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BACKGROUND AND PURPOSE: The Karoon River, located in southwest Iran, has always been considered as an important water source for people in the southward areas. Khuzestan Province is one of the strategic provinces of Iran thus the development of this province is significantly affected by the water pollution of the Karoon River system. Therefore, the current study aims to assess the environmental vulnerability of the Karoon River as well as preparing a classified map of its vulnerabilities using the fuzzy logic method via the geographical information systems (GIS). METHODS: In this study, the required data were gathered from the Water and Electricity Organization of Khuzestan Province. Afterward, the primary maps were created by converting the map of the study origin into a raster format. Then, fuzzy membership was performed by placing the digits in the range of zero and one using the fuzzy membership function. The primary maps were mixed, and finally, the risk map was prepared by applying the fuzzy overly function. RESULTS: According to the results, a clear trend of water quality deterioration exists since water moves from upstream to downstream areas. The ecological vulnerability of the Karoon basin is mostly located at a low-level (78.05 %) rank. The vulnerable areas were ranked extremely high, high, medium, and low as 2.09, 8.09, 12.08, and 78.05, respectively. CONCLUSIONS: Considering that 22 % of the Karoon River drainage basin in Khuzestan province is considered to have a medium to extremely high risk range, it is mandatory for the authorities to take precautions to prevent the entry of polluting sources into this precious river.

This study reports temporal and spatial variations of 16 different species of particulate polycyclic aromatic hydrocarbons (particle-bonded PAHs) in the indoor and outdoor environments of three sampling sites in Bandar Mahshahr city, Iran. A low-volume air sampler was employed to collect size-segregated particulate matter during winter (October to December 2015), and summer (July to September 2016). The results showed that the annual concentrations of indoor and outdoor PM10 and PM2.5 were much higher than the related World Health Organization guidelines. The concentration of total particle-bonded PAHs (TPAHs) was higher in winter than in summer and a significant difference between the two sampling seasons was observed. The indoor and outdoor carcinogenic PAHs to TPAHs concentrations ratios in the sampling sites in summer and winter were as follow: for PM10 40.15-42.51%, PM2.5 41.30-42.97%, and PM1 43.07-44.36%, respectively; furthermore, the smaller the particle size, the higher the percentage of carcinogenic PAHs. 2 ring PAHs had a very small contribution to the total PAHs (about 1%), whereas PAHs with 3-to-4 rings had much larger contributions, ranging from 71.65% to 75.17%. The results demonstrated that as PM size decreased, the proportion of 5-to-6-ring PAHs to the total PAHs increased. Since 5-to-6- ring PAHs are considered to be more toxic, hence more attention should be paid to fine particles. The diagnostic ratios of indoor and outdoor of three sampling sites in both seasons suggested that petrogenic sources, as well as combustion of petroleum and other fossil fuels were the main PAHs sources.

Analyzing the water quality status is one of the most important issues to control the pollution discharged to the river. This helps to obtain the effective environmental management. However, the conventional water quality indices cannot accurately indicate the water quality status, particularly in the presence of the various water quality parameters. In this study 15, water quality parameters of Karun River were categorized based on the similar variation pattern by Self-Organizing Map (SOM) for a 6-year period between 2013 and 2018. Data were then clustered using the k-means nonhierarchical algorithm for determination of the spatio-seasonal pattern of Karun River's water quality and discrimination of the sources of water pollution all along Karun River catchment. The results indicated the dissimilar variation patterns of some water quality parameters which were might due to the different sources of pollutions around the Karun River catchment. Moreover, data clustering by k-means specified 5 different groups collected data. Spatio-seasonal assessment identified the critical stations in the Karun River during a 6-year period. The result of the applied methodology showed an integration of SOM and k-means algorithms gives an insight into the similarities of water quality parameters and analysis of the water quality conditions in critical locations. Therefore, it can be used as a tool for efficient decision making in environmental management.

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 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.

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.

Background: Natural organic matters such as humic acid react with chlorine and produce disinfection by products such as trihalomethanes that are carcinogenic. In this study, shellfish ash was used as a novel adsorbent for removal of humic acid. Methods: The present study was performed under various laboratory conditions including pH, adsorbent dose, contact time, and initial concentration of humic acid. Residual concentrations of humic acid in the samples were determined by a UV-Vis spectrophotometer at 254 nm wavelength. Artificial neural network (ANN) modeling studies were also performed. Results: Elemental analysis showed that the shellfish ash was 98% pure calcium. It was indicated that the maximum adsorption capacity was achieved in acidic conditions (pH = 3) and pHzpc was found to be 10.3. The adsorption data followed the Langmuir model (R2 > 0.9). The adsorption of humic acid followed the pseudo-second-order kinetic (R2 = 0.999). ANN modeling also provided the accurate prediction of humic acid adsorption for testing data (R2 = 0.989). Conclusion: According to the results, shellfish ash is recommended as an effective biosorbent for removal of organic pollutants such as humic acid. Keywords: Adsorption, Humic substances, Kinetics, Shellfish, Neural network

Background: Trihalomethanes (THMs) are the first disinfectant by-products in the drinking water distribution network and 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 water quality parameters and modeled by an artificial neural network (ANN) approach through five water distribution networks (WDNs) and Karoun river in Khuzestan province.Results: This study was conducted from October 2014 to September 2015. Results showed that THMs concentration ranged in five WDNs, including Shoushtar, Ahvaz 2, Ahvaz 3, Mahshahr, Khorramshahr, and Karoun river 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.Conclusions: 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 good ANN potential for estimating THM formation in water sources.

After the early rainfall in the autumn of 2013, respiratory syndromes spread in the Khuzestan province of Iran with the most severity in Ahvaz. There have been recurring outbreaks in recent years. Considering that pollen-derived airborne allergens are regarded as key aeroallergens and the main cause of allergic rhinitis and asthma, this work aimed to forecast total pollen concentration in Ahvaz through an artificial neural network (ANN), followed by evaluating the pollen spatial distribution across the city and the association between pollen concentrations and environmental parameters. The utilized ANN in this work included an input layer with 13 parameters, a hidden layer of five neurons, and an output layer. Data were classified into training, validation, and testing sets. The ANN was implemented with 70% and 80% of data for training. The value of the correlation coefficient for the data validation of these two networks was 0.89 and 0.92, respectively. The results also indicated that despite the difference in the mean concentration of the pollens in various areas of Ahvaz, this difference was not statistically significant (P > 0.05). Furthermore, there was a negative correlation between the concentration of total pollen and relative humidity, precipitation, and air pressure. However, it had a positive correlation with temperature. Consequently, considering the logistical challenges of monitoring bioaerosols in the air, the ANN approach could predict total pollen concentrations. Therefore, in addition to measurements, the ANN technique can be a good tool to enable authorities to mitigate the impact of airborne pollen on people.

A disastrous outbreak of thunderstorm-induced asthma attacks posed a major public health threat in Ahvaz, Iran in autumn of 2013 (>15,000 referrals to hospital emergency rooms and pulmonary clinics). City officials claimed that promoting factors included acid rain, particulate matter, ozone, and polycyclic aromatic hydrocarbon compounds (PAHS) could lead to this phenomenon. After these potential causes were rejected by careful research, the next possible cause that this study addresses is a potential link of the asthma attacks to airborne pollen grains and fungal spores. In order to sample these parameters, a Hirst-type volumetric spore sampler was used, which was installed on the rooftop of the sampling site. Sampling was done during two six-month periods to characterize the regional bioaerosol. The first period included the autumn (September 22 to December 20 of 2016) and winter seasons (December 21 of 2016 to March 19 of 2017), while the second period included the following autumn (September 23 to December 21 of 2017) and winter seasons (December 22 of 2017 to March 20 of 2018). In both studied periods, airborne weed pollen grains, especially Amaranthaceae sp., were by far the greatest contributor to total airborne pollen grain concentrations (50.45%, 3757 pollen grains/m3 and 52.12%, 2392 pollen grains/m3, respectively). Among fungal spores, Cladosporium had the highest concentrations (75.29%, 258677 spores/m3 and 80.33%, 247693 spores/m3, respectively). Findings also showed that concentrations of airborne pollen grains were higher in autumn seasons (6361 and 4167 pollen grains/m3, respectively) in comparison with winter seasons (1085 and 423 pollen grains/m3, respectively), while the concentrations of fungal spores were enhanced in winter seasons (189216 and 160962 spores/m3, respectively) versus autumn seasons (154377 and 147377 spores/m3, respectively). Fungal spores exhibited a uniform distribution during the entire study period, but airborne pollen grains were significantly higher in autumn seasons. The numbers of referrals associated with thunderstorm asthma attacks decreased drastically after implementing Conocarpus erectus tree pruning activities. We concluded that the autumnal thunderstorm asthma attacks in Ahvaz might be triggered by a combination of bio-allergens (fungal spores and airborne pollen grains such as from Conocarpus erectus) and high levels of air pollutants from industrial activity. The results of this work suggest controlling bio-allergen levels by preventive measures such as tree pruning, weed removal, and emasculating urban green infrastructures before the flowering season.

The primary objective of the present study was to evaluate the concentrations and short and long-term excess mortality attributed to PM2.5, NO2, and O3 observed in ambient air of Ahvaz during March 2014 to March 2017 period using the AirQ + software developed by the World Health Organization (WHO), which is updated in 2016 by WHO European Centre for Environment and Health. The hourly concentrations of PM2.5, O3, and NO2 measured at different regulatory monitoring network stations in Ahvaz city were obtained from the Department of Environment (DOE) of the city. Then, for various air quality monitoring stations, the 24-h average concentration of PM2.5, 1-h average of NO2 concentration, and maximum daily 8-h O3 concentrations were calculated using Excel 2010 software. When the maximum daily 8-h ozone means exceeding the value of 35, it was subtracted from 35 to calculate SOMO35 indicator for modeling. Validation of air quality data was performed according to the Aphekom and WHO's methodologies for health impact assessment of air pollution. Year-specific city population and baseline incidence of the health outcomes were obtained. The three-year averages of PM2.5, NO2, and O3 concentrations were 68.95 (±39.86) μg/m3, 135.90 (±47.82) μg/m3, and 38.63 (±12.83) parts-per-billion-volume (ppbv), respectively. SOMO35 values of ozone were 6596.66, 3411.78, and 470.88 ppbv in 2014–2015, 2015–2016, and 2016-2017 years, respectively. The AP and number of natural deaths due to NO2 were higher than PM2.5 except the last year (2016–2017), causing about 39.18%, 40.73%, and 14.39% of deaths within the first, the second, and the third year, respectively. However, for the last year, the natural mortality for PM2.5 was higher than NO2 (34.46% versus 14.39%). The total number of natural mortality caused by PM2.5 and NO2 in all years was 4061 and 4391, respectively. A significant number of deaths was estimated to be attributed to the given air pollutants. It can be concluded that by designing and implementing air pollution control strategies and actions, both health effects and economic losses will be prevented.

In this study, optimization of adsorptive removal of two cationic (methylene blue (MB)) and anionic (acid red 73 (AR 73)) dyes with modified oak waste (MOW) adsorbent was carried out using response surface methodology (RSM). The input variables included initial solution pH (2–10), contact time (20–200 min), adsorbent dosage (0.5–2.5 g/L) and initial dyes concentrations (10–90 mg/L). Furthermore, the surfaces of the adsorbent were modified with different modifiers to find the most efficient adsorbent. The obtained results of comparative studies on dyes adsorption confirm that sodium hydroxide, compared to other modifiers, has the highest ability to activate the surfaces of the oak waste (OW) to remove dyes from the solution. Using NaOH, the removal efficiency of MB and that of AR 73 were obtained to be 96% and 29%, respectively. In addition, results of the optimization study showed that RSM could significantly predict the experimental data of dyes removal with a high correlation coefficient (0.991 for MB and 0.993 for AR 73). The optimum experimental conditions for dyes removal were pH of 6.2, 160 min contact time, 2.0 g/L adsorbent dosage and 70 mg/L initial dye concentrations. Under this condition, the removal efficiency for MB and AR 73 reached 85.36% and 41.27%, respectively. Ultimately, from the findings of this study, it is observed that MOW can be used as an efficient and cost-effective adsorbent for the removal of dyes from the contaminated aqueous solution.

In recent years, concerns over the issue of air pollution have increased as one of the significant environmental and health problems. Air pollutants can be toxic or harmful to the life of plants, animals, and humans. Contrast to primary pollutants, ozone is a secondary pollutant that is produced by the reaction between primary precursors in the atmosphere. The average of air pollutant data was compiled for the purpose of analyzing their correlation with the pulmonary function of students and the FENO biomarker from the air pollutants of the Environmental Protection Agency. According to the average of 3 days, the concentration of ozone in the (S3) region was higher than the other regions, and this level was significantly different from the ANOVA test (p

Total petroleum hydrocarbons (TPHs) are common environmental pollutants in aquatic ecosystems that tend to adsorb onto the sediments. This study aimed to monitor the concentration and assessment of contamination level of TPHs in the sediments of Karun River and Bahmanshir estuary, which will eventually flow into the Persian Gulf. GIS-mapping technique was used to indicate the distribution of TPHs. The samples were collected during dry and wet seasons at 15 monitoring stations. - The maximum amount of TPHs was obtained at station No. 14 in the dry season (225.29 μg/g). The indices of determining the pollution source showed that hydrocarbons in polluted areas were mainly of biogenic origin. Compared to sediment quality guidelines (SQGs) and contamination factor (Cf), the study area sediments were in the conditions of moderate pollution and had a low ecological risk.

This study reports temporal and spatial variations of 16 different species of particulate polycyclic aromatic hydrocarbons (particle-bonded PAHs) in the indoor and outdoor environments of three sampling sites in Bandar Mahshahr city, Iran. A low-volume air sampler was employed to collect size-segregated particulate matter during winter (October to December 2015), and summer (July to September 2016). The results showed that the annual concentrations of indoor and outdoor PM(10) and PM(2.5) were much higher than the related World Health Organization guidelines. The concentration of total particle-bonded PAHs (TPAHs) was higher in winter than in summer and a significant difference between the two sampling seasons was observed. The indoor and outdoor carcinogenic PAHs to TPAHs concentrations ratios in the sampling sites in summer and winter were as follow: for PM(10) 40.15–42.51%, PM(2.5) 41.30–42.97%, and PM(1) 43.07–44.36%, respectively; furthermore, the smaller the particle size, the higher the percentage of carcinogenic PAHs. 2 ring PAHs had a very small contribution to the total PAHs (about 1%), whereas PAHs with 3-to-4 rings had much larger contributions, ranging from 71.65% to 75.17%. The results demonstrated that as PM size decreased, the proportion of 5-to-6-ring PAHs to the total PAHs increased. Since 5-to-6- ring PAHs are considered to be more toxic, hence more attention should be paid to fine particles. The diagnostic ratios of indoor and outdoor of three sampling sites in both seasons suggested that petrogenic sources, as well as combustion of petroleum and other fossil fuels were the main PAHs sources.

This study investigates the concentrations of PM(10)-bound PAHs and their seasonal variations in three cities of Ahvaz, Abadan, and Asaluyeh in Iran. The mean concentrations of PM(10) 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 PM(10) 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 PM(10) 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.

Furfural is an aromatic compound which is mainly used in plastic production and purification of lubricating oils. Furfural removal was investigated using nano-sized zero-valent copper (nZVC or CU•) activated persulfate in aqueous solution by means of artificial neural network modeling. Effect of operational parameters including initial solution pH (3–11), chloride concentration (0.5–4 mM), potassium persulfate (KPS) concentration (0.4–2 mM), Cu nanoparticles dosage (0.01–0.05 g) and furfural concentration (20–100 mg/L) on furfural removal were evaluated. Furthermore, mineralization, kinetic modeling and neural network modeling were also carried out. Result indicated that the highest removal efficiency was achieved at pH 3, nZVC dosage of 0.02 g/L, furfural concentration of 20 mg/L and persulfate concentration of 1.2 mM. In addition, chloride had a dual effect on furfural removal. Neural network modeling also prepared a sensible predictive performance (R2 = 0.98). By application of artificial neural network (ANN) modeling, a good correlation was obtained with training-validation-data. © 2019 Desalination Publications.

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 PM

Due to its high toxicity, phenol is classified as a priority. In this paper, the removal of phenol from aqueous solutions was investigated using EDTA and activated persulfate (PS) with nano zerovalent iron (Fe· or nZVI). The influence of operational parameters such as initial pH, Fe·/EDTA ratio, iron species, chemical additives, and the presence of chloride ion on the efficiency of the process was investigated. An artificial neural network (ANN) model was applied to predict the chemical degradation efficiency. The intermediate compounds produced during the mineralization process in addition to the removal of phenol were also investigated. The decomposition products were analyzed using gas chromatography–mass spectrometry analyzer. The highest removal efficiency was 93.98%, which was obtained at pH = 6.0, Fe·/EDTA = 1 and persulfate (PS) = 7 mM. The main intermediates included phenol, 2-chloro-2-nitro-trimethylphosphine oxide, 4-ethylbenzoic acid, oxalic acid, hexadecanoic acid, succinic acid and benzoquinone. Upon application of the ANN model, a good correlation was obtained with training and test data. In conclusion, our results implied that Fe· along with EDTA as a chelating agent can effectively activate the PS and enable the oxidation process to degrade priority pollutants from contaminated wastewater.

Due to the inevitability of urban flood in presence of the rainfalls exceeding design capacity of urban drainage system, resilience approach has been recently considered instead of the conventional urban drainage management. However, acceptance of resilience approach necessitates considering sustainability in the selection of urban drainage projects due to the various aspect of flood impacts. This paper presents a new integrated framework to show how urban drainage plans are resilient and sustainable. The framework consists of several indicators including technical, economic, social, environmental and planning aspects. On the other hand, the selection of suientropy of the probability distribution p i . In fact, entropy reduces the effect of plan among available suggested plans is complicated in presence of multiplicity of the indicators. A new combined-adaptive multi criteria decision making technique including combination of Adaptive analytical hierarchical process, Entropy and TOPSIS is here introduced to facilitate the decision making process as well as dealing with uncertainties due to the subjective experts’ preferences. Moreover, presented framework are applied on a part of urban drainage system of Tehran, Capital City. Four urban drainage plans are designed and suggested to be evaluated along with existing system in terms of their sustainability and resilience. The results shows the presented framework provide comprehensive information regarding the behavior of the urban drainage plans against urban floods as well as their sustainability for urban managers. In addition, presented framework facilitates and accelerate the complicated process of decision making. Therefore, it can be employed as comprehensive decision support tool for resilient and sustainable urban drainage management.

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., Graphical abstract Image 1

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 (O(3), NO(2), PM(10), PM(2.5), SO(2), 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.

Resilience is defined here considering the behaviour of an urban drainage risk management (UDRM) system in terms of its response and recovery under different rainfall events focusing on institutional and social aspects. A set of indicators is defined to operationalise definition for UDRM systems. To quantify the indicators, a hierarchical fuzzy system is developed to consider uncertainties in the UDRM system's social and institutional parameters and also to prevent the curse of dimensionality. The proposed scheme is tested on a real case study in Tehran in order to evaluate the effectiveness of some urban drainage plans to improve the current urban drainage system in terms of resilience. The results show that the presented indicators may be used as decision making criteria to help urban authorities select urban drainage options based on the behaviour of the UDRM system under different rainfall events.

Recently, atrazine has been increasingly used to control weeds in the corn and sugarcane farms, which affects the water resources and aquatic organisms. In this study, atrazine residual concentrations in water and fish samples of the Shadegan wetland (Iran) were investigated. Furthermore, the health and ecological risk assessment were calculated. A total of fifty water samples were collected from the wetland during three periods with a four-month time interval. Also, ten samples of ten different fish species were analyzed at the same time. The estimation of acceptable daily intake and comparison with international standards indicates danger to adjacent residential areas of wetland. Non-carcinogenic risk analysis showed that total hazard quotient (HQing + HQderm) were below the acceptable limit and there is no danger to the residents of the area. Atrazine concentration in the water and fish samples ranged between 0 and 2175.8 μg/L, and 0 to 35.58 μg/L, respectively. Results showed that 88% of the water samples were higher than EPA and WHO guidelines for drinking water in summer. The concentrations of atrazine in fish samples in summer were more than other seasons. Analysis of ecological risk assessment also showed that considering the average atrazine concentration in three seasons, the risk exposure was very high. This study will be beneficial to the both residents and government officials in management of Shadegan wetland pollution in term of toxic compounds.

Discriminant analysis (DA) and principal component analysis (PCA) as a multivariate statistical technique, were applied for the evaluation of temporal and spatial variation and the interpretation of a large complex water quality data set of the Zohre river basin, monitoring 16 water quality parameters collected over 16 - 45 years (1966 - 2013), in four stations sites (1554 observations for each parameter). Principal component analysis, applied to the data sets of the four studied stations resulted in four, four, four and three latent factors explaining 78.985, 83.828, 77.648, and 77.68 % of the total variance in water quality data sets of Kheirabad, Poleflour, Chambostan and Dehmolla stations, respectively. The factors obtained from PCA analysis indicate that the parameters responsible for water quality variations are mainly related to TDS, EC, Cl, Na, SAR and %Na (natural sources included Gypsum and salt crystal are frequently grown in supratidal, intertidal zones of Zohreh delta and Chamshir faults I and II fault zone, and anthropogenic source caused by sugarcane culture withdrawals, discharging of domestic and industrial wastewaters) in all studied stations. DA provided an important data reduction as it uses only seven parameters (discharge, temperature, electrical conductivity, HCO3- , Cl, %Na and T-Hardness), affording more than 58.5% correct assignations in temporal analysis.

Nowadays, fuzzy logic has been successfully employed to handle the environmental issues. Due to inflexible classifications and interpretative difficulties of crisp data involved in the classic water quality index (WQI), employment of fuzzy logic in water resources problems has been repeatedly recommended for decision support in ecological assessment. As a mixed-ability method, it can both provide balance in suitable and unsuitable observations, which results in incompatible outcomes and can interpret social, economic, and biological effects as membership functions of linguistic terms. Accordingly, this study aims at evaluating the Karun River water quality, based on Mamdani fuzzy approach using developing a fuzzy water quality index (FWQI), a hierarchical approach was conducted to avoid curse of dimensionality in fuzzy rules and to take the relationships between parameters in to account as well 21 parameters in the form of physicochemical, chemical, biological and heavy metal fuzzy groups were processed based on trapezoidal membership functions and the rulesets for considering the relationships between parameters. A global FWQI of 36.78 ± 2.17 has been estimated based on 2010 to 2015 data, while the assessment with NSFWQI and CCMEWQI indices scored 41.8 ± 1.33 and 33.65 ± 2.87, respectively. Based on ANOVA test, changes in global FWQI were not significant in sampling stations. Moreover, results show degradation of the water quality from upstream to downstream of the Karun watershed. These results put forward an argument in necessity of regeneration of the Karun water quality to improve ecological conditions in the region.

In recent years, concerns over the issue of air pollution have increased as one of the significant environmental and health problems. Air pollutants can be toxic or harmful to the life of plants, animals, and humans. Contrast to primary pollutants, ozone is a secondary pollutant that is produced by the reaction between primary precursors in the atmosphere. The average of air pollutant data was compiled for the purpose of analyzing their correlation with the pulmonary function of students and the FENO biomarker from the air pollutants of the Environmental Protection Agency. According to the average of 3 days, the concentration of ozone in the (S

The primary objective of the present study was to evaluate the concentrations and short and long-term excess mortality attributed to PM

Long non-coding RNAs (lncRNAs), are lengthy noncoding transcripts with pivotal roles in biological pathways including cell cycle, apoptosis and chromatin remodeling. Aberrant expression of lncRNAs has been strongly connected with tumor progression and metastasis. However, the prognostic significance of lncRNAs in diffuse large-B-cell lymphoma (DLBCL) remains unclear. In this study, the expression levels of 189 approved lncRNAs were considered in DLBCL patients using several different genomic and transcriptome datasets. The analyses showed that the lncRNA GAS5 allocated the maximum score of RNA dysregulation and can be considered as good choice in DLBCLs' researches.

Long noncoding RNAs (lncRNAs) are lengthy noncoding transcripts which are involved in critical signaling pathways including cell cycle and apoptosis so it is not surprising to see their altered expression in human tumors. Colorectal adenocarcinoma is one the most frequent malignancies worldwide. The role of lncRNAs in colorectal adenocarcinoma is not well understood. To study the significance of lncRNAs in colorectal adenocarcinoma, we retrieved 189 approved lncRNAs from HGNC. The genes were imported into the cBioPortal database for transcriptomic analyses. We queried all the samples from TCGA provisional colorectal adenocarcinoma with RNA-seq v2 data in our study and considered RNA dysregulation with Z-score: ±2. The lncRNA which was altered in most of the patients were considered as "significant lncRNA" for further analyses. We considered the association of candidate lncRNAs with clinicopathologic parameters of samples including tumor disease anatomic site, neoplasm histologic types, tumor stage and survival. We also compute the specificity of the significant lncRNAs expression in colorectal adenocarcinoma comparing with other human cancers in cancer portal. Our analysis showed that lncRNAs SNHG6, PVT1 and ZFAS1 allocated the maximum alteration among the colorectal cases. The expression of SNHG6 and ZFAS1 was more in rectal adenocarcinoma than the colon carcinoma while the PVT1 showed the same expression levels in both tissues. However, we found that upregulation of PVT1 has been reduced the overall survival in patients. Altogether these data showed SNHG6, PVT1 and ZFAS1, are promising candidates for experimental research on colorectal adenocarcinoma to discover novel biomarker for this prevalent cancer.

Despite remarkable advances in urban flood management techniques, pluvial flood damages still occur throughout the world. This may be attributed to uncertainties in the rainfall events which may disrupt the normal performance of an urban drainage system and eventually lead to inundations and damages. Therefore, the conventional urban drainage management approach focusing on system security should be modified. As a new approach to urban drainage management, this paper defines the persistence of a system as the ability of a disturbed system to resist, buffer the effects of variable disturbances and return to accepted level of performance after disturbances and introduces a framework to evaluate the concept of risk management persistence for urban drainage systems based on joint consideration of resilience and resistance standpoints. Based on this perspective, some of the required indicators were selected from the literature and adapted to the present study in order to quantify urban drainage risk management (UDRM) systems persistence against disturbances. Evaluation of urban drainage measures would indicate the level of persistence achieved. As a case study, part of the urban drainage system of city of Tehran–Iran was analyzed using the proposed scheme. Four urban drainage measures including three best management practices (BMPs) and a conventional system were added to the current urban drainage system to assess the performance of various measures in improvement of the persistence of UDRM systems. Results indicate that the analysis of the systems persistence can efficiently enable urban planners to select measures with an insight into the behavior of the UDRM systems faced with disturbances.