Groundwater Contamination and Risk Assessment in Greater Palm Springs DOI Open Access

Warda Khalid,

Muhammad Yousuf Jat Baloch, Asmat Ali

et al.

Water, Journal Year: 2023, Volume and Issue: 15(17), P. 3099 - 3099

Published: Aug. 29, 2023

Groundwater is an essential resource for drinking water, but its contamination with potentially toxic elements and arsenic (As) a global issue. To evaluate As levels in the Coachella Valley, US Geological Survey (USGS) collected 17 groundwater samples. This study looked into distribution, enrichment, hydrogeochemical behavior, health risks associated The comparative analysis between Greater Palm Springs similar regions, could provide valuable insights regional differences common challenges. facies showed dominance of calcium magnesium-bicarbonate-carbonate, indicating permanent hardness salt deposits residual carbonate. Gibbs plot demonstrated that chemical weathering rock-forming minerals evaporation are primary forces impacting chemistry. Geochemical modeling revealed saturation calcite dolomite, under-saturation halite. Principal component identified potential contributory sources groundwater. carcinogenic non-carcinogenic potentials arsenic, cadmium, chromium (VI), lead were calculated using human risk assessment model. For both adults children, highest mean value was observed (8.52 × 10−1), lowest cadmium (1.32 10−3). Children had cumulative from elements. Our research offers crucial baseline data assessing at level, which important reduction remediation programs. show preventative action must be taken to reduce area groundwater, particularly children.

Language: Английский

An appraisal of the local‐scale spatio‐temporal variations of drought based on the integrated GRACE/GRACE‐FO observations and fine‐resolution FLDAS model DOI Creative Commons
Behnam Khorrami, Shoaib Ali, Orhan Gündüz

et al.

Hydrological Processes, Journal Year: 2023, Volume and Issue: 37(11)

Published: Nov. 1, 2023

Abstract The gravity recovery and climate experiment (GRACE) observations have so far been utilized to detect trace the variations of hydrological extremes worldwide. However, applying coarse resolution GRACE estimates for local‐scale analysis remains a big challenge. In this study, new version fine (1 km) Famine early warning systems network Land Data Assimilation System (FLDAS) model data was integrated into machine learning along with evaluate subbasin‐scale water storage, drought. With correlation root mean square error (RMSE) its results, downscaling turned out be very successful in modelling finer TWSA. storage deficit (WSD) Water Storage Deficit Index (WSDI) were used determine episodes severity drought events. Accordingly, two severe droughts (January 2008 March 2009 September 2019 December 2020) discerned Kizilirmak Basin (KB) located Central Türkiye. characterization evaluated based on WSDI, scPDSI, model‐based indices soil moisture percentile (SMSP) groundwater (GWSP). results indicated discrepancies classes different indices. WSDI more correlated GWSP, suggesting high ability monitor as well.

Language: Английский

Citations

12
Force and power requirement for development of cumin harvester: a dynamic approach DOI Creative Commons
Mohit Kumar, P. Sahoo, Dilip Kumar Kushwaha

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 13, 2024

Abstract An experimental setup was developed for simulating the field conditions to determine force and power required cutting cumin crops in dynamic conditions. The effect of cutter bar speeds, forward blade type on requirement were also studied. Experiments carried out at three levels: type. results showed that all factors significantly affected force. followed a decreasing trend with increase speed. Whereas it an increasing maximum blades observed speed 2.00 strokes.s -1 0.46 m.s −1 . idle actual crop determined based obtained validated by drawn from source while operating blades. R 2 values Blade-B1, Blade-B2, Blade-B3 0.90, 0.82, 0.88, respectively. primarily speed, resulting PCR 74.20%, 82.32%, 81.75% Blade-B3, respectively, which had impact 16.60%, 15.27%, 18.25% varied 15.96 58.97 N, 21.08 76.64 30.22 85.31, selected range Blade-B1 18 30% less consumption than Blade-B2

Language: Английский

Citations

4
Groundwater Storage Loss in the Central Valley Analysis Using a Novel Method based on In Situ Data Compared to GRACE-Derived Data DOI
M. L. Stevens, Saul G. Ramirez,

E. Martin

et al.

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: unknown, P. 106368 - 106368

Published: Feb. 1, 2025

Language: Английский

Citations

0
Exploring potential drivers of terrestrial water storage anomaly trends in the Yangtze River Basin (2002–2019) DOI Creative Commons
Jielong Wang, Jielong Wang, Joseph L. Awange

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 58, P. 102264 - 102264

Published: March 5, 2025

Language: Английский

Citations

0
A novel XGBoost-based approach for reconstruction terrestrial water storage variations with GNSS in the Northeastern Tibetan Plateau DOI
Tengxu Zhang,

Zhuohao Wang,

Liangke Huang

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: 659, P. 133255 - 133255

Published: April 10, 2025

Language: Английский

Citations

0
Assessment of landcover impacts on the groundwater quality using hydrogeochemical and geospatial techniques DOI
Javed Iqbal, Gomal Amin, Chunli Su

et al.

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 31(28), P. 40303 - 40323

Published: Sept. 13, 2023

Language: Английский

Citations

9
Machine Learning Algorithms for Predicting the Water Quality Index DOI Open Access
Enas E. Hussein, Muhammad Yousuf Jat Baloch, Anam Nigar

et al.

Water, Journal Year: 2023, Volume and Issue: 15(20), P. 3540 - 3540

Published: Oct. 11, 2023

Groundwater is one of the water resources used to preserve natural sources for drinking, irrigation, and several other purposes, especially in industrial applications. Human activities related industry agriculture result groundwater contamination. Therefore, investigating quality essential drinking irrigation purposes. In this work, index (WQI) was identify suitability irrigation. However, generating an accurate WQI requires much time, as errors may be made during sub-index calculations. Hence, artificial intelligence (AI) prediction model built reduce both time errors. Eighty data samples were collected from Sakrand, a city province Sindh, investigate area’s WQI. The classification learners with raw normalized select best classifier among following decision trees: support vector machine (SVM), k-nearest neighbors (K-NN), ensemble tree (ET), discrimination analysis (DA). These included learner tool MATLAB. results revealed that SVM classifier. accuracy levels training 90.8% 89.2% data, respectively. Meanwhile, testing 86.67 93.33%

Language: Английский

Citations

8
Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models DOI
Javed Iqbal, Chunli Su, Maqsood Ahmad

et al.

Environmental Geochemistry and Health, Journal Year: 2023, Volume and Issue: 46(1)

Published: Dec. 26, 2023

Language: Английский

Citations

7
Quantifying the impact of climate change and urbanization on groundwater resources using geospatial modeling DOI Creative Commons

Junaid Ali,

Fakhrul Islam, Tehmina Bibi

et al.

Frontiers in Earth Science, Journal Year: 2024, Volume and Issue: 12

Published: Aug. 21, 2024

Urbanization poses a significant threat to environmental sustainability, particularly in Pakistan, where uncontrolled urban growth and water mismanagement have exacerbated scarcity climate variability. This study investigates the spatiotemporal impacts of urbanization change on groundwater Lahore District, Pakistan. various parameters were considered execute including land use/land cover (LULC), rainfall, Land Surface Temperature (LST), ground wells population data using advanced techniques such as Random Forest machine learning algorithm, Climate Hazards Group Infrared Precipitation, geographically weighted regression (GWR) analysis. Our findings reveal that has severely impacted table north, northwest, southwest areas. There is negative correlation (−0.333) between quantity level (GWL) annual average LST whereas, p -value (0.75) also showing highly relation GWL area. Whereas positive association (0.666) exist ( 0.333 moderately significant) yearly mean precipitation. research provides crucial insights for policymakers understand effects develop strategies mitigate adverse

Language: Английский

Citations

2
CONTAMINATION IN WATER AND ECOLOGICAL RISK OF HEAVY METALS NEAR A COAL MINE AND A THERMAL POWER PLANT (REPUBLIC OF SRPSKA, BOSNIA AND HERZEGOVINA) DOI Open Access
Ljiljana Stojanović Bjelić, Predrag Ilić, Dragana Nešković Markić

et al.

Applied Ecology and Environmental Research, Journal Year: 2023, Volume and Issue: 21(5), P. 3807 - 3822

Published: Jan. 1, 2023

Water samples were collected near the thermal power plant and coal mine (Gacko, Republic of Srpska, Bosnia Herzegovina) analyzed to measure concentration 33 parameters (pH, temperature, electrical conductivity, alkalinity as CaCO3, total hardness solids, suspended matter, dissolved oxygen, oxygen saturation, biological demand, chemical demand with permanganate, ammonia, nitrite, nitrate, P, PAH, PCBs, phenolic index, mineral oils, detergents, content As, Cd, Cr, Fe, Mn Pb, sulfates, chlorides, fluorides, aerobic organotrophs, coliforms, fecal coliforms streptococci).Determined average mean pH values EC are within reference for class I surface water quality.The in study area is alkaline, a value 8.01.Depending on location, other correspond from V quality classes.The ERI Cr Pb low appreciable.The RI location 1 2 moderate.In locations, risk coefficients low.

Language: Английский

Citations

4