Avicenna Journal of Environmental Health Engineering
Volume:10 Issue: 1, Sep 2023

This study aimed to estimate the burden of diseases (BoD) from environmental cigarette smoke (ECS) exposure. More precisely, the study examined the prevalence of non-communicable diseases (NCDs) related to cigarette smoking in municipal staff based on a prospective cohort study. This cross-sectional study was designed among municipal employees, aged 25-55 years in Qazvin, Iran during 2019-2020. The data on cigarette smoking and environmental exposure were obtained using a standard questionnaire. Finally, systolic blood pressure (SBP) and diastolic blood pressure (DBP), fasting blood sugar (FBS), triglyceride (TG), high-density lipoprotein, and low-density lipoprotein were measured to assess the relation between active cigarette smoking and the prevalence of NCDs, including hypertension and diabetes in workers. The prevalence of cigarette smoking was 16.2%. In addition, 15% of staff were exposed to ECS. The prevalence of anemia and high TG levels in current cigarette smokers was 2.71 (P=0.024) and 1.4 times higher than among non-smokers (P=0.027). The total number of disability-adjusted life years (DALYs) caused by lung cancer, asthma, and ischemic heart disease (IHD) attributable to ECS was 0.65 per 1000 adults annually. Further, the number of 0.058 death was estimated per 1000 adults annually at the workplace. Most deaths were caused by IHD (79%), followed by lung cancer (12%) and asthma (9%). It was revealed that the number of DALYs and deaths attributable to secondhand smoke (SHS) was 0.34 and 0.3 vs. 0.028 and 0.029 per 1000 adults in men and women, respectively. The results demonstrated that exposure to ECS is an important factor in increasing the risk of the prevalence of NCDs and can increase the BoD attributable to cigarette smoking.

Soil poisoning with heavy metals is one of the most significant aspects of environmental research. In the soil treated with Thiobacillus, the effect of sulfur granular municipal solid waste (sulfur granular MSW), humic acid, and nano Fe-oxide on reducing Pb absorption by plants was investigated. Treatments consisted of applying sulfur granular MSW (0 and 30 t/ha), spraying humic acid (0 and 1.5 mmol/L), and using nano Fe-oxide (0 and 2% w/w) in the soil contaminated with Pb (0, 600, and 1200 mg/kg-soil). Plants were collected after 5 months, and the Pb content in the soil and plants was estimated by atomic absorption spectroscopy (AAS). The activity of ascorbate peroxidase (APX) and peroxidases (POX) enzymes were also determined. The use of 30 t/ha of sulfur granular MSW in the Pb-polluted soil reduced Pb concentration in the soil and plant by 13.4% and 15.1 %, respectively, while it increased Fe concentration by 15.2% in the plant. Using nano Fe-oxide (2% w/w) in the Pb-contaminated soil (600 and 1200 mg/kg soil) considerably enhanced Fe content in the plant. Pb absorption by plants was dramatically reduced by foliar application of humic acid. According to the findings of this study, the interaction effects of sulfur granular MSW, nano Fe-oxide, and humic acid significantly reduced the Pb absorption by plants. However, the impacts of soil physico-chemical characteristics and the kind of soil pollutant on plant Pb concentration cannot be ignored.

Awareness of lead (Pb) distribution and zoning in urban, agricultural, and industrial areas is of particular importance in environmental protection planning. This cross-sectional study aimed to determine the distribution of Pb in different uses with different levels of industrial development and human activities in urban areas in Hamadan during 2018. For this purpose, 146 soil samples were collected from 0-20 cm depth and prepared for air-dried laboratory analyses. Then, pH, electrical conductivity (EC), total Pb, and adsorbable Pb in the soil were measured. The results demonstrated that the mean concentration of total Pb is 88 mg/kg. In addition, the mean concentration of Pb in urban, industrial, and agricultural areas was 41 mg/kg, 30 mg/kg, and 17 mg/kg, respectively. Further, the average concentration of adsorbable Pb with a standard deviation of 0.3 in industrial, urban, and agricultural areas was 0.38 mg/kg, 0.16 mg/kg, and 0.06 mg/kg. Land use was an important source of Pb change in the studied soils; however, Pb change did not depend only on land use. Factors such as weather conditions or proximity to roads, as well as previous land uses, could affect the role of existing land use and be effective in the distribution of soil Pb. Studies revealed that the amount of the soil’s adsorbable Pb is more important than that of total Pb in different uses, which should be considered in any soil-related research.

Industrial effluents produce vast amounts of pollutants and account for 20% of industrial wastewater annually. Methylene blue (MB) is one of the most widely used dyes in the medical, pharmaceutical, and textile industries. However, it is toxic to living organisms, and even a short-time exposure to it can be potentially harmful. Therefore, the purpose of this study was to investigate the efficiency of zinc oxide nanoparticles in removing MB from aqueous solutions in the presence of hydrogen peroxide. The effects of various parameters such as pH (3-10), ZnO nanoparticle dose (0.01-0.08 g/L), reaction time (5-50 minutes), initial concentration of MB (20-200 mg/L), and the hydrogen peroxide concentration (0.5-5 mg/L) were studied. The wavelength of maximum absorption (λmax) was 665 nm. The optimal pH value was 5, zinc oxide nanoparticle dose was 0.05 g/L, the initial concentration of MB was 40 mg/L, the concentration of hydrogen peroxide was 2 mg/L, and the contact time was 20 minutes. The efficiency of MB removal was 97.99%. The results showed that zinc oxide nanoparticles in the presence of hydrogen peroxide could remove the MB from aqueous solutions with high efficiency.

Air pollution is a widespread problem in India. The study focuses on forecasting the air quality index (AQI) using time series modeling techniques for the most polluted area of Dehradun City in Uttarakhand state, India. The train test approach of machine learning and Akaike information criterion (AIC) have been used on the monthly data of five years to select the best auto-regressive model. Using the auto-correlation functions (ACF and PACF) and the seasonality component in the time-series dataset, a seasonal auto-regressive moving average (ARMA) model with its minimum AIC has been chosen to forecast the AQI. This model is also validated by comparing its predicted values with the actual values of AQI. The results showed that the seasonal ARMA model of (1,0,0)(1,0,0)12 could forecast AQI based on a stationary dataset. The research also indicates that the asthma patients of the Himalayan Drugs-ISBT region may experience more health effects, especially in winter, due to poor air quality. The model can be helpful for a scientist and the government to take precautionary measures in advance.

The presence of persistent pharmaceutical products in water bodies is a significant problem that obstructs wastewater reuse. This study investigated the adsorption process for removing the recalcitrant antibiotics, including tetracycline (TC), ampicillin (AMP), and amoxicillin (AMOX) from an aqueous solution using a composite biosorbent made from a mixture of palm kernel shell (PKS), Chrysophyllum albidum (CAS), and coconut shell (CS). Simplex centroid design in the Design of Expert (12.0.1.0) was applied to optimize the percentage composition (20-55%) of the composite biosorbent precursor and to remove TC-AMP-AMOX mixtures from the aqueous solution in a batch study. The equilibrium data were fitted to 12 isotherm models and analyzed statistically. The maximum adsorption capacity of 9.12 mg/g, 8.66 mg/g, and 7.11 mg/g was achieved for TC, AMP, and AMOX, respectively, using the biocomposite biosorbent with an optimal mixture of 55% PKS, 20% CAS, and 25% CS. The adsorption behavior of TC, AMP, and AMOX was well-described by the Langmuir/Elovich isotherm (R2=1.000), Hill-DeBoer (R2=0.9953), and Freundlich/ Halsey (R2=0.9898) models, respectively. The obtained results showed that the biocomposite PKS-CAS-CS leverages the individual adsorptive capacity of each constituent to enhance the adsorption process. Moreover, the composite biosorbent demonstrated excellent potential for removing recalcitrant pharmaceuticals from wastewater effectively.

Landfill site selection is a complex decision-making process which requires the evaluation of various environmental, social, and economic criteria. The present research aims to propose a GIS-based Stepwise Weight Assessment Ratio Analysis (SWARA) method for landfill site selection in the east of Lorestan province, Iran. The study used 11 criteria including land use, distance from protected areas, slope, elevation, distance from the water resource, distance from the road, geology, distance from the fault, soil texture, distance from the city, and distance from the village. The results show that the proposed method can be used as a reliable tool for landfill site selection. The method provides decision-makers with a systematic approach to evaluate and rank potential sites based on their suitability. The study found that distance from water resources and soil texture were the most important criteria for landfill site selection in the study area. The proposed method can help decision-makers to identify suitable sites for landfill development considering potential environmental impacts. The study highlights the importance of using the GIS-based SWARA technique for landfill site selection, as it provides a comprehensive and efficient approach to decision-making. The proposed GIS-based SWARA method can be used as a valuable tool for landfill site selection in other regions and can help in achieving sustainable development goals.