Exploring wind direction and SO2 concentration by circular–linear density estimation

Abstract

The study of environmental problems usually requires the description of variables with different nature and the assessment of relations between them. In this work, an algorithm for flexible estimation of the joint density for a circular-linear variable is proposed. The method is applied for exploring the relation between wind direction and SO 2 concentration in a monitoring station close to a power plant located in Galicia (NW-Spain), in order to compare the effectiveness of precautionary measures for pollutants reduction in two different years.

A tractable, parsimonious and flexible model for cylindrical data, with applications

“…The WeiSSVM clearly corresponds to γ = α in (6). All properties of the GGSSVM are obtained along the same lines as our developments in the previous sections, albeit with more involved calculations.…”

A test for directional-linear independence, with applications to wildfire orientation and size

“…A new family of circular-linear distributions based on nonnegative trigonometric sums, which proved to be more flexible in capturing the data structure, was proposed by Fernández-Durán (2007), adapting the method by Johnson and Wehrly (1978). More recently, García-Portugués et al (2013a) exploited the copula representation of the Johnson and Wehrly (1978) family, allowing for a completely nonparametric estimator, which was applied to analyze SO 2 concentrations and wind direction. Nevertheless, the aforementioned methods are designed for the circular-linear case, whereas in our context, a more general tool for handling directional-linear relations is needed, provided that wildfire orientation may be reported in two or three dimensions.…”

A hidden Markov model for the analysis of cylindrical time series

“…Recent studies have focused, for example, on time series of wind directions and pollutant concentrations (García‐Portugués et al. ), wind directions and speeds (Mastrantonio et al. ), wildfire sizes and orientations (García‐Portugués et al.…”

“…Time series of angles and intensities arise often in environmental research. Recent studies have focused, for example, on time series of wind directions and pollutant concentrations (García-Portugués et al, 2013), wind directions and speeds (Mastrantonio et al, 2015), wildfire sizes and orientations (García-Portugués et al, 2014), and wave directions and heights (Bulla et al, 2015;Lagona et al, 2015;Wang et al, 2015). Bivariate sequences of angles and intensities are often referred to as cylindrical time series (Anderson-Cook and Sango Otieno, 2006), because the pair of an angle and an intensity can be represented as a point on a cylinder.…”