Uncertainty characterization for emergy values

While statistical estimation of uncertainty has not typically accompanied published emergy values, as with any other quantitative model, uncertainty is embedded in these values, and lack of uncertainty characterization makes their accuracy not only opaque, it also prevents the use of emergy values in statistical tests of hypotheses. This paper first attempts to describe sources of uncertainty in unit emergy values (UEVs) and presents a framework for estimating this uncertainty with analytical and stochastic models, with model choices dependent upon on how the UEV is calculated and what kind of uncertainties are quantified. The analytical model can incorporate a broader spectrum of uncertainty types than the stochastic model, including model and scenario uncertainty, which may be significant in emergy models, but is only appropriate for the most basic of emergy calculations. Although less comprehensive in its incorporation of uncertainty, the proposed stochastic method is suitable for all types of UEVs. The distributions of unit emergy values approximate the lognormal distribution with variations depending on the types of uncertainty quantified as well as the way the UEVs are calculated. While both methods of estimating uncertainty in UEVs have their limitations in their presented stage of development, this paper provides methods for incorporating uncertainty into emergy, and demonstrates how this can be depicted and propagated so that it can be used in future emergy analyses and permit emergy to be more readily incorporated into other methods of environmental assessment, such as LCA.     

Uncertainty Assessment for Management of Soil Contaminants with Sparse Data

In order for soil resources to be sustainably managed, it is necessary to have reliable, valid data on the spatial distribution of their environmental impact. However, in practice, one often has to cope with spatial interpolation achieved from few data that show a skewed distribution and uncertain information about soil contamination. We present a case study with 76 soil samples taken from a site of 15 square km in order to assess the usability of information gleaned from sparse data. The soil was contaminated with cadmium predominantly as a result of airborne emissions from a metal smelter. The spatial interpolation applies lognormal anisotropic kriging and conditional simulation for log-transformed data. The uncertainty of cadmium concentration acquired through data sampling, sample preparation, analytical measurement, and interpolation is factor 2 within 68.3 % confidence. Uncertainty predominantly results from the spatial interpolation necessitated by low sampling density and spatial heterogeneity. The interpolation data are shown in maps presenting likelihoods of exceeding threshold values as a result of a lognormal probability distribution. Although the results are not deterministic, this procedure yields a quantified and transparent estimation of the contamination, which can be used to delineate areas for soil improvement, remediation, or restricted area use, based on the decision-makers probability safety requirement.     

Evaluation of the Impact of Long-range Transport and Aerosol Concentration Temporal Variations at the Eastern Coast of the Baltic Sea

Ambient particles vary greatly in their ability to affect visibility, climate and human health. The fine fraction of aerosol is responsible for greater and wider effects on human health; thus, investigation of this fraction is very important. Continuous measurements of PM2.5 (particulate matter below 2.5 μm in size) concentrations at the Preila monitoring station started in 2003. During a period of 2 years, the episodes of high daily and semi-hourly concentrations of PM2.5 were measured. These episodes did not depend on the season or time of day. The substantial role of long-range transport of pollutants to these increases in concentration was shown using chemical and statistical analysis. It was found that most of the severe episodes occurred when air masses came from a specific site besides it was established that air masses of different origin were characterized by different mixing layer depth. Lower mixing depth was observed in air masses characterized by higher observed concentrations at the measuring site and vice versa. PM2.5 concentrations showed diurnal and seasonal variations whose pattern reflected the regional origin of the aerosol. The regional pollution level was evaluated by the statistical analysis of PM2.5 concentrations. The background annual average of PM2.5 mass concentration for the eastern coast of the Baltic Sea was 15.1 ± 0.8 μg m⁻³.     

Confidence intervals for the mean of the delta-lognormal distribution

Data that are skewed and contain a relatively high proportion of zeros can often be modelled using a delta-lognormal distribution. We consider three methods of calculating a 95% confidence interval for the mean of this distribution, and use simulation to compare the methods, across a range of realistic scenarios. The best method, in terms of coverage, is that based on the profile-likelihood. This gives error rates that are within 1% (lower limit) or 3% (upper limit) of the nominal level, unless the sample size is small and the level of skewness is moderate to high. Our results will also apply to the delta-lognormal linear model, when we wish to calculate a confidence interval for the expected value of the response variable, given the value of one or more explanatory variables. We illustrate the three methods using data on red cod densities, taken from a fisheries trawl survey in New Zealand.

Some characteristics of the distribution of heavy metals in urban topsoil of Xuzhou,China

An assessment is presented of distribution characteristics of heavy metals in the urban topsoil from the city of Xuzhou. The concentrations of Ag, Al, As, Au, Ba, Be, Bi, Cd, Co, Cr, Cu, Fe, Ga, Hg, Li, Mn, Mo, Ni, Pb, Pd, Pt, Sb, Sc, Se, Sn, V and Zn have been determined from 21 soil samples. Examination of lognormal distribution plots indicates that the diagrams of Al, Be, Fe, Ga, Li, and V are almost linear suggesting that these metals are almost unaffected by anthropogenic activities while the plots for As, Cd, Cu, Pb, Pd, Pt, Se, Zn and others are not linear probably due to anthropogenic activities from which these metals are delivered to the soils. Al is used for mineralogical normalization of these data. An evaluation of background values for topsoil is also carried out by means of lognormal distribution plots. The results show our background values obtained from the lognormal distribution plots are comparable to those values of uncontaminated soils of Xuzhou obtained by previous work except for Cd and Hg. At present, no explanation for the exceptions Cd and Hg can be given.     

基于非共性因子和定量化的滑坡危险性分级评价

滑坡致灾因子的选取和定量化研究是滑坡危险性分级评价的关键技术。滑坡危险性分级评价因子包括共性因子和非共性因子,共性因子在评价中定量化指标具有一致性,不能反映在滑坡个体评价体系中的差异。根据万州滑坡监测预警资料,选取了地层倾角、坡度、高差和滑坡长度与宽度的比值4个非共性因子作为滑坡危险性分级评价因子,运用Lognormal分布模型,建立了评价因子的定量化评价指标。根据建立的5级评价指标体系对万州滑坡资料进行了验证,中危险性以上的滑坡占滑坡总数的95.8%,其中高危险性和极高危险性占74.1%。研究结果表明,该研究方法的评价因子可靠性好,易于测量和获取,可应用于区域滑坡群策群防灾害点的危险性分级评价和滑坡危险性区划。     

Disentangling the effects of heterogeneity, stochastic dynamics and sampling in a community of aquatic insects

A dynamic and heterogeneous species abundance model generating the lognormal species abundance distribution is fitted to time series of species data from an assemblage of stoneflies and mayflies (Plecoptera and Ephemeroptera) of an aquatic insect community collected over a period of 15 years. In each year except one, we analyze 5 parallel samples taken at the same time of the season giving information about the over-dispersion in the sampling relative to the Poisson distribution. Results are derived from a correlation analysis, where the correlation in the bivariate normal distribution of log abundance is used as measurement of similarity between communities. The analysis enables decomposition of the variance of the lognormal species abundance distribution into three components due to heterogeneity among species, stochastic dynamics driven by environmental noise, and over-dispersion in sampling, accounting for 62.9, 30.6 and 6.5% of the total variance, respectively. Corrected for sampling the heterogeneity and stochastic components accordingly account for 67.3 and 32.7% of the among species variance in log abundance. By using this method, it is possible to disentangle the effect of heterogeneity and stochastic dynamics by quantifying these components and correctly remove sampling effects on the observed species abundance distribution.     

Establishment of permit limits for mill effluent

Various governmental regulatory agencies are responsible for establishing regulations on the discharge of chlorinated organic compounds from pulp and paper mills. The procedures for setting permit limits are the basic topic of this article. Different methods of determining permit limits are set forth and discussed in a number of references, including documents of the Environmental Protection Agency. This paper discusses in detail the application of one particular methodology—the lognormal model approach. In this paper we utilize a real data set and include the necessary calculations required to set up permit limits. The basic tenets of the permit process are such that if limits are set too low, operators who are in full regulatory compliance will still be frequently cited, and if limits are set too high, operators who are not in compliance will seldom if ever be cited. Thus there is a great need for these effluent limits to be determined with great care, both to protect the environment receiving the wastewater and the industry producing the wastewater.     

Mapping Soil Gas Radon Concentration: A Comparative Study of Geostatistical Methods

Understanding soil gas radon spatial variations can allow the constructor of a new house to prevent radon gas flowing from the ground. Indoor radon concentration distribution depends on many parameters and it is difficult to use its spatial variation to assess radon potential. Many scientists use to measure outdoor soil gas radon concentrations to assess the radon potential. Geostatistical methods provide us a valuable tool to study spatial structure of radon concentration and mapping. To explore the structure of soil gas radon concentration within an area in south Italy and choice a kriging algorithm, we compared the prediction performances of four different kriging algorithms: ordinary kriging, lognormal kriging, ordinary multi-Gaussian kriging, and ordinary indicator cokriging. Their results were compared using an independent validation data set. The comparison of predictions was based on three measures of accuracy: (1) the mean absolute error, (2) the mean-squared error of prediction; (3) the mean relative error, and a measure of effectiveness: the goodness-of-prediction estimate. The results obtained in this case study showed that the multi-Gaussian kriging was the most accurate approach among those considered. Comparing radon anomalies with lithology and fault locations, no evidence of a strict correlation between type of outcropping terrain and radon anomalies was found, except in the western sector where there were granitic and gneissic terrain. Moreover, there was a clear correlation between radon anomalies and fault systems.