Publications


2021

  1. Estimation of the healthcare waste generation during COVID-19 pandemic in Bangladesh.

    In Science of The Total Environment 2021. 152295; https://doi.org/10.1016/j.scitotenv.2021.152295.

    COVID-19 pandemic-borne wastes imposed a severe threat to human lives as well as the total environment. Improper handling of these wastes increases the possibility of future transmission. Therefore, immediate actions are required from both local and international authorities to mitigate the amount of waste generation and ensure proper disposal of these wastes, especially for low-income and developing countries where solid waste management is challenging. In this study, an attempt is made to estimate healthcare waste generated during the COVID-19 pandemic in Bangladesh. This study includes infected, ICU, deceased, isolated and quarantined patients as the primary sources of medical waste. Results showed that COVID-19 medical waste from these patients was 658.08 tons in March 2020 and increased to 16,164.74 tons in April 2021. A top portion of these wastes was generated from infected and quarantined patients. Based on survey data, approximate daily usage of face masks and hand gloves is also determined. Probable waste generation from COVID-19 confirmatory tests and vaccination has been simulated. Finally, several guidelines are provided to ensure the country's proper disposal and management of COVID-related wastes.

    @article{chowdhury2021estimation,
    title={Estimation of the healthcare waste generation during COVID-19 pandemic in Bangladesh},
    author={Chowdhury, Tamal and Chowdhury, Hemal and Rahman, Md Salman and Hossain, Nazia and Ahmed, Ashfaq and Sait, Sadiq M},
    journal={Science of The Total Environment},
    pages={152295},
    year={2021},
    publisher={Elsevier} }
    1. More crops whilst saving drops using an optimization model—A case from Bangladesh.
      Md. Reaz Akter Mullick, Md Salman Rahman, and Md. Panjarul Haque.

      In Irrigation and Drainage. 2021, 1-19; https://doi.org/10.1002/ird.2649.

      This study aims to determine the optimal use of irrigation water and irrigable area in the Karnafuli Irrigation Project (KIP) of Bangladesh by developing an optimization model using linear programming. The project consists of two units, namely, Halda and Ichamati, and the units are fed with Karnafuli river water. Required data were collected from several government offices and from the CMIP5 model. Considering existing cropping practice, irrigation supply, and future climate, the optimization model was run for four different scenarios. Climate change‐induced effects appeared as non‐significant in the optimization. In the Halda Unit, the existing cropping intensity is 136%, whereas optimal cropping intensity can be increased up to 200%. In the Ichamati Unit, optimal cropping intensity for all scenarios is in between 175% and 200%, where the existing intensity is 150%. An increase in cropping intensity in both the units in optimal scenarios results in higher benefits. At the same time, the implementation of the optimal situation in the KIP can save 20% of the diverted water, indicating the possibility of greater yields and subsequent greater benefits even if the existing water supply is saved. The outcomes of the research have been communicated to the regional extension office for implementation.

      @article{mullick2021more,
      title={More crops whilst saving drops using an optimization model—A case from Bangladesh},
      author={Mullick, Md Reaz Akter and Rahman, Md Salman and Haque, Md Panjarul},
      journal={Irrigation and Drainage},
      publisher={Wiley Online Library} }
      1. Design of a stand-alone energy hybrid system for a makeshift health care center: A case study.
        Tamal Chowdhury, Hemal Chowdhury, Samiul Hasan, Md Salman Rahman, M.M.K.Bhuiya, and Piyal Chowdhury.

        In Journal of Building Engineering. 2021, 40, 102346; https://doi.org/10.1016/j.jobe.2021.102346.

        Worldwide, health care sectors are experiencing massive pressure due to the emergence of COVID-19. Many temporary health care centers have been set up to treat infected patients. Increasing energy consumption in these centers is responsible for both rising energy demand and emission. Implementation of renewable energy-based hybrid stone-alone systems can play a vital role in optimizing increasing energy demand. The aim of this analysis is to design a stand-alone system for a temporary health care center located in Saint Martin Island, Bangladesh. This is the first study which highlights the power management of a hospital load. Homer Pro software is used to design the preliminary model, and the proposed configuration comprises PV/Converter/WIND/Battery/Generator. It is observed that the Levelized cost of the proposed system is $0.4688. This system's Levelized cost of energy (LCOE) is 35% lower than the solar home system (SHS). The payback period (PB), rate of investment (ROI), and internal rate of return (IROR) of the optimized system are seven years, 10, and 13%, respectively. The proposed configuration is environmentally sustainable as it generates 27% less CO2 than a diesel-based fuel system.

        @article{adnan2020improving,
        title={Improving spatial agreement in machine learning-based landslide susceptibility mapping},
        author={Adnan, Mohammed Sarfaraz Gani and Rahman, Md Salman and Ahmed, Nahian and Ahmed, Bayes and Rabbi, Md and Rahman, Rashedur M and others},
        journal={Remote Sensing},
        volume={12},
        number={20},
        pages={3347},
        year={2020},
        publisher={Multidisciplinary Digital Publishing Institute} }

      2. Biofuel production from food waste biomass and application of machine learning for process management.

        In Advanced Technology for the Conversion of Waste into Fuels and Chemicals, Volume 1: Biological Processes. Chapter 3, Woodhead Publishing, 2021; https://doi.org/10.1016/B978-0-12-823139-5.00004-6.

        Food waste (FW) utilization has been harnessed in the recent decades for biofuel production worldwide due to sustainable environmental concern. To attain the upcoming circular economy goal, FW sources have been emphasized notably for biofuel production in several regions of the world. Currently, research has been focused on the advancement of FW conversion technologies. The application of machine learning has been manifested as an eminent innovation to encounter this phenomenon. In many regions worldwide, FWs generated from households, restaurants are dumped in landfills or incinerated to recover energy circular economy and sustainable environment. Therefore, this chapter demonstrated the global FW scenario, related concerns, and several biofuels generated from waste. Various conversion technologies for FW conversion to biofuel production such as biodiesel, bioethanol, methane, hydrogen have been extensively elaborated in this chapter. This chapter also provides a detailed overview of machine learning in the prediction of waste generation. Hence, the prospect of numerous machine learning mechanisms in FW prediction and bioenergy yield has been discussed in this chapter. The overview of this chapter concluded that FW pretreatment to the end products utilization through integrated and machine learning technology approach can play a positive role soon to mitigate the high demand of biofuel and minimize environmental pollution.

        @incollection{chowdhury2021biofuel,
        title={Biofuel production from food waste biomass and application of machine learning for process management},
        author={Chowdhury, Hemal and Chowdhury, Tamal and Barua, Pranta and Rahman, Md Salman and Hossain, Nazia and Khan, Anish},
        booktitle={Advanced Technology for the Conversion of Waste into Fuels and Chemicals},
        pages={77--99},
        year={2021},
        publisher={Elsevier} }

      3. Membrane-based technologies for industrial wastewater treatment and resource recovery.

        In Membrane-Based Hybrid Processes for Wastewater Treatment. Chapter 19, Elsevier, 2021; https://doi.org/10.1016/B978-0-12-823804-2.00005-7.

        Water scarcity for industrial utilization has been appeared as a global issue in this modern era. Different types of wastewater containing heavy metals are continuously contaminating water. To resolve this contamination, various technologies have been adopted to reduce and among them membrane technologies have been considered more effective compared to conventional wastewater treatment technologies due to the isolation capability of nutrients and heavy metals at low concentration. This study demonstrated the comprehensive analysis on the numerous membrane technologies for wastewater pretreatment processes. Furthermore, recovering valuable resources such as nutrients through membrane technologies has been outlined in this chapter what could lead to economic and environmental feasibility. The overview of this chapter summarized that wastewater treatment and resource recovery via implementations of membrane technologies can play an excellent role in near future effectively for wastewater treatment, purification, and resource recovery in chemical, biochemical, food, and manufacturing industrial sector.

        @incollection{chowdhury2021membrane,
        title={Membrane-based technologies for industrial wastewater treatment and resource recovery},
        author={Chowdhury, Tamal and Chowdhury, Hemal and Miskat, Monirul Islam and Rahman, Md Salman and Hossain, Nazia},
        booktitle={Membrane-Based Hybrid Processes for Wastewater Treatment},
        pages={403--421},
        year={2021},
        publisher={Elsevier} }

      4. 2020

        1. An overview of the hydropower production potential in Bangladesh to meet the energy requirements.

          In Environmental Engineering Research. 2020, 26(6), 200514; https://doi.org/10.4491/eer.2020.514.

          Current environmental catastrophes generating from fossil fuel power generation has attracted the attention of energy planners to look for sustainable energy sources. Hydropower is one of the oldest energy sources that have been utilized all over the world to generate electricity, especially in remote areas. Being one of the most densely populated countries, the majority of power demand is fulfilled from fossil fuel. Despite having lots of rivers, Bangladesh has not explored its true potential. So, this paper presents a comprehensive review of the current hydropower potential in Bangladesh. Locations having hydropower potential is evaluated. Different technologies used for hydropower generation have been reviewed. Moreover, global hydropower potential has also been discussed in this study. Based on the economic and environmental study, it is found that small scale hydropower is most feasible in Bangladesh to provide sustainable energy. With a reasonable flow rate, 232 rivers of Bangladesh can be utilized small scale hydropower generation as well as ensuring energy security for remote people. The current study is believed to provide useful information in advancing the generation of hydropower based electricity in Bangladesh.

          @article{islam2020overview,
          title={An overview of the hydropower production potential in Bangladesh to meet the energy requirements},
          author={Islam Miskat, Monirul and Ahmed, Ashfaq and Rahman, Md and Chowdhury, Hemal and Chowdhury, Tamal and Chowdhury, Piyal and M Sait, Sadiq and Park, Young-Kwon and others},
          journal={Environmental Engineering Research},
          year={2020},
          publisher={The Korean Society of Environmental Engineers} }

        2. Improving spatial agreement in machine learning-based landslide susceptibility mapping.
          Mohammed Sarfaraz Gani Adnan, Md Salman Rahman, Nahian Ahmed, Bayes Ahmed, Md. Fazleh Rabbi, and Rashedur M. Rahman.

          In Remote Sensing. 2020, 12(20), 3347; https://doi.org/10.3390/rs12203347.

          Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions.

          @article{adnan2020improving,
          title={Improving spatial agreement in machine learning-based landslide susceptibility mapping},
          author={Adnan, Mohammed Sarfaraz Gani and Rahman, Md Salman and Ahmed, Nahian and Ahmed, Bayes and Rabbi, Md and Rahman, Rashedur M and others},
          journal={Remote Sensing},
          volume={12},
          number={20},
          pages={3347},
          year={2020},
          publisher={Multidisciplinary Digital Publishing Institute} }

        2019

        1. Effect of climate change to irrigation water requirement in an irrigation project of Bangladesh.
          Md Salman Rahman, Md. Reaz Akter Mullick, and Md Panjarul Haque.

          In American Geophysical Union (AGU) Fall Meeting 2019.

          Change in climatic condition results in a significant change to water resources and repercussion to food security and socio-economic condition. Bangladesh is one of the most climate vulnerable countries in the world. This research aims to investigate the effect of climate change to crop water requirement and subsequently crop production in an irrigation project in Bangladesh. The project was established in 2003 and with time the efficiency of this irrigation project has declined; however, people think of severe climate change induced impact behind the low performance of KIP. In this study, the trends of the climatic parameters, ETo, and crop water requirement is estimated month wise by MAKESENS trend model. Two climate change scenarios i.e. RCP-2.6 and RCP-4.5 projected data up to 2099 are used in this study. The trend analysis of historical rainfall (1961-2015) was studied with the Mann Kendall test and Sen's slope method after pre-whitening the data. No significance trend is observed in monthly and annual basis, except for rainfall in May which shows an increasing trend of 1.7 mm/year. In the project area, the month of May is a very less critical period of crop irrigation water requirement. Thus, Climate change induced an effect on rainfall in the KIP is very low. The irrigation project has two units. In Unit 1 (Halda) during Robi season, crops' irrigation water requirement except for mustard was slightly increased due to the decrease of rainfall and increase of ET0 for both RCP-2.6 and RCP-4.5 Scenarios. However, in Unit 2 (Ichamati) there was non-significance in net irrigation water requirement (1990-2015) of all crops at 5% and 1% level of significance. The net irrigation water requirement of Ichamati unit crops was determined for RCP-2.6 and RCP-4.5 climate change scenarios and the baseline net irrigation water requirement was calculated averaging the net irrigation requirement over the period of 1990-2015. Climate change would have a little effect on overall crop water requirement and related crop production. The analysis shows that it is a myth and climate actually has a very insignificant effect on the irrigation water requirement for the study site. Increase in its efficiency, therefore, requires focusing more on the management and distribution of water in KIP rather focusing on climate change.

          @inproceedings{rahman2019effect,
          title={Effect of Climate Change to Irrigation Water Requirement in an Irrigation Project of Bangladesh},
          author={Rahman, Md Salman and Mullick, Md Reaz and Haque, Md Panjarul},
          booktitle={AGU Fall Meeting Abstracts},
          volume={2019},
          pages={PP43D-1621},
          year={2019}
          }

        2. Climate change induced disaster and adaption strategy at coastal region of Bangladesh: a case study on saint martin island.
          Emon Roy, Md Salman Rahman, and Nadia Sultana Nisha.

          In American Geophysical Union (AGU) Fall Meeting 2019.

          Climate change is one of the most challenging issues in the present world especially in the coastal region of Bangladesh. This research will help to find out various problems that people of Saint Martin Island experience due to climate change as well as it will be useful to find out ways where advance adaptive measures can be taken. Using the primary data that are collected through direct observation, questionnaire survey, in-depth interview, Focus Group Discussion (FGD), Key Informant Interview along with the secondary data from different sources including government and non-government organization, this study focuses towards exploring the adaptation processes used by local people as well as government and NGOs. Results of the field survey show that cyclone is the major disaster on this island and 60% disaster happening in the rainy season indicates it as the most hazardous season. Besides, more than one third of total respondents have faced salinity problem and near half the respondents mentioned that sea level is rising gradually every year. Though national, international and government agencies send help in order to improve the vulnerable condition, their help goes in vain due to extreme climatic conditions along with inappropriate distribution, insufficiency and low quality of supplied goods and corruption existing in some organization. Coral reefs, which are highly sensitive to small changes in water temperature, suffered the worst bleaching ever recorded in 1998, with some areas seeing bleach rates of 70 percent and is expected to increase due to rise of sea temperature. Sea level rise, cyclone, storm surge affect the main land of St. Martin's by increasing the beach area day by day and people are losing their agricultural land. Due to salinity intrusion, respondents suffer from drinking, washing, bathing and cooking water and many water borne diseases like skin diseases, typhoid and fever have been the cause of suffering of local inhabitants. These findings suggests that, the adaptive capacity in the study area is not sufficient to cope with the changing environment and adaptability measures should be taken to resist the problems which are highlighted in the study to help the inhabitants to improve education, income, technology and cultivation in the adverse climate condition.

          @inproceedings{roy2019climate,
          title={Climate Change Induced Disaster and Adaption Strategy at Coastal Region of Bangladesh: a Case Study on Saint Martin Island},
          author={Roy, Emon and Rahman, Md Salman and Nisha, Nadia Sultana},
          booktitle={AGU Fall Meeting Abstracts},
          volume={2019},
          pages={NH23B--1004},
          year={2019}
          }
        3. Seasonal weather prediction for Bangladesh based on ENSO condition.
          Md Salman Rahman, Rupom Kanti Dhar, and Md Reaz Akter Mullick.

          In American Geophysical Union (AGU) Fall Meeting 2019.

          Bangladesh, a South Asian developing country is prone to various natural hazards like cyclone, flood, drought, etc. The adverse effects from the disasters are often augmented by the dense population and the indigent economy of this country. Moreover, the global climate change triggers some major changes in the country's average seasonal temperature and rainfall resulting in significant loss to socio-economy. The El Nino Southern Oscillation (ENSO), a recurring climate pattern of the Pacific Ocean which is proven to have great influence over the Pacific region as well as the global climate. ENSO cycle has been successfully used to characterize the hydrological changes of some particular parts of the USA and also some other parts of the world. This research aims to investigate any existing correlation between the ENSO cycle and the hydro-climatic events in Bangladesh. At the same time question arises on whether the correlation can be used for the seasonal weather forecast. Both events based as well as statistical analysis have been carried out to examine the possible correlation between them. Bangladesh Meteorological Department (BMD), Bangladesh Bureau of Statistics and The National Oceanic and Atmospheric Administration (NOAA) are the primary sources of the Rainfall, temperature, flood, cyclone and Southern Oscillation Index (SOI) data which are arranged according to particular ENSO phases and seasonal extent. The analyses show that the hydro-climatic events in Bangladesh particularly its extremities can be interpreted with ENSO phases. Extreme rainfall and flood intensity increase with the strength of the cool phase of ENSO ( La Nina). Severe Cyclonic Storm with Hurricane Intensity hits the coastal area during the pre-monsoon season in ENSO warm phase (El Nino) and during the post-monsoon season in ENSO cool phase(La Nina) more frequently. The findings will help to understand the trends and variability of these hydro-climatic events and based on these, seasonal weather can be predicted. A successful seasonal weather prediction would be proven very useful for the country's agriculture and related as well as for developing hazard management schemes.

          @inproceedings{rahman2019seasonal,
          title={Seasonal Weather Prediction for Bangladesh Based on ENSO Condition},
          author={Rahman, Md Salman and Dhar, Rupom Kanti and Mullick, Md Reaz Akter},
          booktitle={AGU Fall Meeting Abstracts},
          volume={2019},
          pages={A21H--2722},
          year={2019} }
        4. Sustainability impact on Bangladesh due to influx of the Rohingya immigrants.
          Md Salman Rahman and Nadia Sultana Nisha.

          In International Conference on the Rohingya Crisis in Comparative Perspective, UCL Institute for Risk and Disaster Reduction, University College London, UK 2019.

          The aim of the research is to focus on sustainability impact due to commencement of Rohingya people on local inhabitants of Ukhiya Upazila, Cox’s Bazar. The research work is designed to obtain information about various negative consequence both about human life and environmental sustainability that’s the local people suffer due to sudden mass migration of Rohingya people and a prediction about the future negative event that may make possible to occur if proper precaution will not undertake. All the data used are primary and collected through Questionnaire, Focused Group Discussion, Key Informants and analysed from a spatial point of view. The research result indicates that not only the standard living of local inhabitants is lowered, but also the cost of necessary accommodates and transport fair also increased. The immigrant’s people are the main responsible for breaking law and order situation by involving in various social and political crime. Besides this, immigrants also responsible for degrading the environment by various means such as hill destruction by cutting the tree, clearing forest for the nourishment of their fuel necessities. Many communicable disease are being spread from Rohingya to local people. The overall activity of the Rohingya immigrants has a hazardous impact on the overall sustainability of local people as well as Bangladesh. In future, such mass migration is possible to occur in any part of the world, and this research will help to predict sustainability impact due to future mass migration.

      © Copyright 2020 Salman Rahman.