Browsing by Author "Islam, Aznarul"

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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.
Assessing Lake Water Quality During COVID-19 Era Using Geospatial Techniques and Artificial Neural Network Model
(Springer, 2023-04-24) Mohinuddin, S.K.; Sengupta, Soumita; Sarkar, Biplab; Saha, Ujwal Deep; Islam, Aznarul; Islam, Abu Reza Md Towfiqul; Hossain, Zakir Md; Mahammad, Sadik; Ahamed, Taushik; Mondal, Raju; Zhang, Wanchang; Basra, Aimun
The present study evaluates the impact of the COVID-19 lockdown on the water quality of a tropical lake (East Kolkata Wetland or EKW, India) along with seasonal change using Landsat 8 and 9 images of the Google Earth Engine (GEE) cloud computing platform. The research focuses on detecting, monitoring, and predicting water quality in the EKW region using eight parameters—normalized suspended material index (NSMI), suspended particular matter (SPM), total phosphorus (TP), electrical conductivity (EC), chlorophyll-α, floating algae index (FAI), turbidity, Secchi disk depth (SDD), and two water quality indices such as Carlson tropic state index (CTSI) and entropy‑weighted water quality index (EWQI). The results demonstrate that SPM, turbidity, EC, TP, and SDD improved while the FAI and chlorophyll-α increased during the lockdown period due to the stagnation of water as well as a reduction in industrial and anthropogenic pollution. Moreover, the prediction of EWQI using an artificial neural network indicates that the overall water quality will improve more if the lockdown period is sustained for another 3 years. The outcomes of the study will help the stakeholders develop effective regulations and strategies for the timely restoration of lake water quality.
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.
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.
Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta
(2024-01-13) Saha, Asish; Chandra Pal, Subodh; Md. Towfiqul Islam, Abu Reza; Islam, Aznarul; Alam, Edris; Kamrul Islam, Md.
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.
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.
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.
Perturbation in meander behaviour of a subtropical river in India under variable natural and anthropogenic controls
(2024-12-15) Ghosh, Susmita; Islam, Aznarul; Das, Balai Chandra; Pal, Subodh Chandra; Md Towfiqul Islam, Abu Reza; Mallick, Sahil
Meander morphology is perturbed worldwide by human interventions to a certain extent in the Anthropocene. To this end, the present study aims to investigate the meander behaviour, including its deformations, of the Kopai River, a subtropical river in India in the last 50 years (1970–2020). A total of 394 loops (134 loops each in 1970 and 1995 and 126 loops in 2020) were selected for the four river reaches – the upper, middle-upper, middle-lower, and lower reaches. The meandering behaviour was assessed using Mueller's sinuosity index (SI), meander form index, meander shape index, and radius/wavelength ratio. The study found a higher meandering tendency in the lower and upper reaches controlled by fluvial hydraulics, and topographic factors. However, the middle reaches were comparatively stable. The meander loops along the middle reaches regular meander is the dominant meander type, however, more than 50% of meander loops for the lower reaches were intense meander types. Positive extensions were the predominant type of meander deformation in all the reaches. However, few negative extensions are found in the middle-upper and lower reaches, while irregular changes and cut-offs were only found in the lower reach. Thus, the upper and lower reaches exhibited more natural controls dotted with regular meander progression. The middle reaches characterized by higher Bouguer anomaly and steep basement gradient led to channel confinement. Besides, profuse sand mining and the brick kiln industries are perturbing the meander beahviour through human channel straightening, dwarfing the meander evolution in the middle reaches.
Predicting Groundwater Phosphate Levels in Coastal Multi-aquifers A Geostatistical and Data-driven Approach
(Elsevier, 2024-11-15) Abdullah-Al Mamun, Md.; Islam, Abu Reza Md Towfiqul; Aktar, Mst. Nazneen; Uddin, Md Nashir; Islam, Md. Saiful; Pal, Subodh Chandra; Islam, Aznarul; Bari, A.B.M. Mainul; Idris, Abubakr M.; Senapathi, Venkatramanan
Even if you want to make a profit from cryptocurrency, you are worried that you will lose money, and it is difficult to afford it. There are a vast number of papers that study such unpredictable price fluctuations of cryptocurrency. Currently, it is mainstream to use learning deep to predict the price of cryptocurrency. The goal of this research is to predict the price of cryptocurrency over the long-term using deep learning. The algorithms used are LSTM, GRU, and Bi-LSTM. The targeted cryptocurrencies are Bitcoin, Ethereum, Litecoin, and Cardano. Finally, we will compare it with previous research and verify the performance of our model.
Predicting groundwater phosphate levels in coastal multi-aquifers: A geostatistical and data-driven approach
(2024-11-25) Abdullah-Al Mamun, Md.; Md Towfiqul Islam, Abu Reza; Aktar, Mst. Nazneen; Uddin, Md Nashir; Islam, Md. Saiful; Chandra Pal, Subodh; Islam, Aznarul
The groundwater (GW) resource plays a central role in securing water supply in the coastal region of Bangladesh and therefore the future sustainability of this valuable resource is crucial for the area. However, there is limited research on the driving factors and prediction of phosphate concentration in groundwater. In this work, geostatistical modeling, self-organizing maps (SOM) and data-driven algorithms were combined to determine the driving factors and predict GW phosphate content in coastal multi-aquifers in southern Bangladesh. The SOM analysis identified three distinct spatial patterns: K+single bondNa+single bondpH, Ca2+single bondMg2+single bondNO₃−, and HCO₃−single bondSO₄2−single bondPO43−single bondF−. Four data-driven algorithms, including CatBoost, Gradient Boosting Machine (GBM), Long Short-Term Memory (LSTM), and Support Vector Regression (SVR) were used to predict phosphate concentration in GW using 380 samples and 15 prediction parameters. Forecasting accuracy was evaluated using RMSE, R2, RAE, CC, and MAE. Phosphate dissolution and saltwater intrusion, along with phosphorus fertilizers, increase PO43− content in GW. Using input parameters selected by multicollinearity and SOM, the CatBoost model showed exceptional performance in both training (RMSE = 0.002, MAE = 0.001, R2 = 0.999, RAE = 0.057, CC = 1.00) and testing (RMSE = 0.001, MAE = 0.002, R2 = 0.989, RAE = 0.057, CC = 0.998). Na+, K+, and Mg2+ significantly influenced prediction accuracy. The uncertainty study revealed a low standard error for the CatBoost model, indicating robustness and consistency. Semi-variogram models confirmed that the most influential attributes showed weak dependence, suggesting that agricultural runoff increases the heterogeneity of PO43− distribution in GW. These findings are crucial for developing conservation and strategic plans for sustainable utilization of coastal GW resources.
Source Identification and Potential Health Risks from Elevated Groundwater Nitrate Contamination in Sundarbans Coastal Aquifers, India
(Nature Publishing Group, 2024-02-22) Pal, Subodh Chandra; Biswas, Tanmoy; Jaydhar, Asit Kumar; Ruidas, Dipankar; Saha, Asish; Chowdhuri, Indrajit; Mandal, Sudipto; Islam, Aznarul; Islam, Abu Reza Md.Towfqul; Pande, Chaitanya B.; Alam, Edris; Islam, Md Kamrul
In recent years groundwater contamination through nitrate contamination has increased rapidly in the managementof water research. In our study, fourteen nitrate conditioning factors were used, and multi-collinearity analysis is done. Among all variables, pH is crucial and ranked one, with a value of 0.77, which controls the nitrate concentration in the coastal aquifer in South 24 Parganas. The second important factor is Cl-, the value of which is 0.71. Other factors like-As, F-, EC and Mg2+ ranked third, fourth and fifth position, and their value are 0.69, 0.69, 0.67 and 0.55, respectively. Due to contaminated water, people of this district are suffering from several diseases like kidney damage (around 60%), liver (about 40%), low pressure due to salinity, fever, and headache. The applied method is for other regions to determine the nitrate concentration predictions and for the justifiable alterationof some management strategies.
Source identification and potential health risks from elevated groundwater nitrate contamination in Sundarbans coastal aquifers, India
(Scopus, 2024) Pal, Subodh Chandra; Biswas, Tanmoy; Jaydhar, Asit Kumar; Ruidas, Dipankar; Saha, Asish; Chowdhuri, Indrajit; Mandal, Sudipto; Islam, Aznarul; Islam, Abu Reza Md Towfiqul; Pande, Chaitanya B; Alam, Edris; Islam, Md Kamrul
In recent years groundwater contamination through nitrate contamination has increased rapidly in the managementof water research. In our study, fourteen nitrate conditioning factors were used, and multi-collinearity analysis is done. Among all variables, pH is crucial and ranked one, with a value of 0.77, which controls the nitrate concentration in the coastal aquifer in South 24 Parganas. The second important factor is Cl-, the value of which is 0.71. Other factors like-As, F-, EC and Mg2+ ranked third, fourth and fifth position, and their value are 0.69, 0.69, 0.67 and 0.55, respectively. Due to contaminated water, people of this district are suffering from several diseases like kidney damage (around 60%), liver (about 40%), low pressure due to salinity, fever, and headache. The applied method is for other regions to determine the nitrate concentration predictions and for the justifiable alterationof some management strategies..
Source Identification and Potential Health Risks From Elevated Groundwater Nitrate Contamination in Sundarbans Coastal Aquifers, India
(Springer Nature, 2024-02-20) Pal, Subodh Chandra; Biswas, Tanmoy; Jaydhar, Asit Kumar; Ruidas, Dipankar; Saha, Asish; Chowdhuri, Indrajit; Mandal, Sudipto; Islam, Aznarul; Islam, Abu Reza Md.Towfqul; Pande, Chaitanya B.; Alam, Edris; Islam, Md Kamrul
In recent years groundwater contamination through nitrate contamination has increased rapidly in the managementof water research. In our study, fourteen nitrate conditioning factors were used, and multi-collinearity analysis is done. Among all variables, pH is crucial and ranked one, with a value of 0.77, which controls the nitrate concentration in the coastal aquifer in South 24 Parganas. The second important factor is Cl−, the value of which is 0.71. Other factors like—As, F−, EC and Mg2+ ranked third, fourth and fifth position, and their value are 0.69, 0.69, 0.67 and 0.55, respectively. Due to contaminated water, people of this district are suffering from several diseases like kidney damage (around 60%), liver (about 40%), low pressure due to salinity, fever, and headache. The applied method is for other regions to determine the nitrate concentration predictions and for the justifiable alterationof some management strategies.
Spatio-Temporal Assessment of Water Quality of a Tropical Decaying River in India for Drinking Purposes and Human Health Risk Characterization
(Springer Nature, 2023-09-01) Hoque, Md. Mofizul; Islam, Aznarul; Islam, Abu Reza Md Towfiqul; Das, Balai Chandra; Pal, Subodh Chandra; Arabameri, Alireza; Khan, Rituparna
River water pollution and water-related health problems are common issues across the world. The present study aims to examine the Jalangi River’s water quality to assess its suitability for drinking purposes and associated human health risks. The 34 water samples were collected from the source to the mouth of Jalangi River in 2022 to depict the spatial dynamics while another 119 water samples (2012–2022) were collected from a secondary source to portray the seasonal dynamics. Results indicate better water quality in the lower reach of the river in the monsoon and post-monsoon seasons. Principal component analysis reveals that K+, NO3−, and total alkalinity (TA) play a dominant role in controlling the water quality of the study region, while, CaCO3, Ca2+, and EC in the pre-monsoon, EC, TDS, Na+, and TA in the monsoon, and EC, TDS and TA in the post-monsoon controlled the water quality. The results of ANOVA reveal that BOD, Ca2+, and CaCO3 concentrations in water have significant spatial dynamics, whereas pH, BOD, DO, Cl−, SO42−, Na+, Mg2+, Ca2+, CaCO3, TDS, TA, and EC have seasonal dynamics (p < 0.05). The water quality index depicts that the Jalangi River’s water quality ranged from 6.23 to 140.83, i.e., excellent to unsuitable for drinking purposes. Human health risk analysis shows that 32.35% of water samples have non-carcinogenic health risks for all three groups of people, i.e., adults, children, and infants while only 5.88% of water samples have carcinogenic health risks for adults and children. The gradual decay of the Jalangi River coupled with the disposal of urban and agricultural effluents induces river pollution that calls for substantial attention from the various stakeholders to restore the water quality.
Taxonomic approach and potential anthropic indices to understanding cross-sectional morphology and landscape modification of a tropical river Basin, India
(Scopus, 2023-07-09) Ghosh, Susmita; Islam, Aznarul; Román, Adolfo Quesada-; Islam, Abu Reza Md. Towfiqul; Pal, Subodh Chandra; Das, Balai Chandra
During the Anthropocene, human modifications to fluvial landscapes have become a common aspect of their progress and development. The primary objective of this research is to delve into the human-induced alterations on fluvial landscapes at both the channel and basin scales. For channel scale investigation, we classify the channel cross-sections in terms of human interventions and relate them with the potential anthropic (or anthropogenic) geomorphology in the Kopai River basin (KRB) in India. A total of 35 cross-sections (CS) were surveyed at an interval of ~ 3 km from source to mouth, and a perceptional survey was executed among randomly selected 960 respondents in the seven community development blocks. The CS are classified into natural (alluvial and bedrock) and anthropogenic (monatogenic – mining-influenced, traffic- road-stream crossings, hydrogenic-influenced by hydrological projects like dams, and agrogenic- agriculture-influenced) categories following Sźabo’s (1971) taxonomic approach. The statistical difference between natural and anthropic cross-sections is measured using seven hydromorphological characteristics. Basin scale investigation adopting Nir’s index (1983) of potential anthropic geomorphology (IPAG) from 1961 to 2021 depicts that the IPAG is progressively decreasing with time, although the reality is different. We propose to extend the basic notion of the IPAG by incorporating more relevant parameters.
Taxonomic Approach and Potential Anthropic Indices to Understanding Cross-sectional Morphology and Landscape Modification of a Tropical River Basin, India
(Taylor & Francis, 2024-05-13) Ghosh, Susmita; Islam, Aznarul; Quesada-Román, Adolfo; Islam, Abu Reza Md. Towfiqul; Pal, Subodh Chandra; Das, Balai Chandra
During the Anthropocene, human modifications to fluvial landscapes have become a common aspect of their progress and development. The primary objective of this research is to delve into the human-induced alterations on fluvial landscapes at both the channel and basin scales. For channel scale investigation, we classify the channel cross-sections in terms of human interventions and relate them with the potential anthropic (or anthropogenic) geomorphology in the Kopai River basin (KRB) in India. A total of 35 cross-sections (CS) were surveyed at an interval of ~ 3 km from source to mouth, and a perceptional survey was executed among randomly selected 960 respondents in the seven community development blocks. The CS are classified into natural (alluvial and bedrock) and anthropogenic (monatogenic – mining-influenced, traffic- road-stream crossings, hydrogenic-influenced by hydrological projects like dams, and agrogenic- agriculture-influenced) categories following Sźabo’s (1971) taxonomic approach. The statistical difference between natural and anthropic cross-sections is measured using seven hydromorphological characteristics. Basin scale investigation adopting Nir’s index (1983) of potential anthropic geomorphology (IPAG) from 1961 to 2021 depicts that the IPAG is progressively decreasing with time, although the reality is different. We propose to extend the basic notion of the IPAG by incorporating more relevant parameters.
Taxonomic Approach and Potential Anthropic Indices to Understanding Cross-Sectional Morphology and Landscape Modification of a Tropical River Basin, India
(Taylor & Francis Group, 2023-07-18) Ghosh, Susmita; Islam, Aznarul; Quesada-Román, Adolfo; Islam, Abu Reza Md. Towfiqul; Pal, Subodh Chandra; Das, Balai Chandra
During the Anthropocene, human modifications to fluvial landscapes have become a common aspect of their progress and development. The primary objective of this research is to delve into the human-induced alterations on fluvial landscapes at both the channel and basin scales. For channel scale investigation, we classify the channel cross-sections in terms of human interventions and relate them with the potential anthropic (or anthropogenic) geomorphology in the Kopai River basin (KRB) in India. A total of 35 cross-sections (CS) were surveyed at an interval of ~ 3 km from source to mouth, and a perceptional survey was executed among randomly selected 960 respondents in the seven community development blocks. The CS are classified into natural (alluvial and bedrock) and anthropogenic (monatogenic – mining-influenced, traffic- road-stream crossings, hydrogenic-influenced by hydrological projects like dams, and agrogenic- agriculture-influenced) categories following Sźabo’s (1971) taxonomic approach. The statistical difference between natural and anthropic cross-sections is measured using seven hydromorphological characteristics. Basin scale investigation adopting Nir’s index (1983) of potential anthropic geomorphology (IPAG) from 1961 to 2021 depicts that the IPAG is progressively decreasing with time, although the reality is different. We propose to extend the basic notion of the IPAG by incorporating more relevant parameters.
Using unsupervised machine learning models to drive groundwater chemistry and associated health risks in Indo-Bangla Sundarban region
(Scopus, 2024) Jannat, Jannatun Nahar; Islam, Abu Reza Md Towfiqul; Mia, Md Yousuf; Pal, Subodh Chandra; Biswas, Tanmoy; Jion, Most Mastura Munia Farjana; Islam, Md Saiful; Siddique, Md Abu Bakar; Idris, Abubakr M.; Khan, Rahat; Islam, Aznarul; Kormoker, Tapos; Senapathi, Venkatramanan
Groundwater is an essential resource in the Sundarban regions of India and Bangladesh, but its quality is deteriorating due to anthropogenic impacts. However, the integrated factors affecting groundwater chemistry, source distribution, and health risk are poorly understood along the Indo-Bangla coastal border. The goal of this study is to assess groundwater chemistry, associated driving factors, source contributions, and potential non-carcinogenic health risks (PN-CHR) using unsupervised machine learning models such as a self-organizing map (SOM), positive matrix factorization (PMF), ion ratios, and Monte Carlo simulation. For the Sundarban part of Bangladesh, the SOM clustering approach yielded six clusters, while it yielded five for the Indian Sundarbans. The SOM results showed high correlations among Ca2+, Mg2+, and K+, indicating a common origin. In the Bangladesh Sundarbans, mixed water predominated in all clusters except for cluster 3, whereas in the Indian Sundarbans, Cl−-Na+ and mixed water dominated in clusters 1 and 2, and both water types dominated the remaining clusters. Coupling of SOM, PMF, and ionic ratios identified rock weathering as a driving factor for groundwater chemistry. Clusters 1 and 3 were found to be influenced by mineral dissolution and geogenic inputs (overall contribution of 47.7%), while agricultural and industrial effluents dominated clusters 4 and 5 (contribution of 52.7%) in the Bangladesh Sundarbans. Industrial effluents and agricultural activities were associated with clusters 3, 4, and 5 (contributions of 29.5% and 25.4%, respectively) and geogenic sources (contributions of 23 and 22.1% in clusters 1 and 2) in Indian Sundarbans. The probabilistic health risk assessment showed that NO3− poses a higher PN-CHR risk to human health than F− and As, and that potential risk to children is more evident in the Bangladesh Sundarban area than in the Indian Sundarbans. Local authorities must take urgent action to control NO3− emissions in the Indo-Bangla Sundarbans region.