Herbicide Transport in Goodwater Creek ExperimentalWatershed: I. Long‐Term Research on Atrazine1

Lerch, R.N., E.J. Sadler, K.A. Sudduth, C. Baffaut, and N.R. Kitchen, 2010. Herbicide Transport in Goodwater Creek Experimental Watershed: I. Long‐Term Research on Atrazine. Journal of the American Water Resources Association (JAWRA) 1‐15. DOI: 10.1111/j.1752‐1688.2010.00503.x Abstract: Atrazine continues to be the herbicide of greatest concern relative to contamination of surface waters in the United States (U.S.). The objectives of this study were to analyze trends in atrazine concentration and load in Goodwater Creek Experimental Watershed (GCEW) from 1992 to 2006, and to conduct a retrospective assessment of the potential aquatic ecosystem impacts caused by atrazine contamination. Located within the Central Claypan Region of northeastern Missouri, GCEW encompasses 72.5 km² of predominantly agricultural land uses, with an average of 21% of the watershed in corn and sorghum. Flow‐weighted runoff and weekly base‐flow grab samples were collected at the outlet to GCEW and analyzed for atrazine. Cumulative frequency diagrams and linear regression analyses generally showed no significant time trends for atrazine concentration or load. Relative annual loads varied from 0.56 to 14% of the applied atrazine, with a median of 5.9%. A cumulative vulnerability index, which takes into account the interactions between herbicide application, surface runoff events, and atrazine dissipation kinetics, explained 63% of the variation in annual atrazine loads. Based on criteria established by the U.S. Environmental Protection Agency, atrazine reached concentrations considered harmful to aquatic ecosystems in 10 of 15 years. Because of its vulnerability, atrazine registrants will be required to work with farmers in GCEW to implement practices that reduce atrazine transport.     

Most Influential Parameters for the Bed-Load Sediment Flux Equations Used in Alluvial Rivers1

Khorram, Saeed and Mustafa Ergil, 2010. Most Influential Parameters for the Bed-Load Sediment Flux Equations Used in Alluvial Rivers. Journal of the American Water Resources Association (JAWRA): 46(6):1065–1090. DOI: 10.1111/j.1752-1688.2010.00468.x Abstract: Problems of bed-load sediment transport equations in alluvial rivers are addressed in this study where user-friendly parameters were developed. To determine the influences of 300 parameters on the final result, 52 selected bed-load equations for noncohesive particles (sand and gravel separately) were gathered and individually investigated. The influences of discrepancies among the computed and measured datasets were obtained by sensitivity analysis through multilinear regression method. The most influential parameters for the bed-load sediment flux equations used to describe sand particles in alluvial rivers are: the gravitational power due to Shields’ parameter with an energy slope, the universal stream power due to critical Shields’ parameter with an energy slope, the Shields’ parameter ratio, the critical unit stream power, and the Shields’ parameter with energy slope. For gravel particles, the most influential parameters are: the universal stream power due to critical Shields’ parameter with an energy slope, the Shields’ parameter ratio, the gravitational power due to Shields’ parameter with an energy slope, the Shields’ parameter with an energy slope, and the Froude number of the channel. It is expected that researchers working in this field will be able to use these predicted parameters to generate new bed-load sediment flux equations that give results that more closely agree with the actual values measured in alluvial rivers.     

Practitioner insights as a means of setting a context for conservation

A key obstacle to conservation success is the tendency of conservation professionals to tackle each challenge individually rather than collectively and in context. We sought to prioritize barriers to conservation previously described in the conservation literature. We undertook an online survey of 154 practitioners from over 70 countries to ascertain the most important barriers to conservation they faced. We used statistical analyses to identify the key impediments to conservation success and to examine whether these were affected by organizational attributes. Twenty-one barriers were identified. The importance ascribed to those was influenced by continent of operation and organization size, but not by organization age or autonomy (from larger parent organizations). We found the most important barriers to consider when undertaking conservation action were wider issues (e.g., population growth, consumerism, favoring development, and industrial-scale activity), operating environment (e.g., lack of political will, ineffective law enforcement, weak governments, corruption, safety and security), community attributes (e.g., dynamics, conflicts, and education levels), and the way conservation is undertaken (overconfidence, lack of funding, and externally set agendas). However, we advise against applying a one-size-fits-all approach. We propose that conservationists account for the complex socioecological systems they operate in if they are to achieve success.     

Predicting Freshwater Critical Loads from Catchment Characteristics using National Datasets

Maps of freshwater critical loads are used toguide emission strategies for sulphur and nitrogen bothnationally and internationally. Water chemistry data arerequired to calculate critical loads and the production ofnational maps therefore relies on the existence of extensivechemistry datasets. However, the data required to calculatecritical loads are not readily available for all sites. Thisarticle explores how empirical statistical models mightpotentially be used to predict critical loads using nationallyavailable datasets representing a range of catchmentcharacteristics. Initially a global regression model forexplaining freshwater critical load variation across a broadspectrum of catchment types (from lowland agricultural tomountain lakes) throughout mainland Britain is described. Whenattention is focused on more specific catchment types (i.e.upland and non-arable) it is shown that the global model hasless explanatory power. A regionalisation of Great Britain(based on 100 km grid squares) shows that the global modelcannot necessarily be applied successfully within a narrowerregional context. Separate analyses were undertaken on each ofthe regional subsets using backward selection regression. Thevariables emerging as significant predictors variedsubstantially across the regions, as did the explanatory powerof the models. This was also the case when the analysis wasconfined to upland and non-arable catchments. This approachcould be developed so that critical loads assessments can bemade for populations of standing waters rather than simplythose for which water chemistry is available.     

预测铜生物毒性的偏最小二乘回归模型

以黄河水体中的铜作为典型污染物,单细胞淡水藻斜生栅藻作为实验生物,应用偏最小二乘回归方法建立了铜的生物毒性预测模型.结果表明,黄河水体中阳离子(K⁺、Na⁺、Mg²⁺)浓度与铜的半抑制浓度(EC₅₀)成负相关,而水体中的DOC、碱度和阴离子浓度与铜的EC₅₀成正相关;影响铜的生物毒性的主要因子是DOC、pH值、碱度和温度.     

北京市交通路口大气颗粒物污染特征研究(Ⅰ)——大气颗粒物污染特征及其影响因素

研究了北京市典型交通路口大气颗粒物的污染特征及影响因素.于2000年6月在北京市主要交通路口之一的崇文门路口,采集了大气中不同粒径的颗粒物样品,并进行了颗粒物质量浓度、离子浓度和元素质量浓度的分析,同时进行了样品采集期间机动车流量调查.研究结果表明,6月5日大气颗粒物污染严重.6月6日0∶10-3∶50,ρ(PM_2.5)达到最高值.崇文门路口的大气颗粒物污染水平与机动车流量之间无明显的对应关系.交通来源颗粒物是影响交通路口污染水平的首要因素,颗粒物中Al,Ca,Fe和Mg等元素主要来自机动车行驶载带的道路可扬尘,而Cu,Pb等元素主要来自机动车的排放.还指出了今后进行该类研究的主要方向和技术思路.      

绿色大学建设中的生态足迹

生态足迹分析法是评价绿色大学建设水平最好的指标之一.生态足迹分析法有综合法和成分法2种.本文简要介绍了生态足迹成分法的基本原理和算法,并以东北大学为例将该方法应用大学校园的研究.结果表明,东北大学2003年的生态足迹为24787hm2,即需要约25000hm²的生态生产性土地支持学校的各类消费并吸纳所产生的废弃物.其生态效率(即单位生态生产性土地可支持的学生数)为0.94人·hm^-2.生态足迹中,能源消费的足迹最大,占总足迹的2/3多;食物消费和固态垃圾次之,分别占总足迹的21.9%和5.7%.     

废旧电路板的组成与解离特性研究

废旧印刷电路板是电子废弃物的重要组成部分,含有大量金属、玻璃纤维增强树脂、塑料等有回收利用价值的组分,但处理不当会产生严重污染。在分析中,对细碎到粒度为5mm以下的废旧电路板物料中有价资源的赋存状态和解离特性进行了系统的研究,发现金属与非金属的基本解离粒度为1 2mm,解离度为55. 51%;塑料与其他金属(除铜、铁外)是0. 5mm以上废旧电路板物料中的主要组分,树脂与铜是0 .5mm以下物料中的主要组分;物料中平均金属含量为23. 80%,平均铜含量为5. 78%。     

Do natural resources and economic components exhibit differential quantile environmental effects?

In view of the resource curse assumption, the environmental aspects of resource utilization are arguably posing more dangers to human existence. In the Middle East and North Africa (MENA) region, the region that holds more than 60% and 50% of the world's oil and gas reserves respectively, the need to examine the contribution of natural resources to environmental quality among other factors cannot be overemphasized. By leveraging on the novelty of observing the differential impact of natural resources and other economic components such as income and primary energy utilizations across the quantiles of carbon emission, this study implements the quantile regression approach alongside other relevant techniques to analyze data between 1990 and 2018 for selected countries in the MENA region including Saudi Arabia, Iran, Kuwait, Qatar, Algeria, Morocco, Oman, Egypt, and the United Arab Emirates (UAE). The result posits that natural resource utilization generally hampers the environment across the quantiles. However, this negative effect decreases until the 50th quantile before starting to rise again toward the upper quantiles. Additionally, primary energy utilization and globalization respectively worsen and improve environmental quantile, especially toward the upper quantiles while income affirms the inverted U-shaped hypothesis across the entire quantiles. Moreover, there is a statistically significant one-way directional causality from natural resources, economic expansion, primary energy use, and globalization to carbon emission levels. Hence, the study offers environmentally friendly resource utilization policies to the MENA economies and other resource-rich states by extension.     

Quantifying Habitat Requirements of Tree-Living Species in Fragmented Boreal Forests with Bayesian Methods

Abstract:  Quantitative conservation objectives require detailed consideration of the habitat requirements of target species. Tree-living bryophytes, lichens, and fungi are a critical and declining biodiversity component of boreal forests. To understand their requirements, Bayesian methods were used to analyze the relationships between the occurrence of individual species and habitat factors at the tree and the stand scale in a naturally fragmented boreal forest landscape. The importance of unexplained between-stand variation in occurrence of species was estimated, and the ability of derived models to predict species' occurrence was tested. The occurrence of species was affected by quality of individual trees. Furthermore, the relationships between occurrence of species at the tree level and size and shape of stands indicated edge effects, implying that some species were restricted to interior habitats of large, regular stands. Yet for the habitat factors studied, requirements of many species appeared similar. Species occurrence also varied between stands; most of the seemingly suitable trees in some stands were unoccupied. The models captured most variation in species occurrence at tree level. They also successfully accounted for between-stand variation in species occurrence, thus providing realistic simulations of stand-level occupancy of species. Important unexplained between-stand variation in species occurrence warns against a simplified view that only local habitat factors influence species' occurrence. Apparently, similar stands will host populations of different sizes due to historical, spatial, and stochastic factors. Thus, habitat suitability cannot be assessed simply by population sizes, and stands lacking a species may still provide suitable habitat and merit protection.