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Article

Recent Developments in Satellite Remote Sensing for Air Pollution Surveillance in Support of Sustainable Development Goals

by
Dimitris Stratoulias
1,
Narissara Nuthammachot
2,
Racha Dejchanchaiwong
3,4,*,
Perapong Tekasakul
3,5 and
Gregory R. Carmichael
6
1
Asian Disaster Preparedness Center (ADPC), Bangkok 10400, Thailand
2
Faculty of Environmental Management, Prince of Songkla University, Songkhla 90112, Thailand
3
Air Pollution and Health Effect Research Center, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
4
Department of Chemical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
5
Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand
6
Department of Chemical and Biochemical Engineering, University of Iowa, Iowa City, IA 52242, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(16), 2932; https://doi.org/10.3390/rs16162932
Submission received: 6 June 2024 / Revised: 1 August 2024 / Accepted: 2 August 2024 / Published: 9 August 2024
(This article belongs to the Special Issue Stereoscopic Remote Sensing of Air Pollutants: Emission, Formation, and Transport)
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Abstract

:
Air pollution is an integral part of climatic, environmental, and socioeconomic current affairs and a cross-cutting component of certain United Nations Sustainable Development Goals (SDGs). Hence, reliable information on air pollution and human exposure is a crucial element in policy recommendations and decisions. At the same time, Earth Observation is steadily gaining confidence as a data input in the calculation of various SDG indicators. The current paper focuses on the usability of modern satellite remote sensing in the context of SDGs relevant to air quality. We introduce the socioeconomic importance of air quality and discuss the current uptake of geospatial information. The latest developments in Earth Observation provide measurements of finer spatial, temporal, and radiometric resolution products with increased global coverage, long-term continuation, and coherence in measurements. Leveraging on the two latest operational satellite technologies available, namely the Sentinel-5P and the Geostationary Environment Monitoring Spectrometer (GEMS) missions, we demonstrate two potential operational applications for quantifying air pollution at city and regional scales. Based on the two examples and by discussing the near-future anticipated geospatial capabilities, we showcase and advocate that the potential of satellite remote sensing as a, complementary to ground station networks, source of air pollution information is gaining confidence. As such, it can be an invaluable tool for quantifying global air pollution and deriving robust population exposure estimates.

1. Introduction

1.1. Importance of Air Pollution

Population growth, industrialization, concomitant urbanization, and fossil fuel combustion have given rise to the ever-increasing issue of modern pollution. Over the past decades, pollution in its current form, such as air and chemical contamination, has caused an increase in the number of deaths by 66% [1] while specifically air pollution has been considered the leading environmental risk factor [2], with 99% of the global population breathing air pollution exceeding the World Health Organization (WHO) air quality recommended limits [3].
Common anthropogenic sources of air pollution, such as household fuel combustion for cooking and heating, vehicular exhaustion, power generation, industry, and agricultural management practices, emit pollutants into the atmosphere, notably particulate matter (PM), carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2). Specifically, fine PM (defined as particles with an aerodynamic diameter of 2.5 microns or less, henceforth referred to as PM2.5) has been a focal point as it encompasses pollutants capable of entering the bloodstream and penetrating into organ systems with consequent respiratory repercussions [4]. Evidence of its contribution to other adverse health effects, even at very low levels, is growing [5,6,7,8]. PM2.5 relates primarily to cardiovascular and respiratory effects; nevertheless, it has been manifested that it also contributes to a variety of other health complications. Premature mortality has been indicated as a direct consequence, especially in dense urban areas, which have a higher mortality density than rural areas [9]. Indicatively, in a meta-analysis study conducted in China on the effects of ambient PM2.5 and PM10 on the population, Lu et al. [10] found that a 10 μg/m3 increase in PM2.5 was associated with a 0.40% increase in total non-accidental mortality, a 0.63% increase in mortality due to cardiovascular disease, and a 0.75% increase in mortality due to respiratory disease in terms of short-term effects. The respective metrics for an increase of 10 μg/m3 in PM10 were 0.36%, 0.36%, and 0.42%. For a review of the health effects of ambient PM2.5, from a medical perspective, the reader is referred to Feng et al. [5].
Indicatively, only in regard to household air pollution, 2.4 billion people are exposed to dangerous levels annually, with a result of 3.2 million deaths in 2020 [11]. In addition, ambient (outdoor) air pollution is responsible for 4.2 million deaths annually, which raises the toll of cumulative premature deaths to 7 million per year, as reported by the WHO [12]. The latter, in a study conducting a global assessment of exposure to ambient air pollution, estimated that in 2012, 1 out of 9 deaths was a result of air pollution, and approximately 3 million of those were attributed solely to ambient air pollution [13]. The Global Burden Disease study [14] estimated that 6.5 million deaths globally are attributed to air pollution risk. Based on data from the latter study, the 2017 Lancet Commission on Pollution and Health [15] estimated 9 million deaths (16% of all deaths globally) attributed to pollution, out of which 6.67 million are attributed to air pollution. More recently, the WHO [2] estimated that ambient and household air pollution are responsible for approximately 7 million deaths annually, primarily from non-communicable diseases, while Fuller et al. [1], based on data from the Global Burden of Diseases, Injuries, and Risk Factors study (GBD) compiled in 2019, reported that pollution accounts for approximately 9 million deaths per year worldwide or one in six deaths.

1.2. Association with Vegetation and Climate

It is worth noting that air pollution also has an effect on vegetation and may negatively affect crops, forests, grasslands, and ecosystems in general. This may be caused by emissions of NO2 and SO2, acid deposition, or eutrophication of aquatic vegetation, as reported by Stevens et al. [16]. PM may also induce a negative mechanical effect by covering the leaf, and, therefore, reducing light availability, which directly relates to photosynthesis [17]. Of particular importance, ground-level O3 has been demonstrated to cause damage to vegetation, with an increasing amount of observational and experimental evidence. For example, Emberson [18] pinpoints the effect of O3 on agriculture, forests, and grasslands and reports a related 3–16% global yield loss in staple crops in the year 2000, with a consequent 14–26 billion US$ economic loss, which might raise concerns on food supply and security. Specific to crops, in a global-scale study, Mills et al. [19] showed that O3 limits the yields of key crops and is a significant stress comparable in importance with other key stresses.
At a larger scale, air composition is closely linked to the earth’s ecosystems and climate. Several sources of air pollution, primarily fossil fuel combustion, are major sources of greenhouse gas emissions, such as CO2 and black carbon, which have the capacity to alter the climate. This close link between climate change and air pollution has an effect on policies aimed at addressing air pollution and can be part of a strategy to mitigate climate change.

1.3. Economic Impact

Apart from human health and environmental consequences, air pollution has an impact on the economy through direct human loss, such as premature mortality, welfare loss, and lost labor productivity. The World Bank and the Institute for Health Metrics and Evaluation [20] reported that premature deaths attributed to air pollution in 2013 cost globally 5.11 trillion US$ in welfare losses. Landrigan et al. [15] estimated 4.6 trillion US$ in economic losses from pollution in 2015, which translates to 6.2% of global economic output. The World Bank [21] recently estimated the global cost of health damages associated with air pollution to be $8.1 trillion, which translates to 6.1% of the global GDP.
Lost annual labor income due to premature deaths, albeit smaller in magnitude, amounted to 225 billion US$ in 2013, while for South Asia, sums up to 66 billion, which is approximately 1% of the GDP [20]. Apart from the health-related economic burden, air pollution triggers additional losses through damage to agricultural crops, buildings, and infrastructure, as well as indirectly through environmental degradation and climate change.

1.4. Inequality

While air pollution affects the majority of the global population, the distribution of the burden is not equal among countries and groups. It has been widely documented that low- and middle-income countries (LMICs) have a disproportionately higher burden in terms of pollution-induced deaths and economic losses [3,21]. A report by the WHO [13] suggests that in 2012, 87% of pollution-related deaths occurred in LMICs, while more recently, Fuller et al. [1] estimated this percentage to be 92%. This fact is an important aspect, as LMICs host 82% of the world population, and data quality for LMICs is relatively poor compared to high-income countries (HICs); therefore, the former are in a disadvantageous position in terms of monitoring air pollution and conducting exposure assessments. In Southeast Asia specifically, it has been documented that air pollution originating from forest and agricultural fires impacts the poor disproportionately [22]. At the continental level, differences in annual averages and trends are also noticeable. Hammer et al. [23] in a study looking into global estimates of annual PM2.5 concentrations and associated trends during 1998–2018, present a significant positive trend for Asian regions, notably China, India, and Southeast Asia, while the trend for European and American continents is slightly negative. It is worth noting that air pollution is a pertinent issue in developing Asia, driven by the increase in urbanization and the aging population [24].

1.5. Initiatives and SDGs

Meanwhile, scientific experimentation and evidence on the effect of air pollution on human health have accumulated rapidly and reveal adverse health effects observed even at low levels of air pollution in long-term exposure [25,26,27]. Recent studies [6,7,8] have demonstrated that there exists a positive association between mortality and exposure to low PM2.5 and have found no evidence for a threshold; instead, a supra-linear exposure-response function between PM2.5 and mortality was found, which translates into a larger relative effect per additional unit of exposure at low pollutant concentrations than at high concentrations. Based on such evidence, the latest Air Quality Guidelines (AQG) from WHO (released in September 2021, and 15 years after the last recommendation amendment) reconsidered the recommended AQG levels and suggested health benefits yielded by further reducing the annual average and the 24 h average limits of PM2.5 concentration to 5 μg/m3 and 15 μg/m3, respectively [12]. The corresponding thresholds for PM10 were set to 15 μg/m3 and 45 μg/m3, respectively [12].
At the international level, the United Nations has recognized the issue of air pollution as a global health priority in the 2030 Agenda for Sustainable Development adopted in 2015 [28], and these concerns are reflected in the 17 Sustainable Development Goals (SDGs) laid out therein. Specifically, the SDGs indicators relating directly to air quality are described in Table 1. The WHO acts as the custodian of these three air pollution-related indicators [13], and the methodology based on which these indicators are calculated is described in the World Health Organization [13] for 3.9.1 and 11.6.2 by the World Health Organization [2] for 7.1.2.

1.6. Objective

While air quality is evidently an issue of paramount importance to human health, little progress has been made to address this planetary scale and high-impact threat, especially in LMICs, which face the most adverse consequences [1]. The need for global actions and impactful interventions based on the science-policy interface is needed more than ever. For instance, curbing fossil fuels, transitioning to clean energy, and the use of environmentally friendly machinery and practices can all contribute to the actual reduction of air pollution. In the meantime, monitoring progress and quantifying the air pollution in space and time is a necessity and SDGs’ role in this effort is prominent. The advancement of geospatial technology during the last few years has provided new insights for mapping this phenomenon more accurately, and the estimation of SDG indicators based on remote sensing data has demonstrated great potential [29] and future [30] alike. In the current paper, we discuss the usability of these latest developments and available sources from remote sensing satellite sensors tailored to air pollution, and we provide two indicative case studies exhibiting state-of-the-art potential, which is anticipated to open new capabilities of air pollution monitoring in terms of applications such as diurnal changes. We discuss how this technology could be used to advance the use and integration of this information source in the context of air quality-specific SDG indicators.

2. Traditional Sensors, Infrastructure and Methods

2.1. Ground Stations

Authorities, especially those of HICs, have traditionally attempted to operate devices to monitor ambient air pollution. These comprise mainly regulatory-grade sensors installed at locations of concern. However, these systems are expensive to install and maintain. Consequently, the cost is often prohibitive for low-income countries and restricts the installation of large numbers of sensors for HICs. Moreover, the plethora of existing hardware systems induces intra-surveyor errors in studies covering large geographical areas that encompass systems with different specifications, although isolated initiatives providing consistent harmonized measurements globally exist, such as the US Department of State AirNow program. Lastly, given that ambient air pollution emission and the transportation of pollutants through weather systems is a dynamic phenomenon in space and time, these monitoring systems provide a snapshot (typically every hour) of a single location where the station is installed, and the assumption of a point measurement as a representation of a settlement or an area is an insubstantial and inaccurate generalization.
Lately, an increasing interest in low-cost sensors has emerged. Low-cost sensors, as the name implies, are cheaper alternatives to the regulatory-grade stations; however, this decrease in price is accompanied by lower accuracy and fidelity of the data recorded. Most importantly, low-cost sensors tend to saturate at specific levels of pollution, which makes them incapable of providing accurate measurements at high pollution levels, during which such information is presumably most important. In addition, they are sensitive to ambient conditions, specifically relative humidity. Despite the aforementioned limitations, a large installation of a low-cost sensor network system could potentially overcome the limited sample locations that regulatory-ground stations inherit and provide a spatially richer representation of the ambient air pollution of a settlement or area.

2.2. Databases

Eventually, accessibility to both regulatory grade and low-cost sensors depends on the custodian. Several countries provide the data freely, while others do not disclose this information openly. Globally, efforts have been set to redistribute open-source data, such as the OpenAQ, which is a non-profit initiative of the historic and real-time platform containing ‘open-source government-measured and research-grade data’ ((https://openaq.org, accessed 26 July 2024). Another global database is the WHO Global Ambient Air Quality database, which contains annual concentrations of PM (Figure 1) and NO2. It has been updated every 2–3 years since 2011, and the fifth version of the database, released in April 2022, covers 6000 human settlements in 117 countries. However, this database is based on ground measurements and represents the average for the city as a whole rather than individual stations. The database serves as an input for the derivation of the SDG indicators 11.6.2 and 3.9.1.
A closer look at Figure 1 highlights the spatial heterogeneity of the sampling locations. It becomes apparent that most stations are located in Europe, China, and North America, while the rest of the world is represented by a small number of stations. Figure 2 presents the Gridded Population of the World (GPWv4), which is constructed based on the disaggregation of native national census data into a high-resolution global grid [32]. The latter reveals a human-centric view in regard to air pollution exposure. A comparison against the global population distribution shows that a large part of the global population, especially in Africa, South Asia, and South America, is apparently residing in areas with minimal ground station coverage. Satellite-based remote sensing offers an ever-evolving technology for spatially continuous information retrieval from space, often at no acquisition cost. It is in these cases, primarily LMICs, where ground stations are often inadequate, that satellite-based retrievals can valorize their potential and usability.

3. Case Studies

3.1. Examples of Earth Observation

Lately, a surge in interest and initiatives for monitoring the Earth from space has been noticeable. New satellite missions leverage the latest technological capabilities and promise a new lens through which the atmosphere can be observed. In this section, we provide two case studies in which we make use of recently launched satellites and depict the usability and applicability of these products in the framework of monitoring SDGs. These examples, which have the potential to become operational, can be perceived as indicative illustrations of new space services that can augment air quality monitoring capabilities.
Satellite observations have long been used in the framework of air quality. For instance, the WHO and Greenpeace, in an effort to assess global air quality and the associated health impacts, used observations from the MODIS, MISR, and SeaWiFS satellites between 1998 and 2015. The use of historical archives of such satellite data provides a unique vantage point to valorize the consistency inherited by Earth Observation (EO) in the context of long-term analysis and identifying trends. The current and next generation of satellite sensors, nevertheless, offer advanced capabilities and radiometric resolution. Satellites set in a geostationary orbit, such as GOES-East, GOES-West, GOCI, and Himawari 8, provide the opportunity for frequent measurements over a given area, making them suitable for recording with a frequency similar to that of ground stations and hence can observe diurnal changes in phenomena. Low-earth orbit satellites, on the other hand, such as MODIS, VIIRS, and OMI, provide global coverage at daily or sub-daily intervals. This universal data source can be an error-defense mechanism for accurate estimates at the global scale. For example, Hammer et al. [23] estimated a global annual PM2.5 concentration product and related trends between 1998 and 2018 based on the aerosol optical depth (AOD) from satellite products (Figure 3). However, the temporal and spatial resolutions from these two observation orbits are inversely related, and the concurrent fine spatial resolution and frequent acquisition of measurements with high radiometric fidelity have been a long-sought combination. Recently, the launch of modern satellite systems over the Northern Hemisphere has provided unique datasets. In this section, we present two case studies to demonstrate the functionality of this new generation of satellite systems in the framework of air quality monitoring and, specifically, the integration for the calculation of SDGs indicators.

3.2. Sentinel-5P

The Copernicus initiative is an environmental monitoring program from the European Commission that has invested in a fleet of satellites, namely the Sentinels, dedicated to providing measurements of atmospheric composition [34] and are made available freely to the public. The Sentinel-5 Precursor (Sentinel-5P) is the first mission dedicated to monitoring the atmosphere, and the TROPOMI instrument onboard the Sentinel-5P can map a variety of trace gases, such as NO2, O3, CO, SO2, methane (CH4), formaldehyde (CH2O), aerosols, and clouds [35]. Its’ polar orbit allows for a swath width of 2600km and a capability to map daily the entire global atmosphere with a nominal resolution of 7 km × 3.5 km onward its launch date on 13 October 2017. This high spatial representation of the atmospheric constituents from space can provide a picture of air pollution at the city level, and this has been demonstrated in a plethora of recent publications during the COVID-19 pandemic, where anthropogenic influence on air quality has become concurrently evident in several different regions globally (e.g., [36,37,38,39,40,41,42,43]).
In the current experiment, the NO2 mean annual level for 2021 over the city of Bangkok, Thailand, is investigated. The TROPOMI instrument provides columnar estimates of pollutants. The level 3 offline product of the tropospheric vertical column of NO2 was chosen as it is reprocessed with analysis data and is therefore suitable for scientific analysis, despite the fact that it is delivered with a time lag in comparison to near real-time data. A time series of 5168 images acquired from 1st January 2021 to 31st December 2021 was considered. The data were accessed and processed using the Google Earth Engine [44]. The mean value of the time series for the spatial distribution of NO2 was calculated over the Bangkok Metropolitan Region and is depicted in Figure 4.

3.3. GEMS

The Geostationary Environment Monitoring Spectrometer (GEMS) is a mission coordinated by the National Institute of Environmental Research (NIER) of the Ministry of Environment of South Korea with an outlook to monitor the atmosphere over a large part of Asia at hourly intervals [45]. The GEMS instrument, on board the geostationary Korea Multi-Purpose Satellite 2B (GEO-KOMPSAT-2B) satellite launched in February 2020, is able to acquire measurements at hourly intervals with a nominal spatial resolution over Seoul, South Korea, of 7 km–8 km for gases and 3.5 km–8 km for aerosols. PM2.5, one of the third-party products estimated based on AOD, is scheduled to be operationalized in the near future. The fast sampling rate of the instrument, as a consequence of being onboard a geostationary satellite, in combination with the high spatial resolution, indicates the possibility of acquiring sub-daily (diurnal) measurements for fine monitoring of air pollution development over Asia. Figure 5 provides an example of an operational product from the GEMS instrument, depicting the estimated surface PM2.5 concentrations over Asia acquired during a large-scale air pollution event that occurred on 25 February 2022. Figure 6 presents the monthly mean of columnar NO2 and the monthly maximum of columnar SO2 for the month of November 2023. To derive the statistical results, we downloaded the GEMS dataset from the NIER website. The 0245 UTC acquisitions of the Full Central scan for the month of November 2023 were considered. Images were screened for unreasonable and cloudy pixels and averaged for the case of NO2 and extracting the maximum value for the case of SO2. The analysis was performed using the Python programming language after downloading the related products from the NIER GEMS data portal.

4. Discussion

In this section, we discuss how information from this modern constellation of satellites, as per the case studies provided in the previous section, can augment the information available for air quality monitoring and collateral usability in SDGs estimation. Satellite-based performance has been advocated as reasonably strong, able to rival or exceed that of traditional survey-based methods [46]. Correspondingly, EO is increasingly used as a data source for the calculation of SDG indicators, especially environment-related indicators. A study by Andries et al. [47] suggests that EO data could contribute to the estimation of 108 SDG indicators, with 19, 67, and 22 indicators receiving weak, partial, and full support, respectively. Along the same lines, Maso et al. [29] highlighted the great potential of remote sensing data in the computation of SDG indicators. Although there is no specific SDG that focuses exclusively on air pollution, it is often a cross-cutting component in the SDG framework [48].

4.1. Filling the Spatial Gap in Low-Income Countries

A major advantage of EO is the systematic provision of temporally continuous and spatially contiguous measurements with extensive geographic coverage and different levels of granularity. This information cannot be obtained from ground stations at such gridded spatial intervals, as they fail to represent the diversity of atmospheric conditions. Moreover, satellite data, in particular, can acquire measurements over remote areas that might be difficult to reach or impossible to measure on-site. In the context of air pollution, this advantage translates into accessibility to information in areas where monitoring stations are absent or a limited number of them provide insufficient coverage. This is especially relevant and significant for LMICs, which typically have a small number of monitoring stations. Figure 1 depicts a clustered representation of the ground stations. Cumulatively, for all three pollutants, from a total of 6743 settlements, more than half are located in Europe (i.e., 3654), while the African region contains only 59 sampling locations. Considering this distribution and judging against the global gridded map of adjusted population count of Figure 2, it becomes apparent that high population density regions, notably Africa, South Asia, and South America, suffer from inadequate ground monitoring stations. Another bias observed is by income level, where 4226 are categorized as high income and 2517 as low income. Inhabitants of low-income countries are disproportionately affected by air pollution. It is also important to note that the LMICs are the ones, which take the highest toll from air pollution. Therefore, the synthesis of a high population, high exposure to dangerous levels of air pollution, and inadequate ground monitoring stations are creating an ill-fated scenario. It is these areas that can benefit the most from the usability of EO by filling in the data gaps in LMICs, and a related research focus has lately emerged (e.g., [49]).
At the same time, satellite data can also assist the HICs in improving air quality at the national scale by providing nationwide data in a gridded format and, therefore, having a better representation and understanding of the air pollution sources and occurrence of critical events. More importantly, the newest generation of satellite capabilities, primarily the advanced spatial resolution up to a 3.5 km grid, can provide measurements at the sub-city scale and, therefore, open new capabilities in approximating city-scale pollution (example provided in Figure 4). This can be an especially useful tool for SDG indicator 11.6.2, which uses the city as the primary block for reporting. It is worth noting that cities cover approximately 3% of the earth, generate 70% of all carbon emissions, and host 54% of the global population, which is expected to rise to 68% by 2050 [50].
Estimates based on ground data have the disadvantage of providing a highly localized and biased view of air pollution, which is a highly dynamic and complex phenomenon in space and time. This missing information between ground stations can be adequately covered by satellite observations, such as the example of the annual and monthly means of NO2 depicted in Figure 4 and Figure 6, respectively. It is important to note that while PM concentrations are considerably higher in LMICs, this is not the case for NO2, which exhibits a lesser difference between HICs and LMICs [3]. This observation might be explained based on the differing anthropogenic pollution sources responsible for the two pollutants. PM2.5 is a pollutant released during slash-and-burn activities, agricultural residue burning, vegetation fires, indoor and outdoor cooking, and other activities that traditionally take place in LMICs. On the other hand, NO2 is a pollutant mostly associated with urban air pollution, originating from vehicular and industrial activity, which exists in both LICs and HICs. The latter, therefore, is not strongly linked to a country’s economic profile. For example, Bechle et al. [51], in a study investigating the relationships between satellite-derived estimates of NO2 concentration and urban form for 83 cities globally, found that urban NO2 varies non-linearly with the Gross Domestic Product (GDP), while Keola and Hayakawa [52] in a study investigating the effect of lockdown policies on daily NO2 emissions in 173 countries found no robust differences between the regions of HICs and LMICs.

4.2. Continuous and Contiguous Measurements

A major advantage of remote sensing, especially in global studies, is its capacity to provide consistent measurements across space. Single data sources, such as those from a single satellite, offer the advantage of eliminating measurement errors, which might be attributed to various reasons such as different instrumentation used in separate locations, human operation of machinery, perception of qualitative estimation, and environmental parameters. This lack of homogeneity is manifested in the WHO database, which is compiled based on a large number of independent data sources [53]. The primary source consists of official reports obtained by member countries on request, official national and subnational reports, and websites containing PM measurements, ground measurements from the Global Burden of Disease [54], ground measurements for research purposes as reported in Larkin et al. [55] specifically for NO2, regional networks such as the Clean Air Asia [56], and the Air Quality e-reporting [57] database of the European Environment Agency and the US Department of State AirNow [58] program from US embassies and consulates. The document concludes that ‘if such official data were not available, values from peer-reviewed journals were used’. This lack of homogeneity is reported as a limitation in the methodology for estimating the SDG indicator 7.1.2, where the authors explain that data and methodology for exposure assessment at regional and national scales may differ [2] and, consequently, the complexity arising from the use of differing datasets, notably in-situ and satellite, raises concerns over the exposure assessment for ambient air pollution estimations.
Therefore, it becomes apparent that the error introduced from a variety of observation methods and instruments can be reduced when satellite data are used, and the advantage of the latter as a major information source component for global estimates is propounded (e.g., for the health impact of air pollution [23]). Moreover, the global coverage of such products makes them suitable for input to climate change models and scenarios, which are closely linked to atmospheric sciences. Last but not least, the large geographical coverage of satellite observations provides continuous regular observations over entire countries (see Figure 6 compiled from time series of GEMS satellite imagery), hence, making them the only suitable way to quantify transboundary haze pollution.

4.3. The Near Future of Satellite Remote Sensing in Air Pollution

The evolution of satellite remote sensing is impressive, with 6957 active non-military satellites orbiting the earth as of 2023, of which 79% have been launched from 2020 onward [59]. With regard to atmospheric applications, with the advent of Sentinel-5P, which was developed as a fill-in between the Envisat (and particularly the Sciamachy instrument) and the anticipation of the Sentinel-5 mission, a global daily product of major atmospheric constituents is currently provided regularly and is accessible freely and openly. Finer resolution is required in several satellite remote sensing applications, including atmospheric sciences, and is still sought after by end users. For example, the World Health Organization [2] plans for higher spatial resolution products in the future from the current 0.1° x 0.1° grid size. Global products have been available from other past satellites; however, the enhanced radiometric and spatial resolution of Sentinel-5P guarantees a better view of the pollution sources encompassing the sub-city level, as demonstrated in Figure 4. Moreover, the increased spatial resolution not only provides better clarity of information at a finer scale but may also improve the retrieval accuracy of surface PM2.5 from satellite-based AOD. Indicatively, Strandgren [60] in a study investigating the satellite-retrieved AOD spatial resolution effect on ground-level PM concentration prediction reports the correlation between PM2.5 and AOD increased significantly with increasing spatial resolution of the AOD.
At the continental scale, a constellation of satellites with similar capabilities is currently in preparation for a large part of the Northern Hemisphere, where approximately 87% of the global population resides. The GEMS instrument is already in orbit and provides operational data over a large part of Asia (see example in Figure 5), which can be converted into operational scientific products (Figure 6). At the same time, TEMPO has been a sister mission to GEMS covering North America since its launch in April 2023, while the Sentinel-4 mission is anticipated to be launched in the near future and will cover the European continent and part of Africa. These three missions, which will offer level-2 products with similar technical capabilities, will form a constellation of satellites able to monitor a large part of the inhabited and industrialized areas of the Northern Hemisphere at hourly intervals and high spatial resolution, making them suitable for monitoring the diurnal cycle of pollutants with fine detail and therefore open new capabilities in diurnal monitoring and exposure estimates.
In addition, the Multi-Angle Imager for Aerosols (MAIA) project is a health-focused satellite mission dedicated to studying human exposure to air pollution. The main objective of this mission is to assess how PM affects human health by collecting observations of target cities with high pollution levels, with the primary target areas representing densely populated areas. MAIA will provide unprecedented capabilities at 300 m spatial resolution and 1 km for the data products, including the provision of respirable PM concentrations. Although designed as an exploratory non-operational mission, it will provide hindsight and valuable material for studies that will capture details at the ‘community scale characteristics of PM composition’.
By shifting the focus to active remote sensing technology, these satellite instruments may provide details on the vertical structure of the atmosphere. While the information derived from active sensors is not directly used in the estimation of atmospheric pollutants, auxiliary information on temperature, water vapor, and wind can characterize the Planetary Boundary Layer (PBL) and improve air quality predictions, a technology that is mature for operationalization, according to Wang and Menenti [61]. The consideration of the PBL when observing satellite-based atmospheric dispersion can also help to better assess the ground pollution levels, as the PBL height is one of the main factors of the vertical dispersion within the lower atmosphere. PBL is also a relatively poorly observed and modeled layer of the atmosphere [62], which deserves more dedicated research. Experiments of assimilating lidar aerosol measurements into CTMs have already demonstrated improvements in PM2.5 forecast (i.e., [63,64]). In addition, multi-wavelength lidar can quantitatively estimate aerosol size and type (e.g., [65,66]), which is critical information for holistically assessing the air pollution status rather than identifying a single aerosol category, such as is the case of PM2.5.
Zooming out of specific satellite systems and taking into consideration the plethora of technological innovations in sensors, air pollution monitoring in the future can be achieved through integrated systems [67]. This includes the synergy of complementary technologies, such as smart sensors, the Internet of Things, artificial intelligence, remote sensing, and cumulative information processing through machine learning and artificial intelligence. The integration of the aforementioned state-of-the-art technology brings forward innovative solutions as well as challenges. The main one is the increased volume of geospatial data and the concomitant requirements in storing, managing, processing, visualizing, and validating a vast amount of datasets in size and number. Additionally, this increase in size comes with an increase in the variety of data structures and formats, which consequently requires the deployment of more efficient models and data management strategies [68]. Moreover, the plethora of file types and software available for the dissemination and analysis of data raises the issues of interoperability and quality assessment since much of this geospatial data inherits varying levels of accuracy.

4.4. Reliability and Accuracy

The retrieval of surface concentrations of air pollutants from space requires assumptions and conversions that introduce uncertainties into the actual estimation. The main uncertainty is introduced through the fact that satellite observations measure columnar values (as per line of sight from the satellite to the target pixel), which encompasses the contribution of all pollutants at all layers of the atmosphere, which might differ from the actual surface pollution level. The second assumption, especially for PM, is that this cluster of pollutants is estimated based on AOD, which is a unitless measurement and a generic index representing the load of aerosols and PM in the column of the atmosphere. Based on the latter, the ground PM concentration is often approximated through Chemical Transport Models (CTMs) or machine learning (e.g., [23,43,69,70,71]). Nevertheless, AOD may depend on a variety of attributes, such as the size, shape, and chemical composition of aerosols, as well as the wavelength on which the measurement is made. Variations in meteorological conditions and the atmospheric vertical distribution can also strongly influence the PM-AOD relationship [72]. This multi-faceted influence, coupled with the differing modeling methods used in each study, may be the main reason behind the heterogeneity in validation accuracy reported in the literature [73].
Based on the above, satellite-based surface concentration estimations may provide divergent accuracy, as reported in the literature. For instance, Alvarado et al. [74], in a study evaluating satellite observations as an information source for ground-level air quality data in LMICs, found that the uncertainty of the daily average PM2.5 concentration estimated based on satellite data was very large for a given location of a city, and specifically 21–77% for the statistical methods and 48–85% for the CTM-based methods. In a similar global study, Stratoulias et al. [75] investigated site-specific correlations on PM2.5-AOD pairs from AirNow and the Moderate Resolution Imaging Spectroradiometer (MODIS, and found a large variation between stations with R ranging between −0.60 and 0.79). The World Bank [76], in a study assessing the reliability of satellite data for measuring mortality causing air pollution in LMICs, suggests that satellite data may be useful in air quality estimation over large geographical areas or countries; nevertheless, at the scale at which human activity is carried out and in the context of individual human health, they reckon that GLM network strengthening should be prioritized in these countries. However, it should be noted that this study was conducted in nine cities, out of which only one (i.e., Hanoi) is in SE Asia, and only one GLM was considered. The Health Effects Institute [77], based on extensive comparisons between the aforementioned datasets, emphasizes that satellite data can reliably act as indicators of PM2.5 and NO2 exposure in areas where ground data are non-existent or unavailable. The same study also highlights that the accuracy varies by region and is plausibly compromised in regions with a small number of ground stations, as the latter are input parameters in the model estimation. It is, therefore, important to expand the ground monitoring network, as this will also improve the accuracy of the satellite estimates in these regions. This synergy of ground and satellite data sources is an important aspect of understanding that a single data source is not a panacea in most situations for large spatial scale assessments, and the complementarity of data sources is favorable, especially the combination of satellite and ground measurements.
On this note, several studies have suggested a reasonable relationship between ground-level measurements and satellite-derived estimates. For example, Hammer et al. [23], in a study estimating annual PM2.5 concentrations and trends during the period 1998–2018 based on AOD from satellite products, reported a highly consistent annual mean geophysical PM2.5 estimation judged against globally distributed ground stations (R2 = 0.81; slope = 0.90). In a similar but regional study, Yang et al. [78] evaluated the MODIS AOD and PM2.5 associations and found statistically significant site-specific correlations for the majority of the sites considered at 1 km, 3 km, and 10 km levels. In regard to the exposure estimates and connecting remote sensing with health effects, it is important to note the assumption of equitoxicity, which is commonly implicit; however, PM2.5, a term encompassing a variety of air pollution particle categories and certain types of PM2.5, are more toxic than others. For instance, a study by Thurston et al. [79] found that PM2.5 derived from fossil fuel combustion is among the greatest contributors to PM2.5 toxicity. Apart from the variation in the toxicity of PM, there also exists variation in the particle size distribution and sources of PM. Particularly related to human exposure is anthropogenic PM, which consists mainly of small aerosols that can be estimated based on fine mode aerosol optical depth (fAOD). For instance, Lee and Chung [80] showcase, based on fine mode AOD, that overall anthropogenic aerosol emissions reduced in the West and stagnated in Asia from 2001 to 2010. Moreover, several studies have suggested improved surface PM2.5 concentration estimations from satellite data by integrating fine mode fraction information into the aerosol retrieval algorithm. Yan et al. [81] demonstrated that PM2.5 in Xingtai city, China, was more closely correlated with fine mode-Aerosol Optical Thickness (FM-AOT) (r = 0.74) than with the total AOT (r = 0.49). Similarly, Zang et al. [82] demonstrated an improvement in PM2.5 estimations when incorporating fine mode fraction information and emphasized the need for a more accurate fine mode product that enables superior PM2.5 retrieval. In any case, satellite-based retrieval of AOD is still challenging due to the low signal-to-noise ratio, algorithmic constraints, and data gaps in satellite observations, mainly due to cloud contamination [83].
It is also worth noting that the anthropogenic component of air pollution might originate from a large variety of emission sources, such as vehicular, industrial, and agricultural sources, all of which carry a different pollution footprint. Subsequently, following the generation and release of air pollution into the atmosphere from the sources, the co-existence of air pollutants in the atmosphere is determined by complicated chemical and physical processes. In this aspect, emission inventories are crucial in order to have a better understanding of the PM composition at local and regional scales. Last but not least, other particle categories are also critical to human health. Coarse particles (PM2.5–10) are also detrimental, as demonstrated by Liu et al. [84] in an extensive epidemiological investigation including 205 cities across many regions of the globe, in which they demonstrated a significant positive association between short-term exposure to ambient PM2.5–10 and increased risk of total cardiovascular and respiratory mortality, even with adjustment for PM2.5.
Apart from the two ongoing active fields of research mentioned, namely the approximation of surface concentrations via atmospheric columnar measurements and the use of the AOD as a proxy to estimate pollutant concentrations, a few more challenges exist. First, accomplishing a long-term archive of satellite measurements that can provide continuity for past and present data, especially in climatic studies, is an aspect that satellite measurements are often deficient since the satellite missions are designed for a short lifespan, and it is the case that new satellite missions do not always comply with previous missions, which creates gaps in multi-instrument data records [85]. Second, the large volume of data that has been brought forward in the past few years requires new approaches to data processing and algorithmic application. Such approaches may include cloud computing, advanced algorithmic solutions (such as deep learning and generative artificial intelligence), and integrating complementary multi-source information.

5. Conclusions

Clean air is fundamental to human health. Nevertheless, the annual mortality statistics do not indicate any substantial sentiment for improvement. At the city scale, little progress has been made to address air quality issues, with the majority of humans worldwide breathing polluted air [77]. While the SDGs relating to air quality are already making partial use of geospatial data, recent improvements in satellite remote sensing are promising advanced capabilities for observing air pollution from space. LMICs, which typically lack related ground infrastructure, are the ones who can benefit the most from the freely available satellite data. The new constellation of geostationary satellites will introduce the capability of observing diurnal changes over much of the Northern Hemisphere, where the majority of the population lives, and hence provide valuable insights into health exposure to air pollution and sub-city level monitoring, a process that links with the SDG indicator 11.6.2, which uses the city as the primary block for reporting. At the same time, polar orbiting satellites, primarily the ongoing Sentinel-5P mission and anticipated successor Sentinel-5 will provide global coverage that can be leveraged in earth studies and climate change-related evaluations.
Reliable information on air pollution and related exposure is a crucial component of policymaking and necessary for tracking the progress and evaluating the efficacy of policy interventions. Public-sector decision-making has only slightly adopted satellite-based knowledge, perhaps attributed to the recency of the technology [46]; nevertheless, current developments in satellite EO are rapid and stimulating. While the role of EO cannot be directly linked to the reduction of air pollution and more drastic interventions are needed, remote sensing can play a vital role in information provision for policy recommendations and related investments; hence, it can act as a major knowledge source for SDGs. Latest developments and initiatives in EO galvanize the availability of finer spatial, temporal, spectral, and radiometric resolution products with increased global coverage, as well as long-term stability and coherence in measurements. In this framework, the potential of remote sensing as a complementary to ground station networks and source information is gaining confidence and, as such, can be an invaluable tool in deriving robust estimates of population exposure to ambient air pollution at a global scale.

Author Contributions

Conceptualization, D.S.; methodology, D.S.; data curation, D.S.; writing, D.S.; review, D.S., N.N., R.D., P.T. and G.R.C.; funding acquisition, R.D., P.T. and G.R.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received funding support from the NSRF via the Program Management Unit for Human Resources & Institutional Development, Research, and Innovation [grant number B42G670042] and Air Pollution and Health Effect Research Center, Prince of Songkla University.

Data Availability Statement

The data used in the current study are available in the public domain.

Acknowledgments

Special thanks to Beomgeun Jang for his assistance in designing the GEMS figures. This work was supported in part by the NASA SERVIR grant (80NSSC23K0244).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Fuller, R.; Landrigan, P.J.; Balakrishnan, K.; Bathan, G.; Bose-O’Reilly, S.; Brauer, M.; Caravanos, J.; Chiles, T.; Cohen, A.; Corra, L.; et al. Pollution and health: A progress update. Lancet Planet. Health 2022, 6, e535–e547. [Google Scholar] [CrossRef] [PubMed]
  2. World Health Organization. Burden of Disease from Ambient Air Pollution for 2016—Description of Method. 2018. Available online: https://cdn.who.int/media/docs/default-source/air-quality-database/aqd-2018/aap_bod_methods_apr2018_final.pdf?sfvrsn=30ac0d62_3 (accessed on 20 August 2022).
  3. World Health Organization. Billions of People Still Breathe Unhealthy Air: New WHO Data. 2022. Available online: https://www.who.int/news/item/04-04-2022-billions-of-people-still-breathe-unhealthy-air-new-who-data (accessed on 9 July 2024).
  4. Xing, Y.F.; Xu, Y.H.; Shi, M.H.; Lian, Y.X. The impact of PM2.5 on the human respiratory system. J. Thorac. Dis. 2016, 8, E69. [Google Scholar] [PubMed]
  5. Feng, S.; Gao, D.; Liao, F.; Zhou, F.; Wang, X. The health effects of ambient PM2.5 and potential mechanisms. Ecotoxicol. Environ. Saf. 2016, 128, 67–74. [Google Scholar] [CrossRef] [PubMed]
  6. Brunekreef, B.; Strak, M.; Chen, J.; Andersen, Z.J.; Atkinson, R.; Bauwelinck, M.; Bellander, T.; Boutron, M.-C.; Brandt, J.; Carey, I.; et al. Mortality and morbidity effects of long-term exposure to low-level PM2.5, bc, NO2, and O3: An analysis of European cohorts in the ELAPSE project. Res. Rep. Health Eff. Inst. 2021, 2021, 208. [Google Scholar] [PubMed]
  7. Brauer, M.; Brook, J.R.; Christidis, T.; Chu, Y.; Crouse, D.L.; Erickson, A.; Hystad, P.; Li, C.; Martin, R.V.; Meng, J.; et al. Mortality–air pollution associations in low exposure environments (MAPLE): Phase 2. Res. Rep. Health Eff. Inst. 2022, 2022, 212. [Google Scholar] [PubMed]
  8. Dominici, F.; Zanobetti, A.; Schwartz, J.; Braun, D.; Sabath, B.; Wu, X. Assessing adverse health effects of long-term exposure to low levels of ambient air pollution: Implementation of causal inference methods. Res. Rep. Health Eff. Inst. 2022, 2022, 211. [Google Scholar] [PubMed]
  9. Lu, X.; Lin, C.; Li, W.; Chen, Y.; Huang, Y.; Fung, J.C.; Lau, A.K. Analysis of the adverse health effects of PM2.5 from 2001 to 2017 in China and the role of urbanization in aggravating the health burden. Sci. Total Environ. 2019, 652, 683–695. [Google Scholar] [CrossRef] [PubMed]
  10. Lu, F.; Xu, D.; Cheng, Y.; Dong, S.; Guo, C.; Jiang, X.; Zheng, X. Systematic review and meta-analysis of the adverse health effects of ambient PM2.5 and PM10 pollution in the Chinese population. Environ. Res. 2015, 136, 196–204. [Google Scholar] [CrossRef]
  11. World Health Organization. Household Air Pollution. 2022. Available online: https://www.who.int/news-room/fact-sheets/detail/household-air-pollution-and-health (accessed on 9 July 2024).
  12. World Health Organization. Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulphur Dioxide and Carbon Monoxide; WHO Global Air Quality Guidelines; World Health Organization: Geneva, Switzerland, 2021. [Google Scholar]
  13. World Health Organization. Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease; World Health Organization: Geneva, Switzerland, 2016. [Google Scholar]
  14. GBD 2015 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: A systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016, 388, 1659. [Google Scholar] [CrossRef]
  15. Landrigan, P.J.; Fuller, R.; Acosta, N.J.; Adeyi, O.; Arnold, R.; Baldé, A.B.; Bertollini, R.; Bose-O’Reilly, S.; Boufford, J.I.; Breysse, P.N.; et al. The Lancet Commission on pollution and health. Lancet 2018, 391, 462–512. [Google Scholar] [CrossRef]
  16. Stevens, C.J.; Bell, J.N.B.; Brimblecombe, P.; Clark, C.M.; Dise, N.B.; Fowler, D.; Lovett, G.M.; Wolseley, P.A. The impact of air pollution on terrestrial managed and natural vegetation. Philos. Trans. R. Soc. A 2020, 378, 20190317. [Google Scholar] [CrossRef] [PubMed]
  17. Gheorghe, I.F.; Ion, B. The effects of air pollutants on vegetation and the role of vegetation in reducing atmospheric pollution. In The Impact of Air Pollution on Health, Economy, Environment and Agricultural Sources; IntechOpen Limited: London, UK, 2011; Chapter 12; pp. 241–280. [Google Scholar] [CrossRef]
  18. Emberson, L. Effects of ozone on agriculture, forests and grasslands. Philos. Trans. R. Soc. A 2020, 378, 20190327. [Google Scholar] [CrossRef] [PubMed]
  19. Mills, G.; Sharps, K.; Simpson, D.; Pleijel, H.; Frei, M.; Burkey, K.; Emberson, L.; Uddling, J.; Broberg, M.; Feng, Z.; et al. Closing the global ozone yield gap: Quantification and cobenefits for multistress tolerance. Glob. Chang. Biol. 2018, 24, 4869–4893. [Google Scholar] [CrossRef] [PubMed]
  20. World Bank & Institute for Health Metrics and Evaluation. The Cost of Air Pollution: Strengthening the Economic Case for Action (English); World Bank Group: Washington, DC, USA, 2016; Available online: http://documents.worldbank.org/curated/en/781521473177013155/The-cost-of-air-pollution-strengthening-the-economic-case-for-action (accessed on 9 July 2024).
  21. Awe, Y.A.; Larsen, B.K.; Sanchez-Triana, E. The Global Health Cost of PM2.5 Air Pollution: A Case for Action beyond 2021; International Development in Focus; World Bank Group: Washington, DC, USA, 2021. [Google Scholar]
  22. Reddington, C.L.; Conibear, L.; Robinson, S.; Knote, C.; Arnold, S.R.; Spracklen, D.V. Air pollution from forest and vegetation fires in Southeast Asia disproportionately impacts the poor. GeoHealth 2021, 5, e2021GH000418. [Google Scholar] [CrossRef] [PubMed]
  23. Hammer, M.S.; van Donkelaar, A.; Li, C.; Lyapustin, A.; Sayer, A.M.; Hsu, N.C.; Levy, R.C.; Garay, M.J.; Kalashnikova, O.V.; Kahn, R.A.; et al. Global estimates and long-term trends of fine particulate matter concentrations (1998–2018). Environ. Sci. Technol. 2020, 54, 7879–7890. [Google Scholar] [CrossRef]
  24. Rafaj, P.; Kiesewetter, G.; Gül, T.; Schöpp, W.; Cofala, J.; Klimont, Z.; Purohit, P.; Heyes, C.; Amann, M.; Borken-Kleefeld, J.; et al. Outlook for clean air in the context of sustainable development goals. Glob. Environ. Chang. 2018, 53, 1–11. [Google Scholar] [CrossRef]
  25. Hvidtfeldt, U.A.; Severi, G.; Andersen, Z.J.; Atkinson, R.; Bauwelinck, M.; Bellander, T.; Boutron-Ruault, M.-C.; Brandt, J.; Brunekreef, B.; Cesaroni, G.; et al. Long-term low-level ambient air pollution exposure and risk of lung cancer—A pooled analysis of 7 European cohorts. Environ. Int. 2021, 146, 106249. [Google Scholar] [CrossRef]
  26. Papadogeorgou, G.; Kioumourtzoglou, M.A.; Braun, D.; Zanobetti, A. Low levels of air pollution and health: Effect estimates, methodological challenges, and future directions. Curr. Environ. Health Rep. 2019, 6, 105–115. [Google Scholar] [CrossRef]
  27. West, J.J.; Cohen, A.; Dentener, F.; Brunekreef, B.; Zhu, T.; Armstrong, B.; Bell, M.L.; Brauer, M.; Carmichael, G.; Costa, D.L.; et al. What we breathe impacts our health: Improving understanding of the link between air pollution and health. Environ. Sci. Technol. 2016, 50, 4895–4904. [Google Scholar] [CrossRef]
  28. United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development. 2015. Available online: https://sdgs.un.org/publications/transforming-our-world-2030-agenda-sustainable-development-17981 (accessed on 9 July 2024).
  29. Maso, J.; Zabala, A.; Serral, I. Earth Observations for Sustainable Development Goals. Remote Sens. 2023, 15, 2570. [Google Scholar] [CrossRef]
  30. Varotsos, C.A.; Cracknell, A.P. Remote Sensing Letters contribution to the success of the Sustainable Development Goals-UN 2030 agenda. Remote Sens. Lett. 2020, 11, 715–719. [Google Scholar] [CrossRef]
  31. World Health Organization. WHO Ambient Air Quality Database—2022 Update—Status Report. 2022. Available online: https://www.who.int/publications/i/item/9789240047693 (accessed on 9 July 2024).
  32. Doxsey-Whitfield, E.; MacManus, K.; Adamo, S.B.; Pistolesi, L.; Squires, J.; Borkovska, O.; Baptista, S.R. Taking advantage of the improved availability of census data: A first look at the gridded population of the world, version 4. Appl. Geogr. 2015, 1, 226–234. [Google Scholar] [CrossRef]
  33. Center for International Earth Science Information Network—CIESIN—Columbia University. Gridded Population of the World, Version 4 (GPWv4): Population Count Adjusted to Match 2015 Revision of UN WPP Country Totals, Revision 11. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). 2018. Available online: https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-adjusted-to-2015-unwpp-country-totals-rev11 (accessed on 1 August 2022).
  34. Veefkind, J.P.; Aben, I.; McMullan, K.; Förster, H.; De Vries, J.; Otter, G.; Claas, J.; Eskes, H.J.; de Haan, J.F.; Kleipool, Q.; et al. TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sens. Environ. 2012, 120, 70–83. [Google Scholar] [CrossRef]
  35. ESA. Technical Guides, Sentinel-5P TROPOMI, Products and Algorithms. 2020. Available online: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-5p/products-algorithms (accessed on 9 July 2024).
  36. Muhammad, S.; Long, X.; Salman, M. COVID-19 pandemic and environmental pollution: A blessing in disguise? Sci. Total Environ. 2020, 728, 138820. [Google Scholar] [CrossRef]
  37. Bauwens, M.; Compernolle, S.; Stavrakou, T.; Müller, J.F.; Van Gent, J.; Eskes, H.; Levelt, P.F.; Van Der A, R.; Veefkind, J.P.; Vlietinck, J.; et al. Impact of coronavirus outbreak on NO2 pollution assessed using TROPOMI and OMI observations. Geophys. Res. Lett. 2020, 47, e2020GL087978. [Google Scholar] [CrossRef]
  38. Wang, Q.; Su, M. A preliminary assessment of the impact of COVID-19 on environment—A case study of China. Sci. Total Environ. 2020, 728, 138915. [Google Scholar] [CrossRef] [PubMed]
  39. Stratoulias, D.; Nuthammachot, N. Air quality development during the COVID-19 pandemic over a medium-sized urban area in Thailand. Sci. Total Environ. 2020, 746, 141320. [Google Scholar] [CrossRef]
  40. Fan, C.; Li, Y.; Guang, J.; Li, Z.; Elnashar, A.; Allam, M.; de Leeuw, G. The impact of the control measures during the COVID-19 outbreak on air pollution in China. Remote Sens. 2020, 12, 1613. [Google Scholar] [CrossRef]
  41. Sharma, S.; Zhang, M.; Gao, J.; Zhang, H.; Kota, S.H. Effect of restricted emissions during COVID-19 on air quality in India. Sci. Total Environ. 2020, 728, 138878. [Google Scholar] [CrossRef] [PubMed]
  42. Sreekanth, V.; Kushwaha, M.; Kulkarni, P.; Upadhya, A.R.; Spandana, B.; Prabhu, V. Impact of COVID-19 lockdown on the fine particulate matter concentration levels: Results from Bengaluru megacity, India. Adv. Space Res. 2021, 67, 2140–2150. [Google Scholar] [CrossRef]
  43. Mahesh, B.; Sivakumar, V.; Kulkarni, P.; Sreekanth, V. Particulate air pollution in Durban: Characteristics and its relationship with 1 km resolution satellite aerosol optical depth. Adv. Space Res. 2022, 70, 371–382. [Google Scholar] [CrossRef]
  44. Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27. [Google Scholar] [CrossRef]
  45. Kim, J.; Jeong, U.; Ahn, M.H.; Kim, J.H.; Park, R.J.; Lee, H.; Song, C.H.; Choi, Y.-S.; Lee, K.-H.; Yoo, J.-M.; et al. New era of air quality monitoring from space: Geostationary Environment Monitoring Spectrometer (GEMS). Bull. Am. Meteorol. Soc. 2020, 101, E1–E22. [Google Scholar] [CrossRef]
  46. Burke, M.; Driscoll, A.; Lobell, D.B.; Ermon, S. Using satellite imagery to understand and promote sustainable development. Science 2021, 371, eabe8628. [Google Scholar] [CrossRef]
  47. Andries, A.; Morse, S.; Murphy, R.J.; Lynch, J.; Woolliams, E.R. Using Data from Earth Observation to Support Sustainable Development Indicators: An Analysis of the Literature and Challenges for the Future. Sustainability 2022, 14, 1191. [Google Scholar] [CrossRef]
  48. Longhurst, J.; Barnes, J.; Chatterton, T.; De Vito, L.; Everard, M.; Hayes, E.; Prestwood, E.; Williams, B. Analysing air pollution and its management through the lens of the UN sustainable development goals: A review and assessment. WIT Trans. Ecol. Environ. 2018, 230, 3–14. [Google Scholar]
  49. Pinder, R.W.; Klopp, J.M.; Kleiman, G.; Hagler, G.S.; Awe, Y.; Terry, S. Opportunities and challenges for filling the air quality data gap in low-and middle-income countries. Atmos. Environ. 2019, 215, 116794. [Google Scholar] [CrossRef]
  50. World Economic Forum. Net Zero Carbon Cities: An Integrated Approach. Insight Report. 2021. Available online: https://www3.weforum.org/docs/WEF_Net_Zero_Carbon_Cities_An_Integrated_Approach_2021.pdf (accessed on 5 June 2024).
  51. Bechle, M.J.; Millet, D.B.; Marshall, J.D. Effects of income and urban form on urban NO2: Global evidence from satellites. Environ. Sci. Technol. 2011, 45, 4914–4919. [Google Scholar] [CrossRef]
  52. Keola, S.; Hayakawa, K. Do lockdown policies reduce economic and social activities? Evidence from NO2 emissions. Dev. Econ. 2021, 59, 178–205. [Google Scholar] [CrossRef]
  53. World Health Organization. WHO Ambient Air Quality Database—April 2022 Update—Methodology. 2022. Available online: https://cdn.who.int/media/docs/default-source/air-pollution-documents/air-quality-and-health/methodology_who-air-quality-database-2022.pdf?sfvrsn=bdcf9241_10 (accessed on 9 July 2024).
  54. Global Burden of Disease. Institute for Health Metrics and Evaluation. Seattle (WA). 2022. Available online: www.healthdata.org/gbd (accessed on 9 July 2024).
  55. Larkin, A.; Geddes, J.A.; Martin, R.V.; Xiao, Q.; Liu, Y.; Marshall, J.D.; Brauer, M.; Hystad, P. Global land use regression model for nitrogen dioxide air pollution. Environ. Sci. Technol. 2017, 51, 6957–6964. [Google Scholar] [CrossRef]
  56. Clean Air Asia. Paseig City. 2022. Available online: https://cleanairasia.org/ (accessed on 9 July 2024).
  57. Air Quality e-Reporting. Copenhagen: European Environment Agency. 2022. Available online: https://www.eea.europa.eu/data-and-maps/data/aqereporting-9 (accessed on 9 July 2024).
  58. AirNow Department of State. Research Triangle Park (NC). United States Environmental Protection. Agency Office of Air Quality Planning and Standards. 2022. Available online: https://www.airnow.gov/international/us-embassies-and-consulates (accessed on 9 July 2024).
  59. Union of Concerned Scientists (UCS). UCS Satellite Database. 2023. Available online: https://www.ucsusa.org/resources/satellite-database (accessed on 9 July 2024).
  60. Strandgren, J. Study of Satellite Retrieved Aerosol Optical Depth Spatial Resolution Effect on Particulate Matter Concentration Prediction. Master’s Thesis, Lulea University of Technology Study, Norrbotten County, Sweden, 2014. Available online: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-48692 (accessed on 9 July 2024).
  61. Wang, Z.; Menenti, M. Challenges and opportunities in Lidar remote sensing. Front. Remote Sens. 2021, 2, 641723. [Google Scholar] [CrossRef]
  62. National Academies of Sciences, Engineering, and Medicine. Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space. Chapter 3: A Prioritized Program for Science, Applications, and Observations; The National Academies Press: Washington, DC, USA, 2018; Available online: https://doi.org/10.17226/24938 (accessed on 5 June 2024).
  63. Cheng, X.; Liu, Y.; Xu, X.; You, W.; Zang, Z.; Gao, L.; Chen, Y.; Su, D.; Yan, P. Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing. Sci. Total Environ. 2019, 682, 541–552. [Google Scholar] [CrossRef]
  64. El Amraoui, L.; Sič, B.; Piacentini, A.; Marécal, V.; Frebourg, N.; Attié, J.L. Aerosol data assimilation in the MOCAGE chemical transport model during the TRAQA/ChArMEx campaign: Lidar observations. Atmos. Meas. Tech. 2020, 13, 4645–4667. [Google Scholar] [CrossRef]
  65. Perrone, M.R.; De Tomasi, F.; Gobbi, G.P. Vertically resolved aerosol properties by multi-wavelength lidar measurements. Atmos. Chem. Phys. 2014, 14, 1185–1204. [Google Scholar] [CrossRef]
  66. Hara, Y.; Nishizawa, T.; Sugimoto, N.; Osada, K.; Yumimoto, K.; Uno, I.; Kudo, R.; Ishimoto, H. Retrieval of aerosol components using multi-wavelength Mie-Raman lidar and comparison with ground aerosol sampling. Remote Sens. 2018, 10, 937. [Google Scholar] [CrossRef]
  67. Singh, D.; Dahiya, M.; Kumar, R.; Nanda, C. Sensors and systems for air quality assessment monitoring and management: A review. J. Environ. Manag. 2021, 289, 112510. [Google Scholar] [CrossRef] [PubMed]
  68. Li, S.; Dragicevic, S.; Castro, F.A.; Sester, M.; Winter, S.; Coltekin, A.; Pettit, C.; Jiang, B.; Haworth, J.; Stein, A.; et al. Geospatial big data handling theory and methods: A review and research challenges. ISPRS J. Photogramm. Remote Sens. 2016, 115, 119–133. [Google Scholar] [CrossRef]
  69. Xu, J.W.; Martin, R.V.; Van Donkelaar, A.; Kim, J.; Choi, M.; Zhang, Q.; Geng, G.; Liu, Y.; Ma, Z.; Huang, L.; et al. Estimating ground-level PM2.5 in eastern China using aerosol optical depth determined from the GOCI satellite instrument. Atmos. Chem. Phys. 2015, 15, 13133–13144. [Google Scholar] [CrossRef]
  70. Sreekanth, V.; Mahesh, B.; Niranjan, K. Satellite remote sensing of fine particulate air pollutants over Indian mega cities. Adv. Space Res. 2017, 60, 2268–2276. [Google Scholar] [CrossRef]
  71. Park, S.; Shin, M.; Im, J.; Song, C.K.; Choi, M.; Kim, J.; Lee, S.; Park, R.; Kim, J.; Lee, D.-W.; et al. Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea. Atmos. Chem. Phys. 2019, 19, 1097–1113. [Google Scholar] [CrossRef]
  72. Van Donkelaar, A.; Martin, R.V.; Brauer, M.; Boys, B.L. Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environ. Health Perspect. 2015, 123, 135–143. [Google Scholar] [CrossRef] [PubMed]
  73. Zhang, Y.; Li, Z.; Bai, K.; Wei, Y.; Xie, Y.; Zhang, Y.; Ou, Y.; Cohen, J.; Zhang, Y.; Peng, Z.; et al. Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives. Fundam. Res. 2021, 1, 240–258. [Google Scholar] [CrossRef]
  74. Alvarado, M.J.; McVey, A.E.; Hegarty, J.D.; Cross, E.S.; Hasenkopf, C.A.; Lynch, R.; Kennelly, E.J.; Onasch, T.B.; Awe, Y.; Sanchez-Triana, E.; et al. Evaluating the use of satellite observations to supplement ground-level air quality data in selected cities in low-and middle-income countries. Atmos. Environ. 2019, 218, 117016. [Google Scholar] [CrossRef]
  75. Stratoulias, D.; Jang, B.; Nuthammachot, N. Evaluation of daily PM2.5 surface concentrations over 76 major cities and the association with AOD: A global analysis of ambient air pollution. Remote Sens. 2024; in press. [Google Scholar]
  76. World Bank. Getting Down to Earth: Are Satellites Reliable for Measuring Air Pollutants That Cause Mortality in Low-and Middle-Income Countries? International Development in Focus; World Bank Group: Washington, DC, USA, 2022. [Google Scholar]
  77. Health Effects Institute. Air Quality and Health. In Cities: A State of Global Air Report 2022; Health Effects Institute: Boston, MA, USA, 2022. [Google Scholar]
  78. Yang, Z.; Zdanski, C.; Farkas, D.; Bang, J.; Williams, H. Evaluation of Aerosol Optical Depth (AOD) and PM2.5 associations for air quality assessment. Remote Sens. Appl. Soc. Environ. 2020, 20, 100396. [Google Scholar] [CrossRef]
  79. Thurston, G.; Awe, Y.; Ostro, B.; Sanchez-Triana, E. Are All Air Pollution Particles Equal? How Constituents and Sources of Fine Air Pollution Particles (PM2.5) Affect Health; World Bank Group: Washington, DC, USA, 2021. [Google Scholar]
  80. Lee, K.; Chung, C.E. Observationally-constrained estimates of global fine-mode AOD. Atmos. Chem. Phys. 2013, 13, 2907–2921. [Google Scholar] [CrossRef]
  81. Yan, X.; Shi, W.; Li, Z.; Li, Z.; Luo, N.; Zhao, W.; Wang, H.; Yu, X. Satellite-based PM2.5 estimation using fine-mode aerosol optical thickness over China. Atmos. Environ. 2017, 170, 290–302. [Google Scholar] [CrossRef]
  82. Zang, Z.; Li, D.; Guo, Y.; Shi, W.; Yan, X. Superior PM2.5 estimation by integrating aerosol fine mode data from the Himawari-8 satellite in deep and classical machine learning models. Remote Sens. 2021, 13, 2779. [Google Scholar] [CrossRef]
  83. Wei, X.; Chang, N.B.; Bai, K.; Gao, W. Satellite remote sensing of aerosol optical depth: Advances, challenges, and perspectives. Crit. Rev. Environ. Sci. Technol. 2020, 50, 1640–1725. [Google Scholar] [CrossRef]
  84. Liu, C.; Cai, J.; Chen, R.; Sera, F.; Guo, Y.; Tong, S.; Li, S.; Lavigne, E.; Correa, P.M.; Ortega, N.V.; et al. Coarse particulate air pollution and daily mortality: A global study in 205 cities. Am. J. Respir. Crit. Care Med. 2022, 206, 999–1007. [Google Scholar] [CrossRef]
  85. Dubovik, O.; Schuster, G.L.; Xu, F.; Hu, Y.; Bösch, H.; Landgraf, J.; Li, Z. Grand challenges in satellite remote sensing. Front. Remote Sens. 2021, 2, 619818. [Google Scholar] [CrossRef]
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Figure 1. Settlements with available data on PM2.5 concentrations between 2010 and 2019. Adapted from the World Health Organization [31] with permission.
Figure 1. Settlements with available data on PM2.5 concentrations between 2010 and 2019. Adapted from the World Health Organization [31] with permission.
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Figure 2. Global gridded map of the adjusted population count. Adapted from the Gridded Population of the World (GPWv4) dataset. Source: Center for International Earth Science Information Network—CIESIN—Columbia University [33].
Figure 2. Global gridded map of the adjusted population count. Adapted from the Gridded Population of the World (GPWv4) dataset. Source: Center for International Earth Science Information Network—CIESIN—Columbia University [33].
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Figure 3. Global annual mean of geophysical PM2.5 estimates for the year 2015 based on advances in satellite observations. Black dots represent ground stations. Adapted from Hammer et al. [23]. Source: https://pubs.acs.org/doi/10.1021/acs.est.0c01764 (accessed on 30 July 2024). Further permissions related to the material excerpted should be directed to the ACS.
Figure 3. Global annual mean of geophysical PM2.5 estimates for the year 2015 based on advances in satellite observations. Black dots represent ground stations. Adapted from Hammer et al. [23]. Source: https://pubs.acs.org/doi/10.1021/acs.est.0c01764 (accessed on 30 July 2024). Further permissions related to the material excerpted should be directed to the ACS.
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Figure 4. Annual mean of the tropospheric vertical column of NO2 for the year 2021 retrieved from Sentinel-5P satellite over Bangkok, Thailand. The blue dots represent the locations of the regulatory-grade ground stations available in this region. The layers are superimposed over a natural-color satellite image of the city of Bangkok.
Figure 4. Annual mean of the tropospheric vertical column of NO2 for the year 2021 retrieved from Sentinel-5P satellite over Bangkok, Thailand. The blue dots represent the locations of the regulatory-grade ground stations available in this region. The layers are superimposed over a natural-color satellite image of the city of Bangkok.
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Figure 5. An operational product from the GEMS instrument: estimated surface PM2.5 concentrations over Asia acquired on 25 February 2022 (retrieved from the NIER) (left) and monthly mean GEMS AOD (550 nm) image for March 2023 (right).
Figure 5. An operational product from the GEMS instrument: estimated surface PM2.5 concentrations over Asia acquired on 25 February 2022 (retrieved from the NIER) (left) and monthly mean GEMS AOD (550 nm) image for March 2023 (right).
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Figure 6. Reprocessed monthly mean of NO2 (left) and monthly maximum for SO2 (right) from the operational data provided by GEMS for the month of November 2023.
Figure 6. Reprocessed monthly mean of NO2 (left) and monthly maximum for SO2 (right) from the operational data provided by GEMS for the month of November 2023.
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Table 1. SDGs and the respective goals, targets, and indicators relating to air pollution.
Table 1. SDGs and the respective goals, targets, and indicators relating to air pollution.
SDG.
Target.
Indicator		GoalTargetIndicator
3.9.1Ensure healthy lives and promote well-being for all at allBy 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water, and soil pollution and contaminationMortality rate attributed to household and ambient air pollution
11.6.2Make cities and human settlements inclusive, safe, resilient, and sustainableBy 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste managementAnnual mean levels of fine particulate matter (e.g., PM2.5 and PM10) in cities (population-weighted)
7.1.2Ensure access to affordable, reliable, sustainable, and modern energy for allBy 2030, ensure universal access to affordable, reliable, and modern energy servicesProportion of population with primary reliance on clean fuels and technology
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Stratoulias, D.; Nuthammachot, N.; Dejchanchaiwong, R.; Tekasakul, P.; Carmichael, G.R. Recent Developments in Satellite Remote Sensing for Air Pollution Surveillance in Support of Sustainable Development Goals. Remote Sens. 2024, 16, 2932. https://doi.org/10.3390/rs16162932

AMA Style

Stratoulias D, Nuthammachot N, Dejchanchaiwong R, Tekasakul P, Carmichael GR. Recent Developments in Satellite Remote Sensing for Air Pollution Surveillance in Support of Sustainable Development Goals. Remote Sensing. 2024; 16(16):2932. https://doi.org/10.3390/rs16162932

Chicago/Turabian Style

Stratoulias, Dimitris, Narissara Nuthammachot, Racha Dejchanchaiwong, Perapong Tekasakul, and Gregory R. Carmichael. 2024. "Recent Developments in Satellite Remote Sensing for Air Pollution Surveillance in Support of Sustainable Development Goals" Remote Sensing 16, no. 16: 2932. https://doi.org/10.3390/rs16162932

APA Style

Stratoulias, D., Nuthammachot, N., Dejchanchaiwong, R., Tekasakul, P., & Carmichael, G. R. (2024). Recent Developments in Satellite Remote Sensing for Air Pollution Surveillance in Support of Sustainable Development Goals. Remote Sensing, 16(16), 2932. https://doi.org/10.3390/rs16162932

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