Article pubs.acs.org/est
Improving the Accuracy of Daily Satellite-Derived Ground-Level Fine Aerosol Concentration Estimates for North America Aaron van Donkelaar* Dept. of Physics and Atmospheric Science, Dalhousie University, Halifax, N.S., Canada
Randall V. Martin Dept. of Physics and Atmospheric Science, Dalhousie University, Halifax, N.S., Canada Harvard Smithsonian Center for Astrophysics, 407 Cambridge, Massachusetts, USA
Adam N. Pasch Sonoma Technology Inc., Petaluma, CA, USA
James J. Szykman United States Environmental Protection Agency Office of Research and Development, Hampton, VA, USA
Lin Zhang Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
Yuxuan X. Wang Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Institute for Global Change Studies, Tsinghua University, Beijing, China
Dan Chen Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, USA ABSTRACT: We improve the accuracy of daily ground-level fine particulate matter concentrations (PM2.5) derived from satellite observations (MODIS and MISR) of aerosol optical depth (AOD) and chemical transport model (GEOSChem) calculations of the relationship between AOD and PM2.5. This improvement is achieved by (1) applying climatological ground-based regional bias-correction factors based upon comparison with in situ PM2.5, and (2) applying spatial smoothing to reduce random uncertainty and extend coverage. Initial daily 1-σ mean uncertainties are reduced across the United States and southern Canada from ± (1 μg/m3 + 67%) to ± (1 μg/m3 + 54%) by applying the climatological ground-based regional scaling factors. Spatial interpolation increases the coverage of satellite-derived PM2.5 estimates without increased uncertainty when in close proximity to direct AOD retrievals. Spatial smoothing further reduces the daily 1-σ uncertainty to ±(1 μg/m3 + 42%) by limiting the random component of uncertainty. We additionally find similar performance for climatological relationships of AOD to PM2.5 as compared to day-specific relationships.
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INTRODUCTION Fine particulate matter concentration (PM2.5) has been linked to morbidity and mortality caused by cardiac and respiratory illnesses 1−3 and scientific understanding of how PM 2.5 © 2012 American Chemical Society
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compromises human health is still an active area of research.4,5 Daily estimates of air quality help inform citizens of unhealthy air conditions, thereby allowing them to limit activities to reduce exposure. The U.S. Environmental Protection Agency (EPA) set National Ambient Air Quality Standards (NAAQS) for six criteria pollutants, one of which is a 24 h average PM2.5 concentration of 35 μg/m3. Canada-wide standards of 30 μg/ m3 for 24 h average PM2.5 have also been established by the Canadian Council of Ministers of the Environment. The U.S. Air Quality Index6 and the Canadian Air Quality Health Index7 were developed to report the health effects associated with daily PM2.5 concentrations and other air quality constituents to the public. National laws require that air quality estimates be provided to the public in the United States. Ground-based instruments measure local PM2.5 and are presently the only data used for the air-quality indices in the US and Canada. However, the density of ground-based networks limits the effectiveness of spatial interpolation to provide accurate information within the spatial gaps between monitors. These gaps reduce the quality of information available to programs such as AirNow8 that disseminate real-time and forecasted air quality information to the public. Whereas model-based forecasts of ozone (another major component of the U.S. Air Quality Index and the Canadian Air Quality Health Index) are publically available, forecasts and current-day estimates of PM2.5 continue to be developed. Under the Infusing Satellite Data into Environmental Applications (IDEA) program (http://www.star.nesdis.noaa.gov/smcd/spb/aq/) NOAA NESDIS disseminates satellite observations to air quality forecasters and has begun to demonstrate that satellite remote sensing can provide useful data on a daily basis to help fill in these gaps.9,10 Satellite retrievals of total column Aerosol Optical Depth (AOD), a measure of extinction of light passing through the entire atmospheric column, have long been recognized to provide insight into ground level PM2.5 (ref 11). Early studies used fixed statistical relationships between AOD and surface PM2.5 over large regional geographic domains with some success,12 but this relationship varies in time and space because of changing emission types and magnitudes, meteorology, and retrieval biases which impact the vertical profile and scattering properties within the aerosol column. As a result, more recent studies have used additional information about local relative humidity and vertical structure to improve their AOD to PM2.5 relationships (refs 13 and 14). H. Zhang et al.15 used in situ monitors to develop empirical relationships between AOD and PM2.5 for broad spatial regions and seasons across the United States. Liu et al.16 introduced the approach of using a chemical transport model to calculate the relationship between AOD and PM2.5. Van Donkelaar et al.17 extended this approach to relate AOD from the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) instruments to PM2.5 and demonstrated a reasonable level of agreement with long-term mean in situ PM2.5 over North America (r = 0.77, slope = 1.07, n = 1057) and globally (r = 0.83, slope = 0.86, n = 244). This approach was also successfully applied during a major biomass burning event that occurred near Moscow in the summer of 2010 (r2 = 0.85, slope = 1.06) providing evidence of applicability to shortterm PM2.5 estimates during extreme events.18 This article evaluates and describes the process of (1) relating daily retrievals of AOD to PM2.5 under typical aerosol
loadings over the continental United States, (2) reducing systematic errors associated with initial satellite-derived PM2.5 estimates by applying climatological-based regional scaling factors, and (3) extending the spatial coverage of the satellitederived PM2.5 estimates as well as reducing random error using a spatial smoother.
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METHODS PM2.5 Observations. Gravimetric and tapered element oscillating microbalance (TEOM) instruments are two of the most common methods of in situ PM2.5 measurement. Gravimetric instruments, which are typically considered to be the most reliable, measure the mass of aerosol collected on a filter. TEOM instruments monitor the change in natural frequency of an oscillator to infer collected PM2.5 mass and allow for continuous monitoring. Our satellite-derived PM2.5 estimates over the years 2004−2009 were evaluated using measurement sites from the Canadian National Air Pollution Surveillance Network (NAPS, 254 sites), the U.S. Interagency Monitoring of Protected Visual Environments (IMPROVE, 67 Sites) network and the U.S. Environmental Protection Agency Air Quality System (AQS, Federal Reference Method sites only, 1161 Sites). NAPS TEOM measurements that we use have been bias corrected to account for semivolatile particulate matter losses during air sample heating.19 Satellite Observations. The MODIS instrument, onboard both the Terra (launched in 2000) and Aqua (launched in 2002) satellites provides near-daily global coverage at 32 spectral bands. The data obtained from the MODIS instruments have been used for a wide range of applications over the past decade. The MODIS AOD retrieval over land20 uses the top-of-atmosphere reflectance at the 0.47 and 0.66 μm bands to determine AOD over dark surfaces and in the absence of clouds. Dark surfaces are identified through empirical relationships between the 2.1 μm band and visible wavelengths. We use the collection 5 level 2 AOD product (MOD04 and MYD04) for 2004−2009. The spatial resolution of this product is 10 × 10 km at nadir with a lower spatial resolution of 48 km across track × 20 km along track near to the swath edge. Globally, two-thirds of quality-assured Collection 5 MODIS AOD over land are accurate to within ±(0.05 + 15%)21 at the reported wavelength (0.55 μm), although systematic errors can occur regionally.22 The MISR instrument, also onboard the Terra satellite, observes top-of-atmosphere reflectance at four wavelengths (0.446, 0.558, 0.672, and 0.866 μm), each at nine viewing angles (±70.5°, ± 60.0°, ± 45.6°, ± 25.1°, and nadir). MISR takes nine days for global coverage at the equator and two days near the poles in the absence of clouds. The MISR AOD retrieval uses same-scene, multiangle, multispectral observations to infer AOD and aerosol microphysical property information at an 18 km resolution. We use version 22 level 2 MISR AOD data. Globally, two-thirds of MISR AOD retrievals are within ±(0.05 or 20%) of measurements from the Aerosol Robotic Network (AERONET).23 We exclude regions and months in which MODIS and MISR AOD have unusually high bias following the method described by van Donkelaar et al.17 Briefly, AOD uncertainty is limited to ±(0.1 or 20%) using comparisons with AERONET sunphotometer measurements24 at 522 sites within globally defined albedo-based regions. Retrievals with less than 20% fine mode fraction are excluded to limit the impact of coarsemode dominated events. Following the method of Hyer et al.,25 11972
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Figure 1. In situ PM2.5 (a), daily satellite-derived estimates for the original PM2.5 (b), bias-corrected PM2.5,BC (c), smoothed bias-corrected PM2.5,SBC (d), interpolated smoothed bias-corrected PM2.5,ISBC (e), and filtered interpolated bias-corrected PM2.5,FISBC (f) methods for June 27, 2005. Each panel contains the equation of best fit, the correlation coefficient, r, and the number of estimates coincident with in situ observations, n.
www.epa.gov/ttnchie1/net/2005inventory.html), with interannual, and seasonal trends applied to represent temporal variation.34 Canadian emissions are from the Criteria Air Contaminants inventory for 2005 (http://www.ec.gc.ca/inrpnpri/default.asp?lang=En&n=103C06C0-1). We use the eight day Global Fire Emission Database version 2 (GFEDv2)35 biomass burning emissions. The GEOS-Chem aerosol simulation has been extensively evaluated with ground-based36−39 and aircraft measurements.34,40−43 We output simulated AOD at a wavelength of 550 nm. We apply the linearly interpolated simulated ratio of surface PM2.5 and total column AOD (η) from GEOS-Chem to the average of daily filtered MODIS and MISR AOD that has been regridded onto a standardized 0.1° × 0.1° grid. In accordance with the Federal Reference Method, we calculate PM2.5 at 35% relative humidity for direct comparison with in situ measurements. Parts a and b of Figure 1 present both in situ and satellitederived PM2.5 estimates on June 27, 2005 and indicates an aerosol enhancement in the Great Lakes Region. This date is chosen for illustrative purposes and shows that in this case the satellite-derived PM2.5 estimates sharing the same 0.1° × 0.1° grid box as monitoring stations exceed the in situ measurements (slope = 1.97), but are well correlated (r = 0.81, n = 499). We extend our comparison of daily satellite-derived PM2.5 estimates to available in situ measurements for the years 2004, 2006, and 2008. These data are maintained as an independent validation data set throughout this study, whereas odd-year data are used to develop algorithms to reduce error as described in subsequent sections. The 1-σ error at each in situ location (part a of Figure 2) is calculated as the minimum percentage difference between the satellite-derived and in situ PM2.5 that contain 68% of coincident pairs. The average error at these locations is ±(1 μg/m3 + 67%). The absolute error of 1 μg/m3 is included to avoid ratios of small numbers at some locations.
we additionally reduce cloud contamination by applying a buddy check that removes AOD retrievals without neighbors and a textural filter that removes AOD retrievals whose surrounding 5 × 5 pixels have an average above 0.2 and a coefficient of variation greater than 0.5. The majority of filtration occurs in the western U.S., where MODIS retrieval error can be quite large, a feature not seen in the MISR AOD product.17 Lastly we regrid both AOD products onto a common 0.1° × 0.1° using an area weighted average. Estimating Surface PM2.5 Concentrations from AOD. We use the GEOS-Chem chemical transport model v8−03−01 (http://geos-chem.org) to relate AOD to daily PM2.5 for each day of 2004 to 2009. GEOS-Chem predicts the temporal and spatial evolution of atmospheric aerosol and gaseous constituents using meteorological data sets, emission inventories, and equations that represent the physics and chemistry of the atmosphere. We develop the nested North American capability of GEOS-Chem, driven by assimilated meteorology from the Goddard Earth Observing System (GEOS-5) at the National Aeronautics and Space Administration (NASA) Global Modeling Assimilation Office (GMAO) at 1/2° × 2/3° horizontal resolution and 47 vertical levels. Boundary conditions of the nested region are provided by a global GEOS-Chem simulation at 2° × 2.5° horizontal resolution. The GEOS-Chem aerosol simulation includes the sulfate-nitrateammonium system,26 primary,27 and secondary28 carbonaceous aerosols, mineral dust,29 and sea-salt.30 Formation of sulfate and nitrate,26 heterogeneous chemistry,31 and photolysis rates32 are all coupled with the oxidant simulation. GEOS-Chem AOD calculation treats individual aerosol species as an external mixture, whose individual aerosol optical properties are determined using standard Mie calculations of log-normal size distributions, growth factors and refractive indices based upon the Global Aerosol Data Set (GADS) and aircraft measurements.32,33 Emissions for the United States are based upon the EPA’s National Emission Inventory for 2005 (NEI2005, http:// 11973
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PM2.5,SBC is the smoothed, bias-corrected PM2.5 value. i is the index of the m 0.5° × 0.5° grid cells containing satellite-derived PM2.5 estimates. N is the total number of PM2.5,BC estimates within the boundaries of i, to a maximum of 5. The daily to climatological ratios are first averaged onto a 0.5° × 0.5° grid to reduce computational expense and limited to a minimum of 0.1. d is the distance in km between the grid cell of interest and grid cell i, to a minimum of 50 km, or approximately the width of the 0.5° × 0.5° cell. Inverse distance weighting provides a continuous field of PM2.5 estimates, even for locations without direct satellite observation. We explore the level of agreement as a function of a spatial weight parameter, w. We define w as an indicator of the proximity to and number of direct observations used to inform a value of PM2.5,SBC: i
w=
m
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Higher errors are observed in the Great Lakes region, southeastern United States and some mountainous sites, reflecting residual errors in AOD retrievals, and in the simulated AOD to PM2.5 relationship. Reduction of Uncertainty. Systematic errors in AOD retrievals and in simulated factors affecting the relationship of AOD to PM 2.5 (e.g., aerosol vertical profile, aerosol composition and size, relative humidity, aerosol hygroscopicity, and diurnal variation of sources) have the potential to impact satellite-derived estimates of daily mean PM2.5. We apply linear regression to a running 90 day window of the initial satellitederived PM2.5 estimates of a training period (based upon years 2005, 2007, and 2009) to reduce systematic errors and produce a daily bias-corrected satellite-derived PM2.5 (PM2.5,BC). We test its effectiveness on the validation data set. Spatial smoothing, such as inverse distance weighting (IDW), may provide improved daily accuracy when random errors exceed the true spatial variation in PM2.5. Long-term measurements of PM2.5 reveal the presence of fine-scale features due to the influence of aerosol sources on surrounding regions. To partially retain that structure, we include the six year mean satellite-derived PM2.5 values from van Donkelaar et. al,17 which we refer to as the PM2.5 climatology (PM2.5,c). We scale PM2.5,c by the IDW average of the daily PM2.5,BC to PM2.5,c ratio: m
PM 2.5,SBC =
( ) ×( ∑ ( ) N d2 i
m i
PM 2.5,BC PM 2.5,c
N d2 i
) × PM
(2)
Daily calculation of w for each grid cell provides a continuous metric that indicates both the number of observations available to estimate local PM2.5 concentrations and also the distance of those values to the grid cell of interest. The highest values of w will occur where there are a large number of direct satellite observations in close proximity to a given grid cell. We also calculate the effect on the daily PM2.5 estimates of excluding MISR AOD data and using a climatological simulation of η rather than daily values. Preliminary MISR AOD data can take more than 24 h to become available and transport model simulations can be computationally expensive. Finally, air quality estimates are of particular interest during extreme events, such as fires. We therefore examine the performance of satellite-based PM2.5 estimates in these situations.
Figure 2. Uncertainty in the original PM2.5 (a), bias-corrected PM2.5,BC (b) and smoothed bias-corrected PM2.5,SBC (c) satellite-derived estimates. Daily satellite-derived PM2.5 values are within 1 μg/m3 plus XX.X percentage of in situ measurements for two-thirds of coincidently sampled pairs for the validation data set of years 2004, 2006, and 2008. Values in the bottom right of each panel give the mean 1-σ error.
∑i = 1
⎛N⎞ ⎟ d 2 ⎠i
∑ ⎜⎝
RESULTS AND DISCUSSION Bias Correction. Part a of Figure 3 compares the original satellite-derived and in situ running mean PM2.5 at an example site in the eastern United States near the Goddard Space Flight Center (39.1° N, 76.9° W). The original satellite-derived PM2.5 estimates at this location are typically biased high in summer and low in winter. Part b of Figure 3 shows satellite-based and in situ PM2.5 within the 90 day window around July 1 at this site for the odd year training data set of 2005, 2007, and 2009. Linear regression is applied to this window to determine the agreement (slope) and offset between the original satellitebased and in situ PM2.5. This offset is −3.2 μg/m3. A similar linear regression is applied to all in situ monitors. Figure 4 shows the continuous correction field produced by extending those local bias corrections that are statistically significant using an inverse distance weighted mean. The largest regional adjustment in slope (part a of Figure 4) extends from the Great Lakes across the eastern United States. Positive offsets persist across the Great Plains and Prairies, whereas negative offsets are in the southeast United States (part b of Figure 4). We produce and apply this bias correction map for a 90 day window surrounding each day of the Julian year. This bias correction removes most of the observed bias on June 27, 2005 (slope=1.29; r = 0.83), as shown in part c of Figure 1 and reduces mean 1-σ error compared to the original estimates as shown in parts a and b of Figure 2 [±(1 μg/m3 + 54%) vs ± (1 μg/m3 + 67%)]. Spatial smoothing of the biascorrected satellite-derived PM2.5 (part d of Figure 1, slope =
i
2.5,c
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Spatial Interpolation and Uncertainty. We investigate the applicability of spatially interpolated satellite-derived PM2.5 estimates to represent regions without direct satellite observations. Part e of Figure 1 shows the interpolated smoothed bias-corrected satellite-derived PM2.5 estimates (PM2.5,ISBC) over the entire domain on June 27, 2005, and includes regions without direct satellite observations. Comparison with part a of Figure 1 (in situ observations) indicates broad agreement for this particular day, even at locations far from direct observation and suggests that the interpolated values may be representative of PM2.5 in certain cases. Figure 5 shows the error in the PM2.5,ISBC at different levels of w (defined in eq 2) for the odd-year training data set. Error increases with decreasing w reflecting the effect of increased distance from direct observation. Error improves relative to directly observed smoothed bias-corrected satellite-derived PM2.5 estimates (PM2.5,SBC) for w > 0.028, remains similar when w > 0.009, and increases when w < 0.002 (parts c−f of Figure 5). The lowest 25th percentile (w < 0.002, part f of Figure 5) occurs more frequently in the north where observations are sparse due to snow cover, cloudy conditions, and low aerosol concentrations during the winter months. The number of PM2.5 estimates from the direct and interpolated approaches increases by 70% when the minimum w is 0.028 (part b of Figure 5). We identify the range of w for each month at each station over which a 1-σ error is maintained at either 50% or 10% above the direct comparison error. The overall annual mean lower bound of w which satisfies this criterion is 0.03. We take an inverse distance weighted average of these site-specific ranges to produce a continuous field. We limit PM2.5,ISBC to within these ranges to produce a filter interpolated smooth biasborrected estimate, PM2.5,FISBC, and compare with the in situ observations on June 27, 2005. The results are in similar agreement with in situ observations relative to the direct PM2.5,SBC (slope = 1.14, r = 0.84), but the number of coincident samples has increased by 35% (from 499 to 674). Part a of Figure 6 shows the 1-σ error for the entire even-year validation data set is 1 μg/m3 + 47%, similar to the error in the data set with direct observations. Figure 6 also shows the impact of using climatological η, calculated using average daily values from 2005, 2007, and 2009 for each day of the year, and of excluding the MISR AOD retrievals. Overall error levels remain similar, showing mean 1-σ errors of about 50%. The overall impact when using a climatological η is small. Similarly, Figure 7 shows total number of PM2.5,FISBC estimates for the even year validation data set when using daily (parts a and b of Figure 7) and climatological (parts c and d of Figure 7) η, and when MISR AOD is excluded (parts b, d, and f of Figure 7). Differences in sampling reflect the impact of changes to η on PM2.5,ISBC accuracy that subsequently affect the w-based filtering of PM2.5,FISBC. Exclusion of MISR has notable reductions on the number of estimates in the Southwest. The largest increase in values due to interpolation is in the Southeast. Increased coverage is visible across the continent compared to using coincident observations only (parts e−f of Figure 7). Impact of Extreme Events. Finally, we examine the performance of the satellite-derived PM2.5 estimates (using only MODIS AOD and climatological η) during extreme events. Part a of Figure 8 gives the 90th percentile of in situ PM2.5 for the even year validation data set. Part b of Figure 8 gives the 1-σ error of PM2.5,FISBC on days when in situ PM2.5 exceeds the 90th
Figure 3. (a) Comparison of original (blue) and bias-corrected (magenta) satellite-derived PM2.5 with in situ measurements (red) by Julian day for an example location (crosshair in Figure 4). (b) Original satellite-derived PM2.5 estimates versus in situ PM2.5 between May 16 and August 5 for the training data set of years 2005, 2007, and 2009. The solid black line represents the one-to-one line and the dashed line represents the line of best fit.
Figure 4. Inverse-distance weighted average of statistically significant linear regression slopes (a) and offsets (b) between original satellitederived PM2.5 estimates and in situ measurements for the training data set of years 2005, 2007, and 2009. The cross-hair denotes the station location in Figure 3.
1.14; r = 0.84) further reduces its uncertainty to ±(1 μg/m3 + 42%), as shown in part c of Figure 2. The largest improvements are found in the eastern United States and some western regions. In the eastern United States, bias in the AOD retrieval is unlikely as the MODIS retrieval usually performs well in this region; uncertainty in the initial daily AOD to PM 2.5 relationship is more likely. The western bias correction may reflect poorer performance of the AOD retrieval in this area.25 Errors remain elevated in northern regions where in situ monitoring is sparse. 11975
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Figure 5. One-sigma error (XX.X%) and coverage of the directly observed (a) and interpolated smoothed bias-corrected PM2.5,ISBC, for the training data set of years 2005, 2007, and 2009 with respect to spatial weighting parameter, w (c−f). Percentile ranges for w are given in parentheses. (b) The total number of coincidences between satellite-derived PM2.5 estimates and in situ PM2.5 as a function of minimum w.
Figure 6. Observed 1-σ error (XX.X%) with in situ PM2.5 for the even year validation data set. The performance of the filtered interpolated smoothed bias corrected PM2.5,FISBC values is shown in (a). The bottom row (b and d) shows the effect of excluding MISR AOD. The right column (c and d) shows the effect of replacing the daily PM2.5 to AOD relationships with climatology values. Values in the lower right of each panel are the domain mean errors.
Figure 7. Average annual number of the filtered interpolated smoothed bias-corrected satellite-derived PM2.5,FISBC, estimates over the even year validation data set using MISR and MODIS (a) and using MODIS only (b). The center column (c and d) shows the effect of replacing the daily PM2.5 to AOD relationships with climatology values. The right column (e and f) shows the sampling when using only directly observed values. Values in the lower right of each panel are the domain mean number of observations.
percentile and shows a slight reduction in uncertainty (±(1 μg/ m3 + 40%)) suggesting that both the MODIS AOD retrieval and the climatological η are valid even under enhanced loading
conditions. The improved performance could be driven by the increased contribution to top-of-atmosphere reflectance by aerosol which reduces retrieval error. 11976
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Figure 8. Top 90th percentile of measured in situ PM2.5 (a) for the even year validation data set and (b) the corresponding 1-σ error of filtered interpolated smoothed bias-corrected satellite-derived PM2.5,FISBC estimates. Values in the bottom right of each panel are domain averages.
Next Steps. The approach presented here was designed to provide near-real-time information for air quality forecasters to increase the accuracy of PM2.5 estimates between monitoring stations across the United States and Canada. As a next step, the method is being implemented by NOAA for input into the US EPA’s AirNOW program. Future work should examine the extension of these algorithms to forthcoming observations from new instruments such as the Visible Infrared Radiometer Suite (VIIRS) onboard the recently launched Suoi National Polarorbiting Partnership (NPP) satellite, and the Advanced Baseline Imager (ABI) instrument planned the next generation geostationary Geostationary Operational Environmental Satellite RSeries (GOES-R) satellite (scheduled for launch in 2015). Further algorithmic developments to both the AOD retrievals and the AOD to PM2.5 relationship would continue to improve the accuracy of satellite-derived PM2.5 estimates. It will be important to continue to refine these algorithms as the accuracy of AOD retrievals improves over the coming years.
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(5) Lauer, F. T.; Mitchell, L. A.; Bedrick, E.; McDonald, J. D.; Lee, W. Y.; Li, W. W.; Olvera, H.; Amaya, M. A.; Berwick, M.; Gonzales, M.; Currey, R.; Pingitore, N. E., Jr.; Burchiel, S. W. Temporal-spatial analysis of US-Mexico border environmental fine and coarse PM air sample extract activity in human bronchial epithelial cells. Toxicol. Appl. Pharmacol. 2009, 238, 1−10. (6) Environmental Protection Agency, Air quality index reporting: Final rule 40 CFR part 58. United States Federal Register 1999, 64, (149). (7) Stieb, D.; Burnett, R. T.; Smith-Doiron, M.; Brion, O.; Shin, H. H.; Economou, V. A new multipollutant, no-threshold air quality health index based on short-term associations observed in daily timeseries analysis. J. Air Waste Manage. Assoc. 2007, 58 (3), 435−450. (8) Dye, T. S.; Chan, A. C.; Anderson, C. B.; Strohm, D. E., From RAW air quality data to the nightly news: an overview of how EPA’s AIRNOW program operates. 2004. (9) Al-Saadi, J.; Szykman, J. J.; Pierce, R. B.; D., N.; Chu, D. A.; Remer, L.; Gumley, L.; Prins, E.; Weinstock, L.; MacDonald, C.; Wayland, R.; Dimmick, F.; Fishman, J. Improving National Air Quality Forecasts with Satellite Aerosol Observations. Bulletin of the American Meteorological Society 2005, 86, 1249−1261. (10) Hoff, R. M.; Christopher, S. A. Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land? Journal of Air & Waste Management Association 2009, 59, 645−675. (11) Engel-Cox, J. A.; Holloman, C. H.; Coutant, B. W.; Hoff, R. M. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmos. Environ. 2004, 38 (16), 2495−2509. (12) Wang, J.; Christopher, S. A., Intercomparison between satellitederived aerosol optical thickness and PM2.5 mass: Implications for air quality studies. Geophys. Res. Lett. 2003, 30, (21). (13) Di Nicolantonio, W.; Cacciari, A.; Tomasi, C. Particulate Matter at Surface: Northern Italy Monitoring Based on Satellite Remote Sensing, Meteorological Fields, and in-situ Sampling. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2009, 2 (4), 284−292. (14) Schaap, M.; Apituley, A.; Timmermans, R. M. A.; Koelemeijer, R. B. A.; de Leeuw, G. Exploring the relation between aerosol optical depth and PM2.5 at Cabauw, the Netherlands. Atmos. Chem. Phys. 2009, 9, 909−925. (15) Zhang, H.; Hoff, R. M.; Engel-Cox, J. A. The relation between Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth and PM2.5 over the United States: a geographical comparison by EPA regions. Journal of Air & Waste Managements Association 2009, 59, 1358−1369. (16) Liu, Y.; Park, R. J.; Jacob, D. J.; Li, Q. B.; Kilaru, V.; Sarnat, J. A., Mapping annual mean ground-level PM2.5 concentrations using Multiangle Imaging Spectroradiometer aerosol optical thickness over the contiguous United States. J. Geophys. Res., [Atmos.] 2004, 109, (D22). (17) van Donkelaar, A.; Martin, R. V.; Brauer, M.; Kahn, R.; Levy, R.; Verduzco, C.; Villeneuve, P. J. Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest. Disclaimer Although this article has been reviewed and approved for publication by the U.S. Environmental Protection Agency, it may not necessarily reflect official Agency policy.
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ACKNOWLEDGMENTS This work was supported by NASA and by the Government of Canada through the Federal Department of the Environment. We thank Bob Vet and Amy Hou for providing the biascorrected TEOM data. We thank David Holland and three anonymous reviewers for their helpful comments.
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