---

Aims: This study was carried out to determine the effects of urban gaseous pollutants (sulfur dioxide [SO₂], nitrogen dioxide [NO₂], and ozone [O₃]) and climatic factors (temperature and precipitation) on the morphological and anatomical reaction of Platanus orientalis L. in Tehran.
Materials & Methods: Seven districts of this city with a wide spectrum of climatic conditions and diversity of elevation were selected, and gas concentration of SO₂, NO₂, and O₃ was determined. Then, morphological and anatomical parameters of this plant including leaf area, specific leaf area (SLA), wet and dry weight, leaf toughness and thickness, water content, lamina, and main vein were measured and the analysis of their correlations with environmental (climatic and pollutant) factors along with their t-tests were evaluated.
Findings: The results showed that the climatic factors made significant (P < 0.05) changes on SLA, water content, wet and dry weight of green leaves of this plant. In addition, different levels of pollutant gases had significant (P < 0.05) effects on natural toughness, thickness, wet weight, number of spongy parenchyma layers, and the ratio of lamina mesophyll to subvein of leaves.
Conclusion: The results explain that anatomical and morphological characteristics of P. orientalis leaves have been influenced by environmental stresses and changes in these factors show the resistance of the plant to the environmental conditions.

---

---

Economic development is one of the major policies of a country which is concerned to industry and technology on one hand and leads to environmental pollution on the other hand. The experiences of developed countries show that economic development with emphasis on the industry sector, without any attention to environment, can create serious problem against sustainable development. Industrialization has caused increasing energy consumption and therefore air pollution. It's so important to consider the relationship between industrial activities and industrial pollution in developing countries, because industry sector has a basic role in development process of these countries. Therefore the aim of this study is examining and qualifying of linkage between industrial activity and air pollution, using an industry-level dataset of IRAN manufacturing industries during the period 1995-2007. The result of study shows that air pollution is a positive function of energy consumption, industrial activity and physical capital intensity and also is a negative function of labor productivity, fuel price and human skill intensity.

---

---

Shadow economy is an important part of economy in almost all countries especially the developing ones. Most of active firms in this part of economy have negative externality on the environment. Considering the importance of sustainable development and growing international pressures to maintain and support the environment more and more attentions have been drawn to the factors affecting and threatening environmental health. The present paper for the first time considers the role of variables like polity index and active population to total population ratio and how they affect the shadow economy. In addition to the main direct effects of these variables on shadow economy the indirect effects of causal variables through interaction with shadow economy are also examined. Since the relationship between shadow economy and air pollution has been somehow disregarded in economic literature to a large extent in Iran and to some extent at international level the present paper for the first time focuses on the relationship between shadow economy and air pollution. The results indicate that on average the ratio of shadow economy to GDP is 12.25% and a 1% increase in the size of the shadow economy raises the water pollution by 0.17%.

---

Aboozar oil field is located around 75 km south west of Kharg Island, in Bushehr state, southern part of Iran. Aboozar oil and gas complex contains 3 production platforms: Aboozar-A (AA), Aboozar-B (AB) and Aboozar-C (AC). Each of them includes some oil and gas wells which feed the process facilities and in addition each one comprises separate flare system in which separated gas is flared over there. Aboozar field offshore process is limited to oil and gas separation, and the produced gas from the separators is disposed in a common flare system in which more than 36 million standard cubic feet gas mixture are daily being flared. Flaring is the common practice of burning off unwanted, flammable gases via combustion in an open-atmosphere, non-premixed flame. This gas may be deemed uneconomic to process (i.e. if it is far from a gas pipeline or if it is ‘sour’ and contains trace amounts of toxic H2S) or it may occur due to leakages, purges, or an emergency release of gas in a facility. The flaring process can produce different pollutants such as SO2 as an index pollutant which has a substantial role in the environment and human health. SO2 is one the most major pollutants emitted from production platform flares due to gas mixture combustion.This pollutant concentration depends on the composition of gas sent to the flare combustion chamber and also flare combustion efficiency. To specify prevention and mitigation measures, it is needed to know about air pollutants concentration. In highly polluted places, to monitor amount of each pollutant all over the region, we should always measure concentration of pollutant by pollutant emission modeling from the source not to need costly routinely measurement by some special devices. Nowadays, air pollution models are routinely used in environmental impact assessments, risk analysis and emergency planning, and source apportionment studies. A dispersion model is essentially a computational procedure for predicting concentrations downwind of a pollutant source, based on knowledge of the emissions characteristics. These models is based on mathematical calculation and used to estimate pollutants concentration. As the project target, flare modeling as a point source to investigate its concentration around the platforms to understand whether the concentration of pollutant is more than the standard limit value or not was considered. In this project, we have simulated SO2 emission by means of an advanced model based upon the Gaussian model, and we tried to find out SO2 dispersion pathway after flame. In this study, Aboozar production platform flare SO2 dispersion has been simulated by means of AERMOD View software and its concentration was determined. Flare flame length and height was determined, using flare flame net heat release value, to achieve flare effective height. In addition, meteorological data was pre-processed in Rammet View Software to input in main software. Final results showed that SO2 estimated concentration is being exceeded from threshold limit value all over the complex in all living quarters and production and wellhead platforms based upon the American Conference of Governmental Industrial Hygienists standard.

---

Increasing pollution levels due to rapid industrialization and urbanization are now causes of major concern in industrializing countries. Petroleum and chemical processes are responsible for many emissions both into the air. Equipment leaks in chemical and petroleum processing industries are responsible for significant amount of emissions. Even if each individual leak is generally small, it is the largest source of emissions of volatile organic compounds (VOCs) from petroleum industries and chemical manufacturing facilities. Styrene and Acrylonitrile are two major components in the streams of ABS plant of Tabriz Petrochemical Complex which is expected to be released to the atmosphere through various sources such as equipment leaks and tank venting. In the first step of this study the major sources of pollutants emission in the ABS plant were identified considering the PDF and PID of the plant. Then the emission rate of each source was estimated using the emission factors presented by USEPA. An emissions factor is a representative value that attempts to relate the quantity of a pollutant released to the atmosphere with an activity associated with the release of that pollutant. Emission factors are powerful tools for policy makers as they can be used to relate emissions and concentrations. In the last step, the estimated emission rates were used as the input of Industrial Source Complex Short-Term Version 3 (ISCST3) model to predict the ground level concentration of Styrene and Acrylonitrile around the ABS plant. The ISCST3 is steady-state Gaussian plume model which can be used to assess pollutant concentrations from a wide variety of sources associated with an industrial complex. The model is generally applicable for near-field (within 10 km) impact assessment of air pollutant in meteorologically and topographically uncomplex conditions. Among the 54 pumps, 23 compressors and other equipments of the plant, 11 pumps, 8 compressors and 6 storage tanks were identified as the emission sources of considered pollutants. The emission rates of pumps and compressors were estimated using the emission factors presented in AP-42 document of USEPA. The emission estimation of Styrene and Acrylonitrile from six storage tanks has been done using USEPA standard regulatory storage tanks emission model (TANKS 4.0.9a). The emission software program TANKS is developed using emission factors presented in AP-42. The results showed that the compressors are the significant sources of considered pollutants which release about 586 g/day Styrene and 2506 g/day Acrylonitrile to the atmosphere. The emission rate of Styrene and Acrylonitrile from pumps were estimated 36 g/day and 94 g/day, respectively. The results of using TANKS model indicated that Styrene and Acrylonitrile emission rates are 7 g/day and 22 g/day, respectively. The estimated emission rates were used as the input of ISCST3 model to find the ground level concentrations of considered pollutants around ABS plant. The results showed that the maximum level of Styrene was 646 µg/m3 which is below the Reference Concentration (Rfc). In the case of Acrylonitrile the maximum level of estimated concentration was 272 µg/m3 which is higher than Rfc. The implementation of a leak detection and repair (LDAR) program or modifying/replacing leaking equipment with “leakless” components were recommended to reduce the emissions from equipment leaks of ABS plant.

---

 Microbial oil has high similarity to the oil obtained from plants and animals. They are renewable sources that can be used for different purposes such as production of biofuels. Biofuels are renewable, biodegradable, and nontoxic, which makes them highly environmentally friendly. Producing oil from yeasts has more advantages than that from plants. Accordingly, isolation of oleaginous yeasts with high ability of lipid production is highly valuable. A total of 138 yeasts were isolated for the purpose of this study. From this amount, 35 were capable of producing lipid. After extracting lipid, the best strain was selected and, by using PCR method, identified as Rhodotorula mucilaginosa. Optimization was done using the design of experiments; Qualitek-4 (W32b) software was used for analyzing the experimental data. According to the results, Rhodotorula mucilaginosa proved to comprise lipid, dry biomass, and lipid productivity at levels of 10.97 g L ^-1, 18.84 g L^-1, and 58.2% in optimized conditions, respectively. Lipid content on corn stalk and wheat straw hydrolysate was 36.9 and 41.8%, respectively. The extracted lipid was analyzed by FTIR spectroscopy and gas chromatography-mass spectrometry (GC-MS). The study showed high potential of lipid production in Rhodotorula mucilaginosa and also high efficiency of using Taguchi design in optimization of medium condition; therefore, by using this method, the optimization process can be done as best as possible. The economic values of microbial lipid production become more favorable when waste materials with zero or negative economic value are utilized as carbon source. Using bioprocesses such as microbial lipid production from waste materials, the problem of shortage of energy resources, and also air pollutions caused by fossil fuels, could be eliminated. 

---

In Carbon monoxide (CO) is one of the main air pollutant parameters in the atmosphere of Tehran, Iran. Generally, it is difficult to predict and control CO concentration because it is essentially nonlinear time-varying system. Recently, in particular, environmental control such as CO concentration level control is regarded as one of the most important factors in environmental protections. This paper describes forecasting and more specifically uncertainty determination of CO concentration during the modeling process using a support vector machine (SVM) technique. Uncertainty of the air pollution modeling studies highly affected the simulation results. In this regards, it is very important to determine the uncertainty of air pollution models due to consequences on health of people exposed to the pollution. Therefore, this research aims to calibrate, verify, and also determine the uncertainty of support vector machine (SVM) in the process of air pollution modeling in the atmosphere of Tehran. To achive this goal, the SVM model was selected to predict arithmetic average of daily measured CO concentration in the atmosphere of Tehran. In this regards, the SVM model was calibrated and verified using six daily air pollutants include particulate matter with diameter equal or less than 10 micrometer (PM10), total hydrocarbons (THC), nitrogen oxides (NOx), methane (CH4), sulfur dioxide (SO2) and ozone (O3) and also six daily meteorological variables include pressure (Press), temperature (Temp), wind direction (WD), wind speed (WS) and relative humidity (Hum). The data was collected from Gholhak station located in the north of Tehran, Iran, during 2004-2005. Thereafter, the best developed SVM model for predicting the CO concentration was chosen based on determination of coefficient (R2). Finally, to determine the SVM uncertainty, the model was run many times with different calibration data. It led to many different results because of the model sensitivity to the selected calibration data. Then, the model uncertainty in the CO prediction process was evaluated using the width of uncertainty band (d-factor) and the percentage of measured data bracketed by the 95 percent prediction uncertainties (95PPU). Generally, the results confirmed the strong performance of the SVM model in predicting CO concentration in the atmosphere of Tehran. The predicted average daily CO concentrations by SVM model had a good agreement with the measured ones in the Gholahak air quality monitoring station. It was found that the determination of coefficient for calibration and validation of SVM model were equal to 0.89 and 0.88, respectively. Furthermore, the results indicated that the SVM model has an acceptable level of uncertainty in prediction of CO concentration in which the level of d-factor and the percentage of measured data bracketed by the 95PPU in the validation step were 0.74 and 76, respectively. Therefore, The obtained results indicated that the SVM model had an acceptable level of uncertainty in prediction of CO concentration. Therefore, it can be concluded that the SVM model is able to predict the CO concentration in the atmosphere of Tehran while it resulted an acceptable level of uncertainty. Finally, due to the proposed methodology is general, the authors suggest to apply it for analyzing the uncertainty of SVM model in other fields of science and engineering.

---

In recent decades, increasing population density and economic and industrial activities in metropolitan cities has increased traffic volumes and, consequently, increased levels of air pollution. The major source of air pollution in major developing cities is the massive transport of vehicles that use more than standard fuel and energy, and heavy traffic in the streets of these cities is often rooted in problems such as there is a lack of traffic management and traffic culture. One of the important issues in cities and metropolises that face pollution problems and harmful effects is the issue of informing about the future status of air quality and the amount of urban air pollution to the people. This can be achieved through daily or even hourly forecasts of air pollution and preventing people from being exposed to contaminated areas and their irreversible consequences. Therefore, the need to predict the quality of the air and the quantitative estimates of the concentration of pollutants in the aftermath of the equipment makes it felt that in this study, the problem of the predicted hourly concentration of particulate matter (PM2.5) in the district 11 municipalities of Tehran have exceeded 80% of the contaminated days under the influence of this pollutant. The difficulty and uncertainty associated with estimating and predicting the share of road traffic volume at the general level of air quality is the most important factor that can, if properly diagnosed, be very helpful. In order to take into account the effects of varying the volume of different traffic fleets in the process of changes in the concentration of pollutants and air pollution, it is necessary to pay attention to the effects of other influential variables including hydrological variables, geographical variables, etc. To achieve this, The methods of analytic analysis seem to be able to examine all of these effects together and in an omnipresent manner. The method used to predict this study is one of the methods for analyzing neural networks called Support Vector Machine (SVM). Artificial neural networks are important tools in the field of computational intelligence. Different types of artificial neural networks have been introduced, mainly in applications such as classification, clustering, pattern recognition, modeling and approximation of functions (or regression), control, estimation and optimization of the case Are used. Support Vector Machines (SVM) are a special type of neural network that, unlike other types of neural networks (such as multi-layer perceptron MLP and radial base functions of the RBF), instead of minimizing the error, minimize the operational risk of classification or modeling. Slowly This tool is very powerful and can be used in various fields such as classification, clustering and regression. The results of this study showed that SVM models work well in predicting the contribution and time share of road traffic in propagation of particulate matter, and predictions are well-coordinated with observations. It provides the opportunity to be used as an air quality management tool. Variable significance analysis results for SVM models provide this opportunity to be used as a tool for air quality management, in which the sensitivity of models to variations in emissions can be used to evaluate the effectiveness of a The air quality management scenario will test traffic fleet technology, combine the traffic fleet or its volume.

---

Sulfur dioxide (SO₂) is one of the most common and harmful air pollutants. High concentrations of SO₂ can cause stress and limit growth in plants. Some of the plants can resist stress by bacterial symbiosis such as Rhizobium symbiosis. Rhizobium is a beneficial bacterium that enhances plant growth and yield. To study the effects of SO₂ pollution on growth indexes, protein, proline and sulfur contents, 31 days old plants of Trifolium resupinatum (Persian clover), inoculated with native and standard Rhizobium were exposed to the different concentrations of SO₂ (0 as control, 0.5, 1, 1.5 and 2 ppm) for 5 consecutive days. Results showed that inoculation increased leaf area, leaf number, shoot height, root length, shoot fresh and dry weight and protein content of Persian clover but didn’t show any significant effect on proline and sulfur contents. Different concentrations of SO₂ had a significant effect on leaf number, shoot height, root length, shoot fresh and dry weight, protein, proline and sulfur contents but didn’t have effects on leaf area. 0.5 ppm concentration of SO₂ increased growth indexes and protein content. Proline and sulfur contents didn’t change in 0.5 ppm. Increasing SO₂ decreased growth indexes and protein, and increased proline and sulfur contents. Interaction between Rhizobium inoculation and SO₂ treatment improved the stress effects of high concentrations of SO₂ on growth indexes, protein, proline and sulfur contents. It was therefore concluded that Rhizobium can increase tolerance and resistance of this plant to the abiotic stresses such as SO₂ pollution_.

---

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.

---

 

---

---

This paper studies the short-term impacts of air pollutants on mortality rates in a range of diseases including cardiovascular, respiratory and digestive diseases, tumors and cancers. It uses daily data of suspended particulate matters (SPM), CO, NOx, and O3 in six metropolitan areas of Tehran, Ahvaz, Mashhad, Tabriz, Shiraz, and Esfahan during 2011-2015. In order to determine the impact of air pollution on mortality rate, both daily air pollution and its average over the past two weeks is used. In this research, no association was found between daily pollution and mortality rate. However, the results show that increasing CO in the past two weeks causes a significant increase in mortality rates

---

Rapid urbanization and population growth has resulted in increased traffic congestion and consequently air pollution in most major cities, in particular, in the developing countries. Knowledge on the amount of different air pollutants and their spatial and temporal concentrations is of great importance for decision makers on health, environment and air quality estimation in different scales. Mashhad, as a metropolitan, due to its specific religious, socio-cultural and geographical role in the region is declared as one of the most polluted cities of the country. Given that there is a direct relationship between traffic volume data and air pollutants (PM2.5, CO and ), this study attempts to estimate the amount of each pollutant based on traffic volume and some primary weather data. We used empirical models proposed in the literature, such as Baker model and AERMOD, as well as linear regression and nonlinear neural network methods to explore the correlation between traffic volume and air pollutants over a period of six months in the city of Mashhad. The results showed low correlation coefficients between traffic volume and air pollutants in all models, indicating that such models may not be suitable to further estimate air pollutants using only traffic volume and primary weather data. Correlation coefficients were lowest for the pollutant PM2.5 over the time period of the study. Sensitivity analysis demonstrated that vehicle average velocity is by far the most influential variable in the empirical models used.

---

Arak is ranked 71st in the worldchr('39')s most polluted cities. Arakchr('39')s air is more polluted than the Iranian capital in terms of pollutants. The influx of large amounts of pollution into the city shows that a high percentage of pollution is concentrated in a small town, and this volume of pollution is very dangerous for both humans and the environment. In order to reduce air pollution in the city, the Iranian government in 2007 approved a plan called the comprehensive plan to reduce air pollution in Arak. In this comprehensive plan, the construction of a highway called Amirkabir (58 meters) has been approved, which connects the east and west of Arak. In this article, numerically and experimentally, the effect of constructing this highway on the reduction of air pollution in Arak city has been investigated. In the results section, the details of changes in air pollution in Arak city before and after the construction of this highway have been compared. The results show that a 2.75 percent decrease is observed for Nox pollutants, 4 percent decrease is observed for Co, and a change is obtained for So₂. As a result, the implementation of this project at a very high cost will not have a significant impact on improving air pollution in the city of Arak.

---

The quality of manpower is one of the factors affecting the environmental degradation. According to studies, air pollution is affected by its past values, so a dynamic model should be used to study it. Accordingly, using the GMM method, the present study evaluates the impact of human capital on air pollution in Iran during 1981-2019. Human capital is a latent variable in economics and is often replaced by alternative proxies. In this paper, like most previous studies, the research model was firstly estimated using the average proxy of years of schooling (as an indicator of human capital), which resulted in statistical insignificance and theoretical inconsistency in the estimated coefficients. Based on theoretical foundations, it is argued that the human capital index in addition to the education component is influenced by other aspects such as skills and health. Therefore, using fuzzy logic, an indicator for human capital in the Iranian economy has been constructed, so that it includes three main aspects (education, skills and health) of human capital. The results of estimation of the air pollution model using the human capital index showed that improving the level of human capital had a negative effect on air pollution. Therefore, by increasing human capital and improving the quality of manpower, we can expect to reduce air pollution and environmental degradation. In addition, urbanization, industrialization, trade freedom, economic growth and pollution in the previous period had positive and significant effects on air pollution.

---

---