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Global fossil fuel reserves are declining due to differential uses, especially for power generation. Everybody can help to do their bit for the environment by using solar energy. Geographically, Bangladesh is a potential zone for harnessing solar energy. In March 2021, the renewable generation capacity in Bangladesh amounted to 722.592 MW, including 67.6% from solar, 31.84% from hydro, and 0.55% from other energy sources, including wind, biogas, and biomass, where 488.662 MW of power originated from over 6 million installed solar power systems. Concurrently, over 42% of rural people still suffer from a lack of electricity, where solar energy can play a vital role. This paper highlights the present status of various forms of solar energy progress in Bangladesh, such as solar parks, solar rooftops, solar irrigation, solar charging stations, solar home systems, solar-powered telecoms, solar street lights, and solar drinking water, which can be viable alternative sources of energy. This review will help decision-makers and investors realize Bangladesh’s up-to-date solar energy scenario and plan better for the development of a sustainable society.

Global fossil fuel reserves are declining due to differential uses, especially for power generation. Everybody can help to do their bit for the environment by using solar energy. Geographically, Bangladesh is a potential zone for harnessing solar energy. In March 2021, the renewable generation capacity in Bangladesh amounted to 722.592 MW, including 67.6% from solar, 31.84% from hydro, and 0.55% from other energy sources, including wind, biogas, and biomass, where 488.662 MW of power originated from over 6 million installed solar power systems. Concurrently, over 42% of rural people still suffer from a lack of electricity, where solar energy can play a vital role. This paper highlights the present status of various forms of solar energy progress in Bangladesh, such as solar parks, solar rooftops, solar irrigation, solar charging stations, solar home systems, solar-powered telecoms, solar street lights, and solar drinking water, which can be viable alternative sources of energy. This review will help decision-makers and investors realize Bangladesh’s up-to-date solar energy scenario and plan better for the development of a sustainable society.

The U.S. Environmental Protection Agency’s (EPA) Superfund—the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) database—has collected and built an open-source database based on nearly 2000 US soil remediation cases since 1980, providing detailed information and references for researchers worldwide to carry out remediation work. However, the cases were relatively independent to each other, so the whole database lacks systematicness and instructiveness to some extent. In this study, the basic features of all 144 soil remediation projects in four major oil-producing states (California, Texas, Oklahoma and Alaska) were extracted from the CERCLA database and the correlations among the pollutant species, pollutant site characteristics and selection of remediation methods were analyzed using traditional and machine learning techniques. The Decision Tree Classifier was selected as the machine learning model. The results showed that the growth of new contaminated sites has slowed down in recent years; physical remediation was the most commonly used method, and the probability of its application is more than 80%. The presence of benzene, toluene, ethylbenzene and xylene (BTEX) substances and the geographical location of the site were the two most influential factors in the choice of remediation method for a specific site; the maximum weights of these two features reaches 0.304 and 0.288.

The U.S. Environmental Protection Agency’s (EPA) Superfund—the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) database—has collected and built an open-source database based on nearly 2000 US soil remediation cases since 1980, providing detailed information and references for researchers worldwide to carry out remediation work. However, the cases were relatively independent to each other, so the whole database lacks systematicness and instructiveness to some extent. In this study, the basic features of all 144 soil remediation projects in four major oil-producing states (California, Texas, Oklahoma and Alaska) were extracted from the CERCLA database and the correlations among the pollutant species, pollutant site characteristics and selection of remediation methods were analyzed using traditional and machine learning techniques. The Decision Tree Classifier was selected as the machine learning model. The results showed that the growth of new contaminated sites has slowed down in recent years; physical remediation was the most commonly used method, and the probability of its application is more than 80%. The presence of benzene, toluene, ethylbenzene and xylene (BTEX) substances and the geographical location of the site were the two most influential factors in the choice of remediation method for a specific site; the maximum weights of these two features reaches 0.304 and 0.288.

In recent years, prices for photovoltaics have fallen steadily and the demand for sustainable energy has increased. Consequentially, the assessment of roof surfaces in terms of their suitability for PV (Photovoltaic) installations has continuously gained in importance. Several types of assessment approaches have been established, ranging from sampling to complete census or aerial image analysis methodologies. Assessments of rooftop photovoltaic potential are multi-stage processes. The sub-task of examining the photovoltaic potential of individual rooftops is crucial for exact case study results. However, this step is often time-consuming and requires lots of computational effort especially when some form of intelligent classification algorithm needs to be trained. This often leads to the use of sampled rooftop utilization factors when investigating large-scale areas of interest, as data-driven approaches usually are not well-scalable. In this paper, a novel neighbourhood-based filtering approach is introduced that can analyse large amounts of irradiation data in a vectorised manner. It is tested in an application to the city of Giessen, Germany, and its surrounding area. The results show that it outperforms state-of-the-art image filtering techniques. The algorithm is able to process high-resolution data covering 1 km2 within roughly 2.5 s. It successfully classifies rooftop segments which are feasible for PV installations while omitting small, obstructed or insufficiently exposed segments. Apart from minor shortcomings, the approach presented in this work is capable of generating per-rooftop PV potential assessments at low computational cost and is well scalable to large scale areas.

In recent years, prices for photovoltaics have fallen steadily and the demand for sustainable energy has increased. Consequentially, the assessment of roof surfaces in terms of their suitability for PV (Photovoltaic) installations has continuously gained in importance. Several types of assessment approaches have been established, ranging from sampling to complete census or aerial image analysis methodologies. Assessments of rooftop photovoltaic potential are multi-stage processes. The sub-task of examining the photovoltaic potential of individual rooftops is crucial for exact case study results. However, this step is often time-consuming and requires lots of computational effort especially when some form of intelligent classification algorithm needs to be trained. This often leads to the use of sampled rooftop utilization factors when investigating large-scale areas of interest, as data-driven approaches usually are not well-scalable. In this paper, a novel neighbourhood-based filtering approach is introduced that can analyse large amounts of irradiation data in a vectorised manner. It is tested in an application to the city of Giessen, Germany, and its surrounding area. The results show that it outperforms state-of-the-art image filtering techniques. The algorithm is able to process high-resolution data covering 1 km2 within roughly 2.5 s. It successfully classifies rooftop segments which are feasible for PV installations while omitting small, obstructed or insufficiently exposed segments. Apart from minor shortcomings, the approach presented in this work is capable of generating per-rooftop PV potential assessments at low computational cost and is well scalable to large scale areas.

The united nations educational, scientific and cultural organization (UNESCO) considers the historic urban landscapes as the world heritages. Managing historic city centers and maintaining historic cores are the emerging challenges for sustainable urban planning. Today, the historic cores form an important part of the economic, social, environmental, and physical assets and capacities of contemporary cities, and play a strategic role in their development. One of the most important approaches to the development of central textures, especially in historical and cultural cities, is the sustainable urban regeneration approach, which encompasses all aspects of sustainability, such as the economic, social, cultural and environmental aspects. To maintain sustainability and regeneration of historic cores of cities, it is necessary to provide insight into the underlying characteristics of the local urbanization. Furthermore, the fundamental assets are to be investigated as indicators of sustainable regeneration and drivers of urban development. In the meantime, a variety of research and experience has taken place around the world, all of which has provided different criteria and indicators for the development of strategies for the historic cores of cities. The present study, through a meta-analytic and survey method, analyzing the experience and research reported in 139 theoretical and empirical papers in the last twenty years, seeks to provide a comprehensive conceptual model taking into account the criteria and indices of sustainable regeneration in historic cores of cities. The quality of the survey has been ensured using the preferred reporting items for systematic reviews and meta-analysis (PRISMA).

The united nations educational, scientific and cultural organization (UNESCO) considers the historic urban landscapes as the world heritages. Managing historic city centers and maintaining historic cores are the emerging challenges for sustainable urban planning. Today, the historic cores form an important part of the economic, social, environmental, and physical assets and capacities of contemporary cities, and play a strategic role in their development. One of the most important approaches to the development of central textures, especially in historical and cultural cities, is the sustainable urban regeneration approach, which encompasses all aspects of sustainability, such as the economic, social, cultural and environmental aspects. To maintain sustainability and regeneration of historic cores of cities, it is necessary to provide insight into the underlying characteristics of the local urbanization. Furthermore, the fundamental assets are to be investigated as indicators of sustainable regeneration and drivers of urban development. In the meantime, a variety of research and experience has taken place around the world, all of which has provided different criteria and indicators for the development of strategies for the historic cores of cities. The present study, through a meta-analytic and survey method, analyzing the experience and research reported in 139 theoretical and empirical papers in the last twenty years, seeks to provide a comprehensive conceptual model taking into account the criteria and indices of sustainable regeneration in historic cores of cities. The quality of the survey has been ensured using the preferred reporting items for systematic reviews and meta-analysis (PRISMA).