pp´DDT and pp´DDE Accumulation in a Food Chain of Lake Maggiore (Northern Italy): Testing Steady-State Condition

- DOI: http://dx.doi.org/10.1065/espr2006.01.010 Background, Aims and Scope Although pp'DDT usage was strongly limited or banned in most parts of the world during the last three or four decades, the parent compound, its homologues and their metabolites still occur at levels which might pose a risk for many ecosystem components. A case of DDT pollution of industrial origin was discovered in 1996 in Lake Maggiore, the second largest (212 km2) and deepest (370 m) lake in Italy, causing concern for wildlife and human health. The extensive monitoring of many biotic and abiotic compartments which followed from 1998 in order to assess the pollution level and its trend in time, provided a great availability of data referring to DDT contamination of the different fish species of the lake. In this study, the recent contamination levels in selected fish species were compared to those measured in 1998 to evaluate the temporal pollution trend of the lake and its natural recovery, given that no remediation measures were carried out on the contaminated soils and sediments in this time span. Moreover, a modelling approach to test the equilibrium condition between water and pelagic fish species was used. Analytical results of pp'DDT and pp'DDE concentrations in lake water were used as input data in the bioenergetic model by Connolly & Pedersen (1988) to calculate concentrations in two fish species and to compare the predicted and the measured contamination. Methods Sampling and analytical determination of DDT homologues in lake water: Five water sampling campaigns were carried out from May 2002 to February 2004 in three sampling sites of Lake Maggiore. Suspended and dissolved pollutants were determined separately. Quantitative DDT homologue analyses were performed by HRGC coupled with ECD detection by the external standard method. Single water extracts were put together in correspondence with the stratification zones of the water column inferred on the basis of the temperature profile to improve analytical sensitivity. Selection of fish data: Concentrations of DDT and DDE in fishes were selected from recent literature (CIPAIS 2003, 2004). Bioaccumulation model: The bioenergetic model proposed by Connolly & Pedersen (1988) was used to assess the bioaccumulation of pp'DDT and pp'DDE of Alosa fallax (landlocked shad) and Coregonus spp. (whitefish), selected among the different species as representative of a secondary consumer level. Results and Discussion The average concentrations of pp'DDT and pp'DDE in water to be used as input data in the bioenergetic model were obtained considering all the concentrations measured at the three sampling stations in the epylimnion where the fish species considered in this study spend most of their life. The resulting values were 0.05 and 0.16 ng/L for pp'DDT and pp'DDE, respectively. Average measured pp'DDT and pp'DDE concentrations in landlocked shad were 0.81 +/- 0.39 and 1.69 +/- 0.71 mg/kg lipids, respectively, and were 0.29 +/- 0.12 and 1.06 +/- 0.41 mg/kg lipids for the whitefish. Calculated and measured values turned out to be in quite good agreement for pp'DDT, while measured pp'DDE concentrations were higher than expected on the basis of the bioenergetic model in both species. Probably metabolic transformations of pp'DDT accumulated in fish tissues in the past are responsible for the observed differences between calculated and expected pp'DDE concentrations in fish. Conclusions Pelagic fishes of Lake Maggiore seem to maintain the DDT accumulated during their life time and the most efficient mechanism responsible for the fish population recoveries is probably their generation changes; for this reason, equilibrium models cannot be used until negligible pp'DDT concentrations are reached in fish tissues. Recommendations and Outlook The limit proposed for pp'DDT in water by the EU Directive 2000/60, which will come in force in 2008, is 0.2 ng/L, four times higher than the average concentration measured in Lake Maggiore waters. Nevertheless, concentrations measured in Lake Maggiore fish were very close and sometimes exceeded the Maximum residue limits (MRLs) settled by the Italian legislation for foods (0.1 mg/kg w.w. for fish containing 5–20% lipid). It seems, therefore, that the 'environmental quality standard' of 0.2 ng/L cannot guarantee the suitability of fish for human consumption.     

区块链技术在安全生产信息化建设中的应用

为了促进安全生产信息化平台在数据方面进行分析以及整合,基于区块链本身的匿名性、去中心化、安全性、开放性以及独立性等优势,提出将区块链技术应用于安全生产信息化建设,研究表明,区块链技术的应用将能够为企业针对生产安全状态进行有效监督提供最大的帮助,最大限度的实现资源共享,为企业监督、政府部门监管提供便利条件,各企业之间就安全问题进行交流沟通,安全评价服务机构针对各企业安全生产存在的问题提供更有针对性的服务,同时对于提升服务效率以及服务质量有着重要现实意义。     

考虑逆向物流的应急物资配置流程SPN建模分析

应急物资的高效快速配置是降低灾害损失和顺利实施应急救援的有力保障。应急逆向物流包括废旧物资的回收利用以及可重复利用物资的回收再利用,能起到缓解应急物资匮乏,减少环境污染的作用。本文根据随机Petri网理论,构建考虑逆向物流的应急物资配置模型,通过对同构于该模型的马尔可夫链进行仿真,求得各种状态的稳态概率,结合马尔可夫链性质对关键因素进行静态分析和动态分析;通过“雅安地震”的案例应用表明,当地震灾害发生时,此模型可以反映各因素对应急物资配置整体流程的影响,并通过数值变化趋势反映不同条件下应急物资配置的关键环节,可以为灾后救援和应急物资的利用提供理论支持。     

浅谈供应链企业之间动机控制机制

供应链管理强调供应链上节点企业及其活动的整体集成,其管理范围不仅包括企业内部的生产经营活动,而且涉及整体供应链管理技术,并且在管理中特别重视企业之间的合作.基于期望理论从动机控制机制角度浅谈供应链企业之间的运作.图1,参4.     

悬挂链曝气技术应用于河流污染治理

在介绍了悬挂链曝气技术的特点和在复氧曝气方面的适用性的基础上,对该技术的局限性进行了阐述,并认为悬挂链技术为河流污染治理提供了一种新选择。     

On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models

Nonlinear state-space models have been increasingly applied to study population dynamics and data assimilation in environmental sciences. State-space models can account for process error and measurement error simultaneously to correct for the bias in the estimates of system state and model parameters. However, few studies have compared the performance of different nonlinear state-space models for reconstructing the state of population dynamics from noisy time series. This study compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF) and Bayesian nonlinear state-space models (BNSSM) through simulations. Synthetic population time series were generated using the theta logistic model with known parameters, and normally distributed process and measurement errors were introduced using the Monte Carlo simulations. At higher levels of nonlinearity, the UKF and BNSSM had lower root mean square error (RMSE) than the EKF. The BNSSM performed reliably across all levels of nonlinearity, whereas increased levels of nonlinearity resulted in higher RMSE of the EKF. The Metropolis–Hastings algorithm within the Gibbs algorithm was used to fit the theta logistic model to synthetic time series to estimate model parameters. The estimated posterior distribution of the parameter θ indicated that the 95% credible intervals included the true values of θ (=0.5 and 1.5), but did not include 1.0 and 0.0. Future studies need to incorporate the adaptive Metropolis algorithm to estimate unknown model parameters for broad applications of Bayesian nonlinear state-space models in ecological studies.     

生态供应链与生态型设计

生态供应链是可持续发展思想在运作管理领域的具体应用。生态型设计兼顾了经济效益和生态效益，是生态供应链的核心内容。生态型设计的实质是通过整体优化和局部优化来降低各节点企业的环境影响，借助于生态型设计可以把传统供应链代表的单程经济转化成生态供应链代表的循环经济。生态型设计由生态供应链的整体设计和组成元素的设计两部分组成，具体内容包括产品设计、原材料采购、产品生产、产品营销、产品回收和反向物流。     

Bayesian analysis under ordered functions of parameters

Ordered parameter problems arise in a wide variety of real world situations and are dealt with extensively in the literature. Traditional frequentist methods for dealing with these problems are rather complicated theoretically, especially when sample sizes are small. Bayesian methods are not widely used because high dimensional numerical integration is often required. However, Markov chain Monte Carlo methods provide alternatives to such numerical integration and also deal with ordered parameter problems in a straightforward manner. Little is known about the situation where functions of parameters are ordered. Such problems may seem to be of little practical concern initially, but one can readily see their importance in situations where ordering is placed on the means and variances of several normal or Gamma populations. For the Gamma distribution we will present real examples where we will analyze monthly precipitation data from San Francisco, California and Oakland Mills, Iowa. For the San Francisco data we will simultaneously order both monthly precipitation means and variances. For the Iowa data we will place ordering on seasonal average while still estimating monthly means. Our results show that we would obtain sharper, more accurate inference when order restrictions are employed.     

Neighborhood Level Spatial Analysis of the Relationship Between Alcohol Outlet Density and Criminal Violence

Misuse of alcohol is a significant public health problem, potentially resulting in unintentional injuries, motor vehicle crashes, drownings, and, perhaps of greatest concern, serious acts of violence, including assaults, rapes, suicides, and homicides. Although previous research establishes a link between alcohol consumption increased levels of violence, studies relating the density of alcohol outlets (e.g., restaurants, bars, liquor stores) and the likelihood of violent crime have been less common. In this paper we test for such a relationship at the small area level, using data from 79 neighborhoods in the city of Minneapolis, Minnesota. We adopt a fully Bayesian point of view using Markov chain Monte Carlo (MCMC) computational methods as available in the popular and freely available WinBUGS language. Our models control for important covariates (e.g., neighborhood racial heterogeneity, age heterogeneity) and also account for spatial association in unexplained variability using conditionally autoregressive (CAR) random effects. Our results indicate a significant positive relationship between alcohol outlet density and violent crime, while also permitting easy mapping of neighborhood-level predicted and residual values, the former useful for intervention in the most at-risk neighborhoods and the latter potentially useful in identifying covariates still missing from the fixed effects portion of the model.     

Cluster detection using Bayes factors from overparameterized cluster models

In this paper, we consider the use of a partition model to estimate regional disease rates and to detect spatial clusters. Formal inference regarding the number of partitions (or clusters) can be obtained with a reversible jump Markov chain Monte Carlo algorithm. As an alternative, we consider the ability of models with a fixed, but overly large, number of partitions to estimate regional disease rates and to provide informal inferences about the number and locations of clusters using local Bayes factors. We illustrate and compare these two approaches using data on leukemia incidence in upstate New York and data on breast cancer incidence in Wisconsin.