Atmospheric Pollution Research最新文献

{"title":"Environmental impacts and costs of ozone formation in Bangkok Metropolitan Region","authors":"Soe Htet Aung ,&nbsp;Shabbir H. Gheewala ,&nbsp;Ekbordin Winijkul ,&nbsp;Sirima Panyametheekul ,&nbsp;Trakarn Prapaspongsa","doi":"10.1016/j.apr.2025.102450","DOIUrl":"10.1016/j.apr.2025.102450","url":null,"abstract":"<div><div>Ozone formation is an important environmental factor causing impacts on human health and ecosystem. Previous research relating to ozone formation often had limited scopes on direct emissions or focused on limited sectors of cities. This study aimed to quantify environmental impacts and costs due to ozone formation caused by energy generation, industry, agriculture, residential and commercial sectors, transport, fugitive gas emissions and waste treatment in the Bangkok Metropolitan Region (BMR) in 2022. The assessment applied spatially differentiated life cycle assessment framework, quantifying impacts on human health and ecosystem using local and global factors for on-site and supply chain emissions. The baseline situation in 2022 revealed that total emissions (on-site and supply chain) were 3.97E+05 tonnes of NO_x and 1.15E+05 tonnes of NMVOC. NO_x and the transport sector were the main stressor and hotspot causing impacts and costs of ozone formation in BMR. Total impact scores (on-site and supply chain) were 4.39E+02 disability-adjusted life year (human health impact) and 3.50E+01 species.year (ecosystem damage). The impacts were mainly contributed by on-site activities in BMR costing 6 billion Thai Baht. Scenarios were developed focusing primarily on the on-road transport since it was the hotspot causing health impacts and ecosystem damage. The scenarios indicated that upgrading fuel technology from diesel to compressed natural gas and modification of vehicles from diesel to electric were found to be very effective for overall reduction by more than 50% on average for health impacts and by more than 40% on average for ecosystem damage.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102450"},"PeriodicalIF":3.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487697","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":"A significant reduction in the coal contribution to PM2.5 and exposed health risks due to the energy structure transition","authors":"Dongmei Hu ,&nbsp;Mingyang Yuan ,&nbsp;Yulong Yan ,&nbsp;Xiaolin Duan ,&nbsp;Yafei Guo ,&nbsp;Yueyuan Niu ,&nbsp;Wen Yan ,&nbsp;Lin Peng","doi":"10.1016/j.apr.2025.102457","DOIUrl":"10.1016/j.apr.2025.102457","url":null,"abstract":"<div><div>By collecting PM_2.5 samples containing heavy metals in a typical coal resource-based city, we analyzed the interannual variation of heavy metal concentrations over an extended time period (2018–2022). This analysis involved apportioning the sources of these heavy metals and evaluating the carcinogenic and non-carcinogenic health risks posed to different populations via the respiratory route. Results showed that Cd (83.99%), Zn (62.56%), Pb (56.97%), and As (2.60%) were associated with coal combustion, exhibiting decreasing trends. The maximal information coefficient (MIC) indicated that most of the elements with strong correlations were associated with coal combustion. Four sources, namely coal combustion, resuspended dust, traffic emission, and industry, were determined using positive matrix factorization. Cr posed the highest carcinogenic risk, particularly among adults. Coal consumption and its contribution showed significant reductions due to the energy structure transition of coal reduction. Notably, the top three metals in terms of carcinogenic risk were all associated with coal combustion. The carcinogenic risk associated with Cd and As from coal combustion was significantly lower in 2022 than in 2018.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102457"},"PeriodicalIF":3.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453113","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":"Household PM2.5 in a South African urban and rural setting: A comparative analysis using low-cost sensors","authors":"Matthew Benyon ,&nbsp;Ngwako Kwatala ,&nbsp;Tracey Laban ,&nbsp;Thandi Kapwata ,&nbsp;Chiara Batini ,&nbsp;Samuel Cai ,&nbsp;Lisa K. Micklesfield ,&nbsp;Rikesh Panchal ,&nbsp;Siyathemba Kunene ,&nbsp;Sizwe B. Zondo ,&nbsp;Brigitte Language ,&nbsp;Bianca Wernecke ,&nbsp;Scott Hazelhurst ,&nbsp;F. Xavier Gómez-Olivé ,&nbsp;Joshua Vande Hey ,&nbsp;Caradee Y. Wright","doi":"10.1016/j.apr.2025.102459","DOIUrl":"10.1016/j.apr.2025.102459","url":null,"abstract":"<div><div>Household air pollution (HAP) is responsible for millions of premature deaths each year<em>.</em> Exposure to household air pollutants as a risk factor for poor health has not been adequately quantified in many parts of the world, especially Sub-Saharan Africa. We aimed to assess HAP, specifically PM_2.5, and its associations with dwelling and household characteristics in urban (Soweto) and rural (Agincourt) settings in South Africa. We monitored indoor PM_2.5 concentrations in 40 unique households using low-cost sensors, across two study sites and seasons. Low-cost sensors were calibrated by collocation, and associations between dwelling and household characteristics with indoor PM_2.5 concentrations were assessed using a log-linear regression model. PM_2.5 concentrations were greater in urban households in the summer (50 μg/m³ (95% CI: 41–63) and in the winter (82 μg/m³ (95% CI: 62–109)) compared to rural households (summer: 19 μg/m³ (95%: CI 14–26) and winter: 48 μg/m³ (95% CI: 44–53)). The log-linear model (n = 39) explained 74% of the variance in leave-one-out cross validation. Significant associations with household PM_2.5 were observed with the following: the season, study setting, presence of tobacco smoking, presence of incense burning inside the dwelling, and the use of heating. This study found significant variations in HAP concentrations within and across the urban and rural communities, likely influenced by differences in ambient outdoor concentrations and individual behaviours such as incense burning. It is crucial to enhance community and policy maker awareness regarding the dangers of indoor smoking and the harmful effects of burning incense indoors.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102459"},"PeriodicalIF":3.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437105","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":"Research on the impact of land use and meteorological factors on the spatial distribution characteristics of PM2.5 concentration","authors":"Xiaoxia Wang ,&nbsp;Hongtao Zhang ,&nbsp;Zhihai Fan ,&nbsp;Hong Ding","doi":"10.1016/j.apr.2025.102462","DOIUrl":"10.1016/j.apr.2025.102462","url":null,"abstract":"<div><div>Precisely capturing the spatial distribution characteristics of fine particulate matter (PM_2.5) is the key to air pollution prevention and control. Researches suggests that PM_2.5 concentrations is jointly affected by meteorological factors and urban land use. This study comprehensively considered these factors, with land cover, meteorology and road traffic as potential independent variables. The land use regression (LUR) model was used to identify the main influences on the spatial distribution of PM_2.5 concentration under different types of land use, and the importance of these factors was assessed using a random forest (RF) model. The research findings indicate that: (1) The fluctuations of PM_2.5 concentration in residential and industrial land use are relatively severe, ranging from 0 to 50 μg/m³. The changes in commercial and public service land use range from 0 to 40 μg/m³. And the in green space range from 15 to 33 μg/m³ (2) The distribution of PM_2.5 concentration in residential land is primarily influenced by precipitation and relative humidity, with relative importance of 36.3% and 23.9%, respectively. And that in industrial land use is primarily influenced by wind speed, with a relative importance of 30.6%. (3) The most influential determinant of spatial distribution of PM_2.5 concentration is meteorological factors, with relative importance exceeding 62%. The relative importance of road traffic ranges from 15.6% to 24.9%. And that of land cover factors ranges from 9.9% to 22.4%. This study analyzes the coupling connection between urban land use and spatial distribution of PM_2.5 concentration, and elaborates the specific influence of the former on the latter.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102462"},"PeriodicalIF":3.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479229","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":"Unprecedented impacts of meteorological and photolysis rates on ozone pollution in a coastal megacity of northern China","authors":"Jianli Yang ,&nbsp;Chaolong Wang ,&nbsp;Yisheng Zhang ,&nbsp;Sufan Zhang ,&nbsp;Xing Peng ,&nbsp;Xiaofei Qin ,&nbsp;Jianhui Bai ,&nbsp;Lian Xue ,&nbsp;Guan Wang ,&nbsp;Shanshan Cui ,&nbsp;Wenxin Tao ,&nbsp;Jinhua Du ,&nbsp;Dasa Gu ,&nbsp;Xiaohan Su","doi":"10.1016/j.apr.2025.102461","DOIUrl":"10.1016/j.apr.2025.102461","url":null,"abstract":"<div><div>This study investigates the seasonal variations in O₃ levels in Qingdao, a typical coastal city, and quantifies the effects of key photolysis rate constants (<em>J</em>[O¹D] and <em>J</em>[NO₂]), meteorological parameters (RH, TEMP, and SF), and pollutants (ΔCO, PM_2.5, and NO₂) on O₃ levels across different seasons using machine learning. Additionally, the summer months, when photochemical reactions are most active, were analyzed in detail. The results indicate that the factors contributing to summer O₃ levels in order of importance, were RH, ΔCO, SF, PM_2.5, <em>J</em>[O¹D], NO₂, TEMP, WS, and <em>J</em>[NO₂]. RH was the most significant factor, with high humidity levels (&gt;75%) inhibiting O₃ formation. ΔCO, representing regional transport, was the second most influential, suggesting that direct O₃ transport and the delivery of high concentrations of precursors significantly promoted local O₃ production and accumulation. While <em>J</em>[O¹D] and <em>J</em>[NO₂] had different roles in O₃ promotion and depletion, <em>J</em>[O¹D] had a greater impact overall. The temperature in the range of 26 °C–32 °C inhibits O₃ production, When RH exceeded 90%, <em>J</em>[O¹D] accelerates while other photolysis rate constants decline, further suppressing the production of O₃. For comparison, multiple linear regression models were used to develop empirical equations for calculating hourly O₃ concentrations across the four seasons. The results showed that these factors explained 50%, 64%, 61%, and 63% of the O₃ sources in Qingdao for spring, summer, autumn, and winter, respectively. Sensitivity tests on factors influencing summer O₃ concentrations found that MLR could not quantify their contributions to O₃ levels.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102461"},"PeriodicalIF":3.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419419","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":"Evaluating the spatiotemporal variations in atmospheric CO2 concentrations in China and identifying factors contributing to its increase","authors":"Weixin Zhu ,&nbsp;Hong Zhang ,&nbsp;Xiaoyu Zhang ,&nbsp;Haohao Guo ,&nbsp;Yong Liu","doi":"10.1016/j.apr.2025.102458","DOIUrl":"10.1016/j.apr.2025.102458","url":null,"abstract":"<div><div>Understanding the patterns and trends of atmospheric carbon dioxide (CO₂) is essential for comprehending the global carbon cycle and making accurate future climate predictions. CO₂ levels are influenced by complex and often interrelated factors, requiring innovative approaches that can tie place-specific factors with CO₂ concentrations. This study utilized the Orbiting Carbon Observatory-2 (OCO-2) data to explore the changes of CO₂ concentrations in China over the past decade. Additionally, climate parameters, vegetation cover, and anthropogenic activities were combined to explain temporal and spatial changes in CO₂ concentrations, using Geodetector and Multiscale Geographically Weighted Regression (MGWR) model. The results revealed a consistent increase (2.54 ppm/yr) and significant spatial agglomeration (High-High cluster in the east, Low-Low cluster in the west) of CO₂ concentrations in China. The spatial location (<em>q</em> = 0.68) emerged as the primary determinant of CO₂ levels, with population variable (<em>q</em> = 0.55) representing the secondary influencing factor. The interactions among natural elements and anthropogenic activities had substantially elevated CO₂ levels. Compared to the Geographically Weighted Regression (GWR), and Ordinary Least Squares (OLS) models, the MGWR model demonstrated superior capability in revealing the varying spatial scales of influence among different variables, making it more suitable for investigating the impacts of multiple factors on atmospheric CO₂ concentrations. The MGWR revealed significant variations in the optimal bandwidths among different explanatory variables, with temperature, precipitation, and LAI operating at much smaller scales. The findings are expected to provide valuable insights into regional processes influencing CO₂ concentrations and the development of targeted interventions.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102458"},"PeriodicalIF":3.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474202","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":"An integrated feature selection and machine learning framework for PM10 concentration prediction","authors":"Elham Kalantari ,&nbsp;Hamid Gholami ,&nbsp;Hossein Malakooti ,&nbsp;Dimitris G. Kaskaoutis ,&nbsp;Poorya Saneei","doi":"10.1016/j.apr.2025.102456","DOIUrl":"10.1016/j.apr.2025.102456","url":null,"abstract":"<div><div>The Sistan Basin, east Iran is a major dust source, presenting significant atmospheric, ecological, socio-economic, and health challenges. This study employed machine learning (ML) algorithms, including Random Forest (RF), K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), Support Vector Regression (SVR), and Least Absolute Shrinkage and Selection Operator (LASSO), to model and predict PM₁₀ concentrations in Zabol City (2013–2022), utilizing independent meteorological variables such as temperature, relative humidity, wind speed and direction. Feature selection methods — Filter (Information Gain, F-Test, Correlation Coefficient), Wrapper (Recursive Feature Elimination, Sequential Forward/Backward Selection), and Embedded (LASSO, Elastic Net, Ridge Regression, RF Importance) — were applied to identify significant predictors, with embedded methods providing the best balance of simplicity, accuracy, and cost-efficiency. Among the models, RF demonstrated the highest seasonal performance (R² = 0.75) during summer. RF's prediction R² values for PM₁₀ remained above 0.5 in all seasons, consistently outperformed the other models. The WKNN model performed reasonably well across all seasons, ranking second among the models, while the LASSO model demonstrated weaker performance. The SVR model showed satisfactory performance in specific seasons, such as summer and autumn. A common feature of all models was their better performance during summer. Importantly, the models relied solely on readily available meteorological data, enabling accurate predictions of PM₁₀ in this arid region of eastern Iran. The findings highlight the potential of ML techniques for developing air pollution prediction and warning systems, offering valuable support to policymakers in the design of effective pollution control strategies and safeguarding public health.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102456"},"PeriodicalIF":3.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479230","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":"PM2.5 and PM10 concentrations in urban and peri-urban environments of two Pacific Island Countries","authors":"J.J. Hilly ,&nbsp;J. Sinha ,&nbsp;F.S. Mani ,&nbsp;A. Turagabeci ,&nbsp;P. Jagals ,&nbsp;D.S.G. Thomas ,&nbsp;G.F.S. Wiggs ,&nbsp;L. Morawska ,&nbsp;K. Singh ,&nbsp;J. Gucake ,&nbsp;M. Ashworth ,&nbsp;M. Mataki ,&nbsp;D. Hiba ,&nbsp;D. Bainivalu ,&nbsp;L.D. Knibbs ,&nbsp;R.M. Stuetz ,&nbsp;A.P. Dansie","doi":"10.1016/j.apr.2025.102454","DOIUrl":"10.1016/j.apr.2025.102454","url":null,"abstract":"<div><div>Air quality monitoring in most Pacific Island Countries, Territories, and States (PICTS) is minimal, with notable exceptions in Hawai'i and New Caledonia. However, air quality issues are increasingly significant in the region. Existing data on air quality, particularly regarding PM_2.5 and PM₁₀, are limited, with studies focusing on Fiji and New Caledonia. Our research provides the first continuous and comparative air quality monitoring in urban and peri-urban areas of Fiji and the Solomon Islands, and it is the first assessment since the introduction of the 2021 World Health Organization (WHO) Air Quality Guidelines (AQG). This study assesses health risks and air pollution trends to inform governmental recommendations. We collected PM_2.5, PM₁₀, and weather data from Honiara, Solomon Islands (February 2020–August 2023), and Suva, Fiji (April 2021–August 2023). In Honiara, PM_2.5 levels exceeded WHO AQG on 75% of days in urban areas and 51% in peri-urban areas, while PM₁₀ levels surpassed guidelines on 2% of days in both areas. In Suva, urban areas had a 10% exceedance of PM_2.5 guidelines, compared to 13% in peri-urban areas. Annual PM_2.5 averages exceeded WHO guidelines every year, with levels in Suva and Honiara exceeding guidelines by 2–4 times. PM₁₀ levels were 1.5 times higher than WHO AQG in urban Honiara and 1.2 times higher in peri-urban areas. These findings highlight the urgent need for governmental action to establish robust air quality standards and long-term monitoring programs in Fiji and the Solomon Islands to mitigate health risks from poor air quality.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102454"},"PeriodicalIF":3.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471589","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":"Analysis of the horizontal and vertical distribution of a dust weather event in the Tarim Basin based on multi-source observational datasets","authors":"Hong Jiang ,&nbsp;Qing He ,&nbsp;Ruqi Li ,&nbsp;Hao Tang ,&nbsp;Quanwei Zhao ,&nbsp;Hailiang Zhang ,&nbsp;Jinglong Li ,&nbsp;Yongkang Li ,&nbsp;Jingjing Li","doi":"10.1016/j.apr.2025.102455","DOIUrl":"10.1016/j.apr.2025.102455","url":null,"abstract":"<div><div>The study employed multi-source observation data from unmanned aerial vehicles (UAVs), satellites, and LiDAR conduct an observational study on a dust weather event that occurred in the Tarim Basin, China, from May 2 to 4, 2023. The results showed that FY-4A dust storm detection and MODIS aerosol optical depth (AOD) products could effectively observe the horizontal distribution of dust. Dust areas and intensities were identified at the AOD threshold range of 0.54–3.50. The convolutional neural network algorithm dust mask could identify dust structures with more precision compared to traditional FY-4 dust storm detection. Moreover, vertical particulate matter (PM) concentration changes determined by UAVs were analyzed at different altitudes, with low PM concentrations observed at higher altitudes. The dust area obtained through the CALIPSO vertical feature mask product was consistent with the PM concentration changes observed by the UAV. When the visibility value was below 1 km, the ground-based LiDAR 532 nm extinction coefficient (EC), backscatter coefficient (BC), and depolarization ratio (DR) values reached 3.42 km⁻¹, 0.057 km⁻¹sr⁻¹, and 0.47, respectively. The vertical profile changes of EC, BC, and DR were in strong agreement with the vertical profile changes of the PM concentrations by the UAV.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102455"},"PeriodicalIF":3.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419418","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":"Burn parameters affect PAH emissions at conditions relevant for prescribed fires","authors":"Karl Töpperwien ,&nbsp;Guillaume Vignat ,&nbsp;Alexandra J. Feinberg ,&nbsp;Conner Daube ,&nbsp;Mitchell W. Alton ,&nbsp;Edward C. Fortner ,&nbsp;Manjula R. Canagaratna ,&nbsp;Matthias F. Kling ,&nbsp;Mary Johnson ,&nbsp;Kari Nadeau ,&nbsp;Scott Herndon ,&nbsp;John T. Jayne ,&nbsp;Matthias Ihme","doi":"10.1016/j.apr.2025.102438","DOIUrl":"10.1016/j.apr.2025.102438","url":null,"abstract":"<div><div>Wildfire smoke is a health hazard as it contains carcinogenic volatile compounds and fine particulate matter. In particular, exposure to polycyclic aromatic hydrocarbons (PAHs) is a major concern, since these compounds have been recognized as important contributors to the overall carcinogenic risk. In this work, gas and particle-phase PAH emissions from combustion of Eastern White Pine (<em>Pinus strobus</em>) were quantified using time-of-flight mass spectrometry over a range of burn conditions representative of wildfires and prescribed fires, including fuel moisture, heat flux, and oxygen concentration. We found that changing the burn environment lead to a variability of up to 77% in phenanthrene/anthracene emissions. This could explain a large part of the variability in PAH emission factors from biomass combustion reported in the literature. We found that optimal conditions for fuel moisture content of 20–30<span><math><mtext>%</mtext></math></span>, sample heat load of <span><math><mrow><mn>60</mn><mo>−</mo><mtext>70</mtext><mspace></mspace><mtext>kW</mtext><mspace></mspace><mtext>m</mtext><msup><mrow></mrow><mrow><mi>−2</mi></mrow></msup></mrow></math></span>, and oxygen concentrations of 5–15<span><math><mtext>%</mtext></math></span> can significantly reduce the emissions of heavy molar weight PAHs.</div><div>Our analysis showed that the relative carcinogenic risk from PAH exposure can be reduced by more than 50% under optimal conditions. In light of the increasing use of prescribed fire for forest management, the relationship between emissions and burn conditions that we have established provides a guidance for assessing the expected health impact from prescription burns, and can inform strategies to reduce PAH emissions from prescribed fire activities.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 5","pages":"Article 102438"},"PeriodicalIF":3.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430083","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}