Sadaf Javed , Muhammad Imran Shahzad , Imran Shahid
{"title":"揭示不同地形的大气能见度、遥感污染物和气候变量之间的联系:人工智能支持下的数据驱动探索","authors":"Sadaf Javed , Muhammad Imran Shahzad , Imran Shahid","doi":"10.1016/j.apr.2024.102200","DOIUrl":null,"url":null,"abstract":"<div><p>Deteriorating visual range (VR) can cause challenges for the transportation sector, resulting in economic and life losses. Air pollutants, smoke, fog, and many meteorological parameters such as air temperature (T), relative humidity (RH), wind speed (WS), and wind direction (WD) can contribute to light extinction and degrade VR. Advancements in geospatial technologies have triggered artificial intelligence to analyze and model the relationships among environmental and climatological parameters. This paper aims to assess the potential of supervised machine learning models for the parameterization of VR over Pakistan's diverse topography by utilizing meteorological parameters and some pollutants. The daily data from 2005 to 2020 of VR, T, RH, WS, WD, Aerosol Optical Depth (AOD), Nitrogen dioxide (NOx), Sulfate, Sulfur dioxide (SOx), and Dust were acquired. Ten machine learning models, including Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Networks (<span>ANN</span>), Support Vector Machine (SVM), Decision Trees (DT), Gradient Boosting Machine (GBM), Causal, Unbiased, Binned, and Intermittent, Search, and Tree (CUBIST), Multi-Layer Perceptron (MLP), Multivariate Adaptive Regression Splines (MARS), and K-Nearest Neighbor (KNN) were gauged for <span>VR</span> estimation. We also coupled the Bagged Extreme Gradient Boosting (BG-XG) model by combining XGB and bagging technique. BG-XG performed better than the rest of the models, with coefficients of determination of 0.90 for the training and 0.70 to 0.90 for the validation set. Degradation in the VR was highly dependent on the changes in RH followed by SOx and dust associated with anthropogenic emissions. RH, SO<sub>4</sub>, and SO<sub>2</sub> emerged as the most important parameters for the VR decline. Proposed model parameters can be helpful in accurate VR projections and improving severe weather alerts, including analyzing and managing air pollution. This work will also be helpful to improve aviation and transportation safety for pilots, drivers, and automated vehicles to minimize low-visibility accidents.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 9","pages":"Article 102200"},"PeriodicalIF":3.9000,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unveiling the nexus between atmospheric visibility, remotely sensed pollutants, and climatic variables across diverse topographies: A data-driven exploration empowered by artificial intelligence\",\"authors\":\"Sadaf Javed , Muhammad Imran Shahzad , Imran Shahid\",\"doi\":\"10.1016/j.apr.2024.102200\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Deteriorating visual range (VR) can cause challenges for the transportation sector, resulting in economic and life losses. Air pollutants, smoke, fog, and many meteorological parameters such as air temperature (T), relative humidity (RH), wind speed (WS), and wind direction (WD) can contribute to light extinction and degrade VR. Advancements in geospatial technologies have triggered artificial intelligence to analyze and model the relationships among environmental and climatological parameters. This paper aims to assess the potential of supervised machine learning models for the parameterization of VR over Pakistan's diverse topography by utilizing meteorological parameters and some pollutants. The daily data from 2005 to 2020 of VR, T, RH, WS, WD, Aerosol Optical Depth (AOD), Nitrogen dioxide (NOx), Sulfate, Sulfur dioxide (SOx), and Dust were acquired. Ten machine learning models, including Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Networks (<span>ANN</span>), Support Vector Machine (SVM), Decision Trees (DT), Gradient Boosting Machine (GBM), Causal, Unbiased, Binned, and Intermittent, Search, and Tree (CUBIST), Multi-Layer Perceptron (MLP), Multivariate Adaptive Regression Splines (MARS), and K-Nearest Neighbor (KNN) were gauged for <span>VR</span> estimation. We also coupled the Bagged Extreme Gradient Boosting (BG-XG) model by combining XGB and bagging technique. BG-XG performed better than the rest of the models, with coefficients of determination of 0.90 for the training and 0.70 to 0.90 for the validation set. Degradation in the VR was highly dependent on the changes in RH followed by SOx and dust associated with anthropogenic emissions. RH, SO<sub>4</sub>, and SO<sub>2</sub> emerged as the most important parameters for the VR decline. Proposed model parameters can be helpful in accurate VR projections and improving severe weather alerts, including analyzing and managing air pollution. This work will also be helpful to improve aviation and transportation safety for pilots, drivers, and automated vehicles to minimize low-visibility accidents.</p></div>\",\"PeriodicalId\":8604,\"journal\":{\"name\":\"Atmospheric Pollution Research\",\"volume\":\"15 9\",\"pages\":\"Article 102200\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Atmospheric Pollution Research\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S130910422400165X\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric Pollution Research","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S130910422400165X","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Unveiling the nexus between atmospheric visibility, remotely sensed pollutants, and climatic variables across diverse topographies: A data-driven exploration empowered by artificial intelligence
Deteriorating visual range (VR) can cause challenges for the transportation sector, resulting in economic and life losses. Air pollutants, smoke, fog, and many meteorological parameters such as air temperature (T), relative humidity (RH), wind speed (WS), and wind direction (WD) can contribute to light extinction and degrade VR. Advancements in geospatial technologies have triggered artificial intelligence to analyze and model the relationships among environmental and climatological parameters. This paper aims to assess the potential of supervised machine learning models for the parameterization of VR over Pakistan's diverse topography by utilizing meteorological parameters and some pollutants. The daily data from 2005 to 2020 of VR, T, RH, WS, WD, Aerosol Optical Depth (AOD), Nitrogen dioxide (NOx), Sulfate, Sulfur dioxide (SOx), and Dust were acquired. Ten machine learning models, including Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Trees (DT), Gradient Boosting Machine (GBM), Causal, Unbiased, Binned, and Intermittent, Search, and Tree (CUBIST), Multi-Layer Perceptron (MLP), Multivariate Adaptive Regression Splines (MARS), and K-Nearest Neighbor (KNN) were gauged for VR estimation. We also coupled the Bagged Extreme Gradient Boosting (BG-XG) model by combining XGB and bagging technique. BG-XG performed better than the rest of the models, with coefficients of determination of 0.90 for the training and 0.70 to 0.90 for the validation set. Degradation in the VR was highly dependent on the changes in RH followed by SOx and dust associated with anthropogenic emissions. RH, SO4, and SO2 emerged as the most important parameters for the VR decline. Proposed model parameters can be helpful in accurate VR projections and improving severe weather alerts, including analyzing and managing air pollution. This work will also be helpful to improve aviation and transportation safety for pilots, drivers, and automated vehicles to minimize low-visibility accidents.
期刊介绍:
Atmospheric Pollution Research (APR) is an international journal designed for the publication of articles on air pollution. Papers should present novel experimental results, theory and modeling of air pollution on local, regional, or global scales. Areas covered are research on inorganic, organic, and persistent organic air pollutants, air quality monitoring, air quality management, atmospheric dispersion and transport, air-surface (soil, water, and vegetation) exchange of pollutants, dry and wet deposition, indoor air quality, exposure assessment, health effects, satellite measurements, natural emissions, atmospheric chemistry, greenhouse gases, and effects on climate change.