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Article

Monitoring Trends of CO, NO2, SO2, and O3 Pollutants Using Time-Series Sentinel-5 Images Based on Google Earth Engine

1
Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, 00185 Rome, Italy
2
School of Aerospace Engineering (SIA), Sapienza University of Rome, Via Salaria 851-881, 00138 Rome, Italy
3
Department of Civil and Environmental Engineering, Florida International University, Miami, FL 33199, USA
4
Department of Geography and Geospatial Sciences, Kansas State University, 920 N17th Street, Manhattan, KS 66506, USA
5
Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran
*
Author to whom correspondence should be addressed.
Pollutants 2023, 3(2), 255-279; https://doi.org/10.3390/pollutants3020019
Submission received: 1 March 2023 / Revised: 21 April 2023 / Accepted: 25 April 2023 / Published: 22 May 2023

Abstract

:
Air pollution (AP) is a significant risk factor for public health, and its impact is becoming increasingly concerning in developing countries where it is causing a growing number of health issues. It is therefore essential to map and monitor AP sources in order to facilitate local action against them. This study aims at assessing the suitability of Sentinel-5 AP products based on Google Earth Engine (GEE) to monitor air pollutants, including CO, NO2, SO2, and O3 in Arak city, Iran from 2018 to 2019. Our process involved feeding satellite images to a cloud-free GEE platform that identified pollutant-affected areas monthly, seasonally, and annually. By coding in the JavaScript language in the GEE, four pollution parameters of Sentinel-5 satellite images were obtained. Following that, images with clouds were filtered by defining cloud filters, and average maps were extracted by defining average filters for both years. The employed model, which solely used Sentinel-5 AP products, was tested and assessed using ground data collected from the Environmental Organization of Central Province. Our findings revealed that annual CO, NO2, SO2, and O3 were estimated with RMSE of 0.13, 2.58, 4.62, and 2.36, respectively, for the year 2018. The annual CO, NO2, SO2, and O3 for the year 2019 were also calculated with RMSE of 0.17, 2.41, 4.31, and 4.6, respectively. The results demonstrated that seasonal AP was estimated with RMSE of 0.09, 5.39, 0.70, and 7.81 for CO, NO2, SO2, and O3, respectively, for the year 2018. Seasonal AP was also estimated with RMSE of 0.12, 4.99, 1.33, and 1.27 for CO, NO2, SO2, and O3, respectively, for the year 2019. The results of this study revealed that Sentinel-5 data combined with automated-based approaches, such as GEE, can perform better than traditional approaches (e.g., pollution measuring stations) for AP mapping and monitoring since they are capable of providing spatially distributed data that is sufficiently accurate.

Graphical Abstract

1. Introduction

In today’s world, air pollution (AP) is one of the most destructive challenges to the quality of life, especially in developing countries [1]. With the rapid expansion of urbanization and the development of cities, along with rapid population growth, industrialization, and the indiscriminate use of fossil fuels, pollution has increased and exceeded the capacity of the environment to tolerate it [2,3]. As a result, citizens are more likely to suffer from respiratory diseases and suffer from worsened heart and lung conditions. Furthermore, environmental damage, including damage caused by air pollution, costs billions of dollars every year in financial credits, human labor, and other resources [4,5,6].
In recent years, AP has become a leading cause of death in both developing and developed countries [7]. In this regard, the amount of air pollutants in many cities in Iran has reached dangerous levels, including in Tehran, Mashhad, Isfahan, Tabriz, Shiraz, Karaj, Arak and Ahvaz [8,9,10,11]. Among the various sectors that pollute the air in Iran, the transportation and industry sectors both generate the most pollution; the transportation sector alone emits 63.3% of the total nitrogen oxide emissions, 29.3% of sulfur dioxide emissions, 27.5% of carbon dioxide emissions, 24.8% of sulfur trioxide emissions, 98.6% of carbon monoxide emissions, 96.3% of carbon hydride emissions, and 79.2% of suspended particles. Tehran’s air pollution, for instance, is linked to 70% of deaths, according to studies by the Environmental Organization [12,13].
Pollution measuring stations are one of the most accurate methods of measuring pollution. However, measurements are limited to the area around the stations [14,15]. Because of their high installation and maintenance costs, this solution is not widely used in managing and monitoring pollution. By relying solely on ground values, complex air pollution models that include things such as the source, movement path, and chemical characteristics of different types of pollution also have problems [16]. Measurements from pollution stations provide accurate and temporally discrete AP information; however, these measurements are often affected by significant errors associated with them and often result in unrepresentative spatial patterns [17]. Additionally, as temporal resolution and temperature extremes increase, the complexity of AP patterns increases. It is, thus, necessary to use remote sensing methods that have acceptable temporal and spatial resolution in order to overcome these problems [18,19]. In this regard, Sentinel satellites have provided key information in various fields of global monitoring for environmental management programs, understanding and dealing with the effects of climate change, water resources management, hydrology, monitoring the expansion and change of megacities, forests, and agricultural areas, and monitoring plant productivity and health [20,21,22]. Sentinel-5 is the first mission of the Copernicus air pollution control program. Sentinel-5 can be used for identifying ozone, methane, formaldehyde, aerosol, carbon monoxide, NO2, and SO2 gases [23]. Satellite images from Sentinel-5 can be effective in investigating the spatial distribution of pollutants due to their large and global observations of the Earth’s surface. As part of Sentinel-5, the TROPOMI (TROPOspheric Monitoring Instrument) sensor provides daily data on air pollution [24]. There is a wide range of pollutants that can be monitored and imaged using the TROPOMI sensor. There are three different ways to obtain images of these pollutants: real-time images, offline images, and reprocessing images. Almost instantaneous data is available within three hours of data acquisition, and offline data is available several days after imaging [25].
With improvements in the variety and resolution of remote sensing data, various semi-automated and automated methods have been developed, such as Google Earth Engine [26]. GEE is an online computing platform that processes satellite images, spatial data, and geographical data at the petabyte scale [27]. This web-based system provides access to satellite data processing software and algorithms [28]. The GEE makes it relatively easy to access satellite data and other information, cloud computing, and big data processing algorithms [29,30]. The GEE system provides researchers with easy and high-speed access to over thirty years’ worth of free and public data archives, including old images and scientific datasets for large-scale sensing applications [31]. In this way, many limitations related to downloading, storing and processing data are easily overcome [32]. Several researchers have employed GEE for AP retrieval [33,34,35,36,37,38,39].
A review of the literature reveals that few studies have explored the effectiveness of Sentinel 5 images based on GEE for AP retrieval. A primary focus of Sentinel-5 is the study of air quality and composition-climate interactions with the main data products being O3, NO2, SO2, HCHO, CHOCHO, and aerosols. Furthermore, Sentinel-5 will provide daily global coverage for climate, air quality, and ozone/surface applications of CO, CH4, and stratospheric O3 [25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]. Previous studies also mapped and monitored some specific air pollutants, such as O3. Therefore, this study aims: (1) to monitor CO, NO2, SO2, and O3 pollutants in Arak city in the period of 2018 and 2019, (2) to evaluate the effectiveness of Sentinel-5 images for CO, NO2, SO2, and O3 pollutants monitoring, and (3) to explore the efficiency of GEE for AP retrieval.

2. Location of Study Area

Arak city, the capital of the Central Province is located in the central part of Iran (Figure 1). As one of Iran’s major manufacturing cities, Arak is widely regarded as one of the four economic poles of the country. This city is the industrial capital of Iran due to the presence of mother industries, the production of 80% of the country’s energy equipment, and the presence of the biggest aluminum factory, the biggest manufacturer of heavy machinery in the Middle East, the largest gasoline producer, and the largest mineral industries in the country. As a result, it is one of the most polluted cities in Iran (https://markazi.doe.ir, accessed on 1 February 2022). According to the output factors described in Table 1, the fuel consumption of large industries in Arak city and the air pollution caused by this have been estimated based on the available information. Because Arak is one of the most important industrial cores in Iran, fuel consumption in various seasons does not differ significantly from each other [41].
Table 2 shows that other motor vehicles and heating sources contributed less pollution than industries. As shown in Table 1, industries produced 99% of Arak’s air pollution, and the number of pollutants was high as well.

3. Datasets and Methodology

3.1. Datasets

The main objective of this study was to monitor trends in CO, NO2, SO2, and O3 pollutants in Arak city using time-series Sentinel-5 images derived from GEE. To this end, Sentinel-5 AP were employed to monitor CO, NO2, SO2, and O3 pollutants based on GEE from 2018 to 2019. We also used monthly, seasonal (spring (April, May, and June), summer (July, August, and September), fall (October, November, and December), and winter (January, February, and March)), and annual data on atmospheric pollutants for AP monitoring, collected from the Environmental Organization of Central Province for 2018 and 2019 (Table 3). Figure 1(3) shows the location of pollution monitoring stations in Arak city.

3.2. Methodology

To extract pollution maps (CO, NO2, SO2, and O3), the following steps were taken: in the first step, all satellite-based datasets were preprocessed and prepared. Second, results for air pollutants were obtained through JavaScript coding using GEE. As a third step, GEE and Sentinel-5 results were validated based on data from pollution measuring stations. Figure 2 provides a brief review of the methodology used for AP retrieval.

3.2.1. Google Earth Engine (GEE) for AP Retrieval

Google launched GEE in 2010 to store and process Earth observation data in a more reliable and time-efficient way [35]. This platform is valuable when the goal is to process open-access Earth observation data over a large area or long-time interval and in a timely manner [42,43].
To retrieve CO, NO2, SO2, and O3, the Sentinel-5 images were first converted from level 2 to level 3 through the harpconvert tool by the bin spatial operation. Then, after applying the spatial and temporal filters, CO, NO2, SO2, and O3 products from the study area were generated.
Two types of output were then generated, including maps and statistical reports. The results were verified using in situ data obtained from ground-based air pollution stations.

CO Retrieval

The silent killer, CO, is a poisonous and dangerous gas that is odorless, tasteless, and invisible [44]. CO results from the incomplete burning of carbon. As part of Sentinel 5, the CO product is available from 22 November 2018. The features of this product are given in Table 4.

NO2 Retrieval

As a result of human activities, especially the burning of fossil fuels, millions of tons of NO2 are produced every year [45]. In the GEE, NO2 is one of the Sentinel-5 products that provides offline and in-live high-resolution images. The data on this product can be accessed from 7 October 2018. The features of this product are given in Table 5.

SO2 Retrieval

As presented in Table 6, the GEE has provided a product for the analysis of SO2. NO2 data is accessible on the GEE platform from 10 July 2018.

O3 Retrieval

The role of O3 in the thermal structure of the Earth and the balance of solar radiation is critical as it prevents ultraviolet radiation from reaching the Earth’s surface. However, O3 is considered a pollutant when its concentration in the lower atmosphere exceeds the air quality standard threshold [46]. The GEE platform has provided a product to monitor and review this critical issue, which provides a set of high-resolution images in real time. This product’s data can be accessed from 7 October 2018, whose features are listed in Table 7.

3.2.2. Accuracy Assessment

Analyzing the accuracy of a retrieval by inversion compared to a standard assumed to be correct is an important step in image analysis. In this regard, median absolute deviation (MAD) [47], mean square error (MSE) [48], root mean square error (RMSE) [49], and mean absolute percentage error (MAPE) [50] statistical analyses were applied to evaluate the accuracy of results for AP retrieval. Equations (1)–(4) describe the MAD, MSE, RMSE, and MAPE operators.
M A D = t = 1 n   A t F t n
M S E = t = 1 n   A t F t n
R M S E = t = 1 n ( A t F t ) 2 n
M A P E = t = 1 n   A t F t A t n × 100
where n is the number of the AP station in the study area, A t is AP recorded by station, and F t is AP obtained using the Sentinel-5.

4. Results

Using GEE, pollution parameter maps (CO, NO2, SO2, and O3) were extracted. By coding in JavaScript language in the GEE, four pollution parameters of Sentinel-5 satellite images were called. Using filters, the study years (2018 and 2019) and the location (Arak city) were defined. Following that, images with clouds were filtered by defining cloud filters, and average maps were extracted by defining average filters for both years. The results of the spatiotemporal distribution of CO, NO2, SO2, and O3 in Arak are presented in Figure 3, Figure 4, Figure 5, Figure 6, Figure 7 and Figure 8 monthly, seasonally, and annually, respectively.
As we can see in Figure 3, for the year 2018, the highest amount of CO was recorded in July and August (0.029 ppm). November was the month with the lowest amount of CO. According to Figure 3, while the highest amount of NO2 was recorded in December (22.06 ppm), the lowest was recorded in May (12.39 ppm). For SO2, the highest and lowest amounts were related to July (33.54 ppm) and May (25.14 ppm), respectively, as shown in Figure 3. As we can see in Figure 3, the highest amount of O3 was recorded in February (0.148 ppm), while the lowest concentrations of O3 occurred in September and October (0.122 ppm).
According to Figure 4, for the year 2019, the highest amount of CO was recorded in January (0.030 ppm). March was the month with the lowest amount of CO. According to Figure 4, while the highest amount of NO2 was recorded in January (30.19 ppm), the lowest was recorded in April (11.08 ppm). For SO2, the highest and lowest amounts were related to October (45.38 ppm), as shown in Figure 4. As we can see in Figure 4, the highest amount of O3 was recorded in April (0.147 ppm), while the lowest amount of O3 belonged to November (0.117 ppm).
As we can see in Figure 5, for the year 2018, the seasonal highest amount of CO was recorded in spring and summer (0.028 ppm). According to Figure 5, the highest amount of NO2 was found in fall (17.39 ppm), and the lowest was found in spring (12.18 ppm). For SO2, the highest and lowest amounts were related to winter (102.12 ppm) and summer (29.05 ppm), respectively, as shown in Figure 5. As we can see in Figure 5, the highest amount of O3 was recorded in winter (0.142 ppm), while the lowest amount of O3 belonged to summer (0.124 ppm).
According to Figure 6, for the year 2019, the seasonal highest amount of CO was recorded in spring, summer and winter (0.028 ppm). According to Figure 6, while the highest amount of NO2 was recorded in winter (21.96 ppm), the lowest amount was recorded in spring (11.66 ppm). For SO2, the highest and lowest amounts were related to winter (101.25 ppm) and summer (28.01 ppm), respectively, as shown in Figure 6. As we can see in Figure 6, the highest amount of O3 was recorded in spring (0.136 ppm), while the lowest amount of O3 was associated with summer and fall (0.122 ppm).
As we can see in Figure 7 and Figure 8, the annual amount of CO was recorded at about 0.027 ppm, which increased (0.030 ppm) in 2019. According to Figure 7 and Figure 8, the highest amount of NO2 was 13.25 ppm for the year 2018, which increased to about 16.05 ppm in 2019. As we can see in Figure 7 and Figure 8, the annual amount of SO2 was recorded at 35.60 ppm, which increased to 38.05 ppm in 2019. According to Figure 7 and Figure 8, the highest amount of O3 in 2018 was 0.133 ppm, which decreased to 0.129 ppm in 2019.
Table 8 and Table 9 also show the results of the accuracy assessment for annual and seasonal AP retrieval. Our findings revealed the efficiency of Sentinel-5 AP products based on GEE for mapping and monitoring CO, NO2, SO2, and O3. According to Table 8, the annual CO was estimated with MAD, MSE, RMSE, and MAPE of 0.11, 0.017, 0.13, and 4.16, respectively, for the year 2018. In addition, the annual NO2 was calculated with MAD of 2.23, MSE of 8.16, RMSE of 2.58, and MAPE of 10.5, as shown in Table 8. As seen in Table 8, the annual SO2 was estimated with MAD, MSE, RMSE, and MAPE of 4.62, 21.34, 4.62, and 18.20, respectively. Finally, the annual O3 was calculated with MAD of 2.36, MSE of 5.56, RMSE of 2.36, and MAPE of 10.47, as presented in Table 8.
As we can see in Table 8, the annual CO was estimated with MAD, MSE, RMSE, and MAPE of 0.16, 0.03, 0.17, and 6.75, respectively, for the year 2019. Additionally, the annual NO2 was calculated with MAD of 2.03, MSE of 5.83, RMSE of 2.41, and MAPE of 9.2, as shown in Table 8. As seen in Table 8, the annual SO2 was estimated with MAD, MSE, RMSE, and MAPE of 4.31, 18.57, 4.31, and 12.24, respectively. Finally, the annual O3 was calculated with MAD of 4.6, MSE of 21.16, RMSE of 4.6, and MAPE of 16.99, as shown in Table 8.
Table 9 also shows the results of the accuracy assessment for CO, NO2, SO2, and O3 retrieval. According to Table 9, for spring 2018, the seasonal CO, NO2, SO2, and O3 were estimated with RMSE of 0.09, 5.39, 0.70, and 7.81, respectively. In addition, for summer 2018, the seasonal CO, NO2, SO2, and O3 were calculated with RMSE of 0.1, 5.17, 1.17, and 2.83, respectively, as shown in Table 9. As seen in Table 9, the seasonal CO, NO2, SO2, and O3 were estimated with RMSE of 0.29, 4.47, 2.61, and 2.58, respectively, for fall 2018. Finally, for winter 2018, the seasonal CO, NO2, SO2, and O3 were calculated with RMSE of 0.77, 4.80, 3.009, and 2.60, respectively, as shown in Table 9.
According to Table 9, for spring 2019, the seasonal CO, NO2, SO2, and O3 were estimated with RMSE of 0.12, 4.99, 1.33, and 1.27, respectively. In addition, for summer 2019, the seasonal CO, NO2, SO2, and O3 were calculated with RMSE of 0.11, 5.20, 0.67, and 10.35, respectively, as shown in Table 9. As seen in Table 9, the seasonal CO, NO2, SO2, and O3 were estimated with RMSE of 0.069, 1.44, 1.17, and 0.26, respectively, for fall 2019. Finally, for winter 2019, the seasonal CO, NO2, SO2, and O3 were calculated with RMSE of 0.055, 5.23, 0.89, and 1.45, respectively, as shown in Table 9.

5. Discussion

5.1. General Discussion

From a methodological standpoint, the AP can be computed using two methods: ground-based methods and remote sensing technology. Ground-based methods are the main approach to AP retrieval. These methods, however, are time-consuming and expensive. Additionally, they lack frequent records in dynamic environments, such as cities. Over the last decades, satellite-based models have been employed for AP retrieval. The satellite-based technology is considered an innovative technique that can estimate and monitor AP at dense spatial sampling intervals and large scales. The efficiency of satellite-based data and methods such as the Terra Moderate Resolution Imaging Spectroradiometer (MODIS), the Sentinel-5 Precursor (Sentinel-5P), Global Precipitation Measurement (GPM), Soil Moisture Active and Passive (SMAP), the National Centers for Environmental Prediction (NCEP), Climate Forecast System Reanalysis (CFSR), and the Global Land Data Assimilation System (GLDAS) have proven useful for AP retrieval [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38]. This study used Sentinel-5 based on GEE to retrieve monthly, annual and seasonal CO, NO2, SO2, and O3 from 2018 to 2019. The results showed that satellite-derived data and applied methods performed well for AP retrieval (Table 8 and Table 9). The results of this study revealed that remote sensing technology would make AP mapping and monitoring fast and easier in dynamic areas, such as cities. Our findings also showed a strong correlation coefficient between obtained values from pollution measuring stations and Sentinel-5 (Figure 9, Figure 10, Figure 11 and Figure 12). According to Figure 5 and Figure 6, the concentration of pollutants in spring 2019 was the same as 2018 for CO and decreased to about 0.52, 2.13, and 0.1 ppm in 2019 for NO2, SO2, and O3, respectively. The same trend as spring existed for summer 2019 in the concentration of CO, NO2, and SO2, as shown in Figure 5 and Figure 6. The concentration of O3, on the other hand, showed an increase of about 0.02 ppm in summer 2019 compared to summer 2018. As we can see in Figure 5 and Figure 6, while the concentration of CO and NO2 increased by about 0.002 and 4.49 ppm, respectively, from 2018 to 2019 in fall, the concentration of SO2 and O3 decreased by about 3.63 and 0.006 ppm from 2018 to 2019, respectively. The concentration of CO and NO2 increased by about 0.001 and 4.99 ppm, respectively, from 2018 to 2019 in winter. The concentration of SO2 and O3, however, decreased by about 0.87 and 0.008 ppm, respectively, from 2018 to 2019 in this season. In the domain of the annual concentration of pollutants, there was an increase of 0.003, 2.08, and 2.45 ppm for CO, NO2, and SO2, respectively, from 2018 to 2019, as shown in Figure 7 and Figure 8. However, a decrease of 0.004 ppm in the domain of O3 concentration was observed, according to Figure 7 and Figure 8.
According to Figure 9, the correlation coefficients (R2) for CO, NO2, SO2, and O3 in spring 2018 were 0.9509, 0.5692, 0.9285, and 0.5627, respectively. The correlation coefficients between the data obtained from Sentinel-5 and the ground data for summer 2018 were 0.5544, 0.7463, 0.9221, and 0.8601 for CO, NO2, SO2, and O3, respectively, as shown in Figure 9. According to Figure 9, strong correlation coefficients of 0.970, 0.4627, 0.9307, and 0.7841 were found for CO, NO2, SO2, and O3, respectively, in fall 2018. In addition, the correlation coefficients between the data obtained from Sentinel-5 and the ground data for winter 2018 were 0.7117, 0.727, 0.9506, and 0.8748 for CO, NO2, SO2, and O3, respectively, as shown in Figure 9. In general, strong correlation coefficients were estimated between data obtained from remote sensing technology and ground-based data in all seasons, in particular winter for the year 2018. The results of this research are in accordance with reports received from the Environmental Organization of Central Province (https://markazi.doe.ir, accessed on 1 February 2022).
According to Figure 10, the correlation coefficients for CO, NO2, SO2, and O3 in spring 2019 were 0.8974, 0.6174, 0.8959, and 0.5192, respectively. The correlation coefficients between the data obtained from Sentinel-5 data and the ground data for summer 2019 were 0.92, 0.855, 0.8391, and 0.8298 for CO, NO2, SO2, and O3, respectively, as shown in Figure 10. According to Figure 10, strong correlation coefficients of 0.917, 0.6275, 0.9326, and 0.8197 were found for CO, NO2, SO2, and O3, respectively, in fall 2019. In addition, the correlation coefficients between the data obtained from Sentinel-5 and the ground data for winter 2019 were 0.9009, 0.9471, 0.8933, and 0.8332 for CO, NO2, SO2, and O3, respectively, as shown in Figure 10. In sum, the results of the correlation coefficients between the predicted data from remote sensing technology and actual data received from pollution measuring stations demonstrated a difference (about <0.6) from 2018 to 2019 in all seasons. This difference could be because of some missing data received from pollution measuring stations.
From Figure 11, it can be seen that strong correlation coefficients of 0.9509, 0.9344, 0.9344, and 0.8376 for CO, NO2, SO2, and O3, respectively, were obtained in 2018.
According to Figure 12, strong correlation coefficients of 0.9103, 0.8558, 0.9078, and 0.8133 were found for CO, NO2, SO2, and O3, respectively, in 2019.

5.2. Monthly Distribution of AP in 2018 and 2019

Detailed zoning maps of Arak in 2018 showed that the most polluted areas were in the city’s center (Areas 4 and 5), while the least polluted area was 3, which is in accordance with reports received from the Environmental Organization of Central Province (Figure 3). Using satellite images, it was found that the least pollution was recorded in area 3 due to the presence of vegetation and the surrounding gardens. However, the highest distribution of pollution occurred in area 1, which was due to the location of all small and large industries of Arak city in this area, in accordance with collected data from air pollution measuring stations. According to Figure 3, the highest concentration of CO occurred in July and August (0.029 ppm) for the year 2018. The highest concentration of NO2 was also estimated for December (22.06 ppm). The highest concentration of SO2 occurred in August (33.54 ppm). The highest value for O3 was estimated in February, as shown in Figure 3.
The pollution zoning maps for Arak city in 2019 showed the highest levels of pollution in areas 1 and 2, and the least levels of pollution in area 3 (Figure 4). Based on the distribution of pollution using satellite images, it was found that area 3 had the least pollution. This was due to the excellent vegetation and the surrounding gardens. However, area 1 had the highest pollution, which was due to the location of all small and large industries of Arak city in this area, which is in accordance with data received from the Environmental Organization of Central Province. As we can see in Figure 4, the highest concentration of CO occurred in January (0.030 ppm) for the year 2019. The highest concentration of NO2 was also estimated for January (30.19 ppm), while the highest concentration of SO2 belonged to October (45.38 ppm). The highest value for O3 was estimated in April (0.147 ppm), as shown in Figure 4.

5.3. The Effect of Land Use on Air Pollution in Arak City

Based on an analysis of land use and its effect on air pollution, it was found that the most polluted area was the Arak industrial town, which is located in area 1. It has been reported that the industrial factories of Arak play a significant role in the air pollution of Arak city by producing suspended particles, carbonaceous oxides, nitrogenous oxides, ammonia, etc. [41]. The least air pollution was found in area 3 due to the high density of vegetation, which is in accordance with the identified green areas on the land use map.
Arak is surrounded by hills and the city’s highlands are located to the south and the city’s industrial area is to the southeast. The prevailing winds in this area are from the west and southwest. Regardless of other natural factors, due to the average speed (between 7 and 10 K/H) of the prevailing winds, pollution from the industries in the east of this city cannot penetrate into the residential areas [51]. Due to the topography, the south and west of Arak city are surrounded by highlands, and, since local winds are typically from the east and northeast, this factor, as well as the fact that January and April are the months with the greatest percentage of still air, contributed to creating an inversion, which is in accordance with reports received from the Environmental Organization of Central Province.

5.4. Limitation of the Study

Although satellite images are a promising source of data for generating estimates at high spatial resolution on a local scale as they capture some spatial variability, they are limited to generalizing in certain areas [52,53]. Future work should focus on combining additional datasets that are readily available globally (e.g., additional bands of satellite data, normalized difference vegetation index (NDVI)/enhanced vegetation index (EVI), meteorology, digital elevation model (DEM)) that can be combined with meter-scale satellite images to generate better estimates of air pollutants. In addition, future efforts will need to assess the sensitivity of image-based models to images collected with different temporal aspects, such as time of day and season.

6. Conclusions

Recent progress in Earth observation technology, and remote sensing in particular, has turned remote sensing into big data technology, which demands efficient, effective, and cost-effective data-driven methods. Therefore, applying different data-driven approaches and comparing their efficiency can be considered as the state of the art for remote sensing sciences which is the object of the current research. This study employed Sentinel-5 images based on GEE to retrieve CO, NO2, SO2, and O3 parameters. According to our findings, Sentinel-5 images based on GEE turned out to be the most efficient approach for AP retrieval. This study also confirmed a strong correlation between CO, NO2, SO2, and O3 retrieved from Sentinel-5 and air pollution stations.
Our findings confirm that GEE is appropriate to exploit vast amounts of data, and that it can be regarded as a testbed for machine learning algorithms. In the investigation of air pollution in urban areas, satellite images in both time and space can provide optimal management and high accuracy. Considering the fact that most pollution monitoring station data are incomplete due to malfunctioning devices, AP classification from remote sensing images is a challenging task because of the wide range of features that can cause heterogeneity. The present study addresses this complexity by utilizing an automated data-driven platform. This study proves the applicability of Sentinel-5 in GEE for providing a general framework for the monitoring of CO, NO2, SO2, and O3 at various levels and scales. The results of this study contribute to monitoring programs for CO, NO2, SO2, and O3 changes in dynamic environments, such as cities. The results are readily generalizable to more complex Earth feature monitoring.

Author Contributions

Conceptualization, M.K.G.; data curation, H.R. and M.S.; formal analysis, G.L.; investigation, N.M.; methodology, M.K.G., H.R., N.M. and B.S.; software, M.K.G., M.S. and B.S.; supervision, G.L.; validation, G.L.; writing—original draft, M.K.G.; writing—review and editing, G.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location of study area, (1) in Iran, (2) Markazi province, and (3) different areas in Arak city. (46) are examples of observed air pollution in Arak city.
Figure 1. Location of study area, (1) in Iran, (2) Markazi province, and (3) different areas in Arak city. (46) are examples of observed air pollution in Arak city.
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Figure 2. An overview of the methodology used for AP retrieval.
Figure 2. An overview of the methodology used for AP retrieval.
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Figure 3. The monthly spatiotemporal distribution of CO, NO2, SO2, and O3 in January, February, March, April, May, June, July, August, September, October, November, and December for the year 2018.
Figure 3. The monthly spatiotemporal distribution of CO, NO2, SO2, and O3 in January, February, March, April, May, June, July, August, September, October, November, and December for the year 2018.
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Figure 4. The monthly spatiotemporal distribution of CO, NO2, SO2, and O3 in January, February, March, April, May, June, July, August, September, October, November, and December for the year 2019.
Figure 4. The monthly spatiotemporal distribution of CO, NO2, SO2, and O3 in January, February, March, April, May, June, July, August, September, October, November, and December for the year 2019.
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Figure 5. The seasonal spatiotemporal distribution of CO, NO2, SO2, and O3 in Arak for the year 2018 in spring, summer, fall, and winter.
Figure 5. The seasonal spatiotemporal distribution of CO, NO2, SO2, and O3 in Arak for the year 2018 in spring, summer, fall, and winter.
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Figure 6. The seasonal spatiotemporal distribution of CO, NO2, SO2, and O3 in Arak for the year 2019 in spring, summer, fall, and winter.
Figure 6. The seasonal spatiotemporal distribution of CO, NO2, SO2, and O3 in Arak for the year 2019 in spring, summer, fall, and winter.
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Figure 7. The annual spatiotemporal distribution of CO, NO2, SO2, and O3 in Arak for the year 2018.
Figure 7. The annual spatiotemporal distribution of CO, NO2, SO2, and O3 in Arak for the year 2018.
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Figure 8. The annual spatiotemporal distribution of CO, NO2, SO2, and O3 in Arak for the year 2019.
Figure 8. The annual spatiotemporal distribution of CO, NO2, SO2, and O3 in Arak for the year 2019.
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Figure 9. Linear regression correlation between seasonal CO, NO2, SO2, and O3 obtained using Sentinel-5 (predicted data) and pollution measuring stations (actual data) for the year 2018.
Figure 9. Linear regression correlation between seasonal CO, NO2, SO2, and O3 obtained using Sentinel-5 (predicted data) and pollution measuring stations (actual data) for the year 2018.
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Figure 10. Linear regression correlations between seasonal CO, NO2, SO2, and O3 obtained using Sentinel-5 (predicted data) and pollution measuring stations (actual data) for the year 2019.
Figure 10. Linear regression correlations between seasonal CO, NO2, SO2, and O3 obtained using Sentinel-5 (predicted data) and pollution measuring stations (actual data) for the year 2019.
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Figure 11. Linear regression correlations between annual CO, NO2, SO2, and O3 obtained using Sentinel-5 (predicted data) and pollution measuring stations (actual data) for the year 2018.
Figure 11. Linear regression correlations between annual CO, NO2, SO2, and O3 obtained using Sentinel-5 (predicted data) and pollution measuring stations (actual data) for the year 2018.
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Figure 12. Linear regression correlations between annual CO, NO2, SO2, and O3 obtained using Sentinel-5 (predicted data) and pollution measuring stations (actual data) for the year 2019.
Figure 12. Linear regression correlations between annual CO, NO2, SO2, and O3 obtained using Sentinel-5 (predicted data) and pollution measuring stations (actual data) for the year 2019.
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Table 1. Polluting substances in Arak city (in tons per year) caused by fuel consumption in industrial factories.
Table 1. Polluting substances in Arak city (in tons per year) caused by fuel consumption in industrial factories.
Fuel TypeAmount in
Cubic Meters (m3)
Carbon
Monoxide (CO)
Sulfur
Dioxide (SO2)
Nitrogen
Oxides (NOx)
Hydrocarbonate (HC)
Kerosene388,610220.980.580.13
Fuel oil12,728,7896.256588.464.15
Gasoline34,458,61393039015,000148
Burner fuel166,1955.33.79.10.68
Natural gas2,898,477780-8700-
Total50,640,6841721.7959.5910.298153.140
Table 2. Polluting substances in Arak city (in tons per year) produced by motor vehicles and heating sources.
Table 2. Polluting substances in Arak city (in tons per year) produced by motor vehicles and heating sources.
Contaminating SourcesCOSO2NOxHC
Gasoline cars12,420331901536
Motorcycle10,044--3487
Diesel cars310015117391
Home heating sources55729792695
Industrial fuel consumption942,500959,5901,606,840153,140
Refinery production process314,76630,707582318,962
Total1,283,387990,6421,613,896177,611
Table 3. The annual air pollution data (ppm) in Ostandari, Shariati, and Mokhaberat stations from 2018 to 2019.
Table 3. The annual air pollution data (ppm) in Ostandari, Shariati, and Mokhaberat stations from 2018 to 2019.
Name of Station2018
CONO2SO2O3
Ostandari2.9722.336.151.25
Shariati2.3616.9925.3822.54
Mokhaberat2.412.284.181.02
2019
CONO2SO2O3
Ostandari2.962.5125.1421.58
Shariati2.2615.6939.2960.06
Mokhaberat2.2522.3626.7822.03
Table 4. Specifications of CO product.
Table 4. Specifications of CO product.
Band NameUnitMinMaxDescription
CO_column_number_densitymol/m2−2974.64CO concentration.
H2O_column_number_densitymol/m2−46,5363.45844 × 107Water vapor column.
Cloud_heightMeter−83415000Scattered layer height.
Sensor_altitudeMeter828,542856,078Satellite height according to WGS84 geodetic.
Sensor_azimuth_angleDegree−180180Satellite azimuth angle, East and North WGS84.
Sensor_zenith_angleDegree166WGS84 Satellite elevation angle.
Solar_azimuth_angleDegree−180180Sun azimuth angle, East and North angle WGS84.
Solar_zenith_angleDegree980Apex angle of the satellite is the angle away from the vertical.
Table 5. Specifications of NO2 product.
Table 5. Specifications of NO2 product.
Band NameUnitMinMaxDescription
NO2_column_number_densitymol/m2−0.00060.0096NO2 gradient column density ratio
Tropospheric_NO2_columnmol/m2−0.00640.0096Vertical tropospheric column of NO2
Stratospheric_NO2_columnmol/m28.7 × 10−60.0001Stratospheric NO2 vertical column
NO2_slant_column_numbermol/m2−51.4 × 100.003908NO2 gradient column density
Tropopause_pressurePa0.006440.009614Top pause pressure
Absorbing_aerosol_indexPa−14.4310.67Aerosol index
Cloud_fractionfraction01Effective cloud fraction
Sensor_altitudeMeter828,5430.856078Satellite height according to WGS84 geodetic
Sensor_azimuth_angleDegree−180180Satellite azimuth angle, East and North WGS84
Sensor_zenith_angleDegree0.09867WGS84 Satellite elevation angle
Solar_azimuth_angleDegree−180180Sun azimuth angle, East and North angle WGS84
Solar_zenith_angleDegree882Apex angle of the satellite is the angle away from the vertical
Table 6. Specifications of SO2 product.
Table 6. Specifications of SO2 product.
Band NameUnitMinMaxDescription
SO2_column_number_densitymol/m2−480.24Concentration of vertical column of SO2 at ground level.
SO2_column_number_amfmol/m20.13.397Weighted average of cloudy and clear air mass coefficient.
SO2_slant_column_numbermol/m2−0.1470.162SO2 correction column density column slope.
Cloud_fractionFraction01Effective cloud fraction.
Sensor_azimuth_angleDegree−180180Satellite azimuth angle, East and North WGS84.
Sensor_zenith_angleDegree0.0967WGS84 Satellite elevation angle.
Solar_azimuth_angleDegree−180180Sun azimuth angle, East and North angle WGS84.
Solar_zenith_angleDegree880Apex angle of the satellite is the angle away from the vertical.
SO2_column_number_15 kmmol/m200SO2 vertical column density at 15 km.
Table 7. Specifications of O3 product.
Table 7. Specifications of O3 product.
Band NameUnitMinMaxDescription
O3_column_number_densitymol/m20.0250.3048O3 between the surface and the top of the atmosphere.
O3_effective_temperaturek19.92428.11Mass coefficient of cloudy and clear air.
Cloud_fractionFraction01The slope of the O3 condensation column.
Sensor_azimuth_angleDegree−180180Satellite azimuth angle, east and north.
Sensor_zenith_angleDegree0.09866.57WGS84 satellite zenith angle.
Solar_azimuth_angleDegree−180180Sun azimuth angle, East and North angle WGS84.
Solar_zenith_angleDegree8102Apex angle of the satellite is the angle away from the vertical.
Table 8. The results of accuracy assessment for annual AP retrieval.
Table 8. The results of accuracy assessment for annual AP retrieval.
Performance metrics2018
CONO2SO2O3
MAD0.112.234.622.36
MSE0.0178.1621.345.56
RMSE0.132.584.622.36
MAPE4.1610.518.2010.47
2019
MAD0.162.034.314.6
MSE0.035.8318.5721.16
RMSE0.172.414.314.6
MAPE6.759.212.2416.99
Table 9. The results of accuracy assessment for seasonal AP retrieval.
Table 9. The results of accuracy assessment for seasonal AP retrieval.
SeasonPerformance metrics20182019
CONO2SO2O3CONO2SO2O3
SpringMAD0.085.220.54.410.114.581.770.72
MSE0.0829.120.2519.440.00224.903.130.51
RMSE0.095.390.707.810.124.991.331.27
MAPE3.4522.901.6012.731.9117.976.92.78
SummerMAD0.074.571.371.60.114.250.455.84
MSE0.01226.731.872.560.01927.120.2034.10
RMSE0.15.171.172.830.115.200.6710.35
MAPE3.219.206.325.774.4317.672.1032.97
FallMAD0.234.216.851.460.0431.481.370.15
MSE0.0820.0546.922.130.0043.141.870.022
RMSE0.294.472.612.580.0691.441.170.26
MAPE6.7519.1829.586.051.4517.764.40.66
WinterMAD0.644.229.061.470.434.580.80.82
MSE0.6023.1082.082.160.00327.350.640.67
RMSE0.774.803.0092.600.0555.230.891.45
MAPE15.8917.7935.175.471.4327.282.406.36
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Kazemi Garajeh, M.; Laneve, G.; Rezaei, H.; Sadeghnejad, M.; Mohamadzadeh, N.; Salmani, B. Monitoring Trends of CO, NO2, SO2, and O3 Pollutants Using Time-Series Sentinel-5 Images Based on Google Earth Engine. Pollutants 2023, 3, 255-279. https://doi.org/10.3390/pollutants3020019

AMA Style

Kazemi Garajeh M, Laneve G, Rezaei H, Sadeghnejad M, Mohamadzadeh N, Salmani B. Monitoring Trends of CO, NO2, SO2, and O3 Pollutants Using Time-Series Sentinel-5 Images Based on Google Earth Engine. Pollutants. 2023; 3(2):255-279. https://doi.org/10.3390/pollutants3020019

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Kazemi Garajeh, Mohammad, Giovanni Laneve, Hamid Rezaei, Mostafa Sadeghnejad, Neda Mohamadzadeh, and Behnam Salmani. 2023. "Monitoring Trends of CO, NO2, SO2, and O3 Pollutants Using Time-Series Sentinel-5 Images Based on Google Earth Engine" Pollutants 3, no. 2: 255-279. https://doi.org/10.3390/pollutants3020019

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