Modeling Non-Stationary Processes Through Dimension Expansion

In this paper, we propose a novel approach to modeling nonstationary spatial fields. The proposed method works by expanding the geographic plane over which these processes evolve into higher dimensional spaces, transforming and clarifying complex patterns in the physical plane. By combining aspects of multi-dimensional scaling, group lasso, and latent variables models, a dimensionally sparse projection is found in which the originally nonstationary field exhibits stationarity. Following a comparison with existing methods in a simulated environment, dimension expansion is studied on a classic test-bed data set historically used to study nonstationary models. Following this, we explore the use of dimension expansion in modeling air pollution in the United Kingdom, a process known to be strongly influenced by rural/urban effects, amongst others, which gives rise to a nonstationary field

Estimating exposure response functions using ambient pollution concentrations

This paper presents an approach to estimating the health effects of an environmental hazard. The approach is general in nature, but is applied here to the case of air pollution. It uses a computer model involving ambient pollution and temperature input to simulate the exposures experienced by individuals in an urban area, while incorporating the mechanisms that determine exposures. The output from the model comprises a set of daily exposures for a sample of individuals from the population of interest. These daily exposures are approximated by parametric distributions so that the predictive exposure distribution of a randomly selected individual can be generated. These distributions are then incorporated into a hierarchical Bayesian framework (with inference using Markov chain Monte Carlo simulation) in order to examine the relationship between short-term changes in exposures and health outcomes, while making allowance for long-term trends, seasonality, the effect of potential confounders and the possibility of ecological bias. The paper applies this approach to particulate pollution (PM10) and respiratory mortality counts for seniors in greater London (≥65 years) during 1997. Within this substantive epidemiological study, the effects on health of ambient concentrations and (estimated) personal exposures are compared. The proposed model incorporates within day (or between individual) variability in personal exposures, which is compared to the more traditional approach of assuming a single pollution level applies to the entire population for each day. Effects were estimated using single lags and distributed lag models, with the highest relative risk, RR=1.02 (1.01–1.04), being associated with a lag of two days ambient concentrations of PM10. Individual exposures to PM10 for this group (seniors) were lower than the measured ambient concentrations with the corresponding risk, RR=1.05 (1.01–1.09), being higher than would be suggested by the traditional approach using ambient concentrations

Addendum and update to Zidek (2020):Catalogue of species-group names assigned to \u3ci\u3eCopris\u3c/i\u3e Geoffroy, \u3ci\u3eCoptodactyla\u3c/i\u3e Burmeister, \u3ci\u3eLitocopris\u3c/i\u3e Waterhouse, \u3ci\u3eMicrocopris\u3c/i\u3e Balthasar, \u3ci\u3eParacopris\u3c/i\u3e Balthasar, \u3ci\u3ePseudocopris\u3c/i\u3e Ferreira, \u3ci\u3ePseudopedaria\u3c/i\u3e Felsche, \u3ci\u3eSinocopris\u3c/i\u3e Ochi, Kon and Bai, and \u3ci\u3eThyregis\u3c/i\u3e Blackburn (Coleoptera: Scarabaeidae: Scarabaeinae: Coprini)

The catalogue of species-group names of nine coprine genera (Coleoptera: Scarabaeidae: Scarabaeinae: Coprini), published earlier this year, is updated and revised. Presented are species-group taxa overlooked in compiling the catalogue and new species-group taxa described in 2020, i.e. after the cut-off date of the catalogue