Abstract
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.
Highlights
Air quality has attracted more attention in the past 50 years
Ground-level ozone concentration is a key indicator of air quality
We propose a methodology for detecting changes in ground-level ozone concentrations by using entropy
Summary
Air quality has attracted more attention in the past 50 years. Climate change itself may have a direct impact on air quality. Several statistical methodologies have been applied to model the ground-level ozone concentration data, which include multivariate models [2,3], quantile regression [4,5], non-linear time series [6,7,8] and hierarchical Bayesian kriging [9,10]. Most of these approaches assume the temporal homogeneity of the stochastic processes involved, which may not hold over longer time horizons
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