جستجوی مقالات مرتبط با کلیدواژه « آلودگی هوا » در نشریات گروه « علوم پایه »

Air pollution has been raised as one of the most important risk factors contributing to citizens᾽ health and the emergence of environmental problems in recent years. Determining the emission rate and the costs imposed by fossil fuel pollutants on human health is an effective approach that can provide a financial estimation from harmful effects of these compounds for urbanized and industrial managers. This study considered the estimation of emissions, external costs, and ecological footprints of NOx, SO2, SO3, CO, SPM, CO2, CH4, and N2O resulting from the consumption of three types of fossil fuels including natural gas, gasoline, and diesel in an industrial company. 

The scope of study includes an industrial company in the west of Tehran, Iran. Firstly, data about the consumption of fossil fuels was collected in the main consumer sectors such as engine rooms, private and industrial vehicles in 1401. Next, the emission rate of the pollutants was estimated by using the energy balance sheet notified by the Ministry of Energy and Electricity Deputy in 1399. By having the amount of fuel consumed from each source and considering basic amount of the social cost of each pollutant, initial estimation of the social cost of pollutants was calculated. Subsequently, correction factors were assigned and the costs were updated (based on constant prices in 1381). Ultimately, ecological footprint was computed with regard to the total amount of fossil fuels consumed in 1401.     

According to the results, 1943880.64 cubic meters of natural gas were consumed in the engine room sector. Furthermore, 64435 liters of gasoline and 461482 liters of diesel were utilized by private and heavy industrial vehicles, respectively. In 1400, the emission rate of NOx, SO2, SO3, CO, SPM, CO2, CH4 and N2O was 17278.68, 7694.17, 90.34, 25972.46, 6400.79, 88860.01, 187.43, and 78.81, respectively. CO2 pollutant with 4088860.01 kg took into account the highest and SO3 pollutant with 90.34 kg had the lowest emission rate in the year. Based on the emission coefficient obtained from the Iran energy balance in 1399, the social cost for NOx, SO2, CO, SPM, CO2, CH4 were calculated 437081507, 592005417, 205312306, 1160386931, 1722935861, 1659463 Rials per year, respectively, which shows CO2 and CH4 gases impose the highest and lowest social cost to the environment, respectively. Moreover, total social cost based on the exchange rate and inflation rate approach was calculated about 4119381486 and 16383725983 billion Rials, respectively (after the coefficient factor was considered for constant prices in 1381). The calculations related to the estimation of the ecological footprint indicate that gasoline with 22.87 and natural gas with 9.33 hectares have created the greatest and the least ecological footprint. In addition, the total ecological footprint was calculated approximately 50.93 hectares.               

The social cost of pollutants in this study showed different results based on the volume of their emission. While the engine room had the largest share in greenhouse gas emissions with the amount of 26655364.69 Kg, the forklift trucks imposed the largest social cost with the amount of 1808115554 Rials due to the high emission rate of NOx, SPM and SO2 pollutants and also, the higher social cost of these three pollutants compared to the other ones. This method can be used as a model in calculating the social costs of released pollutants within industrial companies and the results can be monitored in the context of their environmental planning so that by identifying the centers of pollution and prioritizing them, correct budgeting should be set to reduce the amount of emissions and social cost of pollutants.

The rapid increase in population, growth of urbanization and industrialization in recent years, which is generally associated with an increase in demand and energy consumption, and as a result, an increase in pollutant emission sources, has exacerbated air pollution as one of the biggest current crises of urban societies and consequently health risks and related social inequalities in terms of time and space. On the other hand, meteorological parameters directly affect the amount of pollutants as well as the duration of their presence in the atmosphere, and the present research was conducted in order to investigate this effect and discover the relationships between criteria air pollutants and atmospheric elements.

In addition to investigating the status of meteorological elements (temperature, precipitation, wind speed, relative humidity, radiation, sunshine hours and cloudiness) and air pollutants (carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3) and particulate matters with aerodynamic diameters less than 10 microns and 2.5 microns (PM10 and PM2.5)) in Tabriz city during 2004-2021, the present study has explored the relationships between pollutants and meteorological parameters in monthly and seasonal time scales using Pearson's correlation test at the 95% confidence level and the effect of these elements on the concentration of pollutants using Multiple Linear Regression (MLR) and Generalized Additive Model (GAM) in R 4.3.1 statistical software.

Based on the results of Pearson correlation analysis, NO2 and PM2.5, SO2 and PM2.5 pollutants and PM2.5 and PM10 pollutants have shown a significant positive correlation in pairs, so it seems that these pollutants have similar emission sources. Also, the results of this research demonstrate that the concentration of air pollutants in Tabriz was affected by weather conditions during the entire statistical period in the monthly and seasonal time scales. NO2 and PM2.5 pollutants had the most negative monthly correlation with the parameters of temperature, wind speed and sunshine hours and the most positive correlation with relative humidity; PM2.5 had the most positive correlation with pressure; CO and SO2 had the most negative correlation with radiation; O3 had a strong positive correlation with temperature, wind speed and sunny hours and the most negative correlation with pressure, relative humidity and cloudiness; and NO2 and PM10 pollutants had the most positive correlation with cloudiness. The results of fitting Multiple Linear Regression (MLR) and Generalized Additive Model (GAM) for each criteria in Tabriz city indicated the better performance of GAM in analyzing the relationships between all air pollutants and the set of independent variables except NO2.

The results of this research indicate that the effect of atmospheric elements on the concentration of pollutants in Tabriz city is different depending on the type of pollutant and at different times, and it can be acknowledged that the effect of a specific meteorological parameter on air pollution is uncertain. However, wind speed, radiation, temperature and air pressure are the most important meteorological elements related to the concentration of pollutants in Tabriz city. Also, the results suggest that both MLR and GAM can describe the variability of the response variable by a set of predictor variables and explain the linear and non-linear relationships between them. However, considering the non-linear relationship between the concentration of atmospheric pollutants and meteorological elements, GAM is able to justify a higher percentage of changes in all criteria atmospheric pollutants except NO2.

From an environmental and human health perspective, air quality management and pollution control from mobile and stationary sources are critically important. Estimating air pollution emissions from vehicles and other mobile sources is a key tool in this field. Given the diversity in vehicle performance under different conditions, this estimation presents significant challenges for researchers and industry professionals. Therefore, the use of accurate and reliable tools for conducting these estimations is essential. In the compilation of national air pollution emission inventories, the International Vehicle Emissions (IVE) modeling software is utilized as a primary tool. This comprehensive software can estimate pollutant emissions from vehicles and other mobile sources, aiding in the development of pollution inventories.

To investigate pollutant emission levels more accurately, this study involved a comparison between data from the IVE model and measured data from zero-kilometer domestic passenger car emissions. These measurements were conducted using the ISQI laboratory dynamometer test and adhering to Euro 4 emission standards and the New European Driving Cycle (NEDC).

The comparison between dynamometer chassis emission test data from domestic vehicles and IVE model-generated data indicates significant differences in emission levels in some cases, highlighting that the IVE model is not closely aligned with reality and requires necessary modifications. Except for one instance where NOx emissions were equivalent with the Quick vehicle, other results showed NOx emissions ranging from 0.01 to 0.05, with the model displaying 0.03. Regarding CO, emissions ranged from 0.26 to 0.96, while the model displayed 0.48. Similarly, HC emissions ranged from 0.03 to 0.08, matching the model's result of 0.03. It is evident that the IVE model cannot accurately reflect real emission values in some instances and requires serious modifications to enhance accuracy in estimating these values.

Managers and decision-makers in air quality and environmental fields should carefully consider research and experimental results. Based on emission analysis, it is clear that the IVE model is not closely aligned with reality and relies on hypothetical conditions for estimating pollutant values, making it unreliable. Necessary improvements are required in the IVE model to enhance assessment quality and performance in air quality and environmental management. Developing approaches and solutions based on accurate and transparent data are crucial and can significantly improve air quality management and pollution reduction. Therefore, the use of accurate and up-to-date data for assessing air pollution from mobile and stationary sources is critically important and requires modification and improvement of the models used for more accurate emission estimations.

Air pollution in third world countries has become a serious crisis for the citizens due to the increase in population and the activities of polluting units. In this regard, in addition to criminal measures, legal systems also use administrative and disciplinary measures to deal with this phenomenon. This article has reviewed and evaluated the evolution of administrative and disciplinary measures against air pollution in the framework of French law as a country that has accumulated legal experience on air pollution for many years. These experiences based on administrative law can be considered as a good solution for countries with air pollution problems, including Iran. The basic purpose of administrative and disciplinary regulations is to prevent, monitor and target or reduce air pollution. The most important of these measures include tax crimes, inspection, determining the permissible limit of pollutants, canceling licenses, and the like. In this article, by using analytical and descriptive methods, it has been tried to answer the basic question that according to the experience of French law, what administrative and disciplinary measures can be identified to deal with air pollution. The most important feature of the French legal regime in the field of dealing with air pollution is the preparation and implementation of plans to deal with air pollution by administrative authorities.

For the last 50 years, activities like urbanization, industrialization and population growth, make air as a significant inseparable part of our life. Air pollution can be defined as the presence of chemicals or toxic compounds in the air to extent that they pose a health risk. Emissions from cars, plant chemicals, dust, pollen and mold spores are introduced as particulate matter (PM). The World Health Organization reported that ambient air pollution causes 4.2 million deaths from strokes, heart disease, lung cancer and chronic respiratory diseases. Of the various pollutants affecting air quality, particulate matter smaller than 2.5 microns is the major air pollution problem (Ścibor et al., 2020). As well, there is growing evidence of the effects of PM10 and PM2.5 on cardiovascular disease (CVD) and respiratory disease (DR).Forecasting air pollutants provides an opportunity to determine the intensity of air pollution in different areas and prevent irreversible impacts. In addition, these models also allow decision-makers to make the right decisions and prepare for the prevention or control of the PMs in the future. Some of the models used in air pollution forecasting studies are auto-regressive Integrated Moving Average (ARIMA), artificial neural network (ANN), Community Multiscale Air Quality Model (CMAQ), the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-CHEM), Fuzzy models, grey model and/or hybrid models. ANN has been used extensively by scientists to provide rapid and parsimonious solutions to mitigate the negative impacts of air pollution worldwide. Neural networks, as an alternative, have been successfully used in air pollution forecasting and have produced accurate results in time series data. Different types of noise and nonlinear structure were present in the data. Hybrid modeling approaches have a wide variety of applications in which numerous methods or attributes are merged to create a more sophisticated model with superior performance in certain scenarios.Urmia is one of Iran's most polluted cities, owing to continuous traffic and traffic congestion, growing CO2 and PM levels, and a lack of knowledge on regulating and locating industrial manufacturing units. Dust from Iraq affects the region, as well as inversion, which occurs 90 days a year, are instances of region-specific air pollution. In addition, the drying of Urmia Lake, which can result in salt storms, is one of the critical concerns that will lead to significant pollution in the near future.In this study, ANN-GA with missing data imputation was used to predict PM2.5 in Urmia, Iran, in the short-term to demonstrate how data-gap filling and preprocessing methods could improve hybrid models' performance.

The concentrations of air pollutants (carbon monoxide, nitrogen dioxide, and sulfur dioxide) as well as meteorological data (temperature, relative humidity, and wind velocity) were used as inputs in this research to predict PM2.5. Air pollution concentrationsand meteorological data over a two-year period were obtained from Monitoring Station No. 3, Urmia municipality, and Iran's meteorology website (Data.irimo.ir).The data was then preprocessed with the Savitsky-Golay filter before being fed into the ANN and ANN-GA networks. Data gaps and imputed data (KNN/SPLINE method) were used as input in each network, and the results were compared.In this study, a single system contains two hidden layers and one output layer. The time series method was used to introduce the data to the network. The data was divided into three parts. 70% of the data is used for training, 15% for validation, and 15% for testing. Data import scenarios were defined in two ways. The first scenario used no imputation, while the second used SPLINE and KNN to fill in data gaps. As a transfer function, a sigmoid (logsig) layer was used for hidden layers, and a linear layer (Purelin) was used for the output layer. The Levenberg-Marquardt algorithm was chosen as the learning algorithm based on the type of problem and the speed of convergence. To improve the results, the number of neurons, repetition parameters, number of permitted evaluations, Levenberg algorithm parameters, and reliability were all adjusted through a trial-and-error process.New ANN-GA network was used in this study and GA was used as a training function. After introducing the data as a time series and selecting the amount of data for each episode of learning, evaluation, and testing, the structure and number of networklayers were created with the "newff" function. The main difference is that the genetic learning process was used instead of the "train" function. It's worth noting that the network layer characteristics in both methods were the same. To learn how to complete the process, the new learning function requires several side processes, including cost function creation, selection, intersection, and mutation. Three methods of roulette selection, tournament selection and random were used in the selection process. To introduce the cost function, weights were taken from those created by the "newff" function. Different values were assigned to the initial population variables, maximum mutation number, and selection pressure coefficient by trial-and-error method. Moreover, two data import scenarios were defined.

Forecasting methods have been considered an important tool in research on air pollution. Among the various pollutants that influence air quality, particles with an aerodynamic diameter of less than 2.5 micrometers (PM2.5) are one of the key issues in air pollution control management. In this study, a model for predicting future concentrations of PM2.5 was developed by the Hybrid Network (ANN-GA). Two methods of data imputation (KNN and SPLINE) were used to minimize training issues and improve network accuracy. PM10, PM2.5, nitrogen dioxide, oxide, carbon monoxide, and weather data were used for predictions. The results show that multi-line neural networks are relatively efficient for predictive purposes but lack sufficient accuracy to predict. The ANN network produced MSE error of 0.023 and coherence coefficient of R 0.543 only with data gap filling methods. In order to improve R and reduce network errors, a genetic algorithm was used in combination with a multi-layer neural network (ANN-GA). As the results showed, MSE and R for hybrid networks (ANN-GA) were improved (R=0.91 and MSE=0.001). In addition, compared to ANN, the R increased by 40 percent and the MSE improved by 95 percent. Thus, it can be concluded that ANN-GA can be used as a powerful and reliable tool for predicting air pollution.

The purpose of this research is to measure air pollutants (especially PM10) in Beyhaqi Terminal of Tehran and to model the release of this pollutant under various conditions.

In order to measure the pollutant of suspended particles with a diameter of 10 microns (PM10), a portable device model Grimm 108/109 was used. In the selected points, sampling was done once a month during the year, three days a month and 3 times a day, and the sampling was done at different times of the day, i.e. morning, noon and evening, according to the increase and decrease of vehicle traffic. And for the purpose of modeling, Austal view, version 7 software was used.

The results showed that the concentration measured in the autumn and winter seasons was higher than the first six months of the year. Since in the standard of the World Health Organization, the daily limit for this pollutant is 50 micrograms per cubic meter, it can be seen that in the first scenario, in some areas (distances less than 300 meters from the source), the concentration of the pollutant exceeded the standard. The rest of the ranges are within the standard range. In the second scenario and based on the emission reduction factor of 70%, it was determined that all areas are within the standard range.

Finally, reducing the stopping time of cars, not turning on lights and using exhaust absorbent filters will help to reduce the emission of suspended particles.

One of the problems of management programs to control and reduce air pollution in Iran, especially in Tehran, is the lack of prioritization and the level of effectiveness of executive steps in order to focus human and financial resources. Therefore, step-by-step performance considering executive priority is a suitable solution to achieve this goal. Using the DPSIR framework in the SOE study of Tehran air pollution provides a systematic and comprehensive method to understand and address this critical issue. It not only helps in diagnosing the problem, but also in formulating and evaluating effective solutions, thereby contributing to the sustainable development of the city. The AHP-DPSIR hybrid model is a suitable model for finding the prioritization of environmental programs with a comprehensive and managerial approach, considering uncertainty with a hierarchical structure. In this study, responses to the improvement of pressure factors, stimulus, current situation and effects were investigated and compared in a hierarchical format, and at the end, their preference level was obtained. Development and improvement of public transportation, improvement of fuel quality, improvement of vehicle emission standard, technical examination of vehicle and finally urban traffic management were recognized as the implementation steps of Tehran's air pollution control and reduction program. The model developed in this study is suggested for use in similar cases, especially in developing countries that face the same management problem as Iran. This model has high flexibility and accuracy in prioritization with a comprehensive approach.

Fuel stations are one of the major sources of BTEX emissions to the atmosphere. Benzene, toluene, ethyl benzene and gazeline isomers are BTEX aromatic hydrocarbons. BTEX compounds are the most abundant volatile organic compounds in atmosphere and surface water.These compounds can affect the health of employees, drivers and surrounding residents. Due to the risks of BTEX compounds on people's health and the possibility of emitting them from fuel stations, examining the concentration of these compounds and controlling them in fuel stations is essential and effective in preventing their complications. The aim of this study is investigation the amount of BTEX in the ambient air of hamadan fuel stations and its surrounding and to compare with occupational and respiratory air standards.

The samples were collected according to the NIOSH method and the SKC adsorbent instruction by the individual sampling pump from indoor air and surrounding of Palestinian, Khansari and Forough fuel stations in Hamadan.The concentrationof BTEX compounds was extracted by dichrometan solvent and measured by GC/FID. The data were analyzed using SPSS software.

The mean of benzene, toluene, ethylenzene and xylene in Khansari fuel station were 1.23, 3.32, 0.31 and 1.43 mg/m3 respectively, in Palestine fuel station was 1.53, 4.14, 0.29 and 1.25 mg/m3 respectively and in Forough fuel station were 1.65, 2.23, 0.42 and 1.22 mg/m3, respectively. The highest concentration of BTEX compounds was located inside the fuel station and their concentration decreased by increasing the distance from the station. The mean concentrations of benzene in the air inside and distance 200m from the fuel stations were higher than EPA and in air inside and distance 50m from fuel station was higher than NIOSH guidelines. Also, the mean concentration of toluene in the air inside the Palestine fuel station was higher than the EPA guidelines.

The results showed that the dispersion of BTEX compounds in Hamadan refueling stations was caused by vehicles and gasoline evaporation during refueling. The reason for the decrease in the concentration of BTEX compounds by increasing the distance from fuel stations can be attributed to the production of pollutant concentrations in the yard stations, temperature conditions, wind direction and vehicles transportation. By increasing the distance from the station, as the production resources are more inside the station, from the concentration of BTEX compounds decreases by increasing the distance. Also, by increasing the distance from station, the wind airflow, lead to the distribution and dispersion of BTEX compounds and decreases its concentration. Employed personnel and drivers inside fuel stationsare confronted with benzene and toluene pollutants. As regards to short-term counterview with BTEX compounds causes effects such as stimulation and sensitivity of the skin, central nervous system problems (fatigue, headache, dizziness and loss of balance). Also in long-term encounters, in addition to the problems mentioned, they can affect the kidney, liver and blood. Therefore, more oversight should be carried out by petroleum product authorities and control actions to maintain the health of the people and reduce the pollutants in fuel stations, including leakage of tanks and pumps, and the possibility of recycling of gasoline vapors during refueling.

These days, the most cost-effective and accessible way to monitor air pollutants is the use of bio-monitors. Therefore, this study was conducted to biologically monitor temporal changes in the concentration of heavy metals suspended in the air using the lichen transplant technique. For this purpose, lichens of the genus Ramalina were applied. It was transferred from an uncontaminated area (Tirkan village, Babol city) in Hyrcanian forests to the study area near the transit road in Noor city. After three periods of three, six, and nine months of exposure, concentrations of Aluminum, Iron, Zinc, Copper, Lead, Nickel, Vanadium, Chromium, Arsenic, Selenium, Cadmium, Molybdenum, Cobalt in lichen samples (15samples per period) was determined by ICP-MS device. The results showed that Aluminum and Cadmium had the highest and lowest concentrations in all periods, respectively (mean614.06and 0.18 μg / g, respectively). With increasing exposure time, the concentration of metals also increased and this increase showed a significant difference in all metals for the first and Three periods (p <0.05). It was also found that due to the average exposure of control rate (3.37) which was much higher than the severe accumulation limit (1.75), the selected lichen has an acceptable ability to biologically monitor changes in heavy metal concentrations. In addition, using an exposure of control rate to evaluate the rate of change is a better alternative than the evaluation of the concentration of trace elements alone.

wrong locations and overloading in the steel industry have led to the creation of environmental problems by this industry. Wind direction, turbulent conditions and fluxes in the atmosphere near the earth's surface are among the most important atmospheric factors affecting the distribution pattern of air pollutants after leaving their emission sources. The purpose of this study is to model the spread of air pollution in a steel factory under prevailing wind conditions.

Using meteorological data, prevailing wind conditions, annual and seasonal windrose were modeled by AERMET software. Suspended particles as the most important pollutant in the area of ​​the steel factory were sampled and then analyzed with using the AERMOD model, particle diffusion and dispersion modeling in two time periods of 24 hours and 6 days.

The prevailing winds in this region are in the north, northwest, and southeast directions, and the highest amount of pollution is related to distances of 5000 to 20000 meters from the location of the chimneys and in the direction of the prevailing wind in the region. Also, the amount of air pollution caused by the factory chimney on a 24-hour time scale is lower than the clean air standard.

In this study, the effectiveness of the AERMOD model in the direction of better management and control of air pollutants and reduction of adverse effects on the environment is investigated, and appropriate solutions to reduce pollution are provided.

Oil and gas production units emit large amounts of air pollutants due to combustion processes and burning of oil and gas wastes and endanger the health of the environment and people. Considering the extent of the activity of this industry in Khuzestan province, in the current study, the range of influence of the flares of the flares of the West Ahvaz oil and gas exploitation complex (number 3) in the winter of 2018 has been investigated.

To carry out this study, NO2, CO2, SO2, CO gases were measured in four directions: north, south, west and east and at distances of 50, 100, 500, 1000, 2000 and 3000 meters by direct reading devices.

NO2, O3, SO2, and CO gas were higher than the standard in the distances near the flare, and PM2.5 and PM10 suspended particles were more than the standard at a distance of 3000 meters from the roads. Regarding PM2.5 and PM10 particles, the results showed that 79.16% of the points were in unhealthy condition for the sensitive and unhealthy groups and 75% of the points were in healthy condition, respectively, more than 83.33% of the measured points in the case of two So2 gas and 75% of the points in CO gas, 66.66% in No2 gas were in very unhealthy condition.

According to AQI index values, O3 and PM10 were generally unhealthy to very unhealthy, and SO2 gas and PM2.5 particles were in worse condition compared to others.

The increased traffic has been followed by many problems in metropolitans, the key of which is air pollution and excessive fuel consumption. Paying attention to public transportation, particularly the bus rapid transit (BRT) system is one of the measures that may be taken, since besides reducing social expenses, it may be very effective in declining air pollution. The main objective of the present research is to study the fuel consumption rate and the emissions rate of various air pollutants including CO2, CH4, and N2O gases in various scenarios of BRT system.

Since traffic and congestion phenomena are complex and dynamic, it is very difficult and sometimes impossible to model them with common mathematical models. To this end, agent-based technologies, highly compatible with these characteristics, can be utilized. In the current research, BRT system’s performance, the fuel consumption rate, and the amount of air pollutants production are estimated using agent-based modeling. This study emphasizes what changes should be made in effective parameters such as bus speed and bus stop time at stations, as well as bus dispatch timing in order to control fuel consumption and reduce pollution factors. This research uses NetLogo software to code the model and run its simulation and considers three different scenarios in line one of BRT system in Tehran (Iran).

following the analysis and comparison of different scenarios, suggestions are made to decline fuel consumption and air pollutants, such as minor changes in the parameters of bus stop times at stations as well as changes in the dispatch time of buses from the terminal in order to reduce fuel consumption and air pollution rates. The results indicate that one of the improved situations was related to the situation of increasing the bus dispatch time parameter and in the bridge scenario, CO2, CH4, and N2O emissions are 1458.6, 1.122, and 11.781, respectively, in one hour of peak passenger time.

According to the results, achieving the goal of reducing fuel consumption and air pollution rates is more suitable in the bridge scenario compared to the other two scenarios. Furthermore, if possible, it is suggested to build bridges at intersections with high traffic, or put the smart traffic light system on the agenda.

If we be aware of the trend of air pollution and the factors affecting pollution, we can reduce these factors and think of important measures for them and work to protect the environment, including the air, which in turn reduces the long run social and economic costs of pollution. The Euro 5 emissions’s standard has been implemented in EU member states since 2009. This law was supposed to be implemented in Iran from 2012, but it faced many obstacles. In this study, due to growing trend of pollution in major cities of the Iran, the emission of carbon monoxide (an indicator of pollution) has been simulated using the system dynamics approach in the period 2021-2042 for the city of Shiraz (as a case study). By continuing the current situation, the population of Shiraz increases through two natural population growth and migration. Appropriate air pollution control strategies have been proposed using public transportation, technical inspection, traffic management, and the offer of alternative fuels. Based on the simulation results in different scenarios, replacing Euro 4 gasoline with Euro 5, carbon monoxide emissions in Shiraz will be significantly reduced in the long run. Otherwise, the continuation of the current trend in 1420, carbon monoxide will become a serious challenge for the city of Shiraz.

Considering the worn-out public transport fleet in Karaj city, it needs to replace the fleet with up-to-date standards. Considering the economic and environmental dimensions, the effectiveness of the measures taken should be evaluated. Therefore, achieving the goal of evaluating the change in the emission of pollutants based on each alternative scenario can help the authorities to adopt air pollution reduction strategies. The purpose of designing scenarios in this research is to estimate the effectiveness of scenarios in the field of replacing the fleet of worn-out single buses in reducing the emission of air pollutants.
Material and First, the classification of buses and units based on parameters such as different systems, year of production, pollution standard, type of fuel consumed and vehicle class have been studied at 1398. Then, scenarios of reducing standard pollutants were designed in two plans, replacing the worn-out fleet with a hybrid fleet, dual-fuel and with fuel consumption, with Euro 4 pollution standard, and finally the scenarios were compared with the basic scenario. Pollution emission coefficients and scenario design were calculated using the International Model of Mobile Vehicle Emission (IVE) model for passages, 1st degree arteries, highways and freeways, with slopes of zero and 22%.
The results of comparing the scenarios showed that by replacing the entire worn-out fleet in terms of age (to the new fleet, scenario four), the maximum reduction in emissions of standard pollutants was 40% (CO, 60% (VOC), 42% (NOX), 86% (Also, the results of the distribution of pollutants in the city of Karaj, with Arc Map software showed that the highest emission of pollutants is related to region 10, and the lowest emission is in region 1, Karaj city.
Discussion and According to the findings, the effectiveness of replacement scenarios of the worn-out fleet (use of hybrid, dual-fuel vehicles with Euro 4 fuel) in reducing the emission of air pollutants has been 40 to 80%. Pollutant reduction scenarios and strategies to increase air quality are useful for policymakers and researchers to better understand the current state of air pollution in the region and are largely operational, requiring timely funding and well-planned planning.

As the population around the world increases, the demand for food and other goods increases. This demand is estimated with extensive production and use of natural resources, which leads to more environmental pollution. Along with other factors of production, energy plays a decisive role in the economic growth of countries and its importance continues to increase. Since a large part of the increase in demand for energy consumption is provided by fossil resources and their consumption leads to the emission of greenhouse gases and air pollution, it seems that economic growth in this way causes more environmental pollution. to be Although the emission of carbon dioxide gas has been closely linked with the growth of national income as one of the axes of the Human Development Index (HDI) in recent decades, it has destructive effects on other key measures of human development, such as life expectancy, because the destruction of the environment not only leads to an increase in the death rate, but also reduces the life expectancy in countries. When pollution and destruction cover the environment, the spread of various diseases and as a result the increase in mortality and the decrease in life expectancy will be obvious for that country and even its neighbors. Pollution sometimes produces such destructive effects that the residents of that area suffer from it and bear its effects for generations to come.Asian Productivity Organization (APO) including 18 member countries (Bangladesh, Cambodia, China, Hong Kong, India, Indonesia, Iran, Japan, South Korea, Malaysia, Mongolia, Nepal, Pakistan, Philippines, Singapore, Sri Lanka, Thailand and Vietnam), In line with the goal of participating in the socio-economic development of the member countries and improving the quality of life for the people of those countries through the promotion of productivity under the spirit of mutual cooperation among the member countries. Considering the rapid expansion of urban society and the dynamic growth of urbanization and its effect on the production of greenhouse gases as one of the most important factors of environmental pollution in different countries, there is an urgent need to fill the void in the relationship between life expectancy, economic growth, energy consumption and urbanization are revealed in the member countries of this organization. Also, according to the conducted surveys and the absence of similar studies, this study is probably the first to examine the amount of carbon dioxide emissions of APO member countries.

The estimation of the model in this study is based on panel data. The distinctive feature of this model is that it is simultaneously able to collect data in the form of time series and cross section and present their results together. Also, in these models, more statistical methods are available to the researcher and as a result, the estimation efficiency increases. This study has investigated air pollution with energy consumption, economic growth, urbanization and life expectancy by using the autoregression model with panel data distribution breaks (ARDL) during the period of 2000-2016. The advantage of using the ARDL method over other methods is that regardless of whether the variables in the model are of I(0) or I(1) type, it is possible to check the convergence relationship between the variables; Therefore, ARDL estimates are unbiased and efficient due to avoiding problems such as endogeneity and autocorrelation. Also, this method simultaneously estimates the long-term and short-term relationships between the dependent variable and other explanatory variables of the model.

Based on the results, the lowest amount of air pollution is related to Nepal and the highest amount is related to Singapore. Also, the countries of Mongolia and China had the lowest and the highest urban population among the studied countries, respectively. The lowest life expectancy is related to Cambodia and the highest life expectancy is related to Hong Kong. In estimating the model, the best interval is Panel ARDL (1,1,1,1,1). The variables of gross domestic product, energy consumption and urbanization have a positive relationship with air pollution, and the variable of life expectancy has a negative relationship with air pollution, which is in accordance with theoretical expectations. Based on this, a one percent increase in GDP in selected APO member countries leads to a 0.44 percent increase in air pollution;Likewise, a one percent increase in urbanization leads to a 0.38 percent increase in air pollution. Also, the results showed that, in the short term, error correction and energy consumption variables are significant at the level of one percent, and other variables are not significant in the short term. The coefficient of the error correction term indicates a significant long-term relationship between the variables of the model. This coefficient is significant at the probability level of one percent and its sign is negative, so it is expected that in each period, about 30% of the deviation of the short-term relationship will be adjusted towards the long-term coefficients. The results obtained from the long-term estimation of the model show that the increase in economic growth, energy consumption and urbanization leads to an increase in air pollution in APO member countries. Also, most of the investigated countries are less developed and developing countries. In addition, based on the results of life expectancy and air pollution, they have an inverse relationship, which is according to theoretical expectations; Because the increase in air pollution causes an increase in health costs, a decrease in the quality of the environment and, as a result, a decrease in life expectancy. Considering the conditions of climate change and the increase in the temperature of the earth, the increase in population and the economic conditions of the studied countries, it is suggested that the use of environmentally friendly technologies and equipment should be expanded in these countries with the cooperation of developed countries and the resources Renewable energy replaces fossil fuel sources to reduce environmental pollution. Moreover, Considering the positive and significant variable of economic growth in the emission of air pollution and considering that improving economic growth is one of the important goals of the economy, measures should be taken by policymakers so that economic growth causes the least damage to the environment.

The purpose of this article is to review scientific research related to air pollution and deaths caused by the corona virus and the costs of premature death and Disability Adjusted Life Years.

The present research method is a review study. The statistical population of the research is all the studies conducted in international and national scientific databases based on the index (DALY) and its relationship with pollutant particles with an emphasis on the pollutant of suspended particles with a diameter of 2.5 microns (PM2.5), as well as the effect of air pollution on The COVID-19 virus and their Willingness to Pay (WTP) in Iran and the world. The research findings were analyzed using the coding technique based on the keywords of air pollution, Contingent Valuation, Disability Adjusted Life Years (DALY) and COVID-19.

survey of environmental disease studies shows the contribution of environmental risk factors including PM2.5, have been according to the DALY index because of death or disability. Also, the study of articles are mostly about statistical analyses on the measured concentrations of PM2.5 and infection and death data by COVID-19 in different countries.

The results of the studies show a strong and significant correlation between the concentration of PM2.5 pollutants and the rate of infection and death caused by the corona virus. which shows that by controlling the concentration of pollutants, the rate of infection and death caused by the corona virus can be reduced.