A review of the CAMx, CMAQ, WRF-Chem and NAQPMS models: Application, evaluation and uncertainty factors

Gao, Zhaoqi; Zhou, Xuehua

doi:10.1016/j.envpol.2023.123183

Cited by 4 publications

(3 citation statements)

References 208 publications

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“…It integrates information from multiple aspects such as emission inventories, meteorological conditions, and chemical mechanisms [37]. The WRF-CMAQ model is widely used to study the formation, migration and transformation, and spatiotemporal distribution characteristics of air pollutants [38]. Zhang et al [39] used the WRF-CAMQ model to determine the reasons for the decreases in PM 2.5 concentrations in the "2 + 26" cities in the Beijing-Tianjin-Hebei (BTH) region during the 2017/18 heating season.…”

Section: Introductionmentioning

confidence: 99%

Analyses and Simulations of PM2.5 Pollution Characteristics under the Influence of the New Year’s Day Effects in China

Shi,

Hou,

Wang

et al. 2024

Atmosphere

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Regional haze often occurs after the New Year holiday. To explore the characteristics of PM2.5 pollutions under the influence of the New Year’s Day effect, this study analyzed the spatiotemporal changes relating to PM2.5 during and around the New Year’s Day holiday in China from 2015 to 2022, and used the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model to study the effects of human activities and meteorological factors on PM2.5 pollutions, as well as the differences in the contributions of different industries to PM2.5 pollutions. The results show that for the entire study period (i.e., before, during, and after the New Year’s Day holiday) from 2015 to 2022, the average concentrations of PM2.5 in China decreased by 41.9% overall. In 2019~2022, the New Year’s Day effect was significant, meaning that the average concentrations of PM2.5 increased by 18.9~46.8 μg/m3 from before to after the New Year’s Day holiday, with its peak occurring (64.3~74.9 μg/m3) after the holiday. In terms of spatial differences, the average concentrations of PM2.5 were higher in the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and central China. Moreover, the Beijing–Tianjin–Hebei region and its surrounding areas, the Chengdu–Chongqing region, the Fenwei Plain, and the middle reaches of the Yangtze River region were greatly affected by the New Year’s Day effect. Human activities led to higher increases in PM2.5 in Henan, Hubei, Hebei, and Anhui on 3 and 4 January 2022. If the haze was accompanied by cloudy days or weak precipitation, the accumulation of surface water vapor and atmospheric aerosols further increased the possibility of heavy pollution. It was found that, for the entire study period, PM2.5 generated by residential sources contributed the vast majority (60~100 μg/m3) of PM2.5 concentrations, and that the main industry sources that caused changes in time distributions were industrial and transportation sources.

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Section: Introductionmentioning

confidence: 99%

Analyses and Simulations of PM2.5 Pollution Characteristics under the Influence of the New Year’s Day Effects in China

Shi,

Hou,

Wang

et al. 2024

Atmosphere

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“…They are also employed globally for operational air quality forecasting (Jena et al, 2021;Koo et al, 2012;Kumar et al, 2012Kumar et al, , 2021Srinivas et al, 2016;Zhang et al, 2012). Air quality models are categorized into two types: 'fully coupled' models, which integrate interactions between chemistry and meteorology, and 'offline' models, where chemistry and meteorology simulations are conducted independently (Gao & Zhou, 2024). Some of state of the art AQ models include the Weather Research and Forecasting (WRF) model coupled with chemistry (WRF-Chem; Grell et al, 2005;Skamarock et al, 2008), WRF-Chem-MADRID (Model of Aerosol Dynamics, Reaction, Ionization and Dissolution, Zhang et al, 2010), CESM2 (Community Earth System Model version 2, Emmons et al, 2020), CHIMERE (Menut et al, 2021), LOTOS-EUROS(v2.0) (Long Term Ozone Simulation European Operational Smog, Manders et al, 2017) and COSMO/MESSy (Consortium for Small Scale Modelling/ Modular Earth Submodel System, Kerkweg & Jöckel, 2012).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the WRF-Chem Performance for gaseous pollutants over the United Arab Emirates

Yarragunta,

Francis,

Fonseca

et al. 2024

Preprint

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Abstract. This study presents a comprehensive evaluation of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in simulating meteorological parameters and concentrations of gaseous pollutants across the United Arab Emirates (UAE) for the months of June and December 2018, representing the contrasting climatic conditions of summer and winter. The assessment of WRF-Chem performance involved comparisons with ground-based observations for meteorological parameters and satellite retrievals from the TROPOspheric Monitoring Instrument (TROPOMI) for gaseous pollutants. The assessment of gaseous pollutants using the WRF-Chem model revealed distinct patterns in the estimation of pollutant levels across different areas and seasons. The comparison with TROPOMI column concentration revealed the model's strengths in simulating tropospheric NO2 and total O3 spatio-temporal patterns, although it had deficiencies in modelling the total CO column concentrations. The model exhibited a strong correlation with TROPOMI retrievals, with correlation coefficients ranging between 0.71 and 0.95 for summer and 0.86 to 0.94 for winter among these gaseous pollutants. It tended to slightly overestimate NO2 levels, with a higher discrepancy observed in summer (0.24 x 1015 molecules/cm2) compared to winter (0.19 x 1015 molecules/cm2). When comparing WRF-Chem to TROPOMI-CO data, the discrepancies were more pronounced, showing an overestimation of 0.48 x 1018 molecules/cm2 in summer and a significant underestimation of 1.13 x 1018 molecules/cm2 in winter. The model consistently underestimated ozone levels in both seasons, by 0.15 x 1018 and 0.20 x 1018 molecules/cm2, respectively. Meteorological evaluations revealed the model's tendency to underestimate the 2-m temperature in summer and overestimate it in winter, with mean biases ranging from -2.17 to +1.19 °C and a Root Mean Square Error in the range of 0.8 to 5.9 °C among the stations. The model showed enhanced performance for the 10-m wind speed and downward shortwave radiation flux, reflecting advancements over previous studies. Therefore, the WRF-Chem model effectively simulates key meteorological parameters and pollutants over the UAE, demonstrating significant regional-scale prediction skills. Areas for further model refinement are also identified and discussed. Integrating model predictions with satellite and ground-based data is emphasized for advancing air quality monitoring and enhancing predictive accuracy of atmospheric pollutants in this region.

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“…To date, there have been limited numerical simulation studies focusing on the 2021 dust storm event in China, and these simulations have shown noticeable errors [37]. Currently, there is a significant research gap in the field of aerosol data assimilation (DA), particularly regarding its application in improving forecasts of extreme dust events such as the one observed in March 2021.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Numerical Simulation of the Dust Event of March 2021: Integrating Aerosol Observations through Multi-Scale 3D Variational Assimilation in the WRF-Chem Model

Mei,

You,

Zhong

et al. 2024

Remote Sensing

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The integration of high-resolution aerosol measurements into an atmospheric chemistry model can improve air quality forecasting. However, traditional data assimilation methods are challenged in effectively incorporating such detailed aerosol information. This study utilized the WRF-Chem model to conduct data assimilation and prediction experiments using the Himawari-8 satellite’s aerosol optical depth (AOD) product and ground-level particulate matter concentration (PM) measurements during a record-breaking dust event in the Beijing–Tianjin–Hebei region from 14 to 18 March 2021. Three experiments were conducted, comprising a control experiment without assimilation (CTL), a traditional three-dimensional variational (3DVAR) experiment, and a multi-scale three-dimensional variational (MS-3DVAR) experiment. The results indicated that the CTL method significantly underestimated the intensity and extent of the severe dust event, while the analysis fields and forecasting fields of PM concentration and AOD can be significantly improved in both 3DVAR and MS-3DVAR assimilation. Particularly, the MS-3DVAR assimilation approach yielded better-fitting extreme values than the 3DVAR method, mostly likely due to the multi-scale information from the observations used in the MS-3DVAR method. Compared to the CTL method, the correlation coefficient of MS-3DVAR assimilation between the assimilated PM10 analysis fields and observations increased from 0.24 to 0.93, and the positive assimilation effect persisted longer than 36 h. These findings suggest the effectiveness and prolonged influence of integrating high-resolution aerosol observations through MS-3DVAR assimilation in improving aerosol forecasting capabilities.

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A review of the CAMx, CMAQ, WRF-Chem and NAQPMS models: Application, evaluation and uncertainty factors

Cited by 4 publications

References 208 publications

Analyses and Simulations of PM2.5 Pollution Characteristics under the Influence of the New Year’s Day Effects in China

Analyses and Simulations of PM2.5 Pollution Characteristics under the Influence of the New Year’s Day Effects in China

Evaluation of the WRF-Chem Performance for gaseous pollutants over the United Arab Emirates

Optimizing the Numerical Simulation of the Dust Event of March 2021: Integrating Aerosol Observations through Multi-Scale 3D Variational Assimilation in the WRF-Chem Model

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