Integrated weighted geospatial analysis and spatiotemporal assessment of key criteria air pollutants for hotspot identification and air quality risk assessment.
Jul 2026· Environmental Toxicology and Chemistry· Vol 45, pp. 2674-2689· 0 citations· 27 references
Medicine
Abstract
Air pollution remains a pressing concern in urban India, affecting human wellbeing and ecosystem sustainability. This investigation explores the spatial and temporal variations in 10 μm particulate matter (PM10), nitric oxides (NOX), and sulfur dioxide (SO2) air pollution in Navi Mumbai, India, from 2014-2023. Data from 23 monitoring points were analyzed using geographic information systems-based methods, including inverse distance weighting and weighted overlay analysis, to generate a comprehensive pollution index. Findings indicate high seasonal variation, with elevated PM10 and NOX levels during premonsoon and winter due to traffic, industrial activity, and unfavorable meteorological conditions. Monsoon rains significantly reduced pollutant levels. Industrial hotspots, particularly in Taloja and Kalamboli, and traffic-heavy corridors, such as Vashi and Nerul, remained persistent pollution sources. A noticeable drop in pollutant concentrations in 2020 coincided with the COVID-19 lockdown, although levels surged in subsequent years. The weighted overlay analysis proved effective in identifying pollution hotspots and offering a comprehensive understanding of air quality risks. Global comparisons highlight the specific challenges of coastal satellite cities, where industrial and harbor emissions contribute to seasonal smog. This study emphasizes the need for targeted emission controls and urban planning interventions to improve air quality and sustainability in rapidly growing regions.
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.
Olawale Victor Oluwatuyi, F. Akinluyi, J. Adeyeye· Discover Atmosphere· 0 citations
Rapid urbanization, industrial expansion, and desert dust transport make India's arid and semiarid cities among the most pollution‐burdened globally. However, chronic multi‐pollutant exposure remains poorly characterized through integrated multicity assessments, despite its role in increasing risks of respiratory disease, cardiovascular morbidity, and premature mortality. This study presents a comprehensive spatiotemporal assessment of ambient air quality across five National Clean Air Programme (NCAP) cities of Rajasthan during 2019–2024. Six years of monitoring data for six criteria pollutants (PM
2.5
, PM
10
, NO
2
, SO
2
, CO, and O
3
) were integrated with geographic information system (GIS)‐based spatial analysis. Four composite indices—air quality index (AQI), pollution load index (PLI), air pollution index (API), and health index (HI)—were also employed to evaluate pollution dynamics, cumulative pollution burden, and associated health risks. Results revealed persistently elevated particulate pollution, with annual PM
2.5
reaching 81.6 µg m
−3
in Jodhpur and exceeding 55 µg m
−3
in Jaipur and Kota, whereas PM
10
peaked at 170.2 µg m
−3
. Concentrations peaked during winter and post‐monsoon seasons due to atmospheric stability and declined sharply during monsoon through wet scavenging. SO
2
and CO remained within Central Pollution Control Board (CPCB) regulatory limits, whereas O
3
exhibited distinct pre‐monsoon photochemical peaks. Multi‐index analysis identified Jodhpur as most pollution‐burdened city (AQI up to 179; HI >4.5). Persistent spatial differences among cities exceeded interannual variability, highlighting structural and climatic drivers. These findings indicate that short‐term or pollutant‐specific interventions alone are insufficient for sustained air quality improvements. Effective NCAP implementation in climatically vulnerable arid environments demands regionally coordinated, multi‐sector strategies addressing both anthropogenic emissions and background dust contributions.
Introduction: Ambient air pollution poses significant public health challenges across urban and semi-urban India. Karnataka, encompassing coastal, plateau, highland, and semi-arid environments, provides an ideal setting for evaluating regional air quality variability and associated health risks. This study assessed the spatio-temporal characteristics of ten criteria and hazardous air pollutants measured at five Central Pollution Control Board (CPCB) Continuous Ambient Air Quality Monitoring Stations (CAAQMS) across Karnataka during 2024.
Materials and methods: Monthly mean concentrations of Particulate Matters (PM₂.₅, PM₁₀), Nitrogen dioxide (NO₂), Sulfur dioxide (SO₂), Carbon monoxide (CO), Ozone (O₃), Ammonia (NH3), and benzene were analysed across four India Meteorological Department (IMD) seasons. Serial autocorrelation was assessed using the lag-1 Kendall statistic prior to applying the Mann–Kendall test and Sen's slope estimator as an exploratory assessment of intra-annual monotonic tendencies. Spatial variability was evaluated using one-way ANOVA with Tukey's HSD post-hoc test and validated using the Kruskal–Wallis test. Inhalation health risks were assessed using the USEPA Risk Assessment Guidance for Superfund (RAGS) methodology by estimating Hazard Quotient (HQ), Hazard Index (HI), and Lifetime Cancer Risk (LCR).
Results: Bengaluru recorded the highest annual mean PM₂.₅ (40.2 μg/ m³) and PM₁₀ (80.4 μg/m³) concentrations, with PM₂.₅ equal to the CPCB National Ambient Air Quality Standard (NAAQS) and PM₁₀ exceeding it by approximately 34%. Annual mean PM₂.₅ concentrations at all stations exceeded the WHO 2021 Air Quality Guideline (15 μg/m³). The southwest monsoon produced the greatest reduction in particulate concentrations. Exploratory Mann–Kendall analysis yielded negative Sen's slopes for PM₂.₅ at all stations, although none were statistically significant (p>0.05). One-way ANOVA confirmed significant spatial heterogeneity among monitoring stations (p < 0.001). Screening-level health risk assessment indicated HI values >1.0 at all stations (1.756–3.290). Estimated PM₂.₅ LCR ranged from 1.76 × 10⁻³ to 3.34 × 10⁻³ and represents hypothetical upper-bound screening estimates rather than regulatory cancer risk values.
Conclusion: Particulate matter remains the dominant air quality concern across Karnataka, particularly in Bengaluru. Strengthened emission control measures, continued multi-year monitoring, and agricultural ammonia management are recommended to support long-term improvements in air quality and public health.
Govindaraju D, Ganesh K. E.· Journal of air pollution and...· 0 citations
Urban thermal dynamics, influenced by air pollution and meteorological variability, are critical for assessing urban resilience in rapidly urbanizing megacities. LST serves as a vital indicator for understanding these interactions under changing climatic and environmental conditions. This study investigates the long-term spatio-temporal variations of LST and criteria air pollutants, their correlations with meteorological parameters and dominant factors influencing LST variability using the Generalized Additive Model (GAM) modelling. MODIS-derived LST and spectral indices, ERA5 meteorological data and pollutant data from the CPCB were retrieved. Results reveal that Premonsoon daytime and nighttime LST varied between 33.88-36.53 °C and 19.57-21.72 °C, respectively, during 2014-2023. The UTFVI analysis found that the urban core falls within the "worse" and "worst" thermal stress categories (> 0.020) at night. Sen's slope found that SO2 shows a persistent increasing trend across all seasons, with a positive slope in winter (4.58 μg/m3 per year). Air temperature positively correlates with pre-monsoon daytime LST (r = 0.86), while wind speed negatively correlates with monsoon nighttime LST (r = -0.95). The GAM model demonstrates predictive performance for both daytime and nighttime LST (R2 > 0.93). SMI dominates daytime LST in all seasons (ΔR2 ≈ 0.004-0.018) whereas nighttime LST is mainly influenced by SO2 (ΔR2 = 0.024) in monsoon and EVI in pre- and post-monsoon (ΔR2 = 0.014 and 0.020). The pollutant variables contribute similarly (ΔR2 ≈ 0.005-0.006) in winter nights. This study reveals the urban thermal-pollution-meteorological dynamics, providing valuable insights for climate-responsive urban planning, heat mitigation strategies and improved air-quality management.
Rainfall–runoff pollution poses a major challenge to urban water quality management, particularly in rapidly developing regions. However, its spatial variability and source characteristics remain inadequately understood. This study investigates the types, concentrations, and sources of pollutants in rainfall runoff across different urban land-use settings, with the aim of providing insights for more effective water management strategies. By quantifying pollutant occurrence frequencies, comparing reported event mean concentrations, and summarizing literature-reported pollution sources, we evaluated the spatial heterogeneity of runoff contamination from a descriptive perspective. The compiled literature data showed descriptive differences in reported pollution levels among land-use types, with relatively high pollutant concentrations frequently reported in residential and traffic areas. These differences should be interpreted as functional-area-based patterns rather than continuous geographic spatial distributions. Pollutants such as chemical oxygen demand (COD), suspended solids (SS), and total nitrogen (TN) frequently exceeded China’s Class V surface water quality standards. Atmospheric deposition and surface litter were the most frequently reported pollution sources, while traffic-related activities were frequently associated with elevated heavy metal concentrations in the reviewed studies. These findings underscore the urgent need for targeted, land-use-specific pollution control strategies that not only reduce runoff pollution but also improve source-control efficiency for sustainable urban water management. This study offers valuable insights that may be transferable to other urban environments worldwide, with important implications for policy development and urban resilience in the face of increasing environmental pressures.
Ziwenqi Yang, Yadan Xue, Lucheng Li et al.· Water· 0 citations
Air pollution emitted from coal-fired power plants may influence ambient air quality and its spatial distribution in surrounding residential areas. This study aimed to analyze the spatial distribution of ambient air pollutants using the Air Pollution Standard Index (ISPU) integrated with Geographic Information System (GIS) analysis around the Celukan Bawang Coal-Fired Power Plant, Bali, Indonesia. Ambient air quality measurements were conducted at 20 monitoring locations within a 1-km radius of the power plant for SO₂, NO₂, PM₂.₅, and PM₁₀. ISPU values were calculated following the Indonesian Ministry of Environment and Forestry Regulation No. 14 of 2020 and spatially interpolated using the Co-Kriging method in ArcGIS. The resulting spatial distribution maps were interpreted together with wind direction, wind speed, topography, and land-use characteristics. All measured pollutant concentrations complied with the Indonesian ambient air quality standards, and the calculated ISPU values were predominantly classified as Good, with PM₂.₅ showing the highest relative ISPU values. Spatial analysis revealed relatively higher pollutant concentrations near the industrial complex and along the dominant wind pathway while remaining within acceptable air quality conditions. The integration of GIS, ISPU, and Co-Kriging provides a practical framework for visualizing spatial variations in ambient air quality and supports environmental monitoring, land-use planning, and evidence-based environmental management in industrial areas
Laurensius Ivander Colorado, I. Wesnawa, I. M. Gunamantha· Jurnal Penelitian Pendidikan...· 0 citations
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