Tracking the origin of trace metals in a watershed by identifying fingerprints of soils, landscape and river sediments

Derakhshan-Babaei, Farzaneh; Mirchooli, Fahimeh; Mohammadi, Maziar; Nosrati, Kazem; Egli, Markus

doi:10.1016/j.scitotenv.2022.155583

Cited by 13 publications

(3 citation statements)

References 89 publications

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“…CC BY 4.0 License. algorithms such as support vector machine, random forest (RF), artificial neural networks (ANN), and multivariate adaptive regression spline have been applied, e.g., for groundwater (Lee et al 2017), gully erosion (Zabihi et al 2018), sediment contamination (Mirchooli et al 2019), dust sources , landslides (Youssef and Pourghasemi 2021), floods (Tehrany et al 2014), and trace elements (Derakhshan-Babaei et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Mapping land degradation risk due to wind and water erosion

Boroughani

Mirchooli

Hadavifar

et al. 2023

Preprint

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Abstract. Land degradation is a cause of many social, economic, and environmental problems. Therefore identification and monitoring of high-risk areas for land degradation are necessary. Despite the importance of land degradation, the topic receives often relatively little attention. The present study aims to create a land degradation map in terms of soil erosion caused by wind and water erosion of semi-dry land. We focus on the Lut watershed in Iran encompassing the Lut Desert that is influenced by both monsoon rainfalls and dust storms. Dust sources are identified using MODIS satellite images with the help of four different indices to quantify uncertainty. The dust source maps are assessed with three machine learning algorithms encompassing artificial neural network (ANN), random forest (RF), and flexible discriminant analysis (FDA) to map dust sources paired with soil erosion susceptibility due to water. We assess the accuracy of the maps from the machine learning results with the metric Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). The maps for water and aeolian soil erosion are used to identify different classes of land degradation risks. The results show that 43 % of the watershed is prone to land degradation in terms of both aeolian and water erosion. Most regions (45 %) have a risk of water erosion and some regions (7 %) a risk of aeolian erosion. Only a small fraction (4 %) of the total area of the region had a low to very low susceptibility for land degradation. The results of this study underline the risk of land degradation for an inhabited region in Iran. Future work should focus on land degradation associated with soil erosion from water and storms in larger regions to evaluate the risks also elsewhere.

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Section: Introductionmentioning

confidence: 99%

Mapping land degradation risk due to wind and water erosion

Boroughani

Mirchooli

Hadavifar

et al. 2023

Preprint

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“…It can integrate data from different sources to create forecasts and discover patterns (Gholami et al, 2020a). In environmental sciences, algorithms such as the support vector machine, random forest (RF), artificial neural network (ANN), and multivariate adaptive regression spline have been applied, e.g., for groundwater (Lee et al, 2017), gully erosion (Zabihi et al, 2018), sediment contamination (Mirchooli et al, 2019), dust sources , landslides (Youssef and Pourghasemi, 2021), floods (Tehrany et al, 2014), and trace elements (Derakhshan-Babaei et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Mapping land degradation risk due to land susceptibility to dust emission and water erosion

Boroughani¹,

Mirchooli²,

Hadavifar³

et al. 2023

SOIL

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Abstract. Land degradation is a cause of many social, economic, and environmental problems. Therefore identification and monitoring of high-risk areas for land degradation are necessary. Despite the importance of land degradation due to wind and water erosion in some areas of the world, the combined study of both types of erosion in the same area receives relatively little attention. The present study aims to create a land degradation map in terms of soil erosion caused by wind and water erosion of semi-dry land. We focus on the Lut watershed in Iran, encompassing the Lut Desert that is influenced by both monsoon rainfalls and dust storms. Dust sources are identified using MODIS satellite images with the help of four different indices to quantify uncertainty. The dust source maps are assessed with three machine learning algorithms encompassing the artificial neural network (ANN), random forest (RF), and flexible discriminant analysis (FDA) to map dust sources paired with soil erosion susceptibility due to water. We assess the accuracy of the maps from the machine learning results with the area under the curve (AUC) of the receiver operating characteristic (ROC) metric. The water and aeolian soil erosion maps are used to identify different classes of land degradation risks. The results show that 43 % of the watershed is prone to land degradation in terms of both aeolian and water erosion. Most regions (45 %) have a risk of water erosion and some regions (7 %) a risk of aeolian erosion. Only a small fraction (4 %) of the total area of the region had a low to very low susceptibility for land degradation. The results of this study underline the risk of land degradation for in an inhabited region in Iran. Future work should focus on land degradation associated with soil erosion from water and storms in larger regions to evaluate the risks also elsewhere.

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“…PTE contamination in urban children's parks (UCPs) is associated with traffic emissions, fuel combustion, tire and brake wear, particles of weathered street dust, waste discharge from industrial activities, weathering particles from buildings and pavement surfaces, urban infrastructure development, wastewater disposal systems, and dumping of municipal solid waste [12][13][14][15][16][17]. Moreover, urban soils are exposed to PTE input from natural sources, wastewater irrigation, and atmospheric deposition [18][19][20][21], resulting in being discharged into water bodies by natural weathering, the erosion process, and anthropogenic activities [22]. Typically, urban runoff contains a range of pollutants that could contaminate water quality [23,24], and such pollutants like PTEs degrade the drinking water sources and leach into groundwater system.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Statistical Methods and GIS-Based Evaluation of Potable Water in Urban Children’s Parks Due to Potentially Toxic Elements Contamination: A Children’s Health Risk Assessment Study in a Developing Country

Ghani,

Nawab,

Ullah

et al. 2023

Sustainability

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Contamination of potentially toxic elements (PTEs) has received widespread attention in urban children’s parks (UCPs) worldwide in the past few decades. However, the risk assessment of PTEs in drinking water sources of UCPs is still unknown particularly in developing countries. Hence, the present study investigated the spatial distribution, sources for PTEs (Cd, Cr, Pb, Ni, and Cu), and health risk assessment in drinking water sources of UCPs in Khyber Pakhtunkhwa, Pakistan. Among PTEs, Cd, Cr, and Pb had low to high concentrations and exceeded the safe limits of WHO and PAK-EPA in most UCPs. PCA results showed high anthropogenic and low natural sources, contributing to the release of PTEs in all UCPs. Heavy-metal pollution index (PTE-PI) results showed low to high pollution levels for all UCPs, with the highest values of 113 and 116 for Sardaryab Park Charsadda (SPC) and Zoo Park Peshawar (ZPP), respectively. Heavy-metal evaluation index (PTE-EI) results also showed low to high pollution levels for all UCPs. UCPs samples (50%) showed low pollution levels in PTE-PI results. To the contrary, UCPs samples (50%) exhibited high pollution levels in PTE-EI results. The non-carcinogenic risk of HQ and HI values of all PTEs were below the permissible limit (<1) for adults and children via ingestion and dermal contact. CR and TCR results showed that PTEs (Cr, Cd, Pb, and Ni) had the highest carcinogenic risk (>1.00 × 10−4) for both adults and children in all UCPs, except Cd and Ni for adults via the ingestion route, while Cr values (>1.00 × 10−4) were exceeded for children in some of the UCPs via the dermal route. Consequently, long-term exposure to toxic PTEs could pose a carcinogenic risk to the local population. Thus, the present study suggests that the government should implement enforcement with firm protocols and monitoring guidelines of environmental regulations to mitigate PTEs originating from anthropogenic sources in order to reduce health risks and improve public health safety in urban areas.

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Tracking the origin of trace metals in a watershed by identifying fingerprints of soils, landscape and river sediments

Cited by 13 publications

References 89 publications

Mapping land degradation risk due to wind and water erosion

Mapping land degradation risk due to wind and water erosion

Mapping land degradation risk due to land susceptibility to dust emission and water erosion

Multivariate Statistical Methods and GIS-Based Evaluation of Potable Water in Urban Children’s Parks Due to Potentially Toxic Elements Contamination: A Children’s Health Risk Assessment Study in a Developing Country

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