Toxicological risk assessment using spring water quality indices in plateaus of Giresun Province/Türkiye: a holistic hydrogeochemical data analysis.
Selin KaradenizFikret UstaoğluHandan AydınBayram YükselPublished in: Environmental geochemistry and health (2024)
Water scarcity is a growing concern due to rapid urbanization and population growth. This study assesses spring water quality at 20 stations in Giresun province, Türkiye, focusing on potentially toxic elements and physicochemical parameters. The Water Quality Index rated most samples as "excellent" during the rainy season and "good" during the dry season, except at stations 4 (40° 35' 12″ North/38° 26' 34″ East) and 19 (40° 44' 28″ North/38° 06' 53″ West), indicating "poor" quality. Mean macro-element concentrations (mg/L) were: Ca (34.27), Na (10.36), Mg (8.26), and K (1.48). Mean trace element values (μg/L) were: Al (1093), Zn (110.54), Fe (67.45), Mn (23.03), Cu (9.79), As (3.75), Ni (3.00), Cr (2.84), Pb (2.70), Co (1.93), and Cd (0.76). Health risk assessments showed minimal non-carcinogenic risks, while carcinogenic risk from arsenic slightly exceeded safe limits (CR = 1.75E-04). Higher arsenic concentrations during the rainy season were due to increased recharge, arsenic-laden surface runoff, and human activities. Statistical analyses (PCA, PCC, HCA) suggested that metals and physico-chemical parameters originated from lithogenic, anthropogenic, or mixed sources. Regular monitoring of spring water is recommended to mitigate potential public health risks from waterborne contaminants.
Keyphrases
- water quality
- health risk
- heavy metals
- risk assessment
- drinking water
- human health
- data analysis
- health risk assessment
- metal organic framework
- south africa
- endothelial cells
- healthcare
- tertiary care
- aqueous solution
- polycyclic aromatic hydrocarbons
- induced pluripotent stem cells
- quality improvement
- loop mediated isothermal amplification
- emergency department
- protein kinase
- ionic liquid
- electronic health record
- sensitive detection