The Analysis of the Effectiveness of Implementing Emission Reduction Measures in Improving Air Quality and Health of the Residents of a Selected Area of the Lower Silesian Voivodship

Sówka, Izabela; Paciorek, Małgorzata; Skotak, Krzysztof; Kobus, Dominik; Zathey, Maciej; Klejnowski, Krzysztof

doi:10.3390/en13154001

Cited by 3 publications

(1 citation statement)

References 3 publications

(2 reference statements)

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“…The CALifornia PUFF Model (CALPUFF) is an example of a diagnostic model [42]. CALPUFF has been utilized to analyze emission fluxes from industrial and urban sites, as well as the associated air quality and health implications [43][44][45][46][47][48]. When applied to complex topography, CALPUFF has been shown to be less accurate when modeling atmospheric dispersion under the thermally stable condition [49].…”

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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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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Evaluation of the CALPUFF model performance for the estimation of the urban ecosystem CO2 flux

Bezyk

Oshurok

Dorodnikov

et al. 2021

Atmospheric Pollution Research

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Public Responses to Air Pollution in Shandong Province Using the Online Complaint Data

Sun

Jin

et al. 2021

IJGI

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As air users, the public is also participants in air pollution control and important evaluators of environmental protection. Therefore, understanding the public perception and response to air pollution is an essential part of improving air governance. This study proposed an analytical framework for public response to air pollution based on online complaint data and sentiment analysis. In the proposed framework, the emotional dictionary of air pollution was firstly constructed using microblog data and complaint data. Secondly, the emotional dictionary of air pollution and the sentiment analysis method were used to calculate public complaints’ emotional intensity. Besides, the spatial and temporal characteristics of air pollution complaint data and public emotional intensity, the complaints content, and their correlation with PM2.5 (particulate matters smaller than 2.5 micrometers) and PM10 were analyzed using address matching, spatial analysis, and word cloud analysis. Finally, the proposed framework was applied to 13,469 air pollution complaint data in Shandong Province from 2012 to 2018. The obtained results indicated that: the public was mainly complaining about the exhaust gas emissions from enterprises and factories. Spatially, the geographical center of complaint data was located in the inland industrial urban agglomeration of Shandong Province. Correlatively, air pollution complaints’ negative emotional intensity was significantly negatively correlated with PM2.5 (−0.73). Moreover, the number of public complaints about air pollution and the intensity of negative emotions also decreased with improved air quality in Shandong Province in recent years.

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The Analysis of the Effectiveness of Implementing Emission Reduction Measures in Improving Air Quality and Health of the Residents of a Selected Area of the Lower Silesian Voivodship

Cited by 3 publications

References 3 publications

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

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

Evaluation of the CALPUFF model performance for the estimation of the urban ecosystem CO2 flux

Public Responses to Air Pollution in Shandong Province Using the Online Complaint Data

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