanmenxia was 39%, 25%, and 11%, respectively. The northern path had the most important impact on Sanmenxia. The results can provide scientific basis for the establishment of severe haze control in Sanmenxia and regional joint control.

Sources of PM2.5 and its responses to emission reduction strategies in the Central Plains Economic Region in China: Implications for the impacts of COVID-19

The Central Plains Economic Region (CPER) located along the transport path to the Beijing-Tianjin-Hebei area has experienced severe PM2.5 pollution in recent years. However, few modeling studies have been performed on the sources of PM2.5, especially the impacts of emission reduction strategies. In this study, the Nested Air Quality Prediction Model System (NAQPMS) with an online tracer-tagging module was adopted to investigate source sectors of PM2.5 and a series of sensitivity tests were conducted to investigate the impacts of different sector-based mitigation strategies on PM2.5 pollution. The response surfaces of pollutants to sector-based emission changes were built. The results showed that resident-related sector (resident and agriculture), fugitive dust, traffic and industry emissions were the main sources of PM2.5 in Zhengzhou, contributing 49%, 19%, 15% and 13%, respectively. Response surfaces of pollutants to sector-based emission changes in Henan revealed that the combined reduction of resident-related sector and industry emissions efficiently decreased PM2.5 in Zhengzhou. However, reduced emissions in only the Henan region barely satisfied the national air quality standard of 75 μg/m3, whereas a 50%–60% reduction in resident-related sector and industry emissions over the whole region could reach this goal. On severely polluted days, even a 60% reduction in these two sectors over the whole region was insufficient to satisfy the standard of 75 μg/m3. Moreover, a reduction in traffic emissions resulted in an increase in the O3 concentration. The results of the response surface method showed that PM2.5 in Zhengzhou decreased by 19% in response to the COVID-19 lockdown, which approached the observed reduction of 21%, indicating that the response surface method could be employed to study the impacts of the COVID-19 lockdown on air pollution. This study provides a scientific reference for the formulation of pollution mitigation strategies in the CPER.

Cross-boundary transport and source apportionment for PM2.5 in a typical industrial city in the Hebei Province, China: A modeling study

Cross-boundary transport of air pollution is a difficult issue in pollution control for the North China Plain. In this study, an industrial district (Shahe City) with a large glass manufacturing sector was investigated to clarify the relative contribution of fine particulate matter (PM2.5) to the city's high levels of pollution. The Nest Air Quality Prediction Model System (NAQPMS), paired with Weather Research and Forecasting (WRF), was adopted and applied with a spatial resolution of 5 km. During the study period, the mean mass concentrations of PM2.5, SO2, and NO2 were observed to be 132.0, 76.1, and 55.5 μg/m3, respectively. The model reproduced the variations in pollutant concentrations in Shahe at an acceptable level. The simulation of online source-tagging revealed that pollutants emitted within a 50-km radius of downtown Shahe contributed 63.4% of the city's total PM2.5 concentration. This contribution increased to 73.9±21.2% when unfavorable meteorological conditions (high relative humidity, weak wind, and low planetary boundary layer height) were present; such conditions are more frequently associated with severe pollution (PM2.5 ≥ 250 μg/m3). The contribution from Shahe was 52.3±21.6%. The source apportionment results showed that industry (47%), transportation (10%), power (17%), and residential (26%) sectors were the most important sources of PM2.5 in Shahe. The glass factories (where chimney stack heights were normally < 70 m) in Shahe contributed 32.1% of the total PM2.5 concentration in Shahe. With an increase in PM2.5 concentration, the emissions from glass factories accumulated vertically and narrowed horizontally. At times when pollution levels were severe, the horizontally influenced area mainly covered Shahe. Furthermore, sensitivity tests indicated that reducing emissions by 20%, 40%, and 60% could lead to a decrease in the mass concentration of PM2.5 of of 12.0%, 23.8%, and 35.5%, respectively.

Evaluation and Bias Correction of the Secondary Inorganic Aerosol Modeling over North China Plain in Autumn and Winter

Secondary inorganic aerosol (SIA) is the key driving factor of fine-particle explosive growth (FPEG) events, which are frequently observed in North China Plain. However, the SIA simulations remain highly uncertain over East Asia. To further investigate this issue, SIA modeling over North China Plain with the 15 km resolution Nested Air Quality Prediction Model System (NAQPMS) was performed from October 2017 to March 2018. Surface observations of SIA at 28 sites were obtained to evaluate the model, which confirmed the biases in the SIA modeling. To identify the source of these biases and reduce them, uncertainty analysis was performed by evaluating the heterogeneous chemical reactions in the model and conducting sensitivity tests on the different reactions. The results suggest that the omission of the SO2 heterogeneous chemical reaction involving anthropogenic aerosols in the model is probably the key reason for the systematic underestimation of sulfate during the winter season. The uptake coefficient of the “renoxification” reaction is a key source of uncertainty in nitrate simulations, and it is likely to be overestimated by the NAQPMS. Consideration of the SO2 heterogeneous reaction involving anthropogenic aerosols and optimization of the uptake coefficient of the “renoxification” reaction in the model suitably reproduced the temporal and spatial variations in sulfate, nitrate and ammonium over North China Plain. The biases in the simulations of sulfate, nitrate, ammonium, and particulate matter smaller than 2.5 μm (PM2.5) were reduced by 84.2%, 54.8%, 81.8%, and 80.9%, respectively. The results of this study provide a reference for the reduction in the model bias of SIA and PM2.5 and improvement of the simulation of heterogeneous chemical processes.

A 6-year-long (2013–2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC

Abstract. A 6-year-long high-resolution Chinese air quality reanalysis (CAQRA) dataset is presented in this study obtained from the assimilation of surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and Nested Air Quality Prediction Modeling System (NAQPMS).This dataset contains surface fields of six conventional air pollutants in China (i.e. PM2.5, PM10, SO2, NO2, CO, and O3) for the period 2013–2018 at high spatial (15 km×15 km) and temporal (1 h) resolutions. This paper aims to document this dataset by providing detailed descriptions of the assimilation system and the first validation results for the above reanalysis dataset. The 5-fold cross-validation (CV) method is adopted to demonstrate the quality of the reanalysis. The CV results show that the CAQRA yields an excellent performance in reproducing the magnitude and variability of surface air pollutants in China from 2013 to 2018 (CV R2=0.52–0.81, CV root mean square error (RMSE) =0.54 mg/m3 for CO, and CV RMSE =16.4–39.3 µg/m3 for the other pollutants on an hourly scale). Through comparison to the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) dataset produced by the European Centre for Medium-Range Weather Forecasts (ECWMF), we show that CAQRA attains a high accuracy in representing surface gaseous air pollutants in China due to the assimilation of surface observations. The fine horizontal resolution of CAQRA also makes it more suitable for air quality studies on a regional scale. The PM2.5 reanalysis dataset is further validated against the independent datasets from the US Department of State Air Quality Monitoring Program over China, which exhibits a good agreement with the independent observations (R2=0.74–0.86 and RMSE =16.8–33.6 µg/m3 in different cities). Furthermore, through the comparison to satellite-estimated PM2.5 concentrations, we show that the accuracy of the PM2.5 reanalysis is higher than that of most satellite estimates. The CAQRA is the first high-resolution air quality reanalysis dataset in China that simultaneously provides the surface concentrations of six conventional air pollutants, which is of great value for many studies, such as health impact assessment of air pollution, investigation of air quality changes in China, model evaluation and satellite calibration, optimization of monitoring sites, and provision of training data for statistical or artificial intelligence (AI)-based forecasting. All datasets are freely available at https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a prototype product containing the monthly and annual means of the CAQRA dataset has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the evaluation of the CAQRA dataset by potential users.

Model Evaluation and Uncertainty Analysis of PM2.5 Components over Pearl River Delta Region Using Monte Carlo Simulations

Sulfate, nitrate, ammonium, organic carbon (OC) and black carbon (BC) are the key components of PM2.5, but predicting their concentrations remains a challenge because of high uncertainties in the modeling. Employing the Nested Air Quality Prediction Modeling System (NAQPMS) developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, this study investigated the uncertainties in Monte Carlo simulations of these aerosols in the Pearl River Delta (PRD) region during 2015. 50 ensemble simulations with a 15 km horizontal resolution were derived by perturbing the emission data for sulfate, nitrate, ammonium, OC and BC from an emission inventory, which is one of the largest sources of uncertainty. Then, surface observations of these species collected from 10 sites across the region for 1 year were used to evaluate the performance of the ensemble simulations. The high correlation coefficients (> 0.74) and low mean biases (< 2 µg m–3) between the mean values of the ensemble and the observation data suggested that the model fairly accurately reproduced spatial and temporal variations in the nitrate, ammonium, OC and BC. However, the predicted sulfate concentrations, which displayed a correlation coefficient of 0.26, were far less reliable, particularly owing to the significant underestimation during winter. Further analysis revealed that uncertainties in the emission data explained most of the discrepancies for the OC and BC, but the mean biases for the sulfate and ammonium, especially during winter, probably stemmed from uncertainties in the heterogeneous reaction modeling.

Quantification of different processes in the rapid formation of a regional haze episode in north China using an integrated analysis tool coupling source apportionment with process analysis

North China Plain (NCP) is one of the most of heavily polluted regions in the world, air pollution associated with haze threatens human health. A high PM2.5 concentration event in the NCP from 30 November to 10 December 2017 was simulated and analyzed by using the Weather Research and Forecasting (WRF) model and the Nested Air Quality Prediction Modeling System (NAQPMS) with an integrated analysis tool coupling source apportionment with process analysis. The weather field simulated by the WRF model and the PM2.5 concentration simulated by NAQPMS agreed well with observations, and the correlation coefficient of PM2.5 between the simulation and observation data remained >0.8 during the study period. We found that this high PM2.5 event can be divided into three phases in NCP: The accumulation of PM2.5 in Phase I was slow and dominated by south or weak winds in the stable boundary layer. In Phase II, PM2.5 concentrations kept a high value in the short term under northern winds resulting from the edge of a cold front. The sustainable high value of PM2.5 concentration in the middle and south of the NCP under northly wind has been less reported. An integrated process contribution analysis and source apportionment technology coupled with NAQPMS showed that the PM2.5 in the north and middle of the NCP were transported to the southern area by the horizontal transmission process. This transport kept PM2.5 high in the south of the NCP and even exceeded the contribution of local emissions. In the Phase III, the strong northern winds from main body of the cold front brought clear air masses and caused the PM2.5 decrease in the whole NCP. The study shows that high dense emissions in the middle of NCP are the main cause for the high PM2.5 events in the whole region, and the north wind could transport the pollutants form upstream region to downstream region, causing the continued high PM2.5 in the middle and south of NCP.

Source apportionment of PM2.5 in the most polluted Central Plains Economic Region in China: Implications for joint prevention and control of atmospheric pollution

With continuous strengthening of emission control actions, the air quality in northern China during winter has been improved in recent years. However, the fine particulate matter (PM2.5) concentration remains high in the Central Plains Economic Region (CPER). The Nested Air Quality Prediction Modeling System coupled with an on-line source-tagged model was applied to investigate the source apportionment of primary and secondary PM2.5 in the most polluted area of China from November 2017 to March 2018. On average, local emissions represent the largest contributor to ambient PM2.5 levels, followed by transport from circumjacent cities at five receptors in northern CPER. Under high PM2.5 concentrations (more than 150 μg/m3), 60%–70% of the primary PM2.5 is freshly emitted on the current day, while the contribution of the current day, one day ago, and two or more days ago are comparable to secondary aerosols. The estimation of contributions based on distance shows that the contribution from regions within 200 km reaches more than 80% of the primary PM2.5, while the contribution from long-range transport within 200–500 km still accounts for more than 20% of the secondary PM2.5 at the receptor sites. These results indicate that control measures of primary particle emission should be effective to reduce the primary PM2.5 levels within local and nearby regions, and control measures of gas phase precursors one or more days in advance should be beneficial to surrounding regions within 500 km. The process analysis results of the change in PM2.5 mass over the northern part of the CPER during increased PM2.5 periods illustrate that the existing particle total transport is smaller than the secondary aerosol chemical production and primary aerosol emissions since the westerly and southerly net inputs are mostly offset by the large net outputs at the east boundary in both cases. This finding indicates that the ambient PM2.5 mass in a selected area is strongly affected by the upstream region; meanwhile, the PM2.5 in this area also spreads to the downstream region. Joint prevention and control measures that cooperate with upstream regions for both primary particles and gas phase precursor emissions are crucial for improving the air quality over polluted areas. As the emission control measures would lead to some economic losses, beneficiary downstream regions could reasonably provide certain economic compensation to upstream regions if emergency control measures are taken.

Modeling Ozone Source Apportionment and Performing Sensitivity Analysis in Summer on the North China Plain

In recent years, air quality issues due to fine particulate matter have been sufficiently treated. However, ozone (O3) has now become the primary pollutant in summer on the North China Plain (NCP). In this study, a three-dimensional chemical transport model (the Nested Air Quality Prediction Model System, NAQPMS) coupled with an online source apportionment module was applied to investigate the sources of O3 pollution over the NCP. Generally, the NAQPMS adequately captured the observed spatiotemporal features of O3 during the period of July 1st to August 31st in 2017 on the NCP. The results of the source apportionment indicated that the contributions of local emissions and transport from the NCP accounted for the largest proportion of O3, with magnitudes of 25% and 39%, respectively. Compared with those in the average monthly results, the local contribution and regional transport during O3 episodes on the NCP increased by 7% and 10%, respectively. Based on sensitivity tests, two thresholds of the sensitivity indicator P(H2O2)/P(HNO3) were detected, at 0.08 and 0.2. Ozone formation in the urban sites of Beijing, Tianjin, and the southern part of Hebei Province was controlled by VOCs, while the other sites were mainly controlled by NOX. Biogenic emissions contributed approximately 18% to O3 formation in July in the southwestern part of Hebei Province.