Physics authors titles recent submissions

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Title: A novel solution for remote sensing of air quality: From satellite reflectance to ground PM2.5

Authors: Tongwen Li a_{, Huanfeng Shen}a,b,c,*_{, Qiangqiang Yuan}b,d_{, Liangpei Zhang}b,e

Affiliations:

a _{School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei, 430079,} China.

b _{The Collaborative Innovation Center for Geospatial Technology, Wuhan, Hubei, 430079,} China.

c _{The Key Laboratory of Geographic Information System, Ministry of Education, Wuhan} University, Wuhan, Hubei, 430079, China.

d_{School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China.} e _{The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote} Sensing, Wuhan University, Wuhan, Hubei, 430079, China.

*_{Corresponding author:}

Huanfeng Shen (shenhf@whu.edu.cn)

Highlights:

 We estimate ground PM2.5 from satellite reflectance rather than satellite-derived AOD.

 The Ref-PM modeling achieves a satisfactory performance with deep learning.

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ABSTRACT

With a large spatiotemporal coverage, the satellite-derived aerosol optical depth (AOD) has been widely used to estimate ground-level PM2.5 concentrations (AOD-PM modeling). However, the retrieval errors exit in the AOD products, and they will be accumulated in PM2.5 estimation. To avoid the intermediate error/process, a novel solution to estimate PM2.5 directly from satellite top-of-atmosphere (TOA) reflectance (denoted as Ref-PM modeling) is proposed. Using multiple linear regression, neural networks, and deep learning to establish the specific relationship between PM2.5, satellite reflectance, and other predictors, the Ref-PM modeling is validated with data from Wuhan Metropolitan Area in 2016. Furthermore, the geographical correlation is incorporated into deep belief network (Geoi-DBN) to better estimate PM2.5 concentrations. The results show that the performance of Ref-PM modeling (cross-validation R2_{=0.64 for DBN) has a competitive advantage than that of conventional} AOD-PM modeling (cross-validation R2_{=0.46 for DBN). Moreover, the out-of-sample} cross-validation R2_{and RMSE for Geoi-DBN in Ref-PM modeling are 0.87 and 9.89}



_{g m}

_/

3_, respectively. On this basis, the daily distributions of PM2.5 with a resolution of 0.01 degree are mapped, and they have similar spatial patterns with ground station measurements. These results demonstrate that the proposed Ref-PM modeling is effective for estimating ground-level PM2.5 concentrations from satellite reflectance. This study will significantly promote the application of satellite remote sensing in environmental monitoring.

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1. Introduction

Fine particular matter (PM2.5, airborne particles less than 2.5



m

in the aerodynamic diameter) has been reported to be associated with many adverse health effects including cardiovascular and respiratory morbidity and mortality (Habre et al., 2014; Madrigano et al., 2013). Previous studies have indicated that the severe PM2.5 pollution resulted in more than 1.2 million premature deaths in 2010 (Lim et al., 2012) and 1.6 million deaths in 2014 (Rohde and Muller 2015). It is thus an urgent need to obtain accurate spatiotemporal distributions of ground-level PM2.5 concentration for the environmental health concerns.

With a large spatiotemporal coverage, the satellite-derived aerosol optical depth (AOD) has been widely used to expand PM2.5 estimation beyond those only provided by ground monitoring stations (Di et al., 2016; Li et al., 2016; van Donkelaar et al., 2016). Many AOD products have been adopted in the estimation of ground PM2.5, including those retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) (Li et al., 2017b), Multi-Angle Implementation of Atmospheric Correction (MAIAC) (Xiao et al., 2017), Multiangle Imaging SpectroRadiometer (MISR) (You et al., 2015), Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) (Paciorek et al., 2008), and Visible Infrared Imaging Radiometer Suite (VIIRS) (Wu et al., 2016). In addition, many models are developed to establish the relationship between AOD and PM2.5 (AOD-PM2.5 relationship), such as multiple linear regression (MLR) (Gupta and Christopher 2009b), geographically weighted regression (GWR) (Hu et al., 2013), back-propagation neural network (BPNN) (Gupta and Christopher 2009a), linear mixed effects (LME) model (Lee et al., 2011), and so on. These models have played an irreplaceable role in modeling the relationship between satellite-derived AOD and

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ground-observed PM2.5 (AOD-PM modeling).

The AOD products are retrieved from satellite top-of-atmosphere (TOA) reflectance by the atmospheric radiative transfer model (e.g., 6S,MODTRAN) (Kaufman et al., 1997a; Levy et al., 2007). Hence, the previous process of satellite-based PM2.5 estimation is to firstly retrieve AOD from satellite TOA reflectance (for AOD-PM2.5 studies, the AOD products are usually provided by others), and subsequently estimate ground-level PM2.5 from satellite-derived AOD. Previous studies have reported that a great uncertainty exists in the retrieval of AOD, especially in urban area (Munchak et al., 2013). The retrieval error of AOD will inevitably be accumulated in the estimation of PM2.5. To avoid the intermediate error/process, whether can we estimate ground-level PM2.5 directly from satellite reflectance?

To estimate ground PM2.5 from satellite reflectance is actually a process to model the relationship between them (denoted as Ref-PM modeling) using statistical approaches. Previous studies (Radosavljevic et al., 2007; Ristovski et al., 2012) adopted neural networks to learn a functional relationship between MODIS observations and ground AOD, and the results show that neural-network-based AOD retrievals were more accurate than the physically based products. In addition, neural network models were developed to estimate ground-level PM2.5 using satellite-derived AOD, and outperform the conventional models (Li et al., 2017b; Wu et al., 2012). We can find that the physical process of AOD retrieval can be simulated by neural networks, and the same to AOD-PM2.5 relationship. Therefore, previous studies provide sufficient evidences for the potential to estimate ground PM2.5 from satellite reflectance, and neural network models are a preferred choice. According to our previous study (Li et al., 2017a), deep learning, which is the further development of neural network,

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may be a better tool for Ref-PM modeling. It is thus promising to estimate ground PM2.5 directly from satellite reflectance.

In this study, we propose a deep learning-based solution to estimate ground-level PM2.5 using satellite reflectance rather than AOD products. The Ref-PM modeling is validated with the data from Wuhan Metropolitan Area (Fig. 1) in 2016. We also establish the AOD-PM modeling using 3-km MODIS AOD products for comparison. The proposed solution for air quality assessment will significantly promote the application of satellite remote sensing in environmental monitoring.

2. Study region and data

Figure 1. Study region and the spatial distribution of PM2.5 stations. (a) The location of Hubei province in China. (b) The boundary of Wuhan Metropolitan Area in Hubei province. (c) The Wuhan Metropolitan Area and the distribution of PM2.5 monitoring stations.

The study region is Wuhan Metropolitan Area (WMA), which is presented in Fig. 1. The study period is a total year of 2016. WMA is located in Hubei province, central China (as shown in Fig. 1 (a) and (b)). To make full use of PM2.5 stations, the monitors in the range with

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latitude of 28.4° ~32.3 ° and longitude of 112.0° ~116.7° are all adopted in our analysis. WMA is an urban group with the center of Wuhan, covering the vicinity 8 cities (Huangshi, Ezhou, Huanggang, Xiaogan, Xianning, Xiantao, Qianjiang, and Tianmen). It has the total population of greater than 30 million, which account for more than a half of the total population of Hubei province. With over 60% of Gross Domestic Product (GDP) of Hubei province, WMA is one of the largest urban groups in central China. In 2007, it is authorize as one of the pilot areas for national resource-saving and environment-friendly society reforming by the Chinese government.

Due to the dense urbanization and industrial activities, WMA is suffering serious air pollution. Wuhan is especially populated with fine-mode particles (Wang et al., 2015), and the mean atmospheric PM2.5 mass concentration was about 160 ± 50

/

g m



(Wang et al., 2014). Moreover, with the rapid economic development, the other cities in WMA (e.g., Ezhou and Huangshi) also have a high level of PM2.5 concentration. It is thus very urgent and necessary to obtain the spatiotemporal information of PM2.5 in this area.

2.1. Ground-level PM2.5 measurements

Hourly PM2.5 concentration data across WMA in 2016 were obtained from the China National Environmental Monitoring Center (CNEMC) website (http://www.cnemc.cn) and the Hubei Provincial Environmental Monitoring Center Station (HPEMCS) website (http://www.hbemc.com.cn/). In this study, 77 CNEMC stations and 27 HPEMCS stations (104 stations in total) are included. The distribution of PM2.5 stations is shown in Fig. 1 (c). We can find that the CNEMC stations are unevenly distributed, while the HPEMCS stations make a good compensation. According to the Chinese National Ambient Air Quality Standard

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(CNAAQS, GB3905-2012), the ground PM2.5 concentrations are measured by the tapered element oscillating microbalance method (TEOM) or with beta attenuation monitors (BAMs or beta-gauge), with an uncertainty of 0.75% for the hourly record (Engel-Cox et al., 2013). We averaged hourly PM2.5 to daily mean PM2.5 data for the estimation of ground-level PM2.5.

2.2. Satellite observations

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B calibrated radiances (MYD02) product was downloaded from the Level 1 and Atmosphere Archive and Distribution System (LAADS) website (http://ladsweb.nascom.nasa.gov). They have a spatial resolution of 1 km at nadir. We extracted top-of-atmosphere reflectance on channels 1, 3 and 7 (B1, B3 and B7 reflectance), and observation angels, i.e., sensor azimuth (SensorA), sensor zenith (SensorZ), solar azimuth (SoarA) and solar zenith (SolarZ), from this product. To screen cloud contamination, the MODIS cloud mask product (MYD35_L2) was adopted. They are available at a resolution of 1 km every day. Furthermore, the MODIS normalized difference vegetation index (NDVI) product (Level 3, MYD13) with a resolution of 1 km every 16 days, were also downloaded from the LAADS website. For a comparison purpose, the MODIS aerosol optical depth (AOD) product of Collections 6 was utilized to establish AOD-PM2.5 model. They have a spatial resolution of 3 km (Munchak et al., 2013).

2.3. Meteorological data

The Goddard Earth Observing System Data Assimilation System GEOS-5 Forward Processing (GEOS 5-FP) (Lucchesi 2013) meteorological data were used in this study. GEOS 5-FP uses an analysis developed jointly with NOAA’s National Centers for Environmental Prediction (NCEP), which allows the Global Modeling and Assimilation Office (GMAO) to

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take advantage of the developments at NCEP and the Joint Center for Satellite Data Assimilation (JCSDA). Details can be found at the website (https://gmao.gsfc.nasa.gov/forecasts/). It has a spatial resolution of 0.25° latitude × 0.3125° longitude. We extracted relative humidity (RH, %), air temperature at a 2 m height (TMP, K), wind speed at 10 m above ground (WS, m/s), surface pressure (PS, kPa), and planetary boundary layer height (PBL, m) from the GEOS 5-FP dataset. Each variable was regridded to 0.01° to be consist with satellite observations.

2.4. Data integration

Firstly, a 0.01-degree grid was created for the data integration, model establishment, and mapping. For each 0.01-degree grid, ground-level PM2.5 measurements from multiple stations are averaged. Meanwhile, we resampled the meteorological data with a spatial resolution of 0.25°× 0.3125° to 0.01° to match with satellite observations. All the data were re-projected to the same coordinate system. Finally, we extracted satellite observations, meteorological parameters on locations where PM2.5 measurement are available.

3. Methodology

The Ref-PM modeling was developed to estimate ground-level PM2.5 from satellite reflectance rather than satellite-derived AOD. Generally, the structure of Ref-PM modeling is shown as Eq. (1), containing dependent variable (PM2.5 concentration) and two types of predictors (satellite observations and meteorological parameters). Details of Ref-PM modeling are illustrated in Fig. 2.





2.5 1, 3, 7, , , , , , , , , ,

PM  f B B B SensorA SensorZ SolarA SolarZ RH WS TMP PBL PS NDVI (1) where B B B1, 3, 7 top-of-atmosphere (TOA) reflectance and observation geometry (sensor

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azimuth, sensor zenith, solar azimuth, and solar zenith) are utilized, inspired by the dark target (DT) algorithm (Kaufman et al., 1997a; Kaufman et al., 1997b).

Figure 2. The schematics of Ref-PM modeling for the estimation of PM2.5.

To model the specific relationship between PM2.5 and predictors, a conventional statistical model (multiple linear regression, MLR) (Gupta and Christopher 2009b), neural networks (back-propagation neural network, BPNN; generalized regression neural network, GRNN) (Gupta and Christopher 2009a; Li et al., 2017b), and a deep learning model (deep belief network, DBN) (Li et al., 2017a) are adopted. On this basis, the spatiotemporal autocorrelation of PM2.5 is incorporated into the Ref-PM modeling. The spatial and temporal terms in conjunction with the above variables can greatly improve the model performance. The details can be seen in somewhere else (Li et al., 2017a), and the deep learning model which considers the spatiotemporal autocorrelation of PM2.5 is denoted as Geoi-DBN. The objective of this study is to develop a new solution (Ref-PM modeling) to remote sensing of PM2.5 concentration. We will thus compare the performance of Ref-PM modeling with that of AOD-PM modeling, based on the above models.

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A 10-fold cross-validation (CV) technique (Rodriguez et al., 2010) was used to test the potential of model overfitting and predictive power. Previous studies usually used sample-based CV (Li et al., 2017b; Ma et al., 2014) or site-based CV (Lee et al., 2011; Xie et al., 2015). For sample-based CV, all samples in the model dataset are randomly and equally divided into ten subsets. One subset is used as testing samples and the rest subsets are used to fit the model for each round of validation. For site-based CV, one monitoring station is used for validation and the remaining stations for model fitting in each round. In this study, we chose sample-based 10-fold CV, which is more widely used (Ma et al., 2016). We adopted the statistical indicators of the coefficient of determination (R2_{), the root-mean-square error} (RMSE,



g m

/

3), the mean prediction error (MPE,



g m

/

3), and the relative prediction error (RPE, defined as RMSE divided by the mean ground-level PM2.5) to evaluate the model performance.

4. Results and discussion

4.1. Descriptive statistics

Fig. 3 shows the histograms and descriptive statistics of the variables in sample dataset. The PM2.5 concentrations range from 5

/

g m



to 256.96



g m

/

3, with an average of 49.43



g m

/

3. The B1, B3, and B7 reflectance are mostly distributed in 0~0.2, with mean values of 0.09, 0.13, and 0.08, respectively. We can find that the satellite reflectance, temperature, relative humidity, surface pressure, height of planetary boundary layer, and NDVI have similar patterns with PM2.5 concentrations, while the satellite observation angles and wind speed have different distributions.

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Figure 3. Histograms and descriptive statistics of Ref-PM modeling variables in the sample dataset.

4.2. Comparison between Ref-PM and AOD-PM modeling

Table 1. Performance comparison between Ref-PM and AOD-PM modeling

Modeling Model fitting Cross-validation

R2 _RMSE _MPE _{RPE (%)} _R2 _RMSE _MPE _{RPE (%)}

Ref-PM (N=4181)

MLR 0.34 22.26 16.37 44.99 0.34 22.19 16.37 44.83 BPNN 0.72 14.55 10.73 29.21 0.59 17.51 12.72 35.39 GRNN 0.74 14.57 10.23 29.51 0.61 17.29 12.29 34.94 DBN 0.77 12.71 9.44 25.68 0.64 16.75 12.11 33.84 AOD-PM

(N=1658)

MLR 0.26 24.12 17.33 47.64 0.27 23.97 17.27 47.31 BPNN 0.48 20.01 14.43 39.68 0.40 21.60 15.26 42.86 GRNN 0.56 19.40 13.34 38.45 0.42 21.43 14.95 42.54 DBN 0.53 19.26 13.47 38.20 0.46 20.57 14.60 40.83 Table 1 shows the performance comparison between Ref-PM and AOD-PM modeling. The sample size of AOD-PM modeling dataset (N=1658) is much smaller than Ref-PM modeling dataset (N=4181). The main reason is that the reflectance data has a finer solution and larger

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coverage than AOD product. Comparing to AOD-PM modeling, the Ref-PM modeling reports a competitive performance. For MLR model, the cross-validation R2_{and RMSE are 0.34,} 22.19



g m

/

3 for Ref-PM modeling, and 0.27, 23.97



g m

/

3 for AOD-PM modeling. For learning-based models, the differences between Ref-PM modeling and AOD-PM modeling is larger, for instance, the cross-validation R2_{of DBN model is 0.64 and 0.46, respectively, with} a variation of 0.18. These results demonstrate that the Ref-PM modeling obtains a better performance than AOD-PM modeling, especially based on learning-based models (BPNN, GRNN, and DBN). However, it should be noted that there is a scale variation between Ref-PM modeling dataset (1 km) and AOD-PM modeling dataset (3 km). We resampled the Ref-PM modeling dataset to 3 km, and reconstruct Ref-PM models. The cross-validation R2 and RMSE of 3 km DBN in Ref-PM modeling is 0.56 and 18.16



g m

/

3. With the same resolution, the 3-km Ref-PM modeling still has some advantages in estimating ground-level PM2.5. Furthermore, the reflectance data and AOD product have different spatial coverage, we evaluate Ref-PM and AOD-PM modeling with their shared data records (N=935), the R2_and RMSE of the former are 0.64 and 14.12



g m

/

3, respectively; 0.48 and 17.64



g m

/

3 for the latter. All these results show that Ref-PM modeling reports a higher accuracy of PM2.5 estimation than AOD-PM modeling, which can be probably attributed to the avoidance of intermediate error.

For both Ref-PM modeling and AOD-PM modeling, DBN model performs the best, followed by GRNN and BPNN models, and MLR gives the poorest performance. The conventional MLR statistical model cannot well represent the nonlinear process of Ref-PM and AOD-PM modeling. Neural networks (BPNN, GRNN) have a better capacity to address

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this issue. With a more complicated structure, deep learning (DBN) show its superior in describe the relationship between PM2.5 and predictors. Allowing for the superiority of the DBN model, we will adopt the DBN model for further analysis of Ref-PM modeling.

4.3. Performance of geo-intelligent Ref-PM modeling

Figure 4. Results of Geoi-DBN (a) model fitting and (b) cross validation.

According to our previous study (Li et al., 2017a), incorporating geographical correlation can greatly improve the accuracy of PM2.5 estimation. Fig. 4 shows the scatter plots of Geoi-DBN model. The R2_{and RMSE of model fitting are 0.88 and 9.66} 3

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g m



, respectively. For cross validation, the Geoi-DBN model achieves a satisfactory result, with R2_{and RMSE} value of 0.87 and 9.89



g m

/

3, respectively. Our results show this study has a great advantage than previous regional studies of PM2.5 estimation in Beijing (R2=0.81, RMSE=18.89



g m

/

3) (Xie et al., 2015) and Yangtze River Delta Region (R2_=0.67, RMSE=15.82



g m

/

3) (Ma et al., 2016). On the other hand, the cross-validation slope for the Geoi-DBN model is 0.87, with an intercept of 6.48



g m

/

3. This means that the proposed approach tends to estimate when the ground PM2.5 concentrations are greater than 50

/

g m



. These results indicate some evidence for estimation bias of our method (Li et al., 2017a). The possible reason for this could be that we used point-based monitoring data and a spatially

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averaged modeling framework. The sampling distribution of monitors in a grid may not give a great estimation of the spatially averaged concentration for that grid.

Furthermore, the seasonal and spatial performance of the Geoi-DBN model was evaluated. Seasonally, the R2_{values for spring (March, April, and May), summer (June, July, and} August), autumn (September, October, and November) and winter (January, February, and December) are 0.85, 0.78, 0.77, and 0.86, respectively. Spatially, the R2_{and RMSE values} between the observed and estimated PM2.5 were calculated, and are shown in Fig. 4. Overall, the Geoi-DBN model has achieved a satisfactory performance, with 81.63% of grids reporting a high R2_{of greater than 0.8. The higher R}2_{and lower RMSE values are found in central} Wuhan, however lower R2_{and higher RMSE values are reported in Huanggang, Xiaogan, and} Xianning. The possible reason for this could be the uneven distribution of monitoring stations.

Figure 5. Spatial distribution of (a) R2_{and (b) RMSE between observed PM}

2.5 and estimated PM2.5. 4.4. Mapping of PM2.5 distribution

On the basis of Geoi-DBN model for Ref-PM modeling, the daily PM2.5 concentration is estimated. Fig. 6 presents daily estimates on some certain days. We selected one day in each season, and these days have as many as valid estimates. As shown in this figure, the derived PM2.5 have similar spatial distributions with ground station measurements. The PM2.5 on

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January 15, 2016 (in winter) has the highest level, while the lowest values are found on July 30, 2016 (in summer).

Figure 6. Daily estimates of PM2.5 on some certain days. The white regions indicate missing data.

To further evaluate our results at daily scale, we calculated RPE between derived PM2.5 and observed PM2.5 on each day, which is shown in Fig. 7. Due to the missing data, only 125 days have valid estimates of PM2.5 covering more than 2 stations. Over 87% of the days have a RPE value of less than 30%. These results demonstrate that the proposed Ref-PM modeling using geo-intelligent deep learning is effective for estimating daily PM2.5 concentrations.

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Figure 7. Distribution of daily RPE between observed PM2.5 and derived PM2.5.

Figure 8. Annual mean distribution of PM2.5 concentrations across WMA in 2016, (a) derived PM2.5, (b) ground-observed PM2.5. It should be noted that we calculated annual mean PM2.5 with valid days greater than 270 from station measurements. Thus, some stations, which are in service from the later year, are not seen in this figure.

Furthermore, the annual mean distribution of PM2.5 in WMA is mapped. Fig. 8 presents the satellite-derived and ground-observed PM2.5 concentrations. Overall, the satellite-derived PM2.5 has a similar spatial pattern with ground measurements. Spatially, higher levels of PM2.5 concentrations are found in Wuhan, Ezhou, and Huangshi cities, while lower values are reported in the other cities. The PM2.5 pollution presents a pattern of “higher center, lower

vicinity”, which is consistent to the urbanization level of these cities. Wuhan, a

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coal-smoke air pollution and exhaust gas of vehicle. While the coal-smoke air pollution are found in Ezhou, Huangshi cities, which are rapidly developing industrial cities. Moreover, agricultural cities and eco-tourism cities including Xianning and Qianjiang have a lower level of PM2.5 pollution.

4.5. Discussion

The satellite reflectance and observation angles are adopted to establish the Ref-PM modeling. The observation angles are used as auxiliary variables to retrieve AOD from satellite TOA reflectance (Kaufman et al., 1997a). We would like to see whether or not they can be eliminated from the input variables. Hence, we further evaluate the influence of observation angles on Ref-PM modeling. The results are presented in Table 2. When the observation angles are not incorporated in the DBN model (Non-angles Ref-PM model), the cross-validation R2_{and RMSE are 0.48 and 19.94} 3

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g m



. However, the model performance is greatly improved (from 0.46 to 0.64 for R2_{) by considering the observation angles. These} results indicate that the satellite observation geometry is important for the Ref-PM modeling.

The radiometric calibration was conducted to obtain satellite TOA reflectance from digital number (DN) signals. It is a linear transformation from DN signals to TOA reflectance, and should have no impact on deep learning. This means that estimating PM2.5 from DN signals should obtain the same results with Ref-PM modeling. However the calibration coefficients vary by day, the relationship between TOA reflectance and DN signals is not linear during the whole study period. Hence, we constructed the model between PM2.5 and DN (DN-PM modeling) for comparison. As shown in Table 2, the DN-PM modeling reports a similar performance with Ref-PM modeling, indicating that the radiometric calibration can probably

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be omitted for estimating PM2.5. However, the objective of this study is to estimate ground PM2.5 by avoiding AOD retrieval, which is conducted using TOA reflectance. The Ref-PM and AOD-PM modeling should be fully compared. Hence the satellite reflectance is adopted in this paper.

Table 2. Performance of various modeling strategies

Models Cross-validation

R2 _RMSE _MPE _{RPE (%)}

Non-angles Ref-PM 0.48 19.94 14.60 40.29

Full Ref-PM 0.64 16.75 12.11 33.84

Full DN-PM 0.63 16.80 12.26 33.67

There are still some space to improve this study. Firstly, the retrieval of AOD often meets a challenge, which is the bright surface (B7>0.15) (Li et al., 2005). How the bright surface affects Ref-PM modeling should been further discussed in future studies. Secondly, the satellite observations used (i.e., B1, B3, B7 reflectance, and angles) are inspired by the inspired by the dark target algorithm, the selection of satellite parameters still has room for researching. Whether or not incorporating more channels (such as B2, B4, and B5 reflectance) into the Ref-PM modeling has a positive effect?

5. Conclusions

To sum up, this study has several advantages. Firstly, a novel solution for remote sensing of air pollution, i.e., estimating PM2.5 directly from satellite reflectance, is proposed. It can avoid the intermediate error/process, and obtain a competitive performance compared with the conventional AOD-PM modeling. Secondly, benefiting from the advanced Geoi-DBN approach, the fine-scale and high accuracy distribution of PM2.5 in WMA are mapped.

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of 0.46 and 20.57

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g m

/

3 , respectively. While a better result (R2_=0.64, RMSE=16.75



g m

/

3) for DBN in the Ref-PM modeling is reported. The results indicate that the proposed Ref-PM modeling is effective for estimating ground-level PM2.5 from satellite reflectance. Furthermore, the out-of-sample R2_{and RMSE for Geoi-DBN model are 0.87 and} 9.89



g m

/

3, respectively. On this basis, the daily distributions of PM2.5 in WMA are mapped, they have similar spatial patterns with ground station measurement. All these results can say that the proposed approach has the capacity to estimate PM2.5 concentration from satellite observations, and provide useful information for pollution monitoring.

In future studies, we will firstly promote the application of the proposed Ref-PM modeling solution to some newly launched satellites. For instance, the Himawari-8 satellite has the capacity to measure atmospheric parameters, but the retrieval algorithm of aerosol has not been fully validated. The proposed Ref-PM modeling may well address this issue, and obtain timely satellite-derived PM2.5 data. Secondly, we would like to make some attempts on the estimation of PM2.5 from high-resolution satellite observations. The existing satellite-based estimates of PM2.5 still have some limitations in pollution monitoring at urban scale.

Acknowledgments

This work was funded by the National Key R&D Program of China (2016YFC0200900) and the National Natural Science Foundation of China (41422108). We are grateful to the China National Environmental Monitoring Center (CNEMC), the Hubei Provincial Environmental Monitoring Center Station (HPEMCS), the Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), the US National Aeronautics and Space Administration (NASA) Data Center for providing the foundational data.

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Table 2. Performance of various modeling strategies

Models Cross-validation

R2 _RMSE _MPE _{RPE (%)}

Non-angles Ref-PM 0.48 19.94 14.60 40.29

Full Ref-PM 0.64 16.75 12.11 33.84

Full DN-PM 0.63 16.80 12.26 33.67

5. Conclusions

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of 0.46 and 20.57



g m

/

3 , respectively. While a better result (R2_=0.64, RMSE=16.75



g m

/



g m

/

Acknowledgments

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Reference

Engel-Cox, J., Kim Oanh, N.T., van Donkelaar, A., Martin, R.V., & Zell, E. (2013). Toward the next generation of air quality monitoring: Particulate Matter. Atmospheric Environment, 80, 584-590

Lee, H., Liu, Y., Coull, B., Schwartz, J., & Koutrakis, P. (2011). A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations. Atmospheric Chemistry and Physics, 11, 7991-8002

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1-km aerosol optical depth from MODIS measurements over Hong Kong. IEEE Transactions on

Geoscience and Remote Sensing, 43, 2650-2658

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Lucchesi, R. (2013). File Specification for GEOS-5 FP. In. GMAO Office Note No. 4 (Version 1.0)

Ma, Z., Hu, X., Huang, L., Bi, J., & Liu, Y. (2014). Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing. Environmental Science & Technology, 48, 7436-7444

Paciorek, C.J., Liu, Y., Moreno-Macias, H., & Kondragunta, S. (2008). Spatiotemporal Associations between GOES Aerosol Optical Depth Retrievals and Ground-Level PM2.5.

Environmental Science & Technology, 42, 5800-5806

Ristovski, K., Vucetic, S., & Obradovic, Z. (2012). Uncertainty Analysis of

Neural-Network-Based Aerosol Retrieval. IEEE Transactions on Geoscience and Remote Sensing, 50, 409-414

Rohde, R.A., & Muller, R.A. (2015). Air Pollution in China: Mapping of Concentrations and Sources. PLOS ONE, 10, e0135749

van Donkelaar, A., Martin, R.V., Brauer, M., Hsu, N.C., Kahn, R.A., Levy, R.C., Lyapustin, A., Sayer, A.M., & Winker, D.M. (2016). Global Estimates of Fine Particulate Matter using a

(6)

Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors.

Environmental Science & Technology, 50, 3762-3772

Wang, L., Gong, W., Li, J., Ma, Y., & Hu, B. (2014). Empirical studies of cloud effects on ultraviolet radiation in Central China. International Journal of Climatology, 34, 2218-2228

Wang, L., Gong, W., Xia, X., Zhu, J., Li, J., & Zhu, Z. (2015). Long-term observations of aerosol optical properties at Wuhan, an urban site in Central China. Atmospheric Environment, 101, 94-102

Wu, Y., Guo, J., Zhang, X., Tian, X., Zhang, J., Wang, Y., Duan, J., & Li, X. (2012). Synergy of satellite and ground based observations in estimation of particulate matter in eastern China.

Science of The Total Environment, 433, 20-30