Air pollution risks to wheat and mustard in Delhi agricultural airshed during the winter cropping season (2022–2025)

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.

Impact of air pollution on sustainable development and environmental justice: Insights from industrial cities of Bangladesh

Urban Air Pollution in Five Major Cities of Rajasthan, India: Spatiotemporal Assessment Using Multi‐Pollutant Indices

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.

Integrated remote sensing analysis of rice field burning and air pollution in Eastern Mazandaran, Iran

Linking ambient air pollution to health risk burden: A comparative study across urban and semi-urban regions of Karnataka, India

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.

LAGGED ASSOCIATION BETWEEN PUNJAB CROP-RESIDUE BURNING AND PM 2.5 POLLUTION IN DELHI-NCR: A NINE-YEAR TIME-SERIES ANALYSIS

Background: Crop residue burning in Punjab, a post harvest agricultural practice, has long been implicated as a significant driver of catastrophic air-quality deterioration in Delhi-NCR during October -November. However, the precise temporal lag and the role of meteorological confounders remain insufficiently clarified in the existing literature. Methods: We assembled a daily time-series dataset spanning nine harvest seasons (2015 - 2024; n = 486 station- days) that integrates satellite-derived Punjab fire counts from NASA FIRMS, ground-level PM2.5 observations from CPCB monitoring stations, and ERA5-Land daily meteorological covariates (wind speed at 850 hPa, planetary boundary layer height, temperature, relative humidity, and rainfall) to control for atmospheric confounding. Cross-correlation, multivariable distributed-lag regression models, and Cochrane-Orcutt autocorrelation correction were employed to characterise the spatiotemporal relationship between burning activity and urban particulate pollution.