Continuous dose-response modeling and risk analysis with the gamma and reciprocal gamma distributions

Kodell and West (1993) describe two methods for calculating pointwise upper confidence limits on the risk function with normally distributed responses and using a certain definition of adverse quantitative effect. But Banga et al. (2000) have shown that these normal theory methods break down when applied to skew data. We accordingly develop a risk analysis model and associated likelihood-based methodology when the response follows either a gamma or reciprocal gamma distribution. The model supposes that the shape (index) parameter k of the response distribution is held fixed while the logarithm of the scale parameter is a linear model in terms of the dose level. Existence and uniqueness of the maximum likelihood estimates is established. Asymptotic likelihood-based upper and lower confidence limits on the risk are solutions of the Lagrange equations associated with a constrained optimization problem. Starting values for an iterative solution are obtained by replacing the Lagrange equations by the lowest order terms in their asymptotic expansions. Three methods are then compared for calculating confidence limits on the risk: (i) the aforementioned starting values (LRAL method), (ii) full iterative solution of the Lagrange equations (LREL method), and (iii) bounds obtained using approximate normality of the maximum likelihood estimates with standard errors derived from the information matrix (MLE method). Simulation is used to assess coverage probabilities for the resulting upper confidence limits when the log of the scale parameter is quadratic in the dose level. Results indicate that coverage for the MLE method can be off by as much as 15% points and converges very slowly to nominal coverage levels as the sample size increases. Coverage for the LRAL and LREL methods, on the other hand, is close to nominal levels unless (a) the sample size is small, say N < 25, (b) the index parameter is small, say k 1, and (c) the direction of adversity is to the left for the gamma distribution or to the right for the reciprocal gamma distribution.     

Direct calculation of likelihood-based benchmark dose levels for quantitative responses

A benchmark dose (BMD) for quantitative responses is a lower confidence limit (LCL) on the effective dose corresponding to a specified risk level r. A commonly adopted method for calculating the BMD is to obtain a pointwise upper confidence curve U(d) on the risk function and then invert this relationship by solving the equation U(d)=r. The solution d is taken to be the BMD. Sciullo et al. (2000) have shown that the coverage achieved by this inversion method is at least as great as the coverage achieved by U (·) but that there is otherwise no general relationship between the two coverage probabilities. The present paper develops a method for direct calculation of the BMD based on the asymptotic distribution of the likelihood ratio statistic. It is further shown that the direct method and the inversion method are equivalent when U (·) is also based on the likelihood ratio. Since the direct method is known to be asymptotically correct, it follows that the LR-based inversion method is also asymptotically correct. However, the direct method is computationally faster and easier to program. Finally, some simulation studies are conducted to assess the small sample coverage probabilities of the direct method when responses follow either a normal or a gamma distribution.     

基于TRMM降水订正数据的祁连山地区最大降水高度带研究

采用TRMM卫星反演月降水数据和气象台站实测降水数据,通过误差评估等数理统计方法验证数据,并结合Kriging法对TRMM降水数据进行订正,以此研究了祁连山地区最大降水高度带的时空变化。结果表明：（1）TRMM降水数据在祁连山地区的整体适用性较好。其中TRMM降水数据与台站实测值的年均降水量相关系数达0.94;季节平均降水量的相关系数分别为春季（0.87）、夏季（0.89）、秋季（0.88）、冬季（0.70）。（2）祁连山地区27个气象台站实测值与TRMM降水数据的相关性较好,但在个别台站误差较大且存在低值高估、高值低估的现象。（3）祁连山地区年均降水量自东向西呈减少趋势。东、中、西三段最大降水高度带分别为4100 m、4500 m、4700 m,年均降水量的垂直变化率分别为16.6 mm/100 m、10.8 mm/100 m、9.8 mm/100 m。（4）1998-2016年祁连山地区东、中、西三段降水量均呈波动增加,最大降水高度带也呈波动上升趋势,祁连山地区年内各季节最大降水高度带按夏、春、秋、冬的次序降低。     

Monitoring of Organochlorine Pesticide Residues in Summer and Winter Vegetables from Agra, India – A case study

Analysis of summer and winter vegetable samples during 2002–2003 for pesticidal contamination was carried out on Gas Chromatograph-Electron Capture Detector with capillary columns. The contamination levels of winter vegetables (average concentration of 4.57, 6.80 and 5.47 ppb respectively for Lindane, Endosulphan and DDT) were found to be slightly higher than the summer vegetables (average concentration of 4.47, 3.14 and 2.82 ppb respectively for Lindane, Endosulphan and DDT). The concentration of these organochlorine pesticides in summer and winter vegetables were well below the established tolerances but continuous consumption of such vegetables even with moderate contamination level can accumulate in the receptor's body and may lead to chronic effects that could be fatal.     

太湖微囊藻光合作用活性的周年动态变化

应用Phyto-PAM浮游植物荧光仪测定了太湖微囊藻光合作用活性的周年变化,并分析了微囊藻光合作用活性参数与环境因子之间的相互关系.结果显示:冬季期间,检测不到微囊藻的光合作用活性;春季期间,微囊藻的最大光量子产量(Fv/Fm)和有效光量子产量(ΔF/Fm')呈现出快速增加趋势;夏季期间,微囊藻的光合作用活性呈现出先增加后下降趋势;秋季期间,微囊藻的光合作用活性呈现出下降趋势.梅梁湾和湖心微囊藻的最大光量子产量分别在0.34~0.55和0.28~0.50之间,平均值分别为0.42和0.39,有效光量子产量分别在0.15~0.38和0.10~0.38之间,平均值分别为0.26和0.23;非光化学荧光淬灭(NPQ)呈现出先增加后下降趋势,在8月达到最大值.光响应曲线(RLC)的3个特征参数从3月到6月呈上升趋势,之后呈现出较大波动性.相关分析结果表明,太湖微囊藻的Fv/Fm、ΔF/Fm'和NPQ之间有显著的正相关性,且均随着水温升高而增加.Fv/Fm和ΔF/Fm'与最大电子传递速率(r ETRmax)之间显著正相关,r ETRmax与TN呈显著正相关;饱和光照强度点(Ik)与TP显著正相关,与TN/TP和NO-3显著负相关.总之,太湖微囊藻的光合作用活性与水华形成及发展动态相适应,控制全球气候变暖和削减氮、磷浓度有利于抑制微囊藻光合作用活性.     

典型北方滨海核电厂温排水混合区边缘温升限值研究

随着我国电力事业的快速发展,电厂循环冷却水(温排水)的余热排放对受纳水体生态环境造成的负面热影响(即热污染)已日益引起社会关注。基于国内现有的温排水排放控制标准可执行性不强的现状,对我国电厂温排水的热污染控制标准的基础技术要素——温排水混合区边缘温升限值进行了研究。以我国北方某典型滨海核电厂址邻近海域的代表性海洋生物为研究对象,以各季节不同受试物种最大临界温度(CTM)的统计分析结果,作为确定该厂址温排水混合区边缘温升限值的主要依据。并结合法规调研法,最终确定该典型滨海核电厂址温排水混合区边缘温升限值的推荐值为3.6℃。     

Optimal Management of a Multispecies Shorebird Flyway under Sea‐Level Rise

Every year, millions of migratory shorebirds fly through the East Asian–Australasian Flyway between their arctic breeding grounds and Australasia. This flyway includes numerous coastal wetlands in Asia and the Pacific that are used as stopover sites where birds rest and feed. Loss of a few important stopover sites through sea‐level rise (SLR) could cause sudden population declines. We formulated and solved mathematically the problem of how to identify the most important stopover sites to minimize losses of bird populations across flyways by conserving land that facilitates upshore shifts of tidal flats in response to SLR. To guide conservation investment that minimizes losses of migratory bird populations during migration, we developed a spatially explicit flyway model coupled with a maximum flow algorithm. Migratory routes of 10 shorebird taxa were modeled in a graph theoretic framework by representing clusters of important wetlands as nodes and the number of birds flying between 2 nodes as edges. We also evaluated several resource allocation algorithms that required only partial information on flyway connectivity (node strategy, based on the impacts of SLR at nodes; habitat strategy, based on habitat change at sites; population strategy, based on population change at sites; and random investment). The resource allocation algorithms based on flyway information performed on average 15% better than simpler allocations based on patterns of habitat loss or local bird counts. The Yellow Sea region stood out as the most important priority for effective conservation of migratory shorebirds, but investment in this area alone will not ensure the persistence of species across the flyway. The spatial distribution of conservation investments differed enormously according to the severity of SLR and whether information about flyway connectivity was used to guide the prioritizations. With the rapid ongoing loss of coastal wetlands globally, our method provides insight into efficient conservation planning for migratory species. Gestión Óptima de una Ruta Migratoria de Múltiples Especies de Aves Costeras Sometida a Incremento del Nivel del Mar     

Potential Impacts of Global Warming on the Diversity and Distribution of Stream Insects in South Korea

Globally, the East Asian monsoon region is one of the richest environments in terms of biodiversity. The region is undergoing rapid human development, yet its river ecosystems have not been well studied. Global warming represents a major challenge to the survival of species in this region and makes it necessary to assess and reduce the potential consequences of warming on species of conservation concern. We projected the effects of global warming on stream insect (Ephemeroptera, Odonata, Plecoptera, and Trichoptera [EOPT]) diversity and predicted the changes of geographical ranges for 121 species throughout South Korea. Plecoptera was the most sensitive (decrease of 71.4% in number of species from the 2000s through the 2080s) order, whereas Odonata benefited (increase of 66.7% in number of species from the 2000s through the 2080s) from the effects of global warming. The impact of global warming on stream insects was predicted to be minimal prior to the 2060s; however, by the 2080s, species extirpation of up to 20% in the highland areas and 2% in the lowland areas were predicted. The projected responses of stream insects under global warming indicated that species occupying specific habitats could undergo major reductions in habitat. Nevertheless, habitat of 33% of EOPT (including two‐thirds of Odonata and one‐third of Ephemeroptera, Plecoptera, and Trichoptera) was predicted to increase due to global warming. The community compositions predicted by generalized additive models varied over this century, and a large difference in community structure in the highland areas was predicted between the 2000s and the 2080s. However, stream insect communities, especially Odonata, Plecoptera, and Trichoptera, were predicted to become more homogenous under global warming. Impacto Potencial del Calentamiento Global sobre la Diversidad y la Distribución de Insectos de Arroyo en Corea del Sur     

A spatially explicit estimate of the prewhaling abundance of the endangered North Atlantic right whale

The North Atlantic right whale (NARW) (Eubalaena glacialis) is one of the world's most threatened whales. It came close to extinction after nearly a millennium of exploitation and currently persists as a population of only approximately 500 individuals. Setting appropriate conservation targets for this species requires an understanding of its historical population size, as a baseline for measuring levels of depletion and progress toward recovery. This is made difficult by the scarcity of records over this species’ long whaling history. We sought to estimate the preexploitation population size of the North Atlantic right whale and understand how this species was distributed across its range. We used a spatially explicit data set on historical catches of North Pacific right whales (NPRWs) (Eubalaena japonica) to model the relationship between right whale relative density and the environment during the summer feeding season. Assuming the 2 right whale species select similar environments, we projected this model to the North Atlantic to predict how the relative abundance of NARWs varied across their range. We calibrated these relative abundances with estimates of the NPRW total prewhaling population size to obtain high and low estimates for the overall NARW population size prior to exploitation. The model predicted 9,075–21,328 right whales in the North Atlantic. The current NARW population is thus <6% of the historical North Atlantic carrying capacity and has enormous potential for recovery. According to the model, in June–September NARWs concentrated in 2 main feeding areas: east of the Grand Banks of Newfoundland and in the Norwegian Sea. These 2 areas may become important in the future as feeding grounds and may already be used more regularly by this endangered species than is thought.     

MULTIVARIATE EXTREME VALUE DISTRIBUTION WITH MIXED GUMBEL MARGINALS1

ABSTRACT: Bivariate and trivariate distributions have been derived from the logistic model for the multivariate extreme value distribution. Marginals in the models are extreme value type I distributions for two-component mixture variables (mixed Gumbel distribution). This paper is a continuation of the previous works on multivariate distribution in hydrology. Interest is focused on the analysis of floods which are generated by different types of storms. The construction of their corresponding probability distributions and density functions are described. In order to obtain the parameters of such a bivariate or trivariate distribution, a generalized maximum likelihood estimation procedure is proposed to allow for the cases of samples with different lengths of record. A region in Northern Mexico with 42 gauging stations, grouped into two homogeneous regions, has been selected to apply the models. Results produced by the multivariate distributions have been compared with those obtained by the Normal, log-Normal-2, log-Normal-3, Gamma-2, Gamma-3, log-Pearson-3, Gumbel, TCEV and General Extreme Value distributions. Goodness of fit is measured by the criterion of standard error of fit. Results suggest that the proposed models are a suitable option to be considered when performing flood frequency analysis.