Atmospheric Pollution Research最新文献

{"title":"Spatiotemporal variability of surface ozone and associated meteorological conditions over the Arabian Peninsula","authors":"Abdulilah Khalid Alduwais ,&nbsp;Hari Prasad Dasari ,&nbsp;Rama Krishna Karumuri ,&nbsp;Harikishan Gandham ,&nbsp;Vankayalapati Koteswararao ,&nbsp;Md Saquib Saharwardi ,&nbsp;Karumuri Ashok ,&nbsp;Ibrahim Hoteit","doi":"10.1016/j.apr.2024.102210","DOIUrl":"https://doi.org/10.1016/j.apr.2024.102210","url":null,"abstract":"<div><p>This study investigates the spatiotemporal variability of surface ozone (O₃) over the Arabian Peninsula (AP) between 2005 and 2019, focusing on the Arabian Gulf (AG). The analysis explores the relationship between surface O₃ data from the Copernicus Atmosphere Monitoring Service (CAMS) with boundary layer height (BLH), 2 m temperature (T2M), downward ultraviolet radiation at the surface (UVB), and 10 m wind speed (WS) and direction from the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5). Also, the study considers Carbon Monoxide (CO) and Nitrogen Oxides (NO_x) surface emissions from the Tropospheric Chemical Reanalysis version 2 (TCR-2). Furthermore, it investigates the impact of El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on surface O₃ variations on a seasonal scale. Surface O₃ observations from 15 ground-based stations across the AP were used to evaluate CAMS-O₃, showing a good agreement between CAMS and the observations. The analysis of mean diurnal variations of CAMS-O₃ and ERA5 reveals that surface O₃ is highest over the eastern parts of the AP, mainly the AG, peaking during summer, followed by spring, fall, and winter. This seasonal cycle is also observed, to a large degree, in BLH, T2M, UVB, and WS. The results also reveal insignificant correlation between surface O₃ and ENSO, but stronger correlation with IOD, especially over the AG during summer and fall. The analysis indicates that elevated T2M and UVB during daytime and elevated BLH during nighttime are significant contributors to increased levels of O₃ over the AG.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 9","pages":"Article 102210"},"PeriodicalIF":4.5,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141325902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the driving factors of carbon emissions in China's transportation industry from a structural adjustment perspective","authors":"Haiying Liu ,&nbsp;Zhiqun Zhang ,&nbsp;Xianzhe Cai ,&nbsp;Dianwu Wang ,&nbsp;Min Liu","doi":"10.1016/j.apr.2024.102224","DOIUrl":"https://doi.org/10.1016/j.apr.2024.102224","url":null,"abstract":"<div><p>Promoting the transport industry to achieve a carbon peak and controlling carbon emissions in various regions of China through structural adjustment are important means to significantly reduce CO₂ emissions. To explore the drivers of carbon emissions from China's transportation sector from 2006 to 2021. And constructs the IPAT-S (IPAT model considering structural adjustment) model, the structural equation is combined to explore the carbon emission reduction path of China's transportation industry from the perspective of structural adjustment. The results show that the structural adjustment is beneficial to alleviate the pressure of emission reduction, carbon emissions from the national transportation sector show a pattern of “low in the north and high in the south”, with the trend gradually shifting to the central region, and the spatial structural effect on carbon emissions has been significantly strengthened, the industrial structure, energy structure and transportation structure still contribute to the carbon emissions of the transportation industry. From the regional level, there are some differences in the impact of structural adjustment on carbon emission reduction in different regions, but optimizing the energy structure will be conducive to carbon emission reduction. In the case of the Northeast region, the energy consumption structure, industrial structure, and transportation structure do not inhibit carbon emissions, and the structural emission reduction in the Central region has begun to bear fruit. The structural adjustment in the eastern and western regions has yet to be improved, which will also provide the government with policy recommendations for structural emission reduction based on regional differentiation.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 9","pages":"Article 102224"},"PeriodicalIF":4.5,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141325903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing a low-cost wireless sensor network for particulate matter monitoring: Implementation, calibration, and field-test","authors":"A. Zafra-Pérez ,&nbsp;J. Medina-García ,&nbsp;C. Boente ,&nbsp;J.A. Gómez-Galán ,&nbsp;A. Sánchez de la Campa ,&nbsp;J.D. de la Rosa","doi":"10.1016/j.apr.2024.102208","DOIUrl":"https://doi.org/10.1016/j.apr.2024.102208","url":null,"abstract":"<div><p>Poor air quality can provoke severe impacts on health, necessitating environmental monitoring of atmospheric particulate matter (PM) to assess potential threats to human well-being. However, traditional continuous air quality monitoring systems are often costly and time-consuming in data treatment. Lately, there is a growing trend towards the use of low-cost wireless PM sensors, providing more detailed information than standard systems. This paper presents a system designed to measure air quality, specifically, a wireless sensor network composed of a distributed sensor network linked to a cloud system. The proposed system can efficiently measure air quality as it is cost-effective, small-sized, and consumes little power. Sensor nodes based on low-power long range (LoRa) motes transmit field measurement data to the cloud via a gateway, and a cloud computing system is implemented to store, monitor, process, and visualise the data. Advanced techniques were included in our cloud for data processing and analysis to optimise the detection of PM. Laboratory and field tests in the historic Riotinto mine validate the system's viability, offering real-time air quality information for nearby populations. Once calibrated, sensors demonstrate high accuracy, presenting mean error of −0.3% and low deviation (R² = 0.96) when compared to regulatory systems for both low (&lt;10 μgPM₁₀/m³) and hazardous concentrations (300 μgPM₁₀/m³), which makes them perfect as early warning systems for atmospheric pollution in mining.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 9","pages":"Article 102208"},"PeriodicalIF":4.5,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141308235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health risk assessments and source apportionment of PM2.5-bound heavy metals in the initial eastern economic corridor (EEC): A case study of Rayong Province, Thailand","authors":"Sawaeng Kawichai ,&nbsp;Susira Bootdee ,&nbsp;Somporn Chantara","doi":"10.1016/j.apr.2024.102205","DOIUrl":"https://doi.org/10.1016/j.apr.2024.102205","url":null,"abstract":"<div><p>This study aimed to determine the metals in ambient PM_2.5 in the expanding industrial metropolitan area of Rayong Province for health risk assessment and source apportionment from May 2022 to April 2023, covering wet and dry seasons. The mean annual PM_2.5 concentration was 15.2 ± 12.0 μg m⁻³, whereas that of wet and dry seasons were 8.4 ± 5.4 μg m⁻³ and 21.8 ± 12.9 μg m⁻³, respectively. The annual PM_2.5 level exceeded the limit set by the World Health Organization (WHO) (5 μg m⁻³) and the standard of Thailand (15 μg m⁻³). A substantial decrease in the Cd, Pb, Zn, Cu, Fe, Mn, and K concentrations was observed during the wet season compared with that of the dry season. The levels of annual Cr in PM_2.5 were 40 times higher than the WHO limit. Cd, Pb, and Zn are tracers of anthropogenic activities. Using the enrichment factor (EF) and I_<em>geo</em>, the contamination of As, Cd, Pb, and Zn suggested that the initial Eastern Economic Corridor (EEC) in Rayong Province was highly polluted. The results of the non-carcinogenic risk indicated that human health was notably affected by toxic metals in PM_2.5, and the Cr-related carcinogenic risk in PM_2.5 exposure suggested a safe or reasonable risk level (10⁻⁶ to 10⁻⁴). Exposure to toxic metals in PM_2.5 increases the risk of developing cancer in adults, potentially owing to the accumulation of these metals within the tissues in the body. Positive matrix factorisation (PMF) suggested that the source apportionment of PM_2.5-bound heavy metals was motor vehicles (34.7%), industrial activities (26.3%), biomass burning (22.7%), and road dust (18.5%).</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 9","pages":"Article 102205"},"PeriodicalIF":4.5,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141325901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantity, size distribution, and sources of leaf-level particulate matter from a major steel plant in SW Ohio: Implications for the spatial footprint of an emitter","authors":"Maral Khodadadi ,&nbsp;Elisabeth Widom ,&nbsp;Mark Krekeler","doi":"10.1016/j.apr.2024.102206","DOIUrl":"https://doi.org/10.1016/j.apr.2024.102206","url":null,"abstract":"<div><p>Despite continued actions to abate harmful air pollutant emissions, air pollution is still a worldwide concern, yet apportioning individual shares of responsibility for pollution is challenging. Here, we present a spatial approach combined with microscopy, elemental composition, and Pb isotopes to trace particulate matter (PM) emissions related to a steel manufacturing plant in Middletown, Ohio. Evergreen leaves were collected in nine sites situated 18 and 32 km upwind and 0–35 km downwind from the steel plant. The relative abundance and size range of spherical Fe-rich particles, as indicators of the steel factory's emissions, were quantified using SEM/EDS. Elemental compositions and Pb isotopes were used for PM source apportionment. The SEM/EDS quantification method was effective for steel particles, while it was less suitable for quantifying fly ash abundances owing to its limitations in detecting ultrafine PM, where fly ash particles are prevalent. Pb isotopes indicated that the average leaf-level PM mass originating from glacial till, steel plant, gasoline, and fly ash, were 44 ± 23, 34 ± 30, 33 ± 17, and 18 ± 11 mg m⁻², respectively, highlighting the steel plant and gasoline as the primary anthropogenic PM sources. <span>Strong</span> correlations between steel spherule mass estimated by MixSIAR and its relative proportion quantified through microscopic investigations (r = 0.94) and pollution load index (r = 0.89) provide support for source apportionment using isotopic methods. The steel spherules quantity decreased exponentially with distance with the steel plant's effective PM footprint extending approximately 32 and 40 km upwind and downwind, respectively, emphasizing its ongoing environmental impact despite pollution control measures.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 9","pages":"Article 102206"},"PeriodicalIF":4.5,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1309104224001715/pdfft?md5=8139d74605ccb793b117861cb4167879&pid=1-s2.0-S1309104224001715-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics of PM2.5 in Hachinohe, the priority pollution control city in Japan","authors":"Meng Sun,&nbsp;Xi Zhang","doi":"10.1016/j.apr.2024.102204","DOIUrl":"10.1016/j.apr.2024.102204","url":null,"abstract":"<div><p>In the urban area of Hachinohe, Japan, PM_2.5 sampling was carried out from May 2015 to February 2021. The average concentration of PM_2.5 during the entire sampling period was approximately 11.7 μg m⁻³, with 4.4 μg m⁻³ for water soluble ions, 3.3 μg m⁻³ for carbonaceous species, 0.5 μg m⁻³ for trace metals, and 3.5 μg m⁻³ for other species. Based on this comprehensive component information, eight sources were quantitatively explored, among which ship emissions (29%), traffic emissions (19%), and secondary organic aerosols (15%) had relatively high contributions for PM_2.5 concentration level. The health risk assessment indicated that the children in Hachinohe City faced serious non-carcinogenic and carcinogenic risks, with corresponding values of 8.0 for HI and 1.2 × 10⁻⁴ for CR. The pollutants from ship emissions, secondary nitrates plus coal combustion, and industrial emissions should be of concern. High risk metals included Pb, As, Sb, V, and Cr(VI). Specifically, ship emissions exhibited the highest concentration (5.5 μg m⁻³) and health risks (HI = 2.2 and CR = 3.0 × 10⁻⁵) in summer; priority should be given to controlling pollution in the Port of Hachinohe. The other two sources had the highest concentration in winter (2.0 and 0.5 μg m⁻³) and were mainly influenced by the polluted air masses from Akita Prefecture, with HI values of 2.4 and 2.5 and CR values of 4.9 × 10⁻⁵ and 3.2 × 10⁻⁵, respectively. Overall, our study comprehensively revealed the characteristics of PM_2.5 in Hachinohe City and conducted an in-depth investigated into its causes of pollution. This information could serve as a scientific basis for developing specific strategies to improve air quality.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 9","pages":"Article 102204"},"PeriodicalIF":4.5,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141281469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measured black carbon deposition over the central Himalayan glaciers: Concentrations in surface snow and impact on snow albedo reduction","authors":"Chaman Gul ,&nbsp;Cenlin He ,&nbsp;Shichang Kang ,&nbsp;Yangyang Xu ,&nbsp;Xiaokang Wu ,&nbsp;Inka Koch ,&nbsp;Joel Barker ,&nbsp;Rajesh Kumar ,&nbsp;Rahat Ullah ,&nbsp;Shah Faisal ,&nbsp;Siva Praveen Puppala","doi":"10.1016/j.apr.2024.102203","DOIUrl":"https://doi.org/10.1016/j.apr.2024.102203","url":null,"abstract":"<div><p>Deposition of ambient black carbon (BC) aerosols over snow-covered areas reduces surface albedo and accelerates snowmelt. Based on in-situ atmospheric BC data and the WRF-Chem model, we estimated the dry and wet deposition of BC over the Yala glacier of the central Himalayan region in Nepal during 2016–2018. The maximum and minimum BC dry deposition was reported in pre- and post-monsoon respectively. Approximately 50% of annual dry deposition occurred in the pre-monsoon season (March to May) and 27% of the annual dry deposition occurred in April. The total dry BC deposition rate was estimated as ∼4.6 μg m⁻² day⁻¹ providing a total deposition of 531 μg m⁻² during the pre-monsoon season. The contribution of biomass burning and fossil fuel sources to BC deposition on an annual basis was 28% and 72% respectively. The annual accumulated wet deposition of BC was 196 times higher than the annual dry deposition. The ten months of observed dry deposition of BC (October 1, 2016 to August 31, 2017 – except December 2016) was ∼39% lower than that of WRF-Chem's estimated annual dry deposition from September 1, 2016 to August 31, 2017 partially due to model bias. The deposited content of BC over the snow surface has an important role in albedo reduction, therefore snow samples were collected from the surface of the Yala Glacier and the surrounding region in April 2016, 2017, and 2018. Samples were analyzed for BC mass concentration through the thermal optical analysis and single particle soot photometer method. The BC calculated via the thermal optical method was in the range of 352–854 ng g⁻¹, higher than the BC calculated through the particle soot photometer method and estimated BC in 2 cm surface snow (imperial equation). The maximum surface snow albedo reduction due to BC was 8.8%, estimated by a widely used snow radiative transfer model and a linear regression equation.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 9","pages":"Article 102203"},"PeriodicalIF":4.5,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141250472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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 ,&nbsp;Muhammad Imran Shahzad ,&nbsp;Imran Shahid","doi":"10.1016/j.apr.2024.102200","DOIUrl":"https://doi.org/10.1016/j.apr.2024.102200","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, 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.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 9","pages":"Article 102200"},"PeriodicalIF":4.5,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141250471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The atmospheric aerosol spatial distribution and tropical intra-seasonal oscillations over the South Asian region","authors":"Binisia Sanatan,&nbsp;V. Vinoj,&nbsp;Kiranmayi Landu","doi":"10.1016/j.apr.2024.102199","DOIUrl":"10.1016/j.apr.2024.102199","url":null,"abstract":"<div><p>Intra-seasonal oscillations (ISO) are well known to modulate the weather phenomena which in turn are known to influence the atmospheric aerosol loading. This study investigates how aerosol loading is modulated in ISO spatio-temporal scales over the Indian region using long-term satellite aerosol optical depth data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, onboard Terra Satellite. It is shown that Madden-Julian Oscillation (MJO) and Equatorial Rossby waves (ER) have the highest effect (15–20% of the mean) followed by Mixed-Rossby-gravity and Tropical depressions (MT), and Kelvin wave (KE) (5–15%). Further, a dipolar pattern in aerosol loading was observed, with poles over the Arabian Sea and the Bay of Bengal. These variabilities were found to be mainly driven by anomalous winds associated with the ISOs. Similar to aerosol, dipolar signatures in the atmospheric aerosol radiative forcing (ARF) were also observed with clearer patterns. However, the forcing poles are not centered exactly where aerosol poles were observed, indicating the effect of differential properties of aerosols on the aerosol radiative forcing. Quantitatively, at the surface level, modulation in ARF is up to 3 Wm^-2 (15%) for MJO and ER, and up to 2 Wm^-2 (5%) for KE and MT; in the atmosphere and at the top of the atmosphere, modulation is up to 2 Wm^-2 (15%) for MJO and ER, and up to 1 Wm^-2 (5%) for KE and MT.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 9","pages":"Article 102199"},"PeriodicalIF":4.5,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acid rain prediction in the Guangdong-Hong Kong-Macao Greater Bay Area using an explainable machine learning framework","authors":"Zeqin Huang ,&nbsp;Jianyu Fu ,&nbsp;Bingjun Liu ,&nbsp;Xinfeng Zhao ,&nbsp;Yun Zhang ,&nbsp;Xiaofei Wang","doi":"10.1016/j.apr.2024.102201","DOIUrl":"https://doi.org/10.1016/j.apr.2024.102201","url":null,"abstract":"<div><p>Acid rain, characterized by pH values lower than 5.6, is a critical natural disturbance of ecosystems, which threatens the sustainability of ecosystems, agriculture, and human society worldwide. However, accurately quantifying the driving factors of acid rain remains challenging due to a changing environment of significant spatial heterogeneity. Here, we established an explainable machine-learning framework (MLF) using 19 meteorological, air pollutant, and land surface variables as model input to construct the pH values of acid rain across the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) during 2006–2021. The MLF includes Extreme Gradient Boosting (XGBoost) for acid deposition prediction and a SHapley Additive exPlanations method (SHAP) for interpreting factor importance. The results indicated that the observed increases in pH values of acid rain are predominantly controlled by the significant decreases in maximum daily sulfur dioxide (SO₂) concentration of air across GBA, with its relative contribution ranging from 16.2% to 31.9% for each city. Changes in the urbanization rate and the proximity to the coast also play significant roles in predicting the pH values of acid rain. Meteorological variables typically have minimal impact on acid rain predictions, with their contribution generally being less than 5%, indicating the complex physical process of acid rain generation. This study enhanced our comprehension of the spatial variability of acid rain drivers across a highly developed region, providing valuable insights and case studies for regions worldwide that frequently experience acid rain.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 9","pages":"Article 102201"},"PeriodicalIF":4.5,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141164288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}