Modelling air quality during the EXPLORE-YRD campaign – Part I. Model performance evaluation and impacts of meteorological inputs and grid resolutions

Li, Lin; Gong, Kangjia; Mao, J.; Hu, Jianlin; Li, Jingyi; Liu, Zhenxin; Liao, Hong; Qiu, Wanyi; Yu, Ying; Dong, Huabin; Guo, Song; Hu, Min; Zeng, Liming; Zhang, Yuanhang

doi:10.1016/j.atmosenv.2020.118131

Cited by 43 publications

(31 citation statements)

References 56 publications

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“…In addition, high seasonal and annual values of R (0.85-0.95 for T; 0.87-0.91 for RH; 0.70-0.85 for WS; and 0.75-0.89 for WD) are found. The WRF performance in this study is comparable to WRF performance in previous studies (Wang et al, 2021;Hu et al, 2016;Sulaymon 260…”

Section: Wrf Model Performancesupporting

confidence: 83%

“…Table 2 and Fig < NMB ≤ -0.05, -10.37 < MB ≤ -1.89). When compared to previous air quality simulation studies (Hu et al, 2016;Wang et al, 2021;Ma et al, 2021;Sulaymon et al, 2021;Li et al, 2021a), the results in this study show a better model performance. The seasonal daily concentrations of NO3are efficiently predicted in Shanghai, 280…”

Section: Cmaq Model Performancementioning

confidence: 49%

“…The simulation began three days prior to each of the study periods, and the results were not (Fu et al, 2020;Sulaymon et al, 2021). Further details about the CMAQ modeling system provided in previous studies (Hu et al, 2016;Liu et al, 2020b) Hu et al (2016) and Wang et al (2021), shown in Table S1,.…”

Section: Model Configuration 145mentioning

confidence: 99%

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Seasonal modeling analysis of nitrate formation pathways in Yangtze River Delta region, China

Sun

Qin

Xie

et al. 2022

Preprint

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Abstract. Nitrate (NO3-) has been the dominant and the least reduced chemical component of fine particulate matter (PM2.5) since the stringent emission control implemented in China in 2013. The formation pathways of NO3- vary seasonally and differ substantially in daytime vs. nighttime. They are affected by precursor emissions, atmospheric oxidation capacity, and meteorological conditions. Understanding NO3- formation pathways provides insights for the design of effective emission control strategies to mitigate NO3- pollution. In this study, the Community Multiscale Air Quality (CMAQ) model was applied to investigate the impact of regional transport, predominant physical processes, and different formation pathways to NO3- and total nitrate (TNO3, i.e., HNO3+NO3-) production in the Yangtze River Delta (YRD) region during the four seasons of 2017. NO3-/PM2.5 and NO3-/TNO3 are the highest in the winter, reaching 21 % and 94 %, respectively. Adjusted gas ratio (adjGR = ([NH3] + [NO3-])/([HNO3] + [NO3-])) in YRD is generally greater than two in different seasons across most areas in YRD, indicating that YRD is mostly in the NH3-rich regime and NO3- is limited by HNO3 formation. Local emissions and regional transportation contribute to YRD NO3- concentrations by 50–62 % and 38–50 %, respectively. Majority of the regional transport of NO3- concentrations is contributed by indirect transport (i.e., NO3- formed by transported precursors reacting with local precursors). Aerosol (AERO, including condensation, coagulation, new particle formation and aerosol growth) processes are the dominant source of NO3- formation. In summer, NO3- formation is dominated by AERO and total transport (TRAN, sum of horizontal and vertical transport) processes. The OH+NO2 pathway contributes to 60–83 % of the TNO3 production, and the N2O5 heterogeneous (HET N2O5) pathway contributes to 10–36 % in YRD. HET N2O5 contribution becomes more important in cold seasons than warm seasons. Within the planetary boundary layer in Shanghai, the TNO3 production is dominated by the OH+NO2 pathway during the day (98 %) in the summer and spring, and by the HET N2O5 pathway during the night (61 %) in the winter. Local contribution dominates the OH+NO2 pathway for TNO3 production during the day, while indirect transport dominates the HET N2O5 pathway at night.

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Section: Wrf Model Performancesupporting

confidence: 83%

Section: Cmaq Model Performancementioning

confidence: 49%

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Seasonal modeling analysis of nitrate formation pathways in Yangtze River Delta region, China

Sun

Qin

Xie

et al. 2022

Preprint

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“…In this study, the Community Multi-Scale Air Quality (CMAQ) ( Byun and Schere, 2006 ) model is applied to investigate the temporal and spatial variations of PM 2.5 and O 3 during the lockdown in the west coast city of Kannur in Kerala State, India. The CMAQ has been widely utilized to evaluate air quality and can provide robust support for numerical simulations of various air pollutants ( Hu et al, 2017 ; Hu et al, 2015 ; Liu et al, 2020 ; Wang et al, 2021a ). In addition to the national lockdown period that began March 24, the Kerala state government imposed a “triple-lockdown” (more tightened restrictive measures compared to the lockdown period) in Kannur beginning April 20 for 20 days, as Kannur was identified as a “hotspot” of COVID-19 and a “red” zone in the state.…”

Section: Introductionmentioning

confidence: 99%

Integrated process analysis retrieval of changes in ground-level ozone and fine particulate matter during the COVID-19 outbreak in the coastal city of Kannur, India

Ye¹,

Rupakheti²,

Huang³

et al. 2022

Environmental Pollution

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“…Furthermore, a high false-alarm rate (FAR) of 49% was obtained. Studies have revealed that the prediction performance of the atmospheric chemical transport model (CTM) is limited by the uncertainties in the meteorological field data used as model input (Seaman, 2000;Doraiswamy et al, 2010;Hu et al, 2010;Jo et al, 2017;Wang et al, 2021), and in emissions (Hanna et al, 2001;Kim and Jang, 45 2014;Hsu et al, 2019). Moreover, the physical and chemical mechanisms in the model cannot fully reflect real-world phenomena (Berge et al, 2001;Liu et al, 2001;Mallet and Sportisse, 2006;Tang et al, 2009).…”

mentioning

confidence: 99%

Development of a deep neural network for predicting 6-hour average PM_2.5 concentrations up to two subsequent days using various training data

Lee

Koo

et al. 2021

Preprint

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Abstract. This study aims to develop a deep neural network (DNN) model as an artificial neural network (ANN) for the prediction of 6-hour average fine particulate matter (PM2.5) concentrations for a three-day period—the day of prediction (D+0), one day after prediction (D+1) and two days after prediction (D+2)—using observation data and forecast data obtained via numerical models. The performance of the DNN model was comparatively evaluated against that of the currently operational Community Multiscale Air Quality (CMAQ) modelling system for air quality forecasting in South Korea. In addition, the effect on predictive performance of the DNN model on using different training data was analyzed. For the D+0 forecast, the DNN model performance was superior to that of the CMAQ model, and there was no significant dependence on the training data. For the D+1 and D+2 forecasts, the DNN model that used the observation and forecast data (DNN-ALL) outperformed the CMAQ model. The root-mean-squared error (RMSE) of DNN-ALL was lower than that of the CMAQ model by 2.2 μgm−3, and 3.0 μgm−3 for the D+1 and D+2 forecasts, respectively, because the overprediction of higher concentrations was curtailed. An IOA increase of 0.46 for D+1 prediction and 0.59 for the D+2 prediction was observed in case of the DNN-ALL model compared to the IOA of the DNN model that used only observation data (DNN-OBS). In additionally, An RMSE decrease of 7.2 μgm−3 for the D+1 prediction and 6.3 μgm−3 for the D+2 prediction was observed in case of the DNN-ALL model, compared to the RMSE of DNN-OBS, indicating that the inclusion of forecast data in the training data greatly affected the DNN model performance. Considering the prediction of the 6-hour average PM2.5 concentration, the 8.8 μgm−3 RMSE of the DNN-ALL model was 2.7 μgm−3 lower than that of the CMAQ model, indicating the superior prediction performance of the former. These results suggest that the DNN model could be utilized as a better-performing air quality forecasting model than the CMAQ, and that observation data plays an important role in determining the prediction performance of the DNN model for D+0 forecasting, while prediction data does the same for D+1 and D+2 forecasting. The use of the proposed DNN model as a forecasting model may result in a reduction in the economic losses caused by pollution-mitigation policies and aid better protection of public health.

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Modelling air quality during the EXPLORE-YRD campaign – Part I. Model performance evaluation and impacts of meteorological inputs and grid resolutions

Cited by 43 publications

References 56 publications

Seasonal modeling analysis of nitrate formation pathways in Yangtze River Delta region, China

Seasonal modeling analysis of nitrate formation pathways in Yangtze River Delta region, China

Integrated process analysis retrieval of changes in ground-level ozone and fine particulate matter during the COVID-19 outbreak in the coastal city of Kannur, India

Development of a deep neural network for predicting 6-hour average PM_2.5 concentrations up to two subsequent days using various training data

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Modelling air quality during the EXPLORE-YRD campaign – Part I. Model performance evaluation and impacts of meteorological inputs and grid resolutions

Cited by 43 publications

References 56 publications

Seasonal modeling analysis of nitrate formation pathways in Yangtze River Delta region, China

Seasonal modeling analysis of nitrate formation pathways in Yangtze River Delta region, China

Integrated process analysis retrieval of changes in ground-level ozone and fine particulate matter during the COVID-19 outbreak in the coastal city of Kannur, India

Development of a deep neural network for predicting 6-hour average PM2.5 concentrations up to two subsequent days using various training data

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Product

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About

Development of a deep neural network for predicting 6-hour average PM_2.5 concentrations up to two subsequent days using various training data