Next Article in Journal
Estimating the Lower Limit of the Impact of Amines on Nucleation in the Earth’s Atmosphere
Next Article in Special Issue
Dimensional Upgrade Approach for Spatial-Temporal Fusion of Trend Series in Subsidence Evaluation
Previous Article in Journal
Synthesis and Surface Thermodynamic Functions of CaMoO4 Nanocakes
Previous Article in Special Issue
High Recharge Areas in the Choushui River Alluvial Fan (Taiwan) Assessed from Recharge Potential Analysis and Average Storage Variation Indexes
Article

Detection of Changes in Ground-Level Ozone Concentrations via Entropy

by 1,*, 2 and 3
1
Department Mathematics and Statistics, York University, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada
2
Department Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China
3
Air Quality Research Division, Science and Technology Branch, Environment Canada, Toronto, Ontario, M3H 5T4, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Entropy 2015, 17(5), 2749-2763; https://doi.org/10.3390/e17052749
Received: 4 March 2015 / Revised: 30 March 2015 / Accepted: 28 April 2015 / Published: 30 April 2015
(This article belongs to the Special Issue Entropy and Space-Time Analysis in Environment and Health)
Ground-level ozone concentration is a key indicator of air quality. Theremay exist sudden changes in ozone concentration data over a long time horizon, which may be caused by the implementation of government regulations and policies, such as establishing exhaust emission limits for on-road vehicles. To monitor and assess the efficacy of these policies, we propose a methodology for detecting changes in ground-level ozone concentrations, which consists of three major steps: data transformation, simultaneous autoregressive modelling and change-point detection on the estimated entropy. To show the effectiveness of the proposed methodology, the methodology is applied to detect changes in ground-level ozone concentration data collected in the Toronto region of Canada between June and September for the years from 1988 to 2009. The proposed methodology is also applicable to other climate data. View Full-Text
Keywords: change-point detection; Box–Cox transformation; entropy; ozone concentration; spatial dependence; simultaneous autoregressive modelling change-point detection; Box–Cox transformation; entropy; ozone concentration; spatial dependence; simultaneous autoregressive modelling
Show Figures

MDPI and ACS Style

Wu, Y.; Jin, B.; Chan, E. Detection of Changes in Ground-Level Ozone Concentrations via Entropy. Entropy 2015, 17, 2749-2763. https://doi.org/10.3390/e17052749

AMA Style

Wu Y, Jin B, Chan E. Detection of Changes in Ground-Level Ozone Concentrations via Entropy. Entropy. 2015; 17(5):2749-2763. https://doi.org/10.3390/e17052749

Chicago/Turabian Style

Wu, Yuehua; Jin, Baisuo; Chan, Elton. 2015. "Detection of Changes in Ground-Level Ozone Concentrations via Entropy" Entropy 17, no. 5: 2749-2763. https://doi.org/10.3390/e17052749

Find Other Styles

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Search more from Scilit
 
Search
Back to TopTop