Tingyuan Li1,2, Xuejiao Deng This email address is being protected from spambots. You need JavaScript enabled to view it.2, Jingyang Chen1, Jie Xu1, Jin Shen3 

1 Guangdong Ecological Meteorological Center, Guangzhou 510640, China
2 Institute of Tropical and Marine Meteorology, China Meteorological Administration (CMA), Guangzhou 510640, China
3 Guangdong Ecological and Environmental Monitoring Center, Guangzhou 510308, China


Received: August 16, 2022
Revised: November 16, 2022
Accepted: January 19, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.220290  


Cite this article:

Li, T., Deng, X., Chen, J., Xu, J., Shen, J. (2023). Comparison and Impact Factor Analysis of Ground PM2.5 Retrieved by Aqua and Himawari-8 Satellite Products in Guangdong, China. Aerosol Air Qual. Res. 23, 220290. https://doi.org/10.4209/aaqr.220290


HIGHLIGHTS

  • Good agreements between satellite-based PM2.5 and ground-based PM2.5 are shown.
  • Himawari-8 performs better than Aqua.
  • Himawari-8 captures the distribution of PM2.5 during haze weather.
  • Satellite-based PM2.5 can reveal a new pollution distribution pattern.
  • Aerosol scale height is the most important parameter in retrieving PM2.5.
 

ABSTRACT


Fine particulate matter (particles sizes < 2.5 µm; PM2.5) concentrations can be obtained at high spatial resolutions using satellite remote sensing. Previous studies on PM2.5 concentrations in Guangdong have used the aerosol optical depth (AOD) determined by MODIS sensors on the Terra and Aqua satellites, which both have a transit time of once per day. In this study, based on AOD data obtained from the Aqua and Himawari-8 (H8) satellites and ground-based measurements, ground PM2.5 concentrations were compared using radiation theory and a semi-empirical model. The temporal resolution of PM2.5 concentrations retrieved by the H8 satellite improved from once per day to once per hour when compared with the temporal resolution of those retrieved by the Aqua satellite. The data from both satellites were consistent with ground-based measurements (R ≥ 0.65 and reaching 0.96 for the annual mean), indicating that the retrieved data could characterize the spatial and temporal variations in ground PM2.5 concentrations in Guangdong Province. The two satellites had similar performances for the annual mean, dry season mean, and hourly data; however, H8 performed better than Aqua for the wet season and monthly averages. The performances of both satellites decreased when relative humidity increased and visibility decreased. H8 performed better at high relative humidity, low visibility, and during the PM2.5 pollution process, thus reflecting the advantage of geostationary satellite in capturing PM2.5 pollution distributions. In addition, the data retrieved by both satellites exhibited higher PM2.5 concentrations near the junctions of some cities and regions, indicating a new pollution distribution pattern after the industrial shift that cannot be observed by ground-based measurements. Aerosol scale height is the most important parameter for retrieving PM2.5 concentrations. Thus, improving the computational accuracy of aerosol scale height may improve the effectiveness of satellite-based PM2.5 concentration retrievals.


Keywords: Satellite-retrieved PM2.5, Impact factor, Himawari-8, Aqua, Comparison


1 INTRODUCTION


Guangdong Province is located in the southernmost part of mainland China and is a major economic province. With rapid economic development, air pollution in the region has become severe (Wu et al., 2007; Li et al., 2018), and the Pearl River Delta (PRD), which is located in the southern central part of Guangdong, has become one of three regions in China with serious air pollution. Although fine particulate matter (particle sizes < 2.5 µm; PM2.5) pollution in Guangdong has improved considerably in recent years, severe PM2.5 pollution processes are still likely to occur under unfavorable meteorological conditions (Wu et al., 2019). Atmospheric visibility is negatively correlated with PM2.5 concentrations (Luo et al., 2019). High PM2.5 concentrations can reduce atmospheric visibility through scattering and absorption effects on visible light, thereby leading to haze, which is an important pollution phenomenon (Hoyle et al., 2011). The negative impacts of high PM2.5 concentrations on the climate, ecological environment, human health, and social economy have received extensive attention from researchers worldwide (Goldberg and Villeneuve, 2021; Hopke and Hill, 2021; Wang et al., 2020; Kollanus et al., 2017; Ngo et al., 2015; Ramanathan et al., 2005).

Accurate pollutant monitoring is a prerequisite to pollution prevention and control. A ground-based air quality and meteorological monitoring network was established for real-time pollution monitoring in Guangdong Province. Haze pollution is distributed in several regions, including the western part of the PRD, the western part of northern Guangdong, the coastal region of eastern Guangdong, and the Zhanjiang region (Deng et al., 2022), and is generally consistent with the distribution of PM2.5 concentrations (Li et al., 2021). Owing to their high cost and operational and maintenance complexities, such monitoring stations are sparse and unevenly distributed, making it difficult to obtain a comprehensive overall distribution of PM2.5 concentrations and haze characteristics. Since 2008, some industries in Guangdong Province have been gradually shifting from the PRD to the eastern and western wings and mountainous areas of northern Guangdong (Central Government Portal, 2008). The lack of in situ monitoring makes the prevention and control of pollution more difficult; therefore, it is particularly important to develop a full-coverage pollution monitoring tool. Unlike traditional ground-based measurements, satellite remote sensing can monitor air quality anywhere worldwide, and its spatial and temporal resolutions are constantly improving. The Himawari-8 (H8) geostationary satellite, launched in 2014, can perform real-time monitoring with high spatial and temporal resolutions (2 km and 10 min, respectively) in Guangdong Province, thereby filling in the blank areas between the ground-based measurements.

Wang and Christopher (2003) found a correlation between satellite remote sensing aerosol optical depth (AOD) and ground-based measurements of PM2.5 in Alabama, USA. Subsequently, researchers have applied various methods to retrieve ground-level PM2.5 and PM10 concentrations using AOD observed by satellite remote sensing, and the results have been promising. Satellite performance varies regionally and is affected by aerosol type, smoke, snow, ice cover, and other factors (Engel-Cox et al., 2004; Gupta et al., 2007; Hoff and Christopher, 2009). Previous studies of ground-level PM2.5 concentrations based on remote sensing satellite data can be broadly classified into two categories (Lin et al., 2015; Goldberg et al., 2019): observation-based methods and simulation-based methods. Observation-based methods include one-dimensional linear models (Zeydan and Wang, 2019), multiple linear regression models (Xu et al., 2021), land use regression models (Lee, 2019), geographically weighted regression models (Gupta et al., 2020), semi-empirical models (Tian and Chen, 2010), and artificial intelligence models (Wei et al., 2020; Xu et al., 2021). Simulation-based methods (Liu et al., 2004; van Donkelaar et al., 2006; Xu et al., 2015; Goldberg et al., 2019) use chemical transport models to retrieve ground-level PM2.5 and consider meteorological conditions, emission sources, and the chemical compositions and physical properties of aerosols. However, the results obtained using simulation-based are limited by uncertainties related to emission sources and aerosol dynamic parameterization schemes. As retrieval methods have continued to develop, their performance has improved considerably in recent years (Lin et al., 2015; Li et al., 2015a; Zhang et al., 2018; Li et al., 2020).

Large amounts of AOD data from satellite sensors, including Advanced Very High Resolution Radiometer (AVHRR), Advanced Himawari Imager (AHI), Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), Geostationary Ocean Color Imager (GOCI), Total Ozone Mapping Spectrometer (TOMS), and Visible Infrared Imaging Radiometer (VIIRS), are available for retrieving ground-based PM2.5, for which data from the MODIS sensors onboard the Terra and Aqua satellites are the most commonly used. Previous studies have focused on the retrieval of ground-level PM2.5 using AOD data from a single satellite; however, a comparative study of the performance of the recently launched H8 satellite and other satellites has not been performed. Substantial research and application of the Aqua satellite products have been conducted in Guangdong. However, because it is only transited once per day, the Aqua products cannot capture sudden pollution events and have great limitations in the application of pollution prevention and control. In addition, the MODIS sensor onboard the Terra satellite has exceeded its expected operational cycle and is expected to stop data transmission in the near future. Data errors have also increased annually as a result of MODIS’s aging problems (Levy et al., 2013; Lyapustin et al., 2014). Therefore, there is an urgent need to use other high-quality and high-resolution satellites for retrieving PM2.5 concentrations. However, there are still relatively few analyses and studies on the H8 products in Guangdong. In this study, the AOD products of the polar-orbiting Aqua satellite and the high-resolution geostationary H8 satellite were used to retrieve ground-level PM2.5 concentrations. The performances of the two satellites were compared, and the retrieval impact factors were analyzed to improve PM2.5 monitoring in Guangdong Province and provide reference data for establishing air quality policies and air pollution forecasting.

 
2 MATERIALS AND METHODS


 
2.1 AOD and Ground-level Data

The MODIS sensor onboard the Aqua satellite conducts global observations once per day in 36 spectral bands (0.4–14.0 µm). AOD data from the Aqua satellite at 0.55 µm (MOD04L2C6) for 2016–2018, with a 3 km × 3 km spatial resolution, was acquired from NASA (http://ladsweb.​nascom.nasa.gov/). The Aqua satellite crosses the equator at approximately 13:30 local time every day. The AHI sensor onboard the H8 satellite has 16 channels covering the visible, near infrared and infrared wavelengths, and can complete a full scan every 10 min. AOD data from the H8 geostationary satellite at 0.55 µm for August 2017–July 2018, with a spatial resolution of 2 km × 2 km and a temporal resolution of 10 min, was obtained from the Guangdong Ecological Meteorological Center database. A description of the retrieval algorithm can be found in Gao et al. (2021a, 2021b), and the AOD products have been proven to be superior to JAXA H8 AOD (Gao et al., 2021b). Hourly ground observations of ground-level PM2.5 concentrations measured at 102 national ambient air quality monitoring stations in Guangdong Province from 2016 to 2018 were obtained from the National Real-time Urban Air Quality Release Platform (https://air.cnemc.cn:​18007/). Hourly surface visibility and relative humidity data from 2016 to 2018 were obtained from 86 national meteorological stations in Guangdong Province. The distribution of the monitoring stations is shown in Fig. S1.

The ground-based PM2.5 data used in the study were the standard data of the national air quality monitoring network, which are obtained using strict operational observation specifications and quality control procedures (State Environmental Protection Administration, 2005; Ministry of Ecological and Environment, 2018), and the data quality is guaranteed. Excessive cloudiness is observed in Guangdong, and the satellite observation is often affected by clouds; therefore, the temporal and spatial matching of satellite AOD and ground-based PM2.5 is needed. In this study, the average AOD within 5 km of the national ambient air quality monitoring station in the same hour is used as the AOD value of this station for the retrieval. The satellite-based PM2.5 concentrations were examined using ground-based PM2.5 concentrations from the same station at the same time, and the correlation coefficient (R) and root mean square error (RMSE) were used as the statistical indicators.

The spatial resolution of the air quality monitoring network in Guangdong Province can reach 1 km in a small number of dense areas, but is lower than 50 km in sparse areas, particularly in non-PRD (N-PRD) regions, with resolutions lower than 100 km. The spatial resolution of PM2.5 concentrations in Guangdong can be improved substantially using satellite remote sensing (> 3 km). The AHI sensor onboard the H8 satellite has high-frequency regional and overall scanning functions, and the single scanning time is very short, which can facilitate the collection of observation data with high spatio-temporal resolution (2 km and 10 min, respectively) and can provide daily variations in PM2.5 concentrations, which is conducive to capture the evolution of PM2.5 pollution processes (especially sudden pollution events).

 
2.2 Data Processing

The algorithm for retrieving ground-level PM2.5 concentrations uses the semi-empirical model given by Lin et al. (2015), which integrates influencing factors such as aerosol hygroscopic growth and mass extinction efficiency and has good performance in the PRD region (Lin et al., 2015, 2016; Li et al., 2020). Hourly ground-level PM2.5 concentrations were retrieved as follows:

 

where H represents the aerosol scale height, RH is the surface relative humidity, and γ represents the integrated humidity effect of hygroscopic growth, mass extinction efficiency, and fine mode fraction. RH0 was set to 40% and the reference RH value under dry conditions. K is an integrated reference value under the condition where RH = RH0.

H at the national meteorological stations was estimated using the following equation:

 

where VIS represents the surface visibility; σa is the surface aerosol extinction coefficient at 0.55 µm and is calculated by surface visibility data based on empirical relationship σa = 3.912/VIS.

Because the weather stations and national ambient air quality monitoring stations are in different locations, the values of H and RH at the national ambient air quality monitoring stations were determined by interpolating the H and RH values at the national meteorological stations. The values of K and γ at the national ambient air quality monitoring stations were calculated using AOD, H, RH, and PM2.5 with Eq. (1). The values of H, RH, K, and γ were interpolated for other regions in Guangdong using Kriging interpolation, and the results were a good representation of the distributions of meteorological conditions, industrial emissions, and anthropogenic activity in southern China (Chi et al., 2016; Li et al., 2020). In the retrieval algorithm, PM2.5 concentrations and visibility cannot be interpolated directly for other regions, as they are strongly influenced by local emission sources.

The retrieval algorithm introduces some assumptions. First, the aerosol extinction coefficient is assumed to decrease exponentially vertically and the parameter H is introduced:

 

Second, the parameter H and the aerosol characteristic are assumed to a smooth variation at the regional scale, so the values of H, K, and γ at the national ambient air quality monitoring stations can be derived by interpolation.

These assumptions may lead to the uncertainties of satellite-based PM2.5 concentrations, and the reduction in the sample size (N) during cloudy conditions may further increase these uncertainties. A ground-based lidar network has been gradually deployed in Guangdong in recent years. The vertical structure of aerosols can be accurately obtained as the improvement of detection means and the assumption of the parameter H can be omitted in the future, which will greatly eliminate these uncertainties. Although in certain cases, the aerosol extinction coefficient does not meet the exponential decreasing pattern under specific meteorological conditions, Wang et al. (2016) found that the aerosol extinction coefficient satisfied the exponential decreasing law on the whole by analyzing seven years of satellite remote sensing data acquired by the sole onboard active polarization lidar detector worldwide (Cloud-Aerosol Lidar with Orthogonal Polarization, CALIOP), which to some extent justified the scientific rationality of the assumption of Eq. (2).

Based on the administrative division method of the Guangdong Statistic Bureau (2014), Guangdong Province is divided into PRD and N-PRD regions, as shown in Fig. S2. Regional PM2.5 pollution episodes were defined as any one or more cities in the region of pollution (daily average PM2.5 > 75 µg m–3).

 
3 RESULTS



3.1 Correlation Analysis of Satellite AOD Products

The accuracy and distribution of PM2.5 results depend heavily on the AOD data. The MODIS AOD products obtained by Aqua are highly reliable and are strongly correlated with ground-based remote sensing AOD provided by Aerosol Robotic Network (correlation coefficient > 0.9; Mao et al., 2021); thus, it is often used for retrieving ground PM2.5 concentrations (van Donkelaar et al., 2006; Lin et al., 2015, 2016; Li et al., 2015b). We compared the Aqua and H8 AOD products in Guangdong to ensure their comparability in retrieving PM2.5 concentrations (Fig. 1). Owing to the inconsistent product resolutions from both satellites, the two datasets were matched temporally and spatially. The H8 AOD was selected within 5 min of the Aqua transit time and the two sets of data were matched to a 0.03° grid for comparison. The AOD products of both satellites were highly correlated, with a grid R greater than 0.9 and a RMSE less than 0.2 in most areas. The afternoon excessive cloudiness in the PRD hinterland resulted in a very small N at this location, and some grids failed to acquire two satellite-matched AOD datasets.

Fig. 1. Correlation between Aqua AOD and Himawari-8 (H8) AOD data in Guangdong from August, 2017 to July, 2018. (a) correlation coefficient (R), (b) sample size (N), (c) root mean square error (RMSE).Fig. 1. Correlation between Aqua AOD and Himawari-8 (H8) AOD data in Guangdong from August, 2017 to July, 2018. (a) correlation coefficient (R), (b) sample size (N), (c) root mean square error (RMSE).

 
3.2 Spatial Distribution of Satellite-retrieved PM2.5

Fig. 2 shows the average PM2.5 distributions simultaneously retrieved by the Aqua and H8 AOD products. Due to variations in the transit time of the Aqua satellite, the H8 AOD data was selected within 5 min of the Aqua transit time for analysis. The retrieved PM2.5 distributions from both satellites were generally consistent, and areas with high PM2.5 concentrations were mainly located in the western part of the PRD. In addition, the retrieved PM2.5 concentrations were higher near administrative boundaries where ground-based measurements are lacking (particularly near the border between the northwestern and southeastern sides of the PRD and N-PRD regions), which is closely related to the recent industrial shift that has occurred in the Guangdong region (Wang et al., 2019). The distribution is consistent with the findings of Li et al. (2015b), who also noted that 75.7% of the areas in the PRD were decreasing while other areas were increasing, which could be influenced by industrial shift. High-resolution satellite monitoring can fill gaps in ground-based measurements and provide a finer distribution of PM2.5 concentrations.

Fig. 2. Distributions of annually averaged simultaneous satellite-based PM2.5 concentrations in Guangdong from August, 2017 to July, 2018. (a) Aqua, (b) H8.Fig. 2. Distributions of annually averaged simultaneous satellite-based PM2.5 concentrations in Guangdong from August, 2017 to July, 2018. (a) Aqua, (b) H8.

With variations in the boundary layer height, PM2.5 often presents obvious diurnal variations, with the lowest concentration occurring in the afternoon (Li et al., 2021). Aqua passes once per day, and monitoring data can only be acquired at approximately 14:00 local time. However, the H8 geostationary satellite provides a more refined technical means to monitor the spatial and temporal variations in PM2.5, which can monitor the daily variations in PM2.5 in Guangdong Province with abundant observation data (Fig. S3). The hourly variations in the H8 satellite-based PM2.5 data were consistent with those in the ground-based PM2.5 data, and the concentrations were similar. In addition, the morning PM2.5 concentrations were substantially higher than those measured in the afternoon, and the afternoon decrease was more pronounced in N-PRD regions than in the PRD (Fig. 3).

Fig. 3. Distributions of annually averaged H8 satellite-based PM2.5 concentrations in Guangdong from August, 2017 to July, 2018. (a) AM, (b) PM. (AM and PM denote periods before and after 12:00 every day, respectively)Fig. 3. Distributions of annually averaged H8 satellite-based PM2.5 concentrations in Guangdong from August, 2017 to July, 2018. (a) AM, (b) PM. (AM and PM denote periods before and after 12:00 every day, respectively)

 
3.3 Comparison of Satellite-retrieved PM2.5

The ground-based and satellite-based PM2.5 concentrations were generally similar (Table S1). The deviations between the two datasets were within 4 µg m–3 for the annual, dry season, wet season, monthly average, and hourly data, with slightly higher satellite-based PM2.5 concentrations. Compared with the 5% and 95% quantiles of the ground-based and satellite-based PM2.5 data, the deviations were larger when the ground-based PM2.5 concentrations were higher. Because of the higher temporal resolution of the H8 dataset, it could observe a somewhat larger range of hourly PM2.5 concentrations (5% and 95% quantile concentrations of 10.0 µg m–3 and 74.0 µg m–3, respectively, for H8 and 13.0 µg m–3 and 67.0 µg m–3, respectively, for Aqua).

As shown in Fig. 4 and Table S2, good agreements were obtained between the Aqua satellite-based and ground-based PM2.5 data, as well as, between the H8 satellite-based and ground-based PM2.5 data, with R values of 0.97 and 0.96, RMSEs of 1.48 and 2.75 µg m–3, and Ns of 102 and 100, respectively. The performances in the PRD and non-PRD regions were comparable, with R ranging from 0.95 to 0.98 and a small RMSE. The ground-based method used in this study is applicable to Guangdong, and its performance is superior to those of other methods (Liu et al., 2014; Xia, 2017).

Fig. 4. Comparison of the annual mean satellite-based and ground-based PM2.5 concentrations in Guangdong. (a) Aqua, (b) H8. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018)Fig. 4. Comparison of the annual mean satellite-based and ground-based PM2.5 concentrations in Guangdong. (a) Aqua, (b) H8. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018)

Guangdong is located in tropical and subtropical monsoon climate zones. The region is influenced by upper-air easterly and westerly wind systems and various types of temperate and tropical weather, leading to distinct dry and wet seasons (Lin et al., 2006). Fig. 5 shows the comparison of the average satellite-based and ground-based PM2.5 concentrations during different seasons, where the dry season extends from October to March and the wet season extends from April to September. Table S3 lists the performances for the different seasons.

Fig. 5. Comparison of seasonally averaged satellite-based and ground-based PM2.5 concentrations in Guangdong (a) PRD, (b) N-PRD. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018. ADS represents Aqua in the dry season, HDS represents H8 in the dry season, AWS represents Aqua in the wet season, HWS represents H8 in the wet season)Fig. 5. Comparison of seasonally averaged satellite-based and ground-based PM2.5 concentrations in Guangdong (a) PRD, (b) N-PRD. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018. ADS represents Aqua in the dry season, HDS represents H8 in the dry season, AWS represents Aqua in the wet season, HWS represents H8 in the wet season)

PM2.5 concentrations were higher in the dry season, and the performances of the two satellites in the dry season were also better than in the wet season. The performances of the two satellites in the dry season were generally comparable, with an R value greater 0.9, while H8 performed better than Aqua in the wet season, with R values of 0.89 and 0.75, respectively. The performance of Aqua in the PRD was better than in the N-PRD region in different seasons, with a larger R and smaller RMSE in the PRD. H8 performed equivalently in different regions during all seasons. In the dry season, Aqua performed slightly better than H8 in the PRD, but worse in the N-PRD region. The performance of H8 was substantially better than that of Aqua in both the PRD and N-PRD regions during the wet season.

Fig. 6 shows a comparison of monthly averaged satellite-based and ground-based PM2.5 concentrations. The monthly averages for different regions are shown in Table S4. Congruency between the monthly averaged satellite-based and ground-based PM2.5 concentrations was shown with R > 0.75, indicating that it is possible to characterize monthly variations in PM2.5 using satellite data. The performance of H8 (R = 0.93, RMSE = 6.57 µg m–3) was substantially better than that of Aqua (R = 0.75, RMSE = 7.71 µg m–3). With regard to the different regions, H8 also performed substantially better, with an R of approximately 0.9, while Aqua had an R < 0.8.

Fig. 6. Comparison of the monthly averaged satellite-based and ground-based PM2.5 concentrations in Guangdong for the (a) Aqua and (b) H8 satellites. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018)Fig. 6. Comparison of the monthly averaged satellite-based and ground-based PM2.5 concentrations in Guangdong for the (a) Aqua and (b) H8 satellites. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018)

To further understand the performances of the satellites, we determined the spatial distributions of R, N, and RMSE for hourly PM2.5 concentrations (Fig. 7). The mean R was 0.65 for H8 and Aqua. The sample size of H8 was 11.2 times larger than that of Aqua (N = 1422 and 127, respectively). Statistically significant correlations for H8 and Aqua at all stations exceeded the 95% confidence level (Zhou and Zheng, 1997), with 100% and 98% of the stations exceeding the 99% confidence level, respectively. The RMSEs at most of the stations were below 20 µg m–3; however, the RMSEs for H8 at some stations were relatively high, with six stations reaching above 40 µg m–3. The correlations in the PRD were better than those in the N-PRD region, with R = 0.71 (N = 1488, RMSE = 23.83 µg m–3) and R = 0.66 (N = 118, RMSE = 15.54 µg m–3) for H8 and Aqua in the PRD, respectively and R = 0.57 (N =1338, RMSE = 26.95 µg m–3) and R = 0.64 (N = 137, RMSE = 14.87 µg m–3) for H8 and Aqua in the N-PRD region, respectively.

Fig. 7. Comparison of the two satellites in retrieving hourly ground-level PM2.5 concentrations at 102 stations in Guangdong: (a) Aqua and (b) H8 satellites. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018)Fig. 7. Comparison of the two satellites in retrieving hourly ground-level PM2.5 concentrations at 102 stations in Guangdong: (a) Aqua and (b) H8 satellites. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018)

 
3.4 Comparisons of Different Scenarios

Relative humidity is the main factor that affects aerosol hygroscopicity, and the growth of aerosol particles can alter atmospheric visibility (Deng et al., 2013; Zhang et al., 2021). The scattering of visible light by aerosols is enhanced as the ambient humidity increases (Waggoner et al., 1981; Sisler and Malm, 1994; Chen et al., 2012). Considerable seasonal differences were observed in the performances of the satellites during the dry and wet seasons. To further understand the effect of relative humidity on performance, a comparison was conducted under different relative humidity levels (Fig. 8). The correlation coefficients for H8 and Aqua at different relative humidities were all above 0.65, with average correlation coefficients of 0.82 and 0.86, respectively. When the relative humidity was higher than 40%, H8 performed better than Aqua, and the performances of both satellites decreased as the relative humidity increased. The sample size of Aqua decreased substantially when the relative humidity was higher than 80%, and Aqua failed to acquire data when the relative humidity was higher than 90%. Unlike Aqua, H8 could still obtain abundant data with a high correlation coefficient when the relative humidity was higher than 80%, which reflects an advantage of geostationary satellites for capturing finer spatial and temporal distributions of AOD and PM2.5.

Fig. 8. Comparison of the two satellites in retrieving average ground-level PM2.5 concentrations at different relative humidity levels in Guangdong. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018)Fig. 8. Comparison of the two satellites in retrieving average ground-level PM2.5 concentrations at different relative humidity levels in Guangdong. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018)

Atmospheric visibility can be used to characterize the cleanliness of the atmosphere and is the main indicator of urban atmospheric quality (Watson, 2002). A comparison of the average satellite-based and ground-based PM2.5 concentrations for different visibility levels is shown in Fig. 9. The mean correlation coefficients at different visibilities for H8 and Aqua were 0.88 and 0.83, respectively. When the visibility was less than 25 km, H8 performed slightly better than Aqua, but Aqua performed slightly better when the visibility was greater than 25 km. As the visibility increased, the performances of both satellites improved, with Aqua exhibiting a more pronounced improvement. After matching the satellite-based and ground-based PM2.5 concentrations, the sample size of H8 for different visibilities was 100, and the correlation coefficient was high (above 0.75); however, Aqua could not obtain any data when the visibility was less than 10 km. The H8 geostationary satellite has significant advantages for capturing PM2.5 pollution during low visibility periods, and the distribution of PM2.5 concentrations retrieved by H8 is beneficial for determining its source and monitoring pollutant generation and dissipation during hazy weather, which can provide a reference for establishing air quality policies and air pollution forecasting.

High PM2.5 concentrations have serious impacts on human health. Disease morbidity and mortality rates increase with increasing PM2.5 concentrations (Hajizadeh et al., 2020; Kasdagli et al., 2022); thus, PM2.5 pollution processes are a priority for air pollution prevention and control. Table S5 presents the performances of the satellites for hourly PM2.5 concentration in different regions when the PM2.5 pollution processes occur. During PM2.5 pollution processes, the two satellites performed well, with R > 0.57 in different regions. The sample size of H8 was substantially larger than that of Aqua, indicating that H8 was more likely to capture PM2.5 generation and dissipation. Among all the samples of the pollution processes, the RMSEs of the H8 satellite-based and ground-based PM2.5 concentrations were substantially higher than those of the Aqua satellite-based and ground-based PM2.5. This phenomenon was mainly due to the fact that H8 can capture a large number of samples with higher concentrations (a total of 5910 pollution data points with hourly concentrations > 75 µg m–3), whereas only 321 pollution data points were captured by Aqua. This increase in RMSE was also observed at high relative humidities and low visibilities (Fig. 8 and Fig. 9), whereas Aqua failed to capture such samples. The dataset at the same station and at the same time as both satellites was extracted for comparison. The performance of H8 in different regions was better than that of Aqua, with a higher correlation coefficient and a lower RMSE between the H8 satellite-based and ground-based PM2.5 concentrations.

Fig. 9. Comparison of the two satellites in retrieving average ground-level PM2.5 concentrations at different visibility levels in Guangdong. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018)Fig. 9. Comparison of the two satellites in retrieving average ground-level PM2.5 concentrations at different visibility levels in Guangdong. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018)


For example, during December 9–11, 2017, PM2.5 pollution occurred in Guangdong for three consecutive days. A total of 13 cities in Guangdong Province experienced PM2.5 pollution on December 10. Fig. 10 shows the variations in PM2.5 from December 8 to 11, 2017. The satellite-based PM2.5 concentrations retrieved by both satellites approached the ground-based measurements. Compared with Aqua, H8 was more likely to capture the temporal changes in PM2.5 pollution processes and the variations in PM2.5 concentrations, which provide scientific support for air pollution prevention and control.

 Fig. 10. Variations in hourly PM2.5 concentrations during December 8–11, 2017 at (a) Panyu Middle School, (b) Nanhaizi, (c) Doumen, (d) Huabaiyuan, (f) Keisuke Peak West, (g) Zhaoqing Central Substation, (h) Chaonan Substation, (i) Meixian New Town, and (j) Mandarin Duck Lake.Fig. 10. Variations in hourly PM2.5 concentrations during December 8–11, 2017 at (a) Panyu Middle School, (b) Nanhaizi, (c) Doumen, (d) Huabaiyuan, (f) Keisuke Peak West, (g) Zhaoqing Central Substation, (h) Chaonan Substation, (i) Meixian New Town, and (j) Mandarin Duck Lake.


4 DISCUSSION


 
4.1 Cloud Cover

Guangdong Province has tropical and subtropical climates, with the Nanling Mountains to the north and South China Sea to the south. Owing to its unique geographic location, Guangdong has both a continental climate with abundant light and heat resources and a maritime climate with abundant rain and humid air (Lin et al., 2006). Strong convective weather with heavy clouds can occur throughout the year in Guangdong. The Aqua and H8 satellites cannot capture AOD data at night or during cloudy conditions, and the degree of cloudiness has a direct impact on the acquisition of AOD data. Fig. S4 shows the ratio of the number of samples acquired over 10 min during the daytime (8–16 h) by both satellites to the total number of observations. Since Aqua only passes over Guangdong once per day, the percentage of samples observed by Aqua satellite was less than 0.5%, which is much lower than that observed by the H8 satellite. The proportion of samples observed by the H8 satellite in most areas ranged from 8% to 16%, and the proportion of samples in the southwestern PRD (a high PM2.5 concentration region) and the southern coast ranged from between 16% to 40%. The degree of cloudiness was very high in Guangdong, and AOD data were not available in most areas for up to 84% of the daytime owing to the influence of clouds.

 
4.1 Factors Impacting PM2.5 Retrieval

According to the analysis presented in Section 3.4, the correlations between satellite-based and ground-based PM2.5 concentrations differed at different relative humidities and visibilities. The retrieval algorithm (Eq. (1)) uses five parameters (AOD, H, RH, K, and γ’), and the main factors impacting satellite-retrieved PM2.5 may differ in different scenarios. To analyze the factors impacting retrieval, M is defined as a parameter that combines aerosol hygroscopic growth, aerosol scale height, and mass extinction efficiency, among others:

 

Eq. (1) can be rewritten as follows:

 

Satellite-retrieved PM2.5 concentrations at different relative humidities can then be calculated as follows:

 

where l is the segmentation of the different relative humidities, which were divided into seven segments (Fig. 8), y represents the y-th group of data under the same relative humidity segment, and  and  represent the average AOD and M values under the l-th relative humidity segment, respectively. In Eqs. (6) and (7), PM2.5(Mly) represents the PM2.5 concentrations calculated from the variation datasets of M, and PM2.5(AODly) represents the PM2.5 concentrations calculated from the variation datasets of AOD.

The standard deviations of the PM2.5(Mly) and PM2.5(AODly) datasets were calculated in the l relative humidity segment, respectively (Table 1). Similarly, the standard deviations of the PM2.5 datasets retrieved from the variations in H, RH, K, and γ', respectively (Table 1) and the standard deviations of the PM2.5 datasets retrieved from the variations in single parameters at different visibilities (Table 2) can also be calculated. The results indicate that the variations in satellite-retrieved PM2.5 are mainly influenced by the M value. When the relative humidity was low (below 50%), the standard deviation of PM2.5(Mly) was 1.9–2.8 times higher than that of the PM2.5(AODly) concentrations. In addition, when the relative humidity was greater than 70%, the difference between the two standard deviations was more than 7.8 times. Among the four parameters affecting the M value, H had the largest impact on the retrieval of PM2.5 concentrations. Higher the relative humidities produced larger standard deviations in the satellite-retrieved PM2.5 concentrations. When the relative humidity was greater than 80%, the standard deviations of the satellite-retrieved PM2.5 concentrations calculated from the variations in H reached more than 230 µg m–3. Uniformly, H had the largest impact on the satellite-retrieved PM2.5 concentrations at different visibilities. The standard deviations of the retrievals decreased as fluctuations in visibility increased. When the visibility was less than 10 km, the standard deviation of the satellite-retrieved PM2.5 concentrations reached the maximum (386 µg m–3).

Table 1. Standard deviations in PM2.5 concentration datasets retrieved from the variations in single parameters at different humidities in Guangdong from August, 2017 to July, 2018 (µg m–3).

Table 2. Standard deviations in PM2.5 concentration datasets retrieved from the variations in single parameters at different visibilities in Guangdong from August, 2017 to July, 2018 (µg m–3).

From the above analysis, the aerosol scale height was found to be the main factor impacting PM2.5 retrievals. Larger standard deviations of the satellite-based PM2.5 concentrations are caused by uncertainties in the aerosol scale height when the relative humidity is higher or the visibility is lower. These phenomena may be due to the large difference in the distribution of the aerosol scale height under high humidity and large visibility conditions, thus leading to a further increase in the interpolation deviations of the aerosol scale height. There have been a few cases of a low-altitude aerosol peak observed via ground-based lidar measurement in recent years, i.e., the aerosol extinction coefficient did not follow the exponential decreasing law. In addition, excessive cloudiness is observed in Guangdong, and the sample size decreases owing to cloud cover at high humidities and low visibilities, which can also further lead to an increase in the interpolation deviations of the aerosol scale height. Ground-based aerosol lidar can determine the vertical distribution of aerosols in the near-surface layer during cloudy conditions. A ground-based aerosol lidar network has been gradually established in Guangdong, and it is necessary to combine the advantages of ground-based lidar measurements and air quality model assimilation technology to improve the effectiveness of satellite-based PM2.5 concentration retrievals in the future. Besides, the spatial and temporal monitoring advantages of both satellites can be combined to further improve PM2.5 monitoring and forecasting through assimilation technology, providing a better reference for policy formulation and air pollution prediction.

 
5 CONCLUSION 


In this study, the ground fine particulate matter (PM2.5) retrieval performances of the polar-orbiting Aqua satellite and the geostationary Himawari-8 (H8) satellite were compared, and the factors impacting the retrievals were analyzed. The major findings of this study can be summarized as follows:

The performances of the Aqua and H8 satellites were good for retrieving ground PM2.5 concentrations, and the retrievals were able to characterize the spatial and temporal variations in ground-level PM2.5. Satellite-based PM2.5 concentrations were in good spatial agreement with ground-based PM2.5 measurements, and could provide finer PM2.5 distributions, thereby observing areas with high PM2.5 concentrations in western Pearl River Delta (PRD), northwestern Guangdong, and coastal regions in eastern Guangdong. H8 performed better than Aqua (Table 3). The correlation coefficients of the H8 satellite-based and ground-based PM2.5 concentrations were 0.96 (RMSE = 2.75 µg m–3), 0.92 (RMSE = 5.06 µg m–3), 0.89 (RMSE = 2.38 µg m–3), and 0.93 (RMSE = 6.57 µg m–3) for the annual, dry season, wet season, and monthly averages, respectively, while those of the Aqua satellite-based and ground-based PM2.5 concentrations were 0.97 (RMSE = 1.48 µg m–3), 0.93 (RMSE = 2.74 µg m–3), 0.75 (RMSE = 4.53 µg m–3), and 0.75 (RMSE = 7.71 µg m–3), respectively. The mean R value for hourly PM2.5 concentrations was 0.65 for H8 and Aqua, and the statistical significance of correlations at all stations exceeded the 95% confidence level.

Table 3. Comparison of the two satellites in retrieving ground-level PM2.5 in Guangdong. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018)

At different relative humidities, visibilities, and PM2.5 pollution levels, H8 performed better than Aqua (Table 4). The mean correlation coefficients for the H8 satellite-based and ground-based PM2.5 in the three scenarios mentioned above were 0.86, 0.88, and 0.77, while the mean correlation coefficients of the Aqua satellite-based and ground-based PM2.5 concentrations were 0.82, 0.83, and 0.70, respectively. When the relative humidity was greater than 40% or the visibility was less than 25 km, the performance of H8 was better than that of Aqua. The performances of both satellites decreased when the relative humidity was greater than 80% and the visibility was less than 15 km. Aqua failed to acquire samples under haze weather conditions (RH > 90% and visibility < 10 km), and the Aqua sample size was substantially smaller than that of H8 for PM2.5 pollution processes.

Table 4. Comparison of the two satellites in retrieving ground-level PM2.5 under different scene in Guangdong. (Aqua data is from 2016 to 2018, and H8 data is from August, 2017 to July, 2018)

Satellite-based PM2.5 concentrations can reveal the distribution of PM2.5 following the recent industrial shift, which cannot be observed by the ground-based monitoring network, indicated by higher PM2.5 concentrations near the administrative boundary of Guangdong, particularly the junction of the northwest and southeast sides of the PRD and non-PRD (N-PRD) regions. In addition, the H8 geostationary satellite could capture the temporal changes in PM2.5 concentrations in Guangdong, which were substantially higher in the morning than in the afternoon, and the afternoon decrease was sharper in the N-PRD than in the PRD.

Aerosol scale height was the main factor impacting PM2.5 retrievals under different scenarios. Higher relative humidities and lower visibilities yielded larger standard deviations of the PM2.5 concentrations retrieved from the variations in a single factor of the aerosol scale height. When the relative humidity was greater than 80%, the standard deviation of the retrieval was more than 230 µg m–3, and when the visibility was less than 10 km, the standard deviation of the retrieval reached 386 µg m–3.

The analysis presented herein indicates the application of high-resolution geostationary satellite PM2.5 retrievals is beneficial for improving the capacity to capture PM2.5 distributions during haze weather and PM2.5 pollution processes in Guangdong, China. Nevertheless, future improvements in the calculation accuracy of aerosol scale height are vital to further enhancing the performance of satellite-retrieved PM2.5 concentrations.

 
ACKNOWLEDGMENTS


This work was funded by the Key-Area Research and Development Program of Guangdong Province (Grant No. 2020B1111360003), the National Key Research and Development Program of China (Grant No. 2019YFC0214605, 2018YFC0213902), the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021A1515011539 and 2019A1515011808), the Science and Technology Innovation Group Project of Guangdong Province Meteorological Department (Grant No. GRMCTD202003), the Science and Technology Research Project of Guangdong Province Meteorological Department (Grant No. GRMC2020M12). We thank the team at JAXA (http://www.​eorc.jaxa.jp/ptree/index.html) for processing and providing H8 products. We are also grateful to the US National Aeronautics and Space Administration (NASA) Data Center for providing the MODIS satellite data.


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