Air Particle Measurement

This digital document is an article from American Journal of Environmental Sciences, published by Science Publications on October 1, 2008. The length of the article is 6472 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available immediately after purchase. You can view it with any web browser.From the author: Key words: [PM.sub.10] concentrations, multiple linear regression, principal component regression, independent component regression, quantile regression, partial least squares regressionCitation DetailsTitle: Prediction of the daily mean [PM.sub.10] concentrations using linear models.(Technical report)Author: J.C.M. PiresPublication: American Journal of Environmental Sciences (Magazine/Journal)Date: October 1, 2008Publisher: Science PublicationsVolume: 4 Issue: 5 Page: 445(9)Article Type: Technical reportDistributed by Gale, a part of Cengage Learning

Published on: 2008-10-01 Released on: 2008-10-21 Format: HTML Binding: Digital 22 pages

Excerpt. © Reprinted by permission. All rights reserved. INTRODUCTION Atmospheric particulate matter is made up of solid and liquid particles suspended in the atmosphere. They are emitted by: (i) natural (volcanic eruptions, seismic activity, forest fires, winds of great intensity or natural particle transport from the dry regions); and (ii) anthropogenic sources (all types of combustion and some industrial processes). In Europe, particulate matter is one of the most important air pollutants responsible for loss of human health (1). In the last decade, several studies about health effects of particulate matter were published (2-5). Long exposure to [PM.sub.10] (particles with diameter smaller than 10 [micro]m) and to [PM.sub.2.5] (particles with diameter smaller than 2.5 [micro]m) has been associated with...