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• Previous studies on contamination spread by flooding undercount sites. • 3.3 million people are at risk of contamination from flooding in US urban areas. • Due to changes in urbanization and climate change these figures are likely to increase. • These risks pose complex challenges for environmental managers and urban planners. Climate change is imposing substantial consequences across physical and social infrastructures. The extent of social disruption and risk to human health are, however, potentially much broader than these general consequences, taken individually, would suggest. To address this gap, we assess the distribution of contaminated sites in the United States (US) and then estimate the impact that flood hazards in urban areas will have on these contaminated sites. Using these measures, we draw inferences about the risk of contamination from climate-impacted extreme weather events, climate adaptation at the local level, social risk and how it is distributed, and a broader understanding of the potential global consequences of climate change. In this paper we address three critical points: 1) the role classification of contaminated sites on our understanding of risk due to climate change; 2) the relationship between contaminated sites and flood risk; and 3) the potential for climate adaption strategies to mediate this risk. We estimate that of the roughly one-third of the US population living in urban areas, up to 3,338,518 people, are living in high-risk flood zones near contaminated sites. Our results suggest severe potential implications for estimates of the negative consequences from climate change and contamination and provide critical insights into the relationship between climate change and the built environment for urban planners and environmental policy makers and managers alike. [ABSTRACT FROM AUTHOR]

Abstract: Obesity and diabetes are increasingly attributed to environmental factors, however, little attention has been paid to the influence of the ‘local’ food economy. This paper examines the association of measures relating to the built environment and ‘local’ agriculture with U.S. county-level prevalence of obesity and diabetes. Key indicators of the ‘local’ food economy include the density of farmers’ markets and the presence of farms with direct sales. This paper employs a robust regression estimator to account for non-normality of the data and to accommodate outliers. Overall, the built environment is associated with the prevalence of obesity and diabetes and a strong local’ food economy may play an important role in prevention. Results imply considerable scope for community-level interventions. [Copyright &y& Elsevier]

Abstract: Publication of a White Paper on “Grand Challenges of the Future for Environmental Modeling” is announced. The White Paper has been prepared for the US National Science Foundation (NSF) in the setting of its several Environmental Observatory initiatives. [Copyright &y& Elsevier]

Hurricanes are one of the most frequent and destructive disasters in the United States. The events are large scale and have relatively long-term impacts. Social networking platforms such as Twitter can provide real-time information for disaster managers and affected populations during large-scale disasters (e.g., hurricanes), but extracting useful information and interpreting data accurately for disaster management is still challenging. Sentiment analysis of social media data helps detect the concerns of affected people and understand individuals' responses on the ground at unprecedented scales, but the method is known to be domain-dependent. The same words or expressions can indicate opposite sentiments in different domains. This paper proposes a domain-specific sentiment analysis approach specifically for tweets posted during hurricanes (DSSA-H). DSSA-H can retrieve hurricane-relevant tweets with a trained supervised-learning classifier, Random Forest (RF), and classify the sentiment of hurricane-relevant tweets based on a domain-adversarial neural network (DANN). We built a dataset of tweets posted during six recent hurricanes and applied the DSSA-H approach for sentiment analysis. After evaluation, we found that each classifier (i.e., RF and DANN) outperforms baseline classifiers and that DSSA-H outperforms two high-performing general sentiment classification approaches when classifying sentiments of tweets posted during hurricanes. We also applied DSSA-H in examining sentiment patterns across six recent hurricanes in the U.S. This domain-specific sentiment analysis approach can be used by the first responders and affected communities to more accurately and rapidly detect crises and emergent events, allocate resources, and assess disaster's impact during hurricanes. DSSA-H contributes to an intelligent and adaptive disaster information system for the data-rich human and the built environment system. • DSSA-H retrieves meaningful tweets relevant to hurricanes precisely. • DSSA-H applies a recurrent neural network with domain adaption to classify sentiments. • DSSA-H outperforms general sentiment analysis approaches in classifying hurricane-relevant tweets. • DSSA-H is useful to detect small-scale crises that trigger negative sentiments in hurricanes. • DSSA can be trained and applied to sentiment analysis of short blogs related to other extreme events. [ABSTRACT FROM AUTHOR]