Despite three decades of political efforts and a wealth of research on the causes and catastrophic impacts of climate change, global carbon dioxide emissions have continued to rise and are 60% higher today than they were in 1990. Exploring this rise ...Read More
Inhalation exposure to environmental tobacco smoke (ETS) particles may increase health risks, but only to the extent that the particles deposit in the respiratory tract. We describe a technique to predict regional lung deposition of environmental tobacco smoke particles. Interpretation of particle size distribution measurements after cigarette combustion by a smoking machine in a test room yields an effective emissions profile. An aerosol dynamics model is used to predict indoor particle concentrations resulting from a specified combination of smoking frequency and building factors. By utilizing a lung deposition model, the rate of ETS mass accumulation in human lungs is then determined as a function of particle size and lung airway generation. Considering emissions of sidestream smoke only, residential exposures of nonsmokers to ETS are predicted to cause rates of total respiratory tract particle deposition in the range of 0.4–0.7 μg / day per kg of body weight for light smoking in a well-ventilated residence and 8–13 μg / day per kg for moderately heavy smoking in a poorly ventilated residence. Emissions of sidestream plus mainstream smoke lead to predicted deposition rates about a factor of 4 higher. This technique should be useful for evaluating health risks and control techniques associated with exposure to ETS particles
We are excited and pleased that from the previous volume (Volume 45, published in 2020), the Annual Review of Environment and Resources joins the ranks of the first handful of Annual Reviews journals that are converting to open access.The conversion uses a new model: Subscribe to Open (S2O).All articles in that and subsequent volumes will be permanently open access to the whole world, and so long as S2O continues, all articles in all past volumes of the Annual Review of Environment and Resources will also be kept accessible to all without a paywall.We are pleased to note that the number of article downloads has seen a huge increase of more than a factor of five since the journal went open access.The Annual Review of Environment and Resources provides authoritative, up-to-date reviews of key issues at the intersections of sustainability, science, technology, and policy.It is a useful resource for researchers and practitioners working on nature-society interactions who want and ought to know the current state of affairs on the topics reviewed.Each review offers critical synthesis of the 150 recent articles from dozens of high-impact journals that would need to be read to keep up to date.Reviews summarize what is known and unknown and identify emerging directions for future research as judged by authorities on that issue.These reviews are valuable for early career scholars in shaping their research trajectory by enabling them to learn new fields quickly and identify new areas for their research.Overall, this journal provides updates and the most recent perspectives on many of the same issues covered more generally in textbooks on environmental science and policy.In effect, five consecutive volumes of the Annual Review of Environment and Resources represent a rolling textbook or desk reference about environment, resources, and society for faculty and students.This journal also serves nonscientist readers professionally charged with making sense of changing environmental issues-for example, journalists, educators, legislative and agency staff, analysts in international organizations, community organizers, and experts engaged in global assessments.
Human interactions with wildlife are a defining experience of human existence. These interactions can be positive or negative. People compete with wildlife for food and resources, and have eradicated dangerous species; co-opted and domesticated valuable ...Read More
Childhood lead exposure through drinking water has long-term effects on cognition and development, and is a significant public health concern. The comprehensive lead testing of public schools entails high expense and time. In prior work, random forest modeling was used successfully to predict the likelihood of lead contamination in the drinking water from schools in the states of California and Massachusetts. In those studies, data from 70% of the schools was used to predict the probability of unsafe water lead levels (WLLs) in the remaining 30%. This study explores how the model predictions degrade, as the training dataset forms a progressively smaller proportion of schools. The size of the training set was varied from 80% to 10% of the total samples in four US states: California, Massachusetts, New York, and New Hampshire. The models were evaluated using the precision-recall area under curve (PR AUC) and area under the receiver operating characteristic curve (ROC AUC). While some states required as few as 10% of the schools to be included in the training set for an acceptable ROC AUC, all four states performed within an acceptable ROC AUC range when at least 50% of the schools were included. The results in New York and New Hampshire were consistent with the prior work that found the most significant predictor in the modeling to be the Euclidean distance to the closest school in the training set demonstrating unsafe WLLs. This study further supports the efficacy of predictive modeling in identifying the schools at a high risk of lead contamination in their drinking water supply, even when the survey data is incomplete on WLLs in all schools.
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