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Pharmaceutical Active Compounds Contamination and Seasonal-Based Ecotoxicity Assessment of the Yamuna River for the Delhi stretch

Alsubih Majed, Khan Roohul Abad

Rocznik Ochrona Środowiska

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2024

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Tom 26

587--602

EN

Yamuna River is the only river in Delhi. This results in heavy reliance on Yamuna to meet Delhi Water demands. This has prompted several research works covering physicochemical, biological, heavy metal concentration, emerging pollutant occurrence, and their risk assessment. This study investigated occurrence, seasonal variation (pre-monsoon, monsoon, and post-monsoon), and risk assessment posed by 7 PhACs at five sampling locations along a 22 km stretch of Yamuna River in Delhi. The samples were collected and analysed in pre-monsoon, monsoon, and post-monsoon seasons. The seven PhACs, comprised of 2 antibiotics (Ciprofloxacin; CIP, sulfamethoxazole; SMZ), 2 NSAIDs (Paracetamol; PCM, Ketoprofen; KPF), 1 anxiety control (Lorazepam, LOR), 1 anticonvulsant (Carbamazepine; CBZ) and 1 statin (Fluvastatin; FUT). The PhACs range of occurrence across three seasons was PCM 75-589 ng L-1, KPF 31-238 ng L-1, CBZ 11-192 ng L-1, LOR 62-462 ng L-1, CIP 48-192 ng L-1, SMZ 192-1534 ng L-1, and FUT 0-421 ng L-1. The seasonal occurrence was in the order of post-monsoon> pre-monsoon> monsoon. PCM, CBZ FUT posed a negligible ecotoxicological risk, LOR posed a low-medium risk, and ketoprofen, ciprofloxacin, and sulfamethoxazole posed high risks. Based on the risk index, all seasons have high ecological risk at every sample point. Discharges of untreated sewage and insufficient and inefficient treated wastewater are the primary contributors of PhACs in the Yamuna River. This study concludes that existing WWTPs need drastic upgrades. Policies and measures should also be developed to prevent untreated wastewater from reaching the Yamuna River. This necessitates further studies to investigate processes suitable for installing and treating wastewater along the longitudinal section of the drain and assess their technical feasibility.

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Impact of Landfill Leachate on Groundwater Quality and Health Risk Assessment in Mohammedia Prefecture, Morocco

El Morabet Rachida, Lamouadene Yasser, Alouane Mohamed, Qahtani Dhafar Ali Abdullah, Khan Roohul Abad

Rocznik Ochrona Środowiska

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2024

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Tom 26

432--448

EN

Several studies have been conducted to identify the potential impact of landfills on groundwater resources. This study evaluates the impact of landfills on groundwater resources in Mohammedia prefecture, Morocco. The groundwater was analysed from 2015 to 2022. The groundwater quality was evaluated based on electrical conductivity, pH, biological oxygen demand, chemical oxygen demand, total Kjeldahl nitrogen, phosphate, suspended solids, dissolved oxygen, ammonia, and total hydrocarbon, aluminium, iron, cadmium, chromium, copper, iron-nickel, zing, and mercury. The assessment was based on the water quality index, leachate pollution index, non-carcinogenic risk assessment, and carcinogenic risk assessment. A leachate pollution index <5 indicates that it poses a severe risk to groundwater resources. The non-carcinogenic risk HQ was determined to be <1, which infers no potential risk. The carcinogenic risk index value of 10-4 indicated that it is within the threshold of acceptable limit. The current study concludes that leachate from the analysed landfills does not infiltrate the groundwater resources of Mohammedia prefecture. However, the leachate pollution, even though it varies, is increasing over time. This is validated by the fact that the landfill is protected with a membrane covering the ground, which inhibits any possible infiltration of soil or water resources. Hence, this study calls for continuous monitoring of groundwater resources in the region. Future studies are required to investigate the groundwater in Mohammedia prefecture in terms of emerging pollutants to identify any potential risk.

3

Evaluation of Sulphur Dioxide Hourly Prediction Using Long Short-term Memory for Summer and Winter Season

Bennis Mohammed, Mohamed Youssfi, El Morabet Rachida, Alsubih Majed, Prayanagat Muneer, Khan Roohul Abad

Rocznik Ochrona Środowiska

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2024

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Tom 26

313--321

EN

Increasing air pollution has necessitated the prediction of pollutants over time. Deterministic, statistical, and machine-learning methods have been adopted to predict and forecast pollutant levels. It aids in planning and adopting measures to overcome the adverse effects of air pollution. This study employs long short-term memory (LSTM). This study used the hourly data from a meteorological station in a low-town area, Mohammedia City, Morocco. The model prediction accuracy was evaluated based on hourly, weekly, and seasonal (summer and winter) readings for the summer and winter of 2019, 2020 and 2021. Root mean square error (RMSE), mean absolute error (MAE) and mean arctangent absolute percentage error (MAAPE) were calculated to evaluate the accuracy of the developed LSTM model. The MAE value of 0.026 was observed to be less in winter than 0.029 during summer in 2019. Also, it was observed that MAE values decreased from Year 2019-2021, indicating increased prediction accuracy. MAAPE also observed a similar trend. However, RMSE values indicated the opposite for 2019 and 2020; in 2021, the RMSE value was 0.21 for summer and 0.14 for winter for hourly readings. Based on the error calculation, the study found weekly hourly readings were the most accurate for predicting SO2 concentration. Also, the LSTM model was more accurate in predicting winter SO2 concentration than in the summer season. Further studies must incorporate local incidences affecting the SO2 concentration into the LSTM model to increase its accuracy.