Sampling strategies in the assessment of long‐term exposures to toxic substances in air

The decision to mitigate exposures from vapor intrusion (VI) is typically based on limited data from 24‐hour air samples. It is well documented that these data do not accurately represent long‐term average exposures linked to adverse health effects. Limited decision guidance is currently available to determine the most appropriate sampling strategy, considering the cost of sampling alternatives along with the economic consequences of exposure‐related health effects. We present a decision model that introduces economic and statistical considerations in evaluating alternative VI sampling methods. The model characterizes the best sampling method by factoring economic and health consequences of exposure, the variability of exposure, the cost of sampling and mitigation, and the likelihood of false‐negatives and false‐positives. Decision‐makers can use results to select the sample size that maximizes net benefit. Conceptual and mathematical models are presented linking biological, statistical, and economic considerations to assess the cost and effectiveness of different sampling strategies. The model relates an average exposure concentration, determined statistically, to abatement costs and to the monetary value of health deterioration. The value of the information provided by different strategies is calculated and used to select the optimum sampling method. Simulations show that longer‐term sampling methods tend to be more accurate and cost‐effective than short‐term samples. The ideal sampling strategy shows significant seasonal variation (it is typically optimal to use longer samples in the winter) and also varies significantly with the stringency of regulatory standards. Longer‐term sample collection provides a more accurate representation of average VI exposure and reduces the likelihood of type I and type II errors. This reduces expected costs of mitigation and exposure (e.g., health consequences, legal and regulatory penalties), which in some cases can be quite significant. The model herein shows how these savings are balanced against the additional costs of longer‐term sampling.     

A comparison of different lysis buffers to assess allele dropout from single cells for preimplantation genetic diagnosis

Single cell polymerase chain reaction (PCR) for preimplantation genetic diagnosis (PGD) requires high efficiency and accuracy. Allele dropout (ADO), the random amplification failure of one of the two parental alleles, remains the most significant problem in PCR-based PGD testing since it can result in serious misdiagnosis for compound heterozygous or autosomal dominant conditions. A number of different strategies (including the use of lysis buffers to break down the cell and make the DNA accessible) have been employed to combat ADO with varying degrees of success, yet there is still no consensus among PGD centres over which lysis buffer should be used (ESHRE PGD Consortium, 1999 ). To address this issue, PCR amplification of three genes (CFTR, LAMA3 and PKP1) at different chromosomal loci was investigated. Single lymphocytes from individuals heterozygous for mutations within each of the three genes were collected and lysed in either alkaline lysis buffer (ALB) or proteinase K/SDS lysis buffer (PK). PCR amplification efficiencies were comparable between alkaline lysis and proteinase K lysis for PCR products spanning each of the three mutated loci (ΔF508 in CFTR 90% vs 88%; R650X in LAMA3 82% vs 78%; and Y71X in PKP1 91% vs 87%). While there was no appreciable difference between ADO rates between the two lysis buffers for the LAMA3 PCR product (25% vs 26%), there were significant differences in ADO rates between ALB and PK for the CFTR PCR product (0% vs 23%) and the PKP1 PCR product (8% vs 56%). Based on these results, we are currently using ALB in preference to PK/SDS buffer for the lysis of cells in clinical PGD. Copyright © 2001 John Wiley & Sons, Ltd.     

Co-ordination for disasters

This paper was prepared in 1978 on the basis of a review of sociological and anthropological fieldwork conducted up to that time. Although theoretical in presentation its conclusions are formulated in accordance with the author's own extensive observations of organizational response to natural disasters in developing countries, especially those having sudden onset. Practical implications for co-ordination agencies and pre-disaster training are drawn.     

Optimal range of plug-in electric vehicles in Beijing and Shanghai

Both China’s national subsidy policies for plug-in electric vehicles (PEVs) purchasers and passenger cars corporate average fuel consumption and new vehicle credit regulation (dual-credit policy) favor long-range 300+ km battery electric vehicles (BEVs) and 80+ km plug-in hybrid electric vehicles (PHEVs). However, these electric vehicles tend to have lower energy efficiency and higher purchase and operation costs. Vehicle with larger batteries can also be less equitable because the subsidies are often provided to more expensive vehicles and wealthier owners. This study takes advantage of a novel dataset of daily driving data from 39,854 conventional gasoline vehicles in Beijing and 4999 PHEVs in Shanghai to determine the optimal range of BEVs and PHEVs within their respective cities. We simulate a model to explore ranges with which PEVs emit less GHGs than that of a baseline hybrid and conventional gasoline vehicle while ensuring that all daily travel demands are met. Our findings indicate that in both cities, the optimal ranges to balance cost and travel demand for BEVs are 350 km or less and for PHEVs are 60 km or less in Beijing and 80 km or less in Shanghai. We also find that to minimize carbon dioxide (CO₂) emissions, the ranges are even lower 10 km in Beijing and 30 km in Shanghai. Our study suggests that instead of encouraging long-range PEVs, governments should subsidize PEV models with shorter ranges. Parallel efforts should also be made to both increase renewable energy over fossil fuels and expand charging facilities. Although individual mobility demand varies, the government could reduce occasional long-distance driving by subsidizing alternative transportation choices. Providing week-long driving trials to consumers before their purchases may help decrease the demand of very long range PEVs by alleviating the range anxiety through a learning process.

     

Guppies and the TIT FOR TAT strategy: preference based on past interaction

Summary The evolution of cooperation requires either (a) nonrandom interactions, such that cooperators preferentially interact with other cooperators, or (b) conditional behaviors, such that individuals act cooperatively primarily towards other cooperators. Although these conditions can be met without assuming sophisticated animal cognition, they are more likely to be met if animals can remember individuals with whom they have interacted, associate past interactions with these individuals, and base future behavior on this information. Here we show that guppies (Poecilia reticulata), in the context of predator inspection behavior, can identify and remember (for at least 4 h) the more cooperative among two conspecifics and subsequently choose to be near these individuals in future encounters. Offprint requests to: L.A. Dugatkin     

A pattern of disasters and victims

Two three-dimensional conceptual models are presented that should help those concerned with disasters and disaster victims to integrate the numerous studies on the topic. The first, concerning disasters, involves matters ecological, technological and social scientific. It covers the elements earth, air, fire, water/liquid, and people. It includes the time dimensions of preparedness, response, recovery and mitigation. The second introduces a six-fold classification of victims, together with dimensions to cover the methods and kinds of delivery of different interventions that might be made available to help them. The suggestion is that, were the appropriately noted victim model placed within the larger disaster model according to the types of disaster study being examined, there might be useful academic, political, and professional implications.     

SI-traceable certification of Cu, Cr, Cd and Pb in sediment and fly ash candidate reference materials

Many fields in environmental analytical chemistry deal with very low limits and thresholds as set by governmental legislations or transnational regulations. The need for the accuracy, comparability and traceability of analytical measurements in environmental analytical chemistry has significantly increased and total uncertainties are even asked for by accreditation bodies of environmental laboratories. This paper addresses achieving these goals to guarantee accuracy, quality control, quality assurance or validation of a method by means of certified reference materials. The assessment of analytical results in certified reference materials must be as accurate as possible and every single step has to be fully evaluated. This paper presents the SI-traceable certification of Cu, Cr, Cd and Pb contents in geological and environmentally relevant matrices (three sediments and one fly ash sample). Certification was achieved using isotope dilution (ID) ICPMS as a primary method of measurement. In order to reduce significantly the number of analytical steps and intermediate samples a multiple spiking approach was developed. The full methodology is documented and total uncertainty budgets are calculated for all certified values. A non-element specific sample digestion process was optimised. All wet chemical digestion methods examined resulted in a more or less pronounced amount of precipitate. It is demonstrated that these precipitates originate mainly from secondary formation of fluorides (essentially CaF2) and that their formation takes place after isotopic equilibration. The contribution to the total uncertainty of the final values resulting from the formation of such precipitates was in general < 0.1% for all investigated elements. Other sources of uncertainty scrutinised included the moisture content determination, procedural blank determination, cross-contamination from the different spike materials, correction for spectral interferences, instrumental background and deadtime effects, as well as the use of either certified values or IUPAC data in the IDMS equation. The average elemental content in the sediment samples was 30-130 micrograms g-1 for Pb, 0.5-3 micrograms g-1 for Cd and 50-70 micrograms g-1 for Cu. Cr was measured in one sample and was about 60 micrograms g-1. The concentrations in the fly ash sample were up to 2 orders of magnitude higher. Expanded uncertainty for the investigated elements was about 3% (coverae factor k = 2) except for Cr, (measured by high resolution ICPMS), for which the expanded uncertainty was about 7% (k = 2).     

Background Levels of Metals in Soils, McMurdo Station, Antarctica

McMurdo Station is the largest research station in Antarctica, with a population that ranges each year from 250 to 1200 people. Because of its size and 40-year history of use, a number of locations around the station have become contaminated with wastes. Soils and sediments in these areas have been shown to contain elevated levels of petroleum-related products, PCBs, other organics, and metals. While some remedial investigations have been conducted, background levels of metals in soils have not been determined. This paper reports on background levels of metals in a natural basalt-derived soil (gray soil) and scoria (soft porous rock used as fill, red soil) near McMurdo Station using two fundamentally different analytical procedures, concentrated acid extraction/analysis and total metals. These data facilitate determining the extent and levels of metal contamination near McMurdo Station and provide reference levels of metals for comparison with existing and future remediation data. There were statistically significant differences between metals concentrations in both gray and red soils, and no correlations between the level of extracted versus total metal. Generally, only a small fraction of a metal was extractable.     

Spatial contaminant heterogeneity: quantification with scale of measurement at contrasting sites

Material within the terrestrial environment is rarely homogeneously distributed, either spatially or temporally. One consequence of heterogeneity is that uncertainty is usually generated in measurements that are taken with the aim of characterising the environment. For example, a measurement of analyte concentration within soil taken from one sampling location on contaminated land can vary substantially when compared against another sample taken at effectively the same nominal location. The measurement uncertainty arising from the heterogeneity can substantially limit the reliability of the interpretations made upon environmental investigations. The sampling uncertainty usually outweighs the analytical uncertainty from the laboratory, often by a factor of 20 or more. One approach to reducing the uncertainty is to design a more suitable sampling strategy. This might be achieved by predicting the degree of heterogeneity prior to the investigation, but this is often difficult to achieve accurately. Another approach, which was investigated here, is to actually characterise the heterogeneity prior to the main investigation using rapid and inexpensive technology, such as in situ measurement techniques. In situ portable X-ray fluorescence (PXRF) and X-ray microprobe (XMP) techniques were employed to test the feasibility of this approach. Two contrasting contaminated land sites were chosen to characterise the two-dimensional spatial heterogeneity of heavy metal contamination in topsoil at a range of scales (50 m to 0.001 m). The spatial heterogeneity of contaminants, expressed as relative standard deviations, was found to differ between the two sites by a factor of two, largely due to the mode of deposition of pollution. The study also indicated that the heterogeneity did not change systematically with the scale of measurement between sampling locations at either site.