By the same authors

From the same journal

From the same journal

Analysis of air pollution data at a mixed source location using boosted regression trees

Research output: Contribution to journalArticle

Published copy (DOI)

Author(s)

Department/unit(s)

Publication details

JournalAtmospheric Environment
DatePublished - 1 Jul 2009
Issue number22-23
Volume43
Number of pages8
Pages (from-to)3563-3570
Original languageEnglish

Abstract

This paper explores the use of boosted regression trees to draw inferences concerning the source characteristics at a location of high source complexity. Models are developed for hourly concentrations of nitrogen oxides (NOX) close to a large international airport. Model development is discussed and methods to quantify model uncertainties developed. It is shown that good explanatory models can be developed and further, allowing for interactions between model variables significantly improves the model fits compared with non-interacting models. Methods are used to determine which variables exert most influence over predicted concentrations and to explore the NOX dependency for each. Model predictions are used to estimate aircraft take-off contributions to total concentrations of NOX and determine how these predictions are affected by annual variations in meteorological conditions and runway use patterns. Furthermore, the results relating to the aircraft contributions to total NOX concentration are compared with those from a more detailed independent field campaign. Finally, we find empirical evidence that plumes from larger aircraft disperse more rapidly from the point of release compared with smaller aircraft. The reasons for this behaviour and the implications are discussed.

    Research areas

  • Aircraft, CART, Emissions, Model averaging, Source apportionment, Stochastic gradient boosting

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations