An evaluation of fungal contamination and its relationship with PM levels in public transportation systems.

Transmission of fungi in the air and its impact on health are regarded as important public health issues. Bioaerosols play an important role in causing or exacerbating infectious diseases, acute toxic effects, allergies, and cardiopulmonary symptoms. As many people use the public transportation system daily, it is necessary to determine the type and manner of dispersal and abundance of airborne fungi in public transport places. Three public transportation systems including a bus station, a train station, and an airport in Ahvaz city (Iran) were examined. At each of these stations, the air samples were taken from inside and outside the hall stations, and in-vehicle. A bio-stage Anderson sampler was used by suctioning air and passing it over a Petri dish containing culture medium Sabouraud Dextrose Agar (SDA). Relative humidity (RH, %), temperature (T, ◦C), and mass concentration of particulate matter (PM1, PM2.5, and PM10, µg/m3) at the sampling points were measured. The highest concentration of airborne fungi was observed in the airport. The concentration of fungi in the ambient air was higher than that in the indoor air of halls and in-vehicle. In all sampling points, the ambient predominant airborne fungi were Cladosporium and Alternaria, while the indoor predominant airborne fungi were Cladosporium, Aspergillus, and Penicillium. The indoor to outdoor ratio showed that the fungi were of an external origin. Due to the influence of the ambient air on indoor air, it is recommended to use proper ventilation and enhance the hygiene level of vehicles in public transportation systems to reduce exposure to environmentally pathogenic bioaerosols.

Atmospheric pollution by potentially toxic elements: measurement and risk assessment using lichen transplants.

The lichen Usnea articulata collected from an unpolluted area was exposed for 6 months at 26 sites for the sample chosenusing a stratified random design, and the content of potentially toxic elements (PTEs) including As, Cd, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Sn, V, and Zn, was assessed using ICP-MS. The health risk for both adults and children was then calculated using the PTEs concentrations. The results showed that despite the hostile urban conditions, transplanted lichens depicted clear deposition patterns of airborne PTEs, mostly associated with industrial sites, where As and other elements showed remarkably high values. The cumulative hazard index was below the risk threshold, both for adults and children. For the entire population (particularly children) residing in areas surrounding industrial sites, As and Cr appeared to be potentially carcinogenic elements.

The impact of COVID-19 pandemic lockdowns on air quality index: a systematic review.

During the outbreak of the novel coronavirus disease 2019 (COVID-19), many countries implemented lockdown policies to control its transmission. These restrictions provided an opportunity to rest and recover the environment. This systematic review (SR) aimed to evaluate the impact of COVID-19 lockdowns on the Air Quality Index (AQI) in countries worldwide. ScienceDirect and PubMed were searched using relevant keywords to identify studies published until March 2020. Overall, 20 studies were included in the SR based on the eligibility criteria. The results show that COVID-19-related lockdown policies positively affect AQI by restricting air-polluting activities, such as transportation, industry, and construction. However, it is important to note that these policies are ineffective in controlling sources of natural air pollution and local dust. The findings of this study emphasize the need for policymakers to approve legislation limiting the sources of air pollutants.

Health risk assessment of workers exposed to lead dust in informal e-waste recycling workshops.

Informal recycling of electronic waste (e-waste) has attracted significant attention due to its economic benefits and rapid growth rate in many parts of the world. Unfortunately, unsafe conditions of recycling workshops possess chronic exposure to workers and lead to elevated blood lead concentrations (BLCs). Upon measuring the lead concentration in the dust of recycling workshops and the e-wastes in southwestern region of Iran, the related health risks were assessed in 30 exposed workers and 30 non-exposed habitants cases based on the determination of BLC. The average BLCs in exposed workers and non-exposed habitants cases were 24 µg/dL and 7 µg/dL, respectively. The geo-accumulation index (Igeo) revealed heavy contamination of dust in informal e-waste recycling workshops (IERWs) (5023 µg/kg) and significantly lower levels in unexposed areas (49 µg/kg). Health risk assessment indicated that lead exposure from IERWs appears to be a potential threat to workers and indirectly to their families.

Using water quality parameters to prediction of the ion-based trihalomethane by an artificial neural network model.

Trihalomethanes (THMs) are the first disinfectant by-products in the drinking water distribution network and are classified as potential carcinogens. The presence of THMs in chlorinated water depends on the pH, water temperature, contact time between water and chlorine, type and dose of disinfection, bromide ion concentration, and type and concentration of natural organic materials (NOMs). In the present study, the formation of THMs was evaluated by six simple and easy water quality parameters and modeled by an artificial neural network (ANN) approach through five water distribution networks (WDNs) and the Karoun River in Khuzestan province. The results of this study that was conducted from October 2014 to September 2015 showed that THM concentration ranged in five WDNs, including Shoushtar, Ahvaz (2), Ahvaz (3), Mahshahr, Khorramshahr, and total WDNs through N.D.-9.39 µg/L, 7.12-28.60, 38.16-67.00, 17.15-90.46, 15.14-29.99, and N.D.-156, respectively. The concentration of THMs exceeded Iran and EPA standards in many cases in Mahshahr and Khorramshahr WDNs. Evaluation of R2, MSE, and RMSE showed the appropriate correlation between measured and modeled THMs, indicating a reasonable ANN potential for estimating THM formation in water sources.

An ontology-based study on water quality: probabilistic risk assessment of exposure to fluoride and nitrate in Shiraz drinking water, Iran using fuzzy multi-criteria group decision-making models.

World Health Organization reports that 2.2 million people die yearly from insufficient sanitary drinking water. This ontology-based study focused on investigating the chemical quality of drinking water through a new water quality index designed by fuzzy multi-criteria group decision-making methods, merged with GIS, and, secondly, surveying non-carcinogenic risk assessment of fluoride and nitrate using Monte Carlo simulation and sensitivity analysis in Shiraz's water sources. F-, NO3-, NO2-, EC, TDS, alkalinity, TH, SO42-, Cl-, and Na were applied in the WQI. The NO3- mean concentrations were 23.15 and 27.66 mg/L in the cold and warm seasons, while the mean concentrations of fluoride were 0.50 and 0.46 mg/L during the cold and warm period. The 95th centiles of fluoride's HQs among infants, children, teenagers, and adults were 0.56, 0.7, 0.49, and 0.4, respectively, in the cold season, which was 0.65 and 0.81, respectively, 0.57 and 0.46 for mentioned groups in the warm season. In comparison, the 95th centiles of nitrate's HQs among infants, children, teenagers, and adults were 1.27, 1.59, 1.13, and 0.9, respectively. The HQs were more than 1 for infants, children, and teenagers, so nitrate can have various adverse effects, whereas fluoride does not adversely affect all aging groups in both seasons. Also, nitrate concentration can increase the non-carcinogenic risk, which the IR and ED lead to the HQ increasing. In contrast, BW has a negative effect on risk increasing. Overall, source management of these parameters can significantly reduce the concentration of nitrate and their adverse human health effect.

Effective bioremediation of petroleum-contaminated saline soil using halotolerant Bacillus strains isolated from the Persian Gulf.

A consortium isolated from the Persian Gulf is evaluated for its ability to bioremediate petroleum-contaminated soils. The soil sample was collected from oil fields of South Western Iran. The crude oil concentrations were set to 1000-10,000 mg/kg, and the sodium chloride concentration was set to 0.5%, 1%, 1.5%, 2%, and 2.5%. Operational parameters including volume (2-20 ml) and soil moisture (25%, 50%, and 100%) were studied consecutively according to one factor at the time of experimental design. A total number of eight different isolates capable of degrading crude oil were isolated from hydrocarbon-contaminated sites (KL1-KL8). The removal efficiency of Total petroleom hydrocarbons (TPH) with an initial concentration of 1000 mg/kg for numbers of bacterial cells per gram soil of 2, 10, and 20 CFU/g was 20.9%, 45%, and 60%, respectively. The removal efficiency of TPHs (initial concentration of 1000 mg/kg) at the end of fifth week for salinity amounts of 0.5%, 1%, 1.5%, 2%, and 2.5% was 10.87%, 22.4%, 25.7%, 68.6%, and 60.5%, respectively. The TPHs biodegradation efficiencies at different soil/water ratios of 25%, 50%, and 100% (slurry) were 12%, 28.7%, and 60.8%, respectively. In sunflowers, there was no statistically significant difference in seed germination for different levels of soil pollution (p > 0.05). The results of the current work suggest that this process is a viable and efficient method for remediating contaminated sites. To enhance the removal results in real soil, a scale-up study should also be conducted.

Temporal profiles of ambient air pollutants and associated health outcomes in two polluted cities of the Middle East.

Dust storms and particulate matters had been increased due to climate change in the Middle East. On the other hand, urbanization and industrialization raised levels of gaseous air pollutants in all big cities. In the current study, air pollution information collected from Environmental Protection Agency of Khuzestan and Tehran containing hourly O3, NO2, CO, SO2, PM10 and PM2.5 concentrations between 2014 and 2015. This study evaluated the air quality of these two cities by Air Quality Index (AQI). As a result, mean concentrations of O3, NO2, PM10 and PM2.5 were higher in Ahvaz than Tehran while Tehran was more pollutant in terms of CO and SO2. Diurnal variations of O3 in weekend were the only trend located above weekday variations along the daytimes. Hourly variations of all pollutants changed with a wider range of concentrations in Ahvaz. Diurnal peaks of all pollutants showed their highest level on Monday as the busiest day in mega city, Tehran with the exception of SO2. PM2.5 was the worst and limiting pollutant for both cities. Accordingly, winter was the most polluted season by 77 and 33 µg m-3 in Ahvaz and Tehran, respectively. Number of clean days was significantly lower in Ahvaz (no-day) than mega city, Tehran (<17 days). The number of unhealthy days was also presented significantly higher in Ahvaz (>186 days). Although, annual PM2.5 concentrations were more in Ahvaz, the higher at-risk population in Tehran caused more health endpoints in the capital of Iran. Consequently, both cities should have their own especial pattern to control air pollution and attributed health damages.

Contrasting Iran's air quality improvement during COVID-19 with other global cities.

BACKGROUND AND PURPOSE: In late 2019, a novel infectious disease (COVID-19) was identified in Wuhan China, which turned into a global pandemic. Countries all over the world have implemented some sort of lockdown to slow down its infection and mitigate it. This study investigated the impact of the COVID-19 pandemic on air quality during 1st January to 30th April 2020 compared to the same period in 2016-2019 in ten Iranian cities and four major cities in the world. METHODS: In this study, the required data were collected from reliable sites. Then, using SPSS and Excel software, the data were analyzed in two intervals before and after the corona pandemic outbreak. The results are provided within tables and charts. RESULTS: The current study showed the COVID-19 lockdown positively affected Iran's air quality. During the COVID-19 pandemic, the four-month mean air quality index (AQI) values in Tehran, Wuhan, Paris, and Rome were 76, 125, 55, and 60, respectively, which are 8 %, 22 %, 21 %, and 2 % lower than those during the corresponding period (83, 160, 70, and 61) from 2016 to 2019. CONCLUSIONS: Although the outbreak of coronavirus has imposed devastating impacts on economy and health, it can have positive effects on air quality, according to the results.

Assessment of incremental lifetime cancer risks of ambient air PM10-bound PAHs in oil-rich cities of Iran.

This study investigates the concentrations of PM10-bound PAHs and their seasonal variations in three cities of Ahvaz, Abadan, and Asaluyeh in Iran. The mean concentrations of PM10 in two warm and cold seasons in Ahvaz were higher and in Abadan and Assaluyeh were lower than the national standard of Iran and the guidelines of the World Health Organization. The Σ16 PAHs concentration in ambient air PM10 during the cold season in Ahvaz, Abadan and Asaluyeh was 244.6, 633, and 909 ng m- 3, respectively, and during the warm season in Ahvaz, Abadan, and Asaluyeh was 242.1, 1570 and 251 ng m- 3, respectively. The high molecular weight PAHs were the most predominant components. The most abundant PAHs species were Pyr, Chr, B [ghi] P, and Flt. The results showed that the total PAHs concentration in the cold and warm seasons was dependent on industrial activities, particularly the neighboring petrochemical units of the city, vehicular exhausts, traffic and use of oil, gas, and coal in energy production. The total cancer risk values as a result of exposure to PAHs in ambient air PM10 in all three cities for children and adults and in both cold and warm seasons were between 1 × 10- 6 and 1 × 10- 4, and this indicates a potential carcinogenic risk. Therefore, considering the various sources of air pollutants and its role on people's health, decision makers should adopt appropriate policies on air quality to reduce the ambient air PAHs and to mitigate human exposure.

On the airborne transmission of SARS-CoV-2 and relationship with indoor conditions at a hospital.

The limited knowledge about the mechanism of SARS-CoV-2 transmission is a current challenge on a global scale. Among possible transmission routes, air transfer of the virus is thought to be prominent. To investigate this further, measurements were conducted at Razi hospital in Ahvaz, Iran, which was selected to treat COVID-19 severe cases in the Khuzestan province. Passive and active sampling methods were employed and compared with regard to their efficiency for collection of airborne SARS-COV-2 virus particles. Fifty one indoor air samples were collected in two areas, with distances of less than or equal to 1 m (patient room) and more than 3 m away (hallway and nurse station) from patient beds. A simulation method was used to obtain the virus load released by a regularly breathing or coughing individual including a range of microdroplet emissions. Using real-time reverse transcription polymerase chain reaction (RT-PCR), 11.76% (N = 6) of all indoor air samples (N = 51) collected in the COVID-19 ward tested positive for SARS-CoV-2 virus, including 4 cases in patient rooms and 2 cases in the hallway. Also, 5 of the 6 positive cases were confirmed using active sampling methods with only 1 based on passive sampling. The results support airborne transmission of SARS-CoV-2 bioaerosols in indoor air. Multivariate analysis showed that among 15 parameters studied, the highest correlations with PCR results were obtained for temperature, relative humidity, PM levels, and presence of an air cleaner.

Effect of long-term exposure to PM2.5 on years of life lost in a populated Middle Eastern city.

From a public health point of view, years of life lost (YLL) is a more important index than the number of deaths to evaluate the effect of risk factors. The objective of the present study was to estimate the burden of disease including years of life lost (YLL) and expected life remaining (ELR) attributed to long-term exposure to PM2.5 in Ahvaz, one of the most polluted cities of the world, during March 2014 through March 2017. AirQ + software was used for the estimation of YLL and ELR due to all natural causes of death. Hourly concentrations of PM2.5 were acquired from the Department of Environment (DoE) of Ahvaz. Several steps were performed to validate the raw air quality data. Only the monitors were included that had minimum data completeness of 75%. Two age groups were selected for this study, including 0-64 and 65 < years. The life table approach was used to estimate YLL and ELR. Annual averages of PM2.5 were 5.2-8 times higher than the air quality guideline (10 µg/m3) set by WHO for long-term exposure to PM2.5. In total, PM2.5 has caused 234,041 years of life lost due to mortality. About 84% of YLLs were attributed to people older than 65 years old. The YLLs of men were higher than those for women. The YLLs in the third year were greater than the first two years. PM2.5 has caused the average age of total population, people aged 0-64 years old, and people > 65 years old decreased by 2.5, 3, and 1.6 years, respectively. These studies indicated that people in a city that the air quality is highly affected by dust storms, industrial emissions, and urban air pollution are significantly at risk. Air pollution control strategies and actions should be designed and executed to improve the quality of ambient air.

Air pollution prediction by using an artificial neural network model.

Air pollutants impact public health, socioeconomics, politics, agriculture, and the environment. The objective of this study was to evaluate the ability of an artificial neural network (ANN) algorithm to predict hourly criteria air pollutant concentrations and two air quality indices, air quality index (AQI) and air quality health index (AQHI), for Ahvaz, Iran, over one full year (August 2009-August 2010). Ahvaz is known to be one of the most polluted cities in the world, mainly owing to dust storms. The applied algorithm involved nine factors in the input stage (five meteorological parameters, pollutant concentrations 3 and 6 h in advance, time, and date), 30 neurons in the hidden phase, and finally one output in last level. When comparing performance between using 5% and 10% of data for validation and testing, the more reliable results were from using 5% of data for these two stages. For all six criteria pollutants examined (O3, NO2, PM10, PM2.5, SO2, and CO) across four sites, the correlation coefficient (R) and root-mean square error (RMSE) values when comparing predictions and measurements were 0.87 and 59.9, respectively. When comparing modeled and measured AQI and AQHI, R 2 was significant for three sites through AQHI, while AQI was significant only at one site. This study demonstrates that ANN has applicability to cities such as Ahvaz to forecast air quality with the purpose of preventing health effects. We conclude that authorities of urban air quality, practitioners, and decision makers can apply ANN to estimate spatial-temporal profile of pollutants and air quality indices. Further research is recommended to compare the efficiency and potency of ANN with numerical, computational, and statistical models to enable managers to select an appropriate toolkit for better decision making in field of urban air quality.