Evaluation of nitrogen and heavy metal pollution in southern Caspian Sea: Risk assessment and modeling approach. (December 2021)

• Record Type: Journal Article
• Authors: Irandoost, Farnaz; Agah, Homira; Eslami, Zahra; Rossi, Loreto; Colloca, Francesco; Khalili, Amir; Costantini, Maria Letizia
• Is Part Of: Marine pollution bulletin. Volume 173:Part B(2021)
• Publication Date: 2021-12
• Subjects: Risk assessment -- Heavy metals -- Nitrogen stable isotopes -- Caspian Sea -- Machine learning approach; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2021.113041
• Languages: English
• ISSNs: 0025-326X

Abstract

Abstract: Industrial, agricultural, and recreational activities dump several pollutants into the Caspian Sea, which is one of the main water bodies of Iran. Therefore, performing risk assessments would be required as part of the monitoring programs. Herein, non-cancer health risk assessment of the consumption of macroalgae was performed and the ecological risk assessment of metal pollution in sediments of the southern Caspian Sea was presented using sediment quality guidelines (SQGs), enrichment factors (EF), contamination factors (CF), contamination degrees (CD), pollution load indices (PLI), geo accumulation indices (Igeo ), and potential ecological risk indices (RI). Next, machine-learning approaches were used to predict the stable isotope value of nitrogen in macroalgae, in which physicochemical information of water and heavy metal levels in macroalgae were used separately and together as predictor variables. Results indicated that simultaneous use of physicochemical properties of water and heavy metal levels resulted in the best prediction of isotopic nitrogen content. Highlights: Ecological risk assessment of metal pollution in sediments of Caspian Sea was studied. Maximum allowable consumption of macroalgae by human was detected. Machine learning approaches were used to predict nitrogen content in macroalgae. Water physicochemical properties and metal levels predicted isotopic nitrogen content.