NUTRIENT CONCENTRATION CRITERIA AND CHARACTERIZATION OF PATTERNS IN TROPHIC STATE FOR RIVERS IN HETEROGENEOUS LANDSCAPES1

ABSTRACT: A method is demonstrated for the development of nutrient concentration criteria and large scale assessment of trophic state in environmentally heterogeneous landscapes. The method uses the River Environment Classification (REC) as a spatial framework to partition rivers according to differences in processes that control the accrual and loss of algae biomass. The method is then applied to gravel bed rivers with natural flow regimes that drain hilly watersheds in New Zealand's South Island. An existing model is used to characterize trophic state (in terms of chlorophyll a as a measure of maximum biomass) using nutrient concentration, which controls the rate of biomass accrual, and flood frequency, which controls biomass loss. Variation in flood frequency was partitioned into three classes, and flow data measured at 68 sites was used to show that the classes differ with respect to flood frequency. Variation in nutrient concentration was partitioned at smaller spatial scales by subdivision of higher level classes into seven classes. The median of flood frequency in each of the three higher level classes was used as a control variable in the model to provide spatially explicit nutrient concentration criteria by setting maximum chlorophyll a to reflect a desired trophic state. The median of mean monthly soluble reactive phosphorus and soluble inorganic nitrogen measured at 68 water quality monitoring sites were then used to characterize the trophic state of each of the seven lower level classes. The method models biomass and therefore allows variation in this response variable to provide options for trophic state and the associated nutrient concentrations to achieve these. Thus it is less deterministic than using reference site water quality. The choice from among these options is a sociopolitical decision, which reflects the management objectives rather than purely technical considerations.     

四川汶川茶园沟泥石流灾害特征及危险度评价

2003年8月9日四川汶川县茶园沟泥石流造成了1人死亡,10人失踪和严重的财产损失.泥石流是由于短历时高强度的大雨激发而形成的.泥石流最大流速为10.8m/s,泥石流洪峰流量为999m3/s,冲出物方量约为5万m3.茶园沟流域内松散固体物质的密度为2.18 g/cm3,含水量为10.7%,空隙率为28%,塑性界限和流性界限分别为18.8%和32.5%,一遇大雨或较长时间的降雨就极易形成泥石流.茶园沟属中度危险的泥石流沟,中度危险的泥石流也造成了严重的人民生命财产的损失,这主要是由于受灾对象,即易损性的特性所决定的.茶园沟泥石流的防灾减灾措施,一是要实行封山育林,停止开矿弃渣;二是要提高公路桥梁的防洪标准,合理布局水利水电设施.     

中国湿地现状综述

湿地是重要的自然资源,对人类生存起着重要作用。本文综述了湿地的含义、功能与 价值、中国湿地的特点与现状,以促进中国湿地的可持续发展与利用。     

池火灾热辐射的数值研究

通过列举储罐火灾事故,提出对池火灾进行研究的重要性。介绍目前池火灾国内外的研究现状及发展情况,描述池火灾燃烧特征和模型。应用化学流体力学基本定律,建立了描述池火灾过程的基本控制方程组,并根据适当的条件选择辐射模型。建立物理模型,做出合理假设,确定初始和边界条件,对池火灾热辐射过程进行数值模拟,得出火焰周围入射热流密度分布图,计算出相邻两罐之间的最小安全距离,应用于工程实际中,给防火间距的制定提供理论依据,计算结果定性合理。     

我国海岸带环境灾害的基本特征与减灾对策

海岸带是自然灾害与社会经济发达区相互重叠的特殊环境地带.我国海岸带环境灾害的基本特征是种类繁多,频次高,破坏性强,与社会经济发展耦合,易形成复杂灾害链等.本文提出了基于可持续发展观点的、以工程建设与生态建设相结合、灾害区划与综合减灾相结合、基础研究与高科技应用相结合的减灾对策.     

“十三五”期间中国生态环境质量变化特征

以国家生态环境监测网监测结果为基础,总结归纳了"十三五"时期中国生态环境质量变化特征和主要环境问题。结果表明:"十三五"期间,全国生态环境质量全面好转,2020年环境空气优良天数比例比"十二五"末期上升5.8个百分点;地表水总体水质由轻度污染转为良好,Ⅰ～Ⅲ类水质断面比例上升17.4个百分点,劣V类断面比例下降9.0个百分点;酸雨污染程度减轻,海洋等环境要素质量稳中向好。但与此同时,后续改善难度加大,全国仍有40%地级及以上城市空气质量超标,4.8%的国土面积发生酸雨,辽河和海河流域仍是轻度污染,部分河流污染较重,地下水以IV类水质为主,农村饮用水源地和地表水水质均受到不同程度污染,近三分之一国土面积县域生态质量为较差和差。总体来看,当前生态环境质量与人民对美好生态环境的需求和美丽中国目标的实现还有较大差距。逐年上升的能源消费总量和不断增长的汽车保有量,增加了生态环境质量继续改善的压力。     

Characterisation of FOGs in grease trap waste from the processing of chickens in Thailand

Industrial firms that kill and process chickens generate wastewater that contains fat, oil, and grease (FOG). The FOGs are located in the fatty waste that is collected by floatation in grease traps. Chemical and physical characterisation of FOGs would provide useful information that would help in the development of methods designed to decrease the extent of pollution caused by disposal of the waste and to utilise commercially some of its lipid constituents. Employing these methods would enhance the profitability and competitive potential of these commercial organisations. Samples of grease trap waste from 14 firms in central Thailand have been examined. Due to the very different schemes of waste management employed by these firms, the physical appearance of their fatty wastes showed considerable variation. The chemical and physical properties of the FOGs present in these wastes showed considerable variation also. Large amounts of free fatty acids (10–70% as oleic acid) were detected in most of the 14 wastes and palmitic, cis-9-oleic, cis,cis-9,12-linoleic, stearic, and palmitoleic acids were the predominant species of free and esterified acids. Most of the FOGs were solid at temperatures below 40 °C. Many of them contained traces of heavy metals (Cu and Pb) and some contained traces of the pesticides dimethoate and cypermethrin. The content of these potentially hazardous substances would have to be considered very carefully before discarding the fatty wastes and during the development of methods designed to isolate their potentially profitable lipid constituents.     

Toward accurate and precise estimates of lion density

Reliable estimates of animal density are fundamental to understanding ecological processes and population dynamics. Furthermore, their accuracy is vital to conservation because wildlife authorities rely on estimates to make decisions. However, it is notoriously difficult to accurately estimate density for wide‐ranging carnivores that occur at low densities. In recent years, significant progress has been made in density estimation of Asian carnivores, but the methods have not been widely adapted to African carnivores, such as lions (Panthera leo). Although abundance indices for lions may produce poor inferences, they continue to be used to estimate density and inform management and policy. We used sighting data from a 3‐month survey and adapted a Bayesian spatially explicit capture‐recapture (SECR) model to estimate spatial lion density in the Maasai Mara National Reserve and surrounding conservancies in Kenya. Our unstructured spatial capture‐recapture sampling design incorporated search effort to explicitly estimate detection probability and density on a fine spatial scale, making our approach robust in the context of varying detection probabilities. Overall posterior mean lion density was estimated to be 17.08 (posterior SD 1.310) lions >1 year old/100 km², and the sex ratio was estimated at 2.2 females to 1 male. Our modeling framework and narrow posterior SD demonstrate that SECR methods can produce statistically rigorous and precise estimates of population parameters, and we argue that they should be favored over less reliable abundance indices. Furthermore, our approach is flexible enough to incorporate different data types, which enables robust population estimates over relatively short survey periods in a variety of systems. Trend analyses are essential to guide conservation decisions but are frequently based on surveys of differing reliability. We therefore call for a unified framework to assess lion numbers in key populations to improve management and policy decisions.     

Spatial Patterns of Breeding Success of Grizzly Bears Derived from Hierarchical Multistate Models

Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low‐elevation wetlands or mid‐elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy—herbaceous alpine ecotones—were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection. Patrones Espaciales del Éxito Reproductivo de Osos Pardos, Derivados de Modelos Jerárquicos Multi‐Estado