Dataset Information

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

ABSTRACT: Satellite-based PM(2.5) monitoring has the potential to complement ground PM(2.5) monitoring networks, especially for regions with sparsely distributed monitors. Satellite remote sensing provides data on aerosol optical depth (AOD), which reflects particle abundance in the atmospheric column. Thus AOD has been used in statistical models to predict ground-level PM(2.5) concentrations. However, previous studies have shown that AOD may not be a strong predictor of PM(2.5) ground levels. Another shortcoming of remote sensing is the large number of non-retrieval days (i.e., days without satellite data available) due to clouds and snow- and ice-cover. In this paper we propose statistical approaches to overcome these two shortcomings, thereby making satellite imagery a viable method to estimate PM(2.5) concentrations. First, we render AOD a robust predictor of PM(2.5) mass concentration by introducing an AOD daily calibration approach through the use of mixed effects model. Second, we develop models that combine AOD and ground monitoring data to predict PM(2.5) concentrations during non-retrieval days. A key feature of this approach is that we develop these prediction models separately for groups of days defined by the observed amount of spatial heterogeneity in concentrations across the study region. Subsequently, these methodologies were applied to examine the spatial and temporal patterns of daily PM(2.5) concentrations for both retrieval days (i.e., days with satellite data available) and non-retrieval days in the New England region of the United States during the period 2000-2008. Overall, for the years 2000-2008, our statistical models predicted surface PM(2.5) concentrations with reasonably high R(2) (0.83) and low percent mean relative error (3.5%). Also the spatial distribution of the estimated PM(2.5) levels in the study domain clearly exhibited densely populated and high traffic areas. The method we have developed demonstrates that remote sensing can have a tremendous impact on the fields of environmental monitoring and human exposure assessment.

SUBMITTER: Lee HJ

PROVIDER: S-EPMC3454441 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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

Lee Hyung Joo HJ Coull Brent A BA Bell Michelle L ML Koutrakis Petros P

Environmental research 20120728

Satellite-based PM(2.5) monitoring has the potential to complement ground PM(2.5) monitoring networks, especially for regions with sparsely distributed monitors. Satellite remote sensing provides data on aerosol optical depth (AOD), which reflects particle abundance in the atmospheric column. Thus AOD has been used in statistical models to predict ground-level PM(2.5) concentrations. However, previous studies have shown that AOD may not be a strong predictor of PM(2.5) ground levels. Another sho ...[more]

PMID: 22841416

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Project description:Long-term PM2.5 exposure has been associated with various adverse health outcomes. However, most ground monitors are located in urban areas, leading to a potentially biased representation of true regional PM2.5 levels. To facilitate epidemiological studies, accurate estimates of the spatiotemporally continuous distribution of PM2.5 concentrations are important. Satellite-retrieved aerosol optical depth (AOD) has been increasingly used for PM2.5 concentration estimation due to its comprehensive spatial coverage. Nevertheless, previous studies indicated that an inherent disadvantage of many AOD products is their coarse spatial resolution. For instance, the available spatial resolutions of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) AOD products are 10 and 17.6 km, respectively. In this paper, a new AOD product with 1 km spatial resolution retrieved by the multi-angle implementation of atmospheric correction (MAIAC) algorithm based on MODIS measurements was used. A two-stage model was developed to account for both spatial and temporal variability in the PM2.5-AOD relationship by incorporating the MAIAC AOD, meteorological fields, and land use variables as predictors. Our study area is in the southeastern US centered at the Atlanta metro area, and data from 2001 to 2010 were collected from various sources. The model was fitted annually, and we obtained model fitting R2 ranging from 0.71 to 0.85, mean prediction error (MPE) from 1.73 to 2.50 ?g m-3, and root mean squared prediction error (RMSPE) from 2.75 to 4.10 ?g m-3. In addition, we found cross-validation R2 ranging from 0.62 to 0.78, MPE from 2.00 to 3.01 ?gm-3, and RMSPE from 3.12 to 5.00 ?gm-3, indicating a good agreement between the estimated and observed values. Spatial trends showed that high PM2.5 levels occurred in urban areas and along major highways, while low concentrations appeared in rural or mountainous areas. Our time-series analysis showed that, for the 10-year study period, the PM2.5 levels in the southeastern US have decreased by ?20 %. The annual decrease has been relatively steady from 2001 to 2007 and from 2008 to 2010 while a significant drop occurred between 2007 and 2008. An observed increase in PM2.5 levels in year 2005 is attributed to elevated sulfate concentrations in the study area in warm months of 2005.

Project description:The countries around the world are dealing with air quality issues for decades due to their mode of production and energy usages. The outbreak of COVID-19 as a pandemic and consequent global economic shutdown, for the first time, provided a base for the real-time experiment of the effect of reduced emissions across the globe in abetting the air pollution issue. The present study dealt with the changes in Aerosol Optical Depth (AOD), a marker of air pollution, because of global economic shutdown due to the coronavirus pandemic. The study considered the countries in south and south-east Asia (SSEA), Europe and the USA for their extended period of lockdown due to coronavirus pandemic. Daily Aerosol Optical Depth (AOD) from Moderate-resolution imaging spectroradiometer (MODIS) and tropospheric column density of NO2 and SO2 from Ozone monitoring instrument (OMI) sensors, including meteorological data such as wind speed (WS) and relative humidity (RH) were analyzed during the pre-lockdown (2017-2019) and lockdown periods (2020). The average AOD, NO2 and SO2 during the lockdown period were statistically compared with their pre-lockdown average using Wilcoxon-signed-paired-rank test. The accuracy of the MODIS-derived AOD, including the changing pattern of AOD due to lockdown was estimated using AERONET data. The weekly anomaly of AOD, NO2 and SO2 was used for analyzing the space-time variation of aerosol load as restrictions were imposed by the concerned countries at the different points of time. Additionally, a random forest-based regression (RF) model was used to examine the effects of meteorological and emission parameters on the spatial variation of AOD. A significant reduction of AOD (-20%) was obtained for majority of the areas in SSEA, Europe and USA during the lockdown period. Yet, the clusters of increased AOD (30-60%) was obtained in the south-east part of SSEA, the western part of Europe and US regions. NO2 reductions were measured up to 20-40%, while SO2 emission increased up to 30% for a majority of areas in these regions. A notable space-time variation was observed in weekly anomaly. We found the evidence of the formation of new particles for causing high AOD under high RH and low WS, aided by the downward vertical wind flow. The RF model showed a distinguishable relative importance of emission and meteorological factors among these regions to account for the spatial variability of AOD. Our findings suggest that the continued lockdown might provide a temporary solution to air pollution; however, to combat persistent air quality issues, it needs switching over to the cleaner mode of production and energy. The findings of this study, thus, advocated for alternative energy policy at the global scale.

Dataset Information

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

Publications

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

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