Designing environmental monitoring networks to measure extremes

This paper discusses challenges arising in the design of networks for monitoring extreme values over the domain of a random environmental space-time field {X _ij } i = 1, . . . , I denoting site and j = 1, . . . denoting time (e.g. hour). The field of extremes for time span r over site domain i = 1, . . . ,I is given by \(\{Y_{i(r+1)}=\max_{j=k}^{k+n-1} X_{ij}\}\) for k = 1 + rn, r = 0, . . . ,. Such networks must not only measure extremes at the monitored sites but also enable their prediction at the non-monitored ones. Designing such a network poses special challenges that do not seem to have been generally recognized. One of these problems is the loss of spatial dependence between site responses in going from the environmental process to the field of extremes it generates. In particular we show empirically that the intersite covariance Cov(Y _i(r+1),Y _i′(r+1)) can generally decline toward zero as r increases, for site pairs i ≠ i′. Thus the measured extreme values may not predict the unmeasured ones very precisely. Consequently high levels of pollution exposure of a sensitive group (e.g. school children) located between monitored sites may be overlooked. This potential deficiency raises concerns about the adequacy of air pollution monitoring networks whose primary role is the detection of noncompliance with air quality standards based on extremes designed to protect human health. The need to monitor for noncompliance and thereby protect human health, points to other issues. How well do networks designed to monitor the field monitor their fields of extremes? What criterion should be used to select prospective monitoring sites when setting up or adding to a network? As the paper demonstrates by assessing an existing network, the answer to the first question is not well, at least in the case considered. To the second, the paper suggests a variety of plausible answers but shows through a simulation study, that they can lead to different optimum designs. The paper offers an approach that circumvents the dilemma posed by the answer to the second question. That approach models the field of extremes (suitably transformed) by a multivariate Gaussian-Inverse Wishart hierarchical Bayesian distribution. The adequacy of this model is empirically assessed in an application by finding the relative coverage frequency of the predictive credibility ellipsoid implied by its posterior distribution. The favorable results obtained suggest this posterior adequately describes that (transformed) field. Hence it can form the basis for designing an appropriate network. Its use is demonstrated by a hypothetical extension of an existing monitoring network. That foundation in turn enables a network to be designed of sufficient density (relative to cost) to serve its regulatory purpose.     

Comparing earthquake insurance programmes: how would Japan and California have fared after the 2010–11 earthquakes in New Zealand?

Earthquakes are insured in high-risk high-income countries only if the public sector is involved. Prototypical examples are the insurance schemes in California (United States), Japan, and New Zealand, but each is structured differently. This paper examines these variations using a concrete case study: the sequence of earthquakes in Christchurch, New Zealand, in 2010–11—the most heavily insured seismic event in history. It assesses what would have been the outcome had the Christchurch insurance system been different, focusing on the California Earthquake Authority (CEA) programme and Japan Earthquake Reinsurance (JER). Overall, the aggregate cost of the earthquake to the New Zealand public insurer (Earthquake Commission) was USD 6.2 billion. If a similar-sized disaster had occurred in Japan and California, homeowners would have received around USD 1.6 billion and USD 0.7 billion, respectively. This paper describes the distributive and spatial patterns of these scenarios and discusses some key policy questions that emerge from this comparison.     

Application of an entropy-based Bayesian optimization technique to the redesign of an existing monitoring network for single air pollutants

We apply the entropy-based Bayesian optimizing approach of Le and Zidek to the spatial redesign of the extensive air pollution monitoring network operated by Metro Vancouver, in the Lower Fraser Valley, British Columbia. This method is chosen because of its statistical sophistication, relative to other possible approaches, and because of the very rich, two-decade long data record available from this network. The redesign analysis is applied to ozone, carbon monoxide and PM_2.5 pollutants.     

Antibiotic and antiparasitic residues in surface water of urban rivers in the Red River Delta (Hanoi,Vietnam): concentrations,profiles, source estimation,and risk assessment

Environmental Science and Pollution Research - Antibiotic residues and antimicrobial resistance in surface water are issues of global concern, especially in developing countries. In this study, the...     

Combining data and simulated data for space–time fields: application to ozone

This paper presents a theory for modeling random environmental spatial-temporal fields that allows simulated data (numerical-physical model output) to be combined with measurements made at fixed monitoring sites. That theory involves Bayesian hierarchical models that provide temporal forecasts and spatial predictions along with appropriate credibility intervals. A by-product is a method for re-calibrating the simulated data to bring it into line with the measurements for certain applications. While the approach covers a broad domain of potential applications, this paper addresses a field of particular importance, ground level ozone concentrations over the eastern and central USA. A univariate model is developed and illustrated with hourly ozone fields. A multivariate alternative is also provided and illustrated with daily concentration fields. The forecasts and predictions they provide are compared with those from other approaches.     

Selecting a binary Markov model for a precipitation process

This paper uses rth-order categorical Markov chains to model the probability of precipitation. Several stationary and non-stationary high-order Markov models are proposed and compared using BIC. The number of parameters increases exponentially by adding the Markov order. Several classes of high-order Markov models are proposed which their increase of number of parameters are modest. For example models that use the number of precipitation days in a period prior to date, temperature of the previous day and sines/cosines periodic functions (to model the seasonality) are considered. The theory of partial likelihood is used to estimate the parameters. Parsimonious non-stationary first order Markov models with few seasonal terms are found optimal using BIC and temperature does not turn out to be a useful covariate. However BIC seems to underestimate the number of seasonal terms. We have also compared the results with AIC in some cases which tends to pick parsimonious models with more seasonal terms and higher order. We also show that ignoring seasonal terms result in picking higher order Markov chains. Finally we apply the methods to build confidence intervals for the probability of periods with no precipitation or low number of precipitation days in Calgary using historical data from 1980 to 2000.     

Effect of petroleum on decomposition of shrub–grass litters in soil in Northern Shaanxi of China

The impacts of petroleum contamination on the litter decomposition of shrub–grass land would directly influence nutrient cycling, and the stability and function of ecosystem. Ten common shrub and grass species from Yujiaping oil deposits were studied. Litters from these species were placed into litterbags and buried in petroleum-contaminated soil with 3 levels of contamination (slight, moderate and serious pollution with petroleum concentrations of 15, 30 and 45 g/kg, respectively). A decomposition experiment was then conducted in the lab to investigate the impacts of petroleum contamination on litter decomposition rates. Slight pollution did not inhibit the decomposition of any litters and significantly promoted the litter decomposition of Hippophae rhamnoides, Caragana korshinskii, Amorpha fruticosa, Ziziphus jujuba var. spinosa, Periploca sepium, Medicago sativa and Bothriochloa ischaemum. Moderate pollution significantly inhibited litter decomposition of M. sativa, Coronilla varia, Artemisia vestita and Trrifolium repens and significantly promoted the litter decomposition of C. korshinskii, Z. jujuba var. spinosa and P. sepium. Serious pollution significantly inhibited the litter decomposition of H. rhamnoides, A. fruticosa, B. ischaemum and A. vestita and significantly promoted the litter decomposition of Z. jujuba var. spinosa, P. sepium and M. sativa. In addition, the impacts of petroleum contamination did not exhibit a uniform increase or decrease as petroleum concentration increased. Inhibitory effects of petroleum on litter decomposition may hinder the substance cycling and result in the degradation of plant communities in contaminated areas.     

Plant extract-mediated synthesis of aluminum oxide nanoparticles for water treatment and biomedical applications: a review

Environmental Chemistry Letters - Nanotechnologies have contributed to disease control and prevention, but conventional synthesis of nanoparticles rarely meets the strict requirements of green...     

Discussion

Environmental and Ecological Statistics -