Dataset Information

10-year spatial and temporal trends of PM2.5 concentrations in the southeastern US estimated using high-resolution satellite data.

ABSTRACT: 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.

SUBMITTER: Hu X

PROVIDER: S-EPMC5619667 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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10-year spatial and temporal trends of PM2.5 concentrations in the southeastern US estimated using high-resolution satellite data.

Hu X X Waller L A LA Lyapustin A A Wang Y Y Liu Y Y

Atmospheric chemistry and physics 20140625 12

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 concent ...[more]

PMID: 28966656

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Project description: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.

Project description:PM2.5 concentrations throughout much of the U.S. have decreased over the last 15 years, but emissions and concentration trends can vary by location and source type. Such trends should be understood to inform air quality management and policies. This work examines trends in emissions, concentrations and source apportionments in two large Midwest U.S. cities, Detroit, Michigan, and Chicago, Illinois. Annual and seasonal trends were investigated using National Emission Inventory (NEI) data for 2002 to 2011, speciated ambient PM2.5 data from 2001 to 2014, apportionments from positive matrix factorization (PMF) receptor modeling, and quantile regression. Over the study period, county-wide data suggest emissions from point sources decreased (Detroit) or held constant (Chicago), while emissions from on-road mobile sources were constant (Detroit) or increased (Chicago), however changes in methodology limit the interpretation of inventory trends. Ambient concentration data also suggest source and apportionment trends, e.g., annual median concentrations of PM2.5 in the two cities declined by 3.2 to 3.6 %/yr (faster than national trends), and sulfate concentrations (due to coal-fired facilities and other point source emissions) declined even faster; in contrast, organic and elemental carbon (tracers of gasoline and diesel vehicle exhaust) declined more slowly or held constant. The PMF models identified nine sources in Detroit and eight in Chicago, the most important being secondary sulfate, secondary nitrate and vehicle emissions. A minor crustal dust source, metals sources, and a biomass source also were present in both cities. These apportionments showed that the median relative contributions from secondary sulfate sources decreased by 4.2 to 5.5% per year in Detroit and Chicago, while contributions from metals sources, biomass sources, and vehicles increased from 1.3 to 9.2% per year. This first application of quantile regression to trend analyses of speciated PM2.5 data reveals that source contributions to PM2.5 varied as PM2.5 concentrations decreased, and that the fraction of PM2.5 due to emissions from vehicles and other local emissions has increased. Each data source has uncertainties, but emissions, monitoring and PMF data provide complementary information that can help to discern trends and identify contributing sources. Study results emphasize the need to target specific sources in policies and regulations aimed at decreasing PM2.5 concentrations in urban areas.

Dataset Information

10-year spatial and temporal trends of PM2.5 concentrations in the southeastern US estimated using high-resolution satellite data.

Publications

10-year spatial and temporal trends of PM<sub>2.5</sub> concentrations in the southeastern US estimated using high-resolution satellite data.

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