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Understanding air quality data using...

University of Southern California.

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Understanding air quality data using nonparametric regression analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Understanding air quality data using nonparametric regression analysis./
Author:	Yoon, Heesong.
Description:	253 p.
Notes:	Adviser: Ronald C. Henry.
Contained By:	Dissertation Abstracts International67-10B.
Subject:	Engineering, Environmental. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3237738
ISBN:	9780542924040

Understanding air quality data using nonparametric regression analysis.
Yoon, Heesong.

Understanding air quality data using nonparametric regression analysis. - 253 p.

Adviser: Ronald C. Henry.

Thesis (Ph.D.)--University of Southern California, 2006.

This research reports on the application of nonparametric regression analysis to air quality data. As required by law, government agencies have been collecting large volumes of ambient air quality data to determine compliance with federal and state air quality standards. Although these data are a perfect candidate for data mining by the nonparametric regression, it has not been applied to air quality data analysis previously.

ISBN: 9780542924040Subjects--Topical Terms:

783782
Engineering, Environmental.

Understanding air quality data using nonparametric regression analysis.
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100 1 $a Yoon, Heesong. $3 1034060
245 1 0 $a Understanding air quality data using nonparametric regression analysis.
300 $a 253 p.
500 $a Adviser: Ronald C. Henry.
500 $a Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5987.
502 $a Thesis (Ph.D.)--University of Southern California, 2006.
520 $a This research reports on the application of nonparametric regression analysis to air quality data. As required by law, government agencies have been collecting large volumes of ambient air quality data to determine compliance with federal and state air quality standards. Although these data are a perfect candidate for data mining by the nonparametric regression, it has not been applied to air quality data analysis previously.
520 $a The data used during this research consisted of about two years of one-hour average concentrations of ultrafine particle number, PM10, CO, NOx, SO 2, and O3, collected by regulatory agencies as part of their routine air quality monitoring. Hourly wind speed and wind direction were also available and were an important part of the analysis. There were four monitoring sites: Atascadero is a rural city in a coastal valley of central California while Long Beach, Glendora, and Upland are all located in the heavily populated Los Angeles County in southern California.
520 $a Three-dimensional nonparametric regression charts showing the effect of wind speed and direction on pollutant concentrations were especially useful in assessing the impact of local sources. Pollutant concentrations in Atascadero were expected to be mainly from local roadway sources and the results from this analysis were consistent with such an expectation. With regards to Long Beach, the results were different from conventional expectation. High concentrations of CO, NOx, and PM10 were not related to nearby heavily used freeways, but were primarily the result of pollution from sources to the north being transported to the site by late night and early morning drainage winds. This effect was especially strong during the winter months. The inland sites, Glendora and Upland, shared many similarities in most results. However, the analysis revealed some important differences between them. Glendora showed more impact from transported pollutants whereas Upland was more directly impacted by local businesses and traffic along adjacent roads.
590 $a School code: 0208.
650 4 $a Engineering, Environmental. $3 783782
650 4 $a Environmental Sciences. $3 676987
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710 2 $a University of Southern California. $3 700129
773 0 $t Dissertation Abstracts International $g 67-10B.
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790 1 0 $a Henry, Ronald C., $e advisor
791 $a Ph.D.
792 $a 2006
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