Nested 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.

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%).

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.

An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application

Abstract. Aerosol vertical stratification is important for global climate and planetary boundary layer (PBL) stability, and no single method can obtain spatiotemporally continuous vertical profiles. This paper develops an online data assimilation (DA) framework for the Eulerian atmospheric chemistry-transport model (CTM) Nested Air Quality Prediction Model System (NAQPMS) with the Parallel Data Assimilation Framework (PDAF) as the NAQPMS-PDAF for the first time. Online coupling occurs based on a memory-based way with two-level parallelization, and the arrangement of state vectors during the filter is specifically designed. Scaling tests demonstrate that the NAQPMS-PDAF can make efficient use of parallel computational resources for up to 25 000 processors with a weak scaling efficiency of up to 0.7. The 1-month long aerosol extinction coefficient profiles measured by the ground-based lidar and the concurrent hourly surface PM2.5 are solely and simultaneously assimilated to investigate the performance and application of the DA system. The hourly analysis and subsequent 1 h simulation are validated through lidar and surface PM2.5 measurements assimilated and not assimilated. The results show that lidar DA can significantly improve the underestimation of aerosol loading, especially at a height of approximately 400 m in the free-running (FR) experiment, with the mean bias (BIAS) changing from −0.20 (−0.14) km−1 to −0.02 (−0.01) km−1 and correlation coefficients increasing from 0.33 (0.28) to 0.91 (0.53) averaged over sites with measurements assimilated (not assimilated). Compared with the FR experiment, simultaneously assimilating PM2.5 and lidar can have a more consistent pattern of aerosol vertical profiles with a combination of surface PM2.5 and lidar, independent extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET). Lidar DA has a larger temporal impact than that in PM2.5 DA but has deficiencies in subsequent quantification on the surface PM2.5. The proposed NAQPMS-PDAF has great potential for further research on the impact of aerosol vertical distribution.

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.

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.

Why do models perform differently on particulate matter over East Asia? A multi-model intercomparison study for MICS-Asia III

Abstract. This study compares the performance of 12 regional chemical transport models (CTMs) from the third phase of the Model Inter-Comparison Study for Asia (MICS-Asia III) on simulating the particulate matter (PM) over East Asia (EA) in 2010. The participating models include the Weather Research and Forecasting model coupled with Community Multiscale Air Quality (WRF-CMAQ; v4.7.1 and v5.0.2), the Regional Atmospheric Modeling System coupled with CMAQ (RAMS-CMAQ; v4.7.1 and v5.0.2), the Weather Research and Forecasting model coupled with chemistry (WRF-Chem; v3.6.1 and v3.7.1), Goddard Earth Observing System coupled with chemistry (GEOS-Chem), a non-hydrostatic model coupled with chemistry (NHM-Chem), the Nested Air Quality Prediction Modeling System (NAQPMS) and the NASA-Unified WRF (NU-WRF). This study investigates three model processes as the possible reasons for different model performances on PM. (1) Models perform very differently in the gas–particle conversion of sulfur (S) and oxidized nitrogen (N). The model differences in sulfur oxidation ratio (50 %) are of the same magnitude as that in SO42- concentrations. The gas–particle conversion is one of the main reasons for different model performances on fine mode PM. (2) Models without dust emission modules can perform well on PM10 at non-dust-affected sites but largely underestimate (up to 50 %) the PM10 concentrations at dust sites. The implementation of dust emission modules in the models has largely improved the model accuracies at dust sites (reduce model bias to −20 %). However, both the magnitude and distribution of dust pollution are not fully captured. (3) The amounts of modeled depositions vary among models by 75 %, 39 %, 21 % and 38 % for S wet, S dry, N wet and N dry depositions, respectively. Large inter-model differences are found in the washout ratios of wet deposition (at most 170 % in India) and dry deposition velocities (generally 0.3–2 cm s−1 differences over inland regions).

Influence of the morphological change in natural Asian dust during transport: A modeling study for a typical dust event over northern China

The effect of the nonsphericity of mineral dust aerosols on its deposition and transport was investigated based on model simulation for a typical dust event over northern China from April 6 to 12, 2018. The settling velocity related to morphological change in dust size was considered in Nested Air Quality Prediction Modeling System (NAQPMS) to simulate the dust spatial distribution. Comparison of these results with observations showed that the model reproduced the temporal variability in the mass concentration of particles along the dust plume pathway. The most frequently reported aspect ratio (λ) was 1.7 ± 0.2 for Asian dust aerosols. Changing the nonsphericity of the particle from typical prolate ellipsoids (λ = 1.7) to spherical ellipsoids (λ = 1) caused an ~3% decrease in the surface dust concentration on average. For particles with diameters >5 μm, nonsphericity caused a change in the surface dust concentration up to 10%, especially at the periphery of the dust source region. The overall effects on the fine dust (<2.5 μm) were not significant. A sensitivity study using a more extreme nonspherical shape (λ = 2) showed that the differences in PM10 concentration were evident, and the surface dust concentration increased by 15 ± 5% as a result of an ~10% decrease in settling velocity. These results confirmed that the effect of the variability in the nonsphericity of Asian dust particles on their regional transport highly depended on synoptical and pollution conditions, and the adoption of a deposition value that changes over time due to this morphological variability could improve the performance of dust modeling and the assessment of climate effects on a global scale, especially for transboundary processes.

Concentration Characteristics and Assessment of Model-Predicted Results of PM2.5 in the Beijing-Tianjin-Hebei Region in Autumn and Winter

This paper discusses the concentration characteristics of PM2.5, as well as its relationship with meteorological factors in autumn and winter (from September to the following February), from 2013 to 2018 in the Beijing-Tianjin-Hebei (BTH) region. The accuracy and uncertainty of the air quality forecast models NAQPMS(nested air quality prediction modeling system), CMAQ(community multiscale air quality modeling system), and CAMx (comprehensive air quality model with extensions) were analyzed based on the model-predicted and measured PM2.5 concentration in autumn and winter from 2015 to 2018. The accuracy of NAQPMS, CMAQ, and CAMx during typical heavy air pollution was also tested. Moreover, methods to improve the accuracy of the model forecast were discussed. The results showed that the mean concentrations of PM2.5 in the BTH region were 122, 98, 82, 99, and 65 μg·m-3 in the five autumn and winter periods, respectively. When the air quality index (AQI) exceeded 150 during each autumn and winter, it reached 229, 198, 210, 204, and 180 μg·m-3, respectively. There were 64 occurrences of heavy regional PM2.5 air pollution in autumn and winter from 2013 to 2018. The average duration was longest in the 2013 to 2014 period, and shortest in the 2017 to 2018 period. The peak concentration and average concentration of PM2.5 decreased year on year, except for the period from 2016 to 2017. In autumn and winter, PM2.5 concentration had a relatively close relationship with relative humidity, wind and sunshine duration, compared with a weak relationship with temperature and air pressure. Regional heavy air pollution always happened under the condition of low wind speed(less than 2 m·s-1),higher relative humidity(greater than 65%),and southwest and northeast wind direction. In addition, the heavy air pollution of PM2.5 in BTH in autumn and winter can be effectively forecasted by NAQPMS, CMAQ, and CAMx. The predicted and measured PM2.5 concentration showed a close relationship. The models performed well in forecasting Zhangjiakou, Chengde, and Qinhuangdao, but by contrast overestimated in Tangshan, Shijiazhuang, Baoding, Beijing, and Tianjin. The uncertainty of emission sources, measured and predicted meteorological data, and the atmospheric chemical reaction mechanism may be the main reasons for the overestimate.

Modeling of aerosol property evolution during winter haze episodes over a megacity cluster in northern China: roles of regional transport and heterogeneous reactions of SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;

Abstract. Regional transport and heterogeneous reactions have been shown to play crucial roles in haze formation over a megacity cluster centered on Beijing. In this study, the updated Nested Air Quality Prediction Model System (NAQPMS) and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were employed to investigate the evolution of aerosols – in terms of the number concentration, size distribution, and degree of aging – in Beijing during six haze episodes between 15 November and 15 December 2016, as part of the Air Pollution and Human Health–Beijing (APHH-Beijing) winter campaign of 2016. The model exhibited reasonable performance not only with respect to mass concentrations of PM2.5 and its components in Beijing but also regarding the number concentration, size distribution, and degree of aging. We revealed that regional transport played a non-negligible role in haze episodes, with contributions of 14 %–31 % to the surface PM2.5 mass concentration. The contribution of regional transport to secondary inorganic aerosols was larger than that to primary aerosols (30 %–63 % and 3 %–12 %, respectively). The chemical transformation of SO2 along the transport pathway from source regions to Beijing was the major source of SO42-. We also found that sulfate formed outside Beijing from SO2 emitted in Beijing; this sulfate was then blown back to Beijing and considerably influenced haze formation. Along the transport pathway, aerosols underwent aging, which altered the mass ratio of the coating of black carbon to black carbon (RBC) and the size distribution of number concentrations. During the episodes, the geometric mean diameter (GMD) increased from less than 100 nm at the initial site to approximately 120 nm at the final site (Beijing), and the RBC increased from 2–4 to 4–8. During haze episodes with high humidity, the average contributions of gas and aqueous chemistry, heterogeneous chemistry, and primary emission to sulfate were comparable. However, their relative contributions varied with pollution levels. Primary emissions had the greatest impact under light to moderate pollution levels, whereas heterogeneous chemistry had a stronger effect under high pollution levels.

Modeling study of ozone source apportionment over the Pearl River Delta in 2015

In recent years, the concentration of fine particulate matter has decreased gradually in the Pearl River Delta (PRD) region, but the ozone (O3) concentration remains high and has become the primary air pollutant. In this study, using a three-dimensional numerical model [nested air quality prediction modeling system (NAQPMS)] coupled with an on-line source apportionment module, the contribution of different source regions and source categories to the O3 concentration in the PRD region was quantified. A comparison with observation data confirmed that the NAQPMS adequately reproduced surface O3 concentrations in different seasons. Compared with biogenic emissions, anthropogenic precursors play a dominant role in O3 production. In Guangzhou city, among different source categories, mobile emission is the largest contributor (accounting for approximately 40%), followed by industry emissions (20%–24%). Regional control measures for solvent use and mobile emissions are effective for reducing O3 concentration. In the PRD region, self-contribution is more significant in daytime (∼40%) than in nighttime (∼10%) on average. Among the source regions outside PRD, the northern part of Guangdong province, Jiangxi province, and Fujian province are important contributors. Within the PRD region, the self-contribution of each city increases by 12%–32% during O3 episodes (>80 ppbv) compared with the annual mean contribution. The contribution of the entire PRD region and the entire Guangdong province is 46%–63% and 63%–74% in PRD cities during O3 episodes. These results indicate that regional collaboration on emission control within PRD or Guangdong province is effective for reducing O3 episodes in the PRD region. In addition, because long-range transport from regions outside Guangdong province played an important role in the O3 concentration in the PRD region, long-term emission control measures throughout China in subsequent years should be propitious to further reduce the annual O3 level and improve air quality in the PRD region.

Investigating secondary organic aerosol formation pathways in China during 2014

The concentrations of organic aerosol (OA) in central and eastern China are much higher than those in Europe and North America. Compared with observations, recent numerical modeling studies have largely underestimated the concentrations of OA, especially the secondary components. Based on the volatility basis set framework, a secondary OA (SOA) module is developed and coupled into the nested air quality prediction modeling system (NAQPMS). This model is applied to investigate the concentration and formation pathways of SOA in central and eastern China during 2014, and the simulation performance is validated through observation. The oxidation of intermediate volatile organic compounds (IVOCs) is significant to SOA formation and could reduce the great discrepancy between simulations and observations. In winter, the contribution of IVOCs accounted for 60%–80% of SOA formation. In spring and autumn, IVOCs and volatile organic compounds (VOCs) have a considerable effect on SOA formation. In summer, the contributions of VOCs reach 40%–60% due to an increase in the emissions of biogenic VOCs. The mass yields and other parameters of IVOCs remain largely uncertain. By using the latest mass yields obtained from chamber experiments, the simulated SOA concentrations in winter increase by 10–20 μg m−3 compared with the base scenario. To more thoroughly understand SOA formation in China, further research on the emission sources and mass yields of IVOCs is warranted.

High-time-resolution source apportionment of PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; in Beijing with multiple models

Abstract. Beijing has suffered from heavy local emissions as well as regional transport of air pollutants, resulting in severe atmospheric fine-particle (PM2.5) pollution. This study developed a combined method to investigate source types of PM2.5 and its source regions during winter 2016 in Beijing, which include the receptor model (positive matrix factorization, PMF), footprint and an air quality model. The PMF model was performed with high-time-resolution measurements of trace elements, water soluble ions, organic carbon and elemental carbon using online instruments during the wintertime campaign of the Air Pollution and Human Health in a Chinese Megacity – Beijing (APHH-Beijing) program in 2016. Source types and their contributions estimated by PMF model using online measurements were linked with source regions identified by the footprint model, and the regional transport contribution was estimated by an air quality model (the Nested Air Quality Prediction Model System, NAQPMS) to analyze the specific sources and source regions during haze episodes. Our results show that secondary and biomass-burning sources were dominated by regional transport, while the coal combustion source increased with local contribution, suggesting that strict control strategies for local coal combustion in Beijing and a reduction of biomass-burning and gaseous precursor emissions in surrounding areas were essential to improve air quality in Beijing. The combination of PMF with footprint results revealed that secondary sources were mainly associated with southern footprints (53 %). The northern footprint was characterized by a high dust source contribution (11 %), while industrial sources increased with the eastern footprint (10 %). The results demonstrated the power of combining receptor model-based source apportionment with other models in understanding the formation of haze episodes and identifying specific sources from different source regions affecting air quality in Beijing.

Estimating PM2.5 Concentrations Based on MODIS AOD and NAQPMS Data over Beijing⁻Tianjin⁻Hebei.

Accurately estimating fine ambient particulate matter (PM2.5) is important to assess air quality and to support epidemiological studies. To analyze the spatiotemporal variation of PM2.5 concentrations, previous studies used different methodologies, such as statistical models or neural networks, to estimate PM2.5. However, there is little research on full-coverage PM2.5 estimation using a combination of ground-measured, satellite-estimated, and atmospheric chemical model data. In this study, the linear mixed effect (LME) model, which used the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), meteorological data, normalized difference vegetation index (NDVI), and elevation data as predictors, was fitted for 2017 over Beijing–Tianjin–Hebei (BTH). The LME model was used to calibrate the PM2.5 concentration using the nested air-quality prediction modeling system (NAQPMS) simulated with ground measurements. The inverse variance weighting (IVW) method was used to fuse satellite-estimated and model-calibrated PM2.5. The results showed a strong agreement with ground measurements, with an overall coefficient (R2) of 0.78 and a root-mean-square error (RMSE) of 26.44 μg/m3 in cross-validation (CV). The seasonal R2 values were 0.75, 0.62, 0.80, and 0.78 in the spring, summer, autumn, and winter, respectively. The fusion results supplement the lack of satellite estimates and can capture more detailed information than the NAQPMS model. Therefore, the results will be helpful for pollution process analyses and health-related studies.

MP CBM-Z V1.0: design for a new Carbon Bond Mechanism Z (CBM-Z) gas-phase chemical mechanism architecture for next-generation processors

Abstract. Precise and rapid air quality simulations and forecasting are limited by the computational performance of the air quality model used, and the gas-phase chemistry module is the most time-consuming function in the air quality model. In this study, we designed a new framework for the widely used the Carbon Bond Mechanism Z (CBM-Z) gas-phase chemical kinetics kernel to adapt the single-instruction, multiple-data (SIMD) technology in next-generation processors to improve its calculation performance. The optimization implements the fine-grain level parallelization of CBM-Z by improving its vectorization ability. Through constructing loops and integrating the main branches, e.g., diverse chemistry sub-schemes, multiple spatial points in the model can be operated simultaneously on vector processing units (VPUs). Two generation CPUs – Intel Xeon E5-2680 V4 CPU and Intel Xeon Gold 6132 – and Intel Xeon Phi 7250 Knights Landing (KNL) are used as the benchmark processors. The validation of the CBM-Z module outputs indicates that the relative bias reaches a maximum of 0.025 % after 10 h integration with -fp-model fast =1 compile flag. The results of the module test show that the Multiple-Points CBM-Z (MP CBM-Z) resulted in 5.16× and 8.97× speedup on a single core of Intel Xeon E5-2680 V4 and Intel Xeon Gold 6132 CPUs, respectively, and KNL had a speedup of 3.69× compared with the performance of CBM-Z on the Intel Xeon E5-2680 V4 platform. For the single-node tests, the speedup on the two generation CPUs can reach 104.63× and 198.50× using message passing interface (MPI) and 101.02× and 194.60× using OpenMP, and the speedup on the KNL node can reach 175.23× using MPI and 167.45× using OpenMP. The speedup of the optimized CBM-Z is approximately 40 % higher on a one-socket KNL platform than on a two-socket Broadwell platform and about 13 %–16 % lower than on a two-socket Skylake platform. We also tested a three-dimensional chemistry transport model (CTM) named Nested Air Quality Prediction Model System (NAQPMS) equipped with the MP CBM-Z. The tests illustrate an obvious improvement on the performance for the CTM after adopting the MP CBM-Z. The results show that the MP CBM-Z leads to a speedup of 3.32 and 1.96 for the gas-phase chemistry module and the CTM on the Intel Xeon E5-2680 platform. Moreover, on the new Intel Xeon Gold 6132 platform, the MP CBM-Z gains 4.90× and 2.22× speedups for the gas-phase chemistry module and the whole CTM. For the KNL, the MP CBM-Z enables a 3.52× speedup for the gas-phase chemistry module, but the whole model lost 24.10 % performance compared to the CPU platform due to the poor performance of other modules. In addition, since this optimization seeks to improve the utilization of the VPU, the model is more suitable for the new generation processors adopting the more advanced SIMD technology. The results of our tests already show that the benefit of updating CPU improved by about 47 % by using the MP CBM-Z since the optimized code has better adaptability for the new hardware. This work improves the performance of the CBM-Z chemical kinetics kernel as well as the calculation efficiency of the air quality model, which can directly improve the practical value of the air quality model in scientific simulations and routine forecasting.