Pub Date : 2026-10-01Epub Date: 2026-09-25DOI: 10.1016/j.apr.2026.103210
Sotirios Papathanasiou, Christos D. Argyropoulos
This paper presents the use of portable air quality monitors (PAQMs) to measure exposure to fine particulate matter (PM2.5) and carbon dioxide (CO2), as well as to investigate their performance and accuracy. It also assesses the practicality of PAQMs, and challenges related to their usage. Two PAQMs were selected to monitor personal exposure in different indoor and outdoor settings in Granada and Almeria, Spain. The obtained measurements showed an annual average PM2.5 exposure at 4.4 μg/m3, which fulfil the World Health Organization (WHO) guideline threshold of 5 μg/m3. However, managing air ventilation was more difficult, resulting in an average annual CO2 concentration of 1077 ppm. This study came across with significant challenges, including data retrieval failures that led to data loss, the difficulty of carrying two devices, inconsistent battery life, and confusing data interpretation for immediate actions, among others. It is concluded that PAQMs are useful for personal environmental awareness, however, their effectiveness is limited by hardware issues and data presentation methods that are not suited for prompting quick behaviour changes. Significant improvements are necessary in device integration, battery longevity and data communication to facilitate and enhance wider use and better results.
{"title":"Evaluating personal air quality monitors for PM2.5 and CO2: Insights from a year-long pilot study","authors":"Sotirios Papathanasiou, Christos D. Argyropoulos","doi":"10.1016/j.apr.2026.103210","DOIUrl":"10.1016/j.apr.2026.103210","url":null,"abstract":"<div><div>This paper presents the use of portable air quality monitors (PAQMs) to measure exposure to fine particulate matter (PM<sub>2.5</sub>) and carbon dioxide (CO<sub>2</sub>), as well as to investigate their performance and accuracy. It also assesses the practicality of PAQMs, and challenges related to their usage. Two PAQMs were selected to monitor personal exposure in different indoor and outdoor settings in Granada and Almeria, Spain. The obtained measurements showed an annual average PM<sub>2.5</sub> exposure at 4.4 μg/m<sup>3</sup>, which fulfil the World Health Organization (WHO) guideline threshold of 5 μg/m<sup>3</sup>. However, managing air ventilation was more difficult, resulting in an average annual CO<sub>2</sub> concentration of 1077 ppm. This study came across with significant challenges, including data retrieval failures that led to data loss, the difficulty of carrying two devices, inconsistent battery life, and confusing data interpretation for immediate actions, among others. It is concluded that PAQMs are useful for personal environmental awareness, however, their effectiveness is limited by hardware issues and data presentation methods that are not suited for prompting quick behaviour changes. Significant improvements are necessary in device integration, battery longevity and data communication to facilitate and enhance wider use and better results.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 10","pages":"Article 103210"},"PeriodicalIF":3.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149153654","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}
Pub Date : 2026-04-01Epub Date: 2025-11-29DOI: 10.1016/j.apr.2025.102836
Shenao Fan, Yunfeng Ma
Ground-level ozone (GLO), produced through reactions between and VOCs under sunlight, is a toxic secondary pollutant that adversely impacts ecosystems and human well-being. This study proposes an interpretable, high-resolution forecasting framework based on a weighted ensemble of machine learning algorithms, including LightGBM, XGBoost, and CatBoost. Using hourly observational data from Shenyang, China (2020–2025), the model incorporates meteorological, temporal, and air quality features to capture annual-scale ozone variability. The ensemble outperformed individual learners, achieving strong predictive accuracy (, MAE = , RMSE = ). To enhance interpretability, SHAP analysis was used to reveal nonlinear interactions among meteorological and chemical drivers. High temperatures and moderate humidity were found to promote ozone formation. Seasonal transport patterns identified by concentration-weighted trajectory (CWT) analysis revealed dominant local photochemistry in summer and stronger long-range influence in winter. The proposed framework offers both predictive accuracy and physical interpretability, supporting early warning and targeted ozone control strategies. Its transferable design enables application to other urban regions with complex atmospheric conditions.
地面臭氧（GLO）是一种有毒的二次污染物，对生态系统和人类福祉产生不利影响。本研究提出了一个可解释的、高分辨率的预测框架，该框架基于机器学习算法的加权集合，包括LightGBM、XGBoost和CatBoost。该模式利用中国沈阳每小时（2020-2025）的观测数据，结合气象、时间和空气质量特征来捕捉年尺度的臭氧变率。集合优于个体学习者，预测准确率较高（R2=0.868, MAE = 10.3μg/m3, RMSE = 14.7μg/m3）。为了提高可解释性，利用SHAP分析揭示了气象和化学驱动因素之间的非线性相互作用。高温和中等湿度有利于臭氧的形成。浓度加权轨迹（CWT）分析的季节输送模式表明，夏季对局地光化学反应起主导作用，冬季对长程影响较强。提出的框架提供了预测准确性和物理可解释性，支持早期预警和有针对性的臭氧控制策略。其可转移的设计使其能够应用于具有复杂大气条件的其他城市地区。
{"title":"Forecasting ground-level ozone in Shenyang using interpretable machine learning: Interaction between air pollutants and climate factors","authors":"Shenao Fan, Yunfeng Ma","doi":"10.1016/j.apr.2025.102836","DOIUrl":"10.1016/j.apr.2025.102836","url":null,"abstract":"<div><div>Ground-level ozone (GLO), produced through reactions between <figure><img></figure> and VOCs under sunlight, is a toxic secondary pollutant that adversely impacts ecosystems and human well-being. This study proposes an interpretable, high-resolution forecasting framework based on a weighted ensemble of machine learning algorithms, including LightGBM, XGBoost, and CatBoost. Using hourly observational data from Shenyang, China (2020–2025), the model incorporates meteorological, temporal, and air quality features to capture annual-scale ozone variability. The ensemble outperformed individual learners, achieving strong predictive accuracy (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>868</mn></mrow></math></span>, MAE = <span><math><mrow><mn>10</mn><mo>.</mo><mn>3</mn><mspace></mspace><mi>μ</mi><mtext>g</mtext><mo>/</mo><msup><mrow><mtext>m</mtext></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span>, RMSE = <span><math><mrow><mn>14</mn><mo>.</mo><mn>7</mn><mspace></mspace><mi>μ</mi><mtext>g</mtext><mo>/</mo><msup><mrow><mtext>m</mtext></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span>). To enhance interpretability, SHAP analysis was used to reveal nonlinear interactions among meteorological and chemical drivers. High temperatures and moderate humidity were found to promote ozone formation. Seasonal transport patterns identified by concentration-weighted trajectory (CWT) analysis revealed dominant local photochemistry in summer and stronger long-range influence in winter. The proposed framework offers both predictive accuracy and physical interpretability, supporting early warning and targeted ozone control strategies. Its transferable design enables application to other urban regions with complex atmospheric conditions.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102836"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584587","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}
The modern way of life has led humans to consume more than 90 % of their everyday lives in indoor spaces like residences, workplaces and public buildings; thus, ensuring a healthy and comfortable environment is crucial. Indoor air quality is one of the most important parameters directly related to the health and comfort of a building's users. The evaluation of indoor air quality, and more precisely the assessment of the factors that affect it, as well as the correlation between chemical substances and their source of origin, is of great research interest. Determining the sources of indoor air pollution is challenging due to the wide range and high variability of indoor pollutants, the intricate physical and chemical processes that serve as primary sources, sinks, and origins of precursors for secondary pollutant formation, as well as the interactions with outdoor environmental factors. All these can be achieved using Source Appointment methods. In this study, air quality measurements were conducted in three office buildings with different characteristics, in the region of Western Macedonia, to determine the concentration of different volatile organic compounds (VOCs), indoors and outdoors. The analysis of these samples was carried out using the GC/MS method. The results of the analysis were used as input to a receptor model and specifically Positive Matrix Factorization (PMF), to detect the sources that contributed to the measured concentrations. The results identified the main sources of VOCs emissions, which reveal indoor and outdoor sources based on building characteristics, outdoor environment and human activities.
{"title":"Study of VOCs levels and sources in three office buildings in Northern Greece","authors":"Giannis Papadopoulos , Vasiliki Skopeliti , Evangelos Tolis , Dikaia Saraga , Giorgos Panaras","doi":"10.1016/j.apr.2025.102837","DOIUrl":"10.1016/j.apr.2025.102837","url":null,"abstract":"<div><div>The modern way of life has led humans to consume more than 90 % of their everyday lives in indoor spaces like residences, workplaces and public buildings; thus, ensuring a healthy and comfortable environment is crucial. Indoor air quality is one of the most important parameters directly related to the health and comfort of a building's users. The evaluation of indoor air quality, and more precisely the assessment of the factors that affect it, as well as the correlation between chemical substances and their source of origin, is of great research interest. Determining the sources of indoor air pollution is challenging due to the wide range and high variability of indoor pollutants, the intricate physical and chemical processes that serve as primary sources, sinks, and origins of precursors for secondary pollutant formation, as well as the interactions with outdoor environmental factors. All these can be achieved using Source Appointment methods. In this study, air quality measurements were conducted in three office buildings with different characteristics, in the region of Western Macedonia, to determine the concentration of different volatile organic compounds (VOCs), indoors and outdoors. The analysis of these samples was carried out using the GC/MS method. The results of the analysis were used as input to a receptor model and specifically Positive Matrix Factorization (PMF), to detect the sources that contributed to the measured concentrations. The results identified the main sources of VOCs emissions, which reveal indoor and outdoor sources based on building characteristics, outdoor environment and human activities.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102837"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584588","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}
Pub Date : 2026-04-01Epub Date: 2025-12-11DOI: 10.1016/j.apr.2025.102854
Anna-Kaisa Viitanen, Alonso Espinosa Mireles de Villafranca, Jonathon Taylor
Exposure to particles with a diameter smaller than 2.5 μm (PM2.5) poses a threat to human health. Monitoring of PM2.5 often relies on sparse networks of standardized instruments, but in many locations, official networks are supplemented by networks of low-cost PM sensors operated by members of the community. This study aims to evaluate the open low-cost PM2.5 sensor networks across Europe by analysing data from Sensor.Community and OpenAQ, assessing completeness and reliability as well as growth, coverage, locations, and correlation in comparison with official measurements. As of 2023, there were 8735 low-cost PM2.5 sensors in Europe publicly sharing data on these networks, or more than three times the number of official stations. Results indicate these sensors are primarily in residential urban areas with higher population densities, higher income levels, and average levels of ambient PM2.5. The analysis shows that although official measurement stations provide more consistent coverage across Europe, in many cities the low-cost PM2.5 sensor network is denser and can potentially complement the official network and provide insights for authorities, researchers and citizens. The rapid growth of openly available low-cost PM2.5 sensor data presents a growing opportunity to enhance the spatial coverage of monitored PM2.5 exposure estimates and understanding of local PM2.5 sources, both indoors and outdoors. However, challenges exist with inconsistent methods for data quality control, absent metadata, and sensor placement and location biases which need to be addressed for example through harmonized measurement protocols, collecting more complete metadata and with advanced data cleaning and processing methods.
{"title":"Community based air pollution measurements in Europe: Opportunities and challenges","authors":"Anna-Kaisa Viitanen, Alonso Espinosa Mireles de Villafranca, Jonathon Taylor","doi":"10.1016/j.apr.2025.102854","DOIUrl":"10.1016/j.apr.2025.102854","url":null,"abstract":"<div><div>Exposure to particles with a diameter smaller than 2.5 μm (PM<sub>2.5</sub>) poses a threat to human health. Monitoring of PM<sub>2.5</sub> often relies on sparse networks of standardized instruments, but in many locations, official networks are supplemented by networks of low-cost PM sensors operated by members of the community. This study aims to evaluate the open low-cost PM<sub>2.5</sub> sensor networks across Europe by analysing data from Sensor.Community and OpenAQ, assessing completeness and reliability as well as growth, coverage, locations, and correlation in comparison with official measurements. As of 2023, there were 8735 low-cost PM<sub>2.5</sub> sensors in Europe publicly sharing data on these networks, or more than three times the number of official stations. Results indicate these sensors are primarily in residential urban areas with higher population densities, higher income levels, and average levels of ambient PM<sub>2.5</sub>. The analysis shows that although official measurement stations provide more consistent coverage across Europe, in many cities the low-cost PM<sub>2.5</sub> sensor network is denser and can potentially complement the official network and provide insights for authorities, researchers and citizens. The rapid growth of openly available low-cost PM<sub>2.5</sub> sensor data presents a growing opportunity to enhance the spatial coverage of monitored PM<sub>2.5</sub> exposure estimates and understanding of local PM<sub>2.5</sub> sources, both indoors and outdoors. However, challenges exist with inconsistent methods for data quality control, absent metadata, and sensor placement and location biases which need to be addressed for example through harmonized measurement protocols, collecting more complete metadata and with advanced data cleaning and processing methods.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102854"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584678","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}
Pub Date : 2026-04-01Epub Date: 2025-11-19DOI: 10.1016/j.apr.2025.102831
V. Sreekanth , Nivedita Barman , Beatriz Cardenas , Daniel Peters , Ramón A. Alvarez , Prakash Doraiswamy
This study presents a comprehensive characterization of BC pollution over Indore, a data-deficient region in central India. Continuous, year-long real-time measurements using a microAeth were conducted as part of the Clean Air Catalyst (CAC) initiative. These BC measurements were complemented by collocated fine particulate matter (PM2.5) and Carbon Monoxide (CO), and surface meteorological observations. Highest BC levels were recorded during the winter (5.2 μg m−3) and post-monsoon (4.8 μg m−3) seasons, while the monsoon season registered the lowest concentrations (1.9 μg m−3). Fossil fuel combustion was the dominant source, contributing approximately 84 % of the BC mass, while biomass burning accounted for 16 %. BC comprised around 6–12 % of the PM2.5 across different seasons. About 70 % of the observed BC originated from local emission sources, primarily due to vehicular and industrial activities. A comparison of MERRA-2 reanalysis BC data with in-situ measurements revealed significant underestimation by MERRA-2 and a low temporal correlation (r = 0.34). These findings (i) support the adoption of evidence-based air pollution mitigation plans by city administrations, (ii) highlight the need for both city-level and regional scale mitigation efforts, (iii) underscore the inadequacy of global reanalysis datasets in capturing urban BC dynamics in central India, and (iv) advocate for long-term ground-based BC monitoring to inform regional emission inventories and assess the effectiveness of mitigation strategies.
{"title":"Atmospheric Black Carbon (BC) characteristics over Indore, a data-deficit region in India","authors":"V. Sreekanth , Nivedita Barman , Beatriz Cardenas , Daniel Peters , Ramón A. Alvarez , Prakash Doraiswamy","doi":"10.1016/j.apr.2025.102831","DOIUrl":"10.1016/j.apr.2025.102831","url":null,"abstract":"<div><div>This study presents a comprehensive characterization of BC pollution over Indore, a data-deficient region in central India. Continuous, year-long real-time measurements using a microAeth were conducted as part of the Clean Air Catalyst (CAC) initiative. These BC measurements were complemented by collocated fine particulate matter (PM<sub>2.5</sub>) and Carbon Monoxide (CO), and surface meteorological observations. Highest BC levels were recorded during the winter (5.2 μg m<sup>−3</sup>) and post-monsoon (4.8 μg m<sup>−3</sup>) seasons, while the monsoon season registered the lowest concentrations (1.9 μg m<sup>−3</sup>). Fossil fuel combustion was the dominant source, contributing approximately 84 % of the BC mass, while biomass burning accounted for 16 %. BC comprised around 6–12 % of the PM<sub>2.5</sub> across different seasons. About 70 % of the observed BC originated from local emission sources, primarily due to vehicular and industrial activities. A comparison of MERRA-2 reanalysis BC data with in-situ measurements revealed significant underestimation by MERRA-2 and a low temporal correlation (r = 0.34). These findings (i) support the adoption of evidence-based air pollution mitigation plans by city administrations, (ii) highlight the need for both city-level and regional scale mitigation efforts, (iii) underscore the inadequacy of global reanalysis datasets in capturing urban BC dynamics in central India, and (iv) advocate for long-term ground-based BC monitoring to inform regional emission inventories and assess the effectiveness of mitigation strategies.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102831"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584682","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}
Pub Date : 2026-04-01Epub Date: 2025-12-13DOI: 10.1016/j.apr.2025.102859
Chang Yan , Xuanming Zhang , Yan Yu , Xin Zheng , Guangming Shi , Fumo Yang
Regional transport of pollutants imparts fluidity and non-local characteristics to PM2.5 pollution, posing significant challenges for timely air quality management. Traditional approaches for identifying transport pathways rely on accurate and near-real-time wind field data, limiting their applicability for near-real-time analysis. In this study, a fully data-driven deep learning framework was developed to infer the major PM2.5 transport channels without requiring precise wind field inputs. Applied to the Sichuan Basin, a region characterized by complex terrain and frequent haze events, the adaptive graph-generated adjacency structure learned from historical pollutant and meteorological data aligns closely with the prevailing directions of air mass movement. High-weighted stations are primarily located in upwind, neighboring, and downwind regions relative to the receptor site, consistent with observed diffusion patterns. The agreement with HYSPLIT results further confirms the model's ability to identify physically meaningful transport pathways. These findings indicate that, even in the absence of accurate wind field data, historical spatiotemporal variations in pollutant concentrations alone can reveal physically interpretable transport patterns. This approach enables near-real-time identification of regional transport dynamics and provides both methodological innovation and practical value for coordinated air quality management in complex basin environments.
{"title":"Identification of regional PM2.5 transport dynamics with an adaptive data-driven model","authors":"Chang Yan , Xuanming Zhang , Yan Yu , Xin Zheng , Guangming Shi , Fumo Yang","doi":"10.1016/j.apr.2025.102859","DOIUrl":"10.1016/j.apr.2025.102859","url":null,"abstract":"<div><div>Regional transport of pollutants imparts fluidity and non-local characteristics to PM<sub>2.5</sub> pollution, posing significant challenges for timely air quality management. Traditional approaches for identifying transport pathways rely on accurate and near-real-time wind field data, limiting their applicability for near-real-time analysis. In this study, a fully data-driven deep learning framework was developed to infer the major PM<sub>2.5</sub> transport channels without requiring precise wind field inputs. Applied to the Sichuan Basin, a region characterized by complex terrain and frequent haze events, the adaptive graph-generated adjacency structure learned from historical pollutant and meteorological data aligns closely with the prevailing directions of air mass movement. High-weighted stations are primarily located in upwind, neighboring, and downwind regions relative to the receptor site, consistent with observed diffusion patterns. The agreement with HYSPLIT results further confirms the model's ability to identify physically meaningful transport pathways. These findings indicate that, even in the absence of accurate wind field data, historical spatiotemporal variations in pollutant concentrations alone can reveal physically interpretable transport patterns. This approach enables near-real-time identification of regional transport dynamics and provides both methodological innovation and practical value for coordinated air quality management in complex basin environments.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102859"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584683","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}
Pub Date : 2026-04-01Epub Date: 2026-02-06DOI: 10.1016/j.apr.2026.102944
Muhammad Hassan Bashir, Asad Abbas, Hamaad Raza Ahmad, Muhammad Tahir Shehzad
Urban roads air pollution from fine particulate matter (PM2.5) is a major environmental concern. Toxic metals associated with PM2.5 pose health risks but remain poorly studied in Pakistan. In present investigation, the dust and PM2.5 bound metals pollution with associated health risks were evaluated by cumulative field measurements, dispersion modelling, and machine learning. For this purpose, dust was collected from solar panels, and airborne PM2.5 was gathered using a low volume sampler for dual seasons (June and December). Along urban roads, the PM2.5 dispersion generated by vehicular activities was simulated using the AERMOD Gaussian dispersion model. Then, utilizing AERMOD outputs and Wheater data for Support Vector Machine (SVM) model were trained to forecast metals (As, Cd, Cr, Ni, Pb, and Zn) in both dust and PM2.5. Elevated concentrations of Zn, Cu, and Cd were observed in PM2.5 and deposited dust, and AERMOD modeled PM2.5 mass concentrations peaked at 115.7 μg/m3, exceeding applicable regulatory limits. The SVM approach attained significant predictive accuracy for metal concentrations (R2 = 0.75, 0.81, 0.85 for As, Cd, and Pb). Health risk perspective, hexavalent chromium (Cr (VI)) posed the substantial noncarcinogenic and carcinogenic risk in both seasons. The observed and SVM predicted metals total cancer risk ranged for PM2.5 (TCR ≤10−4) and dust (TCR <10−6), and hazard index varied for PM2.5 (HI > 1) and dust (HI < 1) in both seasons, respectively. The study was limited by minor roads data and lack of local emission standards; future research should include deeper local modelling and extensive traffic emissions databases.
{"title":"Modelling urban roads particulate matter and airborne traffic-related metals exposure: AERMOD-Support vector machine hybrid framework for Monte Carlo health risk assessment","authors":"Muhammad Hassan Bashir, Asad Abbas, Hamaad Raza Ahmad, Muhammad Tahir Shehzad","doi":"10.1016/j.apr.2026.102944","DOIUrl":"10.1016/j.apr.2026.102944","url":null,"abstract":"<div><div>Urban roads air pollution from fine particulate matter (PM<sub>2.5</sub>) is a major environmental concern. Toxic metals associated with PM<sub>2.5</sub> pose health risks but remain poorly studied in Pakistan. In present investigation, the dust and PM<sub>2.5</sub> bound metals pollution with associated health risks were evaluated by cumulative field measurements, dispersion modelling, and machine learning. For this purpose, dust was collected from solar panels, and airborne PM<sub>2.5</sub> was gathered using a low volume sampler for dual seasons (June and December). Along urban roads, the PM<sub>2.5</sub> dispersion generated by vehicular activities was simulated using the AERMOD Gaussian dispersion model. Then, utilizing AERMOD outputs and Wheater data for Support Vector Machine (SVM) model were trained to forecast metals (As, Cd, Cr, Ni, Pb, and Zn) in both dust and PM<sub>2.5</sub>. Elevated concentrations of Zn, Cu, and Cd were observed in PM<sub>2.5</sub> and deposited dust, and AERMOD modeled PM<sub>2.5</sub> mass concentrations peaked at 115.7 μg/m<sup>3</sup>, exceeding applicable regulatory limits. The SVM approach attained significant predictive accuracy for metal concentrations (R<sup>2</sup> = 0.75, 0.81, 0.85 for As, Cd, and Pb). Health risk perspective, hexavalent chromium (Cr (VI)) posed the substantial noncarcinogenic and carcinogenic risk in both seasons. The observed and SVM predicted metals total cancer risk ranged for PM<sub>2.5</sub> (TCR ≤10<sup>−4</sup>) and dust (TCR <10<sup>−6</sup>), and hazard index varied for PM<sub>2.5</sub> (HI > 1) and dust (HI < 1) in both seasons, respectively. The study was limited by minor roads data and lack of local emission standards; future research should include deeper local modelling and extensive traffic emissions databases.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102944"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584765","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}
Pub Date : 2026-04-01Epub Date: 2025-11-24DOI: 10.1016/j.apr.2025.102827
Gillian Flower , Rochelle Schneider , Karen Exley , Christina Mitsakou , Pierre Masselot , Antonio Gasparrini
PM2.5 and NO2 are two of the most dangerous air pollutants, contributing to substantial excess mortality across Europe. In 2021, the World Health Organization (WHO) revised its air quality (AQ) guidelines, decreasing the recommended annual limit from 10 μg/m3 to 5 μg/m3 for PM2.5, and from 40 μg/m3 to 10 μg/m3 for NO2.
In this study, we analysed concentrations and associated long-term premature mortality across Great Britain from 2008 to 2018 in relation to multiple AQ limits, as determined by WHO guidelines and international/national regulations.
The percentage of the population exposed to PM2.5 concentrations above 10 μg/m3 decreased from 67.6 % to 38.2 % over the study period, whereas those exposed to NO2 levels above 40 μg/m3 remained low. Levels below the new, lower WHO guidelines for PM2.5 and NO2 were reached in only a handful of locations.
Exposure to PM2.5 in 2018 resulted in 36,403 (30,422–42,640) premature deaths, whereas NO2 exposure contributed to 20,175 (7125–32,281) premature deaths. These numbers could be reduced by 46 % and 44 %, respectively, if exceedances beyond the lower cut-offs were avoided.
These findings indicate that complying with the new WHO recommendations would require considerable effort, but they can lead to substantial health benefits.
{"title":"Mortality impacts of long-term PM2.5 and NO2 exposure in Great Britain under national and international air quality limits","authors":"Gillian Flower , Rochelle Schneider , Karen Exley , Christina Mitsakou , Pierre Masselot , Antonio Gasparrini","doi":"10.1016/j.apr.2025.102827","DOIUrl":"10.1016/j.apr.2025.102827","url":null,"abstract":"<div><div>PM<sub>2.5</sub> and NO<sub>2</sub> are two of the most dangerous air pollutants, contributing to substantial excess mortality across Europe. In 2021, the World Health Organization (WHO) revised its air quality (AQ) guidelines, decreasing the recommended annual limit from 10 μg/m<sup>3</sup> to 5 μg/m<sup>3</sup> for PM<sub>2.5</sub>, and from 40 μg/m<sup>3</sup> to 10 μg/m<sup>3</sup> for NO<sub>2</sub>.</div><div>In this study, we analysed concentrations and associated long-term premature mortality across Great Britain from 2008 to 2018 in relation to multiple AQ limits, as determined by WHO guidelines and international/national regulations.</div><div>The percentage of the population exposed to PM<sub>2.5</sub> concentrations above 10 μg/m<sup>3</sup> decreased from 67.6 % to 38.2 % over the study period, whereas those exposed to NO<sub>2</sub> levels above 40 μg/m<sup>3</sup> remained low. Levels below the new, lower WHO guidelines for PM<sub>2.5</sub> and NO<sub>2</sub> were reached in only a handful of locations.</div><div>Exposure to PM<sub>2.5</sub> in 2018 resulted in 36,403 (30,422–42,640) premature deaths, whereas NO<sub>2</sub> exposure contributed to 20,175 (7125–32,281) premature deaths. These numbers could be reduced by 46 % and 44 %, respectively, if exceedances beyond the lower cut-offs were avoided.</div><div>These findings indicate that complying with the new WHO recommendations would require considerable effort, but they can lead to substantial health benefits.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102827"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584581","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}
Pub Date : 2026-04-01Epub Date: 2025-11-19DOI: 10.1016/j.apr.2025.102830
Serpil Yenisoy , Melike Dörter
This review focuses on the efficacy of lichens as biomonitors of atmospheric deposition. It emphasizes the function of lichens in the assessment of trace element pollution and trace element isotope determination in different environments. The importance of foliose and fruticose lichens in lichen biomonitoring, comparison of bulk deposition/aerosol concentrations with the concentrations of transplants and comparison of lichen species with particular reference to the literature of the last decade were investigated. Biomonitoring is a comparatively inexpensive and readily implementable alternative to conventional air sampling methods, although it is subject to certain limitations. While passive biomonitoring uses native organisms, active biomonitoring uses lichens that have been transplanted. The quantitative relationship between the concentrations of elements in lichens and the concentrations in total deposition or in aerosols could be obtained. The accumulation capacity of lichens is influenced by their morphology, substrate and environmental conditions. In addition, the evaluation of trace element isotopes is considered, particularly carbon, nitrogen, sulfur, mercury and lead isotopes, as these are crucial for source apportionment studies. As valuable organisms in environmental monitoring, lichens can contribute to strategies developed to protect public health.
{"title":"Review of the role of elemental and isotopic composition of lichens in air quality assessment","authors":"Serpil Yenisoy , Melike Dörter","doi":"10.1016/j.apr.2025.102830","DOIUrl":"10.1016/j.apr.2025.102830","url":null,"abstract":"<div><div>This review focuses on the efficacy of lichens as biomonitors of atmospheric deposition. It emphasizes the function of lichens in the assessment of trace element pollution and trace element isotope determination in different environments. The importance of foliose and fruticose lichens in lichen biomonitoring, comparison of bulk deposition/aerosol concentrations with the concentrations of transplants and comparison of lichen species with particular reference to the literature of the last decade were investigated. Biomonitoring is a comparatively inexpensive and readily implementable alternative to conventional air sampling methods, although it is subject to certain limitations. While passive biomonitoring uses native organisms, active biomonitoring uses lichens that have been transplanted. The quantitative relationship between the concentrations of elements in lichens and the concentrations in total deposition or in aerosols could be obtained. The accumulation capacity of lichens is influenced by their morphology, substrate and environmental conditions. In addition, the evaluation of trace element isotopes is considered, particularly carbon, nitrogen, sulfur, mercury and lead isotopes, as these are crucial for source apportionment studies. As valuable organisms in environmental monitoring, lichens can contribute to strategies developed to protect public health.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102830"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584582","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}
Pub Date : 2026-04-01Epub Date: 2025-11-20DOI: 10.1016/j.apr.2025.102832
Xiaozeng Xu , Yan Liu , Bo Zeng
As the capital of China, Beijing's air quality monitoring and prediction are the focus of social attention. Through the analysis of the air quality index (AQI) in Beijing over the past decade, the changes in air quality are influenced by various uncertain factors, presenting complex and diverse characteristics, with obvious nonlinear and seasonal features, and a long-term downward trend. To this end, an improved second-order adaptive grey prediction model was proposed to predict annual AQI, and combined with seasonal index (SI) to predict monthly AQI, forming an Air Quality Index Prediction System (AQIFS). Compared to traditional grey models, the new model introduces dynamic power functions to optimize background values, thereby better fitting the nonlinear trends in the sequence. And it combines SI decomposition with monthly data to accurately capture seasonal fluctuations. Experimental results show that the proposed model performs the best in terms of annual average relative percentage error (MRPE) are 2.98 % and 3.82 %, root mean square error (RMSE) are 3.3593 and 3.4244, respectively. Its comprehensive performance is significantly better than other models. This study has made a positive contribution to enriching and improving the methodological system of grey prediction models, providing an effective modeling method for predicting seasonal fluctuations in air quality.
{"title":"Integrated air quality prediction with seasonal index and grey model","authors":"Xiaozeng Xu , Yan Liu , Bo Zeng","doi":"10.1016/j.apr.2025.102832","DOIUrl":"10.1016/j.apr.2025.102832","url":null,"abstract":"<div><div>As the capital of China, Beijing's air quality monitoring and prediction are the focus of social attention. Through the analysis of the air quality index (AQI) in Beijing over the past decade, the changes in air quality are influenced by various uncertain factors, presenting complex and diverse characteristics, with obvious nonlinear and seasonal features, and a long-term downward trend. To this end, an improved second-order adaptive grey prediction model was proposed to predict annual AQI, and combined with seasonal index (SI) to predict monthly AQI, forming an Air Quality Index Prediction System (AQIFS). Compared to traditional grey models, the new model introduces dynamic power functions to optimize background values, thereby better fitting the nonlinear trends in the sequence. And it combines SI decomposition with monthly data to accurately capture seasonal fluctuations. Experimental results show that the proposed model performs the best in terms of annual average relative percentage error (MRPE) are 2.98 % and 3.82 %, root mean square error (RMSE) are 3.3593 and 3.4244, respectively. Its comprehensive performance is significantly better than other models. This study has made a positive contribution to enriching and improving the methodological system of grey prediction models, providing an effective modeling method for predicting seasonal fluctuations in air quality.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"17 4","pages":"Article 102832"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147584583","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}
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