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Microplastics (MPs) are emerging pollutants that pose significant risks to ecosystems due to their inherent toxicity, capacity to accumulate various pollutants, and potential for synergistic impacts. Given these concerns, the focus of this research is on the critical need for effective MPs removal from aquatic environments. Using BBD method, this study aimed to identify the key parameters affecting the removal of MPs by algal biomass from aqueous solutions. The investigation specifically analyzed the effects of varying initial PS concentrations (100 to 900 mg/L), pH values (4 to 10), reaction durations (20 to 40 min), and C. vulgaris dosages (50 to 400 mg/L). Data analysis indicated that QM best described the experimental findings, leading to the identification of optimal conditions for PS removal: a pH of 7.5, a reaction time of 31.90 min, a C. vulgaris dosage of 274.05 mg/L, and a PS level of 789.37 mg/L. Under these conditions, the study achieved a maximum removal efficiency of 73.01% for PS. These outcomes demonstrate the significant potential of C. vulgaris in efficiently removing PS from water. Furthermore, using algae as a green, eco-friendly alternative to conventional chemical coagulants offers a practical and sustainable approach to addressing MPs pollution in our water systems.

One of the most important pollutants is PM 2.5 , which is particularly important to monitor pollutant levels to keep the pollutant concentration under control. In this research, an attempt has been made to predict the concentrations of PM 2.5 using four Machine Learning (ML) models. The ML methods include Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting Regressor (XGBR), Random Forest (RF) and Gradient Boosting Regressor (GBR). The mean and maximum concentration of PM 2.5 were recorded 32.84 µg/m 3 and 160.25 µg/m 2 , respectively, indicating the occurrence of occasional episodes of high pollution levels from 2016 to 2022. The PM2.5 concentrations dropped below 30 µg/m 2 in 2018 due to reduced human activities during COVID-19 lockdowns but PM 2.5 levels were significantly increased because of the ongoing operation of heavy industries post-COVID-19 lockdowns during 2021. The ML models performed very well in predicting the concentrations of PM 2.5 with around 95% of their predictions falling within the factor of the observed concentration. The results presented that among the four ML algorithms, GBR confirmed good model performance compared to the other models, with the lowest MSE (5.33) and RMSE (2.31), as well as high accuracy measures. This suggests that GBR is the best model for reducing large errors, making it more robust in capturing variations in PM2.5 levels. In conclusion, the study proposed a method to obtain high-accuracy PM 2.5 prediction results using ML which are useful for air quality monitoring on a global scale and improving acute exposure assessment in epidemiological research.

Plastic pollution has emerged as a critical global concern, with microplastics increasingly detected across various ecosystems, including the atmosphere. Among indoor sources, hospital laundry units have been identified as significant contributors to airborne microplastic emissions. This study investigates the concentration of inhalable microplastics (MPs (in the air of a hospital laundry environment. In this study, air sampling was conducted at three different time points using a personal air sampler operating at a flow rate of 0.5 L/min for 40 min per sample. Microplastics were characterized using FTIR, SEM, and EDX to ensure accurate identification. FTIR analysis identified the predominant polymer as polyamide (nylon), with characteristic peaks consistent with CH 2 , C = O, and N-H groups. EDX analysis indicated an elemental composition of C (59%), N (32%), O (7%), and P (0.07%). SEM images revealed pronounced diurnal and day-to-day variability, with particle concentrations ranging from 43575 to 66975 particles/m 3 , though statistical analysis showed these variations were not significantly influenced by environmental factors such as humidity and air velocity in this short-term study. Notably, black particles dominated the samples, representing 97% of the MPs. These results underscore the potential for direct inhalation exposure in occupational settings, raising concerns about respiratory health risks for laundry staff and patients. Therefore, further research is needed to inform the development of stricter ventilation standards, occupational safety measures, and regulatory policies to mitigate microplastic emissions in healthcare environments.

Exposure to bioaerosols has been associated with various health issues, including infectious diseases, acute toxicity, allergies, and cancer. This study aimed to assess the concentrations of bacterial and fungal bioaerosols in Mashhad, Iran, and examine their correlation with PM 2.5 levels in outdoor air. Bioaerosol samples were collected at six locations using active sampling techniques. The concentration of airborne bacteria ranged from 36.66 to 89.49 CFU/m³, while fungal concentrations varied between 52.31 and 102.59 CFU/m³. The most frequently identified bacterial species were Staphylococcus epidermidis and Escherichia coli, whereas Aspergillus and Penicillium species were the dominant fungi. A strong positive correlation was observed between bacterial aerosol concentrations and PM 2.5 levels, while airborne fungal concentrations exhibited a moderate positive correlation with PM 2.5 . The strong association between bacterial bioaerosols and PM 2.5 suggests that increased bacterial levels are linked to higher dust concentrations, a pattern consistent with findings from other dust-prone regions worldwide. The HQ values of bioaerosols in six sampling sites were all lower than 1. However, the association between higher bioaerosol concentrations and elevated PM 2.5 levels suggests possible interactions between microbial aerosols and air pollutants. This study underscores the importance of continuous bioaerosol monitoring to mitigate public health risks, particularly in densely populated urban areas where exposure is more pronounced. Additionally, bioaerosols play a role in the natural cycling of biological materials.

Air pollution is the fifth and sixth leading risk factor for global mortality and reduced life expectancy. Studies have established a link between atmospheric pollution and the incidence, hospitalization, and mortality rates of pulmonary tuberculosis (PTB). The city of Zabol has experienced persistent dust storms for several years, with an average annual PM 10 concentration of 206 µg/m³—more than nine times the permissible limit. The results of this study indicate a significant correlation between increased PM 10 levels and the incidence of tuberculosis. To model the disease, machine learning (ML) techniques, including support vector machines (SVM) and k-nearest neighbors (KNN), were employed. Among these, SVM demonstrated the highest accuracy, with a correlation coefficient of 92.4% between the experimental data and the model’s training outputs.

Air pollution poses a significant threat to the health of all living beings on our planet. It has been scientifically established as a crucial factor affecting mortality rates, respiratory illnesses, mental well-being, and overall health. This study aimed to investigate the impact of air pollution and meteorological factors on respiratory disease mortality rates in Mashhad in 2017–2021 using a Random Forest (RF) model. At first, the daily statistics of meteorological parameters (pressure, humidity, temperature, solar radiation) during 2017–2021 were collected. The information related to pollutants pollutants such as PM 2.5 (which is defined as particulate matter with less than 2.5 micrometer diameter and potentially harmful to humans), PM 10 (Particles with a diameter of 10 micrometers or less that can negatively impact both human health and environmental conditions.), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and carbon monoxide (CO) was collected. the mortality statistics from respiratory diseases were collected from the Health system registaration (Sina). Then we used the RF regression model in Excel and Python software to investigate the interaction between the mentioned variables. The escalating trend of air pollution in Mashhad has led to an expected increase in respiratory-related hospitalizations. This necessitates urgent air pollution control measures. Concurrently, the study of pollutants and climatic elements, as substantiated by global epidemiological studies, is crucial. In Mashhad, the second most polluted city in Iran, climatic factors like humidity, sunshine duration, temperature, pressure, and sunlight intensity exacerbate atmospheric pollution levels, impacting human health and ecosystems. The R 2 , RSME, and MAE of RF model are 0.73, 2.52, and 2 which indicate that the model has successfully identified the relationship between input variables and the target variable, and it will exhibit high accuracy in predictions. In this study, the most influential factor was identified when the Variance Inflation (VI) factor reached a value of 0.548. This indicates a strong correlation between this factor and the death rate of patients during the specified period. Furthermore, we analyzed by excluding the day and month plans from our model. The results showed that the factor with the highest coefficient in the executive model was related to pressure, with a VI value of 0.049. This suggests that pressure plays a significant role in our model and has a substantial effect on the death rate of patients. In the study of various pollutants, it was found that PM 10 had the most significant impact on the mortality rate of patients with respiratory conditions, with a VI of 0.039. Following PM 10 , the pollutants with the next highest coefficients of importance were NO 2 (VI = 0.034), SO 2 (VI = 0.033), PM2.5 (VI = 0.029), and CO (VI = 0.025), respectively. Furthermore, the study observed a notable increase in the mortality rate of respiratory patients over time. Specifically, for every five days, the death rate increased by 35.5%. Indeed, climate change and air pollution significantly contribute to the mortality rate from respiratory diseases. Therefore, it is crucial for individuals, particularly those with respiratory conditions, to heed meteorological warnings.

Air pollution, a global health hazard, significantly impacts mortality, cardiovascular health, mental well-being, and overall human health. This study aimed to investigate the impact of air pollution and meteorological factors on cardiovascular mortality rates in Mashhad City, northeastern Iran in 2017–2020. We utilized a Random Forest (RF) model in this study. We gathered daily meteorological data (pressure, humidity, temperature, solar radiation) from 2017 to 2020, pollutant levels (PM 2.5 , PM 10 , SO 2 , NO 2 , CO), and cardiovascular mortality data from the Health System Registration (Sina). The RF model was then applied in Excel and Python to analyze the interplay between these variables. we found that time, air pressure, and temperature significantly impacted cardiovascular mortality. Among pollutants, NO 2 and SO 2 were the most influential. Overall, meteorological factors had a greater impact than pollutants. Furthermore, we discovered that cardiovascular mortality increased with time, higher air pressure, colder seasons, and higher temperatures. Among pollutants, CO, NO 2 , SO 2 , PM 10 , and PM 2.5 significantly impacted mortality rates. These findings highlight the importance of understanding the relationship between diseases, climatic factors, and pollution. Environmental factors like climate change and air pollution play a significant role in cardiovascular mortality. Therefore, it is vital for individuals, especially those with heart conditions, to pay attention to weather alerts.

Gas stations distributed around densely populated areas are responsible for toxic pollutant emissions such as volatile organic compounds (VOCs). This study aims to measure VOCs emission from three different kinds of gas stations to determine the extent of pollution from the gas stations and the most frequent type of VOC compound emitted. The concentrations of ambient VOCs at three refueling stations with a different type of fuels in Mashhad were monitored. The result of this study showed that CNG fuel stations are less polluting than petrol stations. In all the studied sites, the highest concentrations were related to xylene isomers, irrespective of the fuel type. Total VOCs at the supply of both compressed natural gas (CNG) and gasoline stations was 482.36 ± 563.45 µg m −3 . At a CNG station and a gasoline station, total VOC concentrations were 1363.4 ± 1975 µg m −3 and 410.29 ± 483.37 µg m −3 , respectively. The differences in concentrations of toluene and m,p-xylene between the fuel stations can be related to the quality and type of fuel, vapor recovery technology, fuel reserves, dripless nozzles, traffic density in these stations, meteorological conditions and the location of sampling sites. The combination of a sine function and a quadratic function could model the fluctuation behavior of air pollutants like m,p-xylene. In all the sites, the highest concentrations were related to xylene isomers, irrespective of the type of fuel. The changing rate of m,p-xylene pollutant in each station was also modeled in this study.

The study used the AirQ + software developed by the World Health Organization (WHO) to evaluate the health impacts associated with long-term exposure to PM 2.5 in Mashhad, Iran. For this purpose, we analyzed the daily average concentrations of PM 2.5 (with a diameter of 2.5 micrometers or less) registered by the air quality monitoring stations from 2016 to 2021. The levels of PM 2.5 surpassed the Air Quality Guidelines (AQG) limit value of 5 µg/m 3 (annual value) established by WHO. The findings revealed that the burden of mortality (from all-natural causes) at people above 30 years old associated with PM 2.5 exposures was 2093 [95% confidence interval [CI]: 1627–2314] deaths in 2016 and 2750 [95% CI: 2139–3038] deaths in 2021. In general, the attributable mortality from specific causes of deaths (e.g., COPD (chronic obstructive pulmonary diseases), IHD (ischemic heart diseases) and stroke) in people above 25 years old increased between the years, but the mortality from lung cancer was stable at 46 [95% CI: 33–59] deaths in 2016 and 48 [95% CI: 34–61] deaths in 2021. The attributable mortality from ALRI (Acute Lower Respiratory Infection) in children below 5 years old increased between the years. We also found differences in mortality cases from IHD and stroke among the age groups and between the years 2016 and 2021. It was concluded that burden of disease methodologies are suitable tools for regional and national policymakers, who must take decisions to prevent and to control air pollution and to analyze the cost-effectiveness of interventions.

There is insufficient research on bioaerosols in hospital waste decontamination stations. This study aimed to investigate the effect of three-layer and N95 masks in reducing the inhalation risk of bacterial bioaerosols in a waste decontamination station at a teaching hospital. Active sampling was conducted on five different days at three locations: the yard, resting room, and autoclave room in three different modes: without a mask, with a three-layer mask, and with an N95 mask. Bacterial bioaerosols passing through the masks were identified using biochemical tests and polymerase chain reaction (PCR). The median concentration and interquartile range (IQR) of bacterial bioaerosols was 217.093 (230.174) colony-forming units per cubic meter (CFU/m 3 ), which is higher than the recommended amount by Pan American Health Organization (PAHO). The resting room had high contamination levels, with a median (IQR) of 321.9 (793.529) CFU/m 3 of bacterial bioaerosols. The maximum concentration of bioaerosols was also recorded in the same room (2443.282 CFU/m 3 ). The concentration of bacterial bioaerosols differed significantly between using a three-layer or N95 mask and not using a mask (p-value < 0.001). The non-carcinogenic risk level was acceptable in all cases, except in the resting room without a mask (Hazard Quotient (HQ) = 2.07). The predominant bacteria were Gram-positive cocci (33.98%). Micrococci (three-layer mask = 51.28%, N95 mask = 50%) and Coagulase-negative Staphylococci (three-layer mask = 30.77%, N95 mask = 31.82%) were the most abundant bioaerosols passing through the masks. The results obtained are useful for managerial decisions in hospital waste decontamination stations to reduce exposure to bioaerosols and develop useful guidelines.