Journal of Spatial Analysis

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Application of Sentinel 5 satellite imagery in identifying air pollutants Hotspots in Iran
 
Shiva Gharibi¹, Kamran Shayesteh²
1- PhD Student of Environmental Science, Malayer University, Malayer, Iran.
2-Assistant professor, Department of Environmental Sciences, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran
 
k.shayesteh@malayeru.ac.ir
Extended abstract
1- Introduction
Today, poor air quality is one of the most important environmental problems in many cities around the world. Air pollution can have a devastating effect on humans, plants, organisms, and human assets, and efforts are being made to anticipate and analyze the amount of distribution and transmission of air pollutants in order to minimize the adverse effects on air quality and climate. Among the most important air pollutants are (CO), (SO2), (NO2), (O3) and aerosols (AI). Numerous studies have been conducted on the monitoring of these pollutants based on information and statistics from pollution monitoring devices, but the use of satellite images in the field of monitoring and measuring pollutants has been limited. Due to the increasing growth of these pollutants, in this study, an attempt has been made to identify the average spatial concentration of the most important air pollutants as the actual sources of pollution on the scale of Iran from October 2018 to December 2019. Also, identifying the most polluted centers in Iran based on the average of 5 pollutants is another goal of this study. Therefore, the aim of this study is to demonstrate the ability of Sentinel satellite to monitor air pollutants, and the ability of GPW images to produce a population density map for the first time on an Iranian scale.
 
2- Methodology
 Using the Python programming language in the Google Earth Engine program environment, various products related to CO, SO2, NO2, O3 and AI pollutant images, obtained from Sentinel-5 satellite images during the study period and in the scale of Iran, were obtained for monitoring of air pollutants and determination of pollutants focuses. The output variable is defined as a set of images based on the time filter (2019) and the spatial filter (Iran borders). The output of the average concentration of pollutants for each month is calculated separately and annually in these filters. Then, the spatial map of the average concentration of pollutants in the Arc map software was analyzed and statistical information related to the average concentration of these pollutants was processed by SPSS statistical software. To determine the hotspots in terms of all pollutants, the raster location map of each pollutant was classified using the Jenks algorithm. In order to identify the share of provinces and counties, the amount of pollutants was also analyzed by spatial statistics in GIS environment and using the Zonal Statistics command based on the defined administrative boundaries. The G statistic was used for Cluster analysis, and in order to identify Hot Spots and Cold Spots, Getis-Ord Gi statistic (Gi) was used in GIS environment.To determine the population of each province, the latest census information of Iran as well as satellite images related to the fourth version of Gridded Population of World (GPW) product were used. Finally, The Moran index was used to determine the pattern of pollutants distribution and the spatial autocorrelation.
 
3- Results
 Spatial output from the processing of Sentinel-5 satellite images during the study period for identifying air pollution centers in Iran showed that the highest levels of nitrogen dioxide were recorded in the majority of cities in Tehran and Alborz provinces and then in the centers of other provinces. In the case of carbon monoxide, the highest rate is in Tehran and the coasts of the Caspian Sea and Khuzestan, and coastal areas of Bushehr and Hormozgan provinces. The highest amount of ozone is in the northern parts of the provinces of West and East Azerbaijan, Ardabil, Gilan, Mazandaran, Golestan and Northern Khorasan. Most of the dust was in the southern, eastern, southeastern and central provinces of Iran. The highest amount of sulfur dioxide pollutants is recorded in Tehran and then in the provinces of Khuzestan, Kerman, Hormozgan, Bushehr, Markazi, Qom, Isfahan and Khorasan Razavi. Provincially, the highest share of nitrogen dioxide is in the provinces of Tehran, Alborz, Qazvin and Qom. The highest provincial share of carbon monoxide is in Khuzestan, Gilan and Mazandaran provinces. The highest share of dust is in the southeastern provinces, including Sistan and Baluchestan, the highest share of sulfur dioxide is in Khuzestan province, and the highest share of ozone pollution is in the coastal provinces of Caspian Sea. Compliance of the average 5 pollutants with Google Earth images showed that the contaminated areas are located in the cities of Abadan, Imam Khomeini Port, Mahshahr Port and Ahvaz (Khuzestan Province), Tehran, Pakdasht (Tehran Province) and Assaluyeh Port (Bushehr Province). The results of comparing the average concentrations of pollutants in different seasons showed that there was no significant difference between CO, NO2 and O3 pollutants in different seasons, but suspended particles and aerosols in winter and autumn seasons have a significant difference with the amount of this pollutant in spring and autumn. Also, SO2 pollutant in autumn had lower concentrations than other seasons. The results of clustering analysis to determine the status of significant spatial clusters showed that the data are in the confidence range and have spatial auto-correlation and cluster distribution pattern.
 
4- Discussion & Conclusions
 According to Sentinel-5 satellite images, most of the pollution centers in Iran are related to petrochemical industries and refineries, which are located in the cities of Abadan, Imam Khomeini port, Mahshahr port and Ahvaz (Khuzestan province), Assaluyeh port (Bushehr province) and common pollutants. By these centers are NOX, SO2, CO, suspended particles and aerosols. Also, other centers (Tehran, Pakdasht in Tehran province) are located in the most populous urban areas of, which have been identified as hotspots in high production of NO2 and CO, due to high population and urban traffic.  Due to the higher population density of Tehran and Pakdasht than other cities in Iran, air pollution can be more important in these cities. Therefore, the use of satellite imagery to monitor Iran's air pollutants and the location of hotspots can be very cost-effective and time-consuming.
 
Keywords: Air Pollution Monitoring, Sentinel, Satellite Imagery, Polluted Hotspot, Moran’s Index.
 

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Study the Effects of Covid-19 on Air Pollutants by Using Sentinel-5 Satellite Images (Case Study: Metropolises of Tehran, Isfahan, and Mashhad)

Zeinab shogrkhodaei, PHD. Student of Climatology, Faculty of Literature and Humanities, Department of Geography, Razi University
Amanollah Fathnia*, Assistant Professor of Climatology, Faculty of Literature and Humanities, Department of Geography, Razi University
Vahid Razavi Termeh, PHD. Student of GIS, Faculty of Geodesy and Geomantic, K. N. Toosi University.

Introduction
One of the challenges facing the international community right now is Covid-19. This pandemic has caused a comprehensive change in behavior contrary to the usual routine, which can lead to changes in people's lifestyles (Briz-Redón et al., 2021). The prevalence of this disease has not only affected the economy and health, but also the environment (Sohrabi et al., 2020). Among the effects of Covid-19 on the environment are the effects on beaches, noise, surface and groundwater, municipal solid waste, and air quality (Zambrano-Monserrate et al., 2020). The restrictions applied during the Covid-19 era were accompanied by a reduction in greenhouse gas emissions by transport and industry, which affected air quality (Rybarczyk and Zalakeviciute, 2020). Air is a vital element for the survival of all living things, but human activities have caused the release of many harmful pollutants into the atmosphere and endangered human health (Ghorani-Azam et al., 2016). Among the causes of death, air pollution is the fourth leading cause of death in the world after tobacco (WHO, 2020a). Sulfur dioxide, nitrogen oxide, carbon monoxide, and ozone are some of the pollutants that cause short-term or long-term exposure to heart and lung disease (Briz-Redón et al., 2021). Human activities are one of the main sources of air pollutants, so their concentration is expected to decrease during the Covid-19 period (Ghahremanloo et al., 2021).
Materials and methods
In this study, the required data were the average monthly pollutants of sulfur dioxide, nitrogen dioxide, carbon monoxide and ozone before (20 February 2019 to 20 February 2020) and after (20 February 2020 to 20 February 2021) the prevalence of Covid-19 virus. For this purpose, Sentinel-5P satellite images were used to prepare the required data set. The case study included three metropolises of Tehran, Mashhad, and Isfahan. Google Earth Engine was used to access Sentinel-5P satellite images. The final output of the images for each pollutant was interpolated for better display and exposure in GIS software using the kriging method. Then, a T-test was used to compare the differences between the concentrations of contaminants before and after the outbreak of the Covid-19 virus and to evaluate the mean correlation. Based on this test, values that were p-value <0.05 were considered significant. This was considered as a change in the concentration of the contaminant before and after the Covid-19 virus (decreasing or increasing). Those pollutants with a p-value <0.05 were considered unchanged.
Results and Discussion
Analysis of the T-test showed that for pollutants such as sulfur dioxide, nitrogen dioxide, and carbon monoxide in all three metropolises, there was no significant change in their concentration before and after the outbreak of the Covid-19 virus. However, significant changes were observed for ozone pollutants. Also, its concentration trend in all three metropolises has been a decreasing trend. The main sources of emissions of nitrogen dioxide, carbon monoxide, sulfur dioxide, and ozone are related to human activities, including transportation and industry (Ghahremanloo et al., 2021; Cárcel-Carras et al., 2021). Pollutants such as carbon monoxide, nitrogen dioxide and sulfur dioxide are the primary pollutants; It means that they are emitted directly from sources, while ozone is a secondary pollutant and depends on complex and nonlinear atmospheric chemistry (Bekbulat et al., 2021). Given that the concentration of ozone surface decreases significantly with increasing concentration of nitrogen dioxide. When nitric oxide (NO) emissions are high enough, the NO released into the atmosphere converts a large portion of ozone to nitrogen dioxide (Hashim et al., 2021). In addition, in all three cities, when the concentration of nitrogen dioxide increased, we saw a decrease in the amount of ozone concentration. In addition, during the Covid-19 era, many industries that produced primary pollutants, including carbon monoxide, nitrogen dioxide, and sulfur dioxide, were not on the closure list or were telecommuted. Despite the decline in the performance of some activities, important sectors such as manufacturing plants, industrial and mining centers, agriculture, and public transportation have continued to operate even during severe restrictions. The mean difference between the concentrations of nitrogen dioxide before and after the outbreak of Covid-19 was positive. However, this average difference is small. However, the concentration of nitrogen dioxide is slightly increased, especially in cold seasons; Therefore, it can be said that ozone concentration has decreased.

Keywords: Covid-19, Air Pollutants, Remote Sensing, Sentinel-5.


















 

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Determination of flood-prone areas using Sentinel-1 Radar images
(Case study: Flood on March 2019, Kashkan River, Lorestan Province)

Introduction
Although natural hazards occur in all parts of the world, their incidence is higher in Asia than in any other part of the world. Natural phenomena are considered as natural hazards when they cause damage or financial losses to human beings. Iran is also one of the high-risk countries in terms of floods. Until 2002, about 467 floods have been recorded by the country's hydrometric stations. In addition to natural factors such as rainfall, researchers consider human impacts such as destruction of vegetation cover, soil destruction, inefficient management, destruction of pastures and forests, and encroachment on the river are the most important factors for the occurrence and damage of floods in the country. One of the most efficient and emerging tools in flood surveys is the use of radar images. SAR images and flood maps produced by radar images provide researchers valuable and reliable information. Moreover, maps obtained from SAR images help officials to manage the crisis and take preventive measures against floods. The Sentinel-1 satellite is part of the Copernicus program, launched by the European Space Agency, and is widely used in mapping flood-prone areas. The contribution of Sentinel-1 to the application of flood mapping arises from the sensitivity of the backscatter signal to open water. This study aims to determine high-risk and flood-prone areas along the Kashkan River using Sentinel-1 radar images.
Data and Methods
 The study area includes a part of the Kashkan river from Mamolan city to the connection point of this river to Seymareh river, after Pol-dokhtar city. The average annual discharge of the Kashkan river is 33.2 cubic meters per second based on the data of the Pole-Kashkan Station. The length of the river in the study area is about 100 km. To investigate flood-prone areas, we applied pre-processing and image-processing steps to each flood event including SAR images belonging to March 25^th, 2019, March 31^st 2019, and April 2^nd, 2019. SAR images were acquired from ESA Copernicus Open Access Hub. climatic data was downloaded from power.larc.nasa.gov. To create meander cross-sections, the Digital Elevation Model of the studied area was utilized. Cross-sections were created using QGIS software. Pre-processing steps include: applying orbit data, removing SAR thermal noise, calibration of SAR images, de-speckling and topographic correction. In image processing, we applied the Otsu thresholding method to distinguish water pixels from land pixels. In thresholding methods, the histogram of each image is divided into two parts according to the amount of gray composition. The higher the amount of gray (i.e., the pixel tends to be darker), the more pixels represent water, and conversely, the lighter-toned pixels (i.e., pixels that tend to whiten) represent land. The Otsu thresholding method is a commonly used method for water detection in SAR images. It uses an image histogram to determine the correct threshold. The most important feature of the Otsu method is that it is capable of determining the threshold automatically. The Otsu algorithm was applied to all images using MATLAB.
Results
According to the flood maps, on March 25th, 6.51 percent of the study area was flooded, while on March 31th, only 3.96 percent was flooded. This is mainly due to less precipitation on the 31st. On March 25^th the average daily precipitation was 47.46 mm while on 31^st of March the average daily precipitation was 31.64 mm. On April 2nd, however, there was no rainfall, on the day before more than 63 mm of precipitation has occurred. This massive amount of precipitation on the previous day has led to more than 25km2 being flooded in the studied area.
Conclusion
Results showed that meanders and their surrounding areas are the most dangerous sections in terms of flooding. The meander's dynamic and the river's hydrologic processes are essential factors affecting flooding in those sections. Generally, various factors affect flooding and the damage caused by it. This study aimed to determine flooded and flood-prone areas (according to flooded areas in previous events) using new methods in a short time and with high accuracy to use this tool for more accurate zoning and efficient planning in the future. The results showed that radar images are practical, robust, and reliable tools for determining flooded areas, especially for rapid and near-real-time studies of flood events.
Keywords: Floods, Radar images, Sentinel-1Satelitte, Kashkan river