1. Technical Details of Network Assessment Methodology: Concentration Estimation Uncertainty Area of Station Sampling Zone Population in Station Sampling Zone Prepared for EPA OAQPS under Cooperative Agreement Richard Scheffe by Stefan R. Falke and Rudolf B. Husar Center for Air Pollution Impact and Trend Analysis, CAPITA Washington University, St. Louis December 2000

2. Network Assessment Measures AQ Management ActivityGeographic Info. Need Station Measure Risk assessmentPollutant concentration 4 th highest O3 Risk AssessmentPersons/Station Compliance evaluationConc. vicinity to NAAQS Deviation from NAAQS Reg./local source attribution & trackingSpatial coverage Area of Sampling Zone All aboveEstimation uncertainty Conc. & Est. Difference Persons in sampling zone The general framework for Air Quality Network Assessment is presented elsewhere. There are at least five different measures that represent the information need for (1) risk assessment, (2) compliance monitoring and (3) tracking are listed below. This report describes three of the measures by which the networks can be evaluated: Concentration Estimation Uncertainty (Information Value of Station) Area of Sampling Zone Persons in Station Sampling Zone The computational detains of each measure are presented along with illustrative examples.

3. Error Estimation by Cross-Validatation Cross-validation is applied to obtain an estimation error. This involves removing a monitor site from the data base and using the remaining sites to calculate an estimated concentration at the removed monitor location. The estimation error is calculated as the difference between the Estimated - Measured Concentration. The estimate is determined using declustered inverse squared distance weighted (1/r2) interpolation. The nearest 5 sites within a 750 kilometer radius of the estimation location are used in the estimation calculation. Declustering reduces the relative weight of spatially clustered monitor sites during spatial interpolation. It is accomplished by the introduction of two characteristic distances 1) the distance from the monitoring station to the estimation point (R ij ) and 2) the average of the distances between the monitoring site and its surrounding sites. A cluster weight (CW) is defined as the ratio of to R ij. i is the estimation location j is the monitoring site being declustered k is an index of the sites surrounding the monitoring site within a distance R ij of the estimation point r is the average distance between the monitoring site and the sites surrounding it R ij is the distance between the monitoring site and estimation location n is the number of sites within a distance R ij of the monitoring site (including the monitoring site being declustered). A site is clustered if the distances between the monitoring site and its neighboring sites are small (/R ij << 1) compared to the distance between the monitoring site and the estimation point. A feature of this definition is that the degree to which the station is clustered changes depending on the location of the estimation point. For example, a group of sites in a city is considered unclustered when estimating the concentration within the city but is clustered when estimating concentrations in locations substantially outside of the city.

4. Declustering Configurations The sites X 1, X 2, and X 3 are equidistant from the estimation point i and are unclustered Declustered weighing shows the proper allocation of the 1/3 weight to the cluster of sites. There is a cluster of four sites. When applying standard distance weighted interpolation, the cluster will account for 2/3 of estimated value at i while the two single sites each only account for 1/6 of the total weight Standard interpolation applies equal weight; each site has 1/3 of the weight on the estimate at i.

5. Concentration Error, E The concentration error is determined by –selectively removing each site from the database –estimating the concentration at that site by spatial interpolation –setting the error as the difference between the estimated and measured values, E = Est.-Meas. The error estimates in both metric of ozone concentration over the Eastern US ranges between 0-15 %. High estimation error is generally observed over areas with low station density. Low estimation error generally occurs over areas with high station density

6. Concentration Estimation Error (~ 5-6 ppb) For the entire dataset, the measured and estimated data have the same avg. and a slope of 1. The standard deviation of the meas.-est. difference is about 5 ppb, no bias. The estimation error is also random in space. The CastNet network characterizes the non- urban concentrations. The estimation error for CastNet data is the same as for the entire network. This implies that the errors are random.

7. Example: O 3 Station Ranking by Estimation Uncertainty The uncertainty measures the ability to estimate the concentration from other data. The highest uncertainty (red) is found urban stations where the concentrations are highly variable in space and time. The lowest uncertainty (blue) is at remote sites where the concentrations are more homogeneous in space and time From the perspective of estimation uncertainty, the blue stations have the lowest rank.

8. Technical Details of Network Assessment Methodology: Concentration Uncertainty Area of Station Sampling Zone Population in Station Sampling Zone Prepared for EPA OAQPS Richard Scheffe by Stefan R. Falke and Rudolf B. Husar Center for Air Pollution Impact and Trend Analysis, CAPITA Washington University, St. Louis December 2000

9. Station Sampling Zones Every location on the map is assigned to the closest monitoring station. At the boundaries the distance to two stations is equal. Following the above rules, the ‘sampling zone’ surrounding each site is a polygon. The polygons are converted to an ESRI ArcView ‘shapefile’ The the area (km 2 ) of each polygon is calculated ArcView ‘calcarea’ function.

10. Sampling Zone Illustrations (St. Louis, MO) There are three different types of sampling zones: Single monitoring stations that are far from other stations (light blue) have large and symmetric sampling zones. Stations inside clusters (red) in urban areas have small but symmetric sampling zones. Stations on the edge of clusters (yellow) have larger asymmetric, elongated sampling zones.

11. Station Sampling Zones in Different Parts of EUS

12. Sampling Area Distribution Function Over the Eastern US, the average O3 sampling zone is 5900 km 2 or 77x77 km. The distribution of sampling areas is very broad, ranging from 44 km 2 to 98,000 km 2. The 25% of the stations with the smallest area covers only 2.5% of the total EUS area while the upper station quartile covers 70%. In other words, eliminating 25% the ‘smallest’ stations would increase the area of the remaining stations only by 2.5%.

13. Example: Ranking by Area of Sampling Zone The area of the sampling zone is measure of the spatial coverage and uniformity The stations with large sampling areas (red dots) are unclustered remote sites outside of urban areas Conversely, the stations with small sampling areas (blue dots) are in clusters, mostly in urban regions. The clusters with small station areas are located over the NE megalopolis, Chicago, Pittsburgh, St. Louis, etc. They rank lowest in area coverage.

14. Network Assessment Based on Estimation Uncertainty Prepared for EPA OAQPS Richard Scheffe by Stefan R. Falke and Rudolf B. Husar Center for Air Pollution Impact and Trend Analysis, CAPITA Washington University, St. Louis December 2000

15. Population Density The population data are available on census track resolution. The population density is highly textured; it varies by three orders of magnitude over the EUS.

16. Population in Each Station Sampling Zone The population for each monitor was calculated from census tract population data (1990) from available from ESRI.  The station population was calculated by summing the persons in each census tracts located within the sampling zone polygon of the station.  Following the spatial join operation, the population was added as an attribute of each station.

17. Population Distribution Function Over the Eastern US, the average number of persons in an O3 station sampling zone is 220,000. The distribution of population/station varies from virtually zero (hard to measure) to 2.8 million. The 25% of the stations with the smallest population accounts for 4% of the total EUS population while the upper station quartile includes 60% of the total population. In other words, eliminating 25% stations with the lowest population would be affecting only 4% of the EUS population. The population in each sampling zone is weakly correlated with the sampling zone area since the population density varies greatly.

18. Ranking by Population in the Sampling Zone The number of persons in a station’s sampling zone is a scaling factor for the overall health risk. Areas of large population per station (red) are found over the NE megalopolis but also over more remote areas. Small population/station (blue) is generally found remote sites but also in some urban clusters, e.g. Chicago, New Orleans, St. Louis. From the perspective of population coverage, the blue stations have the lowest rank.