Martine I. Bakker

Dietary exposure to dioxins and dioxin-like PCBs in The Netherlands anno 2004

In this study, representative occurrence data for PCDD/Fs and dioxin-like PCBs in food were obtained and used to estimate dietary exposure of the Dutch population. Food composite samples were analyzed as well as single fish and vegetables samples. Total dioxin concentrations in animal products ranged from 0.05 pg TEQ/g product in poultry to 2.5 pg TEQ/g product (using TEF(2006)) in fish (shrimp), with 0.12pg TEQ/g product being the lowest concentrations measured in fish (tuna). In vegetable products, concentrations ranged from 0.00002 pg TEQ/g product (white kale) to 0.19 pg TEQ/g (oils and fats). A long-term dietary exposure distribution was calculated using Monte Carlo Risk Assessment software. The lower bound median exposure of the Dutch population to PCDD/Fs and dioxin-like PCBs was estimated at 0.8 pg WHO-TEQ/kgbw/d, half of which were dioxin-like PCBs. Dairy was the main source (38%) due to its high consumption. Time-trend analysis shows that the exposure to dioxins has further decreased by 35% over the past five years. This is due to lower levels of dioxin-like compounds in most of the foods, mainly influenced by lower levels in meat and milk. The use of the new TEFs gives an exposure reduction of 10% with respect to TEF(1998). Still, 4% of the Dutch population exceeds the exposure limit of 14 pg/kgbw/week as set by the EU.

Comparison of two models for the estimation of usual intake addressing zero consumption and non-normality.

Various models exist for estimating the usual intake distribution from dietary intake data. In this paper, we compare two of these models, the Iowa State University Foods (ISUF) model and the betabinomial-normal (BBN) model and apply them to three different datasets. Intake data are obtained by aggregating over multiple food products and are often non-normal. The ISUF and BBN model both address non-normality. While the two models have similar structures, they show some differences. The ISUF model includes an additional spline transformation for improving the normality of the intake amount distribution, while the BBN model includes the possibility of addressing covariates, such as age or sex. Our analyses showed that for two of the example datasets both models produced similar estimates of the higher percentiles of the usual intake distribution. However, for the third dataset, where the intake amount distribution appear to be multimodal, both models produced different percentile estimates.

A simple steady-state model for carry-over of aflatoxins from feed to cow's milk.

A simple steady-state model is derived from two kinetic one-compartment models for the disposition of aflatoxin B1 (AFB1) and aflatoxin M1 (AFM1) in the lactating cow. The model relates daily intake of AFB1 in feed of dairy cattle and the cows lactation status to resulting concentrations of AFM1 in milk. Moreover, assuming a linear relationship between the cows lactation status and feed intake, the model relates daily milk production and AFB1 concentration in total feed to AFM1 levels in milk. The model explains similar experimental outcomes from different investigations into carry-over of aflatoxins from feed to milk. Although it is difficult to set a permanent limit for AFB1 in feed, the European Union (EU) limit of 5u2009µg AFB1u2009kg−1 concentrate has proved, thus far, to be an appropriate level in preventing the EU limit of 0.05u2009µg AFM1u2009kg−1 milk being exceeded.

Exploring the Uncertainties in Cancer Risk Assessment Using the Integrated Probabilistic Risk Assessment (IPRA) Approach

Current methods for cancer risk assessment result in single values, without any quantitative information on the uncertainties in these values. Therefore, single risk values could easily be overinterpreted. In this study, we discuss a full probabilistic cancer risk assessment approach in which all the generally recognized uncertainties in both exposure and hazard assessment are quantitatively characterized and probabilistically evaluated, resulting in a confidence interval for the final risk estimate. The methodology is applied to three example chemicals (aflatoxin, N-nitrosodimethylamine, and methyleugenol). These examples illustrate that the uncertainty in a cancer risk estimate may be huge, making single value estimates of cancer risk meaningless. Further, a risk based on linear extrapolation tends to be lower than the upper 95% confidence limit of a probabilistic risk estimate, and in that sense it is not conservative. Our conceptual analysis showed that there are two possible basic approaches for cancer risk assessment, depending on the interpretation of the dose-incidence data measured in animals. However, it remains unclear which of the two interpretations is the more adequate one, adding an additional uncertainty to the already huge confidence intervals for cancer risk estimates.

Risk Assessment of N-nitrosodimethylamine Formed Endogenously after Fish-with-Vegetable Meals

The consumption of fish and nitrate-rich vegetables may lead to the formation of the genotoxic carcinogen N-nitrosodimethylamine (NDMA) in the stomach. To assess human cancer risk associated with this formation, a dynamic in vitro gastrointestinal model was used to simulate NDMA formation in the stomach after a fish + vegetable meal. The experimental results were combined with statistical modeling of Dutch food consumption data resulting in predicted exposures to endogenously formed NDMA in the population. The 95th percentile of the long-term exposure distribution was around 4 ng/kg-bw in young children and 0.4 ng/kg-bw in adults. By comparing this exposure with the Benchmark Dose Lower bound (BMDL) 10 for liver cancer in a chronic carcinogenicity study, a chronic margin of exposure (MOE) was calculated of 7000 and 73,000 for young children and adults. Furthermore, the long-term exposure distribution was combined with a dose-response analysis of the liver cancer incidence data to obtain a cancer risk distribution for the human population. The 95th percentile of that distribution was 6 x 10(-6) extra risk for 5-year-old children and 8 x 10(-7) for adults. The liver cancer data allowed for the analysis of the relationship between tumor incidence and time to tumor. For an extra risk of 10(-6), the decrease in time to tumor was conservatively estimated at 3.8 min in the rat, equivalent to 0.1 days in humans. We also combined acute exposure estimates with the BMDL10 from an acute carcinogenicity study for NDMA, resulting in an acute MOE of 110,000. We conclude that the combined consumption of fish and nitrate-rich vegetables appears to lead to marginal increases of additional cancer risk.

Inclusive growth versus pro-poor growth: Implications for tourism development

Inclusive growth and pro-poor growth are terms embraced but not fully understood in the tourism community. This paper discusses the main concepts of inclusive growth and their implication for tourism development across the developing world. Is inclusive growth simply another term for pro-poor in tourism? Discussion of current approaches utilized by the development community and its institutions highlights differences and notes a shift from pro-poor thinking to inclusive growth efforts. Within that context, the authors suggest the need for an improved understanding of the inclusive growth approach in tourism development, particularly for emerging countries.

A Review of “World heritage sites and tourism: global and local relations”: edited by Laurent Bourdeau, Maria Gravari-Barbas and Mike Robinson, New York, Routledge, 2017, 206 pp., £110.00 (hardback), ISBN 9781409470618

does not contain any contributions with an explicit focus on tourism and/or tourists’ impacts on heritage conservation. Given the focus of Heritage, Conservation and Communities, it is understandable that the topics are somewhat limited to the practical issues of capacity building and participation, but given the direct link between tourism and cultural heritage conservation in many parts of the globe, this void is rather surprising. This might also tie to the persistent disciplinary gaps discussed above. Finally, it has to be pointed out that this volume is an outcome of the Engaging Conservation conference, which took place in 2014 at the University of York, UK. The content, therefore, has a very strong focus on York and UK in terms of the featured case studies and theoretical foundations, and nearly all of the contributors have some affiliation with the University of York. There is, nevertheless, a certain geographic variation in the case studies, featuring examples from Japan, India and Spain. In conclusion, Heritage, Conservation and Communities provides a valuable and interesting insight into the world of heritage conservation practitioners, giving a rich overview of the methods, tools and approaches applied in this field. The ethical platform shared by all authors, i.e. prioritization of authentic and meaningful local community involvement, is of high relevance to any society striving for the inclusion of principles of democracy and social justice into all of its processes. A highly valuable message here is that heritage conservation benefits, above everything else, the community itself. As stated by Gill Chitty ‘[c]onservation in practice here is about developing skills in groups and people management, generating honesty and trust in working together to create equity in new partnerships’ (p.9). This book will be, first and foremost, interesting to heritage practitioners and students of heritage studies, who will benefit from multiple conservation approaches and lessons learned, with open discussions about challenges and failures on the way. The book will also be of value to tourism scholars interested in heritage conservation, as well as practitioners of nature conservation, who can benefit from expanding disciplinary horizons and unexpected synergistic interdisciplinary insights.

APROBA-Plus: A probabilistic tool to evaluate and express uncertainty in hazard characterization and exposure assessment of substances.

To facilitate the application of probabilistic risk assessment, the WHO released the APROBA tool. This tool applies lognormal uncertainty distributions to the different aspects of the hazard characterization, resulting in a probabilistic health-based guidance value. The current paper describes an extension, APROBA-Plus, which combines the output from the probabilistic hazard characterization with the probabilistic exposure to rapidly characterize risk and its uncertainty. The uncertainty in exposure is graphically compared with the uncertainty in the target human dose, i.e. the dose that complies with the specified protection goals. APROBA-Plus is applied to several case studies, resulting in distinct outcomes and illustrating that APROBA-Plus could serve as a standard extension of routine risk assessments. By visualizing the uncertainties, APROBA-Plus provides a more transparent and informative outcome than the more usual deterministic approaches, so that risk managers can make better informed decisions. For example, APROBA-Plus can help in deciding whether risk-reducing measures are warranted or that a refined risk assessment would first be needed. If the latter, the tool can be used to prioritize possible refinements. APROBA-Plus may also be used to rank substances into different risk categories, based on potential health risks without being compromised by different levels of conservatism that may be associated with point estimates of risk.