Human Health and Ecological Impacts of Pharmaceuticals and Metals Released by Municipal Wastewater in Tehran: A Life Cycle Impact Assessment.

Pharmaceuticals and metals in municipal wastewater pose significant risks to human and environmental health, necessitating further investigation due to their increasing use and limited data. This study assessed the impacts of commonly prescribed pharmaceuticals and metals in Tehran's municipal wastewater using the USEtox™ model, which quantifies chemical toxicity by integrating fate, exposure, and effect factors. In Tehran, Iran, 61% of untreated wastewater is released into watercourses, while 39% is treated by an activated sludge system and partially reused for agriculture.

Assessment of health risk and burden of disease associated with dietary exposure to pesticide residues through foodstuffs in Iran.

The health risk and burden of disease associated with dietary exposure to pesticide residues in foodstuffs in Iran were assessed. The pesticide residue levels in foodstuffs in the country were determined through systematic review and meta-analysis. The non-carcinogenic risk, carcinogenic risk, and attributable burden of disease were estimated in terms of hazard quotient (HQ), incremental lifetime cancer risk (ILCR), and disability-adjusted life year (DALY), respectively.

Assessing human toxicity and ecotoxicity impacts of agricultural pesticide use in Iran based on the USEtox model.

The human health and ecotoxicity impacts of agricultural pesticide use in Iran in 2022 were estimated. The impacts of agricultural pesticide use in Iran by pesticide, crop, and province were assessed based on the USEtox model in terms of disability-adjusted life year (DALY) for human health and potentially disappeared fraction of freshwater ecosystem species (PDF) for ecotoxicity. The annual mass of agricultural pesticide use in Iran in 2022 was 17,188 tons, consisting of herbicides (46.

Machine learning-based prediction of effluent total suspended solids in a wastewater treatment plant using different feature selection approaches: A comparative study.

Accurately predicting the characteristics of effluent, discharged from wastewater treatment plants (WWTPs) is crucial for reducing sampling requirements, labor, costs, and environmental pollution. Machine learning (ML) techniques can be effective in achieving this goal. To optimize ML-based models, various feature selection (FS) methods are employed.