Imputing Unmeasured Explanatory Variables in Environmental Epidemiology With Application to Health Impact Analysis of Air Pollution

This paper presents the results of a reconsideration of earlier work that finds an association between daily hospital admissions for respiratory distress and daily concentrations of sulphate (lag 1) as well as daily maximum concentrations of ozone (lags 1 and 3). These associations are found even after clustering the data by hospital of admission and accounting for the effects of temperature. We use an adaptation of their generalized estimating equation technique for clustered data, that daily data being for southern Ontario summers from 1983 to 1988. Like them, we adjust for daily maximum temperatures. However, unlike the earlier work returned to ours includes daily average humidity as a potential explanatory variable in our model. Our analysis also differs from theirs in that we cluster the data by census subdivision to reduce the risk of confounding pollutant levels with population size within regions. Moreover, we log-transform the explanatory variables and then high-pass filter the resulting data. We also deviate from the earlier analysis by taking account of measurement error incurred in using surrogate measures of the explanatory variables. To do so we use new methodology designed for our study but of potential value in other applications. That methodology requires a spatial predictive distribution for the unmeasured explanatory variables. Each day about 700 missing measurements for each of these variables can then be imputed over the geographical domain of the study. With these imputations we get a measure of imputation error through the covariance of the predictive distribution. Along with the predictive distribution we require an impact model to link-up with the predictive distribution. We describe that model and show how it uses the imputed measurements of the missing values of the explanatory variables. We also show how through that model, uncertainty about these values is reflected in our analysis and in commensurate uncertainties in the inferences made. Apart from its substantive objectives, our analysis serves to test the new methods with the earlier results serving as a foil. The reassuring qualitative agreement between our findings and the earlier results seems encouraging.     

福建北部海域浙闽沿岸流消亡期浮游植物的种类组成与分布特征

根据2009年4月在福建北部海域(24°47’17.0"~26°48’02.9"N,119°29’36.0"~120°57’13.8"E)所获浮游植物网采样品和同步观测的理化参数,并结合2007年"908专项"春季航次相应范围的调查资料,分析该海域浮游植物种类组成、分布特点、年际变化及其与环境因子的关系.本航次共记录浮游植物3个门类85种,其中硅藻75种、甲藻9种、蓝藻1种.物种组成以广温种为主(占47.06%),其次为暖水种(占36.47%),温带种仅占7.06%.调查区浮游植物丰度平均为137.89×104cells m-3,其平面分布呈现从近岸往外海、从北往南递增的态势.聚类分析显示本海区浮游植物可划分为2个群落,群落Ⅰ主要由近岸低盐种组成,仅分布于受浙闽沿岸流影响较直接的闽江口以北的近岸测站;群落Ⅱ主要为外海高盐种和广温广盐种,广泛分布于受台湾暖流影响较大的闽江口以南及三沙湾外侧水域.主成分分析(PCA)表明,对本海区浮游植物的分布起主导作用的是温度和盐度,而与营养盐的关系不密切,反映调查期间浙闽沿岸流已基本退出本海区,浮游植物的分布主要受制于台湾暖流.浮游植物丰度的平面分布格局与以往的调查结果基本一致,但其群落结构存在年际变化,"908专项"春季航次的优势种为近岸低盐种和广温广盐种,优势种突出,多样性指数低;而本次调查外海性广温种成为优势种之一,多样性指数(H’)和均匀度(J’)均较高,这可能与考察期间受东北季风强度所左右的浙闽沿岸流强弱有关.     

土壤养分空间估测方法研究综述

土壤养分是土壤提供的植物生长发育所必需的营养元素。由于受到自然因素和人为因素的共同作用,土壤养分具有高度的空间变异性。土壤的这种特性不仅表现在区域尺度上,而且也表现在田块尺度上。在研究方法上经历了从传统统计学到地统计学,再到神经网络、地理信息技术以及高精度曲面建模等新方法的不断改进过程。文章从地统计学方法引入到土壤养分空间变异研究中为出发点,论述了国内外基于地统计学的土壤养分空间变异的研究现状,主要包括利用地统计学方法来确定合理的土壤采样数目,土壤养分空间变异的定量化研究,土壤养分空间变异的尺度效应;然后简述了神经网络、地理信息技术、高精度曲面建模等技术在土壤养分空间变异研究中的研究现状和应用。最后对比分析了各种研究方法在应用中存在的缺陷,同时指明了今后应加强作物生长的不同时期土壤养分的空间变异性、土壤在四维空间尺度上的演变机理以及环境信息获取的不确定性等方面的研究。     

The complex relationship between personal sense of connection to animals and self‐reported proenvironmental behaviors by zoo visitors

The global biodiversity crisis requires an engaged citizenry that provides collective support for public policies and recognizes the consequences of personal consumption decisions. Understanding the factors that affect personal engagement in proenvironmental behaviors is essential for the development of actionable conservation solutions. Zoos and aquariums may be some of the only places where many people can explore their relations with wild animals and proenvironmental behaviors. Using a moderated‐mediation analysis of a survey of U.S. zoo and aquarium visitors (n = 3588), we explored the relationship between the sense of connection to animals and self‐reported engagement in proenvironmental behaviors related to climate change and how this relationship is affected by certainty that climate change is happening, level of concern about climate change, and perceptions of effectiveness in personally addressing climate change. We found a significant, directional relationship between sense of connection to animals and self‐reported proenvironmental behaviors. Political inclination within the conservative to liberal spectrum did not affect the relationship. We conclude that a personal sense of connection to animals may provide a foundation for educational and communication strategies to enhance involvement in proenvironmental actions.     

A methodological approach to develop “contaminant migration–population effects” models

The main aim of the present work is to discuss the methodological approaches that underpin the “contaminant migration–population effects” models for the evaluation of the detriment to populations of moving organisms in environmental systems with spatial and time dependent pollution levels. A technique to couple the equations controlling the population dynamics and the pollutant dispersion is described and discussed. The domain of application and the limitations of the methodology are analysed and illustrated by some examples. Possible alternative approaches are briefly presented.     

On the consequences of aggregation and balancing of networks on system properties derived from ecological network analysis

In the ecological network analysis (ENA) of complex flow food webs the assumption is often made that the models characterizing the flows and stocks of ecosystems occur in a steady state where inflows equals outflows. An assessment of the system indices derived from ENA of six balanced and unbalanced system models, respectively, indicate to differences between indices. The aggregation of highly articulated flow models into models with fewer compartments also has drastic effects on the system metrics, particularly on the information indices.     

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.     

A compass for sustainable development

A response to the Nuffield Council on Bioethics Consultation from the Institute of Biology, the Association of Applied Biology, the British Crop Protection Council, the British Ecological Society, the British Electrophoresis Society, the British Grassland Society, and the Institute of Horticulture     

The Aquatic Conservation Strategy of the Northwest Forest Plan

Abstract:  Implemented in 1994, the Aquatic Conservation Strategy of the Northwest Forest Plan was designed to restore and maintain ecological processes for aquatic and riparian area conservation on federal lands in the western portion of the Pacific Northwest. We used decision support models to quantitatively evaluate changes in the condition of selected watersheds. In the approximately 10 years since strategy implementation, watershed condition scores changed modestly, but conditions improved in 64% of 250 sampled watersheds, declined in 28%, and remained relatively the same in 7%. Watersheds that had the largest declines included some where wildfires burned 30–60% of their area. The overall statistical distribution of the condition scores did not change significantly, however. Much of the increase in watershed condition was related to improved riparian conditions. The number of large trees (>51 cm diameter at breast height) increased 2–4%, and there were substantial reductions in tree harvest and other disturbances along streams. Whether such changes will translate into longer-term improvements in aquatic ecosystems across broader landscapes remains to be seen.     

腈醛混合化合物对发光菌联合毒性的QSAR研究

测定了羟基乙腈与系列醛类化合物和对苯二甲醛与系列腈类化合物对发光菌(Photobacterium phosphoreum)的联合毒性，探讨了腈醛混合化合物对发光菌的联合毒性机制，并尝试提出了腈醛混合化合物对发光菌联合毒性的QSAR模型．结果表明，不同的腈醛混合化合物对发光菌的联合毒性不同，联合毒性的大小与腈类化合物和醛类化合物之间化学相互作用的程度紧密相关，采用QSAR模型TU=0．842—0．831σ。(n=8，r^2=0．803，SE=0．222，F=24．415，P=0．003)和TU=-0．348—8．450C^*(n=8，r^2=0．874，SE=0．219，F=41．730，P=0．001)分别定量描述羟基乙腈与系列醛类化合物和对苯二甲醛与系列腈类化合物对发光菌的联合毒性．模型具有较高的稳定性和预测能力．