Extended Abstract
Introduction
Air pollution is one of the most crucial environmental problem in the glob and its impact on human live and ecosystem is undeniable. The International Agency for Research on Cancer introduced air pollution as one of main causes of cancer. Therefore, by monitoring air pollution would be a necessity in industrialized cities. Air quality index include evaluation of the amount of NO2, SO2, O3, CO and Aerosol in the air. As, ground station has limited ability to assess the amount and distribution of these harmful gases in the urban and rural areas, therefore, remote sensing technology become a popular tool in assisting research to shed light on this subject. The current study evaluates air pollution caused by Khatoonabad Copper Smelting Factory using Sentinel P5 satellite images.
Materials & Methods
This research investigates the air pollution created by Khatoonabad Copper Smelting Factory and determines its impact radius, using Google Earth Engine system and Sentinel P5 satellite images. Khatoonabad Copper Smelting Factory is located in the northwest of Kerman province at the latitude of 29 Degree 59 Minute to 30 Degree 32 Minute and longitude of 54 Degree 52 Minute to 55 Degree 55 Minute. By performing the coding operation in the Google Earth Engine system, the images related to the average air pollution for So2 and No2 in the area of 50 km from the factory and in a period of 30 months from 07/04/2018 to 12/30/2021 were obtained. The amount and distribution of pollutants were examined based on one-day, seven-days, fourteen-days, one-month, two-months, three-months, six-months and twelve-months’ time periods from December 2020 to assess the concentration of pollution in the cold months of the year, also for the same time periods from June 2021 to assess the concentration of pollution in the warm months of the year.
In order to map distribution of each pollutants, Natural Break Classification and Hot Spot Analysis methods were performed on the images obtained from Google Earth Engine in GIS. Natural Break Classification method is based on Jenk optimization and classify spatial data based on statistical properties of each input where variances between classes maximize. Hot Spot Analysis methods is a spatial and statistical method that consider spatial autocorrelation among the spatial data to classify the data according to statistical significance of each class. Points that surrounded by high values and they are statistically significant called hot spot and areas that are surrounded by low values and have high negative Z score and low P values ( P value < 0.05) are called cold spot.
Results & Discussion
The results based on an averaged image for the period of 30 months indicated that the amount of So2 from 0.0000987 to 0.000698 (mol/m2) and the amount of No2 from 0.00005854 to 0.00006932 (mol/m2) in the study area that by increasing the distance from the factory, the amount of So2 and No2 decreased. Furthermore, analyzing the average amount of So2 and No2 in different period of daily, weekly, two weeks, and monthly have showed dispersed spatial distributions in warm and cold season of the year. Therefore, Sentinel 5P data in short-term periods such as daily, weekly, two-week and even one-month cannot provide accurate information on the spatial distribution of No2 and So2 in the study area.
In the data obtained from the two-month, three-month, six-month and one-year intervals, the amount of sulfur dioxide concentration has less dispersion than the short-term intervals, and as the time interval increases, the images show less dispersion of sulfur dioxide gas in polluted areas. Therefore, the obtained results indicate that Sentinel 5P images with longer time intervals of two months are able to provide more accurate and logical information about the concentration of sulfur dioxide gas in the area. However, in case of nitrogen dioxide, the imaged longer than two weeks can provide accurate information regarding the spatial distribution of this pollutant in the area.
Hot spot analysis was also performed on the images obtained in one-day, seven-day, fourteen-day, one-month, two-month, and three-month intervals from June in order to investigate the concentration and dispersion of pollution in the hot days of the year. Then the maps obtained from the hot months were compared with the maps of the same period from the cold months of the year. This comparison showed that in the maps obtained from the short-term intervals related to the hot months of the year, the density of hot spots was more observed in areas prone to the presence of sulfur dioxide gas. For example, the one-day image from December showed a lot of dispersion, while the one-day image from June indicated less dispersion and more density of gases in polluted areas. In addition, in the one-week, two-week and one-month maps from December hot spots and cold spots show much greater dispersion compared to similar maps in the same periods from June. However, by comparing the two-months and three-months hot spot maps of the cold months to the same maps of the hot months of the year, almost similar results were obtained, even more density were observed in the hot spot map of longer periods (more than two months) in winter time. The same trend happened by analyzing nitrogen dioxide in the studied area.
 
Conclusion
The results obtained from the classification of images related to sulfur dioxide gas showed that the concentration of sulfur dioxide gas in the area around the desired factory has the highest concentration value and as the distance from the factory increases, the concentration of sulfur dioxide gas decreases. Also, according to the minimum and maximum concentration of sulfur dioxide in the studied area, it is concluded that more sulfur dioxide is observed in the cold months of the year than in the warm months of the year. However, in the cold months the concentration of sulfur dioxide has a greater range of changes than the hot months of the year.
According to the results, the dispersion of sulfur dioxide concentration in short time intervals such as daily, weekly, fortnightly and even one month was very high in these time intervals. As a result, Sentinel 5P images are not able to provide logical and accurate information about the distribution of atmospheric sulfur dioxide concentration in daily, weekly, two-week and one-month intervals. In order to obtain accurate and logical information, images with time intervals longer than one month should be used, and the longer the time interval is, the more reliable the results will be.
The results of the hot spot analysis of the images related to sulfur dioxide concentration also indicated a high concentration of sulfur dioxide gas in the area around the factory. According to the obtained results, the activity of the studied factory can be a reason for the increase in the concentration of sulfur dioxide gas in this area, which has affected a radius of about 4 to 6 kilometers and an area of about 10,700 hectares around the factory.
The results obtained from the classification of images related to nitrogen dioxide gas show that the concentration of nitrogen dioxide in the area around the factory has a higher limit. According to the minimum and maximum concentration of this gas in the study area, it can be concluded that in the hot months of the year, the concentration of nitrogen dioxide gas is higher than in the cold months of the year. Considering the rapid spread of nitrogen dioxide gas in the atmosphere by the wind due to the high dynamics of this gas (Vîrghileanu et al., 2020), it can be concluded that the images obtained from the time intervals of two weeks of more can provide more information about the concentration of nitrogen dioxide in the atmosphere.
The results of the hot spot analysis of the images related to nitrogen dioxide gas showed that in the time intervals of two weeks to two months in the cold months of the year, there are hot spots that indicated the presence of nitrogen dioxide gas in the atmosphere located above the factory. However, in long-term intervals such as three months, six months, one year and thirty months, in the cold and hot months, hot spots are observed towards the northwest and at a distance from the factory.
The result of this research can assist environmentalist and researchers in using and interpreting Sentinel 5P data by considering different periods in cold and warm seasons for making informed decisions.