Spatio-temporal Assessment of Air Quality Across Diverse Land-use Zones in Ranchi, Jharkhand, India

Spatio-Temporal Assessment of Air Quality and AQI in Former Khanh Hoa Province, Vietnam (2021-2024)

Air pollution has become an increasingly important environmental and public health concern, particularly in rapidly urbanizing coastal provinces. This study aimed to evaluate the spatio-temporal variation characteristics of key air quality parameters (TSP, SO₂, NO₂, and CO) and the Air Quality Index (AQI) in former Khanh Hoa Province during the period 2021-2024. Periodic monitoring data collected from 28 monitoring sites across four functional zones (North, South, West, and Central) were analyzed using descriptive statistics, seasonal comparison, Pearson correlation analysis, AQI calculation, and GIS-based spatial interpolation (IDW). The results showed that SO₂ and CO concentrations significantly decreased by 62% and 22%, respectively (p<0.05), while NO₂ remained relatively stable and TSP showed signs of increasing, particularly in 2024. TSP was identified as the dominant pollutant, accounting for more than 95% of AQI deterioration and frequently exceeding the annual average standard in the Central region. Overall air quality was generally classified as “Good” and “Moderate”; however, localized pollution hotspots and short-term unhealthy AQI events were observed, especially during the dry season in urbanized areas with high traffic density and intensive construction activities. Pearson correlation analysis revealed that humidity and wind speed played important roles in pollutant dispersion and washout effects, while noise levels showed significant positive correlations with TSP, NO₂, and CO, indicating the strong influence of transportation-related emissions. These findings suggest that air quality management should prioritize TSP source control, particularly in the Central region, and strengthen seasonal monitoring strategies to support sustainable environmental management in rapidly developing coastal provinces of Vietnam.

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

Citywide modeling and mapping of environmental sound exposure in Dhaka, Bangladesh.

Environmental noise is an increasingly urgent environmental health concern in rapidly growing megacities, yet fine-scale spatial data remain scarce in low- and middle-income cities. We developed the first citywide, spatially explicit estimates of environmental sound exposure for Dhaka city proper, the capital of Bangladesh, using long-duration measurements from 67 monitoring sites across the city and land-use regression models. Land-use regression models were developed for average 24-h (LAeq24h), daytime (Lday), nighttime (Lnight), and day-evening-nighttime (Lden) sound levels, with predictors selected through a forward stepwise regression and performance evaluated using cross-validation. Model performance was strong across metrics (cross-validated R2 = 0.66-0.72; RMSE = 3.4-4.3 dBA). Proximity to major transport corridors and vegetation cover (NDVI) explained much of the spatial variability, reflecting the dominance of traffic-related sources within a dense and highly congested urban environment. Predicted sound levels were elevated across Dhaka during both day and night. Modeled estimates suggest that the vast majority of Dhaka residents, approximately 12 million people, are exposed to outdoor sound levels exceeding the World Health Organization-Europe Region traffic guidelines (Lnight: 45 dBA and Lden: 53 dBA), and an estimated 0.9 million residents are exposed to sound levels >70 dBA, a threshold for noise-induced hearing loss. These findings demonstrate pervasively high environmental sound exposure across Dhaka, and provide an empirical basis for urban noise management, policy evaluation, and future epidemiologic research in rapidly urbanizing megacities.

Twenty-Five Years of Air Quality in Bangladesh: Trends, Seasonality, and Spatial Pollution Regimes

Air pollution is one of Bangladesh's most pressing environmental problems, yet few studies have examined it at a national scale over a long time span. To describe trends, seasonality, geographic patterns, and pollution regimes, this study examines 25 years of hourly air quality data (2000-2025), covering over 3.19 million records, eight pollutants, and 103 cities monitored from 2022 onward following expansion from a single Dhaka station. The composite AQI series showed no significant long-term trend, an artifact of this network expansion rather than a real flattening of pollution, since Dhaka's own AQI rose steadily before the network grew. Seasonality was strong and highly significant, as AQI peaked in January (162.26) and fell to its lowest in July (61.47), tracking the dry and monsoon seasons. Geographically, southeastern coastal cities, including Teknaf and Bandarban, consistently recorded the lowest AQI levels. Dhaka remained the most polluted city, and air quality improved progressively with distance from the capital. K-means clustering of city-level AQI trajectories identified four distinct pollution regimes, ranging from cleaner coastal regions to a rapidly deteriorating Dhaka-Narsingdi cluster with an average increase of 1.84 AQI per year. Correlation analysis showed that PM2.5 and PM10 were the dominant contributors to AQI variation, and so combustion-related emissions are the primary target for air quality management and policy intervention.

Pattern of air pollutants and land surface temperature in Nigeria using space-time pattern mining techniques

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.