Journal of Air Pollution and Health

PM2.5 attributable health impact assessment across urban land-use areas: A comparative study using AirQ+ and BenMAP-CE in Surat, India

2026-06-29T09:23:01+0430

Introduction: Ambient fine Particulate Matter (PM_2.5) pollution is increasingly recognized as a critical environmental issue in rapidly urbanizing and industrializing cities of developing nations. This study aimed to quantify the zone-specific health burden attributable to PM_2.5exposure in Surat, India.
Materials and methods: PM_2.5 data were obtained from a network of low cost sensors operating across three major land-use zones: residential (West), commercial (Central), and industrial (South). PM_2.5data collected over one year (October 2022 to September 2023) were combined with population projections and cause-specific mortality rates from national datasets. Two established Health Impact Assessment (HIA) tools, AirQ+ and BenMAP-CE, were utilized to estimate premature mortality associated with PM_2.5levels exceeding WHO air quality standards.
Results: The industrial zone exhibited the highest annual mean PM2.5 (86.6 µg/m³) and correspondingly the most significant premature mortality burden, primarily from ischemic heart disease, chronic obstructive pulmonary disease, and stroke. The commercial and residential zones exhibited comparatively lower pollution levels; however, notable mortality impacts were associated with higher population densities. Both AirQ+ and BenMAP-CE models produced consistent mortality estimates, highlighting the relationship between pollution concentration and demographic factors in urban health risks. Elevated incidences of acute lower respiratory infections among children under five were also identified in the industrial zone.
Conclusion: In order to lower health hazards, the results highlight the necessity of zone-specific emission reduction measures and additional strengthening of Surat's particle emission trading scheme. The integrated framework offers a practical approach for evaluating urban health and air quality in developing nations.

Health impact of a smoke free policy in small indoor sports facilities in Seoul: Workers’ subjective symptoms and indoor air related job environment in billiard halls and screen golf clubs

2026-06-29T09:23:02+0430

Introduction: This study aimed to evaluate the health impact of a smoke free policy implemented in these facilities in the Seoul metropolitan area, focusing on workers’ subjective symptoms in relation to indoor air related job environment.
Materials and methods: Cross-sectional surveys were conducted among 589 workers employed in billiard halls and screen golf clubs located in Seoul in August 2017 (before policy implementation) and August 2018 (after implementation). Associations between job environment factors and work time symptoms were examined using chi-squared tests or Fisher’s exact tests and multivariable logistic regression, sequentially adjusting for general and work-related characteristics. Across all symptoms, survey year (2018 vs. 2017) was consistently associated with reduced odds of symptom complaints.
Results: Facilities operating mechanical ventilation only or combined natural and mechanical ventilation showed significantly lower odds of all symptoms than those relying solely on natural ventilation. In contrast, workers in facilities with a higher number of windows, facilities where cooking was conducted, or those with more smoking customers generally reported higher odds of symptoms. More frequent ventilation (≥5 times per day) tended to reduce respiratory complaints, whereas insufficient or intermittent ventilation was associated with higher symptom prevalence.
Conclusion: Smoke free regulations, appropriate mechanical ventilation and comprehensive indoor air quality management, including control of cooking and outdoor pollutant infiltration, are needed to further protect the health of workers in these environments. These findings support comprehensive indoor air quality management combining smoke free policies with adequate mechanical ventilation systems in small indoor sports facilities.

Empowering data analysis and machine learning to predict asthma intensity using air pollutants in conjunction with environmental factors

2026-06-29T09:23:02+0430

Introduction: Asthma is a respiratory disease, the severity of which is affected by air pollutants and environmental factors. Predicting asthma severity can help in disease monitoring and control. The objective of this research is to develop a model for predicting the severity of asthma based on environmental and demographical factors using machine learning.
Materials and methods: Data was obtained from different districts in Uttarakhand, India, from government sources. Asthma severity was the output feature or dependent feature, while the input features or independent features were air pollutants such as Particulate Matters (PM_2.5, PM₁₀), Nitrogen dioxide (NO₂), Sulfur dioxide (SO₂), Ozone (O₃), Carbon monoxide (CO), environmental factors (temperature, humidity, wind speed) and socio economic factors (age, gender) in addition to a pollution index. Logistic Regression, Random Forest and XGBoost machine learning models were used for multi-class classification. The metrics for model performance were accuracy, precision, recall and F1-score.
Results: Logistic Regression had the highest accuracy (98%) compared to Random Forest and XGBoost (both 89%). It had goo) with an F1-score of 0.00 (support=1).
Conclusion: Our findings show the potential of machine learning models, especially class performance with F1-scores of 0.99 (class 0) and 0.96 (class1). But all models could not predict the minority class (class 2). Logistic Regression, to predict asthma severity from environmental data. But it has limitations due to the exclusion of various factors like smoking, obesity, genetics, previous asthma, and medication.

Assessing airborne microplastics in urban indoor environments using PM2.5 air exchange rate and polymer characterization for respiratory health risk evaluation

2026-06-29T09:23:02+0430

Introduction: Airborne microplastics have recently emerged as indoor air pollutants in urban environments due to extensive use of synthetic textiles, furnishings, and plastic-based materials. Continuous inhalation exposure may pose respiratory health risks, particularly in densely occupied spaces with poor ventilation. This study quantifies airborne microplastic concentrations in urban indoor environments and evaluates mitigation strategies based on ventilation improvement and material management.
Materials and methods: Air sampling was conducted in residential rooms, classrooms, and office spaces using low-volume active air samplers fitted with quartz microfiber filters (flow rate 16.7 L/min; duration 8 h). Microplastics were identified and counted using optical microscopy, while polymer types were confirmed through Fourier Transform Infra-Red (FTIR) spectroscopy and Scanning Electron Microscopy (SEM) analysis. Environmental parameters including PM_2.5concentration, Air Exchange Rate (AER), relative humidity, temperature, and occupancy density were simultaneously recorded. Respiratory exposure risk was estimated using inhalation dose and Hazard Quotient (HQ) calculations.
Results: Microplastics were detected in all indoor environments, with mean concentrations of 600 particles/m3 in residences, 1050 in offices, and 1180 in classrooms. Fibers dominated (68%), mainly polyester and polypropylene. Higher concentrations were associated with low ventilation (AER 0.4 h−1), high occupancy density (0.85 persons/m2), and elevated PM_2.5
levels (>45 µg/m³). Estimated inhalation exposure ranged from 2.1 to 3.8 particles/kg/day, and HQ exceeded the safe threshold (1.32) in poorly ventilated classrooms. Increasing AER to 1.2 h−1 reduced concentrations by 39%, replacing synthetic textiles lowered fiber proportion to 41%, and reducing occupancy to 0.55 persons/m² decreased inhalation dose to 2.1 particles/kg/day and HQ to 0.78.
Conclusion: Airborne microplastics are prevalent in indoor environments and may contribute to respiratory health risks, especially under low ventilation and occupancy. Enhancing ventilation, indoor materials, and occupancy reduce concentrations and risks, underscoring importance of indoor air management strategies.

Comparison of three functional regression methods on air pollution throughout the first two COVID-19 lockdown phases across 31 Iranian provinces

2026-06-29T09:23:02+0430

Introduction: Exposure to air pollution heightens respiratory vulnerability, particularly during pandemics. The COVID-19 lockdowns in Iran provided a natural experiment to investigate how reduced human activity influenced air quality across 31 provinces. Understanding these environmental responses is vital for informing sustainable public health and pollution mitigation policies.
Materials and methods: Satellite-derived data on air pollutants and air quality and meteorological variables were obtained for all 31 provinces of Iran from Sentinel-5P, the GLDAS-2 dataset developed by National Aeronautics and Space Administration (NASA), and Google Earth Engine (GEE). The study covered two COVID-19 lockdown periods and their corresponding pre-pandemic periods from the previous year. The evaluated air quality indices consisted of Carbon monoxide (CO), Water Vapor (H₂O,) Nitrogen dioxide (NO₂), Ozone (O₃), Sulfur dioxide (SO₂), Absorbing Aerosol Index (AER), and Atmospheric Formaldehyde (HCHO). Meteorological covariates comprised temperature, pressure, precipitation, and wind speed. Sparse temporal data were reconstructed using FDA and FPCA, representing Functional Data Analysis and Functional Principal Component Analysis, respectively. Three Function-on-Function (FOF) regression models standard, smooth, and principal component based were developed, with and without meteorological adjustments. Model performance was assessed using R², AIC, and BIC, representing the coefficient of determination, Akaike Information Criterion, and Bayesian Information Criterion, respectively.
Results: Air pollutant levels significantly declined during both lockdowns compared with the corresponding pre-pandemic periods, with spatial variations influenced by meteorological and industrial factors. Incorporating meteorological covariates markedly improved model accuracy, particularly for NO₂ and CO. The principal component-based FOF model provided the best fit, explaining over 80% of variance in major pollutants.
Conclusion: COVID-19 lockdowns produced measurable, regionally heterogeneous improvements in air quality across Iran. Integrating meteorological adjustments and advanced functional regression approaches enhances environmental modeling and supports evidence-based air pollution control strategies during health emergencies.

Temporal trend of dust storm events and particulate matter (PM2.5 and PM10 ) in Iran's metropolises, 2011-2022

2026-06-29T09:23:03+0430

Introduction: This study investigates the temporal trends of Sand and Dust Storms (SDS) and Particulate Matter (PM_2.5and PM₁₀) concentrations across nine major Iranian cities from 2011 to 2022, assessing long-term air pollution patterns and associated environmental challenges.
Materials and methods: PM concentration data were obtained from air quality monitoring stations. Dust storm events were identified using World Meteorological Organization (WMO) criteria. The primary objective was to analyze spatiotemporal variations and trends, which were statistically assessed using Sen’s slope method.
Results: PM₁₀levels consistently exceeded WHO annual guidelines by 2.13 to 12 times, while PM_2.5levels were 1.37 to 6.89 times higher. Ahvaz recorded the highest cumulative SDS hours (25,318), followed by Yazd (10,062) and Tabriz (9,609), with annual stormy days reaching up to 66. The most pronounced increases in PM₁₀occurred in Ahvaz, Yazd, and Tabriz. Maximum annual PM₁₀concentrations were observed in Ahvaz (179.8 µg/m³in 2013) and Yazd (135.85 µg/m³ in 2022), whereas peak PM_2.5 levels were reported in Ahvaz (57.2 µg/m³ in 2022) and Shiraz (43.37 µg/m³ in 2019).
Conclusion: The escalating intensity and frequency of SDS are linked to regional climate variability, drought, and land degradation. The findings underscore an urgent need for comprehensive mitigation strategies, including desertification control, sustainable land management, and enhanced regional cooperation to address these critical environmental and public health challenges.

Identification and analysis of factors affecting air flow optimization and particle dispersion in clean rooms of electronic industry

2026-06-29T09:23:02+0430

Clean rooms play an important role in electronics industries, specifically for precision manufacturing by strictly regulating pollution control, contaminants, and pressure. Airflow design and particle behavior are key determinants of contamination control performance in clean rooms. Despite technological advances, suboptimal air distribution remains a major contributor to particle dispersion and energy inefficiency. The review systematically examines experimental and modeling studies on airflow management and clean room performance improvement and their real-world application. A search was conducted in Scopus, PubMed, and Web of Science using keywords related to clean rooms, air pollution, and airflow. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, studies published from 2000 up to November 2025 were screened, and 25 studies were selected. To improve air flow and pollution control, various factors were identified along with their strategies and results. The factors identified were classified into four major classifications and their strategies and results were analyzed. These classifications included architectural and structural factors (e.g., room length, floor height), ventilation and air flow factors (e.g., velocity, air change rate, fan-filter-unit configuration), environmental and operational factors (e.g., activity of personnel, SCARA robot, season), and pollutant characteristics (e.g., particle size, and density). Current evidence indicates that performance improvement of clean rooms requires integrating these factors into a multi-objective framework balancing cleanliness, energy demand, and process stability. We propose a framework connecting different parameters to measurable cleanliness and energy efficiency indices.

Systematic review of phytoremediation of airborne benzene and toluene

2026-06-29T09:23:02+0430

Declining Indoor Air Quality (IAQ) in confined spaces and with insufficient ventilation poses serious health risks in industrial and office environments. The presence of volatile organic compounds in indoor air can cause human diseases, highlighting the need for effective and sustainable air quality improvement strategies. Phytoremediation offers an efficient, eco-friendly approach for removing contaminants from air, soil, and water, with plant species differing in their absorption capacities. This systematic review, conducted following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, identifies plant species effective in the phytoremediation of airborne benzene and toluene and examines factors influencing their performance. Comprehensive searches of Scopus, Web of Science, Google Scholar, ProQuest, MagIran, and Irandoc databases retrieved relevant studies through a three-stage screening process (title, abstract, and full-text review). Findings highlight Hedera helix and Epipremnum aureum as the most frequently used and efficient species for benzene and toluene removal. Moreover, enhancing factors such as increasing plant exposure time to pollutants, repeated injection cycles, and modifications to plant characteristics or substrate can significantly improve phytoremediation efficiency. Comparative analysis of conventional and enhanced methods revealed that enhanced phytoremediation plants improve both the rate and extent of pollutant removal and can serve as cost-effective, sustainable, and eco-friendly strategies for controlling IAQ. Findings also suggest that selecting appropriate plant species and designing combined systems can maximize IAQ improvement and promote human health in industrial and office settings. Overall, phytoremediation, particularly using multiple plant species under optimized environmental conditions, can effectively enhance indoor air quality and guide the design of practical phytoremediation systems.