SUPPLEMENTAL MATERIAL
AMBIENT AIR QUALITY EXPOSURE METRICS USED IN STUDY
“Associations between summertime ambient pollutants and respiratory morbidity in New York City:
comparison of results using ambient concentrations versus predicted exposures,” Jones et al.1
In this study, two exposure metrics were used; interpolated daily-averaged observations (ambient
concentration) and estimates generated by the SHEDS exposure model (SHEDS exposure). This
supplemental information explains the technique to (1) interpolate daily-averaged observations for the
study domain and (2) interpolate hourly observations needed for running the SHEDS model. The basic
steps are laid out in the table below:
Processing
Steps
Daily-averaged estimates
Hourly estimates (used to run SHEDS)
PM2.5
Ozone
PM2.5
Ozone
1. Measurement
Data
24-hr averaged
measurements taken
using filter based
Federal Reference
Methods (FRM) were
downloaded from the
EPA AQS site.2
Hourly measurements
taken with UV photometric
FRM monitors2. Daily
maximum 8-hr averaged
concentrations were
calculated by applying an
8-hr moving window. 8-hr
time window with highest
concentration value was
selected for each day. 3
Hourly ozone
concentrations2 were
used directly from UV
photometric FRM
monitors.
2. Interpolation
of data
24-hr averaged
measures were
interpolated to a 1
km grid structure
using the technique
described below
The 1 km grid centerpoint estimates were
spatially joined to
produce estimates for
each census tract
using the technique
described below.
8-hr maximum daily
averaged measures were
interpolated to a 1 km grid
structure using the
technique described below
TEOM continuous PM2.5
mass monitors2 were
used to develop a
diurnal pattern that was
consistent across sites.
This temporal
distribution was applied
to PM2.5 mass
measurements to
allocate daily averaged
concentrations across
the 24 hours of the day.
Hourly data were
interpolated to a 1 km
grid structure using the
technique described
below
3. Attribution of
concentration
estimates to
census tract
Hourly data were
interpolated to a 1 km
grid structure using
the technique
described below
Approach same for all metrics
Measurement data used in study: All available measurements in the study domain that met EPA quality
assurance guidelines were used in the study to derive exposure data. Sources of data used, averaging
approaches and spatial attribution techniques are included in the accompanying manuscript.
While there are several hourly measurement sites in NYC (Figure 1), the TEOM measurements were not
used directly in the kriging, but rather to allocate the daily-averaged PM2.5 mass concentrations into
hourly estimates by applying the diurnal pattern derived from the TEOM measurements. The reason the
TEOM measurements were not directly used in the interpolation process is because the heated sensors
in the instruments are prone to losing volatile mass, making them inaccurate. 3
Monitor Locations: Figure 1 shows the location of the hourly and daily monitoring sites used in the
study.
(a)
(b)
Figure 1: Panel (a) shows location of measurement sites used to derive the daily-averaged ozone and PM2.5 values in 4 NYC
counties during the study time period. Panel (b) shows location of measurement sites for the daily-averaged ozone and PM2.5
values used in the study.
Interpolated Observations. Observations were interpolated by applying a universal kriging approach
defined in the R Statistical geoR package4. This approach applies a Matern spatial correlation function5
to produce daily 1 km x 1 km horizontal resolution maps. For each day of the study, the parameters for
the Matern covariance function were estimated using the restricted maximum likelihood estimation
technique. Kriging was performed to estimate the concentration at the grid center-point. Differences in
the spatial correlation structure along different directions (anisotropy) was also accounted for in the
model. Variograms for each day were examined using cross-validation. An intercomparison of this
approach with other modeling approaches is provided in Garcia et al.6
Representation of titration (ozone). Ozone concentrations measured at a given site may be significantly
different depending on proximity of the monitor to NO sources and on prevalent wind direction and
speed. Monitors far from NO sources may not show low ozone concentrations that result from titration
near NO sources. We examined observations, CMAQ model output and the interpolated estimates to
understand whether the interpolated estimates were capturing areas of titration near NO sources
(Figure 2). The observations show some consistency (around 50 ppb) in the 4-county area (Figure 2(a)).
CMAQ, known for over-predicting titration7, shows lower values, but concentrations are again relatively
consistent across the 4-county area (Figure 2(b)). The interpolated results smooth the observed values
and are also consistent across the 4-county area (Figure 2(c)). This analysis indicates that ozone is fairly
homogeneous for the 4 county domain used in the study. Titration throughout these counties is fairly
consistent (local NO emissions are likely from traffic dispersed throughout the counties), and a strong
land-ocean breeze is predominant in the area helping to mix the pollutants.
(a)
(b)
(c)
Figure 2: 8-hr maximum daily average ozone (ppb) for June 13, 2001 highlighting the lower end of Long Island. Panel (a) is
observations, panel (b) is CMAQ model output and panel (c) is interpolated observations.
Assignment to census tract: Estimates for the 1 km horizontal grid structure were attributed to censustract centroids using the spatial join function in ESRI’s ArcGIS 9.3 application. The join is ‘tract-centric’
which means that each census tract receives the attributes of the 1km grid cell that falls closest to its
centroid. This method was compared against a more rigorous estimation (areal weighted average) and
produced virtually identical results with significantly increased processing time.
1.
2.
3.
4.
5.
6.
7.
Jones RR, Özkaynak H, Nayak S, Garcia V, Hwang S, Lin S. Associations between summertime
ambient pollutants and respiratory morbidity in New York City: comparison of results using ambient
concentrations versus predicted exposures. J Expo Sci Env Epid, submitted
Environmental Protection Agency Air Quality System. Accessed on April 23, 2012 at
http://www.epa.gov/oar/data/aqsdb.html
New York State Department of Environmental Conservation, 2012 Annual Monitoring Network Plan,
Revision 1.1, pgs. 174-197, New York, 2012
R Development Core Team (2012). R: A language and environment for
statistical computing. R Foundation for Statistical Computing.
Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.
Stein M. Interpolation of Spatial Data. Springer, New York, 1999.
Garcia VC, Foley KL, Gego E, Holland DM, Rao ST. A comparison of statistical techniques for
combining modeled and observed concentrations to create high-resolution ozone air quality
surfaces. J Air Waste Manage Assoc, 2010; (60): 586-595.
Appel KW, Gilliland AB, Sarwar G, Gilliam RC. Evaluation of the Community Multiscale Air Quality
(CMAQ) Model Version 4.5: Sensitivities Impacting Model Performance; Part I—Ozone.
Atmospheric Environment (2007), doi: 10.1016/j.atmosenv.2007.08.044.