Prediction of airborne pollen concentrations by artificial neural network and their relationship with meteorological parameters and air pollutants.

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.

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.

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.

Concentrations and health effects of short- and long-term exposure to PM2.5, NO2, and O3 in ambient air of Ahvaz city, Iran (2014-2017).

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.

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.

Long Noncoding RNAs in Colorectal Adenocarcinoma; an in silico Analysis.

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.