Extended Abstract
Introduction
Air pollution is now considered to be one of the most important challenges Iran faces and plays a major role in changes of its climate. Factors such as population growth and the consequent increase in the number of cars, as well as the presence of various (and often old) industries and the energy demand they satisfy have led to an increase in pollution in many Iranian metropolises. As one of the four Iranian industrial hubs, Arak has one of the worst air quality in this country. In addition to the presence of industries, having a relatively high population density (and consequently high traffic congestion level) and various climatic conditions affect the quality of air in Arak. It is essential to accurately measure air pollutants with a high spatial and temporal resolution, determine their distribution pattern and level of effectiveness, and provide provincial and national managers with applicable solutions. Unfortunately, air quality monitoring stations are not sufficiently and properly distributed in Iran. Many Iranian cities do not have even a single air monitoring station and many others have only one station. As the capital city of Markazi province and an industrial city, Arak has only four monitoring stations which are not simultaneously active in many cases. Failing to conduct proper site selection before the installation of ground-based monitoring stations results in local irregularities in the recorded concentration of pollutants. Furthermore, the stations are not usually calibrated on time and thus air quality monitoring observations are disrupted. In these cases, either this data is deleted from the final results or the station will be inactivated (for example, for a week or a month) by authorities. However, it seems that the observations made by these stations still include inaccurate data.
 
Materials and Methods
The present study has introduced a method based on composition and voting to validate the observations made by air quality monitoring stations using Sentinel-5 satellite images. Arak city was used as the study area. Level three images (L3) of the Sentinel-5 TROPOMI sensor received from the Google Earth Engine were used to monitor the concentration of pollutants in the present study. Sentinel-5 is a powerful atmospheric monitoring tool. Equipped with a spectrometer called TROPOMI, the satellite measures ultraviolet radiation reaching the Earth's surface in a high range. TROPOMI sensor is highly capable of imaging and monitoring a large number of pollutants. The present study has compared the concentration of NO2, SO2, CO and ozone pollutants monitored by ground-based stations in Arak city with Sentinel-5 images. Since the time resolution of ground-based observations is higher than satellite observations, a monthly average of pollutants' concentrations was calculated to increase the reliability of observations. In other words, the concentrations of pollutants were compared on a monthly basis. The proposed method has assumed that more accurate sets of ground observations show a higher linear correlation with satellite observations.
In order to select the appropriate set, the number of observations with an acceptable accuracy must be determined. To do so, a method based on a mixture of composition and voting has been used. As previously mentioned, each observation showed average pollutant concentration in a specific month of the study period. The process started with at least four monthly observations. As a result, assuming that all 19 monthly observations were available, 16 subsets were obtained with a maximum linear correlation between ground-based observations and their satellite correspondence which showed the accuracy of the observations. The second step was the proposed voting method which showed that the monthly ground-based observations (for example October 1398) were repeated several times. The high frequency of a monthly observation indicated its higher accuracy. The presence of this particular observation in different permutations has increased the linear correlation coefficient of the observations. Therefore, for an instance a frequency of 15 or 16 for the observation made by the ground-based station in October 2017 indicated high accuracy of the observation.
 
Results and Discussion
The present study has compared the concentration of NO2, SO2, CO and ozone pollutants Using the proposed method, some observations have been identified as outliers or errors. RMSE criterion was used to evaluate the accuracy of the proposed method. Some observations made by the ground-based station were not consistent with other ground-based and satellite observations, and removing them increased the correlation coefficient. Removing outliers from the observations, the RMSE (originally 2%) was improved and reached 47%.
 
Conclusion
Findings indicated that some observations made by ground-based monitoring stations were incorrect, or at least the stations had sometimes failed to exhibit the real general trend of environmental pollution correctly due to local irregularities caused by various reasons, such as improper location or lack of proper calibration.