The presence of contaminants of emerging concern (CECs) such as pharmaceuticals and personal care products (PPCPs), endocrine-disrupting compounds (EDCs), flame retardants (FRs), pesticides, and artificial sweeteners (ASWs) in the aquatic environments remains a major challenge to the environment and human health. In this review, the classification and occurrence of emerging contaminants in aquatic environments were discussed in detail. It is well documented that CECs are susceptible to poor removal during the conventional wastewater treatment plants, which introduce them back to the environment ranging from nanogram per liter (e.g., carbamazepine) up to milligram per liter (e.g., acesulfame) concentration level. Meanwhile, a deep insight into the application of advanced oxidation processes (AOPs) on mitigation of the CECs from aquatic environment was presented. In this regard, the utilization of various treatment technologies based on AOPs including ozonation, Fenton processes, sonochemical, and TiO heterogeneous photocatalysis was reviewed. Additionally, some innovations (e.g., visible light heterogeneous photocatalysis, electro-Fenton) concerning the AOPs and the combined utilization of AOPs (e.g., sono-Fenton) were documented.

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Abstract:In this study, experiments have been conducted to evaluate the organics and nutrients removal from synthetic wastewater by a laboratory scale moving bed biofilm process. For nutrients removal, moving bed biofilm process has been applied in series with anaerobic, anoxic and aerobic units in four separate reactors. Moving bed biofilm reactors were operated continuously at different loading rates of nitrogen and Phosphorus. During optimum conditions, close to complete nitrification with average ammonium removal efficiency of 99.72% occurred in the aerobic reactor. In the aerobic reactor, the average specific nitrification rate was 1.8 g NO x -N kg VSS. The results of the average effluent soluble COD concentration from each reactor showed that denitrification process in the second anoxic reactor consumed most of the biodegradable organic matter. As seen from the results, denitrification rate has increased with increasing NO x -N loading in the second anoxic reactor. The aerobic phosphate removal rate showed a good correlation to the anaerobic phosphate release rate. Moreover, phosphate removal rate showed a strong correlation to the phosphate loading rate in the aerobic reactor. In optimum conditions, the average SCOD, total nitrogen and phosphorus removal efficiencies were 96.9, 84.6 and 95.8%, respectively. This study showed that the moving bed biofilm process could be used as an ideal and efficient option for the total nutrient removal from municipal wastewater.

The phenolic compounds are known by their carcinogenicity and high toxicity as well as creating unpleasant taste and odor in water resources. The present study develops a cost-effective technology for the treatment of water contaminated with phenolic compounds, including Phenol (Ph), 2-chlorophenol (2-CP), and 4-chlorophenol (4-CP). So, two sorbents, rice bran ash (RBA) and biomass of brown algae, Cystoseiraindica, were used and results were compared with the commercially granular activated carbon (GAC). The phenolic compounds were determined using a high performance liquid chromatography (HPLC) under batch equilibrium conditions. The effects of contact time, pH, initial adsorbate concentration, and adsorbent dosages on the removal efficiency were studied. The adsorption data were simulated by isotherm and kinetic models. Results indicated that RBA and GAC had the lowest efficiency for the removal of 2-CP, while the order of removal efficiency for C. indica biomass was as follows: 2-CP > 4-CP > phenol. The efficiency of GAC was higher than those of other adsorbents for all of the phenolic compounds. Furthermore, the adsorption capacity of RBA was found to be higher than that of C. indica biomass. The optimal initial pH for the removal of phenol, 2-CP and 4-CP was determined to be 5, 7, and 7 for RBA, GAC, and algal biomass, respectively. Kinetic studies suggested that the pseudo-second order best fitted the kinetic data.

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Climate change has been described to raise outbreaks of water-born infectious diseases and increases public health concerns. This study aimed at finding out these impacts on cholera infections by using Artificial Neural Networks (ANNs) from 2021 to 2050. Daily data for cholera infection cases in Qom city, which is located in the center of Iran, were analyzed from 1998 to 2016. To determine the best lag time and combination of inputs, Gamma Test (GT) was applied. General circulation model outputs were utilized to project future climate pattern under two scenarios of Representative Concentration Pathway (RCP2.6 and RCP8.5). Statistical downscaling was done to produce high-resolution synthetic time series weather dataset. ANNs were applied for simulating the impact of climate change on cholera. The observed climate variables including maximum and minimum temperatures and precipitation were tagged as predictors in ANNs. Cholera cases were considered as the target outcome variable. Projected future (2020–2050) climate in previous step was carried out to assess future cholera incidence. A seasonal trend in cholera infection was seen. Our results elucidated that the best lag time was 21 days. According to the results of downscaling tool, future climate in the study area by 2050 will be warmer and wetter. Simulation of cholera cases indicated that there is a clear trend of increasing cholera cases under the worst scenario (RCP8.5) by the year 2050 and the highest cholera cases observe in warmer months. The precipitation was recognized as the most effective input variable by sensitivity analysis. We observed a significant correlation between low precipitation and cholera infection. There is a strong evidence to show that cholera disease is correlated with environment variables, as low precipitation and high temperatures in warmer months could provide the swifter bacterial replication. These conditions in Iran, especially in the central parts, may raise the cholera infection rates. Furthermore, ANNs is an executive tool to simulate the impact of climate change on cholera to estimate the future trend of cholera incidence for adopting protective measures in endemic areas.