Show simple item record

dc.contributor.authorPandey, G.
dc.contributor.authorZhang, B.
dc.contributor.authorJian, Le
dc.date.accessioned2017-01-30T10:47:41Z
dc.date.available2017-01-30T10:47:41Z
dc.date.created2013-05-21T20:00:20Z
dc.date.issued2013
dc.identifier.citationPandey, Gaurav and Zhang, Bin and Jian, Le. 2013. Predicting submicron air pollution indicators: A machine learning approach. Environmental Science Processes & Impacts. 15 (5): pp. 996-1005.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/5665
dc.identifier.doi10.1039/c3em30890a
dc.description.abstract

The regulation of air pollutant levels is rapidly becoming one of the most important tasks for the governments of developing countries, especially China. Submicron particles, such as ultrafine particles (UFP, aerodynamic diameter ≤ 100 nm) and particulate matter ≤ 1.0 micrometers (PM1.0), are an unregulated emerging health threat to humans, but the relationships between the concentration of these particles and meteorological and traffic factors are poorly understood. To shed some light on these connections, we employed a range of machine learning techniques to predict UFP and PM1.0 levels based on a dataset consisting of observations of weather and traffic variables recorded at a busy roadside in Hangzhou, China. Based upon the thorough examination of over twenty five classifiers used for this task, we find that it is possible to predict PM1.0 and UFP levels reasonably accurately and that tree-based classification models (Alternating Decision Tree and Random Forests) perform the best for both these particles. In addition, weather variables show a stronger relationship with PM1.0 and UFP levels, and thus cannot be ignored for predicting submicron particle levels. Overall, this study has demonstrated the potential application value of systematically collecting and analysing datasets using machine learning techniques for the prediction of submicron sized ambient air pollutants.

dc.publisherRoyal Society of Chemistry
dc.titlePredicting submicron air pollution indicators: A machine learning approach
dc.typeJournal Article
dcterms.source.volume15
dcterms.source.startPage996
dcterms.source.endPage1005
dcterms.source.issn2050-7887
dcterms.source.titleEnvironmental Science Processes & Impacts
curtin.department
curtin.accessStatusFulltext not available


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record