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The COVID-19 pandemic has had enormous effects on human life and the environment, particularly freshwater ecosystems, on a global scale. Despite its numerous effects, the pandemic has improved the quality of the environment, allowing devastated ecosystems to recover. During the COVID lockdown period, many researchers observed positive amendments in environmental quality in various parts of the world. Due to increased industrialization and urbanization over the past several decades, Dhaka's peripheral rivers have grown highly polluted. In this study, an attempt was made to illustrate the post-covid surface water quality scenario of Bangladesh's hihgly contaminated Turag River. Therefore, for the first time, a GIS-based spatial analysis of water quality index was used to endeavor to quantify 16 water quality parameters (pH, EC, temperature, turbidity, salinity, TDS, DO, Na+, K+ Ca2+, Mg2+ Cl-, Cu, Pb, Fe, and Cd) in the river Turag. Thus, it could serve as a reference work for future researches in Bangladesh. After calculating the WQI for the collected samples, it was determined that the water quality status at all sampling stations was extremely poor because each WQI was greater than 100. Using the Kriging method of interpolation, the encroaching WQS were predicted and visually represented using GIS maps. The findings of this study are particularly concerning, as all recorded WQS samples surpassed the standard limits. Based on these findings, we urged for the rapid deployment of proper water management practices and policies in order to safeguard and manage the water resources.

Global and urban-induced local warming lead to increasing heat risk in cities. The rapid increase in urban population, weak infrastructure, poverty, as well as an ageing population, make the risk more acute in developing cities. However, heat risk is not uniformly distributed and a detailed exploration of the link between urban characteristics and local variations in heat risk is needed to aid targeted mitigation. In this paper, we demonstrate a fine-grained heat risk map using existing data combined with expert opinion in a humid tropical city (Colombo, Sri Lanka) with the objective of highlighting the relative heat risk as a function of physical and socioeconomic conditions across the city. We then simulate the effects of shading and greening on the ‘high’ heat risk areas, and greening on the ‘low’ heat risk areas, to show that a combined approach will be needed to reduce risk at ‘high’ risk areas. In ‘low’ risk areas, maintaining the green cover is crucial to heat risk reduction. The paper, thus, establishes a protocol for detailed heat risk mapping with existing data and points to the differing importance of shading and greening in different parts of the city, thus, showing where, and to what extent, mitigation actions could be beneficial.

Bangladesh is primarily an agricultural country where technological advancement in the agricultural sector can ensure the acceleration of economic growth and ensure long-term food security. This research was conducted in the south-western coastal zone of Bangladesh, where rice is the main crop and other crops are also grown. Land use and land cover (LULC) classification using remote sensing techniques such as the use of satellite or unmanned aerial vehicle (UAV) images can forecast the crop yield and can also provide information on weeds, nutrient deficiencies, diseases, etc. to monitor and treat the crops. Depending on the reflectance received by sensors, remotely sensed images store a digital number (DN) for each pixel. Traditionally, these pixel values have been used to separate clusters and classify various objects. However, it frequently generates a lot of discontinuity in a particular land cover, resulting in small objects within a land cover that provide poor image classification output. It is called the salt-and-pepper effect. In order to classify land cover based on texture, shape, and neighbors, Pixel-Based Image Analysis (PBIA) and Object-Based Image Analysis (OBIA) methods use digital image classification algorithms like Maximum Likelihood (ML), K-Nearest Neighbors (KNN), k-means clustering algorithm, etc. to smooth this discontinuity. The authors evaluated the accuracy of both the PBIA and OBIA approaches by classifying the land cover of an agricultural field, taking into consideration the development of UAV technology and enhanced image resolution. For classifying multispectral UAV images, we used the KNN machine learning algorithm for object-based supervised image classification and Maximum Likelihood (ML) classification (parametric) for pixel-based supervised image classification. Whereas, for unsupervised classification using pixels, we used the K-means clustering technique. For image analysis, Near-infrared (NIR), Red (R), Green (G), and Blue (B) bands of a high-resolution ground sampling distance (GSD) 0.0125m UAV image was used in this research work. The study found that OBIA was 21% more accurate than PBIA, indicating 94.9% overall accuracy. In terms of Kappa statistics, OBIA was 27% more accurate than PBIA, indicating Kappa statistics accuracy of 93.4%. It indicates that OBIA provides better classification performance when compared to PBIA for the classification of high-resolution UAV images. This study found that by suggesting OBIA for more accurate identification of types of crops and land cover, which will help crop management, agricultural monitoring, and crop yield forecasting be more effective.

Social safety, security, and comfort of school-going children during their trip to school have become a subject of anxiety to the parents. It has become a crucial issue in recent times, specially in developing nations. Community-based transport can significantly address social security issues in travel at a reasonable cost and reduce the burden on private mode of transportation. In Dhaka city, school van service already exists to serve school-going children but the service has not been proven as an efficient and formal mode of transport to solve mobility problems. In this connection, this study seeks to identify the problems and prospects of the existing school van service and provide a unique, healthy, safe, and reliable transport mode for children. Applying different tools of the Participatory Rural Appraisal (PRA) method, the problems and solutions have been gleaned from the community. The findings of this study can be applied to a community-managed para-transit systems to improve utility and hence to be useful in resolving issues associated with local transportation. With widespread use, this service has the potential to reduce traffic congestion on major thoroughfares, especially during rush hour.

Purpose Social media creates a public sphere, which functions as the third wheel of governance. This study aims to examine the relationship between Facebook penetration and the World Bank’s World Governance Indicators. Design/methodology/approach First, a Pearson correlation was estimated between Facebook penetration and governance indicators. Second, ordinary least squares analysis was used to examine a variety of additional economic and population factors, including Facebook penetration. In both instances, the colinearity of the variables is examined. Findings The findings indicate that there is no conclusive correlation between Facebook penetration and governance; however, the opposite is true for developing, emerging and least developed countries, though the relationship is not uniform across regions. In addition, per capita gross domestic product and population dynamics, specifically the proportion of the population aged 15–64, have a significant impact on governance measures. The colinearity of the variables suggests that governance is a broad concept, and that the direct correlation between Facebook penetration and governance may be misleading. Research limitations/implications Future research should incorporate panel data from other social media platforms, such as Twitter and Reddit, to better understand temporal factors and the relationship between social media penetration and governance. Practical implications This paper opens up new avenue for investigation on the impact of social media on governance. Social implications This paper can contribute to the complexity of social media as means for voice and accountability. In addition, the paper mentions how social media can be used more neutrally to ensure exposure to diverse perspectives. Originality/value The growing importance of the internet and the popularity of social networking websites are generating a great deal of scholarly attention. To the best of the authors’ knowledge, there is almost no literature that links Facebook penetration and governance. This paper intends to fill this void.

Land and property development process includes a series of multifaceted activities ranging from purchasing to converting it for development purposes and everything in between. The process itself encompasses multiple stakeholders, drivers, and contributions from diversified public and private actors and transaction cost arises out of their complex interaction. Transaction costs incurred during any kind of human interaction (i.e., transactions). Every actor involved in the process wishes to maximize his achievement under various constraints and hence institutional arrangement (i.e., set of humanly devised rules to administer the constraints) is necessary for efficient management of the development process. Therefore, to devise an optimum outcome out of economic and social transactions in the property development process, cooperative and competitive relationships between individuals should be understood from a broader sociopolitical and governance structure. In this research, it is critically argued that the land and property development process should implicate a multifaceted set of formal and informal rules or institutional arrangements to govern the intrinsic interaction, action and thereby reducing the related transaction cost. The central argument is further reproachfully evaluated and implicated in the urban development process through the myopic lens of the Transit-oriented development (TOD) pathway. A vigilant combination of descriptive and explanatory research approaches is adopted to analyze the connection between theory and practice.

With the increasing tension on the global sustainable environment in the urban areas, it is essential to monitor the airborne pollutants and understand the underlying factors that can trigger the situation in a worst-case scenario. Because of its cramped living conditions, excessive coal and fuel usage, and rapid deforestation, the southeast Asian region has historically had worse air quality than the rest of the world. The economic hubs of India and Bangladesh, in particular, have drawn so much attention away from rural regions that unrestrained urbanization is becoming controversial for planners, engineers, and stakeholders in sustainable development. This research combines the two main Asian capital regions, Delhi and Dhaka. It analyzes the change in nitrogen dioxide (NO2) concentration, land surface temperature (LST), and vegetation dynamics across three years (2019–2021) for summer and winter. The NO2 concentration data from Sentinel-5P has been extracted using Google Earth Engine (GEE), and Landsat-8 imagery was utilized for LST, Normalizer Vegetation Index (NDVI), and Enhance Vegetation Index (EVI). The statistical analysis has been carried out by dividing the research regions into one sq. km grid (1512 grids for Delhi and 1485 grids for Dhaka). According to descriptive research, Dhaka's condition is worse than Delhi's, with significant vegetation loss with LST and NO2 concentrations rising. In both research regions, the NO2 concentration is high throughout the winter. The Pearson correlation value demonstrates a negative association between total NO2 concentration and mean NDVI and EVI values and a positive relationship between total NO2 concentration and mean LST. The data have been further assessed using linear regression, which overlaps the correlation result with a maximum R-squared value of 0.2998 for NO2 and EVI in winter 2019.

This study aims to identify the effect of seasonal land surface temperature variation on the COVID-19 infection rate. The study area of this research is Bangladesh and its 8 divisions. The Google Earth Engine (GEE) platform has been used to extract the land surface temperature (LST) values from MODIS satellite imagery from May 2020 to July 2021. The per-day new COVID-19 cases data has also been collected for the same date range. Descriptive and statistical results show that after experiencing a high LST season, the new COVID-19 cases rise. On the other hand, the COVID-19 infection rate decreases when the LST falls in the winter. Also, rapid ups and downs in LST cause a high number of new cases. Mobility, social interaction, and unexpected weather change may be the main factors behind this relationship between LST and COVID-19 infection rates.

In developing countries, rapid and unregulated population growth, along with economic and industrial growth and development have accelerated the rate of changes in land use and land cover (LULC) during the early twenty-first century. One of the most effective ways to assess and manage the land transformation is through quantitative assessment of LULC changes. To find the best classifier for further uses of earth observation data, it is necessary to compare the accuracy of several LULC modelling algorithms. In this paper, four machine learning (ML) algorithms/classifier namely support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF) and decision tree (DT) have been examined using Landsat 8 Operational Land Imager (OLI) sensor data. All the algorithms performed almost in a similar accuracy level, where SVM scores 84.00% of Cohen kappa and 90.74% of overall accuracy. These figures are 82.99% and 90.05% respectively for KNN. For RF, Cohen kappa and overall accuracy recorded 78.24% and 87.62% respectively. DT gained 74.46% for Cohen kappa and 85.51% for overall accuracy. Clearly SVM outperformed other algorithms in low spatial-resolution satellite data. The outcome of the study would help in different stages of LULC modelling. Moreover, it would help the planners and policy makers in formulation and preparation of land-based policy and planning to ensure sustainable living environment.

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.