河流污染物通量估算方法筛选及误差分析

准确估算污染物通量对河流污染物总量控制和水质保护具有重要意义,但常规监测方法通常不能进行连续采样,数据相对比较稀缺,因此,选择恰当的采样间隔和通量估算方法,并对方法可能带来的误差进行估算,对提高通量估算的可靠性具有十分重要的意义.基于此,以江西赣江滁槎水质自动站2005—2007年3个水文年的水量和水质资料为基础,采用Monte Carlo方法模拟时间间隔分别为2、3、5、6、10、15和30 d的河流水质离散采样方案,并计算每种采样方案下的通量.同时,采用偏差(系统误差)和不精确度(离散程度)两个指标,比较了A、B、C、D、E 5种常规通量计算方法的误差分布,并建立了各算法误差随时间间隔变化的相关性曲线,以对河流污染物通量估算方法进行筛选.研究表明,滁槎断面COD_Mn采用瞬时值C_i与时段平均流量 Q_p乘积的方法计算年通量更准确;而NH₄⁺-N由于瞬时通量与流量相关性不显著,采用时段瞬时通量平均计算年通量更准确.     

毛细管GC/FID法测定空气中甲硫醇实验研究

研究了空气中甲硫醇（ＣＨ３ＳＨ）的采样,液氮冷冻浓缩,热解吸和毛细管柱ＧＣ／ＦＩＤ的色谱分离分析方法,在选定条件下,线性范围为０．２－２００μｇ检测下限约０．２μｇ,回收率为９２．６％,重复实验变异系数３．２％,可以满足空气中ＣＨ３ＳＨ的测定,应用于某城市污水厂及污水沟周围空气中甲硫醇的测定,并以ＣＨ３ＳＨ为代表计算了空气的恶臭度,其分布规律可以得到合理的解释,取得了较为满意的结果。     

Network inclusion probabilities and Horvitz-Thompson estimation for adaptive simple Latin square sampling

Consider a survey of a plant or animal species in which abundance or presence/absence will be recorded. Further assume that the presence of the plant or animal is rare and tends to cluster. A sampling design will be implemented to determine which units to sample within the study region. Adaptive cluster sampling designs Thompson (1990) are sampling designs that are implemented by first selecting a sample of units according to some conventional probability sampling design. Then, whenever a specified criterion is satisfied upon measuring the variable of interest, additional units are adaptively sampled in neighborhoods of those units satisfying the criterion. The success of these adaptive designs depends on the probabilities of finding the rare clustered events, called networks. This research uses combinatorial generating functions to calculate network inclusion probabilities associated with a simple Latin square sample. It will be shown that, in general, adaptive simple Latin square sampling when compared to adaptive simple random sampling will (i) yield higher network inclusion probabilities and (ii) provide Horvitz-Thompson estimators with smaller variability.     

A performance indicator for ranked set sampling using ranking error probability matrix

Ranked set sampling (RSS) is a sampling procedure that has been shown to provide more efficient procedures than simple random sampling, in particular the Mann-Whitney-Wilcoxon (MWW) statistic and the empirical distribution function (EDF). We briefly review the work of Bohn (1992) and Stokes and Sager (1988) on the effect of imperfect ranking on the RSS-based MWW test and on the RSS-based EDF, respectively. We propose a model for a ranking error probability matrix which we hope will become a useful tool for evaluating RSS-based statistical procedures     

Are rapid population estimates accurate? A field trial of two different assessment methods

Emergencies resulting in large-scale displacement often lead to populations resettling in areas where basic health services and sanitation are unavailable. To plan relief-related activities quickly, rapid population size estimates are needed. The currently recommended Quadrat method estimates total population by extrapolating the average population size living in square blocks of known area to the total site surface. An alternative approach, the T-Square, provides a population estimate based on analysis of the spatial distribution of housing units taken throughout a site. We field tested both methods and validated the results against a census in Esturro Bairro, Beira, Mozambique. Compared to the census (population: 9,479), the T-Square yielded a better population estimate (9,523) than the Quadrat method (7,681; 95% confidence interval: 6,160-9,201), but was more difficult for field survey teams to implement. Although applicable only to similar sites, several general conclusions can be drawn for emergency planning.     

Impact of environmental factors on the sampling rate of β-blockers and sulfonamides from water by a carbon nanotube-passive sampler

Passive techniques are a constantly evolving approach to the long-term monitoring of micropollutants, including pharmaceuticals, in the aquatic environment. This paper presents, for the first time, the calibration results of a new CNTs-PSDs (carbon nanotubes used as a sorbent in passive sampling devices) with an examination of the effect of donor phase salinity, water pH and the concentration of dissolved humic acids (DHAs), using both ultrapure and environmental waters. Sampling rates (R_s) were determined for the developed kinetic samplers. It has been observed that the impact of the examined environmental factors on the R_s values strictly depends on the type of the analytes. In the case of β-blockers, the only environmental parameter affecting their uptake rate was the salinity of water. A certain relationship was noted, namely the higher the salt concentration in water, the lower the R_s values of β-blockers. In the case of sulfonamides, water salinity, water pH 7–9 and DHAs concentration decreased the uptake rate of these compounds by CNTs-PSDs. The determined R_s values differed in particular when the values obtained from the experiments carried out using ultrapure water and environmental waters were compared. The general conclusion is that the calibration of novel CNTs-PSDs should be carried out under physicochemical conditions of the aquatic phase that are similar to the environmental matrix.     

Geographic sampling of urban soils for contaminant mapping: how many samples and from where

Properly sampling soils and mapping soil contamination in urban environments requires that impacts of spatial autocorrelation be taken into account. As spatial autocorrelation increases in an urban landscape, the amount of duplicate information contained in georeferenced data also increases, whether an entire population or some type of random sample drawn from that population is being analyzed, resulting in conventional power and sample size calculation formulae yielding incorrect sample size numbers vis-à-vis model-based inference. Griffith (in Annals, Association of American Geographers, 95, 740–760, 2005) exploits spatial statistical model specifications to formulate equations for estimating the necessary sample size needed to obtain some predetermined level of precision for an analysis of georeferenced data when implementing a tessellation stratified random sampling design, labeling this approach model-informed, since a model of latent spatial autocorrelation is required. This paper addresses issues of efficiency associated with these model-based results. It summarizes findings from a data collection exercise (soil samples collected from across Syracuse, NY), as well as from a set of resampling and from a set of simulation experiments following experimental design principles spelled out by Overton and Stehman (in Communications in Statistics: Theory and Methods, 22, 2641–2660). Guidelines are suggested concerning appropriate sample size (i.e., how many) and sampling network (i.e., where).

Parameter uncertainties in the modelling of vegetation dynamics—Effects on tree community structure and ecosystem functioning in European forest biomes

Dynamic vegetation models are useful tools for analysing terrestrial ecosystem processes and their interactions with climate through variations in carbon and water exchange. Long-term changes in structure and composition (vegetation dynamics) caused by altered competitive strength between plant functional types (PFTs) are attracting increasing attention as controls on ecosystem functioning and potential feedbacks to climate. Imperfect process knowledge and limited observational data restrict the possibility to parameterise these processes adequately and potentially contribute to uncertainty in model results. This study addresses uncertainty among parameters scaling vegetation dynamic processes in a process-based ecosystem model, LPJ-GUESS, designed for regional-scale studies, with the objective to assess the extent to which this uncertainty propagates to additional uncertainty in the tree community structure (in terms of the tree functional types present and their relative abundance) and thus to ecosystem functioning (carbon storage and fluxes). The results clearly indicate that the uncertainties in parameterisation can lead to a shift in competitive balance, most strikingly among deciduous tree PFTs, with dominance of either shade-tolerant or shade-intolerant PFTs being possible, depending on the choice of plausible parameter values. Despite this uncertainty, our results indicate that the resulting effect on ecosystem functioning is low. Since the vegetation dynamics in LPJ-GUESS are representative for the more complex Earth system models now being applied within ecosystem and climate research, we assume that our findings will be of general relevance. We suggest that, in terms of carbon storage and fluxes, the heavier parameterisation requirement of the processes involved does not widen the overall uncertainty in model predictions.     

Current Trends in Plant and Animal Population Monitoring

Abstract:  Animal and plant population monitoring programs are critical for identifying species at risk, evaluating the effects of management or harvest, and tracking invasive and pest species. Nevertheless, monitoring activities are highly decentralized, which makes it difficult for researchers or conservation planners to get a good general picture of what real-world monitoring programs actually entail. We used a Web-based survey to collect information on population monitoring programs. The survey focused on basic questions about each program, including motivations for monitoring, types of data being collected, spatiotemporal design of the program, and reasons for choosing that design. We received responses from 311 people involved in monitoring of various species and used these responses to summarize ongoing monitoring efforts. We also used responses to determine whether monitoring strategies have changed over time and whether they differed among monitoring agencies. Most commonly, monitoring entailed collection of count data at multiple sites with the primary goal of detecting trends. But we also found that goals and strategies for monitoring appeared to be diversifying, that area-occupied and presence–absence approaches appeared to be gaining in popularity, and that several other promising approaches (monitoring to reduce parameter uncertainty, risk-based monitoring, and directly linking monitoring data to management decisions) have yet to become widely established. We suggest that improved communication between researchers studying monitoring designs and those who are charged with putting these designs into practice could further improve monitoring programs and better match sampling designs to the objectives of monitoring programs.     

Measurement of Atmospheric Deposition Under Forest Canopies: Some Recommendations for Equipment and Sampling Design

Quantification of the forest water flux provides valuable information for the understanding of forest ecosystem functioning. As such, throughfall (and stemflow to a lesser extent) has been frequently measured. Although throughfall collection may seem relatively simple, the requirements to obtain reliable estimates are often underestimated. This review addresses the criteria to take into account when working out the sampling procedure, from the selection of equipment to implementation in the field. Sound sampling of the forest water flux is difficult due to its high spatial and temporal variation. The high costs entailed by the ideal sampling design often prohibit its implementation. Different procedures are available, some of which are compromises between the aim of the study (monitoring or experimental study, short or long term objectives, absolute or relative estimates, quality of the assessment to be achieved) and the available means.