How has a shortage of census and geological information impeded the regularization of artisanal and small‐scale mining?

There is growing consensus that a combination of laissez‐faire policies, ad hoc regulation and debilitating support services has perpetuated socio‐economic and environmental deterioration in the artisanal and small‐scale mining (ASM) industry. However, a lack of anthropological and geological information on ASM prevents many governments both from improving the policy environment of the industry, and from providing more robust extension services to its operators. This article aims to examine more precisely how a deficiency of baseline census and geological data has inhibited industry formalization and undermined many of the measures implemented to address pressing problems at ASM sites. Specifically, it is argued that insufficient knowledge of artisanal mining populations — including their demographic structure — and of areas suitable for ASM activities affects the ability of a government to regularize, as well as to improve, the organization of this largely informal sector of industry. Case studies of Ghana and Zimbabwe are used to illustrate how the undertaking of low‐budget projects in areas of geological prospecting and population analysis could improve the efficiency of ASM assistance.     

Application of non-market valuation to the Florida Keys marine reserve management

The quality of the coral reefs in the Florida Keys is essential to sustain nature-based tourism in the Keys. The recently established marine reserves (MR) are expected to improve the reef environment, particularly coral and fish abundance and diversity. In this paper, a combined model of travel cost and contingent behavior was estimated in order to measure the non-market recreational benefits of reef quality improvements. The results indicated that an average visitor would undertake 43-80% more number of trips to the Florida Keys and experience a 69% increase in the use values per trip, as a result of the MR-induced reef quality improvements. The above non-market value estimates were further applied to evaluating alternative management proposals for funding the MR program. It was found that the annual management costs of the MR program would constitute an insignificant portion--only around one to 2%--of the annual recreational benefits that the MR would generate. The results provide a strong economic justification for designing user-based funding mechanisms in order to make the MR program self-sustaining in the future.     

The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LMNRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.     

ESTIMATING THE MEAN AND VARIANCE OF CENSORED PHOSPHORUS CONCENTRATIONS IN FLORIDA RAINFALL1

ABSTRACT: The total phosphorous (TP) concentrations in the South Florida rainfall have been recorded in weekly intervals with a detection limit (D_L) of 3.5 μg/L. As a large amount of the data is reported as below the D_L, appropriate statistical methods are needed for data analysis. Thus, an attempt was made to identify an appropriate method to estimate the mean and variance of the data. In particular, a method to separate the statistics for the below D_L portion from the estimated population statistics is proposed. The estimated statistics of the censored data are compared with the statistics of the uncensored data available from the recent years’ laboratory records. It was found that the one-step restricted maximum likelihood method is the most accurate for the wet TP data, and that the proposed method to combine the estimated statistics for TP < D_L portion and the sample statistics for TP ≥ D_L portion improves estimates compared to the conventional maximum likelihood estimates.     

RECYCLING INPUT DATA DURING ANALYTIC ELEMENT MODELING NEAR INDIANAPOLIS,INDIANA1

ABSTRACT: Overlapping and adjacent ground water investigations are common in areas where aquifers are threatened by industrial development. In the Indianapolis area in Marion County, Indiana, a patchwork of ground water flow models have been used during the past 20 years to evaluate ground water resources and to determine the effects of local contamination. In every case these ground water models were constructed from scratch. Site specific finite difference grids or finite element meshes inhibit the direct reuse of input data when the area of interest shifts. Because the aquifer is not discretized into a grid or mesh with analytic element models, there are unique opportunities for direct reuse of model input data. In two applications of this principle we illustrate how the newly emerging analytic element method allows a fairly straightforward reuse of model input data from previous models in the same general area. In analytic element models of Central Indiana, streams and their tributaries are represented in different resolutions. Input data items of several modeling studies are stored and cataloged on disk in such a manner that they can be selectively retrieved by a data management program PREPRO. In this manner, a new ground water model can be set up quickly with input data which have been previously defined and tested during model calibration.     

危险评价中的数据环境

本文通过分析危险评价的数据来源、数据的准确程度和危险评价模型，论述了数据环境与危险评价的关系，指出了数据的不确切必然会影响到危险评价质量，但应用数据进行危险评价是完全必要的。关键是如何提高数据的精确程度和模型的真实程度，使评价结果更趋准确、可靠。     

ESTIMATING MEAN AND VARIANCE FOR ENVIRONMENTAL SAMPLES WITH BELOW DETECTION LIMIT OBSERVATIONS1

ABSTRACT: Left-censoring of data sets complicates subsequent statistical analyses. Generally, substitution or deletion methods provide poor estimates of the mean and variance of censored samples. These substitution and deletion methods include the use of values above the detection limit (DL) only, or substitution of 0, DL/2 or the DL for the below DL values during the calculation of mean and variance. A variety of statistical methods provides better estimators for different types of distributions and censoring. Maximum likelihood and order statistics methods compare favorably to the substitution or deletion methods. Selected statistical methods applicable to left-censoring of environmental data sets are reviewed with the purpose of demonstrating the use of these statistical methods for coping with Type I (and Type II) left-censoring of normally and log-normally distributed environmental data sets. A PC program (UNCENSOR) is presented that implements these statistical methods. Problems associated with data sets with multiple DLs are discussed relative to censoring methods for life and fatigue tests as recently applied to water quality data sets.     

FOURIER INFERENCE: SOME METHODS FOR THE ANALYSIS OF ARRAY AND NONGAUSSIAN SERIES DATA1

Fourier inference is a collection of analytic techniques and philosophic attitudes, for the analysis of data, wherein essential use is made of empirical Fourier transforms. This paper sets down some basic results concerning the finite Fourier transforms of stationary process data and then, to illustrate the approach, uses those results to develop procedures for: 1) estimating cloud and storm motion, 2) passive sonar and 3) fitting finite parameter models to nonGaussian time series via bispectral fitting. This last procedure is illustrated by an analysis of a stretch of Mississippi River runoff data. Examples 1), 2) refer to data having the form Y(x_j, y_j, t) for j = 1, …, J and t = 0, …, T-l say, and view that data as part of a realization of a spatial-temporal process. Such data has become common in geophysics generally and in hydrology particularly. The goal of this paper is to present some new statistical procedures pertinent to problems in the water sciences, equally it is to illustrate the genesis of those procedures and how their properties may be approximated.     

PARAMETRIC AND NONPARAMETRIC TESTS FOR DEPENDENT DATA1

ABSTRACT: Simulation and analytical results show that ignoring serial dependence can have serious effects on the performance of the t, sign, and Wilcoxen tests. In particular, the true significance levels of these tests are altered significantly from the intended nominal levels. Modifications for these tests are given and shown to have the correct significance levels. Furthermore, an estimate of serial correlation is suggested for binary data and evaluated by simulation. An application to the toxic contaminants data from the Niagara River concludes the paper.     

Estimation of Emissions of γ-Hexachlorcyclohexane from the Great Lakes–St. Lawrence Ecosystem Using a Coupled Efficient Kalman Filter–Atmospheric Transport–Soil Model

An efficient linear Kalman filter has been combined with a coupled atmospheric transport and soil–air exchange model to determine organochlorine pesticides emissions on the regional scale. In this study, results of -HCH emissions from the Great Lakes–St. Lawrence ecosystem, estimated from the coupled model, are presented and discussed. A source receptor technique is used to identify a priori the locations of emission sources of -HCH, the emissions are then updated through a Kalman filtering procedure which minimizes the weighted difference between the predicted mixing ratios from the coupled model and the measured concentrations over the Great Lakes–St. Lawrence river region. Two experiments using the inverse algorithm are carried out. In the first experiment, the coupled atmospheric transport and soil–air exchange model is implemented to predict -HCH air and soil concentrations. Emissions are then updated every 12 days using the updated soil concentrations and emission factors. However, the updated emissions are not input into the coupled atmospheric transport and soil–air exchange model. On the other hand, in the second experiment the updated emissions are fed back to the coupled model, so that the model is reinitialized in each 12 days. The results from the inverse technique for the year 1995 have been compared with grided -HCH emission inventory in Canada, generated by emission factors. It is shown that the estimated emissions of -HCH are consistent with the measured emissions. It is found that the St. Lawrence valley has larger emissions of -HCH than the Great Lakes region, indicating an opposite distribution to the emission usage inventory, but in agreement with the measured -HCH concentration.