CMAS special session Oct 13, 2010 Air pollution exposure estimation: 1.what’s been done? 2.what’s wrong with that? 3.what can be done? 4.how and what to.

Slides:

Advertisements

Similar presentations

SAMSI Workshop: September 15, 2009 Discussion on Spatial Epidemiology: with focus on Chronic Effects of Air Pollution Discussion on Spatial Epidemiology:

Advertisements

EPA Precursor Gas Training Workshop PM 2.5 Chemical Speciation Network (CSN) Carbon Conversion Joann Rice.

Halûk Özkaynak US EPA, Office of Research and Development National Exposure Research Laboratory, RTP, NC Presented at the CMAS Special Symposium on Air.

Overview of CSN Data Relevant to OC/EC Artifact Adjustments presented by James Flanagan RTI International Davis, CA January 22-23, 2008.

U.S. EPA Office of Research & Development October 30, 2013 Prakash V. Bhave, Mary K. McCabe, Valerie C. Garcia Atmospheric Modeling & Analysis Division.

Carbon Measurements and Adjustments Measurement of organics by IMPROVE & STN networks, Use of blank data to correct carbon concentration measurements,

Critical Issues of Exposure Assessment for Human Health Studies of Air Pollution Michelle L. Bell Yale University SAMSI September 15, 2009.

1 A Fractional AOD Approach to Derive PM2.5 Information Using MISR Data Coupled with GEOS-CHEM Aerosol Simulation Results Yang Liu, Ralph Kahn, Solene.

USING BAYESIAN HIERARCHICAL MODELLING TO PRODUCE HIGH RESOLUTION MAPS OF AIR POLLUTION IN THE EU Gavin Shaddick University of Bath RSS Avon Local Group.

Statistics, data, and deterministic models NRCSE.

Model Choice in Time Series Studies of Air Pollution and Health Roger D. Peng, PhD Department of Biostatistics Johns Hopkins Blomberg School of Public.

GIS in Spatial Epidemiology: small area studies of exposure- outcome relationships Robert Haining Department of Geography University of Cambridge.

Human health applications of atmospheric remote sensing Simon Hales, Housing and Health Research Programme, University of Otago, Wellington, New Zealand.

Health Risks of Exposure to Chemical Composition of Fine Particulate Air Pollution Francesca Dominici Yeonseung Chung Michelle Bell Roger Peng Department.

1 Practical issues and tools for modeling temporal and spatio-temporal trends in atmospheric pollutant monitoring data Paul D. Sampson Department of Statistics.

Halûk Özkaynak US EPA, Office of Research and Development National Exposure Research Laboratory, RTP, NC Presented at the CMAS Special Symposium on Air.

Personal Monitoring for Air Pollution Exposure Philip M. Fine, Ph.D. Atmospheric Measurements Manager South Coast Air Quality Management District CAPCOA.

Jenny Stocker, Christina Hood, David Carruthers, Martin Seaton, Kate Johnson, Jimmy Fung The Development and Evaluation of an Automated System for Nesting.

1 An Update on EPA Attainment Modeling Guidance for the 8- Hour Ozone NAAQS Brian Timin EPA/OAQPS/EMAD/AQMG November 16, 2005.

NERAM 2006 Matching the metric to need: modelling exposures to traffic- related air pollution for policy support David Briggs, Kees de Hoogh and John Gulliver.

1 Key Monitoring Issues Status of PM 2.5 monitoring methodologies Criteria for acceptance of monitors and sites Special considerations for comparing ambient.

CMAS Conference, October 16 – 18, 2006 The work presented here was performed by the New York State Department of Environmental Conservation with partial.

Results of Ambient Air Analyses in Support of Transport Rule Presentation for RPO Workshop November 2003.

Task Force on Health Recent results - Particulate matter Michal Krzyzanowski TFH Chair Head, Bonn Office European Centre for Environment and Health WHO.

Sensitivity of top-down correction of 2004 black carbon emissions inventory in the United States to rural-sites versus urban-sites observational networks.

PM2.5 Model Performance Evaluation- Purpose and Goals PM Model Evaluation Workshop February 10, 2004 Chapel Hill, NC Brian Timin EPA/OAQPS.

Gridding Daily Climate Variables for use in ENSEMBLES Malcolm Haylock, Climatic Research Unit Nynke Hofstra, Mark New, Phil Jones.

Declustering in the Spatial Interpolation of Air Quality Data Stefan R. Falke and Rudolf B. Husar.

Extending Spatial Hot Spot Detection Techniques to Temporal Dimensions Sungsoon Hwang Department of Geography State University of New York at Buffalo DMGIS.

PM Network Assessment: Speciated Network Planning Prepared for EPA OAQPS Richard Scheffe by Rudolf B. Husar Center for Air Pollution Impact and Trend Analysis,

Health effects modeling Lianne Sheppard University of Washington.

Urban vs. Rural Atlanta An assessment of : 1)PM2.5 composition and trends 2)The Atlanta Urban Heat Island Effect.

The Use of Source Apportionment for Air Quality Management and Health Assessments Philip K. Hopke Clarkson University Center for Air Resources Engineering.

Spatial Statistics in Ecology: Continuous Data Lecture Three.

Pushing the Spatio-Temporal Resolution Limit of Urban Air Pollution Maps David Hasenfratz, Olga Saukh, Christoph Walser, Christoph Hueglin, Martin Fierz,

Ambient Air Monitoring Networks 2010 CMAS Conference Chapel Hill, NC October 13, 2010 Rich Scheffe, Sharon Phillips, Wyatt Appel, Lew Weinstock, Tim Hanley,

Spatial Interpolation III

Predicting Long-term Exposures for Health Effect Studies Lianne Sheppard Adam A. Szpiro, Johan Lindström, Paul D. Sampson and the MESA Air team University.

Spatial Interpolation Chapter 13. Introduction Land surface in Chapter 13 Land surface in Chapter 13 Also a non-existing surface, but visualized as a.

Model Evaluation Comparing Model Output to Ambient Data Christian Seigneur AER San Ramon, California.

OBJECTIVES (i) An update of the national analysis a) to assess the confounding and modifying effect of community and neighbourhood level ecological.

Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Office of Research and Development.

Ozone Transport that Impacts on Tribal Land: Case Study Stan Belone Salt River Pima-Maricopa Indian Community.

Presented at the 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008 Identifying Optimal Temporal Scale for the Correlation of AOD and Ground.

GOING FROM 12-KM TO 250-M RESOLUTION Josephine Bates 1, Audrey Flak 2, Howard Chang 2, Heather Holmes 3, David Lavoue 1, Mitchel Klein 2, Matthew Strickland.

Ran TAO Missing Spatial Data. Examples Places cannot be reached E.g. Mountainous area Sample points E.g. Air pollution Damage of data E.g.

Evaluating temporal and spatial O 3 and PM 2.5 patterns simulated during an annual CMAQ application over the continental U.S. Evaluating temporal and spatial.

Exposure Assessment for Health Effect Studies: Insights from Air Pollution Epidemiology Lianne Sheppard University of Washington Special thanks to Sun-Young.

Uncertainty in national-scale soil C inventory estimates Keith Paustian 1,2, Stephen Ogle 2, Jay Breidt 3 1 Dept of Soil and Crop Sciences, Colorado State.

IRAS en het ILM Gerard Hoek Health effects of NO 2 : evidence from recent epidemiological studies Gerard Hoek Institute for Risk Assessment Sciences.

Health Effects of Traffic- Related Particulate Pollution Christine Rioux, MS Doctoral Candidate, Instructor Tufts University.

Technical Details of Network Assessment Methodology: Concentration Estimation Uncertainty Area of Station Sampling Zone Population in Station Sampling.

INTEGRATING SATELLITE AND MONITORING DATA TO RETROSPECTIVELY ESTIMATE MONTHLY PM 2.5 CONCENTRATIONS IN THE EASTERN U.S. Christopher J. Paciorek 1 and Yang.

PM Methods Update and Network Design Presentation for WESTAR San Diego, CA September 2005 Peter Tsirigotis Director Emissions, Monitoring, and Analysis.

1 Preliminary evaluation of the 2002 Base B1 CMAQ simulation: Temporal Analysis A more complete statistical evaluation, including diurnal variations, of.

Assessment of the Speciated PM Network (Initial Draft, November 2004 ) Washington University, St. Louis CIRA/NPS VIEWS Team.

Technical Details of Network Assessment Methodology: Concentration Estimation Uncertainty Area of Station Sampling Zone Population in Station Sampling.

Exposure Prediction and Measurement Error in Air Pollution and Health Studies Lianne Sheppard Adam A. Szpiro, Sun-Young Kim University of Washington CMAS.

N Engl J Med Jun 29;376(26): doi: 10

Objective Analysis and Data Assimilation

Matteo Reggente Giulia Ruggeri Satoshi Takahama

RD Evaluation and Comparison OF Methods to Construct Air Quality Fields for Exposure Assessment haofei yu, jim mulholland, howard chang, ran huang,

Statistical Methods for Model Evaluation – Moving Beyond the Comparison of Matched Observations and Output for Model Grid Cells Kristen M. Foley1, Jenise.

Near-Roadway Health Effects

Using CMAQ to Interpolate Among CASTNET Measurements

Mapping HIV epidemics in sub-Saharan Africa with use of GPS data

J. Burke1, K. Wesson2, W. Appel1, A. Vette1, R. Williams1

Characteristics of Study Subjects and Events, According to the Quintile of Exposure to Fine Particulate Matter (PM2.5)* - Part I Kristin A. Miller et al.

Time-Integrated Particle Measurements : Status in Canada

Evaluation of Models-3 CMAQ Annual Simulation Brian Eder, Shaocai Yu, Robin Dennis, Alice Gilliland, Steve Howard,

Presentation transcript:

CMAS special session Oct 13, 2010 Air pollution exposure estimation: 1.what’s been done? 2.what’s wrong with that? 3.what can be done? 4.how and what to evaluate?

what’s been done? 1.use existing monitoring network(s) 2.estimate “exposure” average monitors nearest monitor inverse-distance weighting spatial statistics (eg, kriging) 3.plug estimate into health model

what’s wrong? 1.basically, exposure measurement error the dogma: if non-differential, bias of health effect estimate to the null 2.what else? features of air monitoring network data intensity - spatial, temporal, components coverage - spatial, geographic covariates 3.unknown impacts on health effect estimates

improvements 1.to monitoring to reflect sources study subject residences/transit/indoors/personal 2.enhance value of monitoring land-use regression

early example Hoek et al. Lancet 2002; 360: local scale - distance to roads (BS & NO 2 ) 2.urban scale - BS/NO 2 regressed on population density (land use regression) 3.regional scale - inverse-distance weighting using population-oriented monitors

use of deterministic air quality models in health studies 1.attempt to address many deficiencies in using monitoring data concentrations at unmonitored locations and times unmeasured pollutants & sources 2.two (at least) ways to use them 1.have monitoring data and estimates of exposure and supplement with AQM predictions 2.start with AQM and supplement with data (“data assimilation”)

evaluating value added 1.performance in predicting “exposure” validation 2.impact on health effect estimates variance, coverage, bias, RMSE 3.role of simulation

problems with using AQMs 1.for pollutants that are relatively homogeneous spatially, we have monitoring data 2.most models smooth and therefore reduce variability addition of covariates with less variability than that of study subjects 3.for pollutants that are not spatially homogeneous, spatial scale often too large

additional points 1.time of exposure for cohort studies

= MESA Air region Chestnut Hill, MA Fall River, MA Jacksonville, FL New Brunswick, NJ Greensboro, NC Des Moines, IA Davenport, IA Phoenix, AZ La Jolla, CA Evanston, IL Epidemiology: WHI-OS and MESA Air cardiovascular cohorts

NPACT 2-week sampling protocols in MESA communities (in addition to CSN data) type of monitoring measurements (sampler) (1) fixed PM 2.5 (2) home outdoor PM 2.5 (3) home indoor PM 2.5 (4) personal PM 2.5 (5) traffic gradient snapshots (6) PM 2.5 /PM 10 snapshots mass, BC, elements/metals (Teflon filter) EC, OC, DTT (quartz filter) EC, OC only NO x, NO 2, O 3, SO 2 (Ogawa sampler) no O 3 no O 3 NO x & NO 2 (1) STN (collocated), roadside, household (3-7/city [26 total]) (2) rotating every 2 weeks > 50 homes/city in 2 seasons (3) rotating ~ 50/city in 2 seasons (4) rotating ~15/city in 2 seasons (5) ~100 sites/city (roadside, near road, population) in 3 seasons (6) ~ 40 sites/city (Chicago, Minneapolis, Winston-Salem) in 2 seasons

NPACT fixed monitoring (red) and CSN (blue) site locations in the 6 MESA cities

Silicon, nickel and EC concentrations at New York STN and MESA Air fixed and home outdoor sites As expected, the spatial distribution of concentrations (quintiles) is different for each species SiNiEC

monitoring data structure is complex

Approach to estimating household concentrations: estimate time trend from temporally rich CSN/STN sites ( and less rich fixed sites ) to estimate time trend ( which can vary over space, esp. for some PM 2.5 components ) at home outdoor sites then, remove time trends from spatially rich ( but temporally sparse ) home outdoor measurements to allow spatial information to be used use spatio-temporal modeling methods, incorporating geographic information

Long-term EC concentrations at individual monitoring sites adjusted for temporal trend in LA solid black line = temporal trend dotted black line = mean of trend black dot = observed value at monitoring site red circle = temporal adjustment for observed value red line = adjusted mean at monitoring site

Measurements Questionnaires Predictions the NPACT study individual exposure estimation