Methods for Characterizing Fine Particulate Matter Using Ground Observations and Remotely Sensed Data: Potential Use for Environmental Public Health Surveillance

Abstract This study describes and demonstrates different techniques for surface fitting daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 μm (PM2.5) for the purpose of inte grating respiratory health and environmental data for the Centers for Disease Control and Prevention (CDC) pilot study of Health and Environment Linked for Information Exchange (HELIX)–Atlanta. It presents a methodology for estimating daily spatial surfaces of ground-level PM2.5 concentrations using the B-Spline and inverse distance weighting (IDW) surface-fitting techniques, leveraging National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) data to complement U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM2.5 from the EPA database for the year 2003 as well as PM2.5 estimates derived from NASA’s satellite data. Hazard data have been processed to derive the surrogate PM2.5 exposure estimates. This paper shows that merging MODIS remote sensing data with surface observations of PM2.5 not only provides a more complete daily representation of PM2.5 than either dataset alone would allow, but it also reduces the errors in the PM2.5- estimated surfaces. The results of this study also show that although the IDW technique can introduce some numerical artifacts that could be due to its interpolating nature, which assumes that the maxima and minima can occur only at the observation points, the daily IDW PM2.5 surfaces had smaller errors in general, with respect to observations, than those of the B-Spline surfaces. Finally, the methods discussed in this paper establish a foundation for environmental public health linkage and association studies for which determining the concentrations of an environmental hazard such as PM2.5 with high accuracy is critical.

[1]  S. Christopher,et al.  Multi year satellite remote sensing of particulate matter air quality over Sydney, Australia , 2007 .

[2]  Altaf Arain,et al.  A review and evaluation of intraurban air pollution exposure models , 2005, Journal of Exposure Analysis and Environmental Epidemiology.

[3]  Modeling atmospheric dust for a public health decision support system , 2005 .

[4]  Jill A. Engel-Cox,et al.  Correlating seasonal averaged in situ monitoring of fine PM with satellite remote sensing data using geographic information system (GIS) , 2004, SPIE Optics + Photonics.

[5]  Michael D. King,et al.  Aerosol properties over bright-reflecting source regions , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[6]  R. Koelemeijer,et al.  Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe , 2006 .

[7]  Tarek Abichou,et al.  Methane flux and oxidation at two types of intermediate landfill covers. , 2006, Waste management.

[8]  L. R. Beck,et al.  Perspectives Perspectives Perspectives Perspectives Perspectives Remote Sensing and Human Health: New Sensors and New Opportunities , 2022 .

[9]  P. Scheff,et al.  Adjusting Tapered Element Oscillating Microbalance Data for Comparison with Federal Reference Method PM2.5 Measurements in Region 5 , 2003, Journal of the Air & Waste Management Association.

[10]  James T. Vogt.  THREE-DIMENSIONAL SAMPLING METHOD FOR CHARACTERIZING ANT MOUNDS , 2007 .

[11]  Jassim A. Al-Saadi,et al.  Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization , 2006 .

[12]  J. Patz.  Guest Editorial: Satellite Remote Sensing Can Improve Chances of Achieving Sustainable Health , 2005, Environmental health perspectives.

[13]  C. Deutsch,et al.  Statistical approach to inverse distance interpolation , 2009 .

[14]  Michelle L Bell,et al.  The use of ambient air quality modeling to estimate individual and population exposure for human health research: a case study of ozone in the Northern Georgia Region of the United States. , 2006, Environment international.

[15]  Jun Wang,et al.  Intercomparison between satellite‐derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies , 2003 .

[16]  D. F. Watson,et al.  A PRECISE METHOD FOR DETERMINING CONTOURED SURFACES , 1982 .

[17]  J. Kukkonen,et al.  Evaluation of inversion strengths and mixing heights during extremely stable atmospheric stratification , 2001 .

[18]  Paul Dierckx,et al.  Curve and surface fitting with splines , 1994, Monographs on numerical analysis.

[19]  Lester L. Yuan,et al.  Comparison of spatial interpolation methods for the estimation of air quality data , 2004, Journal of Exposure Analysis and Environmental Epidemiology.

[20]  Basil W. Coutant,et al.  Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality , 2004 .

[21]  Jun Wang,et al.  Satellite remote sensing of particulate matter and air quality assessment over global cities , 2006 .

[22]  T. Mueller,et al.  Map Quality for Ordinary Kriging and Inverse Distance Weighted Interpolation , 2004 .

[23]  Yang Liu,et al.  Using aerosol optical thickness to predict ground-level PM2.5 concentrations in the St. Louis area: A comparison between MISR and MODIS , 2007 .

[24]  D. Chu,et al.  Improving National Air Quality Forecasts with Satellite Aerosol Observations , 2005 .

[25]  Yang Liu,et al.  Spatiotemporal associations between GOES aerosol optical depth retrievals and ground-level PM2.5. , 2008, Environmental science & technology.

[26]  Sundar A. Christopher,et al.  Seven year particulate matter air quality assessment from surface and satellite measurements , 2008 .

[27]  D. Jacob,et al.  Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing. , 2005, Environmental science & technology.

[28]  D. Watson.  A refinement of inverse distance weighted interpolation , 1985 .

[29]  Kazuhiko Ito,et al.  Monitor-to-monitor temporal correlation of air pollution and weather variables in the North-Central U.S. , 2001, Journal of Exposure Analysis and Environmental Epidemiology.

[30]  E. Kalvelagen.  GAMS/FITPACK: Spline Interpolation with GAMS , 2002 .

[31]  Stanley A. Morain,et al.  Science Data Products for Public Health Decision Support , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.