Md Kaium Hossain

Anthropogenic air pollution represents a significant threat to both environmental and human health, with nitrogen oxides (NO_x) playing a substantial role in the formation of photochemical smog, acid rain, eutrophication, and respiratory diseases. In Bangladesh, NO_x emissions primarily originate from combustion sources such as road transportation, power generation, and industrial activities, while natural sources include lightning, wildfires, and soil emissions. Furthermore, ammonia emissions from fertilizers and livestock exacerbate air quality issues in both urban and rural settings. Despite the acknowledgment of vehicular and industrial contributions, comprehensive and systematic assessments of NO₂ trends across the nation remain scarce. This study presents a complementary, side-by-side assessment of ground-based NO₂ measurements from the Department of Environment (DoE) with atmospheric NO₂ retrieved from the Sentinel-5P TROPOMI Level-3 product via Google Earth Engine (GEE). Spatial distribution patterns of both ground-level and atmospheric NO₂ were analyzed using ArcGIS Pro, along with seasonal and interannual trend assessments from 2018 to 2024. Results indicated pronounced spatial and temporal variability in NO₂ concentrations. The highest levels were consistently recorded over Dhaka and surrounding industrial zones, with moderate accumulation in Chattogram. Winter months (December–February) exhibited hazardous concentrations, with a national maximum of 205.21 µg/m³, while monsoon periods recorded the lowest levels overall, as reflected in its seasonal mean (see below). Seasonal averages indicated the highest concentrations in winter (35.03 µg/m³), followed by pre-monsoon (29.16 µg/m³), with monsoon recording the lowest seasonal mean (19.94 µg/m³). Long-term analysis showed the highest national annual mean NO₂ con-centration in 2018 (44.32 µg/m³), decreasing to 14.81 µg/m³ by 2024, with non-monotonic year-to-year fluctuation in the intervening period (2020–2021 data were unavailable). These findings underscore the necessity for stricter emission regulations and targeted mitigation measures. As a policy recommendation, this may include the implementation of NO_x-selective catalytic reduction (SCR) using NH₃ over metal oxide and zeolite catalysts. This study provides evidence-based insights to support cleaner air initiatives and sustainable environmental management strategies in Bangladesh.

Rapid urbanization and unplanned land development have transformed Dhaka into one of the most densely built metropolitan areas in South Asia, leading to increased surface heat accumulation and growing pressure on urban energy systems. Numerous studies have reported a progressive rise in Dhaka’s Land Surface Temperature (LST), accompanied by a strong negative correlation between the Normalized Difference Vegetation Index (NDVI) and LST and a positive correlation between the Normalized Difference Built-Up Index (NDBI) and LST. However, spatially explicit assessments that integrate multiple geospatial proxies to characterize urban energy-intensity patterns remain limited, particularly in data-scarce cities where direct energy-consumption data are unavailable. The present study aims to develop and map a proxy-based Urban Energy Intensity Index (UEI) for Dhaka by integrating remote sensing, GIS, and AHP-MCDA techniques. Landsat 9 imagery and VIIRS nighttime light data were processed to derive NDVI, NDBI, and LST layers, while building footprints and road networks were extracted from OpenStreetMap to represent urban form. Eight environmental, built-environment, and anthropogenic indicators were standardized and weighted using the Analytic Hierarchy Process (AHP) before integration through a GIS-based weighted overlay approach. Results show that dense, impervious, and intensely illuminated built-up cores exhibit elevated LST and UEI values, while peri-urban and vegetated areas consistently display lower values, producing spatially distinct high-UEI clusters concentrated within Dhaka’s urban core. Because the index is derived from proxy indicators and was not validated against observed energy-consumption data, the findings should be interpreted as relative spatial patterns rather than direct measures of energy use or energy efficiency. These findings demonstrate the effectiveness of integrating remotely sensed and geospatial indicators to capture intra-urban variability in proxy-based urban energy intensity. The study provides a scalable and data-efficient framework for identifying priority zones for urban planning interventions and supports targeted strategies such as urban greening, surface albedo enhancement, and sustainable land-use planning to mitigate thermal stress.

Rapid urban growth and industrial activity have significantly increased air pollution in Chattogram, Bangladesh. Among various pollutants, particulate matter (PM_2.5 and PM₁₀) has emerged as a major concern due to its severe effects on public health and the environment. This study investigates seasonal and temporal variations in air quality and identifies periods of high risk. Data were collected from the Department of Environment, including PM_2.5 and PM₁₀ measurements from 2013 to April 2025 and AQI data from 2022 to April 2025. The analysis focuses on seasonal trends, monthly fluctuations, extreme pollution events, and correlations among pollutants. The study examines which seasons are most polluted and how PM_2.5, PM₁₀, and AQI change over the year. Results indicate that winter experiences the highest pollution levels, while monsoon shows the lowest. Pre-monsoon and post-monsoon show moderate pollution, representing transitional periods with variable air quality. Monthly trends reveal that pollution peaks in January and reaches its lowest point in July, demonstrating clear temporal patterns. Extreme pollution events are most frequent in winter and the pre-monsoon period, emphasizing periods of highest health risk. Correlation analysis shows strong positive associations between PM_2.5, PM₁₀, and AQI (r > 0.80). Machine-learning models demonstrated moderate predictive skill for PM₁₀ forecasting (R² up to 0.65), while PM_2.5 models at Agrabad produced negative R² values, indicating performance worse than a mean-value benchmark; accordingly, long-term PM_2.5 projections at Agrabad are not reported. Exploratory PM₁₀ projections suggest that concentrations may remain above WHO guideline levels through 2030, although these projections should be interpreted cautiously because meteorological drivers were not included in the modelling framework. The findings highlight the need for seasonal interventions to control particulate matter, particularly in winter and pre-monsoon. Measures such as stricter emission regulations, industrial controls, and public awareness campaigns are essential for mitigating health risks. This study provides a comprehensive understanding of air-quality dynamics in Chattogram and offers a framework for targeted pollution-control strategies. Implementing these strategies can improve air quality, protect public health, and support sustainable urban development.

A healthy ecosystem that supports all living forms depends on water, which is the most magnificent natural resource. Considering the acute global water scarcity, it is critical to investigate rainwater gathering techniques that incorporate practical drought mitigation strategies. In addition to providing a selection of the best sites for natural aquifer recharge, the ultimate objective of this work is to determine where rainwater harvesting structures can be built for efficient and successful rainwater harvesting management in the research region. The current work also focuses on identifying possible locations for natural aquifer recharging utilizing RS and GIS methodologies because the water table is rapidly declining. Land use/land cover, slope, soil, runoff, drainage density, and road limitations were the five criteria used to identify RWH locations using GIS-based MCEs. Using the soil and water assessment tool approach, the observed runoff depth varied from 0 to 900 CMS, and the RWH unit's appropriateness ranged from highly suitable to least suitable. The Panjkora River Basin's total area of 1128.395 km2 is the most suitable for building farm ponds, whereas 954.756 km2 is moderately suitable. The suitable area for check dam building is 350.58 km2, while the moderate area is 245.31 km2. According to the results, 1572.518 km2 are the most suitable areas to build gully plug structures in order to prevent soil erosion and store rainwater, while 3742.38 km2 are of moderate appropriateness. In the current study seven local influencing factors, e.g., land use and land cover (LULC), drainage density, slope, population density, water table, elevation, and soil types, were used to accomplish the objective of natural aquifer recharge sites. More than 72% of the basin has been classified as having moderate to very high potential for artificial recharge. The land area is categorized as highly suitable in 30.19% of cases and very high suitability in 1.01% of situations. In terms of hydrology, this spatial correlation is quite important. Highly or very highly suitable locations for recharge are found in many sections of the moderate and poor groundwater zones. Particularly in areas where traditional groundwater extraction is no longer technically or economically feasible, these interventions would encourage vertical infiltration, lessen runoff, and aid in the restoration of dwindling aquifers. Water managers can implement location-specific, data-driven methods to increase climate resilience and long-term water security in the Panjkora Basin by coordinating recharge planning with natural recharge potential and current groundwater stress.