Atmospheric Pollution Research最新文献

{"title":"The influence of different seasonal monsoon seasons on airborne and marine microplastics at selected stations on the west coast of Peninsular Malaysia","authors":"Natasha Arina ,&nbsp;Norfazrin Mohd Hanif ,&nbsp;Yet Yin Hee ,&nbsp;Afiqah Bahirah Ayoub ,&nbsp;Siti Nursarah Mohd Rodzi ,&nbsp;Shik Ahmad Haikal ,&nbsp;Yasmin Zakaria ,&nbsp;Mohd Talib Latif","doi":"10.1016/j.apr.2025.102681","DOIUrl":"10.1016/j.apr.2025.102681","url":null,"abstract":"<div><div>Microplastic (MP) pollution poses a significant environmental threat, particularly as it enters both the atmosphere and the oceans, especially in coastal environments. This is the first study to determine the concentration of MPs in two environmental compartments, the atmosphere and the marine environment on the west coast of Peninsular Malaysia (Port Dickson and Port Klang), across different monsoon seasons. The MPs were extracted using KOH digestion and density separation using ZnCl₂. The quantification of the particles was performed using Nile Red Solution. The results showed the concentrations of MPs in the atmosphere and seawater ranged from 7.16 to 7.62 particles m⁻³ and 156.24 to 221.90 particles L⁻¹, respectively. A significant difference in MP concentrations between sites was observed only in seawater samples, with Port Klang exhibiting higher values. Additionally, airborne samples exhibited significant seasonal variation, with higher MP concentrations recorded during the Northeast monsoon. The observed MPs morphology consisted mostly of fragments (up to 60 %) and fibres (up to 52 %) in the airborne and marine samples, respectively. Additionally, differences in rainfall precipitation and temperature across monsoon seasons significantly impact MP concentrations in marine and airborne samples. The findings highlight the importance of monsoons and coastal activities in influencing MP inputs into the coastal environment, which provides critical insights into MP pollution in the coastal environment.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102681"},"PeriodicalIF":3.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809500","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":"Comparative investigation of airborne particulate matter and culturable fungi in horse stables","authors":"Doris Haas ,&nbsp;Theresa Fritz ,&nbsp;Herbert Galler ,&nbsp;Juliana Habib ,&nbsp;Petra Ofner-Kopeinig ,&nbsp;Michael Kropsch ,&nbsp;Eduard Zentner ,&nbsp;Michael Schalli","doi":"10.1016/j.apr.2025.102683","DOIUrl":"10.1016/j.apr.2025.102683","url":null,"abstract":"<div><div>Today, horses assist us in sports and are partners in our leisure activities. The frequent consumption of roughage and bedding materials, horses and staff are often exposed to high levels of microorganisms in the stable air. The objective of the study was to investigate the emission of particulate matter and culturable fungi in two horse stables of different construction types in urban and rural areas as well as their transmission in the immediate surroundings. The emission measurements were done in an open and a closed stable. To assess the immissions, measurements were conducted at the stables entrance, leeward and windward directions. Samples were carried out using the MAS 100® for airborne fungi and the APC M3® for particulate matter. Additionally, fungi from air, bedding and hay were cultured and identified. The median concentration of the total particles in the closed stable was of 2.57 × 10⁷ N/m³, which was twice as high as in the open stable. The median concentration of the total fungi was 7.29 × 10³ CFU/m³ in both stables. The dominant fungal genus was <em>Aspergillus</em>. In the open stable the median concentration of <em>Aspergillus</em> sp. was of 5 × 10³ CFU/m³ and thus significantly higher (p = 0.001) than in the closed stable. The fungal genera detected in bedding and hay were similar to those in the air of both stables. The findings of this study may contribute to plan and realise horse stables in future, which avoid accumulation of bioaerosols and prevent health hazards to horses and the working team.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102683"},"PeriodicalIF":3.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830463","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":"A two-stage algorithm to estimate ground-level PM2.5 concentration levels in Madrid (Spain) from AOD satellite data and surface proxies","authors":"J.M. Cordero","doi":"10.1016/j.apr.2025.102678","DOIUrl":"10.1016/j.apr.2025.102678","url":null,"abstract":"<div><div>Poor air quality in urban areas is an important health risk; therefore, reducing population exposure to pollutants such as PM_2.5 is a major concern. Health assessments regarding this pollutant have typically relied on the measurements from urban networks of Air Quality Monitoring Stations (AQMS) to assess population exposure. The methods used for the spatial interpolation of observation often lacks a solid physical basis. Mesoscale air quality models provide high spatiotemporally resolved ground-level concentrations based on urban features, including the distribution of pollution sources; however, they are subject to significant uncertainty. In this work, a novel methodology to produce 1 km² resolution maps of ground-level PM_2.5 concentration for the Municipality of Madrid during 2015 is presented. Toward this end, different data sets including: meteorology, satellite observations of atmospheric optical depth (AOD) from MAIAC, emission data, population, land use, and vegetation land cover have been used. Subsequently, we applied extreme gradient boosting (XGBoost) machine learning algorithms in two steps to first fill gaps in the AOD field and then, estimate ground-level PM_2.5 concentration. The predictions of the so-called 2_step_XGBoost algorithm were compared with observations from the all available ground-level PM_2.5 concentration observations from the AQMS in Madrid obtaining a determination coefficient (r²) of 0.96, a RMSE of 1.5 μg/m³, and negligible bias. Additionally, we used a 10-fold cross validation to confirm the robustness of the algorithm and the independency of the dataset used for training (r² of 0.94 ± 0.01, RMSE of 0.40 ± 0.04 and MAE of 0.22 ± 0.02. These results highlight the reliability of this approach for future urban health analysis. In addition, we performed a Feature Importance (FI) analysis that revealed that 2_step_XGBoost identified the planetary boundary layer height (PBLH) as the most influential variable while AOD was found to have relatively low explanatory power, a result that may be contrasted in other case studies.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102678"},"PeriodicalIF":3.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144826513","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":"Temporal variations in the long–term effects of ambient PM2.5 components on pulmonary tuberculosis incidence in mainland China during 2004–2018","authors":"Kang Ma ,&nbsp;Fengman Fang ,&nbsp;Yuesheng Lin ,&nbsp;Xiuya Xing ,&nbsp;Youru Yao ,&nbsp;Fei Tong","doi":"10.1016/j.apr.2025.102679","DOIUrl":"10.1016/j.apr.2025.102679","url":null,"abstract":"<div><div>Ambient fine particulate matter (PM_2.5) has been widely associated with pulmonary tuberculosis (PTB). However, less is known about the impact and temporal variations of specific PM_2.5 components on PTB. In this study, we employed breakpoint regression and distributed lag nonlinear models (DLNM) to assess the temporal variations in the effects of long−term exposure to PM_2.5 and its components, including sulfate (SO₄²⁻), nitrate (NO₃⁻), ammonium (NH₄⁺), organic matter (OM), and black carbon (BC) on the incidence of 14,816,329 PTB cases across 31 provinces in mainland China from 2004 to 2018. We also explored sources of heterogeneity through meta-regression. The study found that long−term exposures to PM_2.5 components were linked to higher monthly PTB incidence. The cumulative relative risks (CRR and 95 % CI) were highest at 1.379 (95 % CI: 1.331−1.428) per interquartile range (IQR) increase in NH₄⁺, followed by 1.361 (95 % CI: 1.314−1.409) for NO₃⁻, 1.331 (95 % CI: 1.282−1.382) for SO₄²⁻, 1.130 (95 % CI: 1.091−1.171) for OM, and 1.126 (95 % CI: 1.084−1.170) for BC. Smear−positive pulmonary tuberculosis (SPPTB) showed greater susceptibility. This association varied significantly across different socioeconomic and healthcare levels. In regions with lower per capita disposable income, urbanization rate, and the number of medical technical personnel per 1000 persons, NO₃⁻ and NH₄⁺ were associated with higher PTB incidence rates. Over time, the effect sizes per unit increase in PM_2.5 components has significantly diminished, but the impact of NH₄⁺, NO₃⁻ and SO₄²⁻ on PTB remains substantial. This observation might be due to reductions in PM_2.5 exposure levels and changes in socioeconomic status factors, such as advancements in the economy and healthcare systems. Our study highlights the temporal variations in the effects of different PM_2.5 components on PTB incidence, identifies key harmful components contributing to PTB, and provides scientific evidence for the PTB prevention and control strategies targeting specific PM_2.5 sources.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102679"},"PeriodicalIF":3.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781518","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":"Exploring different methods to evaluate the Urban Pollution Island intensity based on multi-year observations of aerosol and gases","authors":"Annalisa Di Bernardino ,&nbsp;Margherita Erriu ,&nbsp;Serena Falasca ,&nbsp;Anna Maria Siani","doi":"10.1016/j.apr.2025.102677","DOIUrl":"10.1016/j.apr.2025.102677","url":null,"abstract":"<div><div>This work proposes different methodologies for evaluating the Urban Pollution Island Intensity (UPII), varying the subset of urban air quality stations according to their environmental classification. In-situ observations of particulate matter (PM₁₀ and PM_2.5), nitrogen oxides (NO and NO₂), and ozone (O₃) collected in Rome (Italy) from 2018 to 2023 are examined.</div><div>Urban traffic stations, located near roads with medium-high traffic intensity, always recorded higher concentrations than the urban background sites. The selection of both urban traffic and background stations permits the most representative description of UPII and the pollution levels in the metropolitan area. UPII assumes the largest seasonal variation for PM₁₀ and NO, while the daily cycle highlights a bimodal behaviour, with peaks corresponding to rush hours. NO₂ peaks are shifted by 1 h compared to NO, while for O₃ UPII shows positive values during summertime rush hours. Multi-pollutant air quality indices confirm poor air quality in the city, predominantly during the colder months.</div><div>The findings demonstrate that pollution is mainly attributable to NO₂, PM₁₀, and O₃ in downtown Rome and to PM_2.5 and O₃ in the countryside. The pollution variations suggest the combined effect of emission-based, meteorological-based, and photochemical-based factors, which must be considered when tailored policies for improving air quality are designed.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102677"},"PeriodicalIF":3.5,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781097","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":"Association between air pollution and climate parameters exposure during the periconceptional period and hypospadias risk in Turkish offspring: A case-control study","authors":"Mehmet Semih Demirtas ,&nbsp;Siddika Songul Yalcin ,&nbsp;Mustafa Tusat ,&nbsp;Ismail Ozmen ,&nbsp;Dilek Basar ,&nbsp;Ahmet Salih Calapoglu ,&nbsp;Serdar Siyve ,&nbsp;Orhan Demirtas ,&nbsp;Dilan Altintas Ural","doi":"10.1016/j.apr.2025.102675","DOIUrl":"10.1016/j.apr.2025.102675","url":null,"abstract":"<div><div>We aimed to investigate the associations between hypospadias and contact to air pollutants and climate parameters during the pre- and post-conceptional periods. This study has been carried out as a multicentre case-control study involving 340 children with hypospadias between the ages of 0–3 years and 357 children in the same age range who applied for circumcision. A total of 7 air pollutant (PM_2.5, PM₁₀, NO, NO₂, SO₂, CO, O₃), 5 climate parameters [air temperature, relative humidity (RH), air pressure (AP), wind speed (WS) and precipitation amounts data were obtained. In the multivariate logistic regression analysis, younger maternal age (reference: &gt;35 years) and maternal smoking (reference: no exposure) were significantly associated with a higher likelihood of hypospadias. Conversely, folic acid supplementation intake during pregnancy (reference: no supplementation) and maternal BMI in the range of 25–30 (reference: &lt;25) were associated with a lower likelihood of its occurrence. After adjusting for child-family characteristics, increased interquartile ranges (IQR) of CO, mean temperature, minimum temperature, RH, and AP were positively associated with hypospadias, whereas SO₂ levels and diurnal temperature range (DTR) were inversely associated. Comprehensive analysis of individual and environmental factors further highlighted that younger maternal age, smoking exposure, PM₁₀, and air pressure were positively associated with hypospadias, while a greater DTR was inversely associated. Younger maternal age, smoking exposure, and specific air pollutants, such as PM₁₀ and CO, as well as climatic factors like AP, were positively associated with hypospadias. These findings underscore the importance of mitigating environmental exposures and promoting maternal health interventions to reduce the risk of congenital anomalies like hypospadias. Further longitudinal studies are warranted to confirm these associations and explore underlying mechanisms.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102675"},"PeriodicalIF":3.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144764180","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":"A methodology for estimation of local black carbon and PM2.5 emissions in suburban zones. Case study of Santiago, Chile","authors":"Jessika Rodríguez ,&nbsp;Héctor Jorquera","doi":"10.1016/j.apr.2025.102674","DOIUrl":"10.1016/j.apr.2025.102674","url":null,"abstract":"<div><div>This work presents a methodology that combines CALPUFF dispersion model with novel estimates of local concentrations of black carbon and PM_2.5 coming from fossil fuel (BC_ff, PM_2.5ff) and wood burning (BC_wb, PM_2.5wb), to improve local emission estimates without the need to specify boundary conditions. We have applied this methodology in three suburbs of Santiago, Chile — Colina, Melipilla and San José de Maipo (SJM) — during the cold season of 2021. For PM_2.5wb, local emission rates were estimated at 74.8 ton year⁻¹ for Colina, 93.3 ton year⁻¹ for Melipilla, and 24.3 ton year⁻¹ for SJM, which correspond to 0.5, 0.4, and 0.3 times the 2012 inventory, respectively. For BC_ff, the estimated emission rates were 2.2 ton month⁻¹ in Colina, 0.2 ton month⁻¹ in Melipilla, and 0.2 ton month⁻¹ in SJM, which represent 5.2, 0.7, and 2.8 times the respective values at each site according to the 2017 inventory. For both BC_wb and BC_ff, the peaks in local emissions corresponded to times of the day with higher levels of residential combustion and traffic activity. Dispersion modeling of BC_ff from Zone B (the studied suburbs) to Zone A (Greater Santiago) showed that the highest contributions occurred in July 2021 in the wee hours. Dispersion modeling of PM_2.5wb, PM_2.5ff, BC_wb and BC_ff from Zone A to Zone B and wind trajectory analysis suggest that non-local contributions reaching the studied suburbs originate not only from Santiago but also from nearby and distant localities. This novel methodology may be applied to any suburban area.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102674"},"PeriodicalIF":3.5,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763650","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 illicit drugs in PM10 from Northern Italy using a rapid HPLC-MS/MS method","authors":"Giovanna Mazzi ,&nbsp;Elena Barbaro ,&nbsp;Matteo Feltracco ,&nbsp;Marco Roman ,&nbsp;Emma Camarotto ,&nbsp;Emanuela Canò ,&nbsp;Giada Furlan ,&nbsp;Andrea Gambaro","doi":"10.1016/j.apr.2025.102670","DOIUrl":"10.1016/j.apr.2025.102670","url":null,"abstract":"<div><div>Illicit drugs (IDs) are substances whose production, consumption, and health impact are increasing globally. While their presence in water and soil has been studied, airborne concentrations remain largely unexplored. Airborne IDs could serve as markers of consumption patterns and potentially help identify local drug distribution zones. Few analytical methods have been developed, but preanalytical treatments are often time-consuming. To address this, we developed and validated a reliable, fast analytical method based on HPLC-MS/MS for cocaine, amphetamine, and heroin – three largely consumed IDs. Low Method Detection and Quantification Limits were achieved, with cocaine reaching the lowest reported in the literature (0.02 and 0.08 pg m⁻³, respectively).</div><div>Our methodology was applied to nearly 180 p.m.₁₀ samples collected during December 2023 across thirteen cities in northern Italy. Cocaine was always detected and quantified, whereas amphetamine and heroin were below the detection limit. The highest cocaine concentrations were recorded in Milan (229 pg m⁻³), Collegno (195 pg m⁻³), and Treviso (111 pg m⁻³). A notable increase in cocaine levels over the month suggests a link with holidays, tourism, and the <em>Christmas Depression</em> phenomenon. In some cities, concentration spikes coincided with traditional festivities. Statistical analyses were conducted to explore correlations between cocaine, PM₁₀, inorganic major ions, and atmospheric variables. Negative correlation was found between cocaine concentration and wind in six cities, suggesting multiple local sources. Local available healthcare and drug seizure data were also considered, and an interesting correlation was found between cocaine levels and people taken into care by private services.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102670"},"PeriodicalIF":3.5,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781563","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":"Development and interpretation of PM2.5 estimation model for the Seoul Metropolitan Area using machine learning and explainable AI","authors":"Myeong-Gyun Kim ,&nbsp;Se-Young Kim ,&nbsp;Kwangyul Lee ,&nbsp;Pilho Kim ,&nbsp;Hyoseon Kim ,&nbsp;Hyo-Jong Song","doi":"10.1016/j.apr.2025.102672","DOIUrl":"10.1016/j.apr.2025.102672","url":null,"abstract":"<div><div>PM_2.5 is emitted and formed in the atmosphere through various factors, posing significant health risks to humans. Therefore, accurately estimating PM_2.5 concentrations and analyzing the contributions of individual factors are crucial. A Deep Neural Network (DNN) model was developed for PM_2.5 estimation in the Seoul Metropolitan Area in South Korea, while some machine learning models—Random Forest and Extreme Gradient Boosting—were also built for performance comparison. Among these, the DNN model demonstrated the best performance, with an R² of 0.95, MSE of 12.14, and MAE of 2.6. Based on this, Explainable Artificial Intelligence (XAI) techniques, including Vanilla Gradient and Shapley Additive Explanation (SHAP), were applied to interpret the PM_2.5 estimation model and analyze the contribution of each factor. The contribution analysis for the Seoul Metropolitan Area revealed that NO₃⁻ and NH₄⁺ had the highest contributions to PM_2.5 formation, indicating that secondary formation mechanisms play a dominant role. Furthermore, at high concentrations, the contributions of NO₃⁻, NH₄⁺, and SO₄²⁻ were the highest, and the contributions of metal components and PM₁₀ were higher than the average. In particular, it was observed that NH₄⁺ and K showed a positive correlation with PM_2.5 formation. Future research will focus on refining the model through clustering-based approaches and other enhancements, aiming to deepen the understanding of PM_2.5 formation patterns and provide meaningful insights for policymaking.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 11","pages":"Article 102672"},"PeriodicalIF":3.9,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711247","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":"Indoor ultrafine particle concentrations and their size distributions during festival-associated emissions in urban region","authors":"Monika Sharma ,&nbsp;Mukesh Khare ,&nbsp;Rajeev Kumar Mishra","doi":"10.1016/j.apr.2025.102671","DOIUrl":"10.1016/j.apr.2025.102671","url":null,"abstract":"<div><div>Indoor air quality is crucial for the well-being of residents, as people spend 80 % of their time indoors. This study investigated the impact of festival activities on household indoor air quality in megacity Delhi. The study included different phases and activities during the festival and normal conditions and analyzed the impact of different emission sources. During the festival period, the indoor ultrafine particle concentration was found to be 6.7 × 10⁴ #cm⁻³, which was the highest observed indoor concentration throughout the study. The particle number concentration of the nanoparticles in indoor environment ranged from 10⁴ to 10⁵ #cm⁻³. Ultrafine size range particles contributed up to ∼85 % to the total particle numbers. Fireworks contributed to higher particle numbers in indoors, followed by cooking, dusting, and worshiping. The size distribution pattern of the particles emitted during cooking activities and fireworks was found to be different. Particle size range 10–30 nm contributed ∼31 % to total particle numbers on fireworks day, whereas on a normal day, it contributed only ∼13 %. During normal day, 100–1000 nm size particles contributed ∼50 % to total particle numbers. The diurnal pattern of the indoor environment was different from the outdoor. The outdoor fireworks activities also influenced indoor pollutants with respect to trace metals, which makes the indoor air quality more toxic, and affects the occupants' health.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 12","pages":"Article 102671"},"PeriodicalIF":3.5,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721332","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}