Health-based strategies for overcoming barriers to climate change adaptation and mitigation.

Climate change poses an unequivocal threat to the respiratory health of current and future generations. Human activities-largely through the release of greenhouse gases-are driving rising global temperatures. Without a concerted effort to mitigate greenhouse gas emissions or adapt to the effects of a changing climate, each increment of warming increases the risk of climate hazards (eg, heat waves, floods, and droughts) that that can adversely affect allergy and immunologic diseases. For instance, wildfires, which release large quantities of particulate matter with a diameter of less than 2.5 µm (an air pollutant), occur with greater intensity, frequency, and duration in a hotter climate. This increases the risk of associated respiratory outcomes such as allergy and asthma. Fortunately, many mitigation and adaptation strategies can be applied to limit the impacts of global warming. Adaptation strategies, ranging from promotions of behavioral changes to infrastructural improvements, have been effectively deployed to increase resilience and alleviate adverse health effects. Mitigation strategies aimed at reducing greenhouse gas emissions can not only address the problem at the source but also provide numerous direct health cobenefits. Although it is possible to limit the impacts of climate change, urgent and sustained action must be taken now. The health and scientific community can play a key role in promoting and implementing climate action to ensure a more sustainable and healthy future.

A Statistical Approach for Identifying Private Wells Susceptible to Perfluoroalkyl Substances (PFAS) Contamination.

Drinking water concentrations of per- and polyfluoroalkyl substances (PFAS) exceed provisional guidelines for millions of Americans. Data on private well PFAS concentrations are limited in many regions and monitoring initiatives are costly and time-consuming. Here we examine modeling approaches for predicting private wells likely to have detectable PFAS concentrations that could be used to prioritize monitoring initiatives. We used nationally available data on PFAS sources, and geologic, hydrologic and soil properties that affect PFAS transport as predictors and trained and evaluated models using PFAS data (n~2300 wells) collected by the state of New Hampshire between 2014 and 2017. Models were developed for the five most frequently detected PFAS: perfluoropentanoate, perfluorohexanoate, perfluoroheptanoate, perfluorooctanoate, and perfluorooctane sulfonate. Classification random forest models that allow non-linearity in interactions among predictors performed the best (area under the receiver operating characteristics curve: 0.74 - 0.86). Point sources such as the plastics/rubber and textile industries accounted for the highest contribution to accuracy. Groundwater recharge, precipitation, soil sand content, and hydraulic conductivity were secondary predictors. Our study demonstrates the utility of machine learning models for predicting PFAS in private wells and the classification random forest model based on nationally available predictors is readily extendable to other regions.

Chromogenic Photonic Crystals Enabled by Novel Vapor-Responsive Shape-Memory Polymers.

A new type of shape-memory polymer (SMP) is developed by integrating scientific principles drawn from two disparate fields: the fast-growing photonic crystal and SMP technologies. This new SMP enables room-temperature operation for the entire shape-memory cycle and instantaneous shape recovery triggered by exposure to a variety of organic vapors.