جستجوی مقالات مرتبط با کلیدواژه « Air pollution » در نشریات گروه « عمران »

In response to the Covid-19 pandemic, governments around the world have imposed severe traffic restrictions and presented different scenarios to reduce emissions from traffic sources. By applying the traffic restrictions caused by the Covid-19 epidemic, it was expected to see changes in the concentrations of air pollutants. Therefore, it was decided that the changes of air pollutants as one of the subsets of the environmental index of sustainable urban development during the covid-19 epidemic will be investigated. For this purpose, the aforementioned data are first collected in the four metropolitan cities: Tehran, Karaj, Ahvaz and Tabriz, and then processed and cleaned. After that, a proposed algorithm based on machine learning methods is presented. Machine learning methods decision tree, random forest, support vector machine, Bayesian network and perceptron neural network are applied to the selected features. Investigations showed that the prediction model using decision tree and random forest had the best performance for both recall and accuracy criteria. The research results showed that the effect of restrictions on the concentration of pollutants in different cities is different. Also, the results show that, in general, the application of traffic restrictions during the epidemic period did not have a significant and noticeable effect in reducing the concentration of air pollutants. Also, by comparing the change in air quality index with the death rate during the epidemic period, it was found that there is no relationship between them.

PM 2.5 (particles <2.5 μm in aerodynamic diameter) can be measured by ground station data in urban areas, but the number of these stations and their geographical coverage is limited. Therefore, these data are not adequate for calculating concentrations of Pm2.5 over a large urban area. This study aims to use Aerosol Optical Depth (AOD) satellite images and meteorological data from 2014 to 2017 for spatial distribution simulation of PM 2.5 concentrations over the mega-city of Tehran. The Multilayer Perceptron (MLP), Multiple Linear Regression (MLR), and Decision Tree (DT) models were used to estimate the concentrations of PM 2.5. The results showed that MLP with a root mean square error (RMSE) of 11.46 and R2 coefficient of 0.67 outperformed the MLR and DT models. However, the best model had low prediction accuracy. So, three optimization algorithms, namely, particle swarm optimization (PSO), Genetic Algorithm (GA), and Migration-Based Genetic Algorithm (MBGA) were used to improve the accuracy of the models. The use of GA and MBGA algorithms improved the accuracy of the models significantly and led to the RMSE of 1.71 and R2 of 0.99 for the hybrid model of MBGA-MLP. The proposed hybrid models in this paper can be used to estimate the PM2.5 concentrations.

The development of air pollution control strategies is a relatively complex process that has various aspects; there are also development plans in urban transportation networks, but this models are mainly unable to investigate the simultaneous effects of increasing and decreasing on air pollution. In this research, a dynamic system model has been created to investigate various parameters affecting air pollution compared to their long-term study over 20 years. The model is developed by creating economic, social, environmental and transportation sub-models and their simultaneous relationship to study public transport infrastructure development programs in urban air pollution and evaluates and compares the performance of public transportation and helps to adopt innovative policies for its future planning and management. To evaluate the developed model, the results of 14 criteria in 14 scenarios based on 3 general strategies were reviewed and three-dimensionalization methods were used to assess the policies. The results of the study led to the presentation of the proposed package of public transport policy, which shows that the quality improvement policy of the metro and BRT have been evaluated as the best policies among disposal and absorption policies. The novelty of this paper is the long-term review of various policies as well as all modes of transportation in the system dynamics model, the results of which can be the basis of urban transportation policy.

The aim of this study is to evaluate the effect of nitrogen oxides, volatile organic compounds, and ambient temperature on ozone concentration in the city of Tehran. In this regard, the hourly concentration for ozone and nitrogen oxides for the first half of 1396-98 were used. Also the 2-weeks averaged data for BTEX monitored by passive sampling method and 30-minutes averaged temperature data monitored at Mehrabad airport were used. First, daily variations of ozone and nitrogen oxides with their corresponding indexes were analyzed. Then, the role of nitrogen oxide and nitrogen dioxide on ozone concentration were investigated by statistical analysis. The results show a meaningful relationship between ozone and both nitrogen oxide and nitrogen dioxide with the coefficient of determinations of 0.83 and 0.8, respectively. Although an apparent increasing trend in ozone concentrations has been observed in recent years, however, no similar trends are detected for nitrogen oxide and nitrogen dioxide. The limited available data for BTEX, shows a 17.3% increase from 1394 to 1398. The daytime temperature analysis for the 30 days with the highest ozone concentrations showed an increasing trend from 1396 to 1398. Therefore, it can be concluded that the increasing trends of BTEX concentrations and ambient temperature were probably responsible for the increasing trend of ozone concentration in the study period.

Currently, one of the most important air pollutants in Tehran is fine particulate matter. The composition and sources of these particles are poorly known; therefore, in order to distinguish the status of PM2.5 pollutants from their chemical compounds, 24-hour PM2.5 samples were collected at the main residential station (an Air Quality Control Company site, located in Sharif University of Technology) every six days for a full year from February 2014 to February 2015. The samples were analyzed for ions, organic carbon (including water-soluble and insoluble portions), Elemental Carbon (EC), and all detectable elements. Based on the results of chemical analyses including TOT, GCMS, IC, SF-ICPMS, and ROS methods, Organic Matter (OM) is one of the most important compounds in PM2.5, which on average constitutes 35% of the mass of fine particulate matter. The contribution of PM2.5 sources was determined by the CMB model using the concentration of chemical components of the particles and the profile of possible sources in Tehran. According to previous results, mobile sources (such as gasoline, diesel, and smoking vehicles) as the most important source of particulate matter, accounting for 58% of the total concentration of PM2.5. Road dust, the second significant source contribution, was described as the first loading factor, accounting for 49% of the changes in particle concentration extracted by running the PCA model. Combined model (principal component analysis/multiple linear regression chemical mass balance; PCA/MLR+CMB) comprising two stages has been developed for improving the accuracy of identifying PM2.5 sources. The results show that the second factor of the PCA model including 17% of PM changes is a composite source, and it is influenced by heavy metals from human resources including industries, car brakes, car tires, and car lubricating oils and, in this factor, combustion. Heavy fuels with an average of 65.10% made the largest contribution to the emission of particulate matter associated to factor 2. The insights regarding fine PM in Tehran provided by the results of this paper could be useful in planning effective control strategies and decision-making in Tehran, Iran.

Passenger cars are considered as one of the main sources of air pollution, especially in cities. The large pollutants that are generated from the combustion zone in vehicles, directly and indirectly, can affect the individual and national economy. ‏ In the direct economic losses, the gradual death of green spaces and forests can be opposition to the exposure of pollutants such as nitrogen oxides and ozone, or damage to monuments, objects, and facades of buildings due to corrosion by suspended particulate or matter pollutant compounds‏. to develop respiratory, heart and lung diseases caused by various phenomena of air pollution in public health (Mayer et al., 1999; Jacob et al., 2016). By examining the results from technical examinations and comparing them with the standard of emission in Iran and European countries, it was found that all of the cars reviewed in this paper were consistent with Iranian emission standards, while in comparison to the Euro emission standards they did not have a favorable situation. therefore, the estimation of vehicles emissions, especially the internal vehicles using data collected from technical observation centers, is very necessary and can be achieved by estimating the pollution rate in the planning, reducing the pollution of large cities as well as the prevention of small towns. In fact, if the amount of pollution of vehicles used inside Iran is available, it can be driven by a series of changes in the intended vehicle due to the factors affecting its publication and defining different projects to improve air pollution, as well as the direction of management and control of emissions from vehicles. In recent years, research aimed to investigate the amount of pollutants output, which suggests that, unfortunately, manufacturing cars in terms of environmental standards produce many pollutants (Gulbute et al., 1395). Therefore, in order to determine the exact status of internally produced vehicles, additional research is necessary. The study examined technical examinations, notably technical examinations of passenger cars and the pollution inspection of the seven cars that are very common in Iran. This study is done in East Azerbaijan and Khuzestan province, which are completely different from the climatic point of view, and the results obtained from the two oblasts have been compared.

In recent years, the extension of cities and cars has led to some crises such as increased fuel consumption, air pollution and urban traffic. Often a significant amount of vehicle energy is wasted as a result of sequential braking in high-traffic cities. Thus, the recovery and reuse of braking energy reduces fuel consumption and reduces air pollution. In this paper, we have estimated the average amount of energy and power lost in automobiles in Tehran due to braking in front of traffic lights according to statistics of 2014 and then have calculated the daily amount of energy that can be recycled. Comparing the recoverable energy from these brakes with the daily energy produced by the Parand power plant and the electricity consumption of the houses shows that only this type of braking can save 3% of the energy produced by the Parand power plant, which is equivalent to the electricity consumption of 96,000 residential houses. He searched. The results of the calculations can significantly tempt and justify the design and use of energy regenerative braking systems.

In this study, the amount of pollution and impact of construction machinery was studied on air pollution in the project of construction of Shiraz Urban Train Station. Air pollution parameters were collected and extracted from the EPA station which is near the project implementation site, including PM10, PM2.5 , So2 , No2 , O3 , CO. Some pieces of information were collected from the Urban Train Organization. the type and number of machines involved in the project and their hours of operation from. the data collected from the Meteorological Office, the weather and the amount of dust were determined . Workshop equipment and fuel consumption were calculated based on daily workshop activities of each machine and amount of contamination was determined based on amount of fuel consumed. As well as analysis, review and percentage of air pollutants caused by construction project activity were specified. In compared to average of the pollutants, each of the pollutants caused by workshop activity with average pollutants measured by measurement station, the percentage of pollution caused by workshop activity is CO=46/56%, O3=46/48%, NO249/53%, SO2=60/68%, PM10=39/22, PM2.5=56/08. There was a direct relationship between the pollutants measured by the measuring station activity level of workshop, with highest activity index in the months when workshop showed highest number of pollutants, and at same time, air pollution monitoring station had higher numbers of pollutants in these months. it shows. Devices that have a higher workshop activity index have higher fuel consumption and a higher share of air pollution.

In recent years, air pollution has become one of the most important environmental challenges in large and industrial cities such as Tehran. High concentration of particulate matter with a diameter of less than 2.5 μm (PM2.5), which is known as the main cause of pollution in Tehran, is associated with irreversible effects on human health. Providing spatial-temporal model with high accuracy and speed for forecasting, is an effective way to protect public health against the increase of harmful air pollutants. The rapid growth of computing technologies and the availability of air quality data have provided researchers with the opportunity to provide sophisticated models in the context of machine learning, especially in deep learning to predict the concentrations of various air pollutants. In this study, with the aim of predicting PM2.5 concentrations at different time intervals, a new spatio-temporal deep learning model based on gated recurrent units (GRU) is presented which maintains and extracts temporal and spatial dependencies in the time series of air pollution datasets. The proposed model has been compared with support vector machine regression (SVR) and long-term memory (LSTM) methods as competitive approaches. The data used in this study include the hourly concentration of PM2.5 and meteorological parameters recorded by 13 air pollution monitoring stations and 3 synoptic meteorological stations in Tehran in the period of December, 2016 to February, 2019, respectively. The model presented in this paper with the RMSE of 7.97 μg/m3 and MAE of 5.35 μg/m3 has the best result for predicting air contamination compared to other methods. This model can determine 80% (R2=80) of PM2.5 concentration changes and predict contamination level. The proposed model also proves that it can be used effectively to predict and control air pollution by extracting temporal properties, simultaneous forecasting for all stations and considering spatial correlations.

Air pollution and its adverse effects on the health, economic, and social issues of societies are so high that many countries in the world put it at the head of their economic and social development programs. Particulate matters, PM2.5, which mostly are emitted from gas oil fueled buses in urban environments, is one of the most important pollutants, which has significant effects on the health of individuals and the environment. About 72% of air pollution in Tabriz is due to mobile sources (Kahforooshan and Fatehifar, 2013). PM2.5 concentration in Tabriz was 18 μg/m3 in 2015, while the standard concentration for this pollutant is 12 μg/m3. In this paper, the IVE emission model was used to determine the amount of PM2.5 pollutants emitted from BRT buses in Tabriz, 4 km distance from Shariati intersection to the university intersection as a case study and AERMOD dispersion model was used to determine the contribution of this fleet to the concentration of PM2.5 pollutant which is measured at three air quality monitoring stations.

U-Turns are an important and unconventional tools for the improvement of highway operations. In some cities of Iran, signalized intersections were replaced by mid-block median u-turns which have different design from foreign counterparts. Recent studies on the performance of u-turns led to controversial results since they did not apply systematic approach for accurate evaluation, studies were not conducted for peak and nonpeak periods, and the resultant effects of eliminating delays at the intersection and spending more time for making a u-turn were not considered simultaneously. Therefore a research was conducted in which operations of signalized intersection were simulated and operational models were deployed to obtain travel times and speeds at u-turns. At high and medium volumes, u-turns increase travel speed of vehicles by 20 (km/h) and decrease speed by 10 (km/h) at low volumes. As the volume ratio of turning vehicles to highway through vehicles rises, u-turns ability to improve highway mobility rises. At Bagheri-Golbarg and Bagheri-Resalt intersections (in Tehran), fuel consumption declines by 1,623 and 4,244 liters per day, although minor street vehicles daily consumption rises by 1,269 liters. The daily emission of CO and HC declines and NOX emission grows. In brief, u-turns have strong performance at high and medium volumes and poor performance at low volumes while it may have some negative impacts on minor street vehicles. There is high risk of collision with geometric elements of u-turns. This result is in concordance with studies that state total amount of crashes increase by 13.2 percent at u-turns.

Achieving the lowest emission rate of urban air pollutants, requires an effective management of mobile air polluting sources. To address this type of management, not only high quality vehicles should be recruited but also the quality of transportation such as amount, slope, and traffic patterns (i.e., steady vs. interrupted flow) should be considered. Therefore, a number of methods are emerged to control the steadiness of traffic flow through traffic network instruments such as traffic lights or ramp metering schemes. In this study, attempts have been made to model a steady traffic flow on the Sadr Overpass to mitigate the least air pollutants production Modeling the optimized traffic volume entering and leaving the ramps whilst maintaining an acceptable service level using a mathematical linear programming technique is presented. Furthermore, a simulation has been conducted using an IVE model to estimate the amount of emissions. The results indicate that temporary closure of ramps in the east-west direction could lead to a steady flowrate on the overpass which decreases the amount of CO and NOx by %54 and %25, respectively. Similarly, in the West-East direction, deploying a cyclic monitoring of traffic flow in the ramp discharging into Modarres Expressway, results in reduction of CO and NOX by %42 and %41, respectively.

In the late February 2020, the first positive cases of the novel coronavirus, COVID-19, were confirmed in Iran, and the World Health Organization updated the status of the global outbreak from epidemic to pandemic in mid-March 2020. The rapid outbreak of the virus intervened a significant portion of socioeconomic activities, leaving behind some serious questions on the main factors intensifying the infection and the morality rates. Although the primary impacts of the outbreak have been extensively explored at the global and regional scales since its emergence, the impacts of the environment on the viral spread are still poorly understood. The goal of this paper is to review the most recent scientific findings on the spatiotemporal correlation between the air pollution and the mortality rate due to COVID-19. These researches are based on statistical analysis of the ground and satellite-based recorded data on PM2.5,PM10, and NOx across the United States, China, Italy, and England. The majority of these studies also consider data on population intensity, meteorological variables, migration rate, age, and health service quality to guarantee the validity of the findings by excluding the possible impacts imposed by these stressors. The results suggest that there exists a significant positive correlation between the concentration of the aforementioned air pollutants and the infection and mortality rates due to COVID-19. While long-term exposure to NOx has been associated with hypertension, heart and cardiovascular diseases, and chronic obstructive pulmonary disease, high concentration of PM2.5 and PM10 pollutants additionally enhances the mortality rate by facilitating the transmit of pathogenic agents through the fine particulate matters in the air. Regarding the drastic air pollution condition during the cold seasons in the most populated cities across Iran, the conclusions of this study can guide policy makers towards an effective planning and management of the COVID-19 crisis in such seasons.

Traffic management in cities necessitates the implementation of comprehensive strategies and correct scheduling of demand management in order to reach sustainable development goals. Transportation is the main contributor to urban air pollution imposing high cost to communities. Emission from mobile sources in Tehran is responsible for 85 percent of the total air pollutant emissions. Therefore, assessment of emission rates in different districts may be used as the base for air quality management decisions. Due to the complexity of effective policies that lead to environmental sustainability for reducing the emissions of air pollutants caused by transportation using Multi Criteria Decision Making (MCDM) approaches could be an effective and the most appropriate approach.
 Sadr overpass is one of the east to west main corridors in Greater Tehran Area and embeds a large amount of traffic volume. Therefore, assessing different and alternative traffic scenarios and its modeling incorporating air pollution concerns, promotes imposition of the most environmentally preferred traffic demand management policies. This study aims to investigate different alternatives to access Sadr Overpass of Tehran using different ramps and estimating the air pollution caused by the traffic volumes in each access mode. These scenario alternatives have been evaluated using MCDM. Therefore, the different access routes via ramps of Sadr Overpass to its main lanes are considered in terms of the two formerly implemented scenarios. The first implemented scenario is defined as the air pollution caused by the traffic volume due to limitation of access that was implemented before 21 June of 2017. In this period of time, in the east to west direction, the limitation of access to Sadr Overpass was imposed via lower level Sadr ramp in between 7 to 10 AM and during the closure of this ramp, vehicles could access the overpass and Niayesh Tunnel via Qeytarieh and Kaveh ramps. In other side of the overpass, the first ramp leading the lower level is closed at 15 to 21 and vehicles could not access Sadr Expressway via this ramp. The second scenario is defined at the period of time that the limitation of access in both directions, was imposed all over during the day time permanently that is from 21 June 2017 till now.  Air pollution caused by each mode of transportation is modeled using IVE that is an International Vehicle Emission Model to simulated emissions from motor vehicles. The IVE model uses local vehicle technology levels and its distribution and includes emission factors for estimating the air pollutants. Furthermore, these scenarios have been compared using Multiple-Criteria Decision Making approach and the evaluated criteria are the emission rates of motor vehicles, velocity and level of service (LOS) of the expressway. The results show that the evaluated scenarios are ranked as per their level of priority as the first and the second implemented scenarios, respectively. Also, it is shown that in the east to west direction, closure of lower level Sadr ramp in the morning peak time of traffic volume reduces the emission rates of CO pollutants by 10 percent  in that time. Similarly, in the west to east direction, limiting the access to the lower level Sadr ramp during 16-17 hours reduces the CO emissions by 3.5 percent.

In recent years, due to the dramatic increase of air pollutants preparation of right air pollution maps and the development of instantaneous air pollution monitoring systems attention of researchers has attracted. According to the data obtained from air pollution monitoring stations, they were not isolated from the error and some of their observations are missing; therefore, it is necessary to use a robust model to refine the data and predict the observations. In the present study, the data’s of three stations in Tehran of air pollutants corrected and predicted by Kalman filter. Results showed that an average accuracy of 92% for the Kalman filter.

Asthma is an untreatable but manageable disease that has increased dramatically during the last fifty years due to contamination of the surrounding environment with triggers such as air pollution and has become one of the common chronic diseases throughout the world. Researchers consider allergies and environmental stimuli to be one of the essential components of asthma management programs. Predicting the impact of these factors, identifying potential risk areas for asthma exacerbation and informing patients about preventing exposure to them in all places and times are an appropriate and effective approach in managing the disease in the long-run. For this reason, specialists consider that the design, development, and promotion of asthma management programs are vital to controlling asthmatic exacerbations. Today, with the development of information technology tools and influence web geography information system in health area, the design of managing programs for controlling asthma has undergone fundamental changes by removing spatial and temporal constraints. The tool is available at any location and time and provides appropriate information about the living environment of patients and useful information about the role of air pollution factors in allergic asthma exacerbations.
Study area: The study area is Tehran city in the article. We collected required data from patients with asthmatic exacerbations referring to one of the pulmonary hospitals (1702 asthmatic patients) in the city. The air pollutants data also were prepared from pollutant stations in Tehran. In two databases were merged in a unique geographical database.
Materials & In this study, we have developed an asthma management system based on air pollution using tree-based classification. Our research process includes four main steps. Firstly, required data is collected and prepared in a geographical database as spatial data. Then with the help of tree-based classification algorithm, predictive model of asthmatic exacerbations is made in Rstudio and its performance is assessed. Then with help of if-then rules of the model, it was prepared a toolbox in ArcGIS to reproduced maps of the prediction daily, because the conditions for air pollution factors change on a daily basis, and it is necessary for these maps to be updated continuously. In the final step, it was published these maps to the users in the Web, which will be realized with the help of ArcGIS Server tools. Patients can view these maps through ArcGIS Online (via the browsers of desktop and mobile devices).
Results & In this study, we have developed a tool based on decision tree algorithm integrated spatial data with accuracy 82% and the web-based geographic information system to provide daily maps of the occurrence of asthma exacerbations based on air pollution to alert patients about potentially dangerous areas and places which asthma exacerbates. This tool, which is available on browsers of both of web and smartphones platforms, improves patient adherence to asthma management programs and significantly raises their awareness about of an environmental-related disease. With the help of these maps, they will have a powerful self-management tool that can decide whether or not they will be present in these areas. Our asthma management system provides the useful and valuable map for users with asthma. Patients could use this tool in every place and every time without any limitation.  Our system improves adherence ‘s patients to asthma management plans Compared with traditional and paper-based tools. Patients inform about surrounding environment continually with the practical system to avoid places that are hazardous to their health. Therefore, we provide a suitable environment for users with help of various tools Arc GIS that is available for all users without extra cost. These maps are easy to understand and users can easily use it with a low level of literacy.

In recent years, technology development and using various tools have led to produce environmental contaminations. Radon (Rn-222) is one of the dangerous and fatal indoor air pollution. According to Environment Protection Agency (EPA) and many other international organizations, Radon is major cause of lung cancer after smoking. In present research, Radon measurements have been done in 30 dwellings of Tehran using AEOI devices. According to research achievements, the average Radon concentration in Tehran is measured 104 Bq/m3. Maximum and minimum concentrations are 460.2 Bq/m3 in west and 31 Bq/m3 in north respectively. Risk assessment was done using EPA method. Results showed that independent of age, the lung cancer death rates and risk were 14 times or 12 times (for males and females respectively) greater for ever smokers than never smokers. In conclusion, females are more at risk than males. It was determined people who were older than 70 and also ever smokers are more at risk that must be followed up.

Air pollution as a silent murderer of metropolitan areas demanded huge amounts of attractions. During the past few decades, after London 1954 black days, the world encountered a novel problem which was made by anthropologic actions. Scientific researches for scrutinizing the air pollution and its effects on humankind and the environment, started and improved after chronic influences of contaminations which in this era prognostication of pollutants and finding the relationships between parameters out, seems to be undeniable. Ozone as a tropospheric gas, has severe impacts on the all creatures while the human beings are more delicate in conjunction with this gas where it can destroy ability lungs and cause asthma and other pulmonary diseases. In the present article, the two most prevailing approaches for prediction, applied to the forecast tropospheric ozone value considering eight other photochemical precursors and meteorological parameters. Sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) and particulate matters (PM2.5, PM10) as photochemical precursors, and also humidity, air temperature and wind speed as meteorological parameters, after data preparation, used for ground level ozone prognostication in Tehran, Iran, with a condensed population where suffers from severe air contaminations and high rate of daily death, related to the air pollution. Used data series, have been collected from 22 regions of the cited city during 2 years (2014 and 2015). Two evaluation criteria, root mean square error (RMSE) and correlation coefficient (R), selected for comparison of applications. Support vector machine (SVM) and artificial neural networks (ANN) as capable soft computing approaches which have been used in numerous areas of science, opted in this research. Support vector machine with classification of other eight parameters and by 286 vectors as a classifier and 97 border vectors, sorted the 70 percent of data sets as training and the residual amount of parameters used as testing data sets. Radial basis function (RBF) selected as Kernel function. Artificial neural network works as like as human brains and neurons between layers transfer datasets and process them during the run time, where in the recent paper the layer number of the created network is one for hidden layer and one for the output layer and 10 neurons have been selected for hidden layer and one for the output layer. Network type of this system is feed-forward with back propagation and TRAINLM used as training function and LEARNGDM used for adaption learning function. Both approaches depicted reliable and acceptable results, where RMSE and R values for support vector machine, respectively 0.0774 and 0.8456, also artificial neural network resulted 0.0914 for RMSE and 0.8396 for R, which are reasonable outcomes. As the outcomes for training datasets were better than the results for testing datasets, both approaches showed acceptable performances because of over-training controlling, which is a serious and prevalent difficulty of soft computers. Support vector machine, with lower root mean square error and higher correlation coefficient selected as better application for ground level ozone prediction. These series of studies are supportive for calibration of measuring systems and due to their expensiveness, soft computing is the most reliable and affordable substitute for the past machines. Also the analysis of tolerances among the parameters illustrated that CO, Temperature and NO2 are the most effective where, PM2.5 had the least amount impact on O3 forecasting process.