Using meteorological normalisation to detect interventions in air quality time series
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...
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Veröffentlicht in: | The Science of the total environment 2019-02, Vol.653, p.578-588 |
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Sprache: | eng |
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Zusammenfassung: | 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.
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•Detecting the influence of air quality interventions is important.•Changes in meteorology over time complicate air quality intervention analysis.•Meteorological normalisation was applied in two locations to explore interventions.•The changes detected in the normalised time series were associated to interventions.•The non-black-box nature of the procedure allows for interpretation of results. |
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ISSN: | 0048-9697 1879-1026 |
DOI: | 10.1016/j.scitotenv.2018.10.344 |