PM Network Assessment: Speciated Network Planning Prepared for EPA OAQPS Richard Scheffe by Rudolf B. Husar Center for Air Pollution Impact and Trend Analysis, CAPITA Washington University, St. Louis Draft, March, 2001

Spatial Distribution of PM2.5 Species: Uncertain The spatial distribution of PM2.5 species (e.g. sulfates, organics, dust) over the US is not well characterized. The main PM2.5 speciation data are provided by the evolving IMPROVE network which is focused on remote parks and wilderness areas, mostly in the West. The spatial coverage over the populated Eastern US and the Pacific Coast was marginal. The sparse IMPROVE coverage over the East (16 sites) can only characterize the gross features of the spatial pattern East of the Mississippi. The annual PM2.5 concentration estimated from the new FRM network (~700 stations) shows significant spatial texture over the Eastern US and the West Coast

Combined Speciated PM Network

The Combined PM2.5 Speciation Network: How to design it to be effective? The new planned PM2.5 Speciation Network is intended to aid the implementation of the PM2.5 and Haze regulations. It will be an extension of the IMPROVE network (yellow dots). Speciation sites have already been (tentatively?) located by the states (black marks) EPA has identified a set of long term speciation trends sites (green marks). In the following analysis, the combination will be taken as a single Speciation Network consisting of 263 sites.

Background on AQ Network Assessment Monitoring the ambient concentrations provide the necessary sensory input to the various aspects of AQ management. Efforts are under way to implement new monitoring systems for ozone precursors (PAMS) as well as for PM2.5 mass and composition. At the same time the performance of the existing ozone and PM monitoring sites are re-assessed for possible re-location or elimination. EPA OAQPS has initiated a program to make the existing and developing AQ monitoring networks more responsive to the needs of AQ management. This work is a progress report by the CAPITA group on methodology to assess the performance of the planned speciated PM2.5 monitoring networks.

Monitoring Network Evaluation: Multiple Criteria Monitoring networks need to support multiple aspects of air quality management including risk assessment, compliance monitoring and tracking the effectiveness of control measures. Multiple purposes may require very different network designs. For example, health risk characterization requires sampling of the most harmful species over populated areas during episodes; tracking of emission-concentration changes requires broad regional sampling for establishing pollutant budgets. In general, an AQ monitoring network is characterized by the spatial distribution of sampling stations, temporal sampling pattern and the species measured. The methodology described in this report is focused on evaluating the geographic features of the network. The consideration of the temporal and species aspects of network evaluation is left for future work. In particular, the relative value of individual existing or planned stations (compared to the value of other stations) is evaluated.

‘Information Value’ of Network Stations The information value contributed by a specific station is composed of two major parts: –How much the station contributes to reduce the estimated ambient concentration error in it’s sampling zone – the concentration error, E. –How important is the station from the perspective of the specific network objective – the receptor sensitivity weight, W. The information value, I, is defined here as I = E x W. Additional factors, such as the concentration level, C, could also be included. In this illustration, the concentration error reduction, E, is estimated from the existing data by selectively removing the individual sites from the data. As an example, the receptor sensitivity weight, W, can be taken as the number of persons in the ‘sampling zone’ of each site.

Station Sampling zones The ‘sampling zone’ surrounding each site was crudely estimated as a polygon. Single monitoring stations that are far from other stations have large sampling zones. Station clusters in urban areas have small sampling zones.

Population The population data are available on census track resolution. The population data were re-mapped onto a ~5 km grid The population density is highest over the urban- metropolitan areas. The PM/ozone monitoring station density is also highest over these areas The population weight factor, W, isthe number of persons in the sampling zone of each site.

PM Network Evaluation: Spatial, Temporal, Parameter Coverage Spatial Coverage Spatial density (number of stations) Spatial uniformity (clustering) Temporal Coverage Temporal density (sampling frequency) Temporal uniformity (sampling uniformly, intermittently) Parameter Coverage Number of parameters (# of speciation parameters, auxiliary info) Speciation focus (sulfate, dust, smoke…focus)

Network Layout: Uniform or Clustered? The existing monitoring networks for O3, PM2.5 and weather parameters show very different strategies: The monitoring ozone network is highly clustered in around populated areas (top). Evidently, O3 regulatory network is laid out with to focus on areas ‘where the people are’. The recently established FRM PM2.5 network is less clustered (center). On the other hand, the automated surface weather observing system, ASOS is uniformly distributed in space for broad spatial coverage (bottom). Clearly, the layout of these networks is tailored for different purposes. Click on images for full resolution

Network Evaluation Combining Subjective and Objective Steps 1.First, select multiple evaluation criteria i.e. risk assessment, compliance monitoring, trend tracking etc. This is a subjective procedure driven by the network objectives. 2.Decide on specific measures that can represent each criterion, i.e. number of persons in the sampling zone of each station; concentration etc. The selection of the suitable measures is also somewhat subjective. 3.Calculate the numeric value of each measure for each station in the network. This can be performed objectively using well defined, transparent algorithmic procedures. 4.Rank the stations according the each measure. This yields a separate rank value for each measure. For example, a station may be ranked 5 by day-max O3 and ranked 255 by persons in the sampling zone. This step can also be performed objectively. 5.Weigh the rankings, i.e. set the relative importance of various measures. This involves comparing ‘apples and oranges’ and it is clearly subjective. 6.Add the weighed rankings to derive the overall importance of the station and rank the stations by this aggregate measure. Use the aggregate ranking to guide decisions on network modifications. The proposed network evaluation methodology combines subjective and objective methods for network evaluation. Below is the outline of the hybrid procedure.

Network Evaluation Using Independent Measures The approach is illustrated with the PM2.5 network using independent measures of performance. The different measures represent the information need for (1) risk assessment, (2) compliance monitoring and (3) tracking. The methodology allows easy incorporation of additional measures. These are all measures of the network benefits. Other benefits measures (temporal, species) should also be incorporated. For cost-benefit analysis, the cost of the network operation should also be incorporated. AQ Management ActivityGeographic Info. Need Station Measure Compliance evaluationConc. vicinity to NAAQS Deviation from NAAQS Reg./local source attribution & trackingSpatial coverage Area of Sampling Zone All aboveEstimation uncertainty Meas. & estimate difference Risk assessment Pollutant concentration Annual PM Concentration

The Independent Measures of Network Performance In this assessment, four independent measures are used to evaluate the AQ monitoring network performance. As the Network Assessment progresses, additional measure will be incorporated. Further details about the five measures can be found in separate presentations pertaining each measure. (See links below. Note: Use the Back arrow on the browser to return to this presentation). Pollutant Concentration is a measure of the health risk. According to the NAAQS, a relevant statistic is the annual average PM2.5 concentration. The station with the highest annual average PM2.5 mass concentration is ranked #1.Pollutant Concentration Deviation from NAAQS measures the station’s value for compliance evaluation. The station ranking is according to the absolute difference between the station value and the annual average PM2.5 (15  g/m 3 ). The highest ranking is for the station whose concentration is closest to the standard. Stations well above or below the standard concentration are ranked low.Deviation from NAAQS Spatial coverage measures the geographic surface are each station covers. The highest ranking is for the station with the largest area in it’s sampling zone. This measure assigns high relative value to remote regional sites and low value to clustered urban sites with small sampling zones.Spatial coverage Estimation uncertainty measures the ability to estimate the concentration at a station location using data from all other stations. The station with the highest deviation between the actual and the estimated values (i.e. estimation uncertainty) is ranked #1. In other words, the stations who’s values can not be estimated accurately from other data are ranked (valued) high.Estimation uncertainty

Ranking by Annual PM2.5 Concentration The daily max concentration is a factor in health risk. The stations with the highest annual PM2.5 levels (red) are located over the Southeast. The stations with the lowest PM2.5 levels (blue) are located in the Southeast. The stations above the 75%- ile PM2.5 concentration (black rectangles) are located in industrial East and in California. The stations ranked lowest (25 percentile) are located over the mountainous regions.

Ranking by Deviation from NAAQS The deviation from the NAAQS ( 15  g/m 3 ) measures the station importance for compliance. The stations closest to the NAAQS (black dots) are found along the boundaries of the 15  g/m 3 contour line. The stations with large +/- deviation from NAAQS (white circles) are clustered over pristine West.

Ranking by Estimation Uncertainty The uncertainty measures the ability to estimate the concentration from other data. The highest uncertainty of estimation (black dots) is found in California. This is due to the strong concentration difference between low and high elevation sites. The lowest uncertainty (white circles) is is observed throughout the country.

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 (black rectangles) are unclustered remote sites outside of urban areas Conversely, the stations with small sampling areas (white circles) are in clusters, mostly in urban regions. The stations with with large sampling zones are located over in the mid-section of the country extending from Texas to N. Dakota.

Aggregate Ranking of Stations Aggregation of rankings is not simply a weighing the rankings since it involves subjective judgments. However, once the relative weights of different rankings are available, (from the negotiation process) the current methodology allows their incorporation into the assessment The following pages illustrate several aggregate station rankings.

Summary of the Network Evaluation Methodology: Establish network evaluation criteria using subjective judgment. Calculate objective measures for each criteria for each site using existing data. Using the uniform standards across the network, rank stations according to each criteria using these objective measures. Subjectively weigh the rankings to establish the aggregate ranking.

Qualitative Annotations: Compliance Criteria