{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:58:22Z","timestamp":1760241502206,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,18]],"date-time":"2018-04-18T00:00:00Z","timestamp":1524009600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recent use of satellite observations of aerosol optical depth (AOD) to characterize surface concentrations of particulate matter (PM) air pollution has proven extremely valuable in estimating exposures for health effects studies. While the spatial resolutions of satellite data provide far better coverage than existing fixed site surface monitoring stations, they are not able to capture atmospheric processes such as dilution of primary pollutants that vary at small spatial scales. As a result, small-scale variability due to highly localized sources such as traffic may be poorly represented, which in turn may lead to exposure measurement error in epidemiological analyses. Using a fixed spatial grid representing 4.4 km Multiangle Imaging SpectroRadiometer (MISR) aerosol observations, we examined the spatial variability in fine and coarse mode PM (PM2.5 and PM2.5\u201310 respectively) measured at ground monitors from a unique spatially-dense sampling campaign in Southern California. We found that while the variance in measured PM2.5 differed seasonally (warm 6.82 \u03bcg2\/m6 and cool 24.5 \u03bcg2\/m6) across the study region, the average subgrid (&lt;4.4 km) variance did not (warm 2.03 \u03bcg2\/m6 and cool 2.43 \u03bcg2\/m6) and was significantly smaller. On the other hand, ground monitor PM2.5\u201310 concentrations showed large variance in warm (18.6 \u03bcg2\/m6) and cool (20.6 \u03bcg2\/m6) seasons, as well as seasonal differences in subgrid variance (warm 8.90 \u03bcg2\/m6 and cool 3.28 \u03bcg2\/m6). Geostatistical analysis of the semivariance as a function of distance indicated that variability in measured PM2.5 and PM2.5\u201310 concentrations was relatively constant for spatial scales of one to five kilometers, but there was evidence of small-scale (~500 m) variability in PM2.5\u201310 concentrations in the cool season. The lack of small-scale spatial variability in the warm season was likely due to large photochemical contributions to regional PM2.5, and greater regional contributions to PM2.5\u201310 from windblown dust. In contrast, in the cool season there tends to be greater localized concentrations from primary traffic sources due to stronger nocturnal inversions and delayed morning winds reducing dilution that contribute to greater spatial heterogeneity. Overall, these results suggest that regional contributions tend to dominate PM2.5, and spatial resolutions of satellite observations including the 4.4 km MISR and 3 km MODIS aerosol products aptly capture relevant spatial variability. Coarse PM2.5\u201310 can have seasonally dependent localized contributions, leading to small-scale variability below current satellite aerosol product resolutions.<\/jats:p>","DOI":"10.3390\/rs10040623","type":"journal-article","created":{"date-parts":[[2018,4,19]],"date-time":"2018-04-19T10:12:01Z","timestamp":1524132721000},"page":"623","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Characterization of Subgrid-Scale Variability in Particulate Matter with Respect to Satellite Aerosol Observations"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3802-8829","authenticated-orcid":false,"given":"Meredith","family":"Franklin","sequence":"first","affiliation":[{"name":"Division of Biostatistics, University of Southern California, Los Angeles, CA 90089, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1330-1529","authenticated-orcid":false,"given":"Olga V.","family":"Kalashnikova","sequence":"additional","affiliation":[{"name":"NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1374-5074","authenticated-orcid":false,"given":"Michael J.","family":"Garay","sequence":"additional","affiliation":[{"name":"NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scott","family":"Fruin","sequence":"additional","affiliation":[{"name":"Division of Environmental Health, University of Southern California, Los Angeles, CA 90089, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1080\/10473289.2006.10464485","article-title":"Health effects of fine particulate air pollution: Lines that connect","volume":"56","author":"Pope","year":"2006","journal-title":"J. Air Waste Manage. Assoc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1038\/sj.jes.7500530","article-title":"Association between PM2.5 and all-cause and specific-cause mortality in 27 US communities","volume":"17","author":"Franklin","year":"2007","journal-title":"J. Expo. Sci. Environ. Epidemiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1161\/CIR.0b013e3181dbece1","article-title":"Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association","volume":"121","author":"Brook","year":"2010","journal-title":"Circulation"},{"key":"ref_4","first-page":"1276","article-title":"Childhood exposure to ambient air pollutants and the onset of asthma: An administrative cohort study in Qu\u00e9bec","volume":"1276","author":"Doucet","year":"2016","journal-title":"Environ. Health Perspect."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1183\/09031936.00083406","article-title":"Air pollution and development of asthma, allergy and infections in a birth cohort","volume":"29","author":"Brauer","year":"2007","journal-title":"Eur. Respir. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1136\/thoraxjnl-2012-203159","article-title":"Associations of children\u2019s lung function with ambient air pollution: Joint effects of regional and near-roadway pollutants","volume":"69","author":"Urman","year":"2014","journal-title":"Thorax"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1038\/jes.2011.45","article-title":"Predictors of intra-community variation in air quality","volume":"22","author":"Franklin","year":"2012","journal-title":"J. Expo. Sci. Environ. Epidemiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1111\/j.0006-341X.2005.030821.x","article-title":"Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models","volume":"61","author":"Fuentes","year":"2005","journal-title":"Biometrics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1175\/JAM2538.1","article-title":"Using CMAQ for exposure modeling and characterizing the subgrid variability exposure estimates","volume":"46","author":"Isakov","year":"2007","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1289\/ehp.0800123","article-title":"Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information","volume":"117","author":"Liu","year":"2009","journal-title":"Environ. Health Perspect."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1289\/ehp.1408646","article-title":"Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter","volume":"123","author":"Martin","year":"2015","journal-title":"Environ. Health Perspect."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.rse.2017.05.002","article-title":"Size-resolved particulate matter concentrations derived from 4.4 km resolution size-fractionated Multi-angle Imaging SpectroRadiometer (MISR) aerosol optical depth over Southern California","volume":"196","author":"Franklin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1038\/sj.jea.7500388","article-title":"A review and evaluation of intraurban air pollution exposure models","volume":"15","author":"Jerrett","year":"2005","journal-title":"J. Expo. Anal. Environ. Epidemiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.atmosenv.2012.08.038","article-title":"Spatial variation of PM2.5, PM10, PM2.5 absorbance and PMcoarse concentrations between and within 20 European study areas and the relationship with NO2\u2014Results of the ESCAPE project","volume":"62","author":"Eeftens","year":"2012","journal-title":"Atmos. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.atmosenv.2013.10.063","article-title":"Spatial variation in particulate matter components over a large urban area","volume":"83","author":"Fruin","year":"2014","journal-title":"Atmos. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6444","DOI":"10.1016\/j.atmosenv.2005.07.030","article-title":"A review of intraurban variations in particulate air pollution: Implications for epidemiological research","volume":"39","author":"Wilson","year":"2005","journal-title":"Atmos. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1097\/01.ede.0000181630.15826.7d","article-title":"Spatial analysis of air pollution and mortality in Los Angeles","volume":"16","author":"Jerrett","year":"2005","journal-title":"Epidemiology"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.atmosenv.2013.04.015","article-title":"A regionalized national universal kriging model using Partial Least Squares regression for estimating annual PM2.5 concentrations in epidemiology","volume":"75","author":"Sampson","year":"2013","journal-title":"Atmos. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1214\/08-AOAS204","article-title":"Practical large-scale spatio-temporal modeling of particulate matter concentrations","volume":"3","author":"Paciorek","year":"2009","journal-title":"Ann. Appl. Stat."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1021\/es2025752","article-title":"Exposure assessment for estimation of the global burden of disease attributable to outdoor air pollution","volume":"46","author":"Brauer","year":"2012","journal-title":"Environ. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"84013","DOI":"10.1088\/1748-9326\/9\/8\/084013","article-title":"Using satellite data to develop environmental indicators","volume":"9","author":"Levy","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.rse.2006.05.022","article-title":"Using aerosol optical thickness to predict ground-level PM2.5 concentrations in the St. Louis area: A comparison between MISR and MODIS","volume":"107","author":"Liu","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_23","first-page":"1","article-title":"Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing","volume":"111","author":"Martin","year":"2006","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5621","DOI":"10.1002\/jgrd.50479","article-title":"Optimal estimation for global ground-level fine particulate matter concentrations","volume":"118","author":"Martin","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"574","DOI":"10.3155\/1047-3289.60.5.574","article-title":"An improved method for estimating surface fine particle concentrations using seasonally adjusted satellite aerosol optical depth","volume":"60","author":"Weber","year":"2010","journal-title":"J. Air Waste Manag. Assoc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2989","DOI":"10.5194\/amt-6-2989-2013","article-title":"The Collection 6 MODIS aerosol products over land and ocean","volume":"6","author":"Levy","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.5194\/amt-6-1829-2013","article-title":"MODIS 3 km aerosol product: Algorithm and global perspective","volume":"6","author":"Remer","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nichol, J.E., and Bilal, M. (2016). Validation of MODIS 3 km resolution aerosol optical depth retrievals over Asia. Remote Sens., 8.","DOI":"10.3390\/rs8040328"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9296","DOI":"10.1002\/jgrd.50712","article-title":"Enhanced Deep Blue aerosol retrieval algorithm: The second generation","volume":"118","author":"Hsu","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_30","first-page":"1","article-title":"Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm","volume":"116","author":"Lyapustin","year":"2011","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_31","first-page":"18","article-title":"Development and assessment of a high spatial resolution (4.4 km) MISR aerosol product using AERONET-DRAGON Data","volume":"17","author":"Garay","year":"2016","journal-title":"Atmos. Chem. Phys. Discuss."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1109\/TGRS.2002.801584","article-title":"Performance of the MISR instrument during its first 20 months in earth orbit","volume":"40","author":"Diner","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4248","DOI":"10.1002\/2015JD023322","article-title":"An analysis of global aerosol type as retrieved by MISR","volume":"120","author":"Kahn","year":"2015","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_34","first-page":"1","article-title":"Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth validation based on 2 years of coincident Aerosol Robotic Network (AERONET ) observations","volume":"110","author":"Kahn","year":"2005","journal-title":"J. Geophys. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1289\/ehp.0901623","article-title":"Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: Development and application","volume":"118","author":"Martin","year":"2010","journal-title":"Environ. Health Perspect."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2993","DOI":"10.1016\/j.atmosenv.2004.02.045","article-title":"Variability of particulate matter concentrations along roads and motorways determined by a moving measurement unit","volume":"38","author":"Weijers","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1111\/j.1467-9876.2011.01035.x","article-title":"Combining spatial information sources while accounting for systematic errors in proxies","volume":"61","author":"Paciorek","year":"2011","journal-title":"J. R. Stat. Soc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.atmosenv.2011.11.006","article-title":"An approach to characterize within-grid concentration variability in air quality models","volume":"49","author":"Ching","year":"2012","journal-title":"Atmos. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1164\/ajrccm.159.3.9804144","article-title":"A study of twelve Southern California communities with differing levels and types of air pollution: II. Effects on pulmonary function","volume":"159","author":"Peters","year":"1999","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.atmosenv.2005.08.041","article-title":"Development and evaluation of personal respirable particulate sampler (PRPS)","volume":"40","author":"Lee","year":"2006","journal-title":"Atmos. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kokhanovsky, A.A., and de Leeuw, G. (2009). Retrieval of aerosol properties over land using MISR observations. Satellite Aerosol Remote Sensing Over land, Springer.","DOI":"10.1007\/978-3-540-69397-0"},{"key":"ref_42","first-page":"D15204","article-title":"A climatology of aerosol optical and microphysical properties over the Indian subcontinent from 9 years (2000\u20132008) of Multiangle Imaging Spectroradiometer (MISR) data","volume":"115","author":"Dey","year":"2010","journal-title":"J. Geophys. Res."},{"key":"ref_43","unstructured":"Cressie, N.A. (2015). Statistics for Spatial Data, John Wiley & Sons. Revised Edition."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"D07S08","DOI":"10.1029\/2004JD004627","article-title":"Seasonal and spatial variability of the size-resolved chemical composition of particulate matter (PM10) in the Los Angeles Basin","volume":"110","author":"Sardar","year":"2005","journal-title":"J. Geophys. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1038\/jes.2010.24","article-title":"Community-level spatial heterogeneity of chemical constituent levels of fine particulates and implications for epidemiological research","volume":"21","author":"Bell","year":"2010","journal-title":"J. Expo. Sci. Environ. Epidemiol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1097\/01.ede.0000181308.51440.75","article-title":"Childhood asthma and exposure to traffic and nitrogen dioxide","volume":"16","author":"Gauderman","year":"2005","journal-title":"Epidemiology"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1289\/ehp.1103516","article-title":"Residential traffic-related pollution exposures and exhaled nitric oxide in the children\u2019s health study","volume":"119","author":"Eckel","year":"2011","journal-title":"Environ. Health Perspect."},{"key":"ref_48","unstructured":"Health Effects Institute (2010). Traffic Related Air Pollution: A Critical Review of the Literature on Emissions, Exposure, and Health Effects, Health Effects Institute. Special Report 17."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1289\/ehp.0800360","article-title":"Limitations of remotely sensed aerosol as a spatial Proxy","volume":"117","author":"Paciorek","year":"2009","journal-title":"Environ. Health Perspect."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1289\/EHP575","article-title":"Comparing the health effects of ambient particulate matter estimated using ground-based versus remote sensing exposure estimates","volume":"552","author":"Jerrett","year":"2017","journal-title":"Environ. Health Perspect."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/623\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:01:12Z","timestamp":1760194872000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/4\/623"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,18]]},"references-count":50,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,4]]}},"alternative-id":["rs10040623"],"URL":"https:\/\/doi.org\/10.3390\/rs10040623","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,4,18]]}}}