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• W2080694019 abstract "Prediction of meteorological and air quality variables motivates a lot of research in the atmospheric sciences and exposure assessment communities. An interesting related issue regards the relative predictive power that can be expected at different time scales, and whether it vanishes altogether at certain ranges. An improved understanding of our predictive powers enables better environmental management and more efficient decision making processes. Fractal analysis is commonly used to characterize the self-affinity of time series. This work introduces the Continuous Wavelet Transform (CWT) fractal analysis method as a tool for assessing environmental time series predictability. The high temporal scale resolution of the CWT enables detailed information about the Hurst parameter, a common temporal fractality measure, and thus about time scale variations in predictability. We analyzed a few years records of half-hourly air pollution and meteorological time series from which the trivial seasonal and daily cycles were removed. We encountered a general trend of decreasing Hurst values from about 1.4 (good autocorrelation and predictability), in the sub-daily time scale to 0.5 (which implies complete randomness) in the monthly to seasonal scales. The air pollutants predictability follows that of the meteorological variables in the short time scales but is better at longer scales." @default.
• W2080694019 created "2016-06-24" @default.
• W2080694019 creator A5016772127 @default.
• W2080694019 creator A5019391961 @default.
• W2080694019 date "2010-05-13" @default.
• W2080694019 modified "2023-10-07" @default.
• W2080694019 title "Studying the Time Scale Dependence of Environmental Variables Predictability Using Fractal Analysis" @default.
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• W2080694019 doi "https://doi.org/10.1021/es903495q" @default.
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