Optimal estimation for global ground‐level fine particulate matter concentrations (original) (raw)
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Environmental Health Perspectives, 2010
Background: Epidemiologic and health impact studies of fine particulate matter with diameter < 2.5 µm (PM 2.5 ) are limited by the lack of monitoring data, especially in developing countries. Satellite observations offer valuable global information about PM 2.5 concentrations. Objective: In this study, we developed a technique for estimating surface PM 2.5 concentrations from satellite observations. Methods: We mapped global ground-level PM 2.5 concentrations using total column aerosol optical depth (AOD) from the MODIS (Moderate Resolution Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) satellite instruments and coincident aerosol vertical profiles from the GEOS-Chem global chemical transport model. results: We determined that global estimates of long-term average (1 January 2001 to 31 December 2006) PM 2.5 concentrations at approximately 10 km × 10 km resolution indicate a global population-weighted geometric mean PM 2.5 concentration of 20 µg/m 3 .
Assessment of PM2.5 concentrations over bright surfaces using MODIS satellite observations
Remote Sensing of Environment, 2015
Aerosol optical depth (AOD) MODIS PM 2.5 Mixed effects models Exposure to particles with an aerodynamic diameter smaller than 2.5 μm (PM 2.5) adversely impacts human health. In many geographical regions where ground PM 2.5 monitoring is spatially sparse and unsuitable for environmental health inference, satellite remote sensing can potentially be used for estimating human exposure to PM 2.5. However, retrieval of the aerosol optical depth (AOD) using the Dark Target (DT) algorithm is uncertain in many regions worldwide (e.g. western USA, the Middle East and central Asia) due to low signal-to-noise ratio as a result of high surface reflectivity in the spectral bands used by the algorithm. In this study we use the Deep Blue (DB) algorithm as well as a combined DB-DT algorithm for AOD retrievals. The AOD products are used to predict ground PM 2.5 using mixed effects models and the daily calibration approach. Models for the two study areas (Israel and San Joaquin Valley, Central California) were developed independently and then compared to each other. Using the AOD DB within a mixed effects model considerably improved PM 2.5 prediction in high reflectance regions, revealing in both study areas enhanced model performance (in terms of both R 2 and the root mean square prediction error), significant increase in the spatiotemporal availability of the AOD product, and improved PM 2.5 prediction relative to using AOD DT retrievals.
Summary During the last years, attention has been devoted to the possibility to monitor surface Particulate Matter (PM) concentrations using satellite observations. The capability of NASA sensors MODIS-on board Terra and Aqua platforms-to retrieve tropospheric aerosol optical properties has been exploited to verify the degree of correlation between satellite Aerosol Optical Depth (AOD) at 550 nm and surface-level PM2. 5. However, several factors affect the relationship between columnar AOD and PM.
High-Resolution Satellite Mapping of Fine Particulates Based on Geographically Weighted Regression
—Satellite-retrieved aerosol optical depth (AOD) has been increasingly utilized for the mapping of fine particulate matter (PM 2.5) concentrations. An accurate estimation and mapping of PM 2.5 concentrations depends on the high-resolution AOD data and a robust mathematical model that takes into account the spatial nonstationary relationship between PM 2.5 and AOD. Take the core portion of the Beijing–Hebei–Tianjin (Jing-Jin-Ji) urban agglomeration as case study (the most seriously polluted region in China). Land use, population, meteorological variables, and simplified aerosol retrieval algorithm-retrieved AOD at 1-km resolution are employed as the predictors for the geographically weighted regression (GWR) and the ordinary least squares (OLS) model to map the spatial distribution of PM 2.5 concentrations. The GWR model shows significant spatial variations in PM 2.5 concentrations over the region than the traditional OLS model, which reveals relative homogeneous variations. Validation with ground-level PM 2.5 concentrations demonstrates that PM 2.5 concentrations predicted by the GWR model (R 2 = 0.75, RM SE = 10 μg/m 3) correlate better than those by the OLS model (R 2 = 0.53, RM SE = 16 μg/m 3). These results suggest that the GWR model offered a more reliable way for the prediction of spatial distribution of PM 2.5 concentrations over urban areas. Index Terms—Aerosol optical depth (AOD), geographically weighted regression (GWR), moderate resolution imaging spectro-radiometer (MODIS), PM 2.5 , simplified aerosol retrieval algorithm (SARA), urban area.
Journal of the Air & Waste Management Association, 2007
We develop a method that uses both the total column aerosol optical depth (AOD) and the fractional AOD values for different aerosol types, derived from Multiangle Imaging SpectroRadiometer (MISR) aerosol data, to estimate ground-level concentrations of fine particulate matter (PM2.5) mass and its major constituents in eastern and western United States. Compared with previous research on linking column AOD with ground-level PM2.5, this method treats various MISR aerosol components as individual predictor variables. Therefore, the contributions of different particle types to PM2.5 concentrations can be estimated. When AOD is greater than 0.15, MISR is able to distinguish dust from non-dust particles with an uncertainty level of approximately 4%, and light-absorbing from non-light-absorbing particles with an uncertainty level of approximately 20%. Further analysis shows that MISR Version 17 aerosol microphysical properties have good sensitivity and internal consistency among different mixture classes. The retrieval uncertainty of individual fractional AODs ranges between 5 and 11% in the eastern United States, and between 11 and 31% in the west for non-dust aerosol components. These results provide confidence that the fractional AOD models with their inherent flexibility can make more accurate predictions of the concentrations of PM2.5 and its constituents.
Atmospheric Measurement Techniques, 2015
Ground-based observations have insufficient spatial coverage to assess long-term human exposure to fine particulate matter (PM 2.5 ) at the global scale. Satellite remote sensing offers a promising approach to provide information on both short-and long-term exposure to PM 2.5 at local-toglobal scales, but there are limitations and outstanding questions about the accuracy and precision with which groundlevel aerosol mass concentrations can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual average PM 2.5 and discontinuous satellite observations of columnar aerosol optical depth (AOD). We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing estimates for application in health-effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN) includes a global federation of ground-level monitors of hourly PM 2.5 situated primarily in highly populated regions and collocated with existing ground-based sun photometers that measure AOD. The instruments, a three-wavelength nephelometer and impaction filter sampler for both PM 2.5 and PM 10 , are highly autonomous. Hourly PM 2.5 concentrations are inferred from the combination of weighed filters and nephelometer data. Data from existing networks were used to develop and evaluate network sampling characteristics. SPARTAN filters are analyzed for mass, black carbon, watersoluble ions, and metals. These measurements provide, in a variety of regions around the world, the key data required to evaluate and enhance satellite-based PM 2.5 estimates used for assessing the health effects of aerosols. Mean PM 2.5 concentrations across sites vary by more than 1 order of magnitude. Our initial measurements indicate that the ratio of AOD to ground-level PM 2.5 is driven temporally and spatially by the vertical profile in aerosol scattering. Spatially this ratio is also strongly influenced by the mass scattering efficiency.
Journal of the Air & Waste Management Association, 2007
We use the fractional aerosol optical depth (AOD) values derived from Multiangle Imaging Spectroradiometer (MISR) aerosol component measurements, along with aerosol transport model constraints, to estimate groundlevel concentrations of fine particulate matter (PM 2.5 ) mass and its major constituents in the continental United States. Regression models using fractional AODs predict PM 2.5 mass and sulfate (SO 4 ) concentrations in both the eastern and western United States, and nitrate (NO 3 ) concentrations in the western United States reasonably well, compared with the available ground-level U.S. Environment Protection Agency (EPA) measurements. These models show substantially improved predictive power when compared with similar models using total-column AOD as a single predictor, especially in the western United States. The relative contributions of the MISR aerosol components in these regression models are used to estimate size distributions of EPA PM 2.5 species. This method captures the overall shapes of the size distributions of PM 2.5 mass and SO 4 particles in the east and west, and NO 3 particles in the west. However, the estimated PM 2.5 and SO 4 mode diameters are smaller than those previously reported by monitoring studies conducted at ground level. This is likely due to the satellite sampling bias caused by the inability to retrieve aerosols through cloud cover, and the impact of particle hygroscopicity on measured particle size distributions at ground level.
High resolution aerosol data from MODIS satellite for urban air quality studies
Open Geosciences, 2014
The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not suitable for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM 2 5 as measured by the 27 EPA ground monitoring stations was investigated. These results were also compared to conventional MODIS 10 km AOD retrievals (MOD04) for the same days and locations. The coefficients of determination for MOD04 and for MAIAC are R 2 =0.45 and 0.50 respectively, suggested that AOD is a reasonably good proxy for PM 2 5 ground concentrations. Finally, we studied the relationship between PM 2 5 and AOD at the intra-urban scale ( 10 km) in Boston. The fine resolution results indicated spatial variability in particle concentration at a sub-10 kilometer scale. A local analysis for the Boston area showed that the AOD-PM 2 5 relationship does not depend on relative humidity and air temperatures below~7°C. The correlation improves for temperatures above 7 -16°C. We found no dependence on the boundary layer height except when the former was in the range 250-500 m. Finally, we apply a mixed effects model approach to MAIAC aerosol optical depth (AOD) retrievals from MODIS to predict PM 2 5 concentrations within the greater Boston area. With this approach we can control for the inherent day-to-day variability in the AOD-PM 2 5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance. Our results show that the model-predicted PM 2 5 mass concentrations are highly correlated with the actual observations (out-of-sample R 2 of 0.86). Therefore, adjustment for the daily variability in the AOD-PM 2 5 relationship provides a means for obtaining spatially-resolved PM 2 5 concentrations.
Global chemical composition of ambient fine particulate matter for exposure assessment
Environmental science & technology, 2014
Epidemiologic and health impact studies are inhibited by the paucity of global, long-term measurements of the chemical composition of fine particulate matter. We inferred PM2.5 chemical composition at 0.1° × 0.1° spatial resolution for 2004-2008 by combining aerosol optical depth retrieved from the MODIS and MISR satellite instruments, with coincident profile and composition information from the GEOS-Chem global chemical transport model. Evaluation of the satellite-model PM2.5 composition data set with North American in situ measurements indicated significant spatial agreement for secondary inorganic aerosol, particulate organic mass, black carbon, mineral dust, and sea salt. We found that global population-weighted PM2.5 concentrations were dominated by particulate organic mass (11.9 ± 7.3 μg/m(3)), secondary inorganic aerosol (11.1 ± 5.0 μg/m(3)), and mineral dust (11.1 ± 7.9 μg/m(3)). Secondary inorganic PM2.5 concentrations exceeded 30 μg/m(3) over East China. Sensitivity simula...
Journal of the Air & Waste Management Association
We use the fractional aerosol optical depth (AOD) values derived from Multiangle Imaging Spectroradiometer (MISR) aerosol component measurements, along with aerosol transport model constraints, to estimate groundlevel concentrations of fine particulate matter (PM 2.5 ) mass and its major constituents in the continental United States. Regression models using fractional AODs predict PM 2.5 mass and sulfate (SO 4 ) concentrations in both the eastern and western United States, and nitrate (NO 3 ) concentrations in the western United States reasonably well, compared with the available ground-level U.S. Environment Protection Agency (EPA) measurements. These models show substantially improved predictive power when compared with similar models using total-column AOD as a single predictor, especially in the western United States. The relative contributions of the MISR aerosol components in these regression models are used to estimate size distributions of EPA PM 2.5 species. This method captures the overall shapes of the size distributions of PM 2.5 mass and SO 4 particles in the east and west, and NO 3 particles in the west. However, the estimated PM 2.5 and SO 4 mode diameters are smaller than those previously reported by monitoring studies conducted at ground level. This is likely due to the satellite sampling bias caused by the inability to retrieve aerosols through cloud cover, and the impact of particle hygroscopicity on measured particle size distributions at ground level.