Associations of trace elements in aerosol at the south Adriatic coast

We applied statistical methods to the data of a 5-year sampling period to characterize the features of trace elements in airborne particulate matter at the South Adriatic Coast. The analysis of surface soil samples was also performed. The data were processed by cluster analysis, enrichment factor determination and receptor rose statistics. It is shown that the principal element association consists of Fe, Mn and Ti originating from surface material resuspension. Hg and Se were shown to originate from more complex natural and anthropogenic processes. A strong emission source of Cd and Se was identified in the SSE direction.Selected article from the Regional Symposium on Chemistry and Environment Krusevac, Serbia, June 2003, organized by Dr. Branimir Jovancicevic.     

Using the bootstrap with two-phase adaptive stratified samples from multiple populations at multiple locations

Recently the two-phase adaptive stratified sampling design proposed by Francis (1984) has been extended by Manly et al. (2002) for situations where several biological populations are sampled simultaneously, and where this is done at several different geographical locations in order to estimate population totals or means. The method uses the results from a first phase sample to decide how best to allocate a second phase sample to locations and strata, in order to maximise a criterion (based on estimated coefficients of variation) that measures the accuracy of estimation for population totals, for all variables at all locations. One potential problem with this method is bias in the estimators of the population totals and means. In this paper bootstrapping is considered as a means of overcoming these biases. It is shown using model populations of Pacific walrus and shellfish, based on real data, that bootstrapping is a useful tool for removing about half of the bias. This is also confirmed from some simulations using artificial data.     

Variance estimation in the EMAP strategy for sampling discrete ecological resources

The United States Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP) is designed to describe status, trends and spatial pattern of indicators of condition of the nation's ecological resources. The proposed sampling design for EMAP is based on a triangular systematic grid and employs both variable probability and double sampling. The Horvitz-Thompson estimator provides the foundation of the design-based estimation strategy used in EMAP. However, special features of EMAP designed to accommodate the complexity of sampling environmental resources on a national scale require modifications of standard variance estimation procedures as well as development of new techniques. An overview of variance estimation methods proposed for application to EMAP's sampling strategy for discrete resources is presented.     

Estimating percentiles from composite environmental samples when all observations are nondetectable

Many environmental sampling problems involve some specified regulatory or contractual limit (RL). Often the interest is in estimating the percentile of the underlying contaminant concentration distribution corresponding to RL. The focus of this paper is on obtaining a point estimate and a lower confidence limit for that percentile when all observations are nondetectable, with the ith observation known to be less than some detection limit DLI, where DLi RL. Since composite samples are being considered, it is not unreasonable to assume an underlying normal distribution.     

Desirable design characteristics for long-term monitoring of ecological variables

Long-term environmental monitoring places a set of demands on a sampling strategy not present in a survey designed for a single time period. The inevitability that a sample will become out of date must be a dominant consideration in planning a long-term monitoring programme. The sampling strategy must be able to accommodate periodic frame update and sample restructuring in order to address changes in the composition of the universe and changes in the perception of issues leading to new questions and concerns. The sampling strategy must be capable of adapting to such changes while maintaining its identification as a probability sample and its capacity to detect trends that span the update occasions. These issues are examined with respect to sub-population estimation, post-stratification via conditioning, and sample enlargement and reduction. Design features that involve complex sample structure create potentially serious difficulties, whereas an equal probability design permits greater adaptability and flexibility. Structure should be employed sparingly and in awareness of its undesirable effects.     

Resampling from stochastic simulations

To model the uncertainty of an estimate of a global property, the estimation process is repeated on multiple simulated fields, with the same sampling strategy and estimation algorithm. As opposed to conventional bootstrap, this resampling scheme allows for spatially correlated data and the common situation of preferential and biased sampling. The practice of this technique is developed on a large data set where the reference sampling distributions are available. Comparison of the resampled distributions to that reference shows the probability intervals obtained by resampling to be reasonably accurate and conservative, provided the original and actual sample has been corrected for the major biases induced by preferential sampling.Andre G. Journel is a Professor of Petroleum Engineering at Stanford University with a joint appointment in the Department of Geological and Environmental Sciences. He is, also, Director of the Stanford Center for Reservoir Forecasting. Professor Journel has pioneered applications of geostatistical techniques in the mining/petroleum industry and extended his expertise to environmental applications and repository site characterization. Most notably, he developed the concept of non-parametric geostatistics and stochastic imaging with application to modeling uncertainty in reservoir/site characterization. Although the research described in this article has been supported by the United States Environmental Protection Agency under Cooperative Agreement CR819407, it has not been subjected to Agency review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

Copper distribution in chemical soil fractions and relationships with maize crop yield

Agricultural practices can lead to copper accumulation in soils and at high concentration it can become toxic for plants. One common toxic effect of copper on plants is a decrease of crop yield. Here, we studied 1) the crop yield of maize grown on plots of a soil intentionally enriched with copper sulphate and 2) the possible relationship between the copper concentration in chemical soil fractions and the maize crop yield. Anthropogenic copper is mainly bound to manganese oxides, to iron oxides and to the organic matter. Maize (Zea maize L.) was grown on outdoor experimental plots. The crop yield was evaluated for three development stages: the 6–10 leaf stage, the female flowering stage and the maturity stage, 2, 4 and 6 years after the soil copper enrichment. Strong crop yield reductions, proving a toxic effect of copper on maize growth were noted 2 years after the copper input at the maturity stage and 4 years after the copper input at the 6–10 leaf stage. Variations in maize crop yield are described with linear multiple regression equations including the variable copper content in soil, and other variables when needed such as soil pH, soil organic carbon level and the climatic variables, the precipitation rate and the ambient temperature. The crop yield study at the 6–10 leaf stage and at the female flowering stage does not provide significant regression equations, while the crop yield study at the maturity stage does. Request variables for the models are the total copper content or the copper bound to the organic matter and the meteorological data. Electronic Publication     

Application of adaptive cluster sampling to low-density populations of freshwater mussels

Freshwater mussels appear to be promising candidates for adaptive cluster sampling because they are benthic macroinvertebrates that cluster spatially and are frequently found at low densities. We applied adaptive cluster sampling to estimate density of freshwater mussels at 24 sites along the Cacapon River, WV, where a preliminary timed search indicated that mussels were present at low density. Adaptive cluster sampling increased yield of individual mussels and detection of uncommon species; however, it did not improve precision of density estimates. Because finding uncommon species, collecting individuals of those species, and estimating their densities are important conservation activities, additional research is warranted on application of adaptive cluster sampling to freshwater mussels. However, at this time we do not recommend routine application of adaptive cluster sampling to freshwater mussel populations. The ultimate, and currently unanswered, question is how to tell when adaptive cluster sampling should be used, i.e., when is a population sufficiently rare and clustered for adaptive cluster sampling to be efficient and practical? A cost-effective procedure needs to be developed to identify biological populations for which adaptive cluster sampling is appropriate.     

水质在线监测中的若干问题

以环境水质自动监测子站的采样和配水系统为切入点，针对不同环节上极易出现的问题，分别提出具体的解决方案。     

Bayesian analysis under ordered functions of parameters

Ordered parameter problems arise in a wide variety of real world situations and are dealt with extensively in the literature. Traditional frequentist methods for dealing with these problems are rather complicated theoretically, especially when sample sizes are small. Bayesian methods are not widely used because high dimensional numerical integration is often required. However, Markov chain Monte Carlo methods provide alternatives to such numerical integration and also deal with ordered parameter problems in a straightforward manner. Little is known about the situation where functions of parameters are ordered. Such problems may seem to be of little practical concern initially, but one can readily see their importance in situations where ordering is placed on the means and variances of several normal or Gamma populations. For the Gamma distribution we will present real examples where we will analyze monthly precipitation data from San Francisco, California and Oakland Mills, Iowa. For the San Francisco data we will simultaneously order both monthly precipitation means and variances. For the Iowa data we will place ordering on seasonal average while still estimating monthly means. Our results show that we would obtain sharper, more accurate inference when order restrictions are employed.