Economic evaluation of hazardous healthcare waste treatment systems.

The cost estimation and assessment of healthcare waste treatment systems (HCWTSs) for preventing financial and environmental damage are essential. This work reports economic analyses of treatment of hazardous-infectious waste based on WHO approach in HCWTSS of 43 hospitals in Tehran, Iran. The waste generation rate for total hospital waste in 43 HCWTSS was 4.42 ± 2.77 kg/active-bed/day. The mean of chemical, sharps, infectious, and general wastes in 43 HCWTSS were 13.79 ± 19.71, 30.29 ± 37.46, 336.28 ± 291.31, and 539.6 ± 383.13 kg/day, respectively. Economic analyses proved that general hospitals spent 1.63 times more than specialized hospitals on treating hazardous-infectious waste per year. The annual cost of treating each kilogram of hazardous healthcare waste in studied HCWTSS was 0.3 dollars. A range of total annual costs in 43 HCWTSS was limited to 7.9-118 thousand dollars. The results of ANOVA test demonstrated that the age and performance levels of hospitals significantly affect the annual capital and operating costs, respectively. Hence, improving recycling knowledge and increasing source-separated recycling should be considered to control the costs in HCWTSS. The results of this work have implications for the hospital managers in especially developing countries to evaluate previously unknown economic analyses and policies and take action to control wasted costs in HCWTSS.

Occurrence and removal of psychiatric pharmaceuticals in the Tehran South Municipal Wastewater Treatment Plant.

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.

Characteristics and health effects of particulate matter emitted from a waste sorting plant.

Solid waste components can be recycled in waste paper and cardboard sorting plants (WPCSP) through a multistep process. This work collected 15 samples every six days from each of the 9 points selected to study the processes taking place in a WPCSP (135 particulate matter samples total). Examining the concentration and size fraction of particulate matter (i.e., PM1, PM2.5 and PM10) in WPCSP is an essential issue to notify policy makers about the health impacts on exposed workers. The major activities for increasing of the concentration of PM in various processing units in the WPCSP, especially in hand-picking routes I and II were related to manual dismantling, mechanical grinding, mechanical agitation, and separation and movement of waste. The results of this work showed that a negative correlation between temperature and particulate matter size followed the order PM10 > PM2.5 > PM1. Exposure to PM2.5 and PM10 in the WPCSP lead to possible risk (HI = 5.561 and LTCRs = 3.41 × 10-6 to 9.43 × 10-5 for PM2.5 and HI = 7.454 for PM10). The exposure duration and the previous concentrations had the most effect on the ILCRs and HQs for PM2.5 and PM10 in all sampling sites. Hence, because WPCSP are infected indoor environments (I/O ratio > 1), the use of control methods such as isolation of units, misting systems, blower systems equipped with bag houses, protective equipment, a mechanical ventilation system, and additional natural ventilation can reduce the amount of suspended PM, enhance worker safety, and increase the recycling rate.

Characteristics and assessing biological risks of airborne bacteria in waste sorting plant.

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 < 1, an acceptable level, the indoor/outdoor ratio (1.5 ≤ I/O ≤ 6.6) of airborne bacteria typically exceeded the threshold value (I/O > 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.

Environmental risk assessments of multiclass pharmaceutical active compounds: selection of high priority concern pharmaceuticals using entropy-utility functions.

This research aimed to identify high-risk pharmaceutically active compounds (PhACs) by analyzing occurrence (O), persistence (P), bioaccumulation (B), and toxicity (T) of 62 drugs which are widely used in Iran. A comprehensive approach was taken in risk assessment of the selected PhACs and in their prioritization using multiple-criteria decision analysis (MCDA) such as utility functions and principal component analysis (PCA). In practice, assigning weight to each criterion (i.e., O, P, B, and T) for risk assessment of PhACs is a challenge. In this research, the impact of giving both equal and unequal weight to each criterion by using a quantitative entropy method was studied. For risk assessment, two exposure approaches (consumption rate and occurrence of PhACs) and three MCDA approaches (PCA and utility functions with and without equal weights for each criterion) were compared. The utility function using equal weights for all O, P, B, and T criteria showed that thioridazine, pimozide, chlorpromazine, sertraline, clomipramine, and aripiprazole were at the highest level of risk, with concern score of 0.75, 0.75, 0.67, 0.58, 0.58, and 0.58, respectively. Unequal weight approach included additional compounds such as fluoxetine, citalopram, and methadone as a priority. All three MCDA approaches showed that sedatives and antidepressants were prevalent PhACs in the risk-based priority lists. However, the exposure-based approaches showed antibiotics and analgesics as the pharmaceutical of the highest priority. Overall, selection of the high priority concern pharmaceuticals depends on the prioritization approach employed. However, the utility function using unequal weights is a more conservative and effective approach for prioritization.

Assessment of co-composting of sewage sludge, woodchips, and sawdust: feedstock quality and design and compilation of computational model.

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.

Dataset on specifications, carcinogenic and non-carcinogenic risk of volatile organic compounds during recycling paper and cardboard.

Emissions of volatile organic compounds (VOCs) were studied during paper and cardboard recycling from a paper and cardboard solid waste recycling factory (PCSWRF). Data are summarized in this article for the following quantities for a PCSWRF during the winter in Tehran, Iran: VOC concentrations (µg m-3), the percentage of detected VOCs, exposure indices (Ei) of individual and total VOCs (TVOCs), inhalation lifetime cancer risk (LTCR) of VOCs, the hazard quotient (HQ) of VOCs, sensitivity analysis (SA) for VOC exposure in different age groups (birth to <81), and Spearman's rank correlation coefficients (r) between VOC concentrations and meteorological parameters. For more insight please see "Characteristics and Health Effects of Volatile Organic Compound Emissions during Paper and Cardboard Recycling"[1], https://doi.org/10.1016/j.scs.2019.102005.

Comparative study of predicting hospital solid waste generation using multiple linear regression and artificial intelligence.

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 (R2) 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 R2 value and 0.003-0.008 for average MSE value, indicated relative superiority of Kernel-based models compared to Neuron based (average R2 = 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.

An innovative swimming pool water quality index (SPWQI) to monitor and evaluate the pools: design and compilation of computational model.

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.

A case study of BTEX characteristics and health effects by major point sources of pollution during winter in Iran.

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 <81) and as a function of distance (0-250 m) from the center of the stations. For all monitoring stations, the average values of benzene, toluene, ethylbenzene, and xylene in winter were 466.09 ±â€¯132.25, 873.13 ±â€¯233.51, 493.05 ±â€¯141.22, and 910.57 ±â€¯145.40 µg m-3, respectively. The mean wintertime ratios of T/B for the 12 stations ranged from 1.69 to 2.04. Furthermore, there was no significant relationship between the concentration of BTEX with either the specific month or distance from the center of stations (p > 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 < 1). The results of this work are applicable to similar areas that are heavily populated with vehicular traffic. This study motivates a closer look at mitigation strategies to limit the health effects of carcinogenic emissions such as benzene and ethylbenzene from gas/CNG stations.