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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.

This book encourages children to create friendship with birds.

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.