Members’ Publication

Rapid urbanization and population growth have introduced a new phenomenon called “light pollution”, which is the result of ungoverned use of artificial lighting. Natural land cover is being replaced by impervious surface to meet the growing demand. As a result, urban areas are getting warmer compared to the surrounding rural areas. Scholarly literature confirms the relationship between light pollution and air temperature. However, the nature of direct relationship is yet to be explored. Using remote sensing and weather station data, this research reveals the nature of relationship between these two variables. The analysis confirms that a significant relationship exists which can be explained by geographically weighted regression (GWR) in a far better way compared to ordinary least square (OLS) regression. According to the GWR, overall 50% change in air temperature is influenced or affected by light pollution, where in urban areas the impact of light pollution on air temperature is higher compared to rural areas. This research also unearths that light pollution is increasing at a dissolute rate, four-fold in 10 years. With this said, considering the nexus between light pollution and air temperature along with its other negative effects the authorities are expected to take measures to reduce light pollution.

Purpose Seismic vulnerability evaluation of various public structures, especially school buildings, is very crucial for designing hazard mitigation initiatives in seismic prone areas. The city of Mymensingh is at great risk of earthquake because of its geographical location, geological structure and proximity to active faults. The city is famous for its ancient and renowned educational institutes that need to be evaluated for understanding the seismic performance of the building during an earthquake. This study aims to evaluate the seismic vulnerability of educational buildings of Mymensingh city using rapid visual screening (RVS) and index based approach. Design/methodology/approach RVS procedure includes field survey and secondary source assessment for evaluating structural vulnerability attributes. Analytical hierarchy process is applied to develop an index focusing on systematic attributes of vulnerability based on expert opinions. Then, a composite vulnerability map is developed combining both structural and systematic vulnerability score providing an equal weight. Findings This study evaluates the seismic vulnerability of 458 educational buildings of Mymensingh city and the result shows that 23.14% educational building has high, 46.29% has moderate and 26.86% has moderately low and only 3.71% buildings has the low seismic vulnerability. This study expected to be helpful in resource targeting and prioritizing seismic hazard mitigation activities for education buildings of Mymensingh city. Originality/value This study endeavors to present a comprehensive vulnerability assessment method by integrating RVS and index based approach that incorporates both structural and systematic dimensions of vulnerability. The result is expected to be helpful in the formulation of disaster prevention policy for vulnerable educational buildings and development of the earthquake-resistant building codes for the new building construction in Mymensingh city. Keywords

Sustainable development, Road services, User satisfaction, Factor Analysis, Linear Regression, Analytical Hierarchy Process (AHP).

Sustainable rice production in the coastal region of Bangladesh is threatened due to various climatic impacts. The study investigated the current scenario of rice production and identified the linkage among different input variables related to rice production such as seed, urea, pesticide, etc. in three districts of Patuakhali, Khulna, and Cox’s Bazar, using a log–log model and panel data analysis. The Stochastic Model of multiple regression analysis helped to identify the most important input variables. The input factors found common in all three study areas were seed. The log–log model was used to identify how climatic factors influenced these input variables. Identifying such factors would help to ensure sustainable agriculture production in the future. Seed cost showed a negative elasticity in Patuakhali, while urea cost showed a negative elasticity in Khulna region. Time series forecasting of rice production up to 2030 in the study areas was examined using the best fitted ARIMA model. While rice production showed an increasing trend in two regions, it did not show a satisfactory upward trend in Kutubdia, which is mostly related to their mismanagement of fertilizer use and irrigation water scarcity. The analysis from the research indicated that an area-specific adaptation plan is required to sustain the agricultural production, as the input variables for rice production were not found similar for three different study districts where a specific attention needs to be given. Moreover, to address the nonexistence of quality panel data for rice production, measures have been introduced which includes an online data collection system using a mobile application.

The livelihood of the people in the dynamic coastal regions of Bangladesh is highly dependent on nature and thus is vulnerable to climatic events. The research study developed the Fishermen Livelihood Vulnerability Index (LVI) to measure the vulnerability of the fishing communities living in the coastal regions of Bangladesh. The weighted average method was used to measure the FLVI and the binary logistic regression was used to find out the determinants of vulnerability. The variables to measure vulnerability such as exposure, sensitivity, and adaptive capacity were taken from the IPCC climate vulnerability framework. The results, based on the findings of a survey of 164 households from 3 coastal districts (Patuakhali, Khulna & Cox’s Bazar) indicated that the coastal fishing communities are more vulnerable (FLVI = 0.48) compare to the people living in other parts of Bangladesh. The study also revealed that the livelihood activities of the coastal fishermen are mainly affected by the impacts of natural disasters. Spatial maps were prepared to show the distribution of the vulnerable areas with indicating the houses according to their level of vulnerability. The study also identified the most significant factors of vulnerability. As part of the analysis, it was found that the variables that had the most significant impact on fishermen livelihood vulnerability were natural disasters, diseases due to climate change, NGO and microcredit, income, and piracy problem. The study also identified that vulnerability factors are highly influenced by spatial characteristics and thereby identification of location specific factors is crucial in assessing and calculating vulnerability score. The study concluded that if location specific fishermen vulnerability factors are identified after certain interval with the active engagement of local communities can generate valuable information for developing appropriate strategy to secure their livelihood in a sustainable way.

Pedestrians are the most vulnerable road users and their risky behaviours make the situation worse. This study aimed to assess the crash risk associated with various pedestrian groups and their road crossing behaviours at intersections in Dhaka, Bangladesh. A two-stage, multi-criteria decision-making approach—analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS)—was used for risk assessment. In the first stage, five risk assessment criteria: nearest vehicle speed, nearest vehicle type, vehicle flow, interrupted by vehicle, and minimum gap were identified from the literature, and these criteria were weighted by the AHP. In the second stage, risk associated with various pedestrian groups and their road-crossing behaviours were quantified and ranked by the TOPSIS. The results indicate that male and young pedestrians had higher risk among pedestrian groups. Furthermore, an oblique crossing type, a running crossing pattern, mobile phone usage while crossing, carrying medium-weight baggage, a rolling gap crossing type, high crossing speed, and cross by using conflict zone were found as the riskiest behaviours. Besides, using crosswalk while crossing, single-stage and two-stage crossing types, perpendicular crossing type, and group crossing were found to be the safest behaviours.

The intensity and formation of urban heat island (UHI) phenomena are closely related to land use/land cover (LULC) and land surface temperature (LST) change. The effect of UHI can be described quantitatively by urban thermal field variance index (UTFVI). For measuring urban health and ensuring sustainable development, the analysis of LST and UTFVI are receiving boosted attention. This study predicted LULC, seasonal (summer & winter) LST, and UTFVI variations using machine learning algorithms (MLAs) in Cumilla City Corporation (CCC), Bangladesh. Landsat 4–5 TM and Landsat 8 OLI satellite images were used for 1999, 2009, and 2019 to predict future scenarios for 2029 and 2039. MLAs such as Cellular Automata (CA) and Artificial Neural Network (ANN) methods were used to predict the future change in LULC, LST, and UTFVI. The result suggests that, in the year 2029 and 2039, the urban area will likely to be increased by around 8 % and 11 %, where significant decrease will be taken place in green cover by 9 % and 14 %. If the rapid urban growth continues, more than 30 % of the CCC area will likely to be experienced more than 33 ◦C temperature and strongest UTFVI effect in the year 2029 and 2039. In addition, an average 4 ◦C higher LST was recorded in the urban area compared with vegetation cover. In urban construction practice, avoiding concentrated impermeable layers (built-up areas) and increasing green covers, are effective ways of mitigating the effect of UTFVI. This study will contribute in achieving sustainable development and provide useful insights to understand the complex relationship among different elements of urban environments and promotion of city competence.

According to the statement of Intergovernmental Panel on Climate Change (IPCC), since the late 19th century, the global mean land surface temperature (LST) has increased 20C due to the increase in greenhouse gas emissions and rapid land use/land cover change (LULCC) from human activities. The dramatic increase in LST leads to sever urban heat island (UHI) effect. In the last few decades, massive LULCC transition leads to higher LST in Bangladesh's different city space. The dramatic decrease in green land and water bodies with a massive increase in the urban builtup area influence LST in Chattogram city. This study estimates the existing and future dynamic of LST using remote sensing techniques. A series of Landsat TM/OLI images collected in the summer season were used to explore LST variation from 1999 to 2019. Also, the correlation between LST and normalized difference water index (NDWI), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference bare soil index (NDBSI) is estimated. Finally using artificial neural network predict the future LST for the year 2029 and 2039 to identify the impact of future LST increase in climate change. The study results suggest that an overall increase in LST is 2.50C for the last 20 years. Correlation results indicate that LST shows a strong positing correlation with NDBI and a negative correlation with NDWI and NDVI. Simulation results indicate that LST will increase 20C (2029) and 30C (2039) compare with 2019. A significant increase in LST will create environmental degradation and ecological imbalance in Chattogram city. Higher LST hotspot areas were found in urban built-up areas, which are the most vulnerable to the UHI effect. The findings will help the city authorities to prepare suitable strategies to reduce the UHI effect and improve the city's climate change scenario.