Browsing by Author "Pal, Subodh Chandra"

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A Critical Review and Prospect of NO2 and SO2 Pollution Over Asia
(Elsevier, 2023-06-10) Jion, Most. Mastura Munia Farjana; Jannat, Jannatun Nahar; Mia, Md. Yousuf; Ali, Md. Arfan; Islam, Md. Saiful; Ibrahim, Sobhy M.; Pal, Subodh Chandra; Islam, Aznarul; Sarker, Aniruddha; Malafaia, Guilherme; Bilal, Muhammad; Islam, Abu Reza Md Towfiqul
Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are two major atmospheric pollutants that significantly threaten human health, the environment, and ecosystems worldwide. Despite this, only some studies have investigated the spatiotemporal hotspots of NO2 and SO2, their trends, production, and sources in Asia. Our study presents a literature review covering the production, trends, and sources of NO2 and SO2 across Asian countries (e.g., Bangladesh, China, India, Iran, Japan, Pakistan, Malaysia, Kuwait, and Nepal). Based on the findings of the review, NO2 and SO2 pollution are increasing due to industrial activity, fossil fuel burning, biomass burning, heavy traffic movement, electricity generation, and power plants. There is significant concern about health risks associated with NO2 and SO2 emissions in Bangladesh, China, India, Malaysia, and Iran, as they pay less attention to managing and controlling pollution. Even though the lack of quality datasets and adequate research in most Asian countries further complicates the management and control of NO2 and SO2 pollution. This study has NO2 and SO2 pollution scenarios, including hotspots, trends, sources, and their influences on Asian countries. This study highlights the existing research gaps and recommends new research on identifying integrated sources, their variations, spatiotemporal trends, emission characteristics, and pollution level. Finally, the present study suggests a framework for controlling and monitoring these two pollutants' emissions.
A New Approach from Public Behavioral Attitudes and Perceptions Towards Microplastics: Influencing Factors, and Policy Proposals
(Elsevier, 2024-07-01) Al Masud, Abdulla; Islam, Abu Reza Md Towfiqul; Al Mamun, Abdullah; Alam, G.M. Monirul; Arabameri, Alireza; Bari, A.B.M. Mainul; Pal, Subodh Chandra; Rakib, Md Refat Jahan; Senapathi, Venkatramanan; Bodrud-Doza, Md.; Idris, Abubakr M.; Malafaia, Guilherme
This research paper addresses the urgent environmental concern of microplastic (MP) emissions, focusing on the behavioral attitudes and perceptions of the general populace in Shyamnagar Upazila, Bangladesh. Against the backdrop of escalating MP pollution globally, this study investigates the level of awareness and the factors influencing public engagement in mitigating MP prevalence. Leveraging survey data from 350 respondents, the ordered logistic regression (OLR) and boosted regression tree (BRT) models are employed for comprehensive data analysis. The findings expose a concerning lack of awareness about MPs, as only 12% of respondents possessed prior knowledge, and a notable 63% remained uninformed about MP pollution. The OLR model reveals a positive correlation between heightened awareness of MPs and an increased willingness to take action. Gender differences become evident, with women exhibiting greater willingness than men to mitigate MP emissions, and environmental practitioners displaying heightened motivation. The BRT model underscores construction materials and industrial pollution as the primary influential factors amplifying MP pollution. These insights not only illuminate the existing scenario but also provide a basis for fostering favorable behavioral attitudes and perceptions to mitigate the prevalence of MPs within the coastal milieu.
A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models
(Scopus, 2024) Alam, Md Mahfuz; Akter, Mst. Yeasmin; Reza, Abu; Islam, Md Towfiqul; Mallick, Javed; Kabir, Zobaidul; Chu, Ronghao; Arabameri, Alireza; Pal, Subodh Chandra; Masud, Md Abdullah Al; Costache, Romulus; Senapathi, Venkatramanan
Evapotranspiration (ETo) is a complex and non-linear hydrological process with a significant impact on efficient water resource planning and long-term management. The Penman-Monteith (PM) equation method, developed by the Food and Agriculture Organization of the United Nations (FAO), represents an advancement over earlier approaches for estimating ETo. Eto though reliable, faces limitations due to the requirement for climatological data not always available at specific locations. To address this, researchers have explored soft computing (SC) models as alternatives to conventional methods, known for their exceptional accuracy across disciplines. This critical review aims to enhance understanding of cutting-edge SC frameworks for ETo estimation, highlighting advancements in evolutionary models, hybrid and ensemble approaches, and optimization strategies. Recent applications of SC in various climatic zones in Bangladesh are evaluated, with the order of preference being ANFIS > Bi-LSTM > RT > DENFIS > SVR-PSOGWO > PSO–HFS due to their consistently high accuracy (RMSE and ). This review introduces a benchmark for incorporating evolutionary computation algorithms (EC) into ETo modeling. Each subsection addresses the strengths and weaknesses of known SC models, offering valuable insights. The review serves as a valuable resource for experienced water resource engineers and hydrologists, both domestically and internationally, providing comprehensive SC modeling studies for ETo forecasting. Furthermore, it provides an improved water resources monitoring and management plans.
A systematic review of the nexus between climate change and social media: present status, trends, and future challenges
(Scopus, 2024-10-14) Sultana, Bebe Chand; Prodhan, Md. Tabiur Rahman; Alam, Edris; Sohel, Md. Salman; Bari, A. B. M. Mainul; Pal, Subodh Chandra; Islam, Abu Reza Md. Towfiqul; Islam, Md. Kamrul
: Social media and climate change are some of the most controversial issues of the 21st century. Despite numerous studies, our understanding of current social media trends, popular hot topics, and future challenges related to climate change remains significantly limited. This research presents a systematic review of climate change and social media for the first time. Review the studies published between 2009 and 2022 in places like Google Scholar, Science Direct, Web-of-Science, Scopus, ResearchGate, and others. For this systematic review, we found 1,057 articles. Forty-five articles were the most relevant according to our goals and study design, which followed the PRISMA framework. The results of this review demonstrate that Twitter is the most popular platform. Every year, we identify rising trends in the number of publications. Past studies often focused on just one social media site, like Twitter (n = 26) or Facebook (n = 5). Although most studies focus on the United States, the study area is primarily “all over the world.” This study offers a theoretical framework by examining the relationship between social media platforms and the discourse surrounding climate change. It looked into how social media trends influence public perception, raise awareness, and spur action on climate change. In practical terms, the study focuses on important and trending topics like nonbelievers and climate change. The contribution consists of synthesizing the body of research, providing insights into the state of the digital world, and suggesting future lines of inquiry for the field of social media and climate change studies. We highlighted the studies’ quality assessment result of “moderate quality.” This systematic review provides information about how climate change is now portrayed on social media and lays the groundwork for further study in this area.
A systematic review of the nexus between climate change and social media: present status, trends, and future challenges
(Scopus, 2024-10-14) Sultana, Bebe Chand; Prodhan, Md. Tabiur Rahman; Alam, Edris; Sohel, Md. Salman; Bari, A. B. M. Mainul; Pal, Subodh Chandra; Islam, Md. Kamrul; Islam, Abu Reza Md. Towfiqul
Social media and climate change are some of the most controversial issues of the 21st century. Despite numerous studies, our understanding of current social media trends, popular hot topics, and future challenges related to climate change remains significantly limited. This research presents a systematic review of climate change and social media for the first time. Review the studies published between 2009 and 2022 in places like Google Scholar, Science Direct, Web-of-Science, Scopus, ResearchGate, and others. For this systematic review, we found 1,057 articles. Forty-five articles were the most relevant according to our goals and study design, which followed the PRISMA framework. The results of this review demonstrate that Twitter is the most popular platform. Every year, we identify rising trends in the number of publications. Past studies often focused on just one social media site, like Twitter (n = 26) or Facebook (n = 5). Although most studies focus on the United States, the study area is primarily “all over the world.” This study offers a theoretical framework by examining the relationship between social media platforms and the discourse surrounding climate change. It looked into how social media trends influence public perception, raise awareness, and spur action on climate change. In practical terms, the study focuses on important and trending topics like nonbelievers and climate change. The contribution consists of synthesizing the body of research, providing insights into the state of the digital world, and suggesting future lines of inquiry for the field of social media and climate change studies. We highlighted the studies’ quality assessment result of “moderate quality.” This systematic review provides information about how climate change is now portrayed on social media and lays the groundwork for further study in this area.
Anthropogenic Drivers Induced Desertification Under Changing Climate
(Elsevier, 2023-12-17) Pal, Subodh Chandra; Chatterjee, Uday; Chakrabortty, Rabin; Roy, Paramita; Chowdhuri, Indrajit; Saha, Asish; Islam, Abu Reza Md. Towfiqul; Alam, Edris; Islam, Md Kamrul
A study of the extended desertification due to anthropogenic causes under climate change (CC) associated with its impact is presented here. Desertification, the main environmental issue, severely impacts agricultural output, causing poverty and economic instability in a nation like India. The regional distribution of desertification was determined using the RF and MaxEnt models. The western, central, and southern portions of the nation are very high, high, and moderately susceptible to desertification, respectively, according to the RF model. The MaxEnt model indicates that the western, central, and southern parts of the country exhibit a significant susceptibility to desertification, with the eastern parts also showing a moderate level of vulnerability. The remaining portion of this region, mainly in the north, east, and northeast, is particularly resistant to desertification. The outcome demonstrated that the country's desertification process had expanded from the west to the south. However, there are some spatial differences associated with the mentioned part of the country. This relevant information is crucial for decision maker of this country to take suitable remedies in regard to the reduction of the intensity of desertification.
Application of Bagging and Boosting Ensemble Machine Learning Techniques for Groundwater Potential Mapping in a Drought-prone Agriculture Region of Eastern India
(Springer, 2024-09-02) Halder, Krishnagopal; Srivastava, Amit Kumar; Ghosh, Anitabha; Nabik, Ranajit; Pan, Subrata; Chatterjee, Uday; Bisai, Dipak; Pal, Subodh Chandra; Zeng, Wenzhi; Ewert, Frank; Gaiser, Thomas; Pande, Chaitanya Baliram; Islam, Abu Reza Md. Towfiqul; Alam, Edris; Islam, Md Kamrul
Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies. In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond.
Application of Optimal Subset Regression and Stacking Hybrid Models To Estimate COVID-19 Cases in Dhaka, Bangladesh
(Springer, 2023-08-16) Md, Abu Reza; Islam, Towfiqul; Elbeltagi, Ahmed; Mallick, Javed; Fattah, Md. Abdul; Roy, Manos Chandro; Pal, Subodh Chandra; Shahjaman, Md.; Patwary, Masum A.
The COVID-19 outbreaks revealed a severe healthcare crisis with many loopholes in the global healthcare system. It is more crucial to quantify COVID-19 cases when COVID-19 occurs in humid to semi-humid climatic conditions. There are issues with a lack of meteorological and air pollution data and future information on COVID-19 mortality, as is the case in Bangladesh. To deal with this issue, the present research aims to apply four single artificial intelligence models, including additive regression (AR), M5P tree (M5P), random subspace (RSS), and support vector machine (SVM), and construct their stacking hybrid ensemble models for predicting COVID-19 mortality cases at five sites in greater Dhaka City, Bangladesh. The proposed methods were developed using a total of eight input datasets that included climatic factors such as relative humidity, temperature, precipitation, wind speed, and air pollutants including sulfur dioxide (SO2), ozone (O3), carbon monoxide (CO), and nitrate oxide (NO2). Various input data combinations are appraised according to predictive performance, utilizing statistical tests and graphical presentation. The datasets were categorized into two classes (68:32) for model generation (training data) and model validation (testing data) with a fivefold cross-validation technique. Results show that SVM is superior to other AR, M5P, and RSS models (R2 testing = 0.86–0.91, MAE = 1.33–2.02, RMSE = 3.12–3.85, RAE% = 19.68–29.86, and RRSE% = 40.58–41.01). The sensitivity analysis findings reveal a higher sensitivity for all input parameters selected except CO in the predictive results. Relative humidity, wind speed, and SO2 were the three input parameters that most influenced the results of subset regression and sensitivity analysis. The SVM is a promising method because it can predict COVID-19 mortality in greater Dhaka City with fewer input parameters. The suggested model developed in this research produced satisfactory outcomes in COVID-19 mortality prediction. It will be a new method for future COVID-19 prevention for policymakers and health experts. Serious social concern and robust public health measures may lessen the environmental impact of COVID-19 cases.
Assessing metal(loid)s-Induced long-term spatiotemporal health risks in Coastal Regions, Bay of Bengal: A chemometric study
(Scopus, 2024) Aktar, Shammi; Islam, Abu Reza Md. Towfiqul; Mia, Md Yousuf; Jannat, Jannatun Nahar; Islam, Md Saiful; Masud, Md Abdullah Al; Idris, Abubakr M.; Pal, Subodh Chandra; Senapathi, Venkatramanan
Despite sporadic and irregular studies on heavy metal(loid)s health risks in water, fish, and soil in the coastal areas of the Bay of Bengal, no chemometric approaches have been applied to assess the human health risks comprehensively. This review aims to employ chemometric analysis to evaluate the long-term spatiotemporal health risks of metal(loid)s e.g., Fe, Mn, Zn, Cd, As, Cr, Pb, Cu, and Ni in coastal water, fish, and soils from 2003 to 2023. Across coastal parts, studies on metal(loid)s were distributed with 40% in the southeast, 28% in the south-central, and 32% in the southwest regions. The southeastern area exhibited the highest contamination levels, primarily due to elevated Zn content (156.8 to 147.2 mg/L for Mn in water, 15.3 to 13.2 mg/kg for Cu in fish, and 50.6 to 46.4 mg/kg for Ni in soil), except for a few sites in the south-central region. Health risks associated with the ingestion of Fe, As, and Cd (water), Ni, Cr, and Pb (fish), and Cd, Cr, and Pb (soil) were identified, with non-carcinogenic risks existing exclusively through this route. Moreover, As, Cr, and Ni pose cancer risks for adults and children via ingestion in the southeastern region. Overall non-carcinogenic risks emphasized a significantly higher risk for children compared to adults, with six, two-, and six-times higher health risks through ingestion of water, fish, and soils along the southeastern coast. The study offers innovative sustainable management strategies and remediation policies aimed at reducing metal(loid)s contamination in various environmental media along coastal Bangladesh.
Assessment of Soil Heavy Metal Pollution and Associated Ecological Risk of Agriculture Dominated Mid-Channel Bars in a Subtropical River Basin
(Springer Nature Limited, 2023-07-09) Hoque, Md. Mofizul; Islam, Aznarul; Islam, Abu Reza Md. Towfiqul; Pal, Subodh Chandra; Mahammad, Sadik; Alam, Edris
The elevated concentrations of heavy metals in soil considerably threaten ecological and human health. To this end, the present study assesses metals pollution and its threat to ecology from the mid-channel bar’s (char) agricultural soil in the Damodar River basin, India. For this, the contamination factor (CF), enrichment factor (EF), geoaccumulation index (Igeo), pollution index, and ecological risk index (RI) were measured on 60 soil samples at 30 stations (2 from each station, i.e., surface and sub-surface) in different parts of the mid-channel bar. The CF and EF indicate that both levels of char soil have low contamination and hence portray a higher potential for future enrichment by heavy metals. Moreover, Igeo portrays that soil samples are uncontaminated to moderately contaminated. Further, pollution indices indicate that all the samples (both levels) are unpolluted with a mean of 0.062 for surface soils and 0.048 for sub-surface soils. Both levels of the char have a low potentiality for ecological risk with an average RI of 0.20 for the surface soils and 0.19 for the sub-surface soils. Moreover, Technique for order preference by similarity to ideal solution (TOPSIS) indicates that the sub-surface soils have lower pollution than the surface soils. The geostatistical modeling reveals that the simple kriging technique was estimated as the most appropriate interpolation model. The present investigation exhibits that reduced heavy metal pollution is due to the sandy nature of soils and frequent flooding. However, the limited pollution is revealed due to the intensive agricultural practices on riverine chars. Therefore, this would be helpful to regional planners, agricultural engineers, and stakeholders in a basin area.
Ecosystem richness degradation assessment from elevated hydro-chemical properties of Chilka Lake, India
(Scopus, 2024-06-16) Ruidas, Dipankar; Pal, Subodh Chandra; Saha, Asish; Mandalb, Sudipto; Islam, Aznarul; Islam, Abu Reza Md. Towfiqul
A hydrogeochemical analysis of Chilka Lake Ramsar sites has been conducted to measure the surface water quality status and ecological suitability for aquatic habitats. Degree of contamination, water quality index and ecological risk index field-based techniques were employed to analyse the hydro-chemical properties using 48 water samples from across the entire Ramsar region. Among them, 14 predominant factors were identified. The Ramsar area is categorized into five water quality and ecological risk zones with reference to Indian and World Health Organization water quality standards. This field-based study reveals that in the southern, southeastern, and central parts, about 9.89% and 16.12% of areas experienced very poor and poor water quality standards, respectively. Thus, this research work will help government authorities and policymakers formulate appropriate strategies and regulations for controlling and reducing the contamination levels in the Chilka Ramsar region.
Evaluation of Groundwater Contamination and Associated Human Health Risk in a Water-Scarce Hard Rock-Dominated Region of India
(Elsevier, 2023-11-15) Biswas, Tanmoy; Pal, Subodh Chandra; Ruidas, Dipankar; Saha, Asish; Shit, Manisa; Islam, Abu Reza Md. Towfiqul; Islam, Aznarul; Costache, Romulus
Groundwater is the most precious resource on the earth's surface, providing fresh drinking water for human beings and supplying water for plants to survive. The shortage, unavailability, and pollution of fresh drinking water is an emerging issue in almost every part of the world. To this end, the present study intends to evaluate the quality of groundwater and the associated health hazard risk of the Bankura district. To conduct the present study, 55 groundwater samples were collected across the Bankura district with intensive field investigation in the dry season, 2021 to assess the groundwater quality and associated health risks. For this study, we have selected 15 groundwater causative parameters to seek the NO3−, F− and Fe heavy metal groundwater quality (GWQ) status and impact of GWQ on human health by utilizing the degree of contamination (CD), GWQI and human health hazard index. Gibbs's diagram confirmed that the source of the groundwater pollutants is more geogenic than anthropogenic inputs for the Bankura district. The study's findings indicate that the GWQ of 25% area of the Bankura district is poor and unsafe for drinking purposes. In comparison, 15% of the groundwater contains good quality and is safe for drinking, and various pollutants moderately contaminate the remaining areas. The human health hazard index exhibited the same pattern as the GWQI map of the Bankura district. This unique finding of the present study will be helpful for the decision-makers, local well-being authorities and disaster management team to manage and mitigate the issue of GWQ of Bankura district and surroundings more sustainably.
Extreme exposure of fluoride and arsenic contamination in shallow coastal aquifers of the Ganges delta, transboundary of the Indo-Bangladesh region
(2024-01-01) Ruidas, Dipankar; Pal, Subodh Chandra; Biswas, Tanmoy; Saha, Asish; Md. Towfiqul Islam, Abu Reza
Globally, shallow aquifer groundwater (GW) has been severely affected in recent decades for both geogenic and anthropogenic reasons. The hydro-geochemical characteristics of the GW change inconsistently with the addition of unwanted inorganic trace elements into the GW aquifer of the Indo-Bangladesh delta region (IBDR), such as arsenic (As) along with fluoride (F−) contamination. Contaminated GW can have a negative impact on drinking water supplies and agricultural output. GW pollution can have serious adverse effects on the environment and human health. Thus, the GW quality of this region is deteriorating progressively, and human health threatening by various life-threatening disorders. Hence, the current study concentrated on the GW quality evaluation and prediction of possible health issues in the IBDR due to elevated contamination of As along with F− within GW aquifers by considering sixteen causative. Field survey-based statistical methods such as entropy quality index (EWQI) combined with health risk index (HRI) was implemented for evaluating the As and F− sensitivity with the help of correlation testing and principal component analysis. The study's outcome explains that a substantial portion of the IBDR has been vastly experiencing inferior GW quality, environmental issues, and health-related problems in dry and wet seasons, correspondingly for As and F− exposure. Piper diagram verified the suitability of water that almost 55% of GW across the study area’s aquifers are unfit for drinking as well as cultivation of crops. Sensitivity analysis and the Monte Carlo simulation method were also applied to assess the contaminant's concentration level and probable health risk appraisal. The present study concludes that the elevated exposure of As and F− pollution has to be monitored regularly and prevent unwanted GW contamination through implementing sustainable approaches and policies to fulfil the sustainable development goal 6 (SDG-6) till 2030, ensuring the most basic human right of clean, safe, and hygienic water.
Flood Hazard Forecasting and Management Systems: A Review of State-of-the-art Modelling, Management Strategies and Policy-practice Gap
(Elsevier, 2024-06-15) Ruidas, Dipankar; Pal, Subodh Chandra; Saha, Asish; Roy, Paramita; Pande, Chaitanya B.; Islam, Abu Reza Md. Towfiqul; Islam, Aznarul
The effects of flood disasters on human society have now taken precedence in today's world; despite improvements in flood hazard and exposure models, there is still a shortage of knowledge regarding regional and temporal susceptibility patterns. Thus, building real-time flood prediction models for early warning to the public has become more popular over the years due to the frequent development of flood hazards around the world and their catastrophic impacts; the technique and ability of flood hazard modelling to accurately anticipate and identify flood-prone or affected locations has significantly improved, meeting the goal of policymakers. Till now, enormous state-of-the-art modelling approaches such as deep learning (DL), machine learning (ML) and metaheuristic models have been introduced for proper flood-prone area demarcation and early warning systems. Henceforth, our present research provides an understanding of the applicability, advantages, disadvantages, and uncertainties of the previously applied state of the modelling approaches based on the global climate change scenario; it also deals with flood-occurring drivers including hydrogeological, geomorphological, and socioeconomic perspectives; globally several developed and developing countries have employed different flood mitigation strategies but those are failed to fulfil expected outcomes due to a lack of knowledge on practical protection levels, suitable observation, surveillances, management plans, and passive funding sources for such techniques. This work will assist future researchers in creating notable flood hazard modelling techniques by considering current research constraints. This will serve as a valuable tool in the future and aid in closing the adopted policy practice gap.
Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning
(2024-03-14) Costache, Romulus; Pal, Subodh Chandra; B. Pande, Chaitanya; Md. Towfiqul Islam, Abu Reza; Alshehri, Fahad; Abdo, Hazem Ghassan
Among the various natural disasters that take place around the world, flood is considered to be the most extensive. There have been several floods in Buzău river basin, and as a result of this, the area has been chosen as the study area. For the purpose of this research, we applied deep learning and machine learning benchmarks in order to prepare flood potential maps at the basin scale. In this regard 12 flood predictors, 205 flood and 205 non-flood locations were used as input data into the following 3 complex models: Deep Learning Neural Network-Harris Hawk Optimization-Index of Entropy (DLNN-HHO-IOE), Multilayer Perceptron-Harris Hawk Optimization-Index of Entropy (MLP-HHO-IOE) and Stacking ensemble-Harris Hawk Optimization-Index of Entropy (Stacking-HHO-IOE). The flood sample was divided into training (70%) and validating (30%) sample, meanwhile the prediction ability of flood conditioning factors was tested through the Correlation-based Feature Selection method. ROC Curve and statistical metrics were involved in the results validation. The modeling process through the stated algorithms showed that the most important flood predictors are represented by: slope (importance ≈ 20%), distance from river (importance ≈ 17.5%), land use (importance ≈ 12%) and TPI (importance ≈ 10%). The importance values were used to compute the flood susceptibility, while Natural Breaks method was used to classify the results. The high and very high flood susceptibility is spread on approximately 35–40% of the study zone. The ROC Curve, in terms of Success, Rate shows that the highest performance was achieved FPIDLNN-HHO-IOE (AUC = 0.97), followed by FPIStacking-HHO-IOE (AUC = 0.966) and FPIMLP-HHO-IOE (AUC = 0.953), while the Prediction Rate indicates the FPIStacking-HHO-IOE as being the most performant model with an AUC of 0.977, followed by FPIDLNN-HHO-IOE (AUC = 0.97) and FPIMLP-HHO-IOE (AUC = 0.924).
Hydro-chemical Based Assessment of Groundwater Vulnerability in the Holocene Multi-aquifers of Ganges Delta
(Springer Nature, 2024-01-02) Saha, Asish; Pal, Subodh Chandra; Islam, Abu Reza Md. Towfiqul; Islam, Aznarul; Alam, Edris; Islam, Md. Kamrul
Determining the degree of high groundwater arsenic (As) and fluoride (F−) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F− concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F− contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.
Land Use and Climate Change-Induced Soil Erosion Mapping in a Sub-Tropical Environment
(Informa UK Limited, trading as Taylor & Francis Group., 2023-10-27) Pal, Subodh Chandra; Chakrabortty, Rabin; Islam, Abu Reza Md. Towfiqul; Roy, Paramita; Chowdhuri, Indrajit; Saha, Asish; Islam, Aznarul; Costache, Romulus; Alam, Edris
One of the most important aspects of the ‘sub-tropical’ monsoon-influenced environment is the issue of ‘soil erosion’ and its related ‘land degradation’. On the other hand, the climate in this area has become quite extreme. According to this viewpoint, it is important to research a future ‘soil erosion’ scenario in front of the probable effects of climate change and land use change. For the objective of assessing the extent of soil erosion in this area, this study took into account both the USLE and the RUSLE. Compared to the USLE that has been validated, RUSLE has a comparatively greater quantitative efficiency. In RUSLE, the ‘very high’ (>20) and ‘high’ (15–20) ‘soil erosion’ zones tend to be associated with the ‘north-western, western, south-western, and southern’ regions of the river basin. The ‘soil erosion’ that will occur in the future has been estimated by taking into account the projected rainfall, land use and land cover (LULC). ‘Soil erosion’ has increased from the previous time to the projected time. Predicted R factor values for SSP 585 range from 399.92 to 493.72. In addition, a growing erosion tendency associated with increased shared socio-economic pathways (SSPs) has been found.
Microplastics in sediment and surface water from an island ecosystem in Bay of Bengal
(2024-01-15) Mia, Md. Sonir; Md. Towfiqul Islam, Abu Reza; Ali, Mir Mohammad; Siddique, Md. Abu Bakar; Pal, Subodh Chandra; Idris, Abubakr M.; Senapathi, Venkatramanan
Microplastics (MPs) have garnered global attention as emerging pollutants in aquatic and terrestrial ecosystems. Despite their significance, studies on MP pollution have overlooked a biodiverse island ecosystem in the northeast Bay of Bengal. Hence, the current study is a pioneering effort to delve into this issue with the island. This research embodies the first comprehensive report exploring the presence of MP pollution in sediment and surface water and their influencing factors along Sandwip island in the northeast Bay of Bengal. The average MP concentration was 305 ± 37.16 (items/kg) in sediment and 106.14 ± 22.57 (items/m3) in surface water. Fragments emerged as the predominant type in sediment (78.77%) and surface water (54.64%) samples. Fourier Transform Infrared Spectroscopy identified three plastic polymers, the most abundant being polyethylene (56%) and polypropylene (41%). Anthropogenic activities, particularly fishing practices, improper waste disposal, and inadequate waste management strategies, were pinpointed as potential sources of MP contamination on the island. MP concentrations in water and sediment correlated positively with pH and organic matter (p < 0.000), indicating important factors influencing MP distribution. The spatial distribution and hotspots of MPs followed significant human routes. By shedding light on the extent of MPs' presence and their potential sources, this study contributes essential insights that can inform effective environmental management strategies for the island's future well-being.
Microplastics in the coral ecosystems: A threat which needs more global attention
(2024-03-01) Biswas, Tanmoy; Pal, Subodh Chandra; Saha, Asish; Ruidas, Dipankar; Shit, Manisa; Md. Towfiqul Islam, Abu Reza; Malafaia, Guilherme
Microplastics (MPs) are one of the leading pollutants on the earth's surface and are found almost everywhere, including in aquatic environments. Microplastic pollution (MPP) on the oceanic surface is causing widespread concern among scientists and researchers worldwide due to its life-threatening impact on underwater living organisms. Excessive production of MP components and unscientific disposal caused severe issues for the marine ecosystem. Invertebrate corals and coral reefs in tropical and sub-tropical countries have suffered immensely from MP pollution and have shown a gradually decreasing trend of coral reef concentration on the oceanic surface. The bioaccumulation and ingestion of MP debris by the coral polyps have hindered the growth of the corals and forced coral bleaching. Most of the previous studies on MP have focused on the sources and distribution of MP. However, little is known about coral bleaching and its adverse impacts on the coral ecosystem. Therefore, the present review focuses on identifying sources, their global distribution, and the adverse effects of MP on the marine ecosystem, with particular reference to corals and coral reef ecosystems. The current study's findings highlighted that only a few countries and a few researchers worldwide have worked with MP pollution and its life-threatening impact on coral ecosystems. Earlier researchers showed fish communities' vulnerability to MPP in the aquatic environment. Hence, the present work recommends researching MPP and the worldwide threats to coral ecosystems. Apart from this, the production of MP should be minimized after the identification of MP sources, regular monitoring of aquatic ecosystems, recycling of MP elements, and strict government policies to slow down the dreadful impact of MP on coral reef ecosystems and marine environments.
Personal Protective Equipment-Derived Pollution During COVID-19 Era: A Critical Review of Ecotoxicology Impacts, Intervention Strategies, and Future Challenges
(Elsevier, 2023-05-13) Hasan, Mehedi; Islam, Abu Reza Md. Towfiqul; Jion, Most. Mastura Munia Farjana; Rahman, Md. Naimur; Peu, Susmita Datta; Das, Arnob; Bari, A.B.M. Mainul; Islam, Md. Saiful; Pal, Subodh Chandra; Islam, Aznarul; Choudhury, Tasrina Rabia; Rakib, Md. Refat Jahan; Idris, Abubakr M.; Malafaia, Guilherme
During the COVID-19 pandemic, people used personal protective equipment (PPE) to lessen the spread of the virus. The release of microplastics (MPs) from discarded PPE is a new threat to the long-term health of the environment and poses challenges that are not yet clear. PPE-derived MPs have been found in multi-environmental compartments, e.g., water, sediments, air, and soil across the Bay of Bengal (BoB). As COVID-19 spreads, healthcare facilities use more plastic PPE, polluting aquatic ecosystems. Excessive PPE use releases MPs into the ecosystem, which aquatic organisms ingest, distressing the food chain and possibly causing ongoing health problems in humans. Thus, post-COVID-19 sustainability depends on proper intervention strategies for PPE waste, which have received scholarly interest. Although many studies have investigated PPE-induced MPs pollution in the BoB countries (e.g., India, Bangladesh, Sri Lanka, and Myanmar), the ecotoxicity impacts, intervention strategies, and future challenges of PPE-derived waste have largely gone unnoticed. Our study presents a critical literature review covering the ecotoxicity impacts, intervention strategies, and future challenges across the BoB countries (e.g., India (162,034.45 tons), Bangladesh (67,996 tons), Sri Lanka (35,707.95 tons), and Myanmar (22,593.5 tons). The ecotoxicity impacts of PPE-derived MPs on human health and other environmental compartments are critically addressed. The review's findings infer a gap in the 5R (Reduce, Reuse, Recycle, Redesign, and Restructure) Strategy's implementation in the BoB coastal regions, hindering the achievement of UN SDG-12. Despite widespread research advancements in the BoB, many questions about PPE-derived MPs pollution from the perspective of the COVID-19 era still need to be answered. In response to the post-COVID-19 environmental remediation concerns, this study highlights the present research gaps and suggests new research directions considering the current MPs' research advancements on COVID-related PPE waste. Finally, the review suggests a framework for proper intervention strategies for reducing and monitoring PPE-derived MPs pollution in the BoB countries.