Chemical degradation-based methods including oxidation have shown great promise for controlling benzene, toluene, ethylbenzeneandxylene isomers (BTEX) in waste gas. This study presents an approach in which the emission of BTEX compounds in a bituminous waterproofing (BW) production unit located in the city of Delijan, Iran has been controlled through process modification. The process is modified by introducing a thermal oxidation unit using an incinerator design. The process simulation has been performed with Aspen Hysys software and, key parameters in the oxidation process are identifiedandoptimized. Finally, the environmentalandeconomic performances of the incinerator were assessed to provide a decision support tool for the selection of this approach. Finally, the environmentalandeconomic performances of the incinerator have been assessed to provide a decision support tool for the selection of this approach. The results indicated that the formation of the oxidation unit had prevented the release of BTEX pollutants up to 98.5%. Moreover, the economic analysis illustrated that the rate of return on investment in the proposed project is 0.27. Thus, the potential for attracting capital will have positive impacts on the environmentalandeconomic indicators of the region.

Air pollution damages children’s health in many different ways, through both chronic and acute effects. The aims of our research are to reveal the indoor air quality levels in schools. Subject and indoor air measurements were performed in 34 primary schools located in the Central Anatolia region. PM10, PM2.5, CO2, CO, CH2O, relative humidity, temperature, and total bacteria and fungus levels were measured. In the urban region, mean PM1 was higher than the other regions(p=0.029). PM10 and PM2.5 were higher in schools in rural areas. According to CO2 measurements, only one school was identified to be below the upper limit recommended by the WHO. Total microorganism concentration was exceeded in 44.1% of classrooms. Indoor PM1, PM2.5, PM10, CO2, total bacteria and fungus levels were high and above recommended limits. Human activities, movements of students could be considered the most important indoor factors for particle matter increase. Indoor parameters could be lowered by organizing the school environment.

The advent of advanced features of soft computing can be used to solve complex problems which are more non-linear and messy. Many of the applications have been analysed and validated by the researchers through soft computing approach in the past.Neural Networks (NN) with appropriate selection of training parameters is implemented apart from conventional mathematical model. In this paper, analysis is made on the estimation of PM10 air quality in selected regions of Chennai district by wavelet approach with energy spectrograms. After analysing the results, NN of multilayer feed forward back propagation algorithm forecasts the air quality of selected regions. Discrepancies in selecting the training parameters of NN’s have been overcome by trial and error basis. This work will be helpful in proving the powerful tool of NN to forecast short term nonlinear parameters and the predicted results will give us the clear design of existing problem and thecontrol measures need to be implemented.

Today, indoor air pollution is a major concern. So far, many quantitative and qualitative studies have been conducted on particulate matter pollution in closed environments, but not much research has been done to measure air pollution in subway station. In this study, we have investigated the concentrations of PM10, PM2.5 and TSP particles in 12 underground stations on the oldest and main Tehran metro line, in two seasons, autumn and spring. For sampling suspended particles, we have used a portable direct reading device for monitoring suspended-particles (HAZDUST EPMA5000). We also used Pair T- test to compare the particle concentrations in different modes of the ventilation system (on, off, and inlet air) and Three-way variance analyze. According to the results, the mean concentrations of PM2.5-PM10 - TSP values in line-1 on the station platforms are significantly higher in spring than in autumn, off state of the ventilation system than on state of the ventilation system (P <0.001). Also, the concentration of particles measured in the air of subway stations is higher in the off state of ventilation systems, compared to Inlet air to stations (P<0.001). There is a correlation between concentration of particles measured in different sampling season, condition of the ventilation mode (on, off, inlet air) (P<0.001). Improving the efficiency of ventilation systems (equipped with a suitable filter) and fan in stations is suggested as one of the factors to reduce the concentration of particles, especially in spring.

Exposure to airborne particulates is a major occupational hazard especially for outdoor workers who spending time outdoors at ground level getting exposed to traffic fumes and roadside dust. Aim of this study was to assess respiratory health symptoms and determine the change of lung functions of the roadside vendors and its association with traffic-related exposures and their working experience. A cross-sectional study was conducted in key market places of Kolkata – Gariahat (GH), Esplanade-Park Street (EP), Shyambazar-Hatibagan (SH) and Behala (BE). Particulate (PM10 and PM2.5) levels and meteorological parameters (wind speed, temperature and relative humidity) were monitored in the morning, afternoon and night over the period of October 2019 to February 2020. Lung function status (FEV1, FVC, FEV1/FVC ratio and PEF) was measured for 111 purposively selected participants. PM concentration was observed higher in the morning and night peak hours for all sites. At SH area the average occupational exposure level for PM10 and PM2.5 were observed as 1502.22 μg/m3h and 684.01 μg/m3h. Percentage predicted FEV1 (%FEV1) of street vendors was found decreasing with their work experience and the worst-case scenario was observed in the EP area, with the corresponding value being 70.75%, 49.15% and 47.3% for less than 10 years, 10 to 20 years and more than 20 years participation respectively. The higher particulate burden was observed to have declining lung function status of the street vendors. A strong policy framework should be adopted to improve outdoor working environment for outdoor workers.

Air quality prediction is highly important in view of the health impacts caused by exposure to air pollutants in urban air. This work has presented a model based on support vector machine (SVM) technique to predict daily average carbon monoxide (CO) concentrations in the atmosphere of Tehran. Two types of SVM regression models, i.e. -SVM and -SVM techniques, were used to predict average daily CO concentration as a function of 12 input variables. Then, forward selection (FS) technique was applied to reduce the number of input variables. After converting 12 input variables to 7 using the FS, they were fed to SVM models (FS-(-SVM) and FS-(-SVM)). Finally, a comparison among SVM models operation and previously developed techniques, i.e. classical regression model and artificial intelligent methods such as ANN and adaptive neuro-fuzzy inference system (ANFIS) was carried out. Determination of coefficient (R2) and mean absolute error (MAE) for -SVM (-SVM) were 0.87 (0.40) and 0.87 (0.41), respectively, while they were 0.90 (0.39) and 0.91 (0.35) for ANN and ANFIS, respectively. Results of developed SVM models indicated that both FS-(-SVM) and FS-(-SVM) regression techniques were superior. Furthermore, it was founded that the performance of FS-(-SVM) and FS-(-SVM) models were generally a bit better than the best FS-ANFIS and FS-ANN solutions for short term forecasting of CO concentrations.

The life cycle of diesel fuel in non-passenger vehicles was assessed for all registered vehicles in Qatar as of November 2017. The Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model was used as a source of normalized data to evaluate diesel fuel emissions for all non-passenger vehicle categories. This work aims at estimating the emissions from all non-passenger diesel vehicles in Qatar and evaluating the impact of the fuel life cycle assessment. The emissions of CO2, NOx, CO, SO2, VOC, black carbon (BC), organic carbon, fine particulates PM2.5, and coarse particulates PM10 were evaluated. SO2 emissions were found to be dominant during the well to pump (WTP) stage of the life cycle assessment (LCA) process, while the pump to wheel (PTW) stage was found to be dominated by CO, VOC, PM10, PM2.5, and BC emissions. NOx and organic carbon emissions were virtually the same during both stages. Total greenhouse gas emissions amounted to 5367 kt of CO2 equivalent (CO2-eq) in 2017 as compared with that in 2014 (5277 kt), the only reported value in Qatar for transportation emissions. In addition, several mitigation strategies are proposed to ensure sustainability in the transport sector and to minimize the negative impact of diesel fuel emissions in the country.

The present study analyzes air quality for Carbon monoxide (CO), in Esfahan with the measurements taken in three different locations to prepare average data in the city. The average concentrations have been measured every 24 hours, every month and every season with the results showing that the highest concentration of CO occurs generally in the morning and at the beginning of night, while the least concentration has been found in the afternoon and early morning. Monthly concentrations of CO show the highest values in August and the lowest values in February. The seasonal concentrations show the least amounts in spring, while the highest amounts belong to summer. Relations between the air pollutant and some meteorological parameters have been calculated statistically, using the daily average data. The data include Temperature (min, max), precipitation, Wind Direction (max), Wind Speed (max), and Evaporation, considered independent variables. The relations between the pollutant concentration and meteorological parameters have been expressed by multiple linear regression equations for both annual and seasonal conditions, using SPSS software. Analysis of variance shows that both regressions of ‘enter’ and ‘stepwise’ methods are highly significant, indicating a significant relation between the CO and different variables, especially for temperature and wind speed in annual condition. RMSE test shows that among different prediction models, stepwise model is the best option.

The quick growth of vehicles is due to fast urbanization in mega cities during last decades. This phenomenon has serious impacts on air quality, as emission from mobile vehicles is the major source of air pollution. As a result, any attempt to reduce the emitted air pollutants is needed. This study aims at improving the fuel quality in transporting system with particular emphasis on taxis in Tehran in 2014. As a clean fuel, Euro IV is being used to reduce the emission of pollution, toxic substances, and greenhouse gases. A bottom-up approach to evaluate vehicular emission, using IVE (International Vehicle Emission) model in Tehran, has been presented, which employs the local vehicle technology and its distributions, vehicle soak distributions, power based driving factors, and meteorological parameters to evaluate the emission, itself.  Results show that the most abundant air pollutant (CO) has been reduced by 87.6% due to the clean fuel consumption (Euro IV). Also, the emission rates of the predominant toxic pollutant (Benzene) decreased by 98.7%. As a clean fuel, Euro IV managed to increase the emitted amount of CO2 and NH3. It can be concluded that upgrading transportation system with updated fuel quality is an essential step to improve air quality in Tehran.

The present research analyzes air quality in Ahvaz, a city in the south of Iran, paying special attention to sulfur dioxide (SO2). In order to prepare the average data in the city, measurements have been carried out between 2009 and 2010 in two different locations. Relations between sulfur dioxide and some meteorological parameters have been calculated statistically, using the daily average data. The wind data (velocity, direction), relative humidity, temperature, sunshine periods, evaporation and rainfall have been considered as independent variables. The RMSE Test showed that among different prediction models, the stepwise one is the best option. The average concentrations have been calculated for every 24 hours, during each month and each season. Results show that the highest concentration of sulfur dioxide occurs generally in the morning while the lowest concentration is found before the sunshine. In case of the monthly concentrations of sulfur dioxide, the highest value belongs to January, while the lowest one occurs in October. And as for the seasonal concentrations, it has been shown that the highest amounts belong to winter. Results show that quantities of SO2 in different seasons as well as the entire year can be estimated by climate parameters. Results also indicate that the relations between the SO2 and meteorological parameters are stronger than the entire year during the seasons.