Assessment of PM₂.₅ related health impacts along a major traffic corridor in Lagos using the WHO AirQ⁺ model

Industrial Dominance of Urban NO2 and Associated Health Risks: A Multi-Source Analysis in the Southern Region of Rio de Janeiro State

Driven by regional demand for stringent emission controls and the associated public health impacts, this study investigates tropospheric NO2 concentrations in Volta Redonda, a major metallurgical hub in Rio de Janeiro State. Initially, TROPOMI data were used to establish spatiotemporal patterns from 2019 to 2024, revealing that Volta Redonda maintains higher NO2 levels than similarly sized urban centers. A detailed analysis of the ‘weekend effect’ showed a minimal 3% reduction in NO2 concentrations during the weekend in Volta Redonda, compared with a 23% drop in Rio de Janeiro city, suggesting that industrial sources of this pollutant predominate over vehicular traffic. This finding is further supported by an analysis of Climate TRACE emissions data, which shows that industrial NOx emissions exceed transportation emissions in this industrial-urban cluster. Local ground station measurements, combined with satellite observations, indicate that the nearby industrial zone is likely the primary source of NO2. Finally, the study quantifies the human cost through a Relative Risk Index (RRI) analysis. Results indicate that a 1 µg/m3 increase in minimum NO2 concentrations leads to a 34% rise in pediatric hospitalizations and a 25% increase among older people, offering critical evidence for regional emission regulations and public health strategies.

Assessment of PM2.5 exposure and respiratory health risks for vulnerable populations in urban underpass: a predictive MLR approach in Chiang Mai, Thailand

Integrated Assessment of Air Pollution Sources, Public Health Impacts, and Data Gaps in Adana Province: Evidence-Based Policy Interventions

This study comprehensively evaluates air pollution in Adana, one of Türkiye’s key industrial, urban, and transportation hubs. Using monitoring data from 2023–2025, the concentrations of major pollutants—PM2.5., PM10, SO2, NO2, CO, and O3—were analyzed. The results were compared with standards set by the World Health Organization (WHO), the European Union (EU), and Türkiye’s national regulations, showing that PM2.5 and PM10 thresholds were frequently exceeded at several stations. A significant finding was that data capture rates remained below 60%, indicating reliability gaps in the monitoring infrastructure and weakening evidence-based policymaking. Primary sources of pollution include coal-fired power plants, the use of low-quality fuels, open burning of agricultural residues, and vehicular emissions. These factors are aggravated by Adana’s geographic and meteorological conditions, such as temperature inversions and low wind speeds, which facilitate pollutant accumulation and increase public health risks. Statistical analyses revealed notable rises in SO2 and particulate matter concentrations during winter due to residential heating, coinciding with higher respiratory illness rates. The study recommends reducing coal dependency, expanding access to natural gas, promoting sustainable transportation, restricting agricultural waste burning, and improving data transparency. The Adana case highlights air pollution as both an environmental and social justice issue.

Greenspace, land use, and PM₂.₅ across pandemic phases: Insights from a community scientist-led mobile sensing campaign.

Regulatory stationary monitoring networks record regional records for PM₂.₅, whereas local mobile monitoring can capture variability in mixed-use municipalities during a targeted sampling campaign. A community scientist-led mobile air quality monitoring campaign was undertaken using low-cost air sensors to measure fine particulate matter (PM₂.₅) across Norwood, Ohio, for two summer periods spanning the initial COVID-19 pandemic phase (2020) and the recovery/reopening phase (2021). A meteorology-adjusted difference-in-differences framework was used to quantify year- and time-of-day-specific shifts in PM₂.₅ levels. The results indicate that higher PM₂.₅ concentrations in 2021 were not fully explained by meteorological factors. To identify local spatial trends, land use regression models were developed at the street level using linear regression. Results showed that in the 2021 afternoon model, PM₂.₅ was higher within 50 m of office and manufacturing areas, while the Normalized Difference Vegetation Index (NDVI) within 100 m was inversely associated with PM₂.₅ in this campaign. Cross-validated R2 ranged from 0.17-0.25 in 2020 to 0.55-0.61 in 2021, with the strongest performance in the 2021 afternoon model. In addition, health survey scores showed that physical functioning and vitality varied across community wards. These scores provided neighborhood-level context for community prioritization, where air monitoring concerns may overlap with broader health-related community needs. This study offers a transferable community science monitoring workflow for neighborhoods with limited regulatory air quality coverage by combining sampling and quality control protocols, low-cost sensor-based meteorological adjustment, and regression modeling.

Integrated assessment of air pollution dynamics and health impacts in Myanmar: Evidence from ground monitoring, satellite observations, and modeling.

Least Developed Countries (LDCs), such as Myanmar, experience disproportionately high air pollution burdens due to limited regulatory capacity, inadequate monitoring, and political instability. This study provides a multi-source assessment of air pollution dynamics and potential health impacts in Myanmar, combining ground-based monitoring, satellite observations, and statistical modeling to inform evidence-based policymaking. Ground-based data (2019-2024) from two monitoring stations in Yangon revealed an average PM2.5 concentration of 24.4 μg/m3 (SD = 18.6), with the highest seasonal mean in winter (39.4 μg/m3; November-February). Analysis of particulate matter (PM) components showed high contributions of sulfate (SO42-) in both winter and summer, followed by nitrate (NO3-) in winter and Ca2+ in summer. A health impact assessment estimated that compliance with the WHO PM2.5 guideline (5 μg/m3) could have prevented 2,106 (95% confidence interval 345-3,818) premature deaths (aged ≥ 30) annually in Yangon during 2020-2021. A generalized additive model revealed that relative humidity and wind speed were significantly associated with PM2.5, whereas the daily maximum 8 h average (MDA8) O3 showed a limited meteorological association with relative humidity. National daily means of TROPOMI HCHO and NO2 were strongly correlated (Pearson's r = 0.754; p = 0.001), and HCHO/NO2 ratios indicated predominantly NOx-sensitive regimes across Myanmar. These findings suggest that O3 air quality can be substantially improved by reducing NOx emissions. Despite Myanmar's limited data, this study provides a comprehensive spatiotemporal assessment of air pollution hotspots and their associated health burdens, thereby informing targeted emission control strategies.

Low-Cost Sensor-Based Spatial Screening of Urban Air Quality in a Medium-Sized City: A Case Study in Alba-Iulia, Romania

Urban air pollution remains a major public health concern, with road traffic representing one of the dominant sources of particulate matter and volatile organic compounds in growing cities. This study evaluates the level of chemical air pollution in Alba-Iulia municipality (Romania) through two measurement campaigns (May 2025 and October 2025), carried out at 26 and 19 points, respectively, selected based on traffic intensity and population vulnerability criteria (schools, kindergartens, hospitals, and the food market). Measurements targeted PM2.5, PM10, total volatile organic compounds (TVOC) and formaldehyde (HCHO), as well as carbon dioxide (CO2), correlated with road traffic intensity. Statistical analysis revealed significant differences between the two seasons for PM2.5, PM10, and CO2 (p < 0.01), as well as a strong correlation between particulate matter concentrations and vehicle counts (r = 0.60–0.95), consistent with road traffic being an important local contributor to particulate matter, though correlation alone cannot establish source dominance in a strict causal sense. A multiple regression controlling for both traffic and season explained over 73% of the variance in PM2.5 and PM10 and showed that traffic and season each contribute independently to particulate levels. Both mean PM2.5 and PM10 exceeded WHO and EU limit values in both campaigns, and the low-cost sensor over-read absolute concentrations by roughly 2–4× relative to the official monitoring network, so absolute values should be treated as orientative. Despite this bias, the consistent spatial and seasonal patterns show that portable low-cost sensors can reliably rank exposure hotspots and support the targeting of traffic-mitigation measures, such as selective catalytic reduction (SCR) systems, in medium-sized cities that lack dense reference monitoring networks.