Use of satellite-based aerosol optical depth and spatial clustering to predict ambient PM2.5 concentrations

Lee, Hyung Joo; Coull, Brent A.; Bell, Michelle L.; Koutrakis, Petros

doi:10.1016/j.envres.2012.06.011

Cited by 103 publications

(81 citation statements)

References 37 publications

(39 reference statements)

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“…This study also proved that high resolution GEOS AOD may be a better predictor of urban PM 2.5 than rough resolution MODIS AOD [35]. Lee et al [64] found that the R 2 value of MEM could reach 0.830 if missing AOD value were filled using a combination of cluster analysis and generalized additive models. In the mid-Atlantic region, Kloog et al [34] improved the MEM by adopting IPW for non-random missing AOD data, and obtained a value of 0.850 for the cross-validation of R 2 .…”

Section: Theory Background and Applicationmentioning

confidence: 80%

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: 80%

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

et al. 2016

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“…It is interesting to see that within the urban areas of major cities in southern New England, the annual mean PM2.5 concentrations are obviously high as compared to their surrounding rural forest areas, but their spatial variations tend to be low. This might be because of the regional impacts of the transported PM2.5 pollution on these cities, as reported by a previous study [23]. …”

Section: Spatiotemporal Estimation Of the Pm25 Concentrationmentioning

confidence: 74%

Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States

Zhang

et al. 2017

Remote Sensing

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Degraded air quality by PM 2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM 2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM 2.5 concentration, little research was focused on the use of remotely sensed nighttime light (NTL) imagery. This study evaluated the merits of using NTL satellite images in predicting ground-level PM 2.5 at a regional scale. Geographically weighted regression (GWR) was employed to estimate the PM 2.5 concentration and analyze its relationships with AOD, meteorological variables, and NTL data across the New England region. Observed data in 2013 were used to test the constructed GWR models for PM 2.5 prediction. The Vegetation Adjusted NTL Urban Index (VANUI), which incorporates Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI into NTL to overcome the defects of NTL data, was used as a predictor variable for final PM 2.5 prediction. Results showed that Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL imagery could be an important dataset for more accurately estimating PM 2.5 exposure, especially in urbanized and densely populated areas. VANUI data could obviously improve the performance of GWR for the warm season (GWR model with VANUI performed 17% better than GWR model without NDVI and NTL data and 7.26% better than GWR model without NTL data in terms of RMSE), while its improvements were less obvious for the cold season (GWR model with VANUI performed 3.6% better than the GWR model without NDVI and NTL data and 1.83% better than the GWR model without NTL data in terms of RMSE). Moreover, the spatial distribution of the estimated PM 2.5 levels clearly revealed patterns consistent with those densely populated areas and high traffic areas, implying a close and positive correlation between VANUI and PM 2.5 concentration. In general, the DMSP/OLS NTL satellite imagery is promising for providing additional information for PM 2.5 monitoring and prediction.

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“…There are extensive studies investigating the PM2.5-AOD relationship by the use of either an empirical statistical method (Engel-Cox et al, 2004;Liu et al, 2005Liu et al, , 2009Gupta et al, 2006;Koelemeijer et al, 2006;Gupta and Christopher, 2008;Paciorek et al, 2008;Di Nicolantonio et al, 2009;Schaap et al, 2009;Lee et al, 2012;Sorek-Hamer et al, 2013;Strawa et al, 2013;Chudnovsky et al, 2014;Ma et al, 2014) or a chemical transportation model (Liu et al, 2004;Van Donkelaar et al, 2006Kessner et al, 2013;Xu et al, 2015). In these studies, aerosol vertical distributions are estimated based on model simulation or under an assumption that aerosols are well mixed within the boundary layer and then decrease exponentially with height.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing of PM2.5 during cloudy and nighttime periods using ceilometer backscatter

Joseph

Min

et al. 2017

Atmos. Meas. Tech.

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Abstract. Monitoring PM2.5 (particulate matter with aerodynamic diameter d ≤ 2.5 µm) mass concentration has become of more importance recently because of the negative impacts of fine particles on human health. However, monitoring PM2.5 during cloudy and nighttime periods is difficult since nearly all the passive instruments used for aerosol remote sensing are not able to measure aerosol optical depth (AOD) under either cloudy or nighttime conditions. In this study, an empirical model based on the regression between PM2.5 and the near-surface backscatter measured by ceilometers was developed and tested using 6 years of data (2006 to 2011) from the Howard University Beltsville Campus (HUBC) site. The empirical model can explain ∼ 56, ∼ 34 and ∼ 42 % of the variability in the hourly average PM2.5 during daytime clear, daytime cloudy and nighttime periods, respectively. Meteorological conditions and seasons were found to influence the relationship between PM2.5 mass concentration and the surface backscatter. Overall the model can explain ∼ 48 % of the variability in the hourly average PM2.5 at the HUBC site when considering the seasonal variation. The model also was tested using 4 years of data (2012 to 2015) from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, which was geographically and climatologically different from the HUBC site. The results show that the empirical model can explain ∼ 66 and ∼ 82 % of the variability in the daily average PM2.5 at the ARM SGP site and HUBC site, respectively. The findings of this study illustrate the strong need for ceilometer data in air quality monitoring under cloudy and nighttime conditions. Since ceilometers are used broadly over the world, they may provide an important supplemental source of information of aerosols to determine surface PM2.5 concentrations.

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Use of satellite-based aerosol optical depth and spatial clustering to predict ambient PM2.5 concentrations

Cited by 103 publications

References 37 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

Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States

Remote sensing of PM2.5 during cloudy and nighttime periods using ceilometer backscatter

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