中国旅游业绿色发展效率时空演变特征及影响机理

绿色发展是旅游业可持续发展理念的重要组成部分,是旅游业奉行以人为本、生态至上和全面发展的新价值观。在梳理旅游业绿色发展概念及内涵基础上,构建旅游业绿色发展效率评价体系,运用SBM-Undersirable模型、核密度估计、空间马尔科夫链等方法,探讨2008—2018年中国31个省（市、自治区）旅游业绿色发展效率（TGDE）时空演化特征及影响机理。研究发现：（1）时间和空间变化方面,TGDE总体处于中等偏下水平,时间上呈“W”型变化形态,“下降—上升—调整”阶段特征显著;空间呈“东—中—西”递减分布,内部差异为西部地区>东部地区>中部地区,低、中、高效率由“金字塔”向“菱形”结构转变,高效率地区集中于东部沿海,中等效率多分布于中西部地区,低效率位于胡焕庸线两侧。（2）动态演进方面,TGDE始终存在两极分化现象,但区域协调性逐步增强,具有较强平稳性,难以实现跨越式发展,空间向上转移省份比较集中,以中西部为主,向下调整省份较少,且存在明显的空间溢出效应,溢出影响具有不对称性。（3）影响机理方面,总体上,经济水平、产业结构、政府规制、教育水平和旅游资源影响因子与TGDE间存在显著的正向关系,对外开放程度的作用不显著,但各因子的影响程度、作用机理及条件具有较强地域性。     

基于贝叶斯的苯并(α)芘PBPK模型优化与健康风险评估应用

应用基于生理的药代动力学(PBPK)模型预测苯并(α)芘(BaP)暴露的人体内部剂量,基于贝叶斯的马尔科夫链蒙特卡洛模拟(MCMC)方法对模型参数进行校准和优化,最后运用已优化的模型对BaP内暴露基准值进行推导.研究发现,基于贝叶斯的MCMC方法对模型后验参数校准后,模型精度明显提高,两个数据集验证结果显示残差平方和分别降低了72%和94%.PBPK模型以BaP和子代谢物3-羟基苯并(α)芘(3-OHBaP)的体内动力学过程为结构基础,模拟BaP体内浓度分布大小为脂肪>肾脏>皮肤>缓慢灌注组织>快速灌注组织>静脉血>肝脏;3-OHBaP体内浓度分布大小为肾脏>快速灌注组织>脂肪>肺>静脉血>缓慢灌注组织>肝脏>皮肤.敏感性分析显示,快速灌注组织-血分配系数对模型输出影响最大,灵敏度系数超过了200%;排泄系数影响最小,只有肾小球过滤率K_BR的灵敏度系数超过了1%.以美国国家环境保护局推荐的参考浓度2.0×10^-6mg/m³为外暴露安全基准值,基于PBPK模型推导了职业暴露的BaP生物监测当量(BE),结果显示BE值为0.405pmol/mol肌酐(尿液3-OHBaP平均浓度),为基于人体内暴露剂量水平进行定量健康风险评估奠定了基础.     

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.     

系统安全态势的马尔科夫预测模型建立及应用

基于马尔科夫链预测理论,分析研究了系统安全态势的定性与定量预测问题,建立了系统安全态势预测模型.定性预测侧重安全态势的升、降趋势分析,定量预测以系统万人死亡率为依据,划分四个安全状态,从而实现系统安全状态定量预测.定性与定量预测相互检验、相互补充.实例研究表明,马尔科夫定性与定量相结合的预测模型结构简单,计算方便,符合系统安全态势预测特征要求,是系统安全宏观管理的重要参考依据之一.     

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。     

Modeling Diapause Termination of Rhipicephalus Appendiculatus using Statistical Tools to Detect Sudden Behavioral Changes and Time Dependencies

This paper presents statistical methodology to analyze longitudinal binary responses for which a sudden change in the response occurs in time. Probability plots, transition matrices, and change-point models and more advanced techniques such as generalized auto-regression models and hidden Markov chains are presented and applied on a study on the activity of Rhipicephalus appendiculatus, the major vector of Theileria parva, a fatal disease in cattle. This study presents individual measurements on female R. appendiculatus, which are terminating their diapause (resting status) and become active. Comprehending activity patterns is very important to better understand the ecology of R. appendiculatus. The model indicates that activity and non-activity act in an absorbing way meaning that once a tick becomes active it shows a tendency to remain active. The change-point model estimates that the sudden change in activity happens on December 10. The reaction of ticks on acceleration and changes in rainfall and temperature indicates that ticks can sense climatic changes. The study revealed the underlying not visually observable states during diapause development of the adult tick of R. appendiculatus. These states could be related to phases during the dynamic event of diapause development and post-diapause activity in R. appendiculatus.     

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.     

Studying crop sequences with CarrotAge,a HMM-based data mining software

We have developed a knowledge discovery system based on high-order hidden Markov models for analyzing spatio-temporal data bases. This system, named CarrotAge , takes as input an array of discrete data – the rows represent the spatial sites and the columns the time slots – and builds a partition together with its a posteriori probability. CarrotAge has been developed for studying the cropping patterns of a territory. It uses therefore an agricultural drench database, named Ter-Uti , which records every year the land-use category of a set of sites regularly spaced. The results of CarrotAge are interpreted by agronomists and used in research works linking agricultural land use and water management. Moreover, CarrotAge can be used to find out and study crop sequences in large territories, that is a main question for agricultural and environmental research, as discussed in this paper.     

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.