Field study of atmospheric boundary layer observation in a hilly Gobi Desert region and comparison with the CALMET/CALPUFF model

Cui, Huiling; Rentai, Yao; Long-quan, Chen; Lv, Miaomiao; Xin, C. T.; Wu, Q D

doi:10.1016/j.atmosenv.2020.117576

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…This is particularly the case for predicting pesticide dispersion from spray drift. In fact, an increasing number of studies in recent years have coupled CALPUFF and WRF for investigating and predicting atmospheric dispersion of pollutants ( Cui et al, 2020 ; Deb et al, 2014 ; Guo et al, 2020 ; Lee et al, 2014 ; Wu et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

Towards quantifying atmospheric dispersion of pesticide spray drift in Yuma County Arizona

Yuan,

Arellano,

Knickrehm

et al. 2024

Atmospheric Environment

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Section: Methodsmentioning

confidence: 99%

Towards quantifying atmospheric dispersion of pesticide spray drift in Yuma County Arizona

Yuan,

Arellano,

Knickrehm

et al. 2024

Atmospheric Environment

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“…When applied to complex topography, CALPUFF has been shown to be less accurate when modeling atmospheric dispersion under the thermally stable condition [49]. In addition, CALPUFF is shown to overpredict the turbulent diffusion, and as a result, underpredict the concentration of pollutants when compared to observations or more accurate models [50]. CALPUFF is also shown to suffer from lack of adequate meteorological forcing for its wind field and dispersion predictions.…”

Section: Modeling Techniques To Quantify Emission Fluxesmentioning

confidence: 99%

Machine Learning to Predict Area Fugitive Emission Fluxes of GHGs from Open-Pit Mines

et al. 2022

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Greenhouse gas (GHG) emissions from open-pit mines pose a global climate challenge, which necessitates appropriate quantification to support effective mitigation measures. This study considers the area-fugitive methane advective flux (as a proxy for emission flux) released from a tailings pond and two open-pit mines, denominated “old” and “new”, within a facility in northern Canada. To estimate the emission fluxes of methane from these sources, this research employed near-surface observations and modeling using the weather research and forecasting (WRF) passive tracer dispersion method. Various machine learning (ML) methods were trained and tested on these data for the operational forecasting of emissions. Predicted emission fluxes and meteorological variables from the WRF model were used as training and input datasets for ML algorithms. A series of 10 ML algorithms were evaluated. The four models that generated the most accurate forecasts were selected. These ML models are the multi-layer perception (MLP) artificial neural network, the gradient boosting (GBR), XGBOOST (XGB), and support vector machines (SVM). Overall, the simulations predicted the emission fluxes with R2 (-) values higher than 0.8 (-). Considering the bias (Tonnes h−1), the ML predicted the emission fluxes within 6.3%, 3.3%, and 0.3% of WRF predictions for the old mine, new mine, and the pond, respectively.

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“…derived by various meteorological factors [27][28][29]. The CALPUFF atmospheric dispersion model system consists of the CALMET (a diagnostic 3-D meteorological model) meteorological module [30,31], the CALPUFF plume transport module, the CALPOST (California puff post-processing model) post-processing module [32,33], and modules for pre-processing conventional ground station meteorological data, geographic data, altitude data, and precipitation data. The data pre-processing module generates pre-processed input files needed for the CALPUFF model from the pre-collected data, and in this study these inputs contain geographic data and data from ground-based weather monitoring stations.…”

Section: Model Introduction and Parametersmentioning

confidence: 99%

Atmospheric Pollution Impact Assessment of Brick and Tile Industry: A Case Study of Xinmi City in Zhengzhou, China

Xie

2021

Sustainability

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The brick and tile industry was selected to investigate the impact of pollutants emitted from such industry on air quality. Based on the 2018 Zhengzhou City Census data and combined with field sampling and research visits, an emission inventory of the brick and tile industry in Xinmi City was established using the emission factor method. Based on the established emission inventory, the concentrations of SO2, NOX, and PM2.5 emitted by 31 brick and tile enterprises were then predicted using the CALPUFF model(California puff model, USEPA), which had been evaluated for accuracy, and the simulation results were compared with the observed results to obtain the impact of pollutant emissions from the brick and tile industry on air pollution in the simulated region. Results show that SO2, NOX, and PM2.5 emissions from the brick and tile industry in the study area in 2018 were 564.86 tons, 513.16 tons, and 41.01 tons, respectively. The CALPUFF model can simulate the characteristics of meteorological changes and pollutant concentration trends, and the correlation coefficient of the fit curve between the pollutant observed data and the simulated data was higher than 0.8, which can reproduce the impact of key industrial point sources on air quality well. The simulated concentration values and spatial and temporal distribution characteristics of SO2, NOX, PM2.5 in spring, summer, autumn, and winter were obtained from the model simulations. The contribution of pollutant emissions from the brick and tile industry to the monthly average concentrations of SO2, NOX, and PM2.5 in the simulated region were 6.58%, 5.38%, and 1.42%, respectively, with the Housing Administration monitoring station as the receptor point. The brick and tile industry should increase the emission control measures of SO2 and NOX, and at the same time, the emission control of PM2.5 cannot be slackened.

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Field study of atmospheric boundary layer observation in a hilly Gobi Desert region and comparison with the CALMET/CALPUFF model

Cited by 7 publications

References 19 publications

Towards quantifying atmospheric dispersion of pesticide spray drift in Yuma County Arizona

Towards quantifying atmospheric dispersion of pesticide spray drift in Yuma County Arizona

Machine Learning to Predict Area Fugitive Emission Fluxes of GHGs from Open-Pit Mines

Atmospheric Pollution Impact Assessment of Brick and Tile Industry: A Case Study of Xinmi City in Zhengzhou, China

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