Assessment of industrial effluent impacts on soil physiochemical properties in Kwashe Industrial Area, Iraq Kurdistan Region.

Meshabaz, Ronak A.; Umer, Mustafa Ismail

doi:10.1088/1755-1315/1120/1/012037

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…In the Iraqi Kurdistan region, despite extensive SOM research, there is a noticeable absence of detailed digital SOM maps. While Gravi and Ibrahim [18] employed the ASD Field Spec 3 spectroradiometer for SOM levels in Duhok province, others still rely on time-tested laboratory methods [19,20].…”

Section: Introductionmentioning

confidence: 99%

Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach

Khalaf,

Mustafa,

Fayyadh

2023

Applied Sciences

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Soil organic matter (SOM) is an essential component of soil fertility that plays a vital role in the preservation of healthy ecosystems. This study aimed to produce an SOM-level map of the Batifa region in northern Iraq. Random forest (RF) and extreme gradient boosting (XGBoost) models were used to predict the SOM spatial distribution. A total of 96 soil samples were collected from the surface layer (0–30 cm) of both cropland and soil areas in Batifa. In addition, remote sensing data were obtained from Landsat 8, including bands 1–7, 10, and 11. Supplementary variables such as the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), brightness index (BI), and digital elevation model (DEM) were employed as tools to predict SOM levels across the region. To evaluate the accuracy of the RF and XGBoost models in predicting SOM levels, statistical metrics, including mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2), were used, with 80% of the data used for prediction and 20% for validation. The findings of this study revealed that the XGBoost model exhibited higher accuracy (MAE = 0.41, RMSE = 0.62, and R2 = 0.92) in predicting SOM than the RF model (MAE = 0.65, RMSE = 0.96, R2 = 0.79). Band 10, DEM, SAVI, and NDVI were identified as the most important predictors for both the models. The methodology employed in this study, which utilizes machine learning models, has the potential to map SOM in similar settings. Furthermore, the results offer significant insights for the stakeholders involved in soil management, thereby facilitating the enhancement of agricultural techniques.

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Section: Introductionmentioning

confidence: 99%

Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach

Khalaf,

Mustafa,

Fayyadh

2023

Applied Sciences

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Changing soil properties influenced vegetation diversity along effluent‐dominated river courses in dry areas of north‐western India

Singh,

Nagora,

Haksar

et al. 2024

Water & Environment J

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Improving the environmental quality of effluent‐dominated seasonal rivers is a fundamental challenge for sustaining life in drylands, where people utilize contaminated water to produce food, which ultimately accumulates in the food chain. Preventing further contaminations and phytoremediation are needed to avoid environmental degradation and health risks. This study aimed at analysing water quality and its impacts on soil and vegetation at effluent‐impacted, river‐edge and non‐polluted (control) micro‐habitats at five sites along Luni, Bandi and Jojari Rivers each in western Rajasthan. Soil and water samples were collected and analysed, and vegetation was recorded. River water exhibited high pH (7.60–8.60), electrical conductivity (EC; 2.45–38.20 dS m−1), total dissolved solid (TDS; 1.26–30.86 g L−1), alkalinity (24.0–250.0 mg L−1) and Na (1.50–30.00 g L−1), K (29.0–1100.0 mg L−1), Ca (136.0–3800.0 mg L−1) and Mn (0.05–83.92 mg L−1) concentrations and low NH4‐N, NO3‐N, PO4‐P and heavy metals. Species numbers ranged between 32 along Luni and 20 along Jojari. Soil pH, EC, PO4‐P and shrub richness (R), diversity (H′) and evenness (e') were highest for Bandi, whereas NH4‐N, NO3‐N, K and tree R and H′ were highest for Jojari River. Soil variables decreased and plant diversity increased downstream. Concentrations of Cd, Zn, Cr, Cu and Pb were above the acceptable limit in leaf of Salvadora persica and Prosopis juliflora. Principal component analysis (PCA) revealed significant relationships between different variables of river water, soil, and vegetation and 11 PCA axes. Conclusively, industry effluents negatively affected water, soil and river ecology. Effluent‐impacted soils had high salinity and less diversity adapted by salt‐tolerant species, complementary effects of which contribute to restoring the modified ecosystem and urban greening. Although people utilize effluent‐contaminated water in irrigation, precautions should be taken to avoid environmental and animal/human health risks.

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Assessment of industrial effluent impacts on soil physiochemical properties in Kwashe Industrial Area, Iraq Kurdistan Region.

Cited by 2 publications

References 18 publications

Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach

Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach

Changing soil properties influenced vegetation diversity along effluent‐dominated river courses in dry areas of north‐western India

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