Abstract: Clean air is a necessity for human well-being and health. Air pollution is a major threat to humans and other organisms and is considered as one of the environmental challenges. Today, with the increase in air pollution, the need to know more about the causes of its occurrence has been raised. The various consequences of air pollution have made air quality monitoring and control inevitable in all societies at the forefront of environmental issues. In recent years, air pollutants have caused serious risks to human health and the environment. One of these pollutants Tropospheric Ozone is the cause of health and environmental problems, especially respiratory problems and lung dysfunction and asthma attacks. Other effects of tropospheric Ozone can be reduced lung capacity, cough, chest pain, sore throat, condition Nausea, damage to plants (growth disorders and the effect on germination) and reduced tire life, hence, it is necessary to know and study the tropospheric Ozone in large and industrial cities. Tropospheric Ozone is a pollutant because it plays an effective role in converting primary pollutants into secondary pollutants. Therefore, the aim of this study was to investigate the trend of changes in tropospheric Ozone concentration with meteorological parameters, Ozone precursors (nitrogen dioxide and nitrogen oxides). In this study, data from Kermanshah synoptic station and air quality station of Kermanshah General Department of Environmental Protection (Ziba Park station) in a long-term period of 10 years (2007-2016) have been used. Also, in this study using sensor images The OMI satellite Aura was surveyed in February and July 2016. Tropospheric Ozone is known as a pollutant in Kermanshah. Therefore, no systematic studies have been conducted on the recognition of tropospheric Ozone and the relationship between tropospheric Ozone and meteorological parameters in Kermanshah over a long period of time. Tropospheric Ozone and its relationship with changes in nitrogen oxides, nitrogen dioxide and synoptic parameters in Kermanshah were studied and the correlation between tropospheric Ozone concentration and meteorological parameters was studied by Pearson test and the relationship between them was studied by linear regression. Based on the results. The maximum concentration of Ozone occurs in the afternoon between 14:00 and 17:00 and the maximum amounts of nitrogen oxides occur at night and in the early morning of the year. Also, the study of seasonal changes in Ozone concentration showed that in warm seasons due to the conditions of tropospheric Ozone formation, including the intensity of sunlight, temperature and time of radiation and the presence of pollutants including nitrogen oxides, the concentration of tropospheric Ozone was much higher. Ozone concentrations are highest in June, July, August, and spring and summer. The results also showed that there is a direct relationship between solar radiation and Ozone concentration. Simultaneously with increasing solar radiation, it increases the air temperature, which increases the photochemical activity and thus increases the Ozone concentration. This can be seen in the warm months of the year (June, July and August). Wind speed is also directly related to the concentration of tropospheric Ozone. As the wind speed increases, the reactants mix faster and the tropospheric Ozone concentration increases. However, precipitation is inversely related to the concentration of tropospheric Ozone, which decreases with the occurrence of precipitation in the months associated with the onset of precipitation, and in the dry months of the year, the concentration is increasing. Therefore, meteorological factors and parameters play an important role in tropospheric Ozone changes. Which can be seen by linear regression and Pearson test. The results of the study of nitrogen dioxide and nitrogen oxides showed that the highest concentration of nitrogen dioxide and nitrogen oxides during the day is the opposite of the concentration of tropospheric Ozone and the lowest concentration of Ozone occurs in summer due to increased solar radiation, increased oxidation of di Nitric oxide and nitrogen oxides, and as a result increase the concentration of tropospheric Ozone, in autumn and winter, this amount has an increasing trend. Therefore, the trend of changes in tropospheric Ozone concentration is the opposite of the concentration of nitrogen dioxide and nitrogen oxides, which can be seen in the daily, monthly and seasonal sections, which linear regression and Pearson test show this important and OMI sensor images confirm this fact. In conclusion of this study, all parameters related to the concentration of pollutants along with meteorological parameters have been effective factors in the concentration of tropospheric Ozone. Keywords: air pollution, meteorological parameters, Ozone tropospheric, NO2, NOX

As one of the hazardous pollutants, Ozone (O3), has significant adverse effects on urban dwellers' health. Predicting the concentration of Ozone in the air can be used to control and prevent unpleasant effects. In this paper, an attempt was made to find out two empirical relationships incorporating multiple linear regression (MLR) and gene expression programming (GEP) to predict the Ozone concentration in the vicinity of Zrenjanin, Serbia. For this purpose, 1564 data sets were collected, each containing 18 input parameters such as concentrations of air pollutants (SO2, CO, H2S, NO, NO2, NOx, PM10, benzene, toluene, m-and p-xylene, o-xylene, ethylbenzene), and meteorological conditions (wind direction, wind speed, air pressure, air temperature, solar radiation, and relative humidity (RH)). In contrast, the output parameter was Ozone concentrate. The correlation coefficient and root mean squared error for the MLR were 0. 61 and 21. 28, respectively, while the values for the GEP were 0. 85 and 13. 52, respectively. Also, to evaluate these two methods' validity, a feed-forward artificial neural network (ANN) with an 18-10-5-1 structure has been used to predict the Ozone concentration. The correlation coefficient and root mean squared error for the ANN were 0. 78 and 16. 07, respectively. Comparisons of these parameters revealed that the proposed model based on the GEP is more reliable and more reasonable for predicting the Ozone concentrate. Also, the sensitivity analysis of the input parameters indicated that the air temperature has the most significant influence on Ozone concentration variations.

The objective of this study was to analyze spatial patterns of ground-level Ozone concentration in the Dallas-Fort Worth, Texas metropolitan area. Average daily maximum eight-hour Ozone concentration, number of days with concentrations exceeding 75 ppb, average outside air temperature, and resultant wind direction at 20 monitoring stations were compiled for January-December, 2013. Recent estimates of population and vehicle miles traveled were also compiled for 12 counties with Ozone monitoring station (s). Ozone levels and resultant wind directions were mapped for representative months in each season. On several days from May-October, eight-hour Ozone measurements exceeded 75 ppb. September, followed by August, produced the highest Ozone concentrations, as well as the most observations exceeding 75 ppb. Late spring and summer months also showed the greatest range in Ozone concentrations; during this time period, sustained southeasterly winds caused distinct clusters of high Ozone concentration at the northern perimeter of the study area. However, Ozone concentrations at individual monitoring stations were not associated with population or vehicle miles traveled in counties occupied by those stations.

Growth of population and over consumption of fossil fuel has caused an increase in the degree of pollution in the last decade. Owing to the environmental awareness, it seems necessary to find a way to predict the amount of pollutants such as Ozone which is toxic. Neuro-fuzzy systems are a powerful tool for modeling and simulation of such processes. In this work, we have adopted ANFIS algorithm for predicting the level of Ozone concentration in the city of Mashhad. The inputs of the algorithm are the concentration of hydrocarbons (except methane), mono and dioxide of nitrogen, temperature, wind speed and direction. For the output, the concentration of Ozone has been chosen. The percentage of error for this work are 1.8 and 2.4 for the training and trial data, respectively. The result of this research reveals that the error resulted from LSE are lower compared to the combined algorithm.

Ground surface Ozone is one of the most dangerous pollutants that has significant harmful effects on the residents of urban areas. The purpose of this study is to identify the factors affecting Ozone concentration and modeling its changes using satellite data and different machine learning methods in Tehran. For this purpose, pollutant concentration and meteorological data were used along with the satellite product of land surface temperature (LST) in the period from 2015 to 2021. After calculating the correlation between Ozone concentration and independent parameters, Ozone concentration modeling was done in five different modes in terms of input parameters and learning method and applying data refinement. In the first and second mode, modeling was done using pollutant concentration and meteorological data through multivariate linear regression method. The only difference between these two modes is the filtering of the input data using the WTEST method in the second mode. In the third mode, the LST product was added to the input data, and in the fourth and fifth mode, Ozone modeling was done using multilayer neural network and recurrent neural network, respectively. The comparison of the five modes showed that the modeling of the first to fifth stages with adjusted coefficient of determination of 0.5, 0.64, 0.69, 0.74 and 0.8 were able to recover the Ozone concentration, respectively. It was also found that among different pollutants, nitrogen monoxide, nitrogen dioxide and nitrox have the greatest impact on Ozone concentration, just as temperature, humidity and wind speed are the most influential among meteorological data. Although the use of WTEST statistics led to the identification and elimination of inconsistencies and errors in the observations of pollution measurement stations, the neural network learning method showed better performance in modeling than multivariate regression due to its less sensitivity to noise. As a notable result, adding the LST product to the input data brought a 5% increase in accuracy in estimating Ozone concentration.

Background and Objectives: Weather pollution, caused by Ozone (O3) in metropolitans, is one of the major components of pollutants, which damage the environment and hurt all living organisms.Therefore, this study attempts to provide a model for the estimation of O3 concentration in Tabriz at two pollution monitoring stations: Abresan and Rastekuche.Materials and Methods: In this research, Artificial neural networks (ANNs) were used to consider the impact of the meteorological and weather pollution parameters upon O3 concentration, and weight matrix of ANNs with Garson equation were used for sensitivity analysis of the input parameters to ANNs.Results: The results indicate that the O3 concentration is simultaneously affected by the meteorological and the weather pollution parameters. Among the meteorological parameters used by ANNs, maximum temperature and among the air pollution parameters, carbon monoxide had the maximum effect.Conclusion: The results are representative of the acceptable performance of ANNs to predict O3 concentration. In addition, the parameters used in the modeling process could assess variations of the Ozone concentration at the investigated stations.

Background: Aerobic exercise, Ozone therapy and cell therapy have each been independently proposed as treatments for osteoarthritis. Therefore, the present study aimed to investigate the effect of aerobic exercise with Ozone and cell therapy on serum levels of anandamide concentration in osteoarthritis rats. Materials and Methods: Forty-five male rats were randomly divided into 9 groups, including 1 group: healthy control, 2-patient control, 3-exercise, 4-Ozone therapy, 5-cell therapy, 6-exercise+Ozone therapy, 7-exercise+cell therapy, 8-Ozone+cell therapy and 9-Ozone+cell therapy+exercise. Cell groups alone and combination cell therapy received 1×106cells/kg, and Ozone groups alone and Ozone combination therapies received 20μ, g/ml of Ozone in 3 replications before exercise. The training program consisted of running on a treadmill at a speed of 16 meters per minute for 8 weeks. Forty-eight hours after the last exercise session, serum levels of anandamide were measured by Elisa. Results: Findings showed that the serum level of anandamide in the osteoarthritis patient group was significantly reduced compared to the healthy group that exercise and Ozone therapy led to a significant increase in the serum anandamide level,but cell therapy could not have a significant effect on increasing serum levels of anandamide. The peak of this increase was seen in Ozone+cell therapy and the interaction of all three interventions simultaneously. Conclusion: Although aerobic exercise and Ozone therapy each independently have a positive effect on anandamide in people with osteoarthritis, but the interaction of all three interventions simultaneously with Ozone+cell therapy is more effective.