Organochlorine contaminants in seabird eggs from the Pacific coast of Canada, 1971–1986

Eggs were collected from seven seabird species at colonies on the British Columbia coast from 1983 to 1986 and analyzed for organochlorine contaminants. Total PCB levels (wet weight) were highest in double-crested cormorants (Phalacrocorax auritus) from the Fraser estuary (2.91 mg kg^-1) and the Strait of Georgia (3.79 mg kg^-1). Highest DDE levels were in fork-tailed storm-petrels (Oceanodroma furcata) from the Queen Charlotte Islands (1.68 mg kg^-1). Organochlorine levels were generally lower in eggs from the mid 1980s than in those collected in the early 1970s. Organochlorine levels in Pacific alcids and hydrobatids foraging in offshore locations were compared to those in the same or ecologically similar species from the Canadian Atlantic coast. DDT- and HCH-related compounds were higher in Pacific populations while levels of dieldrin, oxychlordane, and HCB were generally lower. With the exception of -HCH, levels of all measured organochlorines were lower in cormorants breeding in the Fraser River estuary than in cormorants from the St. Lawrence River estuary on the Atlantic coast.     

An application of circular-linear correlation analysis to the relationship between Freon concentration and wind direction in Woods Hole,Massachusetts

Two statistical tests for correlation between a circular variate and a linear variate are presented. The tests are applied to a small data set concerning Freon-12 concentration and wind direction in Woods Hole, Massachusetts during the summer of 1987. A significant correlation is found. Further analysis suggests that this directional effect is related to onshore Freon-12 release.Woods Hole Oceanographic Institution Contribution Number 6750     

Estimation of the Thermal Sensitivity of a Dwelling to Variations in Architectural and Behavioural Parameters

The provision of energy for households is a major contributor to greenhouse gas emissions in the domestic sector. There is significant scope for energy savings and thus emission reduction in this sector. This paper constructs a bridge between thermal analysis and environmental assessment in the occupancy stage of the dwelling life cycle, approaching both methods as being on a common system Environment–Dwelling–Inhabitant. The importance of the local level in the thermal analysis and assessment of either a dwelling or an inhabitant's behaviour is demonstrated. It is shown that the researcher can choose between natural experiment, computer experiment and mathematical modelling to analyze the system. Such a choice is discussed for two particular methods of thermal analysis. Thermal Nomograms are the result of mathematical modelling, and the Energy Score Sheet is the result of a computer experiment in energy rating. Despite being developed for the purpose of thermal analysis, these methods can also be utilized for environmental assessment at the local level. The discussion centres on Australian conditions where the majority of the population resides in a relatively benign climate wherein behavioural alterations have significant potential for energy savings and environmental impact. The purpose of the paper is to emphasise benefits apart from energy minimisation to promote the use of energy efficient housing strategies.     

Water quality test based on dielectrophoretic measurements of fresh water algae Selenastrum capricornutum

A method is described whereby dielectrophoresis of algal cells is used to perform rapid water quality analysis, specifically detecting the presence of CuSO4. The dielectric collection spectrum of the fresh water alga Selenastrum capricornutum was determined for a range of concentrations of CuSO4 from 25 mg L(-1) to 0.25 mg L(-1) for exposure times of 15 min and 18 h. In all cases increasing the concentration of CuSO4 reduced cell collection, but a step reduction was observed in collection between 2 mg L(-1) and 5 mg L(-1). This method has potential for forming a rapid, low-cost test for water quality with broad specificity and significantly reduced analysis time compared to current methods.     

Determination of biomass burning emission factors: Methods and results

Biomass burning, in a broad sense, encompasses different burning practices, including open and confined burnings, and different types of vegetation. Emission factors of gaseous or particulate trace compounds are directly dependent both on the fuel type and the combustion process. Emission factors are generally calculated by stoichiometric considerations using the carbon mass balance method, applied either to combustion chamber experiments or to field experiments based on ground-level measurements or aircraft sampling in smoke plumes. There have been a number of experimental studies in the last 10 years to investigate wildfires in tropical, temperate, or boreal regions. This article presents an overview of measurement methods and experimental data on emission factors of reactive or radiatively active trace compounds, including trace gases and particles. It focuses on fires in tropical regions, that is, forest and savanna fires, agricultural burns, charcoal production, use of fuelwood, and charcoal combustion.     

Further studies using X-ray fluorescence to sample lead contaminated carpeted surfaces

The sampling of carpeted surfaces to test for lead contamination primarily focuses upon vacuum techniques. Vacuum sampling techniques, however, require time-consuming, expensive laboratory analysis of the dusts obtained and are unable to determine total lead load on the carpet. X-ray fluorescence (XRF) analysis is an on-site, inexpensive, non-destructive, quick technique for predicting metals levels in a variety of media, such as water, soil, filter paper and painted surfaces. A 1992 study of the feasibility of XRF to analyze for lead and soil loadings on carpeted surfaces indicated that XRF can detect lead at a low enough level to warrant further study. This paper expands this earlier study and developes lead and soil loading calibration curves for three different carpet types based upon XRF lead L-beta peak areas and XRF iron and barium K-alpha peak and background areas. Results indicate that variation in the data can be reduced through modifications of the XRF analysis technique, thus reducing the statistically determined detection level, and that carpet type does affect the calibration. Detection levels of approximately 70 mg/m² for lead and 5 g/m² for soil were obtained. Overall, good agreement was found between results of this study and the earlier one. XRF shows excellent potential for quantitative analysis of lead on carpeted surfaces.     

Factor analysis of trace-element data from tree-bark samples in The Netherlands

Monte Carlo-assisted factor analysis has been applied to a data set of 20 trace-element concentrations in tree-bark samples obtained from 123 locations in The Netherlands, with the aim to investigate the suitability of bark as a biomonitor for air pollution. A Monte Carlo approach was used to give more insight to the uncertainties and significance levels of the factor analysis results. Notwith-standing a rather strong influence of soil material on the concentration levels, factor analysis enabled the identification of five significant pollution source types, all of which corresponded with source types found in an earlier biomonitoring study in The Netherlands using epiphytic lichens. A more detailed comparison with the lichen results showed a remarkable difference in lead concentrations between bark and lichen. It was concluded that bark can be successfully employed as a biomonitor for air pollution. The power of factor analysis to adequately determine the soil contribution may render extensive sample washing procedures superfluous.     

Characterizing soils for hazardous waste site assessments

This paper provides a review and justification of the minimum data needed to characterize soils for hazardous waste site assessments and to comply with the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA). Scientists and managers within the regulatory agency and the liable party need to know what are the important soil characteristics needed to make decisions about risk assessment, what areas need remediation and what remediation options are available. If all parties involved in characterizing a hazardous waste site can agree on the required soils data set prior to starting a site investigation, data can be collected in a more efficient and less costly manner. Having the proper data will aid in reaching decisions on how to address concerns at, and close-out, hazardous waste sites.This paper was prepared to address two specific concerns related to soil characterization for CERCLA remedial response. The first concern is the applicability of traditional soil classification methods to CERCLA soil characterization. The second is the identification of soil characterization data type required for CERCLA risk assessment and analysis of remedial alternatives. These concerns are related, in that the Data Quality Objective (DQO) process addresses both. The DQO process was developed in part to assist CERCLA decision-makers in identifying the data types, data quality, and data quantity required to support decisions that must be made during the remedial investigation/feasibility study (RI/FS) process. Data Quality Objectives for Remedial Response Activities: Development Process (US EPA, 1987a) is a guidebook on developing DQOs. This process as it relates to CERCLA soil characterization is discussed in the Data Quality Objective Section of this paper.     

Use of cluster and principal component analyses to profile areas in California where ground water has been contaminated by pesticides

An empirical approach to profiling areas of ground water contamination by pesticides was devised that did not rely upon determining the level of vulnerability between land areas and that did not assume any particular pathway for ground water contamination. Climatic and soil data were obtained for 1-square mile sections of land in California where pesticide residues had been found in well water samples and the detection was attributed to legal agricultural applications. These sections were designated as known contaminated (KC) sections. Climate and soil data were also obtained for sections which lacked either well sampling data or a positive pesticide detection. These sections were designated as candidate sections. Statistical procedures were used to cluster groups of KC sections first with respect to climate characteristics and then with respect to soil characteristics. Principal components analysis (PCA) was used to construct a statistical profile of soil variables for each cluster of KC sections. A method based on the PCA was developed to compare the similarity of soil profiles derived for each KC section cluster to individual candidate sections. Since the profiling scheme was based only on data from KC sections, candidate sections that did not match any KC cluster profile could only be considered dissimilar to contaminated sections, receiving a status of not-classified. This profiling method is flexible and it can be revised to incorporate updated well sampling information.