Estimating PM2.5 in Xi'an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models

You, Wei; Zang, Zengliang; Pan, Xiaojun; Zhang, Lifeng; Chen, Dan

doi:10.1016/j.scitotenv.2014.11.024

Cited by 103 publications

(51 citation statements)

References 65 publications

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“…If a large proportion of AOD data was missing, we could select certain buffer areas for each spatial observation point and fill in the vacancy according to the corrected Akaike Information Criterion. Thus, spatial distribution of regression parameter gained, and the GWR model could explain the effects of the spatial autocorrelation within a certain area when spatial aggregation occurred for a certain variable [87,105,107,128]. …”

Section: Theory Background and Applicationmentioning

confidence: 99%

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

et al. 2016

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This study reviewed the prediction of fine particulate matter (PM 2.5 ) from satellite aerosol optical depth (AOD) and summarized the advantages and limitations of these predicting models. A total of 116 articles were included from 1436 records retrieved. The number of such studies has been increasing since 2003. Among these studies, four predicting models were widely used: Multiple Linear Regression (MLR) (25 articles), Mixed-Effect Model (MEM) (23 articles), Chemical Transport Model (CTM) (16 articles) and Geographically Weighted Regression (GWR) (10 articles). We found that there is no so-called best model among them and each has both advantages and limitations. Regarding the prediction accuracy, MEM performs the best, while MLR performs worst. CTM predicts PM 2.5 better on a global scale, while GWR tends to perform well on a regional level. Moreover, prediction performance can be significantly improved by combining meteorological variables with land use factors of each region, instead of only considering meteorological variables. In addition, MEM has advantages in dealing with the AOD data with missing values. We recommend that with the help of higher resolution AOD data, future works could be focused on developing satellite-based predicting models for the prediction of historical PM 2.5 and other air pollutants.

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Section: Theory Background and Applicationmentioning

confidence: 99%

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

et al. 2016

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“…Aerosol optical thickness (AOT), derived from satellite observations, is a dimensionless measure of aerosol abundance and atmospheric extinction of radiance over the entire air column. Previous researches have revealed a strong positive relationship between satellite-based measurements of AOT and ground-based measurements of particulate matters smaller than 2.5 μm and 10 μm in aerodynamic diameters (PM2.5 and PM10, respectively) [15][16][17][18][19][20][21][22][23][24][25]. A summary of linear AOT/PM relationships, intercepts and correlation coefficients in several previous studies is shown in Table 1.…”

Section: Introductionmentioning

confidence: 98%

Retrieval of High-Resolution Atmospheric Particulate Matter Concentrations from Satellite-Based Aerosol Optical Thickness over the Pearl River Delta Area, China

Yang

Wang

2015

Remote Sensing

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Satellite remote sensing offers an effective approach to estimate indicators of air quality on a large scale. It is critically significant for air quality monitoring in areas experiencing rapid urbanization and consequently severe air pollution, like the Pearl River Delta (PRD) in China. This paper starts with examining ground observations of particulate matter (PM) and the relationship between PM10 (particles smaller than 10 μm) and aerosol optical thickness (AOT) by analyzing observations on the sampling sites in the PRD. A linear regression (R 2 = 0.51) is carried out using MODIS-derived 500 m-resolution AOT and PM10 concentration from monitoring stations. Data of atmospheric boundary layer (ABL) height and relative humidity are used to make vertical and humidity corrections on AOT. Results after correction show higher correlations (R 2 = 0.55) between extinction coefficient and PM10. However, coarse spatial resolution of meteorological data affects the smoothness of retrieved maps, which suggests high-resolution and accurate meteorological data are critical to increase retrieval accuracy of PM. Finally, the model provides the spatial distribution maps of instantaneous and yearly average PM10 over the PRD. It is proved that observed PM10 is more relevant to yearly mean AOT than instantaneous values.

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“…The most widely used satellite parameter is aerosol optical depth (AOD) (Hoff and Christopher, 2009). Many satellite instruments own the capacity to provide AOD products, and have been applied to the monitoring of PM2.5, for instance, the Moderate Resolution Imaging Spectroradiometer (Li et al, 2017b) and Multiangle Imaging SpectroRadiometer (You et al, 2015) on board Earth Observing System (EOS) satellites (i.e., Terra and Aqua) etc.…”

Section: Introductionmentioning

confidence: 99%

REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM_2.5 USING SATELLITE REMOTE SENSING

Zhang

Shen

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Satellite remote sensing has been reported to be a promising approach for the monitoring of atmospheric PM2.5. However, the satellite-based monitoring of ground-level PM2.5 is still challenging. First, the previously used polar-orbiting satellite observations, which can be usually acquired only once per day, are hard to monitor PM2.5 in real time. Second, many data gaps exist in satellitederived PM2.5 due to the cloud contamination. In this paper, the hourly geostationary satellite (i.e., Harawari-8) observations were adopted for the real-time monitoring of PM2.5 in a deep learning architecture. On this basis, the satellite-derived PM2.5 in conjunction with ground PM2.5 measurements are incorporated into a spatio-temporal fusion model to fill the data gaps. Using Wuhan Urban Agglomeration as an example, we have successfully derived the real-time and seamless PM2.

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Estimating PM2.5 in Xi'an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models

Cited by 103 publications

References 65 publications

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

Retrieval of High-Resolution Atmospheric Particulate Matter Concentrations from Satellite-Based Aerosol Optical Thickness over the Pearl River Delta Area, China

REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM_2.5 USING SATELLITE REMOTE SENSING

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Estimating PM2.5 in Xi'an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models

Cited by 103 publications

References 65 publications

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

Retrieval of High-Resolution Atmospheric Particulate Matter Concentrations from Satellite-Based Aerosol Optical Thickness over the Pearl River Delta Area, China

REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM2.5 USING SATELLITE REMOTE SENSING

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REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM_2.5 USING SATELLITE REMOTE SENSING