Hydrogeochemical evaluation and corresponding health risk from elevated arsenic and fluoride contamination in recurrent coastal multi-aquifers of eastern India

Jaydhar, Asit Kumar; Pal, Subodh Chandra; Saha, Asish; Islam, Abu Reza Md. Towfiqul; Ruidas, Dipankar

doi:10.1016/j.jclepro.2022.133150

Cited by 57 publications

(22 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RUSLE is a widely applied empirical model for estimating the average annual soil loss or erosion rate (Sujatha and Sridhar, 2021). Collected datasets were assembled using various data mining techniques coupled with machine learning algorithms to generate thematic layers which served as the input for the RUSLE model (Ruidas et al, 2021;Ruidas et al, 2022a;Ruidas et al, 2022b;Ruidas et al, 2022c;Jaydhar et al, 2022). This model incorporates five factors related to rainfall/ precipitation, soil/land, topography/landscape, LULC, and conservation practices.…”

Section: Estimating Parameters Of Revised Universal Soil Loss Equatio...mentioning

confidence: 99%

GIS integrated RUSLE model-based soil loss estimation and watershed prioritization for land and water conservation aspects

Singh

Sur

Al‐Ansari

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

Land degradation has become one of the major threats throughout the globe, affecting about 2.6 billion people in more than 100 countries. The highest rate of land degradation is in Asia, followed by Africa and Europe. Climate change coupled with anthropogenic activities have accelerated the rate of land degradation in developing nations. In India, land degradation has affected about 105.48 million hectares. Thus, modeling and mapping soil loss, and assessing the vulnerability threat of the active erosional processes in a region are the major challenges from the land and water conservation aspects. The present study attempted rigorous modeling to estimate soil loss from the Banas Basin of Rajasthan state, India, using GIS-integrated Revised Universal Soil Loss Equation (RUSLE) equation. Priority ranking was computed for different watersheds in terms of the degree of soil loss from their catchments, so that appropriate conservation measures can be implemented. The total area of Banas basin (68,207.82 km2) was systematically separated into 25 watersheds ranging in area from 113.0 to 7626.8 km2. Rainfall dataset of Indian Meteorological Department for 30 years (1990–2020), FAO based Soil map for soil characterization, ALOS PALSAR digital elevation model for topographic assessment, and Sentinal-2 based land use and land cover map were integrated for modeling and mapping soil erosion/loss risk assessment. The total annual soil loss in the Banas basin was recorded as 21,766,048.8 tons. The areas under very low (0–1 t ha-1 year-1), low (1–5 t ha-1 year-1), medium (5–10 t ha-1 year-1), high (10–50 t ha-1 year-1) and extreme (>50 t ha-1 year-1) soil loss categories were recorded as 24.2, 66.8, 7.3, 0.9, and 0.7%, respectively, whereas the respective average annual soil loss values were obtained as 0.8, 3.0, 6.0, 23.1, and 52.0 t ha-1 year-1. The average annual soil loss among different watersheds was recorded in the range of 1.1–84.9 t ha-1 year-1, being highest (84.9 t ha-1 year-1) in WS18, followed by WS10 (38.4 t ha-1 year-1), SW25 (34.7 t ha-1 year-1) and WS23 (17.9 t ha-1 year-1), whereas it was lowest for WS8 (1.1 t ha-1 year-1). Thus, WS18 obtained the highest/top priority rank in terms of the average annual soil loss (84.9 t ha-1 year-1) to be considered as the first priority for land and water conservation planning and implementation. The quantitative results of this study would be useful for implementation of land and water conservation measures in the problematic areas of the Banas basin for controlling soil loss through water erosion.

show abstract

Section: Estimating Parameters Of Revised Universal Soil Loss Equatio...mentioning

confidence: 99%

GIS integrated RUSLE model-based soil loss estimation and watershed prioritization for land and water conservation aspects

Singh

Sur

Al‐Ansari

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…VIF computes the extent of correlation between an independent factor and other factors in a model, and it is interpreted as VIF = 1 means factors are not correlated, VIF between 1 and 5 means factors are moderately correlated, and VIF >5 indicates high correlation among the factors. TOL is the reciprocal of VIF, and its values are between 0 and 1 (Chen and Chen, 2021;Jaydhar et al, 2022;Zhang et al, 2022). VIF and TOL are computed using the following functions: Triggering factor Rainfall Uncertain 0ཞ60, 60ཞ80, 80ཞ100, 100ཞ110, 110ཞ120, 120 mm above…”

Section: Multicollinearity Analysis Of Landslide Conditioning Factorsmentioning

confidence: 99%

“…The metrics are expressed in the equations below: Ac and ka close to 1 indicate that the model is reliable, while close to 0 means the model is not reliable and has very poor performance (Landis and Koch, 1977;Ruidas et al, 2022a). Also, when AUC is almost 1 implies that the model is perfect while when AUC = .5 means the model is inaccurate (Huang and Ling, 2005;Jaydhar et al, 2022). Frontiers in Environmental Science frontiersin.org…”

Section: Evaluation Metricsmentioning

confidence: 99%

Landslide susceptibility mapping using O-CURE and PAM clustering algorithms

Mwakapesa

Lan²,

Nanehkaran³

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

Landslide susceptibility mapping (LSM) is a crucial step during landslide assessment and environmental management. Clustering algorithms can construct effective models for LSM. However, a random selection of important parameters, inconsideration of uncertain data, noise data, and large datasets can limit the implementation of clustering in LSM, resulting in low and unreliable performance results. Thus, to address these problems, this study proposed an optimized clustering algorithm named O-CURE, which combines: the traditional Clustering Using REpresentatives algorithm (CURE), that is, efficient for large datasets and noise data, the partition influence weight (PIW)-based method to enhance the selection of sample sets and the city block distance (CIBD) for processing of the uncertain data in CURE clustering during LSM modeling. A database containing 293 landslide location samples, 213 non-landslide samples, and 7 landslide conditioning factors was prepared for the implementation and evaluation of the method. Also, a Multicollinearity analysis was conducted to select the most appropriate factors, and all the factors were acceptable for modeling. Based on O-CURE, landslide density, and the partitioning around medoids (PAM) algorithm a susceptibility map was constructed and classified into very high (33%), high (18%), moderate (24%), low (13%), and very low (12%) landslide susceptible levels. To evaluate the performance of the O-CURE model, five statistic metrics including accuracy, sensitivity, specificity, kappa, and AUC were applied. The analysis shows that O-CURE obtained accuracy = .9368, sensitivity = .9215, specificity = .9577, kappa = .8496, and AUC = .896 is an indication of high-performance capability. Also, the proposed method was compared with the CURE algorithm, three existing clustering methods, and popular supervised learning methods. From this assessment, O-CURE outperformed the other clustering methods while showing significant and more consistent performance than the supervised learning methods. Therefore, we recommend that the O-CURE model and the constructed map can be useful in assessing landslides and contribute to sustainable land-use planning and environmental management in light of future disasters.

show abstract

“…Cadmium is known to cause lung damage, kidney damage and fragile bones which can result in death [27,28]. Exposure to arsenic from food and water leads to cancer, skin lesions, cardiovascular disease, and diabetes [29][30][31][32]. Lead reduces brain development, causes anemia, body weakness, kidney, and brain damage [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Toxic Heavy Metals in Soil and Plants from a Gold Mining Area, South Africa

Raji¹,

Palamuleni²

2023

Heavy Metals - Recent Advances

View full text Add to dashboard Cite

The mining of mineral deposits abundant in South Africa has led to the release of toxic heavy metals into the environment. The aim of this study was to investigate heavy metal pollution from a gold mining area. The concentrations of arsenic, cadmium, lead, and zinc were analyzed in soil and plants found within 500 m and 1000 m radius of a gold mine established in North-West. The concentrations of these heavy metals were determined using Inductively Coupled Plasma Mass Spectrometry. The results showed that the concentrations of the studied heavy metals were all below the national and international threshold but higher than the concentrations found several kilometers away from the mining area, the natural background concentration. The results from the pollution load index shows that the topsoil is contaminated for the selected heavy metals. There was accumulation of the studied heavy metals in the studied Eragrostis hypnoides plant’s leaves and roots. In this study, it was revealed that zinc and cadmium bioaccumulated in the plant via the soil. These findings suggests that the consumption of agricultural products from farms within the 1 km radius of the mining site could be detrimental to the wellbeing of direct and indirect consumers.

show abstract

Hydrogeochemical evaluation and corresponding health risk from elevated arsenic and fluoride contamination in recurrent coastal multi-aquifers of eastern India

Cited by 57 publications

References 59 publications

GIS integrated RUSLE model-based soil loss estimation and watershed prioritization for land and water conservation aspects

GIS integrated RUSLE model-based soil loss estimation and watershed prioritization for land and water conservation aspects

Landslide susceptibility mapping using O-CURE and PAM clustering algorithms

Toxic Heavy Metals in Soil and Plants from a Gold Mining Area, South Africa

Contact Info

Product

Resources

About