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.     

Heavy metal accumulation in Littoraria scabra along polluted and pristine mangrove areas of Tanzania

The periwinkle Littoraria scabra was collected at polluted and pristine mangrove sites along the Tanzanian coastline, including Msimbazi, Mbweni (i.e. Dar es Salaam) and Kisakasaka, Nyamanzi and Maruhubi (i.e. Zanzibar). Periwinkles were morphologically characterized, sexed and their heavy metal content was determined using ICP-MS. Analysis revealed that L. scabra from polluted areas contained higher soft tissue heavy metal levels, were significantly smaller and weighed less compared to their conspecifics from the unpolluted mangroves. The current morphological observations may be explained in terms of growth and/or mortality rate differences between the polluted and non-polluted sites. Although a variety of stressors may account for these adverse morphological patterns, our data suggest a close relationship with the soft tissue heavy metal content. Compared to soft tissue heavy metal levels that were measured in L. scabra along the same area in 1998, most metals, except for arsenic, chromium and iron have decreased dramatically.     

Das Phänomen des Hörens: Ein interdisziplinärer Diskurs

Teil I s. Heft 7, S. 300