Response surface methodology (RSM) approach was used for optimization of the process parameters and identifying the optimal conditions for the removal of both trihalomethanes (THMs) and natural organic matter (NOM) in drinking water supplies. Co-precipitation process was employed for the synthesis of magnetic nano-adsorbent (sMNP), and were characterized by field emission scanning electron microscopy (SEM), trans-emission electron microscopy (TEM), BET (Brunauer-Emmett-Teller), energy dispersive X-ray (EDX) and zeta potential. Box-Behnken experimental design combined with response surface and optimization was used to predict THM and NOM in drinking water supplies. Variables were concentration of sMNP (0.1 g to 5 g), pH (4–10) and reaction time (5 min to 90 min). Statistical analysis of variance (ANOVA) was carried out to identify the adequacy of the developed model, and revealed good agreement between the experimental data and proposed model. The experimentally derived RSM model was validated using t-test and a range of statistical parameters. The observed R2 value, adj. R2, pred. R2 and “F-values” indicates that the developed THM and NOM models are significant. Risk analysis study revealed that under the RSM optimized conditions, a marked reduction in the cancer risk of THMs was observed for both the groups studied. Therefore, the study observed that the developed process and models can be efficiently applied for the removal of both THM and NOM from drinking water supplies.
This is the first study to assess human health risks due to the exposure of ‘repurposed’ pharmaceutical drugs used to treat Covid-19 infection. The study used a six-step approach to determine health risk estimates. For this, consumption of pharmaceuticals under normal circumstances and in Covid-19 infection was compiled to calculate the predicted environmental concentrations (PECs) in river water and in fishes. Risk estimates of pharmaceutical drugs were evaluated for adults as they are most affected by Covid-19 pandemic. Acceptable daily intakes (ADIs) are estimated using the no-observed-adverse-effect-level (NOAEL) or no observable effect level (NOEL) values in rats. The estimated ADI values are then used to calculate predicted no-effect concentrations (PNECs) for three different exposure routes (i) through the accidental ingestion of contaminated surface water during recreational activities only, (ii) through fish consumption only, and (iii) through combined accidental ingestion of contaminated surface water during recreational activities and fish consumption. Higher risk values (hazard quotient, HQ: 337.68, maximum; 11.83, minimum) were obtained for the combined ingestion of contaminated water during recreational activities and fish consumption exposure under the assumptions used in this study indicating possible effects to human health. Amongst the pharmaceutical drugs, ritonavir emerged as main drug, and is expected to pose adverse effects on r human health through fish consumption. Mixture toxicity analysis showed major risk effects of exposure of pharmaceutical drugs (interaction-based hazard index, HI
int
: from 295.42 (for lopinavir + ritonavir) to 1.20 for chloroquine + rapamycin) demonstrating possible risks due to the co-existence of pharmaceutical in water. The presence of background contaminants in contaminated water does not show any influence on the observed risk estimates as indicated by low HQ
add
values (<1). Regular monitoring of pharmaceutical drugs in aquatic environment needs to be carried out to reduce the adverse effects of pharmaceutical drugs on human health.
This study aimed at developing a model for predicting the formation of trihalomethanes (THMs) in drinking water supplies. Monitoring of THMs in five major water treatment plants situated in the Eastern part of India revealed high concentration of THMs (231-484 μg l(-1)). Chloroform was predominant, contributing 87-98.9% to total THMs. Seasonal variation in THMs levels dictated that the concentration were higher in autumn than other seasons. Linear regression analysis of data indicated that TOC is the major organic precursors for THMs formation followed by DOC and UV254. Linear and non-linear predictive models were developed using SPSS software version 16.0. Validation results indicated that there is no significant difference in the predictive and observed values of THMs. Linear model performed better than non-linear one in terms of percentage prediction errors. The model developed were site specific and the predictive capabilities in the distribution systems vary with different environmental conditions.
This study identified ecological and human health risks exposure of COVID-19 pharmaceuticals and their metabolites in environmental waters. Environmental concentrations in aquatic species were predicted using surface water concentrations of pharmaceutical compounds. Predicted No-Effect Concentrations (PNEC) in aquatic organisms (green algae, daphnia, and fish) was estimated using EC
50
/LC
50
values of pharmaceutical compounds taken from USEPA ECOSAR database. PNEC for human health risks was calculated using the acceptable daily intake values of drugs. Ecological PNEC revealed comparatively high values in algae (Chronic toxicity PNEC values, high to low: ribavirin (2.65 × 10
5
μg/L) to ritonavir (2.3 × 10
−1
μg/L)) than daphnia and fish. Risk quotient (RQ) analysis revealed that algae (Avg. = 2.81 × 10
4
) appeared to be the most sensitive species to pharmaceutical drugs followed by daphnia (Avg.: 1.28 × 10
4
) and fish (Avg.: 1.028 × 10
3
). Amongst the COVID-19 metabolites, lopinavir metabolites posed major risk to aquatic species. Ritonavir (RQ = 6.55) is the major drug responsible for human health risk through consumption of food (in the form fish) grown in pharmaceutically contaminated waters. Mixture toxicity analysis of drugs revealed that algae are the most vulnerable species amongst the three trophic levels. Maximum allowable concentration level for mixture of pharmaceuticals was found to be 0.53 mg/L.
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