Prediction of arsenic concentration in groundwater of Chapainawabganj, Bangladesh: machine learning-based approach to spatial modeling.

Groundwater in northwestern parts of Bangladesh, mainly in the Chapainawabganj District, has been contaminated by arsenic. This research documents the geographical distribution of arsenic concentrations utilizing machine learning techniques. The study aims to enhance the accuracy of model predictions by precisely identifying occurrences of groundwater arsenic, enabling effective mitigation actions and yielding more beneficial results. The reductive dissolution of arsenic-rich iron oxides/hydroxides is identified as the primary mechanism responsible for the release of arsenic from sediment into groundwater. The study reveals that in the research region, alongside elevated arsenic concentrations, significant levels of sodium (Na), iron (Fe), manganese (Mn), and calcium (Ca) were present. Statistical analysis was employed for feature selection, identifying pH, electrical conductivity (EC), sulfate (SO4), nitrate (NO3), Fe, Mn, Na, K, Ca, Mg, bicarbonate (HCO3), phosphate (PO4), and As as features closely associated with arsenic mobilization. Subsequently, various machine learning models, including Naïve Bayes, Random Forest, Support Vector Machine, Decision Tree, and logistic regression, were employed. The models utilized normalized arsenic concentrations categorized as high concentration (HC) or low concentration (LC), along with physiochemical properties as features, to predict arsenic occurrences. Among all machine learning models, the logistic regression and support vector machine models demonstrated high performance based on accuracy and confusion matrix analysis. In this study, a spatial distribution prediction map was generated to identify arsenic-prone areas. The prediction map also displays that Baroghoria Union and Rajarampur region under Chapainawabganj municipality are high-risk areas and Maharajpur Union and Baliadanga Union are comparatively low-risk areas of the research area. This map will facilitate researchers and legislators in implementing mitigation strategies. Logistic regression (LR) and support vector machine (SVM) models will be utilized to monitor arsenic concentration values continuously.

Elevated Uptake and Translocation Patterns of Heavy Metals in Different Food Plants Parts and Their Impacts on Human Health.

Irrigation with contaminated wastewater is a common practice in cultivation of crops and vegetables in many developing countries due to the scarcity of available fresh water. The present study has investigated the transfer and mobilization trends of heavy metals in different crops and vegetables plants grown in contaminated soil and waterbody. The translocation patterns of metals from polluted sources into different organs of plants bodies such as roots and edible parts and associated health risks have been evaluated simultaneously. Total of 180 different environmental samples including food plants, agricultural soil, and irrigation water were collected and analyzed. Heavy metal concentrations (Fe, Ni, Mn, Pb, Cu, Cd, As) in water, soil, and different parts of crops and vegetable plants were compared with the permissible levels reported by FAO/WHO, EU, and USEPA. Different metals contents within the food plants were found to be in the order of Fe > Mn > Ni > Cu > Pb > Cd > As. Pollution load index (PLI) data indicate that soil is highly polluted with Cd as well as moderately contaminated by As and Cu. Bioconcentration factor (BCF) analysis showed excessive accumulation of some heavy metals in crops and vegetables. Target hazard quotient (THQ) and target carcinogenic risk (TCR) analysis data showed higher carcinogenic and non-carcinogenic risks for both adult and children from the consumption of metal-contaminated food items. The results of metal pollution index (MPI), estimated daily intake (EDI), and hazard index (HI) analyses demonstrated the patterns of metals pollution in different food plants.

Hydro-chemical characteristics and groundwater quality evaluation in south-western region of Bangladesh: A GIS-based approach and multivariate analyses.

The study focuses on the chemistry of groundwater and if it is suitable for drinking and for use in agriculture using water quality indices, GIS mapping, and multivariate analyses in Sharsa Upazila, Jashore district, Bangladesh. In this study, the concentration of NH4+, K+, Ca2+, EC, Turbidity overstep BDWS drinking standards in 69 %, 14 %, 100 %, 40 % (WHO), 73 % of samples respectively. The value of Water Quality Indices (WQI) results inferred that the maximum specimen was held good quality for drinking uses, and the values distributed central eastern part to the south-eastern part were good quality water in the selected studied area. The study area's PH, EC, SAR, Na (%), TH, and NO3- values were mapped using GIS tools to show their spatial distribution. The cluster and correlation matrix analyses are used to validate for Principle Component Analysis (PCA). The five PCA results exhibited that the presence of EC, turbidity, K+, SO42- and NO3- was significant and was caused by both geogenic (rock weathering and cation exchange) and anthropogenic (agrochemicals, animal feedback) factor. According to the hydro-geochemical data, the maximum number of samples is of the Ca-Mg-HCO3-Cl type and is dominated by rocks. The irrigation water indices like MH, KR, SAR, and %Na indicate show high-quality groundwater for irrigation purposes. Most of the samples were satisfactory and compiled with WHO and Bangladeshi criteria for standard drinking water guideline values.

Receptor model-based sources and risks appraisal of potentially toxic elements in the urban soils of Bangladesh.

Rapid urbanization and industrial development have prompted potentially toxic elements (PTEs) in urban soil in Bangladesh, which is a great concern for ecological and public health matters. The present study explored the receptor-based sources, probable human health and ecological risks of PTEs (As, Cd, Pb, Cr, Ni, and Cu) in the urban soil of the Jashore district, Bangladesh. The USEPA modified method 3050B and atomic absorption spectrophotometers were used to digest and evaluate the PTEs concentration in 71 soil samples collected from eleven different land use areas, respectively. The concentration ranges of As, Cd, Pb, Cr, Ni, and Cu in the studied soils were 1.8-18.09, 0.1-3.58, 0.4-113.26, 0.9-72.09, 2.1-68.23, and 3.82-212.57 mg/kg, respectively. The contamination factor (CF), pollution load index (PLI), and enrichment factor (EF) were applied to evaluate the ecological risk posed by PTEs in soils. Soil quality evaluation indices showed that Cd was a great contributor to soil pollution. The PLI values range was 0.48-2.82, indicating base levels to continuous soil degradation. The positive matrix factorization (PMF) model showed that As (50.3 %), Cd (38.8 %), Cu (64.7 %), Pb (81.8 %) and Ni (47.2 %) were derived from industrial sources and mixed anthropogenic sources, while Cr (78.1 %) from natural sources. The highest contamination was found in the metal workshop, followed by the industrial area, and brick filed site. Soil from all land use types revealed moderate to high ecological risk after evaluating probable ecological risks, and the descending order of single metal potential ecological risk was Cd > As > Pb > Cu > Ni > Cr. Ingestion was the primary route of exposure to potentially toxic elements for both adults and children from the study area soil. The overall non-cancer risk to human health is caused by PTEs for children (HI=0.65 ± 0.1) and adults (HI=0.09 ± 0.03) under USEPA safe limit (HI>1), while the cancer risks from exclusively ingesting As through soil were 2.10E-03 and 2.74E-04 for children and adults, respectively, exceeding the USEPA acceptable standard (>1E-04).

Contamination of Selected Toxic Elements in Integrated Chicken-Fish Farm Settings of Bangladesh and Associated Human Health Risk Assessments.

The presence of trace elements in the environment can contaminate a food chain of an agro farm in various ways. Integrated chicken-fish farms (i.e., where poultry chicken and fish are cultivated in same places) are getting popular nowadays to meet the demands of a balanced diet. The present study conducted a health risk assessment on the basis of selected heavy metal (i.e., Cr and Pb) and metalloid (i.e., As) contamination in this type of farm in Bangladesh. Samples of various types were collected from different farms between September 2019 and March 2020. The concentrations of the elements were checked by Flame-AAS and HG-AAS. Our findings demonstrated that the elements' concentrations in fishes were simultaneously induced by the habitation and bioaccumulation through the food chain of the farm. The concentrations of As and Pb in the chicken parts and Cr and As in some fishes were greater than the highest limits set by different permissible standards. Overall, the metal concentration obtained in different samples was in descending order: sediment > droppings > different fish parts > various species of chicken > pond water. Among the pollutants, As gave target hazard quotient (THQ) values higher than 1 for all the species, suggesting health risks from the intake of fishes and chicken. However, there was non-target cancer risk present while considering all the elements together. Notably, the study found carcinogenic risks of As, Pb, and Cr for humans due to poultry and/or fish consumption; the identified health risks associated with the integrated farming setting will be crucial in further tackling strategies. Investigation of the possible sources of heavy metals in commercial chicken feeds and regular monitoring of groundwater used for agro-farming are highly recommended to reduce the burden.

Human health risk ​and receptor model-oriented sources of heavy metal pollution in commonly consume vegetable and fish species of high Ganges river floodplain agro-ecological area, Bangladesh.

This study was intended to assess heavy metal contents and sources in commonly consumed vegetables and fish collected from the Jashore district of Bangladesh and to evaluate the probable human health risks via the ingesting of those vegetables and fish species. A total of 130 vegetable and fish samples were analyzed for As, Mn, Cu, Cr, Ni, and Pb concentration by an atomic absorption spectrophotometer. Metals and metalloids like As, Pb, and Cr in vegetable species were greater than the maximum allowable concentration (MAC), while Pb and cu in fish species exceeded the MAC. Pollution evaluation index values were ranges from 0.40-10.35 and 1.53-2.78 for vegetable and fish species, respectively, indicating light to serious pollution. Lactuca sativa followed by Cucurbita moschata, Amaranthus gangeticus for vegetables and Channa punctate, Oreochromis mossambicus, followed by Dendrobranchiata for fish are the most contaminated food items. The positive matrix factorization model showed that As (81.9%), Ni (48%), Cr (49.6%), Mn (46%), Pb (44.3%), and Cu (44.4%) for vegetable species and As (86.9%), Ni (90.5%), Mn (67.6%), Pb (65.3%), Cr (57%) and Cu (46.2%) for fish species were resulting from agrochemical, atmospheric emission, irrigation, contaminated feed, and mixed sources. The self-organizing map and principle component analysis indicates three spatial patterns e.g., As-Mn-Cu, Pb-Cr, and Ni in vegetables and As-Mn-Cr, Cu-Ni, and Pb in fish samples. The THQ values for single elements were less than 1 (except As for vegetables and Pb for fish species) for all food items but the HI values for all of the vegetables (2.18E+00 to 2.04E+01) and fish (1.07E+00 to 9.39E+00) samples were exceeded the USEPA acceptable risk level (HI > 1E+00). While the cancer risks only induced by As for all vegetables and fish species, which exceeded the USEPA safe level (TCR>1E-04). Sensitivity analysis indicates that metal concentration was the most responsible factor for carcinogenic risk.

Arsenic, iron, and manganese in groundwater and its associated human health risk assessment in the rural area of Jashore, Bangladesh.

This study investigated groundwater pollution and potential human health risks from arsenic, iron, and manganese in the rural area of Jashore, Bangladesh. Study results show that the mean value of groundwater pH is 7.25 ± 0.31, with a mean conductivity of 633.94 ± 327.41 µs/cm, while about 73, 97, and 91% of groundwater samples exceeded the Bangladesh drinking water standard limits for As, Fe, and Mn, respectively. Groundwater pollution evaluation indices, including the heavy metal pollution index, the heavy metal evaluation index, the degree of contamination, and the Nemerow pollution index, show that approximately 97, 82, 100, and 100% of samples are in the high degree of pollution category, respectively. Spatial distribution exhibited that the study area is highly exposed to As (73%), Fe (82%), and Mn (46%). In the case of non-carcinogenic health risk via oral exposure, about 94% of samples suggest a high category of risk for infants, and 97% of samples are found to be at high risk for children and adults. The carcinogenic risk of arsenic via an oral exposure pathway suggests that approximately 97% of the samples are found to be at high risk for infants, and all of the samples are at high risk for both adults and children.

Novel micro-structured carbon-based adsorbents for notorious arsenic removal from wastewater.

The contamination of groundwater by arsenic (As) in Bangladesh is the biggest impairing of a population, with a large number of peoples affected. Specifically, groundwater of Gangetic Delta is alarmingly contaminated with arsenic. Similar, perilous circumstances exist in many other countries and consequently, there is a dire need to develop cost-effective decentralized filtration unit utilizing low-cost adsorbents for eliminating arsenic from water. Morphological synthesis of carbon with unique spherical, nanorod, and massive nanostructures were achieved by solvothermal method. Owing to their intrinsic adsorption properties and different nanostructures, these nanostructures were employed as adsorption of arsenic in aqueous solution, with the purpose to better understanding the morphological effect in adsorption. It clearly demonstrated that carbon with nanorods morphology exhibited an excellent adsorption activity of arsenite (about 82%) at pH 3, remarkably superior to the two with solid sphere and massive microstructures, because of its larger specific surface area, enhanced acid strength and improved adsorption capacity. Furthermore, we discovered that iron hydroxide radicals and energy-induced contact point formation in nanorods are the responsible for the high adsorption of As in aqueous solution. Thus, our work provides insides into the microstructure-dependent capability of different carbon for As adsorption applications.