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For growing cities, solid waste management is a serious concern, focusing on household waste because it accounts for a significant portion of municipal solid waste. An accurate estimation of the quantity and characteristics of solid waste mixtures from diverse income levels is required for effective city-wide solid waste management planning. The purpose of this study is to assess the production and characteristics of domestic solid waste produced by different socio-economic groups in three wards (no. 17, 23 and 24) of Khulna city. Moreover, various factors that influence the pace of waste production have been critically investigated. The finding suggests that, household waste generation rate ranges from 0.421 to 5.81 kg/day, average of 1.917 kg/day and 0.476 kg/capita/day. The waste composition analysis depicts that kitchen garbage is the most prevalent component. The waste generation rate has a positive relationship with income level and household size. Nonetheless, the findings and proposed essential steps of this study would be beneficial in assisting policymakers in refining plans for controlling the municipal waste effectively.

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In recent decades, with the rapid growth of urbanization, the amount of carbon emission in urban areas has been globally accelerated. In this study, the carbon storage capacity of different small administrative regions (wards) of Khulna city has been estimated based on four major sinks types- trees, vegetations, soils, and water bodies. To estimate carbon storage capacity, the total coverage area of each of the sink’s types has been estimated by Sentinel-II satellite image data of 2019 through supervised image classification of remote sensing techniques. A comparison of more carbon-absorbing capacities between the wards has been assessed through GIS techniques as well. Results show that the north-west part of Khulna city has some extent of carbon sinks. However, the lower portion in the southern part is congested with built-up areas and has a huge lacking of carbon sinks. The obtained amount of carbon about 9306992 kg, 7733698 kg, 7328298 kg, and 6030692 kg have been absorbed through respective trees, vegetations, soils, and water bodies which is about 31%, 25%, 24%, and 20% of total absorption, respectively. The result shows that carbon absorption capacity is very high in ward no. 3 whereas ward no. 1, 2, 4, 6, 9, 14, 16, and 31 are high carbon-absorbing wards. Ward no 8, 13, 15, 17, 18 and 24 are moderate, ward no 5, 10, 12, 21, 22, 25, 26, 27, 28, 29 and 30 are lower and ward no 7, 11, 19, 20 and 23 are very lower carbon-absorbing wards of Khulna city. The findings of this study opt to provide valuable insights amongst significant stakeholders, i.e., local people, urban planners, policy-makers, and non-government organizations regarding the emission and absorption of carbon.

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.