Monitoring and controlling air quality parameters form an important subject of atmospheric and environmental research today due to the health impacts caused by the different pollutants present in the urban areas. The support vector machine (SVM), as a supervised learning analysis method, is considered an effective statistical tool for the prediction and analysis of air quality. The work presented here examines the feasibility of applying the SVM to predict the ozone and particle concentrations in two Tunisian cities, namely Tunis and Sfax. We used the SVM with the linear kernel, SVM with the polynomial kernel and SVM with the RBF kernel to predict the ozone and particle concentrations in Tunisia for one year. The RBF kernel produced good results for the two pollutants with 0% error rate. Polynomial and linear kernels produced sufficiently low errors for the pollutants, at 9.09% and 18.18%, respectively. Discriminant Analysis (DA) was selected to analyze the datasets of two air quality parameters, namely ozone O3 and Suspended Particles SP. The DA results show that the spatial characterization allows for the successful discrimination between the two cities with an error rate of 4.35% in the case of the linear DA and 0% in the case of the quadratic DA. A thematic map of Tunisia was created using the MapInfo software.

The estimation of pollution fields, especially in densely populated areas, is an important application in the field of environmental science due to the significant effects of air pollution on public health. In this paper, we investigate the spatial distribution of three air pollutants in Tehran’s atmosphere: carbon monoxide (CO), nitrogen dioxide (NO2), and atmospheric particulate matters less than 10 μm in diameter (PM10μm). To do this, we use four geostatistical interpolation methods: Ordinary Kriging, Universal Kriging, Simple Kriging, and Ordinary Cokriging with Gaussian semivariogram, to estimate the spatial distribution surface for three mentioned air pollutants in Tehran’s atmosphere. The data were collected from 21 air quality monitoring stations located in different districts of Tehran during 2012 and 2013 for 00UTC. Finally, we evaluate the Kriging estimated surfaces using three statistical validation indexes: mean absolute error (MAE), root mean square error (RMSE) that can be divided into systematic and unsystematic errors (RMSES, RMSEU), and D-Willmot. Estimated standard errors surface or uncertainty band of each estimated pollutant surface was also developed. The results indicated that using two auxiliary variables that have significant correlation with CO, the ordinary Cokriginga scheme for CO consistently outperforms all interpolation methods for estimating this pollutant and simple Kriging is the best model for estimation of NO2 and PM10. According to optimal model, the highest concentrations of PM10 are observed in the marginal areas of Tehran while the highest concentrations of NO2 and CO are observed in the central and northern district of Tehran.

Exhaust emissions contribute greatly to air pollution, the social cost of which may occur as danger to human health, attracting huge medical expenses, causing absenteeism and hence loss of productivity. These are incentives to reduce exhaust emissions to the barest minimum. Two major cities in Ghana, Accra and Kumasi, are struck by vehicular traffic jams especially during rush-hours and are grappling with the situation perceived to be worsened by driving pattern, a travel-related characteristic with a tendency to increase vehicular emission and hence, atmospheric pollution. Driving patterns were studied in the Kumasi Metropolis using questionnaires purposively administered to drivers who visited the Driver and Vehicles Licensing Authority. Parameters were analyzed with SPSS. Results indicate that drivers plied highway (90.0%), feeder (6.7%) and urban (3.3%) roads. Drivers (90%) had no knowledge of how driving patterns contribute to emissions, effect of idle and hot emissions and hot-and-cold starts dynamics. This could explain the failure of drivers to allow vehicle engines to stabilize for over 5 min and also to put off engines when stuck in traffic. Drivers changed speed as often as 4 times/km due to vehicle congestion and intermittent traffic lights, intersections and roundabouts. This may explain the difficulty in maintaining constant speed; thereby compelling drivers to exhibit frequent gear-changing behaviours as well as unstable and inconsistent speed profiles, as the commonest driving patterns. Such characteristics potentially increase energy consumption, emission level and abatement cost significantly and therefore, call for intensified educational programmes aimed at curbing this environmental peril.

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.