Record Display for the EPA National Library Catalog
RECORD NUMBER: 28 OF 1156
|OLS Field Name||OLS Field Data|
|Main Title||An approach for the preliminary assessment of TSP concentrations.|
|Author||Pace, Thomas G.|
|CORP Author||Environmental Protection Agency, Research Triangle Park, NC. Monitoring and Data Analysis Div.|
|Publisher||U.S. Environmental Protection Agency,|
|Stock Number||PB-286 201|
|Subjects||Air--Pollution--Sampling. ; Air--Pollution--Statistics.|
|Additional Subjects||Particles ; Urban areas ; Monitoring ; Sampling ; Concentration(Composition) ; Sites ; Assessments ; Linear regression ; Numerical analysis ; Sources ; Statistical analysis ; Land use ; Tables(Data) ; Nitrogen oxides ; Industrial wastes ; Combustion products ; Sulfates ; Inorganic nitrates ; Sulfur oxides ; Air quality data ; Air pollution sampling ; Total suspended particulates|
|Collation||67 pages : graphs, charts ; 28 cm|
Air quality data for Total Suspended Particulate (TSP) in 13 U.S. urban areas was examined. The data from 142 monitoring sites were grouped so that residential and commercial sites in non- or light-industrial urban areas could be examined. A relationship between height and concentration was noted at the sites with nearby ground-level activity due to traffic, parking, etc., such that the concentration decreased exponentially with increasing height of the monitor above ground. No such relationship was found at sites with no ground-level activity. Commercial and industrial sites were found to be near ground-level activity in 90 percent of the cases examined while residential sites were virtually never located near such activity. The entire data base was then examined using a multiple regression procedure to estimate the relative impacts of non-industrial, general industrial, and steel mill influences on TSP levels. Non-industrial influences were found to account for over half of the total concentration estimate in all cases. Several potential applications of the linear regression technique are suggested. It can be used as a screening technique for examining TSP data to identify sites with unusual concentrations or to provide a preliminary estimate of sources of TSP. It can be used to interpret the variations in TSP data by estimating siting effects and it can help to identify the causes of discrepancies between predictions obtained with dispersion models and observations.
Appendices included. Includes bibliographical references.
This document describes the technical basis, uses and limitations of an approach for making a preliminary assessment of annual Total Suspended Particulate (TSP) data. This approach was developed using a statistical analysis of ambient data. It defines average values for TSP based on several siting, land use and industrial descriptors. It is hoped that this document will prove useful to agencies and others who are interested in understanding the sources of TSP. Ambient levels of TSP reflect the combined impact of many sources and source types which collectively contribute to TSP levels. These levels are above the National Ambient Air Quality Standard for TSP in many areas of the country. As an aid in identifying these sources and their relative contributions to annual average TSP levels, a data base of 142 sites in 13 urban areas was compiled. Each site was visited and information on monitor placement and the surrounding neighborhoods was obtained. The sites represented a mix of underdeveloped, residential, commercial and industrial land use. The 14 urban areas visited represented a variety of industrial and non-industrial urban centers and spanned the country geographically. Five components were identified as a result of the site visits and preliminary and analysis as comprising most if not all of the ambient TSP concentration. Four of the components (primary non-urban background, urban sulfates and nitrates, local sources and urban activity) are generally associated with sources other than industrial primary stack emissions. These four components collectively are referred to as non-industrial components. They were found to contribute significantly to observed TSP levels in all cities and at all site types (except undeveloped) in varying ratios. The impact of the fifth component, industrial primary stack emissions (called industrial component) was found to be restricted primarily to industrial site types except when major steel making facilities were near residential or commercial areas. Using statistical analysis, this document estimates average contributions to observed annual TSP concentrations attributable to each of these five components. Of the five components, primary non-urban background and urban sulfates and nitrates are estimated directly from measurements taken in non-urban areas and chemical analysis of sulfate and nitrate. The average contribution of local source and urban activity components was estimated empirically from the data base gathered in the non-industrial cities. Using these average values as a guide and referring to information for each of the 142 sites in the data base, an estimate was made of the total non-industrial component. A multiple linear regression technique was next used to estimate the average contribution of the industrial component to TSP levels. Thus, this document describes the derivation of average values for each of the five components comprising TSP annual averages. These average values were used to compose estimates of total annual TSP levels for sites in two test cities. These estimates were compared with actual observations and were found to be a reasonably accurate approximation of the observed levels. The fraction of variance in the observed data which is explained by the regression equation (R2) was .70 and .79 for the two test cities. The empirically derived relationships between observed annual TSP and the five previously described components of TSP can be used to estimate TSP levels. This estimate can be useful in several ways: 1) The estimate can be compared with the actual concentration at a site to identify those situations which differ substantially from the norm. Thus, such an estimate becomes a screening technique for identifying abnormal influences. It can also be used as a screening technique for areas without monitors. 2) The estimate can be further broken down using data from previous analyses to provide a preliminary estimate of source categories contributing to TSP levels. This preliminary estimate can be refined through more extensive analysis or used in those situations where a more refined estimate (by atmospheric diffusion models or from measurements such as filter analysis or special sampling) is precluded by time or resource constraints. 3) The estimate can be useful in interpreting monitoring data and in identifying possible siting anomalies. 4) Comparing the estimate with dispersion model predictions may help identify the causes of discrepancies between predictions obtained with dispersion models and observations, such as certain improper emission factors or use of a different grid size.