PM Monitoring Network Design Tool and Resources

1. PM Monitoring Network Design Tool and Resources Planning Document from 1996 dfdshg

2. Background • The promulgation of the proposed PM2.5 air quality standard will require the creation of a suitable PM2.5 monitoring network. Proper network design requires substantial resources including experience, data and tools. In many state and local agencies such resources are not available but there is considerable knowledge about the local conditions. • The current thinking is that the general guidance and tools for the new network design is to be prepared by the federal EPA, while the detailed design, implementation and evaluation of the network is to be conducted primarily by state or local teams.(Frank, 1996). • The design and evaluation of monitoring networks has many subtle features. Some aspects of network design can use the methods of science. Other aspects are a craft, requiring experience, craftsmanship and tools. Yet other parts of network design are literally art in that they are dependent on the intuition of the designer. • The goal of this project is to aid the science and the craft of network design including design rationale, methods, tools and to provide the necessary information resources.

3. Network Assessment/Design • Air quality monitoring network design is about layout and operation such that it best characterizes AQ for the specific network purpose. • At the minimum, the best design needs to: • 1.      Reduce the uncertainty of concentration estimate for the unmonitored data domain • 2.      Incorporate and weigh the conditions imposed by the network purpose.

4. Optimal Network Design • An inherent problem of optimal network design lies in the following paradox: • In order to design a network to ‘best’ characterize the pollutant, it is necessary to know the pollutant patterns. • If the pollutant patterns are indeed known, what is the purpose of designing a network • In reality, air quality monitoring network design is somewhat less paradoxical. • Usually there are some monitoring data available as a guidance for full network design. • The goal may be simply to rearrange the network to better serve the needs. • The purpose of the design may also be to reduce the network, so the actual AQ pattern may be known reasonably well.

5. Network Design Theory(1) • The theory of network design is largely an optimization problem. The goal of the optimization is to minimize the uncertainty of the (purpose-weighed) exposure estimates at the unmonitored data domain. Cost may also be a factor. • Below is a simple model for the evaluation of network performance toward a specific purpose. • The basic assumption is that the contribution of a monitoring site can be measured by the combination (product) of two independent functions: • one is related to the pollutant exposure and • a relevancy-function determined by the purpose(s) of the network G(x,y,t) = E(c(x,y,t))* R(x,y,t) G(x,y,t)- relevance -weighed exposure function applicable throughout the data domain E(c(x,y,t)) - pollutant exposure function which depends on c(x,y,t) c(x,y,t) - ambient concentration as a function of space and time R(x,y,t)- relevancy function which is to be defined by the network purpose (is there a better name for this?) The reason for modifying the pollutant exposure function E with the relevancy function R is to assure that irrelevant data receive less weight, while highly relevant data receive proper consideration. For instance, for human exposure monitoring, sites that have low population density are less relevant.

6. Uncertainty of Network Data Value • Throughout the data space, the relevance-weighed exposure function, G, has an uncertainty since both the exposure and the relevancy factor have their respective uncertainties, UE and UR at the unmonitored location and times. (Or for that matter even at the monitored domain.). One can then define UG as the uncertainty of G. • UG(x,y,t) = f [UE (c(x,y,t))* UR (x,y,t)] • The function, f, could be the standard error-propagation function, i.e. the square root of the sum of the error squares. • In the past, this apparent paradox was resolved by network designers by basing the network layout primarily on the relevancy function, R. In the case of SLAMS for example, the monitoring sites were placed roughly in proportion to the population density. [Is this how it went?].

7. Network Performance Measures • At this time there is no generally accepted definition of network performance, however there are several statistical criteria that can be used to define performance based on existing monitoring data. These include: • 1.      Direct cross-validation with data (selectively removing sites) • 2.      Maximum information (Caselton and Zidek, 1984) • 3.      Minimum relative uncertainty (Venkatram, 1988) • 4.      Minimum variogram estimate (Warrick and Myers, 1987) • A common characteristic of these algorithms is that each provides a measure of uncertainty for concentration estimates based on monitoring data, hence they are suitable for the evaluation of network performance. • A detailed exploration of these methods is currently underway at CAPITA by Stefan Falke as part of his PhD dissertation. Inputs from the entire project team would be helpful for this task.[Done in 1999]

8. Computer Aided Network Design Tools • Ideally, network design should be guided by objective methods using the set goals, available data, and clear algorithmic methods. It is, however, clear that the network design has to incorporate many factors that do not lend themselves to analytical modeling. Such factors may include site availability and accessibility, costs, existing long-term monitoring data, etc. The incorporation of all these factors in the design process is best accomplished by designers who are familiar with the local circumstances. For this reason it is desirable to develop design methodology and a set of tools and data resources and to support the local designers. • It is assumed that the following aspects of network design are given to a designer: the goal of the network; The geographic area of the network coverage; Monitoring data for air quality and related variables; Geography and other contextual information.

9. Network Design Tool Specification • ·        The design tool should allow the display of existing air quality data on spatial maps and time charts. • ·        The spatial display on maps should allow the superposition of multiple data layers to provide spatial context. These should include major emission point sources, population density, topography, major roads, rivers etc. • ·        The tool should allow the placement of network locations on the map including an updated display of the estimated change of information value contributed by each network configuration. • The resulting tool should allow the designers to lay out and explore potential network configurations while at the same time showing the information value measure of the network. Incorporation of multiple potential value criteria may be desirable.

10. Illustration of the Network Design Tool • An illustration of the tool’s appearance is given in Figure 1. It consists of a Map view, Time view, and Information view. The Map view, consisting of multiple data layers, would allow the addition, removal, and dragging of stations from the new monitoring network. As stations are added, the information view would automatically display the current information value metric as a feedback to the designer. • The specification and evaluation of the tools should involve the project team. The implementation of these tools is to be the responsibility of CAPITA.

11. Data and Knowledge Resources • Existing AQ data. The main data set in support of new network design consists of the existing monitoring data for the region. This data set is used to establish the pattern of exposure as well as to estimate the estimation of information value added by each station. The air quality data should be augmented by other air quality related parameters. These may include emission fields for point and area sources, surrogate variables (e.G. PM10 as a surrogate for PM25), possibly wind rose data, etc. • Relevancy function data. It is presumed that the major relevancy function will be related to population density. Zip code level population data need to be prepared for the information value analysis. • Geographic context. Further context for the network design may be added by geographic data on topography, major roads, rivers, and lakes. Political boundaries at zip code, county, state, and national level would also help the design process. • Knowledge resources. The knowledge resources in support of local designers would include tutorials on design rational, design procedure guidelines, and network evaluation procedures.[John, how do you like your main task to be tucked away in this corner here!]

12. Network Assessment through a Community • The PM25 network design process across the country can be aided by interaction among the designers and data analysts. A community of network designers, with common interests, could share a stable forum to show their designs, (e.g. through Web pages); receive feedback, to learn from others, and if needed, to act collectively. Such a community can be a Web-based ‘virtual’ community, that transcends the boundaries of states, and their brick-and-mortar institutions. • The Web-based network designer’s forum is not a necessity to the project, but it is exposed here for consideration and comments by the project team. CAPITA is interested in this aspect of the project in the context of SMARTS (http://capita.wustl.edu/smarts/smarts3.htm).

13. References • Caselton W.F. and Zidek J.V. (1984) Optimal monitoring network designs. Stat. Prob. Lett.2, 223-227 • Frank N.H. (1996) Regulatory Monitoring Strategy for Revised PM NAAQS: A Blueprint for a New National Monitoring Program for Particulate Matter. EPA-OAQPS. Draft Feb 8, 1996, Do not Cite or Quote. • Program for Particulate Matter. EPA-OAQPS. Draft Feb 8, 1996, Do not Cite or Quote. • Venkatram, A. (1988) On the use of kriging in the spatial analysis of acid precipitation data. Atmospheric Environment22, 1963-1975. • Warrick A. W. and Myers D.E. (1987) Optimization of sampling locations for variogram calculations. Wat. Resour. Res. 23, 496-500