By the same authors

From the same journal

Using meteorological normalisation to detect interventions in air quality time series

Research output: Contribution to journalArticle

Author(s)

Department/unit(s)

Publication details

JournalScience of the Total Environment
DateAccepted/In press - 25 Oct 2018
DateE-pub ahead of print - 28 Oct 2018
DatePublished (current) - 25 Feb 2019
Volume653
Number of pages11
Pages (from-to)578-588
Early online date28/10/18
Original languageEnglish

Abstract

Interventions used to improve air quality are often difficult to detect in air quality time series due to the complex nature of the atmosphere. Meteorological normalisation is a technique which controls for meteorology/weather over time in an air quality time series so intervention exploration (and trend analysis) can be assessed in a robust way. A meteorological normalisation technique, based on the random forest machine learning algorithm was applied to routinely collected observations from two locations where known interventions were imposed on transportation activities which were expected to change ambient pollutant concentrations. The application of progressively stringent limits on the content of sulfur in marine fuels was very clearly represented in ambient sulfur dioxide (SO2) monitoring data in Dover, a port city in the South East of England. When the technique was applied to the oxides of nitrogen (NOx and NO2) time series at London Marylebone Road (a Central London monitoring site located in a complex urban environment), the normalised time series highlighted clear changes in NO2 and NOx which were linked to changes in primary (directly emitted) NO2 emissions at the location. The clear features in the time series were illuminated by the meteorological normalisation procedure and were not observable in the raw concentration data alone. The lack of a need for specialised inputs, and the efficient handling of collinearity and interaction effects makes the technique flexible and suitable for a range of potential applications for air quality intervention exploration.

Bibliographical note

© 2018 The Authors.

    Research areas

  • Air pollution, Data analysis, Machine learning, Management, Random forest

Discover related content

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

View graph of relations