Air-quality research in India has predominantly focused on large metropolitan regions, leaving plateau-based Tier-II cities comparatively underexamined. This study assessed the spatio-temporal variability of air quality across five land-use zones in Ranchi, Jharkhand: industrial, traffic, residential, agricultural, and eco-sensitive. Monthly observations collected during 2022–2023 yielded 120 site-month records, and the National Air Quality Index framework was applied to PM₂.₅, PM₁₀, SO₂, and NO₂. Site-level differences were evaluated using one-way analysis of variance, while dominant-pollutant regimes and seasonal patterns were examined to identify spatial and temporal contrasts. Mean AQI differed significantly among sites (F(4, 115) = 11.04, p < 0.001). The traffic site recorded the highest mean AQI (267.7 ± 27.2), followed by the industrial site (243.2 ± 16.9), indicating sustained pollution pressure in high-emission zones. The eco-sensitive site recorded a higher mean AQI (175.1 ± 105.4) than the residential site (158.6 ± 58.0), suggesting that local vegetation did not consistently prevent episodic deterioration. PM₂.₅ was the most frequent AQI-determining pollutant, particularly at the traffic and industrial sites, whereas PM₁₀ governed several abrupt episodes at the agricultural and eco-sensitive locations. Winter generally showed the highest and most variable AQI, while the monsoon produced lower and less dispersed values, reflecting seasonal differences in pollutant accumulation and removal. Overall, the findings demonstrate marked land-use and seasonal heterogeneity in Ranchi. They further indicate that air-quality monitoring and management should address both persistent emissions at traffic and industrial locations and episodic pollution affecting peri-urban, agricultural, and eco-sensitive zones within the rapidly expanding plateau-based urban environment.
The adverse health effects of exposure to ambient air pollution are well established; however, limited spatial coverage of regulatory monitoring networks constrains comprehensive assessment, particularly in regions with complex terrain. The increasing availability of low-cost sensors provides a promising approach to overcome these limitations. In this study, a network of TSI BlueSky low-cost PM2.5 monitors was deployed to investigate the spatial and temporal variability of PM2.5 concentrations across four physiographic regions of Nepal: Eastern Terai, Western Terai, Inner Terai, and Mid hills. The Eastern Terai consistently recorded the highest PM2.5 concentrations, followed by Western Terai, with winter levels at several sites far exceeding both national standards and WHO air quality guidelines. PM2.5 concentrations in the Inner Terai and Mid Hill regions were comparatively lower, though still of significant public health concern during winter months. A marked increase in PM2.5 concentrations across the Terai belt immediately following the monsoon season indicates the influence of transboundary pollution, with elevated levels persisting throughout the winter period. A sharp rise in PM2.5 concentrations across all study regions during April highlights the substantial contribution of forest fire activity to regional air quality degradation. Urban–rural paired site analysis revealed that PM2.5 pollution is not exclusively an urban phenomenon, as rural sites such as Lumbini and Sauraha recorded concentrations equal to or exceeding those of their urban counterparts. Coefficient of Divergence analysis confirmed that industrially influenced sites display markedly distinct pollution signatures compared to proximate paired sites. These findings underscore the necessity for strengthened monitoring networks and regionally tailored mitigation strategies that account for local emissions, transboundary pollution, and topographic influences on PM2.5 across Nepal.
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.
Kigali, like many cities in sub-Saharan Africa, faces rapid urban growth alongside the need to manage air pollution and protect public health. Despite its policy efforts, systematic data on nitrogen oxides (NOx: NO2 and NO), key indicators of combustion-related pollution, have been limited. We applied a standardized protocol to characterize city-scale spatial and temporal patterns of NOx across Kigali. Between November 2022 and December 2023, we collected weekly integrated NO2 and NO samples (n = 630 each) at 130 sites representing diverse land-use types. NO2 concentrations ranged from 1.3 to 61.9 µg/m3 (mean 13.9 µg/m3), with annual-equivalent frequently exceeding the WHO annual guideline (10 µg/m3) in urban areas. Exceedances occurred in 39% of sparsely residential, 89% of commercial/industrial, and 99% of densely populated residential sites. NO2 concentrations were significantly higher in urban versus rural areas (18.2 vs. 6.3 µg/m3), near major roads (19.9 vs. 11.6 µg/m3), and at lower elevations (15.4 vs. 9.0 µg/m3). The highest levels were observed in the densely populated districts of Kicukiro and Nyarugenge. Overall, NO2 and NO exhibited strong spatial gradients related to land use, traffic, population density, and topography, highlighting the importance of targeted urban planning and air quality management in rapidly growing cities.
Air pollution remains a major environmental and public health challenge in Romania, where exceedances of European Union air quality standards persist. However, a national-scale assessment identifying regions of simultaneous multi-pollutant burden and coherent worsening trends has been lacking. Here we analyse hourly concentrations of PM2.5, PM10, NO2, and O3 from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis over 2013-2024, validated against 317 in situ stations-pollutant validation series. The Mann-Kendall test, Sen's slope estimator, and Local Indicators of Spatial Association (LISA) are used to quantify trends and detect spatially coherent clusters. CAMS demonstrates a solid correlation with the measured data for various pollutants: PM2.5 shows a strong correlation (coefficient of 0.83), followed closely by O3 with 0.82, and PM10 at 0.78. However, the performance for NO2 is noticeably weaker, with a correlation coefficient of only 0.48. Atmospheric pressure serves as the primary factor influencing pollutant levels, surpassing other variables. Following this, the proximity to urban centers and the height of the boundary layer also emerge as significant determinants in understanding air quality dynamics. Trend analysis reveals seasonally structured patterns: winter PM2.5 and PM10 increases of up to ＋10 μgm-3decade-1 in northern Transylvania with maxima recorded in the Cluj area of +10.01 μgm-3decade-1 (95% CI: 3.91-15.62 μgm-3decade-1) for PM2.5 and +10.22 μgm-3decade-1 (95% CI: 5.19-17.38 μgm-3decade-1) for PM10, and widespread summer O3 increases exceeding ＋15 μgm-3decade-1 in southeastern and western Romania. LISA clustering identifies High-High trend regions in northwestern Transylvania, the Bucharest-Giurgiu corridor, and northeastern Romania. Conversely, Low-Low clusters in southwestern Romania and the Galaţi area reflect lignite-fired power plant decommissioning and industrial restructuring, respectively. Combining a composite pollution-burden score with LISA clustering delineates priority areas where multi-pollutant exposure is persistently elevated and continues to increase, providing spatially explicit evidence for geographically targeted air-quality management.
