Members’ Publication

Assessing fish habitat suitability in a data-scarce tidal river is often challenging due to the absence of continuous water quantity and quality records. This study comprises an intensive field study on a 42 km reach which recorded bathymetry and physical water quality parameters(pH, electroconductivity, dissolved oxygen, and total dissolved solids) testing and corresponding water levels and velocity. Frequent water sampling was conducted on 17 out of 90 locations for laboratory water quality tests. Based on this, an interpolation technique, i.e., Inverse DistanceWeighted (IDW), generates a map in a Geographic Information System (GIS) environment using ArcGIS software to determine the river water quality parameters. Additionally, a hydrodynamic model study was conducted to simulate hydraulic parameters using Delft3D software followed by water quality distribution. During validation, the Delft3D-simulated water quality could reasonably mimic most field data, and GIS featured dissolved oxygen. The overall water quality distribution showed a lower dissolved oxygen level (~3 mg/l) in the industrial zone compared to the other two zones during the study period. On the other hand, these validated hydraulic properties were applied in the Physical Habitat Simulation Model (PHABSIM) set up to conduct the hydraulic habitat suitability for Labeo rohita (Rohu fish). Thus, the validated model could represent the details of habitat suitability in the studied river for future decision support systems, and this study envisaged applying it to other similar rivers (4) (PDF) Suitability Assessment of Fish Habitat in a Data-Scarce River. Available from: https://www.researchgate.net/publication/364279688_Suitability_Assessment_of_Fish_Habitat_in_a_Data-Scarce_River [accessed Sep 11 2023].

Rajshahi City Corporation (RCC) has been marked as a geologically vulnerable in terms of earthquake. However, no scientific method has been determined to figure out the influencing factors. This is the first approach to detect the vulnerability of RCC area. In this point of view, the liquefaction hazard map has the innate power to evaluate the potentiality of earthquake-prone areas. The liquefaction map was prepared using sixty borehole profiles data among 30 wards of RCC. The most widely popular Seed’s method (1971) is employed to calculate liquefaction potential index (LPI) using an earthquake scenario of magnitude M7.0 with peak ground acceleration of 0.12 g (BNBC 2010 draft version). Based on LPI value, Rajshahi city area has been divided into four different liquefaction severity categories (i.e., very low, low, high, and very high) and finally mapped through ArcGIS for the complete visualization. This initial study might be helpful for engineers and planners to plan and execute different structural scheme in this region. This paper also summarizes the calculation procedure of determining LPI from available literature to build up a single framework.

Environmental degradation can be predicted and managed in a sustainable manner by the perodic analysis of the Land Use/Land Cover (LULC) change pattern, which not only helps to revitalize the environment but also helps to improve future land-use policies. With the Rohingya influx in 2017, the Kutupalong Mega Camp area in Bangladesh is at a severe risk of environmental degradation as the area is experiencing remarkable LULC change. The aim of this research is to illustrate the LULC change in the Kutupalong Mega Camp before and after the refugee influx, as well as its impact on the surrounding environment because of this change. The spatial and temporal variation of the LULC is analyzed from the classified multi-temporal Landsat images for years-2015, 2018, and 2021. The study reveals gradual decrease in forest cover of the area, which is replaced by the increasing human settlements. The study found an inverse relation between the refugee influx and the vegetation cover, where a positive relation to the bare land and settlement exists. The area experienced about ten times increase in human settlements during 2015–2021, which resulted deforestation of surrounding forest cover. Between 2015 to 2021, 74 % of forest cover of the studied area has been cleaned up for newer settlements, with an increase of wetland to meet the needs of increasing refugee population which has made the scenario worse. We also noticed an increase of Land Surface Temperature (LST) within a short period, where the average temperature increase rate is 0.06% during 2015–2018 and 0.01% during 2018–2021. The ecosystem, wild-habitat, and the thermal environment has been disturbed to a great extent due to this drastic change of forest cover mostly by the increasing anthropogenic activities in this area. The study represents the present scenario in comparison to its natural setting just a few years ago, and may serve as a guidance for the concerned authorities and international humanitarian organizations to develop a sustainable, comprehensive, and environment-friendly land management plan in order to protect the surrounding forest-ecology as well as the humanitarian works.

Particulate matter (PM2.5) is one of the major threats to public health, particularly Dhaka City in Bangladesh, frequently cited as one of the worst cities in the World in terms of air quality. This study examines the effects of six environmental (land surface temperature (LST), digital elevation model (DEM), water vapor concentration, wind speed, rainfall, and normalized difference vegetation index (NDVI)) and six economic factors (population density, road density, gross domestic product (GDP), poverty rate level, and percentage of low-income groups in rural and urban setting) on PM2.5 concentration in five industrial cities of Bangladesh using geographically weighted regression modelling (GWR) and machine learning (ML) tools. The mean annual rate of PM2.5 concentration increased by > 42% during 2002–2020 in all cities. Dhaka and Narayanganj districts were affected the most. Goodness-of-fit (R2) was 93% (environmental factors) and 73% (economic factors). Environmental factors: LST (100%) and water vapor concentration (100%) were correlated positively with PM2.5, while DEM (100%), rainfall (83%), NDVI (81%), and wind speed (84%) had a negative relationship at 95% confidence level. β-coefficients of DEM (p < 0.02), LST (p < 0.01), water vapor concentration (p < 0.01), NDVI (p < 0.02), and poverty rate (p < 0.01) were correlated negatively. Moreover, machine learning has extracted a good prediction of PM2.5, ranging the R2 between 0.79 and 0.86%. This study can be replicated in other cities by incorporating socio-economical, local geo-environmental, and meteorological with other air pollutants.