Living with arsenic in the environment: An examination of current awareness of farmers in the Bengal basin using hybrid feature selection and machine learning

Enhancing precision flood mapping: Pahang's vulnerability unveiled

Malaysia, particularly Pahang, experiences devastating floods annually, causing significant damage. The objective of the research was to create a flood susceptibility map for the designated area by employing an Ensemble Machine Learning (EML) algorithm based on geographic information system (GIS). By analyzing nine key factors from a geospatial database, flood susceptibility map was created with the ArcGIS software (ESRI ArcGIS Pro v3.0.1 x64). The Random Forest (RF) model was employed in this study to categorize the study area into distinct flood susceptibility classes. The Feature selection (FS) method was used to ranking the flood influencing factors. To validate the flood susceptibility models, standard statistical measures and the Area Under the Curve (AUC) were employed. The FS ranking demonstrated that the primary attributes to flooding in the study region are rainfall and elevation, with slope, geology, curvature, flow accumulation, flow direction, distance from the river, and land use/land cover (LULC) patterns ranking subsequently. The categories of 'very high' and 'high' class collectively made up 37.1% and 26.3% of the total area, respectively. The flood vulnerability assessment of Pahang found that the Eastern, Southern, and central regions were at high risk of flooding due to intense precipitation, low-lying topography with steep inclines, proximity to the shoreline and rivers, and abundant flooded vegetation, crops, urban areas, bare ground, and rangeland. Conversely, areas with dense tree canopies or forests were less susceptible to flooding in this research area. The ROC analysis demonstrated strong performance on the validation datasets, with an AUC value of >0.73 and accuracy scores exceeding 0.71. Research on flood susceptibility mapping can enhance risk reduction strategies and improve flood management in vulnerable areas. Technological advancements and expertise provide opportunities for more sophisticated methods, leading to better prepared and resilient communities.

Unveiling the link between arsenic toxicity and diabetes: an in silico exploration into the role of transcription factors

Arsenic-induced diabetes, despite being a relatively newer finding, is now a growing area of interest, owing to its multifaceted nature of development and the diversity of metabolic conditions that result from it, on top of the already complicated manifestation of arsenic toxicity. Identification and characterization of the common and differentially affected cellular metabolic pathways and their regulatory components among various arsenic and diabetes-associated complications may aid in understanding the core molecular mechanism of arsenic-induced diabetes. This study, therefore, explores the effects of arsenic on human cell lines through 14 transcriptomic datasets containing 160 individual samples using in silico tools to take a systematic, deeper look into the pathways and genes that are being altered. Among these, we especially focused on the role of transcription factors due to their diverse and multifaceted roles in biological processes, aiming to comprehensively investigate the underlying mechanism of arsenic-induced diabetes as well as associated health risks. We present a potential mechanism heavily implying the involvement of the TGF-β/SMAD3 signaling pathway leading to cell cycle alterations and the NF-κB/TNF-α, MAPK, and Ca2+ signaling pathways underlying the pathogenesis of arsenic-induced diabetes. This study also presents novel findings by suggesting potential associations of four transcription factors (NCOA3, PHF20, TFDP1, and TFDP2) with both arsenic toxicity and diabetes; five transcription factors (E2F5, ETS2, EGR1, JDP2, and TFE3) with arsenic toxicity; and one transcription factor (GATA2) with diabetes. The novel association of the transcription factors and proposed mechanism in this study may serve as a take-off point for more experimental evidence needed to understand the in vivo cellular-level diabetogenic effects of arsenic.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43188-024-00255-y.

Improving the efficiency of machine learning in simulating sedimentary heavy metal contamination by coupling preposing feature selection methods

Sediment cores were collected from Taihu Lake in China. The chronology was determined by radionuclide. Heavy metals and magnetic properties of each core slice were assessed, respectively. The concentrations of most heavy metals in sediments surged at 20 cm from the surface, accompanying the increase in the concentrations of single-domain magnetic particles. This may be resulted from the influence of anthropic activities on the lake's environment after the 1970s. Two feature selection methods, random forest (RF) and maximal information coefficient (MIC), were combined with support vector machine (SVM) model to simulate heavy metals, with the inclusion of selected magnetic and physicochemical parameters. Compared with the modeling results obtained with the full set of parameters, a reasonable simulation performance was obtained with RF and MIC. RF performed better than MIC by increasing the R² of simulation models for Cd, Cr, Cu, Pb, and Sb. For heavy metals with high ecological risks (As, Cd, Cr, Hg, Pb, Sb), the correlation coefficients for observed and predicted data ranged from 0.73 to 0.97 with only 14-27% of the parameters selected by RF as input variables. The RF-RBF-SVM enabled heavy metal predictions based on the magnetic properties of the lake sediments.

Arsenic Elevated Groundwater Irrigation: Farmers' Perception of Rice and Vegetable Contamination in a Naturally Arsenic Endemic Area

Arsenic (As) in groundwater and its accumulation in agricultural produces has caused serious threats to human health. The majority of current research on As mainly focuses on the technical aspects while bypassing the social perspectives. Farmers are the prime stakeholders as well as executors of agricultural strategies, and their adaptation largely depends on how they perceive the risk for which a mitigation strategy is proposed. This study aims to explore how rice and vegetable farmers perceive As accumulation in their rice and vegetables as well as explore current crop- and body-loading status, the subsequent health consequences of As, and alleviation possibilities with mitigation strategies and to investigate if there is an association between their socioeconomic status and their level of perception. Results reveal that one-fourth of the farmers gave a positive message regarding the As-contamination scenario in rice and vegetables. Although 10 farmers' socioeconomic characteristics were positively significant, distinctive emphasis should be given to five predictor variables explaining 88% variances: knowledge, direct participation in farming, information sources used, participant education, and organizational participation. Path analysis depicts that direct participation in farming presents the highest positive total effect (0.855) and direct effect (0.503), whereas information sources show the highest positive indirect effect (0.624). The mean As content in all five locations was statistically significant at the 5%, 5%, 0.1%, 1%, and 1% probability levels in scalp hairs, rice, vegetables, soils, and irrigation water, respectively. The first principal component (PC1) explains 92.5% of the variation. Significant variations were primarily explained by As levels in irrigation water, rice grain, and soil. Farmers' perception is far behind the actual field status of As level and its transfer. Therefore, intensified priorities should be administered on the farmers' characteristics contributing to variances in perception. The findings can be utilized for policy formulation in all As-endemic nations. More multidisciplinary research can be undertaken on farmers' attitude towards adopting As-mitigation techniques, with a focus on the socioeconomic position found to influence farmers' perceptions.

Human health impact due to arsenic contaminated rice and vegetables consumption in naturally arsenic endemic regions

Rice and vegetables cultivated in naturally arsenic (As) endemic areas are the substantial source of As body loading for persons using safe drinking water. However, tracing As intake, particularly from rice and vegetables by biomarker analysis, has been poorly addressed. This field investigation was conducted to trace the As transfer pathway and measure health risk associated with consuming As enriched rice and vegetables. Purposively selected 100 farmers from five sub-districts of Chandpur, Bangladesh fulfilling specific requirements constituted the subjects of this study. A total of 100 Irrigation water, soils, rice, and vegetable samples were collected from those farmers' who donated scalp hair. Socio-demographic and food consumption data were collected face to face through questionnaire administration. The mean As level in irrigation water, soils, rice, vegetables, and scalp hairs exceeded the acceptable limit, while As content was significant at 0.1%, 5%, 0.1%, 1%, and 0.1% probability levels, respectively, in all five locations. Arsenic in scalp hair is significantly (p ≤ 0.01) correlated with that in rice and vegetables. The bioconcentration factor (BCF) for rice and vegetables is less than one and significant at a 1% probability level. The average daily intake (ADI) is higher than the RfD limit for As. Both grains and vegetables have an HQ (hazard quotient) > 1. Maximum incremental lifetime cancer risk (ILCR) showed 2.8 per 100 people and 1.6 per 1000 people are at considerable and threshold risk, respectively. However, proteinaceous and nutritious food consumption might have kept the participants asymptomatic. The PCA analysis showed that the first principle component (PC1) explains 91.1% of the total variance dominated by As in irrigation water, grain, and vegetables. The dendrogram shows greater variations in similarity in rice and vegetables As, while the latter has been found to contribute more to human body loading compared to grain As.

Identifying risk factors in explaining women's anaemia in limited resource areas: evidence from West Bengal of India and Bangladesh

Background

Anaemia among women is a public health problem with associated adverse outcomes for mother and child. This study investigates the determinants of women’s anaemia in two Bengals; West Bengal (a province of India) and Bangladesh. These two spaces are inhabitated by Bengali speaking population since historic past. The study argues that open defecation, contraceptive method use and food consumption patterns are playing crucial role in explaining anaemia.

Methods

Using non-pregnant women belonging to different religious groups, we analyzed a total of 21,032 women aged 15–49 from the nationally representative cross-sectional surveys, i.e., Bangladesh Demographic Health Survey (BDHS-VI, 2011) and National Family Health Survey (NFHS round 4, 2015–16). We performed spatial, bivariate and logistic regression analyses to unfold the important risk factors of anaemia in two Bengals.

Results

The prevalence of anaemia was 64% in West Bengal and 41% in Bangladesh. The significant risk factors explaining anaemia were use of sterilization, vegetarian diet and open defecation. Further, women who used groundwater (tube well or well) for drinking suffered more from anaemia. Also, younger women, poor, less educated and having more children were highly likely to be anaemic. The study also indicates that those who frequently consumed non-vegetarian items and fruits in West Bengal and experienced household food security in Bangladesh were less prone to be anaemic. Hindus of West Bengal, followed by Muslims of that state and then Hindus of Bangladesh were at the higher risk of anaemia compared to Muslims of Bangladesh, indicating the stronger role of space over religion in addressing anaemia. Unlike West Bengal, Bangladesh observed distinct regional differences in women's anaemia.

Conclusions

Propagating the choices of contraception mainly Pill/ injection/IUDs and making the availability of iron rich food along with a favourable community environment in terms of safe drinking water and improved sanitation besides better education and economic condition can help to tackle anaemia in limited-resource areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13806-5.

Predicting sustainable arsenic mitigation using machine learning techniques

This study evaluates state-of-the-art machine learning models in predicting the most sustainable arsenic mitigation preference. A Gaussian distribution-based Naïve Bayes (NB) classifier scored the highest Area Under the Curve (AUC) of the Receiver Operating Characteristic curve (0.82), followed by Nu Support Vector Classification (0.80), and K-Neighbors (0.79). Ensemble classifiers scored higher than 70% AUC, with Random Forest being the top performer (0.77), and Decision Tree model ranked fourth with an AUC of 0.77. The multilayer perceptron model also achieved high performance (AUC=0.75). Most linear classifiers underperformed, with the Ridge classifier at the top (AUC=0.73) and perceptron at the bottom (AUC=0.57). A Bernoulli distribution-based Naïve Bayes classifier was the poorest model (AUC=0.50). The Gaussian NB was also the most robust ML model with the slightest variation of Kappa score on training (0.58) and test data (0.64). The results suggest that nonlinear or ensemble classifiers could more accurately understand the complex relationships of socio-environmental data and help develop accurate and robust prediction models of sustainable arsenic mitigation. Furthermore, Gaussian NB is the best option when data is scarce.

Arsenic exposure from drinking water and staple food (rice): A field scale study in rural Bengal for assessment of human health risk

Arsenic is a well-known carcinogen with emerging reports showing a range of health outcomes even for low to moderate levels of exposure. This study deals with arsenic exposure and associated increased lifetime cancer risk for populations in arsenic-endemic regions of rural Bengal, where arsenic-safe drinking water is being supplied at present. We found a median total exposure of inorganic arsenic to be 2. 9 μg/Kg BW/day (5th and 95th percentiles were 1.1 μg/Kg BW/day and 7.9 μg/Kg BW/day); with major contribution from cooked rice intake (2.4 µg/Kg BW/day). A significant number of households drank arsenic safe water but used arsenic-rich water for rice cooking. As a result, 67% participants had inorganic arsenic intake above the JEFCA threshold value of 3 μg/Kg BW/day for cancer risk from only rice consumption when arsenic contaminated water was used for cooking (median: 3.5 μg/Kg BW/day) compared to 29% participants that relied on arsenic-free cooking water (median: 1.0 µg/kg BW/day). Arsenic in urine samples of study participants ranged from 31.7 to 520 µg/L and was significantly associated with the arsenic intake (r = 0.76); confirming the preponderance of arsenic exposure from cooked rice. The median arsenic attributable cancer risks from drinking water and cooked rice were estimated to be 2.4 × 10-5 and 2.7 × 10-4 respectively, which further emphasized the importance of arsenic exposure from staple diet. Our results show that any mitigation strategy should include both drinking water and local staple foods in order to minimize the potential health risks of arsenic exposure.