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Psychiatric drugs released by humans in wastewater have received more attention because of their potential risks for aquatic organisms. In this study, the occurrence of the two most common groups of psychiatric drugs (sedatives-hypnotics-anxiolytics and antidepressants) were evaluated in the Tehran South Municipal Wastewater Treatment Plant. All the target sedatives-hypnotics-anxiolytics (alprazolam, phenobarbital, and thioridazine) and antidepressants (fluoxetine, citalopram, sertraline, and venlafaxine) were observed in influent and secondary clarification (SC) effluent. Thioridazine (164.25 ± 218.74 ng/L) and citalopram (672.53 ± 938.56 ng/L) had the highest mean concentrations in the influent, while alprazolam (5.09 ± 2.33 ng/L) and citalopram (776.97 ± 1088.01 ng/L) had the highest concentrations in the SC effluent. The higher concentrations of the psychiatric drugs, except thioridazine, were detected in the SC effluent compared to the concentrations in the influent. The increased drugs concentrations, with negative removal efficiencies, were more distinctive in the cold season samples. Psychiatric drugs processed in the chlorination unit followed a completely different pattern compared to the drugs in the biological treatment unit. All the drugs' concentrations, except thioridazine, decreased in the chlorination unit, ranging between 27 ± 14% for alprazolam and 75 ± 10% for citalopram. However, the mean concentrations of the detected drugs were as follows: sertraline (11.96 ± 11.62 ng/L) and venlafaxine (184.94 ± 219.74 ng/L) which could cause environmental and ecological concerns.

Examining the concentration and types of airborne bacteria in waste paper and cardboard sorting plants (WPCSP) is an urgent matter to inform policy makers about the health impacts on exposed workers. Herein, we collected 20 samples at 9 points of a WPCSP every 6 winter days, and found that the most abundant airborne bacteria were positively and negatively correlated to relative humidity and temperature, respectively. The most abundant airborne bacteria (in units of CFU m−3) were: Staphylococcus sp. (72.4) > Micrococcus sp. (52.2) > Bacillus sp. (30.3) > Enterococcus sp. (24.0) > Serratia marcescens (20.1) > E. coli (19.1) > Pseudomonas sp. (16.0) > Nocardia sp. (1.9). The lifetime average daily dose (LADD) for the inhalation and dermal routes for the intake of airborne bacteria ranged from 3.7 × 10−3 ≤ LADDInhalation ≤ 2.07 × 101 CFU (kg d)−1 and 4.75 × 10−6 ≤ LADDDermal ≤ 1.64 × 10−5 CFU (kg d)−1, respectively. Based on a sensitivity analysis (SA), the concentration of airborne bacteria (C) and the exposure duration (ED) had the most effect on the LADDInhalation and LADDDermal for all sampling locations. Although the Hazard Quotient of airborne bacteria was HQ 2), indicating worker’s exposure to an infected environment. Therefore, in the absence of sufficient natural ventilation the indoor ambient conditions of the WPCSP studied should be controlled by supplying mechanical ventilation.

PURPOSE: A successful hospital solid waste (HSW) management needs an accurate estimation of waste generation rates. The conventional regression methods upon increasing the number of input variables hardly can predict the HSW generation rate and require more complex modeling. In return, application of machine learning methods seems to be able to increase the power of predicting the produced wastes. METHODS: To predict the HSW, Multiple Linear Regression(MLR) and several Neuron- and Kernel-based machine learning methods were employed to analyze data from hospitals of Karaj metropolis. The number of wards, active and occupied beds, staffs and inpatients, and ownership type and activity years of hospital were defined as the model inputs. In addition, proposed models performance was evaluated based on coefficient of determination (R(2)) and Mean-Square Error (MSE). RESULTS: The performance of Neuron- and Kernel-based machine learning methods indicated that both models were satisfactory in predicting HSW. However, the better results of 0.82–0.86 for average R(2) value and 0.003–0.008 for average MSE value, indicated relative superiority of Kernel-based models compared to Neuron based (average R(2) = 0.68–0.74, average MSE = 0.009–0.023) and MLR models. Number of staffs and hospital ownership type were the most influential model variables in predicting the HSW generation rate. CONCLUSIONS: The machine learning methods could interpret the relationship between waste generation rate and model inputs, appropriately. Thus, they may play an effective role in developing cost-effective methods for suitable HSW management. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s40201-018-00324-z) contains supplementary material, which is available to authorized users.

Response surface methodology (RSM) and artificial neural network (ANN) were applied to investigate the chemical coagulation/precipitation process in the treatment of water contaminated with humic acid and processed kaolin. Accordingly, the removal efficiencies of total organic carbon (TOC), turbidity, and colour were considered to assess the effectiveness of the process. The proposed models were evaluated based on determination coefficient (R2) and Mean-Square Error (MSE). Although both models could satisfactorily interpret the correlation of targets with model inputs, i.e. pH and coagulant dosage, the ANN exhibited a slightly better fit to the process data than the RSM, based on comparison between determination coefficients (R2) and Mean-Square Error (MSE). R2, MSE, and error values of 0.990–0.999, 0.112–3.569 and 0.000–1.660% for ANN were respectively demonstrated. To investigate the predictive performance of both models, some additional experiments were subsequently carried out at obtained optimum conditions by genetic algorithm technique. TOC, colour, and turbidity removal efficiencies of 58.8, 93.0, and 100% were demonstrated at identified optimum conditions (i.e. pH 6.7 and 3 mM of ferric chloride). The results revealed a low deviation from their predicted values with maximum errors of 1.90 for RSM and 1.66 for ANN. Also, our results suggest that setting the RSM before ANN could considerably improve its prediction weaknesses.

Composting process of sewage sludge requires a preprocessing step in order to prepare the appropriate mixture of dewatered sludge (Xs) with amendment (Xa), bulking agent (Xb), and/or recycled materials (Xr). This research aimed to develop a novel mathematical model for finding an optimal mixture ratio of dewatered sludge with the aforementioned influencing elements on co-composting process. Seven feasible scenarios were presented and the best one was selected in viewpoint of technical and economic perspectives. The optimum mixture was prepared and its quality was evaluated in the terms of physical, chemical, and microbial characteristics. The optimum mixture was loaded in an aerated static pile composting reactor in order to evaluate the quality of the final compost product. If the test results were not in compliance with the USEPA standards, the model was iteratively modified to fulfill the desired objective. The model was validated using the experimental results. The mixture of Xs:Xa:Xb:Xr with a weight ratio 7.4:1.0:1.4:2.3 allowed optimal moisture content (59.8 ± 0.5%), organic matter (80.0 ± 2.6%), dry matter (40.2 ± 0.6%), C/N ratio (28.0 ± 1.6), and free air space (> 30%) across the composting pile. The final product of compost met the heavy metal and microbial requirements for land application. It can be concluded this mathematical model is a promising method for selecting the optimal amount and type of materials for preparing the initial mixture of co-composting process.

This research aimed to develop an index known as swimming pool water quality index (SPWQI) for quantifying the water pollution potential. The index is a quantitative tool to show the data on the water quality of swimming pools consistently. It makes summarizing the complex data on water quality possible and paves the way for its communication to the general public and decision makers. In this study, the SPWQI has been formulated according to the literature review and Delphi method. According to literature review and the total judgment of the panelist (23 experts specialized in environmental health engineering), 13 physicochemical and biological parameters along with their weights were selected for inclusion in the SPWQI. The results indicated the possibility of calculating a single numerical value (i.e., SPWQI), which indicates the combined effect of significant selected parameters on water quality. In addition, biological composition (47%) had a strong preference weighting than physical and chemical parameters in evaluating the index. Next, it was attempted to implement a novel and user-friendly mathematical model for evaluating the SPWQI. Then, the model accuracy and performance was confirmed by a case study. Overall, it could be said that the assessment of SPWQI sheds light on the concurrent power of several pollutants and can help the authorities to decide upon water quality management and treatment approaches.

This study characterized spatio-temporal variations in the concentration of benzene, toluene, ethylbenzene, and xylene (BTEX) compounds in the vicinity of gas and compressed natural gas (CNG) stations in Tehran, Iran. Health risk assessment (HRA) was computed using Monte Carlo simulations (MCS) for evaluating inhalation lifetime cancer risk (LTCR), the hazard quotient (HQ), and sensitivity analysis (SA) for BTEX exposure in different age groups (birth to 0.05). Factors promoting BTEX formation in the study region were fuel evaporation and gas/CNG station emissions. The LTCRs for the target compounds in the winter for different age groups and distances from the center of stations was limited to 2.11 × 10−4 to 1.82 × 10−3 and 2.30 × 10−4 to 2.01 × 10−3, respectively, which exceeded proposed values by U.S. EPA. Moreover, the HQs for BTEX for three age groups and distances were limited to between 2.89 × 10−5 and 9.33 × 10−2, which were lower than the acceptable limit (HQs

Background and objectives: phenol, cyanide, and chromium (VI) are toxic pollutants. They can be discharged from different industries simultaneously such as iron and steel industry, coal mining, automobile manufacturing of parts. Presence of these pollutants in water and wastewater is a serious hazard and these substances lead to undesirable effects on both the environment and human. Thus, control of these contaminants is essential for human health and environment. The main objective of this study was to assess the efficiency of Fenton process for simultaneous removal of phenol, cyanide, and chromium (VI) from synthetic wastewater. Materials and methods: This study is an experimental study in lab scale that was carried out in a batch system. Variations of this study including pH, chemicals concentration (Fe2+ and H2O2 and molar ratio Fe2+/H2O2), and reaction time were investigated. Cyanide, phenol, and total chromium were determined respectively by colorimetric method (spectrophotometer), high-per...