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

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.

Evaluating the suitability of sentinel-5P satellite data for air quality monitoring in Tehran.

This study evaluates the applicability of Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) satellite observations for air quality monitoring in Tehran, a megacity characterized by complex topography and persistent air pollution challenges. A comprehensive comparison was conducted between ground-based measurements from 22 air quality monitoring stations and Sentinel-5P Level-2 products, including carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and the Ultraviolet Aerosol Index (UVAI) over the period 2019-2024. Data preprocessing included outlier removal using the Interquartile Range method, Min-Max normalization, and Air Quality Index analysis. Agreement between satellite and ground observations was evaluated using Pearson and Spearman correlation coefficients and the Kling-Gupta Efficiency metric. In addition, spatiotemporal pollutant trajectories derived from both datasets were compared. The results revealed substantial differences between satellite-derived and ground-based observations. Among the investigated pollutants, NO₂ exhibited the strongest agreement, whereas CO, SO₂, and O₃ showed generally weak correspondence. Spatiotemporal trajectory analyses also demonstrated notable discrepancies between satellite-derived and ground-based pollutant distributions, reflecting differences in spatial representativeness and measurement characteristics. Comparisons involving particulate matter should be interpreted cautiously because UVAI represents atmospheric aerosol loading rather than direct surface particulate matter concentrations. The observed discrepancies are primarily attributed to the spatial resolution of Sentinel-5P, temporal sampling limitations, and the complex meteorological and topographical conditions of Tehran, which generate highly localized pollution patterns. The findings indicate that Sentinel-5P has limited standalone capability for near-surface urban air quality assessments in complex environments, but remains valuable for identifying regional pollution gradients, long-term trends, and broader atmospheric patterns. The results support the development of hybrid monitoring frameworks that combine satellite observations with ground-based measurements, meteorological information, and advanced data-fusion techniques. More broadly, this study provides guidance for improving satellite-based air quality assessment and supports the design of more effective monitoring systems for rapidly urbanizing regions worldwide.

SATELLITE AND GROUND-BASED AIR QUALITY ASSESSMENT IN AL-DIWANIYAH

Objective: Ambient atmospheric pollution represents a critical threat to human health globally. Within Iraq, air quality has drastically diminished due to harsh climatic dust events and expanding unregulated anthropogenic factors. The primary goal of this investigation is to establish a thorough comparative analysis of air quality across the urban and rural sectors of Al-Diwaniyah Province. Method: A comparative cross-sectional design was carried out from mid-September 2025 through January 2026 across six strategic sampling locations. Ground-level monitoring utilized a calibrated Temtop M2000 device to log particulate metrics, while space-borne observations were acquired via the Sentinel-5P TROPOMI satellite for gaseous pollutants. A composite Z-score air quality index (AQI) evaluated safety thresholds, with statistical analysis processed through SPSS v26 software. Results: Ground measurements revealed that average PM2.5 (34.87 µg/m³) and PM10 (101.37 µg/m³) drastically outpaced WHO safety limits (p < 0.001). Satellite monitoring demonstrated a significant abundance in tropospheric NO₂ (63.54 µmol/m²) and O₃. The index classified 73.3% of the total observations as unhealthy for sensitive demographics. Urban sectors displayed significantly higher concentrations of PM2.5, PM10, and CO₂ than rural environments (p < 0.001), though rural locations showed a high prevalence (80.0%) of air quality categorized as hazardous for sensitive groups. Novelty: Al-Diwaniyah Province experiences severe atmospheric degradation. While urban centers undergo heavier pollution loads from concentrated traffic and fuel combustion, rural zones present distinct ambient threats to vulnerable populations from agricultural operations and dust.

Assessing the spatiotemporal characteristics of urban air pollution in Ado-Ekiti and its environs, southwest Nigeria

Air pollution poses significant environmental and public health challenges in rapidly urbanising regions of sub-Saharan Africa, where ground-based monitoring infrastructure remains limited. This study examined the spatiotemporal distribution of key air pollutants in Ado-Ekiti and its environs, Nigeria, from 2019 to 2024. Columnar concentrations of carbon monoxide (CO) and formaldehyde (HCHO) were retrieved from Sentinel-5P, while MODIS aerosol optical depth data was used to estimate particulate matter (PM2.5 and PM10). Meteorological variables (rainfall, wind speed, and wind direction components u and v) were derived from the Weather Research and Forecasting model, and one week of ground-based measurements of pollutants were collected to enable correlation analysis with satellite-derived estimates. All datasets were aggregated to monthly and annual timescales, resampled to a 1 km spatial resolution and re-projected to UTM Zone 31 N. Results indicated that particulate matter dominated the pollutant profile, with annual mean concentrations of 59.37–65.29 µg/m³ for PM2.5 and 89.35–99.51 µg/m³ for PM10, exceeding WHO guideline limits. Peak concentrations occurred during the Harmattan season, with PM₂.₅ > 100 µg/m³ and PM₁₀ > 200 µg/m³, driven primarily by Saharan dust transport and local activities. Mann-Kendall trend analysis revealed significant increasing trends in HCHO, PM2.5, and PM10, whereas CO showed no significant trend. Satellite-derived particulate estimates showed positive but weak correlations with ground-based particulate concentrations (r < 0.20), whereas rainfall significantly reduced particulate levels, and meridional winds (v) facilitated long-range PM₁₀ transport. This study provides a significant integrated satellite model for air quality assessment in data-scarce urban environments while providing evidence to support targeted emission control strategies.

Spatio-temporal characterisation of air quality along the coastal regions of Kerala based on CPCB data

Seasonal air pollution dynamics, source apportionment, and health risk in industrial zones of southern Vietnam: a five-year integrated assessment

Industrial expansion in southern Vietnam has intensified concerns regarding seasonal air pollution dynamics and associated health risks under tropical monsoon conditions. However, assessments linking interannual variability, source structure, and population-specific health risks in tropical industrial zones remain limited. This study monitored ambient air quality at 37 sites across nine industrial zone clusters in southern Vietnam over five consecutive years (2021–2025), with biannual campaigns during the dry season (March) and rainy season (October). Two-way ANOVA identified significant effects of both year and season on concentrations of PM10, SO2, NO2, CO, NH3, and H2S (all p < 0.05), with consistently higher dry-season levels, while noise remained invariant. Principal component analysis revealed clear seasonal separation along the primary axis (39.5% variance explained), and hierarchical cluster analysis confirmed season-dominated multivariate structure. Non-negative matrix factorization resolved four stable PM10-related source factors, with Factors 3 and 4 showing pollutant signatures consistent with combustion- and sulfur-associated emissions and contributing most strongly during the dry season. The observed seasonal patterns are associated with differences in meteorological conditions and emission activities between the dry and rainy seasons. Probabilistic assessment identified H2S, PM10, and NO2 as dominant risk drivers, among which H2S posed the greatest exposure concern across population groups, with a mean hazard quotient (HQ) reaching 10.26 in the dry season for the high-exposure group and P(HQ ⩾ 1) ≈ 1.000. GIS-based composite pollution mapping further revealed persistent high-risk hotspots in the East–Southeast industrial corridor, where high-risk area coverage increased from 27% in 2021 to approximately 36% during 2022–2025, a pattern spatially associated with industrial clustering, transport connectivity, and seasonal meteorological conditions. Overall, the findings suggest that the observed seasonal differences in source-related pollutant patterns, pollutant accumulation, and population health risk are consistent with the combined influence of meteorological conditions and seasonal emission activities in tropical industrial environments, providing evidence to support season-specific emission control and targeted public health protection strategies.