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.     

Use of Multispecies Occupancy Models to Evaluate the Response of Bird Communities to Forest Degradation Associated with Logging

Forest degradation is arguably the greatest threat to biodiversity, ecosystem services, and rural livelihoods. Therefore, increasing understanding of how organisms respond to degradation is essential for management and conservation planning. We were motivated by the need for rapid and practical analytical tools to assess the influence of management and degradation on biodiversity and system state in areas subject to rapid environmental change. We compared bird community composition and size in managed (ejido, i.e., communally owned lands) and unmanaged (national park) forests in the Sierra Tarahumara region, Mexico, using multispecies occupancy models and data from a 2‐year breeding bird survey. Unmanaged sites had on average higher species occupancy and richness than managed sites. Most species were present in low numbers as indicated by lower values of detection and occupancy associated with logging‐induced degradation. Less than 10% of species had occupancy probabilities >0.5, and degradation had no positive effects on occupancy. The estimated metacommunity size of 125 exceeded previous estimates for the region, and sites with mature trees and uneven‐aged forest stand characteristics contained the highest species richness. Higher estimation uncertainty and decreases in richness and occupancy for all species, including habitat generalists, were associated with degraded young, even‐aged stands. Our findings show that multispecies occupancy methods provide tractable measures of biodiversity and system state and valuable decision support for landholders and managers. These techniques can be used to rapidly address gaps in biodiversity information, threats to biodiversity, and vulnerabilities of species of interest on a landscape level, even in degraded or fast‐changing environments. Moreover, such tools may be particularly relevant in the assessment of species richness and distribution in a wide array of habitats. Uso de Modelos de Ocupación para Múltiples Especies para Evaluar la Respuesta de las Comunidades de Aves a la Degradación de Bosques Asociada con la Tala     

Dynamics of a low‐density tiger population in Southeast Asia in the context of improved law enforcement

Recovering small populations of threatened species is an important global conservation strategy. Monitoring the anticipated recovery, however, often relies on uncertain abundance indices rather than on rigorous demographic estimates. To counter the severe threat from poaching of wild tigers (Panthera tigris), the Government of Thailand established an intensive patrolling system in 2005 to protect and recover its largest source population in Huai Kha Khaeng Wildlife Sanctuary. Concurrently, we assessed the dynamics of this tiger population over the next 8 years with rigorous photographic capture‐recapture methods. From 2006 to 2012, we sampled across 624–1026 km² with 137–200 camera traps. Cameras deployed for 21,359 trap days yielded photographic records of 90 distinct individuals. We used closed model Bayesian spatial capture‐recapture methods to estimate tiger abundances annually. Abundance estimates were integrated with likelihood‐based open model analyses to estimate rates of annual and overall rates of survival, recruitment, and changes in abundance. Estimates of demographic parameters fluctuated widely: annual density ranged from 1.25 to 2.01 tigers/100 km², abundance from 35 to 58 tigers, survival from 79.6% to 95.5%, and annual recruitment from 0 to 25 tigers. The number of distinct individuals photographed demonstrates the value of photographic capture–recapture methods for assessments of population dynamics in rare and elusive species that are identifiable from natural markings. Possibly because of poaching pressure, overall tiger densities at Huai Kha Khaeng were 82–90% lower than in ecologically comparable sites in India. However, intensified patrolling after 2006 appeared to reduce poaching and was correlated with marginal improvement in tiger survival and recruitment. Our results suggest that population recovery of low‐density tiger populations may be slower than anticipated by current global strategies aimed at doubling the number of wild tigers in a decade.     

Accident modelling and analysis in process industries

Accident modelling is a methodology used to relate the causes and effects of events that lead to accidents. This modelling effectively seeks to answer two main questions: (i) Why does an accident occur, and (ii) How does it occur. This paper presents a review of accident models that have been developed for the chemical process industry with in-depth analyses of a class of models known as dynamic sequential accident models (DSAMs). DSAMs are sequential models with a systematic procedure to utilise precursor data to estimate the posterior risk profile quantitatively. DSAM also offers updates on the failure probabilities of accident barriers and the prediction of future end states. Following a close scrutiny of these methodologies, several limitations are noted and discussed, and based on these insights, future work is suggested to enhance and improve this category of models further.     

Modelling fertility: an application of count regression models

Often the lifecycle data occur as count of the vital events and are recorded as integers. The purpose of this article is to model the fertility behavior based on religious, educational, economic, and occupational characteristics. The responses of classified groups according to these determinants are examined for significant influence on fertility using Poisson regression model (PRM) based on the National Family Health Survey -3 dataset. The observed and predicted probabilities under PRM indicate modal value of two children for the Poisson distribution modeled data. Presence of dominance of two child in the data motivates the authors to adopt multinomial regression model (MRM) in order to link fertility with various socioeconomic indicators responsible for fertility variation. Choice of the explanatory factors is limited to the availability of data. Trends and patterns of preference for birth counts suggest that religion, caste, wealth, female education, and occupation are the dominant factors shaping the observed birth process. Empirical analysis suggests that both the models used in the study perform similarly on the sample data. However, fitting of MRM by taking birth count of two as comparison category shows improved Akaike information criterion and consistent Akaike information criterion values. Current work contributes to the existing literature as it attempts to provide more insight into the determinants of Indian fertility using Poisson and MRM.     

应用构型一致的显式方法预测结构冲击动响应

目的 发展构型一致的显式方法,以提高拉格朗日有限元对冲击、碰撞及侵彻问题计算的精度和健壮性。方法 为更准确地描述和计算结构大变形,基于任意参考构型理论,建立构型一致的动量方程拉格朗日有限元格式。为避免网格间侵入现象,通过结合前增量位移的时间积分方法和共轭梯度算法,使得显式增量步更新后,仍满足接触非侵入条件。结果 通过对大变形碰撞及侵彻问题的模拟,并与LS-DYNA软件结果对比,验证了计算方法的可靠性。结论 基于构型一致的显式算法可有效抑制大变形碰撞过程的单元畸变,提高计算程序的健壮性和预测准确度,使该算法在模拟大变形碰撞问题方面具有优势。     

Revealing photon transmission in an ultraviolet reactor: Advanced approaches for measuring fluence rate distribution in water for model validation

Fluence rate (FR) distribution (optical field) is of great significance in the optimal design of ultraviolet (UV) reactors for disinfection or oxidation processes in water treatment. Since the 1970s, various simulation models have been developed, which can be combined with computational fluidic dynamic software to calculate the fluence delivered in a UV reactor. These models strive for experimental validation and further improvement, which is a major challenge for UV technology in water treatment. Herein, a review of the simulation models of the FR distribution in a UV reactor and the applications of the current main experimental measurement approaches including conventional flat-type UV detector, spherical actinometer, and micro-fluorescent silica detector (MFSD), is presented. Moreover, FR distributions in a UV reactor are compared between various simulation models and MFSD measurements. In addition, the main influential factors on the FR distribution, including inner-wall reflection, refraction and shadowing effects of adjacent lamps, and turbidity effect are discussed, which is helpful for improving the accuracy of the simulation models and avoiding dark regions in the reactor design. This paper provides an overview on the simulation models and measurement approaches for the FR distribution, which is helpful for the model selection in fluence calculations and gives high confidence on the optimal design of UV reactors in regard to present methods.     

液氨储罐泄漏扩散模型的改进研究

针对液氨储罐孔洞泄漏的实际工况,综合考虑泄漏及由于泄漏导致液氨闪蒸造成 的罐压变化,以及储罐的许用压力,对其连续泄漏过程进行了分析,以此改进现有的泄漏扩 散后果分析模型,获得较忽略这些因素更为严重的后果.最后针对灾难性事故发生前连续泄 漏最大持续时间的影响因素进行了分析.结果表明,环境温度与初始罐压对其影响较大,是决定...     

The influences of climate and hydrology on population dynamics of waterbirds in the lower Murrumbidgee River floodplains in Southeast Australia: Implications for environmental water management

Restoration of waterbird diversity and abundance is a key objective of river system management in Australia. Therefore, understanding the effects of climatic and hydrological variables on waterbird population dynamics is fundamental for successful river restoration programs. We investigated the population dynamics of waterbirds (total abundance) and seven functional waterbird groups in the floodplains of lower Murrumbidgee River. We found a general declining abundance trend from 1983 to 2007, except for the deep water foragers. We modelled the relative contribution of the climatic and hydrological factors to waterbird population decrease using the generalized additive model (GAM) framework after identifying the negative binomial distribution. Most of the seven functional groups were positively related to both annual rainfall and water usage, defined as the total water volume intercepted by the river reach, and the models indicated that rainfall was slightly more important. Temperature also played a role in waterbird abundance: the maximum summer temperature negatively influenced the abundance of dabbling ducks, shoreline foragers and fish eaters, while the minimum winter temperature positively affected the abundance of dabbling ducks and shoreline foragers. Overall, our results support the practice of providing environmental water for sustaining waterbird populations. However, environmental water provision is likely to be most effective when timed to coincide with antecedent rainfall.     

Disentangling the effects of heterogeneity, stochastic dynamics and sampling in a community of aquatic insects

A dynamic and heterogeneous species abundance model generating the lognormal species abundance distribution is fitted to time series of species data from an assemblage of stoneflies and mayflies (Plecoptera and Ephemeroptera) of an aquatic insect community collected over a period of 15 years. In each year except one, we analyze 5 parallel samples taken at the same time of the season giving information about the over-dispersion in the sampling relative to the Poisson distribution. Results are derived from a correlation analysis, where the correlation in the bivariate normal distribution of log abundance is used as measurement of similarity between communities. The analysis enables decomposition of the variance of the lognormal species abundance distribution into three components due to heterogeneity among species, stochastic dynamics driven by environmental noise, and over-dispersion in sampling, accounting for 62.9, 30.6 and 6.5% of the total variance, respectively. Corrected for sampling the heterogeneity and stochastic components accordingly account for 67.3 and 32.7% of the among species variance in log abundance. By using this method, it is possible to disentangle the effect of heterogeneity and stochastic dynamics by quantifying these components and correctly remove sampling effects on the observed species abundance distribution.