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Application of Ground and Space Based Remote Sensing for Air Pollution

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2019) | Viewed by 41465

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Dr. Lam Yun Fat Nicky

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Guest Editor

School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong
Interests: regional/local air pollution modeling; intercontinental long-range transport of air pollution; impact assessment of future climate change; global climate and air quality downscaling; stratospheric ozone transport

Dr. Ka Lok Chan

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Guest Editor

Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Weßling, Germany
Interests: remote sensing of atmospheric composition; radiative transfer modeling; global chemical transport modeling; long-range transport of atmospheric pollutants; numerical inversion methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our great pleasure to organize the Special Issue of “Applications of remote sensing observations on climate and air quality” in the journal Remote Sensing.

This Special Issue on the applications of remote sensing observations on climate and air quality, is including but not limited to the applications of ground- and space-based remote sensing of particle and gas phase chemicals in regional and global scales. The goal of this Special Issue is to explore and improve the cross-applications of remote sensing measurements on climate and air quality modeling. This section particularly focuses on the cross-validation of chemical transport model simulations, inverse modeling or other inversion techniques for pollutant emissions estimation and data assimilation using ground- and space-based remote sensing data. Original research combining model simulation and remote sensing observation for the characterization of the physical and chemical properties of air pollution and/or climate interaction, or spatial–temporal analysis of pollution episodes are also welcome. Other topics of interest for this Special Issue are artificial intelligence or machine learning approaches for the determination of spatio-temporal information on air pollution using remote sensing observations.

We invite scientists to contribute research and review articles that explore the following topics:

Validation/inter-model comparison of chemical transport model simulation using remote sensing
Inverse modeling or other inversion techniques for emissions estimation using remote sensing data
Data assimilation using remote sensing observations
Model simulation and remote sensing observations of pollution transport
Characterization of physical and chemical properties of air pollution and climate interaction
Artificial intelligent or machine learning approaches for the determination of spatio-temporal information on air pollution using remote sensing observations

Please feel free to disseminate this announcement to any colleagues who might be interested.

Kind regards,

Dr. Lam Yun Fat Nicky
Dr. Ka Lok Chan
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

Air pollution
Air quality model
Chemical transport model
Climate interaction
Satellite remote sensing
Pollutant emissions inversion
Inverse modeling
Data assimilation
Machine learning

Published Papers (11 papers)

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Research

19 pages, 7554 KiB

Open AccessEditor’s ChoiceArticle

Regional Atmospheric Aerosol Pollution Detection Based on LiDAR Remote Sensing

by Xin Ma, Chengyi Wang, Ge Han, Yue Ma, Song Li, Wei Gong and Jialin Chen

Remote Sens. 2019, 11(20), 2339; https://doi.org/10.3390/rs11202339 - 09 Oct 2019

Cited by 26 | Viewed by 4577

Abstract

Atmospheric aerosol is one of the major factors that cause environmental pollution. Light detection and ranging (LiDAR) is an effective remote sensing tool for aerosol observation. In order to provide a comprehensive understanding of the aerosol pollution from the physical perspective, this study investigated regional atmospheric aerosol pollution through the integration of measurements, including LiDAR, satellite, and ground station observations and combined the backward trajectory tracking model. First, the horizontal distribution of atmospheric aerosol wa obtained by a whole-day working scanning micro-pulse LiDAR placed on a residential building roof. Another micro-pulse LiDAR was arranged at a distance from the scanning LiDAR to provide the vertical distribution information of aerosol. A new method combining the slope and Fernald methods was then proposed for the retrieval of the horizontal aerosol extinction coefficient. Finally, whole-day data, including the LiDAR data, the satellite remote sensing data, meteorological data, and backward trajectory tracking model, were selected to reveal the vertical and horizontal distribution characteristics of aerosol pollution and to provide some evidence of the potential pollution sources in the regional area. Results showed that the aerosol pollutants in the district on this specific day were mainly produced locally and distributed below 2.0 km. Six areas with high aerosol concentration were detected in the scanning area, showing that the aerosol pollution was mainly obtained from local life, transportation, and industrial activities. Correlation analysis with the particulate matter data of the ground air quality national control station verified the accuracy of the LiDAR detection results and revealed the effectiveness of LiDAR detection of atmospheric aerosol pollution. Full article

(This article belongs to the Special Issue Application of Ground and Space Based Remote Sensing for Air Pollution)

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18 pages, 3196 KiB

Open AccessArticle

Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM_2.5 Measurements in Shenzhen City, China

by Jiansheng Wu, Jingtian Liang, Liguo Zhou, Fei Yao and Jian Peng

Remote Sens. 2019, 11(19), 2223; https://doi.org/10.3390/rs11192223 - 24 Sep 2019

Cited by 13 | Viewed by 3643

Abstract

Satellite-derived aerosol optical depth (AOD) is widely used to estimate surface PM_2.5 concentrations. Most AOD products have relatively low spatial resolutions (i.e., ≥1 km). Consequently, insufficient research exists on the relationship between high-resolution (i.e., <1 km) AOD and PM_2.5 concentrations. Taking Shenzhen City, China as the study area, we derived AOD at the 16-m spatial resolution for the period 2015–2017 based on Gaofen-1 (GF-1) satellite images and the Dark Target (DT) algorithm. Then, we extracted AOD at spatial scales ranging from 40 m to 5000 m and applied vertical and humidity corrections. We analyzed the correlation between AOD and PM_2.5 concentrations, and the impacts of AOD correction and spatial scale on the correlation. It was found that the DT-derived GF-1 AOD at different spatial scales had statistically significant correlations with surface PM_2.5 concentrations, and the AOD corrections strengthened the correlations. The correlation coefficients (R) between AOD at different spatial scales and PM_2.5 concentrations were 0.234–0.329 and 0.340–0.423 before and after AOD corrections, respectively. In spring, summer, autumn, and winter, PM_2.5 concentrations had the best correlations with humidity-corrected AOD, uncorrected AOD, vertical and humidity-corrected AOD, and uncorrected AOD, respectively, indicating a distinct seasonal variation of the aerosol characteristics. At spatial scales of 1–5 km, AOD at finer spatial scales generally had higher correlations with PM_2.5 concentrations. However, at spatial scales <1 km, the correlations fluctuated irregularly, which could be attributed to scale mismatches between AOD and PM_2.5 measurements. Thus, 1 km appears to be the optimum spatial scale for DT-derived AOD to maximize the correlation with PM_2.5 concentrations. It is also recommended to aggregate very high-resolution DT-derived AOD to an appropriate medium resolution (e.g., 1 km) before matching them with in situ PM_2.5 measurements in regional air pollution studies. Full article

(This article belongs to the Special Issue Application of Ground and Space Based Remote Sensing for Air Pollution)

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16 pages, 4082 KiB

Open AccessArticle

Ground-Based MAX-DOAS Observations of CHOCHO and HCHO in Beijing and Baoding, China

by Zeeshan Javed, Cheng Liu, Muhammad Fahim Khokhar, Wei Tan, Haoran Liu, Chengzhi Xing, Xiangguang Ji, Aimon Tanvir, Qianqian Hong, Osama Sandhu and Abdul Rehman

Remote Sens. 2019, 11(13), 1524; https://doi.org/10.3390/rs11131524 - 27 Jun 2019

Cited by 25 | Viewed by 4248

Abstract

Glyoxal (CHOCHO) and formaldehyde (HCHO) trace gases were successfully retrieved from a multi-axis differential optical absorption spectroscopy (MAX-DOAS) system in Beijing (39.95°N, 116.32°E) and Baoding (39.15°N, 115.40°E), China. The measurements of these trace gases span the period from May 2017 to April 2018. Higher levels of trace gases were observed in Beijing most likely due to increased transport and industrial activities compared to Baoding. Different time scales were analyzed from seasonal to daily levels. Seasonal variation categorized by wintertime maximum and summertime minimum was observed for CHOCHO, while for HCHO maximum values were recorded during summer at both observation points. Variations in the diurnal cycle of trace gases were examined. The results are consistent with strong links to photo-oxidations of VOCs for HCHO production, whereas the CHOCHO diurnal variation can be related to anthropogenic effects in the evening. Weekends didn’t have any significant effect on both HCHO and CHOCHO. We investigated the temperature dependency of HCHO and CHOCHO. HCHO shows positive correlation with air temperature, which strengthened the argument that HCHO production is linked to photo-oxidation of VOCs. CHOCHO is anti-correlated with air temperature. This suggests that photolysis is a major sink for CHOCHO in Beijing and Baoding. We also investigated the relationship between CHOCHO and HCHO VCDs with enhanced vegetation index (EVI) data obtained from MODIS, which represents a direct relation with biogenic emissions. The positive correlations were observed among monthly mean HCHO VCDs and monthly mean EVI at both monitoring stations. The strong correlation of HCHO with EVI found, suggests that oxidation of isoprene and HCHO production is strongly related, while negative correlation was observed among CHOCHO VCDs and EVI. Full article

(This article belongs to the Special Issue Application of Ground and Space Based Remote Sensing for Air Pollution)

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12 pages, 2229 KiB

Open AccessArticle

The Radiance Differences between Wavelength and Wavenumber Spaces in Convolving Hyperspectral Infrared Sounder Spectrum to Broadband for Intercomparison

by Di Di, Min Min, Jun Li and Mathew M. Gunshor

Remote Sens. 2019, 11(10), 1177; https://doi.org/10.3390/rs11101177 - 17 May 2019

Cited by 4 | Viewed by 2802

Abstract

Converting the hyperspectral infrared (IR) sounder radiance spectrum to broadband is a common approach for intercomparison/calibration. Usually the convolution is performed in wavenumber space. However, numerical experiments presented here indicate that there are brightness temperature (BT) differences between wavelength and wavenumber spaces in convolving hyperspectral IR sounder spectrum to broadband. The magnitudes of differences are related to the spectral region and the width of the spectral response functions (SRFs). In addition, the central wavelength and central wavenumber should be determined separately in wavelength and wavenumber spaces, respectively; they cannot be converted to each other directly for broadband BT calculations. There exist BT differences (BTDs) between interpolating the resolution of SRF to hyperspectral IR sounder spectrum, and vice versa, for convolution. This study provides clarity on convolution, central wavelength/wavenumber determination, and spectral resolution matching between broadband SRFs and hyperspectral IR sounder radiances for intercomparison/calibration. Full article

(This article belongs to the Special Issue Application of Ground and Space Based Remote Sensing for Air Pollution)

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13 pages, 23211 KiB

Open AccessArticle

Alternate Mapping Correlated k-Distribution Method for Infrared Radiative Transfer Forward Simulation

by Feng Zhang, Mingwei Zhu, Jiangnan Li, Wenwen Li, Di Di, Yi-Ning Shi and Kun Wu

Remote Sens. 2019, 11(9), 994; https://doi.org/10.3390/rs11090994 - 26 Apr 2019

Cited by 15 | Viewed by 3313

Abstract

The alternate mapping correlated k-distribution (AMCKD) method is studied and applied to satellite simulations. To evaluate the accuracy of AMCKD, the simulated brightness temperatures at the top of the atmosphere are compared with line-by-line radiative transfer model (LBLRTM) or the observed data which are from Advanced Himawari Imager (AHI) on board the Himawari-8, as well as Medium Resolution Spectral Imager (MERSI) on board the Fengyun-3D. The result of AMCKD is also compared with the algorithm of Radiative Transfer for the Television Observation Satellite Operational Vertical Sounder (RTTOV). Under the standard atmospheric profiles, the absolute errors of AMCKD in all longwave channels of AHI and MERSI are bounded by 0.44K compared to the benchmark results of LBLRTM, which are more accurate than those of RTTOV. In the most cases, the error of AMCKD is smaller than the NEDT at ST, while the error of RTTOV is larger than the instrument noise equivalent temperature (NEDT) at scene temperature (ST). Under real atmospheric profile conditions, the errors of AMCKD increase, because the input data from ERA-Interim reanalysis dataause bias in the satellite remote sensing results. In the most considered cases, the accuracy of AMCKD is higher than RTTOV, while the efficiency of AMCKD is slightly slower than RTTOV. Full article

(This article belongs to the Special Issue Application of Ground and Space Based Remote Sensing for Air Pollution)

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15 pages, 4735 KiB

Open AccessArticle

Preliminary Studies on Atmospheric Monitoring by Employing a Portable Unmanned Mie-Scattering Scheimpflug Lidar System

by Zhi Liu, Limei Li, Hui Li and Liang Mei

Remote Sens. 2019, 11(7), 837; https://doi.org/10.3390/rs11070837 - 08 Apr 2019

Cited by 26 | Viewed by 3999

Abstract

A portable unmanned Mie-scattering Scheimpflug lidar system has been designed and implemented for atmospheric remote sensing. The Scheimpflug lidar system employs a continuous-wave high-power 808 nm laser diode as the light source and the emitted laser beam is collimated by an F6 lens with a 100 mm aperture. Atmospheric backscattering light is collected by a F5 lens with a 150 mm aperture and then detected by a 45° tilted image sensor. The separation between the transmitting and the receiving optics is about 756 mm to satisfy the Scheimpflug principle. Unmanned outdoor atmospheric measurements were performed in an urban area to investigate system performance. Localized emissions can be identified by performing horizontal scanning measurements over the urban atmosphere for 107° approximately every 17 min. The temporal variation of the vertical aerosol structure in the boundary layer has also been studied through zenith scanning measurements. The promising result shows great potential of the present portable lidar system for unmanned atmospheric pollution monitoring in urban areas. Full article

(This article belongs to the Special Issue Application of Ground and Space Based Remote Sensing for Air Pollution)

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20 pages, 5464 KiB

Open AccessArticle

Long-Term Trends of Atmospheric CH₄ Concentration across China from 2002 to 2016

by Xiaodi Wu, Xiuying Zhang, Xiaowei Chuai, Xianjin Huang and Zhen Wang

Remote Sens. 2019, 11(5), 538; https://doi.org/10.3390/rs11050538 - 05 Mar 2019

Cited by 25 | Viewed by 3839

Abstract

Spatiotemporal variations of atmospheric CH₄ from 2002 to 2016 across China were detected, based on the Atmospheric Infrared Sounder (AIRS) sixth-layer CH₄ concentration. The CH₄ concentration showed good consistency with the ground measurements of surface CH₄ concentration from the World Data Centre for Greenhouse Gases (WDCGG) (R² = 0.83, p < 0.01), indicating that the remotely-sensed CH₄ reflected the spatial and temporal variations of surface CH₄ concentration. Across China, three hotspots of CH₄ concentration were found in northern Xinjiang, the northeast of Inner Mongolia/Heilongjiang, and the Norgay plateau in northwest Sichuan. The CH₄ concentration showed obviously seasonal variations, with the maximum CH₄ concentration occurring in summer, followed by the autumn, winter, and spring. Furthermore, the CH₄ concentration showed significantly increasing trends across China, with the rate of increase ranging from ~0.29 to 0.62 ppb·month⁻¹, which would bring a 0.0019~0.014 mK potential rise in surface temperature response over China. In particular, the most rapidly increasing rates occurred in the Qinghai-Tibet plateau, while relatively low rates occurred in southeast China. Full article

(This article belongs to the Special Issue Application of Ground and Space Based Remote Sensing for Air Pollution)

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21 pages, 5519 KiB

Open AccessArticle

Observation of CO₂ Regional Distribution Using an Airborne Infrared Remote Sensing Spectrometer (Air-IRSS) in the North China Plain

by Ruwen Wang, Pinhua Xie, Jin Xu, Ang Li and Youwen Sun

Remote Sens. 2019, 11(2), 123; https://doi.org/10.3390/rs11020123 - 10 Jan 2019

Cited by 4 | Viewed by 3813

Abstract

Carbon dioxide (CO₂) is one of the most important anthropogenic greenhouse gases (GHG) and significantly affects the energy balance of atmospheric systems. Larger coverage and higher spatial resolution of CO₂ measurements can complement the existing in situ network and satellite measurements and thus improve our understanding of the global carbon cycle. In this study, we present a self-made airborne infrared remote sensing spectrometer (Air-IRSS) designed to determine the regional distribution of CO₂. The Air-IRSS measured CO₂ in the spectral range between 1590 and 1620 nm at a spectral resolution of 0.45 nm and an exposure time of 1 s. It was operated onboard an aircraft at a height of 3 km with a velocity of 180 km/h, and a spatial resolution of 50.00 m × 62.80 m. Weighting function modified differential optical absorption spectroscopy (WFM-DOAS) was used to analyze the measured spectra. The results show that the total uncertainty estimated for the retrieval of the CO₂ column was 1.26% for airborne measurements over a large region, and 0.30% for a fixed point, such as power points or factories. Under vibration-free static conditions, the on-ground Air-IRSS observations can adequately reproduce the variations observed by Greenhouse Gases Observing Satellite (GOSAT) with a correlation coefficient (r) of 0.72. Finally, we conducted an airborne field campaign to determine the regional distribution of CO₂ over the North China Plain. The regional distribution of CO₂ columns over four cities of Xing-tai, Hengshui, Shijiazhuang, and Baoding were obtained with the GPS information, which ranged from 2.00 × 10²¹ molec cm⁻² to 3.00 × 10²¹ molec cm⁻². The CO₂ vertical distributions were almost uniform below a height of 3 km in the area without CO₂ emission sources, and the highest values were found over Baoding City. Full article

(This article belongs to the Special Issue Application of Ground and Space Based Remote Sensing for Air Pollution)

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13 pages, 5277 KiB

Open AccessArticle

Assessing Effect of Targeting Reduction of PM_2.5 Concentration on Human Exposure and Health Burden in Hong Kong Using Satellite Observation

by Changqing Lin, Alexis K. H. Lau, Xingcheng Lu, Jimmy C. H. Fung, Zhiyuan Li, Chengcai Li and Andromeda H. S. Wong

Remote Sens. 2018, 10(12), 2064; https://doi.org/10.3390/rs10122064 - 19 Dec 2018

Cited by 9 | Viewed by 3789

Abstract

Targeting reduction of PM_2.5 concentration lessens population exposure level and health burden more effectively than uniform reduction does. Quantitative assessment of effect of the targeting reduction is limited because of the lack of spatially explicit PM_2.5 data. This study aimed to investigate extent of exposure and health benefits resulting from the targeting reduction of PM_2.5 concentration. We took advantage of satellite observations to characterize spatial distribution of PM_2.5 concentration at a resolution of 1 km. Using Hong Kong of China as the study region (804 satellite’s pixels covering its residential areas), human exposure level (c_ρ) and premature mortality attributable to PM_2.5 (Mort) for 2015 were estimated to be 25.9 μg/m³ and 4112 people per year, respectively. We then performed 804 diagnostic tests that reduced PM_2.5 concentrations by −1 μg/m³ in different areas and a reference test that uniformly spread the −1 μg/m³. We used a benefit rate from targeting reduction (BRT), which represented a ratio of declines in c_ρ (or Mort) with and without the targeting reduction, to quantify the extent of benefits. The diagnostic tests estimated the BRT levels for both human exposure and premature mortality to be 4.3 over Hong Kong. It indicates that the declines in human exposure and premature mortality quadrupled with a targeting reduction of PM_2.5 concentration over Hong Kong. The BRT values for districts of Hong Kong could be as high as 5.6 and they were positively correlated to their spatial variabilities in population density. Our results underscore the substantial exposure and health benefits from the targeting reduction of PM_2.5 concentration. To better protect public health in Hong Kong, super-regional and regional cooperation are essential. Meanwhile, local environmental policy is suggested to aim at reducing anthropogenic emissions from mobile and area (e.g., residential) sources in central and northwestern areas. Full article

(This article belongs to the Special Issue Application of Ground and Space Based Remote Sensing for Air Pollution)

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14 pages, 3116 KiB

Open AccessArticle

The Influence of Instrumental Line Shape Degradation on the Partial Columns of O₃, CO, CH₄ and N₂O Derived from High-Resolution FTIR Spectrometry

by Youwen Sun, Cheng Liu, Kalok Chan, Wei Wang, Changong Shan, Qihou Hu and Jianguo Liu

Remote Sens. 2018, 10(12), 2041; https://doi.org/10.3390/rs10122041 - 14 Dec 2018

Cited by 8 | Viewed by 3512

Abstract

High resolution Fourier transform infrared (FTIR) measurement of direct sunlight does not only provide information of trace gas total columns, but also vertical distribution. Measured O₃, CO, CH₄, and N₂O can be separated into multiple partial columns using the optimal estimation method (OEM). The retrieval of trace gas profiles is sensitive to the instrument line shape (ILS) of the FTIR spectrometer. In this paper, we present an investigation of the influence of ILS degradation on the partial column retrieval of O₃, CO, CH₄, and N₂O. Sensitivities of the partial column, error, and degrees of freedom (DOFs) of each layer to different levels of ILS degradation for O₃, CO, CH₄, and N₂O are estimated. We then evaluate the impact of ILS degradation on the long-term measurements. In addition, we derive the range of ILS degradation corresponding to the acceptable uncertainties of O₃, CO, CH₄, and N₂O results. The results show that the uncertainties induced by the ILS degradation on the absolute value, error, and the DOFs of the partial column are altitude and gas species dependent. The uncertainties of the partial columns of O₃ and CO are larger than those on CH₄ and N₂O. The stratospheric partial columns are more sensitive to the ILS degradation compared to the tropospheric part. Our result improves the understanding of the ILS degradation on the FTIR measurements, which is important for the quantification of the measurement uncertainties and minimizes the bias of the inter-comparison between different measurement platforms. This is especially useful for the validation of satellite observations, the data assimilation of chemical model simulations, and the quantification of the source/sink/trend from the FTIR measurements. Full article

(This article belongs to the Special Issue Application of Ground and Space Based Remote Sensing for Air Pollution)

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17 pages, 11715 KiB

Open AccessArticle

Retrieval of Daily PM_2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing–Tianjin–Hebei Region, China

by Lijuan Li, Baozhang Chen, Yanhu Zhang, Youzheng Zhao, Yue Xian, Guang Xu, Huifang Zhang and Lifeng Guo

Remote Sens. 2018, 10(12), 2006; https://doi.org/10.3390/rs10122006 - 11 Dec 2018

Cited by 20 | Viewed by 3199

Abstract

Exposure to fine particulate matter (PM_2.5) is associated with adverse health impacts on the population. Satellite observations and machine learning algorithms have been applied to improve the accuracy of the prediction of PM_2.5 concentrations. In this study, we developed a PM_2.5 retrieval approach using machine-learning methods, based on aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observation System (EOS) Terra and Aqua polar-orbiting satellites, near-ground meteorological variables from the NASA Goddard Earth Observing System (GEOS), and ground-based PM_2.5 observation data. Four models, which are orthogonal regression (OR), regression tree (Rpart), random forests (RF), and support vector machine (SVM), were tested and compared in the Beijing–Tianjin–Hebei (BTH) region of China in 2015. Aerosol products derived from the Terra and Aqua satellite sensors were also compared. The 10-repeat 5-fold cross-validation (10 × 5 CV) method was subsequently used to evaluate the performance of the different aerosol products and the four models. The results show that the performance of the Aqua dataset was better than that of the Terra dataset, and that the RF algorithm has the best predictive performance (Terra: R = 0.77, RMSE = 43.51 μg/m³; Aqua: R = 0.85, RMSE = 33.90 μg/m³). This study shows promise for predicting the spatiotemporal distribution of PM_2.5 using the RF model and Aqua aerosol product with the assistance of PM_2.5 site data. Full article

(This article belongs to the Special Issue Application of Ground and Space Based Remote Sensing for Air Pollution)

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