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

Atmospheric Environment

Volumn 43, Issue 22-23, 2009, Pages 3563-3570

  Carslaw, David C ^a   Taylor, Paul J ^b  

^a UNIVERSITY OF LEEDS   (United Kingdom)

^b ATKINS   (United Kingdom)

Author keywords

Aircraft; CART; Emissions; Model averaging; Source apportionment; Stochastic gradient boosting

Indexed keywords

CART; EMISSIONS; MODEL AVERAGING; SOURCE APPORTIONMENT; STOCHASTIC GRADIENT BOOSTING;

AIR QUALITY; AIRCRAFT; COMMUNICATION CHANNELS (INFORMATION THEORY); CONCENTRATION (PROCESS); FORECASTING; NITRIC OXIDE; NITROGEN OXIDES; UNCERTAINTY ANALYSIS;

STOCHASTIC MODELS;

NITROGEN OXIDE;

AIRCRAFT EMISSION; ATMOSPHERIC POLLUTION; MODELING; NITROGEN OXIDES; POLLUTANT SOURCE; REGRESSION ANALYSIS; STOCHASTICITY;

AIR POLLUTION; AIRCRAFT; AIRPORT; ARTICLE; CONTROLLED STUDY; INFERENTIAL STATISTICS; METEOROLOGY; PLUME DISPERSION; PREDICTION; PRIORITY JOURNAL; REGRESSION ANALYSIS;

EID: 67649280364     PISSN: 13522310     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2009.04.001     Document Type: Article

References (33)

1. AQEG, 2004. Nitrogen dioxide in the United Kingdom. Tech. Rep., Report prepared by the Air Quality Expert Group for the Department for Environment, Food and Rural Affairs; Scottish Executive; Welsh Assembly Government; and Department of the Environment in Northern Ireland, March 2004.
2. Breiman L. Bagging predictors. Machine Learning 24 2 (1996) 123-140
3. Bruno F., Cocchi D., and Trivisano C. Forecasting daily high ozone concentrations by classification trees. Environmetrics 15 2 (2004) 141-153
4. Carslaw D., Beevers S., Ropkins K., and Bell M. Detecting and quantifying aircraft and other on-airport contributions to ambient nitrogen oxides in the vicinity of a large international airport. Atmospheric Environment 40 28 (2006) 5424-5434
5. Carslaw D., Ropkins K., Laxen D., Moorcroft S., Marner B., and Williams M. Near-field commercial aircraft contribution to nitrogen oxides by engine, aircraft type and airline by individual plume sampling. Environmental Science and Technology 42 6 (2008) 1871-1876
6. Comrie A.C. Comparing neural networks and regression models for ozone forecasting. Journal of the Air & Waste Management Association 47 6 (1997) 653-663
7. De'ath G. Boosted trees for ecological modeling and prediction. Ecology 88 1 (2007) 243-251
8. Efron B., and Tibshirani R. An Introduction to the Bootstrap (1993), Chapman & Hall
9. Elith J., Leathwick J.R., and Hastie T. A working guide to boosted regression trees. Journal of Animal Ecology 77 4 (2008) 802-813
10. X emissions to exposure in West London. Environmental Modelling & Software 21 4 (2006) 477-485
11. Friedman J.H. Greedy function approximation: a gradient boosting machine. Annals of Statistics 29 5 (2001) 1189-1232
12. Friedman J.H. Stochastic gradient boosting. Computational Statistics & Data Analysis 38 4 (2002) 367-378
13. Friedman J.H., and Popescu B.E. Predictive learning via rule ensembles. Annals of Applied Statistics 2 (2008) 916 10.1214/07-AOAS148
14. Gardner M.W., and Dorling S.R. Statistical surface ozone models: an improved methodology to account for non-linear behaviour. Atmospheric Environment 34 1 (2000) 21-34
15. Guardani R., Nascimento C.A.O., Guardani M.L.G., Martins M., and Romano J. Study of atmospheric ozone formation by means of a neural network-based model. Journal of the Air & Waste Management Association 49 3 (1999) 316-323
16. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2001), Springer-Verlag, New York
17. 2 emission ratios measured from in-use commercial aircraft during taxi and takeoff. Environmental Science & Technology 38 22 (2004) 6078-6084 0013-936X
18. Herndon S.C., Rogers T., Dunlea E.J., Jayne J.T., Miake-Lye R., and Knighton B. Hydrocarbon emissions from in-use commercial aircraft during airport operations. Environmental Science and Technology 40 14 (2006) 4406-4413
19. ICAO, 2007. Aircraft Engines Emissions Databank.
20. IPCC. Aviation and the Global Atmosphere, Intergovernmental Panel on Climate Change. Tech. Rep. (1999), Cambridge University Press
21. Kuebler J., Russell A.G., Hakami A., Clappier A., and vanden Bergh H. Episode selection for ozone modelling and control strategies analysis on the Swiss plateau. Atmospheric Environment 36 17 (2002) 2817-2830
22. Kukkonen J., Partanen L., Karppinen A., Walden J., Kartastenpaa R., Aarnio P., Koskentalo T., and Berkowicz R. Evaluation of the OSPM model combined with an urban background model against the data measured in 1997 in Runeberg Street, Helsinki. Atmospheric Environment 37 8 (2003) 1101-1112
23. Lawrence R., Bunn A., Powell S., and Zambon M. Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote Sensing of Environment 90 3 (2004) 331-336
24. Leathwick J.R., Elith J., Francis M.P., Hastie T., and Taylor P. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Marine Ecology-Progress Series 321 (2006) 267-281
25. McHugh C.A., Carruthers D.J., and Edmunds H.A. ADMS and ADMS-urban. International Journal of Environment and Pollution 8 3-6 (1997) 438-440
26. McHugh, C., Williams, M., Price, C., Lad, C., 2007. Air Quality Studies for Heathrow: Base Case, Segregated Mode, Mixed Mode and Third Runway Scenarios Modelled Using ADMS-Airport. Cambridge Environmental Research Consultants, 15 November 2007. URL http://www.dft.gov.uk/consultations/closed/heathrowconsultation/technicalreports/.
27. Moisen G.G., Freeman E.A., Blackard J.A., Frescino T.S., Zimmermann N.E., and Edwards T.C. Predicting tree species presence and basal area in Utah: a comparison of stochastic gradient boosting, generalized additive models, and tree-based methods. Ecological Modelling 199 2 (2006) 176-187
28. Peace H., Maughan J., Owen B., and Raper D. Identifying the contribution of different airport related sources to local urban air quality. Environmental Modelling & Software 21 4 (2006) 532-538
29. PSDH, 2006. Project for the Sustainable Development of Heathrow: Report of the Airport Air Quality Technical Panels. Tech. Rep., Department for Transport.
30. R Development Core Team. R: a Language and Environment for Statistical Computing (2008), R Foundation for Statistical Computing, Vienna, Austria. 3-900051-07-0. URL http://www.R-project.org
31. Ridgeway, G., 2007. GBM: Generalized Boosted Regression Models. R Package Version 1.6-3. http://www.i-pensieri.com/gregr/gbm.shtml.
32. Schafer K., Jahn C., Sturm P., Lechner B., and Bacher M. Aircraft emission measurements by remote sensing methodologies at airports. Atmospheric Environment 37 37 (2003) 5261-5271
33. Sorokina D., Caruana R., Riedewald M., and Fink D. Detecting statistical interactions with additive groves of trees. ICML '08: Proceedings of the 25th International Conference on Machine Learning (2008), ACM, New York, NY, USA 1000-1007