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1、Estimation of Spatial Air Pollutant Concentration Fields from Observation DataStefan FalkeAdvisor: Rudolf HusarA thesis proposal presented in partial fulfillment of the requirements of the degree of Doctor of Science in Environmental EngineeringWashington UniversityMarch 13, 1997AbstractReliable air
2、 pollutant concentration fields are essential for environmental researchers, epidemiologists, and policy-makers in such activities as air quality pattern and trend analysis, exposure assessment, and monitor network design. The goal of this research is to develop new methodologies for estimating spat
3、ial air pollution concentration fields from observation data and to provide uncertainty measures associated with the estimated concentration fields. The proposed interpolation methodologies will combine physically based and statistically based methods to estimate concentrations at unknown locations.
4、 Pollutant transport will be incorporated using wind speed and wind direction data. Topographical data will be employed to account for the elevation dependence of pollutant concentrations. Surrogates will be utilized to increase the spatial resolution of the data. For instance, visibility observatio
5、ns are surrogates for fine mass concentrations because of the strong correlation between fine mass concentrations and visibility degradation. Statistical methods, such as inverse distance weighted interpolation and kriging, will be employed to aid in relating concentrations at monitored locations to
6、 non-monitored locations. The uncertainty of the interpolation will be assessed using cross validation and covariance analysis techniques. Existing techniques account for uncertainties associated with the spatial configuration of the observed data as well as any redundant information they contain. T
7、hese techniques will be extended to assess the influence of incorporating emission, wind, topographical, and surrogate data. The new interpolation scheme will be applied to generate tropospheric ozone and particulate matter concentration fields for the coterminous U.S.ContentsI. SPECIFIC AIMS AND SI
8、GNIFICANCE11.1 Statement of Problem11.2 Objectives21.3 Significance22. BACKGROUND32.1 Estimation Methods32.1.1 Statistical32.1.2 Surrogate Aided52.1.3 Physically Based52.2 Uncertainty Measures73. APPROACH TO THE THESIS83.1 Review of Ozone and Particulate Matter Spatial Characteristics83.2 Review of
9、Ozone and PM Source Receptor Relationship83.3 Collection and Interpretation of Ozone and PM Datasets93.4 Estimation of Unknown Concentrations103.4.1 Statistically Based Methods103.4.2. Surrogate Aided103.4.3 Physically Based Methods113.5 Measurement of Uncertainty114. PROGRESS REPORT134.1 Visibility
10、 and PM10 as Surrogates for PM2.5 Concentrations134.2 Elevation Correction of PM10 Concentration Fields164.3 Declustering in Spatial Estimation184.4 Spatial Structure of Eastern U.S. Ozone204.5 Integration of work and future additions224.5.1 Potential Difficulties and Limitations224.5.2 Work Schedul
11、e225. REFERENCES CITED23APPENDIX A. - MAPS OF PM2.5 OVER THE U.S. DERIVED FROM REGIONAL PM2.5 AND SURROGATE VISIBILITY AND PM10 MONITORING DATA.A-IAPPENDIX B. - INCORPORATING TOPOGRAPHY IN THE SPATIAL INTERPOLATION OF POLLUTANT CONCENTRATIONS.B-IAPPENDIX C. - DECLUSTERING IN THE SPATIAL INTERPOLATIO
12、N OF AIR QUALITY DATA.C-I1. Specific Aims and Significance1.1 Statement of ProblemEnvironmental researchers, policy makers, and epidemiologists have a need for spatially complete air pollution concentrations for activities such as air quality pattern and trend analysis, monitor network design, and e
13、xposure assessment. Monitoring networks have been established with the objective of providing knowledge of ambient air pollutant concentrations and their impact on human health and welfare. Monitors provide valuable information at their locations but monitoring networks leave large gaps in areas whe
14、re it is desirable to have an understanding of the concentrations. There is a need for estimation methods that provide reliable air pollutant concentrations at non-monitored locations. Existing estimation methods are primarily statistically based and hampered by their exclusion of physical factors i
15、nfluencing ambient pollutant concentrations, such as topography and meteorology. For illustrative purposes, Figure 1 displays a contour plot of estimated ozone concentrations generated from distance weighted interpolation. The high concentrations in St. Louis are excessively dispersed over much of s
16、outhcentral Missouri because no sites exist in the area and no additional information was incorporated to restrict the bias.Figure 1. Contour map of 90th percentile daily maximum ozone concentrations in Missouri with monitoring stations superimposed (squares). The contour was generated using inverse
17、 distance weighted. Rivers (blue lines) and interstates (brown lines) are included as visual guides.1.2 ObjectivesThe general objective is to improve the quality of spatially estimated air pollutant concentration fields for the coterminous U.S. Specifically, new methods will be developed for estimat
18、ing air pollution concentrations that will be derived from observation data. supplemented with measures of uncertainty associated with the estimates. based on physical and chemical principles. aided by spatial statistical techniques. applied to tropospheric ozone and particulate matter concentration
19、s.1.3 SignificanceAdequate spatial resolution estimated concentration fields can be applied: as input in exposure assessment models outlined in the EPA Particulate Matter and Ozone Criteria Document (U.S. EPA, 1996a; U.S. EPA, 1996b). for aiding network design. Knowledge of the current status of a n
20、etwork can be used as a feedback to improve the station configuration by rearrangement of addition/subtraction of sites. (Holland et al, 1994). in support of NAAQS. Estimated concentration fields have been used in the past by the EPA Office of Air Quality Standards and Planning in the standard setti
21、ng process.(Falke and Husar, 1998c; Falke and Husar, 1996). in the assessment of air quality models (Meiring, et al., 1997). to supplement remote sensing (satellite imagery).2. BackgroundMonitoring networks are established to study the air quality in specified regions. The collected samples characte
22、rize the pollutant concentrations at the monitoring locations, but to gain insights into the concentrations at other locations, estimation methods and techniques to assess their uncertainties have been developed.2.1 Estimation MethodsA review of the literature reveals numerous spatial air pollutant
23、concentration estimation techniques. The approaches generally fall into three categories: statistical, surrogate aided, and physically based.2.1.1 Statistical2.1.1.1 Inverse Distance Weighted InterpolationThe general theory underlying inverse distance weighted interpolation is that points closer to
24、the estimation location are more influential than points farther away (Watson, 1992). The estimate is obtained from a weighted average of the relevant stations with stations closest to the estimation point receiving the largest weights.Figure 2 displays an example configuration of monitoring station
25、s and estimation location. where ci is the estimated concentration at location i,n is the number of monitoring sites,cj is the concentration at monitoring site j,wij is the weight assigned to monitoring site j.The weights are determined from the distances between estimation point and monitoring site
26、s, rij, so thatwhere n is the power-law of distance weighting.Figure 2. Configuration of monitoring sites and estimation location.Each of the sites has their concentration weighted by the inverse of their distance from the estimation point. The weighted concentrations are then summed and divided by
27、the sum of the weights to ensure that the weights sum to one.2.1.1.2 GeostatisticalMuch of the current work aimed at improving the interpolation process for air pollutant concentrations involves geostatistical techniques. A frequently invoked technique, kriging, accounts for the spatial variability
28、in the data as well as their spatial distribution (Isaaks and Srivastava, 1989). Kriging was originally developed as a statistical tool in the mining industry for estimating ore deposits. Like simple distance weighted interpolation, kriging is based on the separation distance between the monitoring
29、sites and the estimation location with the estimate being a linear combination of weighted concentrations at neighboring monitoring stations. Kriging distinguishes itself in that the weights are determined by minimizing the estimation variance. The estimation variance is derived through covariances
30、that are dependent on a random variable model called the variogram. The variogram model is developed by comparing the concentrations between all pairs of monitoring stations. The distance separating each monitoring station pair is used to place the pair in a distance bin and the covariance is calcul
31、ated for all stations pairs within each bin. Plotting the covariance values against the bin distances results in the sample variogram. A function is fitted to the sample variogram and is applied to the error minimization. Subsequently, the kriging weights are derived as,where wij is the weight assig
32、ned to monitoring site j,Cjxj-1 is a matrix of that contains the covariance between all pairs of monitoring station,Dij is a vector that contains the covariance between monitoring sites j and the estimation point i.The covariance vector, Dij, can be interpreted as the weights obtained in inverse dis
33、tance weighted interpolation except the distances are statistical in nature in that they account for the covariance between the monitoring sites and estimation points as well as their separation distance. The covariance matrix, Cjxj-1, accounts for the separation distances and covariances between al
34、l pairs of monitoring sites. This allows kriging to incorporate aspects of the monitoring network that simple interpolation schemes do not, namely the clustering and redundancy of sites. Kriging has been applied to atmospheric variables such as wind speed and direction, acid precipitation, troposphe
35、ric ozone, and precipitation (Lefohn et al., 1987; Seilkop and Finkelstein, 1987; Eynon, 1987; Venkatram, 1988; Palomino and Martin, 1994; Liu and Rossini, 1996). 2.1.2 Surrogate AidedSecondary variables related to the primary variable being estimated can improve the estimation process, especially i
36、n areas of sparse primary variable monitoring. The secondary variables are usually highly correlated with the primary variable and can be thought of as surrogates for the primary variable. Willmott, et al, (1995a) advanced their estimation of temperature fields by incorporating a second air temperat
37、ure field that is sampled over a different time period but at a higher spatial resolution. The higher resolution surrogate data are related to the lower resolution temperatures and the relationship is applied to inverse distance weighted interpolation.Cokriging is an extension to simple kriging that
38、 includes surrogate data. The cross correlation between the surrogates and the primary variable is used to reduce the estimation variance in the kriging system (Phillips, et al., 1997)2.1.3 Physically BasedPhysically based methods incorporate the laws of nature to which the pollutant is subject. The
39、 laws are incorporated into the estimation process as models that focus on the causal relationships between the primary and secondary variables.Willmott, et al, (1995b) introduced a modified spatial interpolation scheme for air temperature data. They incorporate digital elevation model (DEM) data an
40、d the lapse rate relationship to adjust the temperature estimates according to elevation. High resolution measured temperature data from an earlier period are used to increase the spatial resolution of the measured temperature for interpolation. The measured temperature data is adjusted to sea level
41、 using the environmental lapse rate. The sea level temperatures are interpolated and then the interpolated temperatures are adjusted to actual elevation using the lapse rate again. The modified interpolation is 35% more accurate for estimating air temperature data than simple interpolation technique
42、s.Geographical Information Systems (GIS) are becoming accessible for environmental data analysis and facilitate the use of multiple data sets to exploit relations among the data. Ollinger et al. (1995) used the high resolution of an elevation data base with deposition data to conduct regression anal
43、ysis and then used the derived relations to improve the resolution of sulfur and nitrogen deposition maps in the northeastern U.S.Lee, et al, (1997) used an interpolation scheme driven by a GIS to generate ozone concentraiton fields. The method calculates a Potential Exposure Surface (PES) which is
44、based on two physical assumptions. One, the areas downwind of locations with large ozone precursor emissions and experiencing higher temperatures and low cloud cover will have a greater potential for higher ozone concentrations and secondly, areas in close proximity to each other with similar PES va
45、lues will have similar actual ozone exposure. They use annual emission inventories and assume that ozone exposure is a function of the amount of upwind NOx emissions, temperature, and cloud cover. NOx is spatially dispersed from the 1443 emission sources using wind direction and a decay function. El
46、evation is accounted for by imposing the restrictions that if the terrain height is between 500-1500 meters, 50% of the plume passes over and if its higher than 1500 meters of any cell within 20 km of it, none of the plume gets through. Temperature and cloud cover are incorporated so that at high te
47、mperatures and low cloud cover there is a high PES and low temperature and high cloud cover there is a low PES. They found the resulting maps more realistic than those produced using simple distance weighted interpolation but were unable to provide any quantitative metrics for comparison.Loibl et al
48、., (1994) extended the pure statistical approach of kriging estimation of tropospheric ozone by incorporating an elevation and diurnal cycle dependence model to improve the resolution of ozone concentrations in the complex terrain of Austria.2.2 Uncertainty MeasuresThe jack knife method of cross validation has been extensively used to compare pollution estimation methods by providing a measure of interpolation performance at loca
