Fine Particle Maps Derived from Regional PM2.5 and PM10 Data

Contents:

• Background and Rationale
• Approach
• Fine Mass Concentrations
• PM10 as a Fine Particle Surrogate
• PM10 Data Processing
• PM10-Fine Mass Relationship
• Data Flow for Fine Particle Mass Map Preparation
• Resulting Fine Mass Concentration Maps
• Limitations and Possible Improvements to Fine Particle Maps
• Appendix A

Fine particles are known contributors of visibility degradation, acid deposition, and regional climate change. Recent epidemiological studies also indicate fine particles as causal factors in human health effects. For this and other reasons, the Environmental Protection Agency is considering the promulgation of ambient concentration standards for fine particles. In this report, the term fine particles refers to the mass concentration of particles below 2.5 µm (nominal). In other contexts, the fine-coarse particle size cut may be anywhere between 1 and 2.5 µm.

The spatial and temporal pattern and trends of fine particles over the U.S. is not well established since systematic fine particle monitoring has begun only in the 1980s and at limited number of sites. On the other hand, estimating the fine particle effects on human health and other effects requires higher resolution than what is provided by existing fine particle monitoring networks. Higher spatial and time resolution particle concentrations can be estimated by several methods:

1. Integration and fusion of multiple fine particle mass concentration data sets.
2. Use of surrogate variables for spatial and temporal extrapolation.
3. Application of properly calibrated and verified regional and local atmospheric transmission models as data extrapolators.

This report applies the surrogate method 2, by utilizing the existing higher resolution visibility data for the interpolation of measured fine particle concentrations.

Data Sets

Our current understanding of the US national fine mass concentration (FM) pattern arises primarily from non-urban monitoring networks, the Interagency Monitoring of Protected Visual Environment (IMPROVE) and Northeast States for Coordinated Air Use Management (NESCAUM). Additional FM data are also available through EPA's Aerometric Information Retrieval System (AIRS).

The IMPROVE/NESCAUM non-urban aerosol concentrations are measured at remote sites, away from urban-industrial activities. The sites are located mostly in national parks and wilderness areas. Size-segregated aerosol mass and chemical composition data are available for 50 sites, through the IMPROVE (Eldred et al., 1987; Eldred et al., 1988; Eldred et al., 1989) and NESCAUM (Flocchini et al., 1990, Poirot et al., 1990) networks. The PM10 and PM2.5 mass concentrations are sampled and analyzed on separate filters. The sampling frequency was generally twice a week for 24-hours. The PM2.5 samples are analyzed for chemical composition which make the data sets suitable for chemical mass balance computations (e.g. Gebhart and Malm, 1987; Schichtel and Husar, 1991; Sisler et al., 1993; Eldred and Cahill, 1994; Sisler and Malm, 1994). The IMPROVE/NESCAUM aerosol data are available from 1988 through 1993.

An example of eastern fine mass concentration (IMPROVE/NESCAUM) pattern is shown below.

Fine Mass Concentrations
Quarter 1 (1988-92)	Quarter 2 (1988-92)	Quarter 3 (1988-92)	Quarter 4 (1988-92)

The Figures show the location of the monitoring sites, the magnitude of annual fine mass concentration at these sites as well as the estimated contour lines. The contours drawn for the eastern US are derived from only 15-20 stations. Given the sparseness of the data, the contour lines are to be taken as guides to the eye and not as actual pattern. The coarseness of the contoured data in the Figure confirms the need to develop higher resolution fine mass concentration maps.

The AIRS network consists mainly of urban monitoring sites. The total PM10 needed to be classifed into urban and nonurban groups so that the nonurban could be merged with the IMPROVE/NESCAUM data set. The procedure used to accomplish this task was to define regions in the airs domain, analyze each region for a base concentration, and declare anything below this base as nonurban. The regions used in the AIRS network are shown in following figure.

There are seven regions which adhere to general spatial boundaries. Each regional rectangle also displays a number which indicates the base concentration level determined for the particular region. The base values were determined by looking at annually averaged PM10 concentrations for individual sites. The base was chosen at a level which appeared to separate source influenced sites from rural sites. Sites that were signficantly below the higher concentration group were grouped as rural.
The resulting "nonurban" and "nonrural" maps are shown below. Of the nonurban classifcation, approximately 15 AIRS sites measured PM2.5 and these sites were merged with the IMPROVE/NESCAUM fine mass after an AIRS-IMPROVE/NESAUM relation was derived.

AIRS - IMPROVE/NESCAUM Fine Mass Relation

To utilize the the PM2.5 data sites in the AIRS network in conjunction with the IMPROVE/NESCUAM data, the two networks needed to be related. The site latitudes and longitudes of IMPROVE/NESCAUM were used to extract PM10 from the AIRS nonurban grids shown above. The extracted AIRS PM10 concentration was compared with the measured PM10 from the IMPROVE/NESCAUM database. Some sites were removed from the analysis. Washington D.C. was an urban influenced IMPROVE site and possessed a high PM10 measured concentration. Likewise, Acadia National Park showed a high PM10 measurement possibly because of the influence of sea salt spray. For each of the quarters, the AIRS concentrations were consistently higher than the corresponding IMPROVE/NESCAUM concentrations. It was found that an offset of the AIRS data produced an adequate correlation for the two networks as shown below. The AIRS fine mass was fused with the IMPROVE/NESCAUM data by the formula,

A fine fraction (PM2.5/PM10) was caluclated for each of the 15 AIRS sites sampling fine mass. This fraction was multiplied by the offset of 6.5 to determine by how many µg.m3 to adjust the measured fine mass so that it matched the levels of the IMPROVE/NESCAUM dataset. The resulting tables and maps are shown below.

IMPROVE/NESCAUM and NonUrban AIRS PM2.5
Quarter 1 (1988-92)	Quarter 2 (1988-92)	Quarter 3 (1988-92)	Quarter 4 (1988-92)

PM10 as a Fine Particle Surrogate

The excellent spatial and temporal coverage of the PM10 data base can be utilized only after careful site by site scrutiny for interference and anomalous behavior.

PM10
Quarter 1 (1988-92)	Quarter 2 (1988-92)	Quarter 3 (1988-92)	Quarter 4 (1988-92)

The data quality control of the PM10 data was implemented using visual inspection of daily time series for each station, and comparing the time series for neighboring sites.

For purposes of spatial-temporal trend analysis, raw PM10 (every sixth day) observations were summarized as quarterly averages.

The spatial patterns of PM10 concentrations are presented as contour maps. The contouring procedure is described in Appendix A. The resulting contours as well as the corresponding stations, PM10 data (the square size is proportional to PM10 concentration at a monitoring site) are shown. The shades are 10 ug/m3 apart.

PM10-Fine Mass Relationship

Nonurban PM10 to NonUrban Fine Mass Ratio
Quarter 1 (1988-92)	Quarter 2 (1988-92)	Quarter 3 (1988-92)	Quarter 4 (1988-92)

The PM10/FM ratio is a key variable in the present data fusion process. The use of PM10 as a fine mass surrogate relies on a spatially smooth PM10/FM ratio. In general, the spatial pattern is smooth.

High values of PM10/PM2.5 will arise either due to higher than expected PM10, or lower than expected PM2.5.

The procedure for creating fine particle maps using the PM10 surrogate is shown the data flow chart. The Figure contains two types of components: the shaded boxes represent operators or (processors) that take input data, manipulate them, and produce an output. The oval elements represent the data inputs/outputs to/from the operators. The numbers adjacent to the data elements are sequential step output counters.

Data Flow for Fine Particle Map

The first operations involving data, step numbers 1 and 2, entail aggregating daily PM10 and fine mass data to quarterly averages. The quarterly PM10 and fine mass data were constructed in table formats, containing columns for site name, longitude, latitude, and associated PM10 or fine particle concentration values.

The tables were transformed to grid format using an operator called the Contourer. The inputs consist of the table, as well as user specified inputs that include a distance weight function, a radius of influence, a minimum required number of data points within the radius, a maximum number of data points used in the calculation of the grid value, and the dimensions for the output grid.

The next step, 6, was to produce a quarterly PM10/PM2.5 ratio table. This was accomplished using a ratio table producing operator. The inputs to the operator were the quarterly PM10 grid and the quarterly fine mass table. Since the fine mass data were in table format, each concentration value was associated with a particular latitude and longitude. The PM10 data were in the form of a 120 X 80 grid. The operator read through the two inputs and from the fine mass table it extracted the longitude, latitude, and fine mass concentration data. The operator used the longitude and latitude values from the table as search parameters in the grid. It extracted PM10 values from the grid nearest the latitude and longitude from the fine particle table. The operator then divided the PM10 value by the corresponding fine particle concentration value. An output table was produced containing the latitude, longitude, and PM10/PM2.5 ratio value.

The PM10/fine mass ratio table was sent through the Contourer to produce a ratio grid. This process is identical to that described in the conversion of Bext tables to grids. The resulting PM10/fine mass ratio grid was used with the quarterly PM10 grid as inputs to the multiplier operator which produced annual fine particle grids. The multiplier read through the two input grids and multiplied the PM10 values by the inverse of the ratio values to calculate a visibility fine particle concentration grid.

The resulting grid is shown in the PM10 corrected fine mass maps. The display and printing of all the maps was accomplished through the MapEdit program.

The resulting quarterly fine mass maps are shown below.

PM10 Corrected Fine Mass
Quarter 1 (1988-92)	Quarter 2 (1988-92)	Quarter 3 (1988-92)	Quarter 4 (1988-92)

A comparison of the fine mass concentration maps using only the fine mass data with the PM10-augmented FM maps shows significantly more detail for the latter. The fused fine mass concentrations show spatial heterogeneity over both the eastern and western US. It is likely, however, that in reality the spatial gradients are much stronger than depicted, particularly over urban centers and topographically confined valleys of the western states.

a single grid cell.

The fine particle maps of the conterminous US presented here have known limitations. The causes of these limitations and possible remedies in future work are stated below.

Inadequate PM2.5 concentration data

An improvement of the coverage of the local hot spot concentrations could be achieved by the gathering and proper inclusion of more fine particle concentration data in the spatial analysis work. The candidate data sets for this purpose include fine particle data in the national AIRS system, as well as numerous short term and geographically limited fine particle data sets that have been gathered over the US, e.g. Philadelphia, St. Louis, Salt Lake City, Los Angeles, Grand Canyon region, Oregon, and Washington. Prudent fusion of these data sets would provide a direct sensory input for more spatially resolved fine particle maps.

Use of PM10 as a surrogate and interpolator

A possible improvement would arise from the combination, i.e. data fusion of several different fine particle surrogates in addition to PM10. Such surrogates may include concentrations of visibility observations, concentration of aerosol sulfate or other chemical species and the possible use of aerosol remote sensing by geostationary and polar orbiting meteorological satellites.

Spatial interpolation schemes

An improvement in the interpolationpolation could be achieved through the consideration of other physical or chemical factors that inhibit or enhance the spatial dispersion of air pollutants. The most obvious of these factors is the terrain elevation. The use of elevation in the contouring process would necessitate certain assumptions regarding the elevation dependence of aerosol concentration. Using this approach for example, high elevation mountain peaks would be assigned low concentrations even though they may be in the vicinity of high concentration valley sites.

Physico-chemical modeling

An important data transformation processes used in this work is contouring. Most of the spatial patterns are presented on contour maps. The contours were derived from the point observations using a spatial extrapolation scheme. In the first step, the data from the random locations were projected to a uniform grid with 120x80 nodes that covers the conterminous U.S. The gridding used inverse distance squared (1/r2) as the station weighing factor. The extrapolations outside the U.S. boundaries were trimmed to eliminate spurious values.

Contourer Pseudocode

Contour
	Functionality:	transform data from table format into grid format
	Input:	Table - a set of data points
		WeightFunc - distance weight function
		Radius - distance constraint
		MinPoints - minimum required number of data points within Radius
		MaxPoints - maximum number of data points used in calculation of a grid cell
	Output:	Grid - uniformly distributed set of data points
function Contour(out Grid, in Table, in WeightFunc, in Radius, in MinPoints, in MaxPoints)
{
	for each Cell in Grid
		CalcCell
}

CalcCell
	Functionality:	calculate value of a grid cell given a table of data points around it
function CalcCell(in out Cell, in Table, in WeightFunc, in Radius, in MinPoints, in MaxPoints)
{
	PointList = an empty list of table points
	for each point in Table that is not null
		if the distance between the point and the cell is less than Radius then
			add point to the PointList, sorted by distance
	Cell value = Interpolate PointList
}

Interpolate
	Functionality:	interpolate over a sorted list of data points
function Interpolate(in PointList, in WeightFunc, in MinPoints, in MaxPoints)
{
	if number of points in PointList < MinPoints return null value

	TotalWeight = 0
	Sum = 0
	WeightExp = 0 if WeightFunc = 1
		    1 if WeightFunc = 1/r
		    2 if WeightFunc = 1/r2, etc
	
	for first MaxPoints in PointList
		Dist = point distance from cell
		Weight = 1 / (Dist^WeightExp)
		TotalWeight = TotalWeight + Weight
		Sum = Sum + Weight * point value
		
	if TotalWeight = 0 return null value
	Sum = Sum / TotalWeight
	return Sum