Long-Term Assessment of Ambient Air Quality, Meteorological Influence, and Transboundary Transport in a Tier-II Indian City

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

Air Quality Based on Particulate Matter Concentrations (TSP, PM10, and PM2.5) in Paal Dua, Manado City

The Paal Dua area of Manado City is an urban area characterized by intensive traffic and commercial activities that may contribute to elevated particulate matter concentrations in ambient air. This study aimed to determine and analyze Total Suspended Particulate (TSP), PM10, and PM2.5 concentrations at three sampling sites with different environmental characteristics and to evaluate their compliance with ambient air quality standards specified in Government Regulation No. 22 of 2021, Annex VII. Measurements were conducted for 24 hours at each site using a High Volume Air Sampler (HVAS) and the gravimetric method, following SNI 7119.3:2017 for TSP, SNI 7119.15:2016 for PM10, and SNI 7119.14:2016 for PM2.5. Although all methods employ the same gravimetric principle, they differ in particle-size-selective inlet specifications. TSP concentrations ranged from 6.19 to 24.46 µg/m³, PM10 from 1.38 to 10.89 µg/m³, and PM2.5 from 0.24 to 3.83 µg/m³. The highest TSP and PM10 concentrations were observed at Site 1, near a road and market area, while PM2.5 concentration at Site 3 exceeded that at Site 2. This spatial difference was presumably associated with prevailing wind direction and surrounding building configuration, although meteorological and statistical analyses are required for confirmation. All measured particulate concentrations were below ambient air quality standards during August 2025. These results represent conditions during the sampling period and cannot be generalized to the entire Paal Dua area. Periodic monitoring with broader spatial and temporal coverage is recommended. The study provides information on particulate matter distribution across locations with different emission sources and urban configurations.

Thirty-Year Variability and Meteorological Controls of PM2.5 over Bengaluru, India (1995–2025): Evidence from MERRA-2 Reanalysis

Meteorological drivers of ambient PM2.5 pollution at urban traffic intersections in Accra, Ghana

Influence of meteorological factors and anthropogenic emissions on air pollutant concentrations

Characterizing the Seasonal Particulate Matter (PM 2.5 , PM 10 ) Concentrations in Nine Indian Cities, Using Exploratory Seasonal Analysis (ESA), Supervised Statistical Methods and Contextual Appraisal

Elevated ambient particulate matter levels have become a critical urban sustainability concern in India, endangering health and well‐being (UN SDG 3) of a vast section of the population. We present an exploratory seasonal analysis (ESA) of the ambient PM 2.5 and PM 10 concentrations, for nine Indian cities, using daily archival records from the Central Control Room for Air Quality Management (CAQM) for 2023. The ESA results revealed distinct seasonality in both PMs, varying as post‐monsoon (most polluted) > winter > summer > monsoon (cleanest). The National Capital Region (NCR: Delhi, Gurugram, Noida) appeared the most vulnerable to PM pollution, with Delhi as the most polluted among the nine cities, while Varanasi, the cleanest. Jaipur and Ahmedabad revealed concerns over windblown dust in summer, elevating the PM 10 risks. Seasonal PM 2.5 /PM 10 ratios indicated that besides fuel combustion in general, there is need for more location‐specific air quality control mechanisms, addressing local sources. For each city, about 9%–10% of the days appeared as “outlier”, representing sudden high spikes in PM levels. Both PMs exceeded the WHO health safety thresholds for about 90%–95% of days, even during monsoon, while for 99–100% of the days in winter and post‐monsoon times indicating year‐round public health concerns. Hierarchical Clustering (HCA) offered a spatio‐temporal (city‐season) PM 2.5 ‐vulnerability ranking scheme comprising of four statistically distinguishable clusters: Cluster 1 (148.67 ± 17.94 µg.m −3 ) > Cluster 3 (79.469 ± 1.51 µg.m −3 ) > Cluster 4 (59.90 ± 6.31 µg.m −3 ) > Cluster 2 (32.64 ± 7.24 µg.m −3 ). Cluster 1 represented the NCR cities for post‐monsoon and winter, while cluster 2 included all nine cities for monsoon and summer. Overall, HCA outputs could help setting up a joint action plan, bringing together various city municipal bodies to address the PM pollution. Applying Multiple Linear Regression (MLR) to the HCA clusters individually, revealed significant ( p < 0.05) statistical associations between meteorological parameters and PM2.5 concentrations in HCA clusters 1 (R 2 : 0.76) and 3 (R 2 : 0.67), while significantly lower for clusters 4 (R 2 : 0.52), and negligible for Cluster 2 (R 2 : 0.39). In Cluster 1 and 3, relative humidity and rainfall (monsoon season) exhibited strong inverse statistical relationships with PM 2.5 levels, consistent with potential scavenging and wet deposition processes to reduce the PM 2.5 risks, which was aggravated during post‐monsoon and winter. The latter occurred due to atmospheric stagnation (low wind speed, low solar radiance, cooler temperatures) that trapped the PM 2.5 near the ground. Meteorological influences, however, were minimal for HCA cluster 2 (R 2 : 0.39), indicating importances of other factors (e.g., built‐up types and geometry; emission sources types, vegetation type and density) that need to be researched further. In the final sections, we reflect on a future vision about reimagining the urban transport sector, aligning policies with UN SDGs 7 (clean energy), 9 (infrastructure, and innovation) 16 (institution building) and 17 (forging multi‐sectoral partnerships) to address the particulate pollution crises.