Mobile sources from administrative service commutes significantly contribute to air pollutant emissions in metropolises, underscoring the need for travel demand management (TDM) and referral reduction strategies. A software-oriented approach is crucial in metropolises like Karaj due to the high commuting volume. Evaluating pollutant emissions across scenarios offers insights for effective air pollution reduction strategies. Scenarios aim to assess air pollution management, considering software and hardware aspects. Data collection involved field interviews and questionnaires for individuals commuting to administrative offices. These challenges and considerations informed the classification of the studied vehicle fleet based on system types, production years, emission standards, fuel types, and vehicle classes. We designed scenarios to minimize standard pollutants by reducing in-person visits to administrative offices and replacing the fleet with hybrid and natural gas vehicles. Results were compared with the baseline scenario, computing emissions using the International Vehicle Emission Model (IVE). The comparative analysis highlighted that substantial pollutant reduction comes from combined commuting reduction and a decrease in referral numbers. TDM emerged as the most cost-effective strategy, executed with principled planning. In conclusion, this study's scenario exploration provides insights for policymakers and urban planners. Adopting a software-oriented approach to mitigate air pollutant emissions through commute reduction and strategic TDM can significantly enhance air quality and curb traffic-related pollution in cities like Karaj.

In this study, a multitude of statistical tools were used to examine PM10 concentration trends and their seasonal behavior from 2015 to 2021 in Tehran. The results of the integrated analysis have led to a better understanding of current PM10 trends which may be useful for future management policies. The Kruskal – Wallis test indicated the significant impact of atmospheric phenomena on the seasonal fluctuations of PM10. The seasonal decomposition of PM10 time series was conducted for better analysis of trends and seasonal oscillations. The seasonal Mann-Kendall test illustrated the significant possibility of a monotonic seasonal trend of PM10 (p = 0.026) while showing its negative slope simultaneously (Sen = -1.496). The forecasting procedure of PM10 until 2024 comprised 15 time series models which were validated by means of 8 statistical criteria. The model validation results indicated that ARIMA (0,1,2) was the most satisfactory case for predicting the future trend of PM10. This model estimated the concentration of PM10 to reach approximately 79.04 (µg/m3) by the end of 2023 with a 95% confidence interval of 51.38 – 107.42 (µg/m3). Overall, it was concluded that the use of the aforementioned analytical tools may help decision-makers gain a better insight into future forecasts of ambient airborne particulate matter.

This research aims to investigate the effect of virtual education during the COVID-19 outbreak on air pollution indicators in Tehran. The study uses quantitative methods, including One-Way ANOVA, to analyze the air pollution indicators before and during the COVID-19 pandemic. Data on air pollution indicators in Tehran from 2018, 2019, and 2020 were collected from Tehran Air Control Company and compared using statistical tests. The year 2019 represents virtual education, while 2018 and 2020 represent face-to-face education. The examined indicators include particulate matters with a diameter less or equal than 2.5μ (PM2.5), SO2, NOX (i.e., NO2 and NO), O3, and CO. The results of variance analysis show significant differences in the PM2.5and NOX indices between virtual and face-to-face training days. Follow-up tests by Toki and Scheffé indicate that in 2019, when education was fully virtual, the levels of these pollutants were lower compared to 2018 and 2020. However, there were no significant differences in the SO2, O3, and CO indices during the days of virtual education compared to the years before and after. This suggests that virtual education during the COVID-19 outbreak contributed to pollution reduction by reducing traffic to educational organizations and its indirect effects.

In recent times, the brick kiln contributes to air pollution is one of the most emerging issues worldwide. In this research work, the Peshawar city, ambient air quality was measured, using a fixed air monitoring station to evaluate the impact of gaseous emission from brick kilns on ground level. In this study, the portable gas analyzer (PG-250) was used to quantify brick-based emitting carbon monoxide (CO), sulfur dioxide (SO2) and nitrogen oxide (NOx) from 3 brick kilns in the city of Peshawar. It was noticed that the average concentration of SO2 and NOx exceeds the National Environmental Quality Standards (NEQS) of Pakistan specifically, in terms of air quality. The brick kilns in District Peshawar have shown negative effects on the environment. It is necessary to take various measures to monitor the brick kiln embosom regularly before it becomes a significant risk for individuals. In conclusion, the impact of air pollution on physical activity and sedentary behavior at a specific time may be different.

Mitigation of atmospheric pollution has been a topic of concern over the past decades. In this study, tree rings have been used to reconstruct past climates as well as to assess the effects of recent climatic and environmental changes on tree growth. Vehicular emission is one of the major sources of pollutants in the atmosphere and this study focused on the Haatso-Atomic road which over the years has been a spot for heavy vehicular traffic. Swietenia mahagoni (Mahogany) tree was logged and the rings counted and age determined to be 61 years spanning from 1957 to 2018. X-ray fluorescence (XRF) was used to investigate the presence of the following heavy metals. Heavy metals (Cu, Mn, Zn, Pb, Cd and Ni) which ranged from (3.15—9.84mg/kg), (2.58 – 5.49 mg/kg), (8.18 – 15.78mg/kg), (0.12—0.60 mg/kg), (0.01—0.09 mg/kg) and (0.10 – 0.99 mg/kg) respectively, from vehicular emissions were determined for annual rings spanning from 1957 to 2018 and surprisingly an increasing trend was observed with some the heavy metals exceeding WHO guidelines. Tree growth rates were calculated through ring width measurements and related to annual precipitation data spanning over the sampling period. It was observed that wet seasons correlate with high growth rates of trees while low precipitations seasons related to low or no growth rate of trees.

Defined as any substance in the air that may harm humans, animals, vegetation, and materials, air pollution poses a great danger to human health. It has turned into a worldwide problem as well as a huge environmental risk. Recent years have witnessed the increase of air pollution in many cities around the world. Similarly, it has become a big problem in Iran. Although ground-level monitoring can provide accurate PM2.5 measurements, it has limited spatial coverage and resolution. As a result, Satellite Remote Sensing (RS) has emerged as an approach to estimate ground-level ambient air pollution, making it possible to monitor atmospheric particulate matters continuously and have a spatial coverage of them. Recent studies show a high correlation between ground level PM2.5, estimated by RS on the one hand, and measurements, collected at regulatory monitoring sites on the other. As such, the present study addresses the relation between air pollution and satellite images. For so doing, it derives RS estimates, using satellite measurements from Landsat satellite images. Monitoring data is the daily concentration of PM2.5 contaminants, obtained from air pollution stations. The relation between the concentration of pollutants and the values of various bands of Landsat satellite images is examined through 19 regression models. Among them, the Ensembles Bagged Trees has the lowest Root-Mean-Square Error (RMSE), equal to 21.88. Results show that this model can be used to estimate PM2.5 contaminants, based on Landsat satellite images.

This study was carried out to analyze the variations of Benzene, Toluene, and para- Xylene (BTp-X) present in the urban air of Delhi. These pollutants can enter into the human body through various pathways like inhalation, oral and dermal exposure posing adverse effects on human health. Keeping in view of the above facts, six different locations of Delhi were selected for the study during summer and winter seasons (2016-2017). The concentrations of BTp-X on online continuous monitoring system were analyzed by chromatographic separation in the gaseous phase followed by their detection using a Photo Ionization Detector (PID). The concentrations of BTp-X were found maximum at a high traffic intersection area as 68.35±48.26 µg/m3 and 86.84±32.55 µg/m3 in summer and winter seasons respectively and minimum at a residential area as 4.34±2.48 µg/m3 and 15.42±9.8 µg/m3 in summer and winter seasons respectively. The average BTp-X concentrations of summer and winter seasons were found as 9.88, 20.68, 28.52, 49.75, 64.04, and 77.59 µg/m3 at residential, institutional, commercial, low traffic intersection, moderate traffic intersection and high traffic intersection areas respectively. Clearly, it has been found that the concentrations of these compounds were more on the traffic areas indicating that the vehicles are the major emission source. Hence, it may be concluded that the number of vehicles along with the high traffic congestion on the city streets and roads results in more accumulation of aromatic compounds and deteriorate the urban air quality.

Management of solid wastes is considered as an economic and environmental issue in the building stone industry. The current study uses raw and calcined calcareous sludge, generated in the stone cutting factories, in order to remove sulfur dioxide. Sludge characterization has been performed, using X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX) analyses. The removal experiments of sulfur dioxide have conducted under different humid contents and adsorbent doses. The results showed that the higher the adsorbent dosage and humidity content, the greater the SO2 adsorption.. The calcination process at temperatures of 400, 500, 600, and 700℃ revealed that with rising calcination temperature and humidity content, the adsorbent capability is enhanced considerably. This method could be developed for the management of stone sludge produced from the stone cutting industry through its conversion into an effective and low-cost adsorbent for desulfurization process.

In the present study, air quality analyses for ozone (O3) were conducted in Shiraz, a city in the south of Iran. The measurements were taken from 2011 through 2012 in two different locations to prepare average data in the city. The average concentrations were calculated for every 24 hours, each month and each season. Results showed that the highest concentration of ozone occurs generally in the afternoon while the least concentration was found in the morning and at midnight. Monthly concentrations of ozone showed the highest value in August and June while the least value was in December. The seasonal concentrations showed the least amounts in autumn while the highest amounts were in spring. Relations between the air pollutant and some meteorological parameters were calculated statistically using the daily average data. The wind data (velocity, direction), relative humidity, temperature, sunshine periods, evaporation, dew point, and rainfall were considered as independent variables. The relationships between concentration of pollutant and meteorological parameters were expressed by multiple linear regression equations for both annual and seasonal conditions using SPSS software. Root mean square error (RMSE) test showed that among different prediction models, stepwise model is the best option.

Air pollution is a challenging issue in some of the large cities in developing countries. Air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. Several methods exist to analyze air quality. Among them, we applied the dynamic neural network (TDNN) and Radial Basis Function (RBF) methods to predict the concentrations of ground-level ozone in Karaj City in Iran. Input data included humidity, hour temperature, wind speed, wind direction, PM2.5, PM10 and benzene, which were monitored in 2014. The coefficient of determination between the observed and predicted data was 0.955 and 0.999 for the TDNN and RBF, respectively. The Index of Agreement (IA) between the observed and predicted data was 0.921 for TDNN and 0.9998 for RBF. Both methods determined reliable results. However, the RBF neural network performance had better results than the TDNN neural network. The sensitivity analysis related to the TDNN neural network indicated that the PM2.5 had the greatest and benzene had the minimum effect on prediction of ground-level ozone concentration in comparison with other parameters in the study area.