Journal of Atmospheric Science Research

Investigating the Impact of Meteorological Parameters on PM2.5 Concentrations and Air Quality Index Using Regression Analysis

Neelam Tanwar — 2025-09-07

Air pollution from fine particulate matter (PM2.5) poses a serious health risk in rapidly urbanizing areas like Bhopal, India. This study investigates the influence of meteorological factors temperature, humidity, rainfall, and wind speed on PM2.5 concentrations and the Air Quality Index (AQI) from January 2022 to December 2023. PM2.5 levels ranged from 1.0 µg/m³ to 938.0 µg/m³, with an average AQI of 119, indicating moderate pollution. Correlation analysis indicated that higher temperatures were sometimes associated with elevated PM2.5 episodes due to enhanced photochemical activity, but regression analysis revealed an overall negative association, suggesting stronger atmospheric dispersion at higher temperatures. Wind speed consistently reduced PM2.5, while humidity and rainfall supported pollutant removal. Regression models explained 18.1% of PM2.5 and 29.7% of AQI variability. Ridge regression reinforced the dominant influence of temperature and humidity. AQI was modelled alongside PM2.5 to align with its practical role in public communication and policy, despite being largely driven by PM2.5. These findings highlight the role of meteorological conditions in shaping urban air quality and emphasize the need for targeted interventions during stagnant, high-pressure episodes. By focusing on Bhopal, this study contributes valuable city-specific knowledge to the broader discourse on air pollution in rapidly developing regions of India. The study reinforces the need to incorporate meteorological forecasting into urban air quality management strategies.

Is Climate Change Fuel to Increase Intense Tropical Cyclones in the North Indian Ocean?

Krishna K. Muni — 2025-09-03

In recent years, severe cyclonic storm (SCS) activity is increased in the North Indian Ocean (NIO), particularly in the central Arabian Sea and Bay of Bengal. In the present study investigate the role of climate change on increasing intense severe cyclonic storms (Cat 4 & 5) at each 2.5° × 2.5° grid boxes in the NIO. Inter-governmental on Climate Change (IPCC) AR4 Model data sets for cyclone genesis parameters, sea surface temperature (SST) from Hadley Centre, cyclone tracks from Indian Meteorological Department and Joint Typhoon Warning Center during 1891–2010 is used. The study reveals stimulating results that the frequency of SCS increased during 1970–2010 (19) compared with 1891–1969 (12) at north of 15° N lat, but south of 15° N it is reversed in the Arabian Sea (AS) during southwest monsoon season(JJAS). In the Bay of Bengal (BoB) the scenario is reversed when compared with the Arabian Sea and the frequency of SCS was decreased in climate change environment. Strong latitudinal shift and increase the frequency of SCS is observed in the Arabian Sea during climate change scenario (1970–2010), but such phenomena is not noticed in the BoB. The main reason is reduction in vertical wind shear (easterly shear) along with increase in SSTs. To find out the relationship between the frequency of SCS and cyclone genesis parameters I did statistical tests, they also showed good results.

Exploration of Vulnerability of Temperature Changes in Southeastern Coastal Islands of Bangladesh through the 2 Decades of Spatiotemporal Data

Prabal Barua — 2025-09-17

Bangladesh is one of the most vulnerable countries to climate change-related disasters and economic loss and damage. This study examines 20 years of satellite-derived land surface temperature (LST) data to investigate seasonal trends, changes in land use and land cover (LULC), and the relationship between temperature changes and the most common mangrove species in the Coastal islands of Bangladesh. The most noticeable temperature changes happened in the pre-monsoon and monsoon seasons. In December, on the other hand, there was a statistically significant cooling trend of −0.041 ℃ per year. At the same time, forest cover has been shrinking by an average of 26.36 km² per year, while coastal water bodies have been growing by 23.44 km² per year. Cluster analysis shows that temperatures change a lot from month to month outside of the pre-monsoon season. This suggests that the climate is unstable and could push the system beyond ecological thresholds. SARIMA modelling demonstrated 98.12% accuracy in predicting temperatures, highlighting the importance of temporal analysis in forecasting future stress thresholds. Species-specific temperature clustering shows how different mangrove species can handle heat: Ceriops decandra is more common in locations with higher temperatures, while Heritiera fomes is more common in areas with lower temperatures. These patterns show that ecosystem resilience is becoming less stable; therefore, we need to move from passive Conservation to proactive, species-informed, and thermally adaptive management practices.

Meteorological Determinants of PM2.5 and PM10 Concentrations During the Transition Season in Campo Grande, Central Brazil

Amaury de Souza — 2025-09-13

Air pollution remains a pressing environmental issue in Brazilian cities, particularly during the dry season when meteorological conditions favor pollutant accumulation. This study investigates the influence of meteorological variables on PM₂.₅ and PM₁₀ concentrations in the urban atmosphere of Campo Grande, Mato Grosso do Sul, Brazil, during the transition period between the wet and dry seasons (March to June 2021). Data were obtained from the air quality monitoring station at the Federal University of Mato Grosso do Sul (UFMS), including daily measurements of particulate matter and meteorological parameters such as temperature, humidity, precipitation, atmospheric pressure, wind speed, and wind direction. Descriptive statistics, Pearson’s correlation, multiple linear regression, and Principal Component Analysis (PCA) were employed to explore the relationships between meteorological drivers and particulate matter. Results revealed that relative humidity and precipitation are negatively correlated with PM concentrations, indicating their role in atmospheric cleansing through wet deposition. Conversely, wind speed and atmospheric pressure were positively associated with PM levels, suggesting pollutant transport or accumulation under stable atmospheric conditions. The PM₂.₅/PM₁₀ ratios of 0.55 (1-hour) and 0.44 (24-hour) point to a predominance of fine particles from anthropogenic sources. The findings highlight the complexity of pollutant-meteorology interactions and underscore the need to incorporate meteorological data into air quality forecasting and management strategies. This approach is especially critical for medium-sized tropical cities that experience seasonal climate extremes and are subject to both urban and biomass-burning emissions.