فصلنامه پژوهش در بهداشت محیط
سال نهم شماره 3 (پاییز 1402)

Nitroaniline is a derivative of aniline known for its high toxicity, potential carcinogenicity, and mutagenic effects. A practical method for removing these compounds from aqueous solutions is applying advanced oxidation processes (AOPs). This study aims to investigate the efficiency of the activated persulfate process in removing nitroaniline from aqueous solutions under controlled laboratory conditions.

To conduct this study, the impact of various variables, including pH levels (ranging from 3 to 11), contact time (15 to 120 minutes), nitroaniline concentration (ranging from 1 to 30 mg/L), and persulfate concentration (ranging from 100 to 800 mg/L), on synthetic samples was examined. Post-treatment, the remaining nitroaniline concentration was measured using a UV-vis spectrophotometer at 530 nm. Independent t-tests and descriptive statistics, including mean and standard deviation, were employed to analyze the acquired data.

The activated persulfate process with pulsed light achieved the highest nitroaniline removal efficiency at 98.2%. This result was obtained under specific conditions: a contact time of 90 minutes, a pH level of 8, an activated persulfate concentration of 600 mg/L, and a nitroaniline concentration of 1 mg/L. Our findings indicate that the photocatalytic process of persulfate activated by pulsed light adheres to the second-order kinetic model.

Based on the outcomes of this study, the persulfate process activated by pulsed light displayed significant effectiveness in removing nitroaniline from aqueous solutions. Therefore, owing to its high removal efficiency, cost-effectiveness, and straightforward management, it is recommended to employ this process to remove organic and resistant pollutants from aqueous solutions.

Tetracycline represents the most prevalent antibiotic group in production and utilization and is extensively employed for the prophylaxis and treatment of infectious diseases in both human and veterinary medicine. The primary objective of this investigation was to assess the efficacy of electro-activated persulfate (EC/PS/HR) for eliminating tetracycline from aqueous solutions.

This study was conducted in a batch mode utilizing an electro-activated persulfate (EC/PS/HR) system. All experiments were carried out under constant temperature conditions. Response surface methodology (RSM) in conjunction with a central composite design (CCD) was employed to optimize the variables associated with the electro-activated persulfate and hydrogen peroxide process, including pH, current density, and the persulfate/hydrogen peroxide molar ratio, with the aim of tetracycline removal. Data analysis in this study was performed using Data Designer 8.0.6 software.

The results of this study revealed the use of a quadratic model to predict the impact of independent variables on the efficiency of tetracycline removal in the process. The exceedingly low (p <0.0001) and the high correlation coefficient (R2) of the obtained model signify a robust correlation between experimental and predicted data. The optimal conditions for achieving maximum efficiency in the degradation of tetracycline through electro-activated persulfate were determined to be a pH of 5.6, a persulfate/hydrogen peroxide molar ratio of 1.1, and a current density of 31 mA. Under these conditions, tetracycline degradation reached approximately 95.2%.

Based on the findings of this investigation, it can be deduced that the advanced oxidation process relying on electro-activated persulfate (EC/PS/HR) is capable of eliminating contaminants in aqueous environments, influenced by various factors such as hydrogen peroxide dosage, catalyst concentration (persulfate), and pH. The study highlights the capability of the electro-activated persulfate (EC/PS/HR) hybrid process to decompose recalcitrant pollutants like tetracycline from aqueous environments. Overall, the electro-activated persulfate process demonstrates promise for the degradation of tetracycline in aqueous solutions.

Drinking water the primary source of fluoride essential for bodily requirements. Its appropriate concentration health as a preventive measure against caries. Consequently, this study aims to ascertain the spatiotemporal variations in fluoride concentration within the drinking water of Hamadan province through the utilization of a Geographic Information System (GIS).

The present research is a descriptive-analytical investigation conducted across nine cities within Hamadan province in 2019.Data about the fluoride on concentrations in the drinking water of Hamadan province were obtained from the Health Vice-Chancellor of Hamadan University of Medical Sciences. ArcGIS version 10/.8 software was employed to elucidate the spatial and temporal fluctuations in fluoride ion concentration, while SPSS version 16 software was utilized for data. analysis. A significance level of 0.05 was considered across all tests.

The study findings revealed that the highest annual fluoride concentration was observed in Kabudarahang city (0.98 ± 0.38 mg/l), whereas the lowest annual average was recorded in Tuyserkan city (0.58 ± 0.35 mg/l). Overall, the mean fluoride concentration in the latter half of the year (0.77 ± 0.42 mg/liter) was slightly higher than in the first half of the year (0.71 ± 0.43 mg/liter). Nevertheless, this discrepancy did not reach statistical significance (p>0.05). 

The outcomes of this investigation demonstrate that, on the whole, the average fluoride concentration throughout all regions of Hamadan province falls within the optimal range thereby averting potential risks associated with excessive or substandard fluoride levels for consumers.

Nitrates have long been considered indicative of drinking water quality and a critical concern for human health. The evolution of advanced models for water quality management has spurred decision-makers to incorporate artificial intelligence technologies into water quality planning. This study aims to employ the AdaBoost model, one of the cutting-edge models in water quality management, to predict nitrate concentrations in groundwater using pH and EC (Electrical Conductivity) as input variables.

Initially, the study analyzed the Pearson correlation matrix and subsequently determined the input variables for multiple AdaBoost models with varying hyperparameters. A sensitivity and dependence analysis of the model's input variables was conducted to assess their impact on nitrate prediction.

The results obtained from the AdaBoost model reveal R-squared (R2) values of 0.915 for the training dataset and 0.924 for the test dataset. Additionally, the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) scores for the training dataset were recorded as 1.02, 1.01, 0.823, and 7.3%, respectively. For the test dataset, these metrics were observed in the order of 0.228, 0.477, 0.375, and 3.2%. The model's sensitivity analysis identified the pH variable as the most influential factor in nitrate prediction.

The model analysis demonstrates that the proposed method performs well in predicting nitrate concentrations. This approach holds significant potential for implementation as an intelligent system for forecasting water quality parameters.

Oral and dental health is crucial to public health. The primary indices used to assess oral and dental health are DMFT and CPITN indices. This study aimed to evaluate the oral and dental health status of 11 to 13-year-old students in Mashhad City and its relationship with their water consumption and other related factors.  This cross-sectional analytical study was conducted in 2021 on 1,232 students. Sampling was done from all hand-dug wells, semi-deep wells, deep wells, and qanats (underground channels) in the Mashhad region in 2021. All drinking water parameters were measured according to the standards outlined in "Standard Methods for the Examination of Water and Wastewater." Data were analyzed using SPSS software version 20. The mean DMFT index among Mashhad students was 0.98. The average fluoride content in drinking water was 0.38 milligrams per liter(mg/L), lower than the permissible fluoride level in drinking water. According to the relevant statistical tests, there was a significant relationship (p<0.05) between the DMFT index and daily water consumption, fluoride content in drinking water, and daily flossing. Furthermore, a significant relationship (p<0.05) was found between the CPITN index and toothbrush usage.     Given the significant association between fluoride content in drinking water and daily flossing with the DMFT index, it is essential to maintain fluoride levels in drinking water within the standard range. Moreover, oral health care practices, such as flossing and toothbrushing, should be taught to students.

Increased waste production during patient treatment emerges as a serious issue following infectious disease outbreaks. The current study aimed to investigate the effect of the COVID-19 outbreak on medical waste production in Mashhad, Iran.

In this study, 4 referral (R) and 2 non-referral (NR) hospitals were examined. The quantity and quality of waste produced before (2018-2019) and during COVID-19 (2019-2020) were extracted from hospital records and noted in standard medical waste management checklists. Also, the number of hospital visits due to COVID-19 was determined and entered into the study by referring to the Treatment Deputy of Mashhad University of Medical Sciences.

The results showed that the mean waste production was 449.50 ± 49.45 kg/day and 436.04 ± 107.55 kg/day in 2018-2019 (before COVID-19) and 2019-2020 (during the outbreak) in R1, respectively, 3000.26 ± 227.46 kg/day and 3279.72 ± 153.00 in R2, 544.53 ± 107.10 kg/day and 666.32 ± 156.68 in R3, and 476.72 ± 15.96 kg/day and 635.41 ± 54.88 kg/day in R4. Also, the mean waste production was 219.6 ± 36.90 and 199.06 ± 49.58 kg/day in NR1 and 112.1 ± 29.55 kg/day and 108.8±59.1 kg/day in NR2 before and during COVID-19, respectively. Field surveys showed that despite the increased amount of waste production, all waste management stages, such as collection, transfer, and disposal, are being implemented in accordance with the guidelines.

The results of this study showed that the medical waste production rate is related to the prevalence of diseases. Considering the importance of medical waste management, it requires careful and correct application of national and international guidelines.

In the contemporary context, hazardous waste management has gained prominence within global environmental regulations. However, impediments pose significant challenges to effectively managing such waste. This study aims to identify ten barriers within the hazardous waste management paradigm, drawing from literature sources and insights from experts in the field.

The empirical investigation conducted in 2022 solicited the viewpoints of five managers and experts affiliated with a waste management firm in Kermanshah province, Iran. The decision-making techniques of DEMATEL (Decision-Making Trial and Evaluation Laboratory) and interpretive structural modeling were employed to comprehensively comprehend the internal interrelationships and hierarchical structure of these barriers in hazardous waste management.

The findings reveal that the primary obstacles, as discerned through the hybrid approach combining decision-making trial and evaluation with interpretive structural modeling, are the lack of public awareness regarding hazardous waste recycling and the absence of comprehensive policies and regulations addressing hazardous waste issues. Notably, the deficiency in public awareness regarding hazardous waste recycling emerges as a causal factor, achieving a score of 0.54. This factor falls within the category of autonomous obstacles, exerting a potent influence with a strength rating of 10 and a relatively weak dependency of 1. Conversely, the inadequacy of policies and regulations addressing hazardous waste is identified as a causal factor with a score of 0.47, belonging to the category of connecting obstacles. It exhibits a robust penetration capability with a rating of 10 and a relatively strong dependency power of 6.

The proposed methodology is a valuable tool for policymakers and decision-makers, enabling them to discern the interconnectedness of barriers and their hierarchical arrangement within the hazardous waste management framework.

One of the fundamental problems of air pollution is that it often affects large areas of various land uses, such as cities and agricultural products hundreds of kilometers away from the source of pollutants, or results in cumulative effects with other industries. This research aims to quantify the concentration of pollutants in residential areas around industrial areas.

In this study, the AERMOD software was used for modeling air pollutants. This software uses meteorological data, digital elevation models, and information about pollutant sources. It assesses and quantifies air pollution levels related to PM10, SOX, NOx, and CO pollutants in a specific location.

The results of this study indicate that in these 28 population points surrounding the targeted industrial land use, pollutant concentrations in both scenarios with and without background concentrations did not exceed the standard limits for any pollutant. The most significant pollutant in this research was NOx, which showed the slightest difference from the permissible pollution limit. Furthermore, due to the closer proximity of pollutants to environmental standards in this region, increased loading of industrial land uses can lead to various health, economic, and social problems.

The findings of this research demonstrate that to assess and quantify pollutant concentrations in the areas surrounding industrial pollutant points, it is advisable to consider background pollution in addition to modeling point sources for greater accuracy in the direction of sustainable development in such areas.