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- W2949344517 abstract "Mobile air pollution monitoring is an effective means of collecting spatially and temporally diverse air pollution samples. These observations are often used to predict long-term air pollution concentrations using temporal adjustments based on the time-series of a fixed location monitor. Temporal adjustments are required because the time-series is often incomplete at each spatial location. We describe a method-fusion temporal adjustment that has been demonstrated to improve the accuracy of long-term estimates from incomplete time-series data. Our adjustment approach combines the techniques of using a log transformation to modify the air pollution samples to a near normal distribution and incorporates the long-term median of a reference monitor to mediate the effects of estimate inflation created by outliers in the data. We demonstrate the approach with hourly Nitrogen Dioxide observations from Paris, France in 2016. Method-Fusion Benefits: •Log transformations control for estimate inflation created by log normally distributed data.•Adjusting data with the long-term median, rather than the mean, controls for estimate inflation.•Produces more accurate long-term estimates than other adjustments independent of the pollutant being estimated." @default.
- W2949344517 created "2019-06-27" @default.
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- W2949344517 date "2019-01-01" @default.
- W2949344517 modified "2023-09-24" @default.
- W2949344517 title "Improving long-term air pollution estimates with incomplete data: A method-fusion approach" @default.
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- W2949344517 doi "https://doi.org/10.1016/j.mex.2019.06.005" @default.
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