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Residential landscapes support human well-being and ecological functioning in urban ecosystems. Trees and native plants in yards and neighborhoods positively influence satisfaction, an important component of human well-being and quality of life. However, these patterns may not hold true in arid ecoregions, where the composition of desert vegetation contrasts the tall, broad-leafed trees of temperate regions. The effects of xeric, desert-like landscaping on satisfaction with the neighborhood environment are especially important to consider given the large amount of resources required to support people’s propensity for grassy yards. Although place satisfaction is related to pro-environmental behavior, the multi-scalar relationship between yard management decisions and satisfaction with the neighborhood environment has yet to be established. Here, we test the social-ecological dynamics of landscape preferences, satisfaction with trees and desert plants, and management intensity in residential yards and neighborhoods throughout the desert city of Phoenix, Arizona, USA. We found that wealthy neighborhoods close to desert open space were intensively managed and supported the highest levels of satisfaction. However, there was no direct relationship between satisfaction and management intensity for people who preferred xeric landscaping. Instead, management intensity for people with xeric preferences was related to demographics, such as income and home ownership. There was a relationship between satisfaction and management intensity for people with mesic preferences, suggesting a resource cost to maintain satisfaction for lush, green landscaping in a desert city. Overall, our study supports the assertion that changing yard landscaping may not result in desired changes for high-input management practices.

High nighttime urban air temperatures increase health risks and economic vulnerability of people globally. While recent studies have highlighted nighttime heat mitigation effects of urban vegetation, the magnitude and variability of vegetation-derived urban nighttime cooling differs greatly among cities. We hypothesize that urban vegetation-derived nighttime air cooling is driven by vegetation density whose effect is regulated by aridity through increasing transpiration. We test this hypothesis by deploying microclimate sensors across eight United States cities and investigating relationships of nighttime air temperature and urban vegetation throughout a summer season. Urban vegetation decreased nighttime air temperature in all cities. Vegetation cooling magnitudes increased as a function of aridity, resulting in the lowest cooling magnitude of 1.4 °C in the most humid city, Miami, FL, and 5.6 °C in the most arid city, Las Vegas, NV. Consistent with the differences among cities, the cooling effect increased during heat waves in all cities. For cities that experience a summer monsoon, Phoenix and Tucson, AZ, the cooling magnitude was larger during the more arid pre-monsoon season than during the more humid monsoon period. Our results place the large differences among previous measurements of vegetation nighttime urban cooling into a coherent physiological framework dependent on plant transpiration. This work informs urban heat risk planning by providing a framework for using urban vegetation as an environmental justice tool and can help identify where and when urban vegetation has the largest effect on mitigating nighttime temperatures.

Access to air conditioned space is critical for protecting urban populations from the adverse effects of heat exposure. Yet there remains fairly limited knowledge of the penetration of private (home air conditioning) and distribution of public (cooling centers and commercial space) cooled space across cities. Furthermore, the deployment of government-sponsored cooling centers is likely to be inadequately informed with respect to the location of existing cooling resources (residential air conditioning and air conditioned public space), raising questions of the equitability of access to heat refuges. We explore the distribution of private and public cooling resources and access inequities at the household level in two major US urban areas: Los Angeles County, California and Maricopa County, Arizona (whose county seat is Phoenix). We evaluate the presence of in-home air conditioning and develop a walking-based accessibility measure to air conditioned public space using a combined cumulative opportunities-gravity approach. We find significant variations in the distribution of residential air conditioning across both regions which are largely attributable to building age and inter/intra-regional climate differences. There are also regional disparities in walkable access to public cooled space. At average walking speeds, we find that official cooling centers are only accessible to a small fraction of households (3% in Los Angeles, 2% in Maricopa) while a significantly higher number of households (80% in Los Angeles, 39% in Maricopa) have access to at least one other type of public cooling resource such as a library or commercial establishment. Aggregated to a neighborhood level, we find that there are areas within each region where access to cooled space (either public or private) is limited which may increase heat-related health risks.

Synoptic climatology has a long history of research where weather data are aggregated and composited to gain a better understanding of atmospheric effects on non-atmospheric variables. This has resulted in an applied scientific discipline that yields methods and tools designed for applications across disciplinary boundaries. The spatial synoptic classification (SSC) is an example of such a tool that helps researcher bridge methodological gaps between disciplines, especially those studying weather effects on human health. The SSC has been applied in several multi-discipline projects, and it appears that there is ample opportunity for growth into new topical areas. Likewise, there is opportunity for the SSC network to be expanded across the globe, especially into mid-latitude locations in the southern hemisphere. There is some question of the utility of the SSC in tropical locations, but such decisions must be based on the actual weather data from individual locations. Despite all of the strengths and potential uses of the SSC, there are some research problems, some locations, and some datasets for which it is not suitable. Nevertheless, the success of the SSC as a cross-disciplinary method is noteworthy because it has become a catalyst for collaboration.

Background: Extreme heat is a leading weather-related cause of mortality in the United States, but little guidance is available regarding how temperature variable selection impacts heat–mortality relationships. Objectives: We examined how the strength of the relationship between daily heat-related mortality and temperature varies as a function of temperature observation time, lag, and calculation method. Methods: Long time series of daily mortality counts and hourly temperature for seven U.S. cities with different climates were examined using a generalized additive model. The temperature effect was modeled separately for each hour of the day (with up to 3-day lags) along with different methods of calculating daily maximum, minimum, and mean temperature. We estimated the temperature effect on mortality for each variable by comparing the 99th versus 85th temperature percentiles, as determined from the annual time series. Results: In three northern cities (Boston, MA; Philadelphia, PA; and Seattle, WA) that appeared to have the greatest sensitivity to heat, hourly estimates were consistent with a diurnal pattern in the heat-mortality response, with strongest associations for afternoon or maximum temperature at lag 0 (day of death) or afternoon and evening of lag 1 (day before death). In warmer, southern cities, stronger associations were found with morning temperatures, but overall the relationships were weaker. The strongest temperature–mortality relationships were associated with maximum temperature, although mean temperature results were comparable. Conclusions: There were systematic and substantial differences in the association between temperature and mortality based on the time and type of temperature observation. Because the strongest hourly temperature–mortality relationships were not always found at times typically associated with daily maximum temperatures, temperature variables should be selected independently for each study location. In general, heat-mortality was more closely coupled to afternoon and maximum temperatures in most cities we examined, particularly those typically prone to heat-related mortality. Citation: Davis RE, Hondula DM, Patel AP. 2016. Temperature observation time and type influence estimates of heat-related mortality in seven U.S. cities. Environ Health Perspect 124:795–804; http://dx.doi.org/10.1289/ehp.1509946

Transportation planning agencies around the world are increasingly seeking to develop transportation systems that encourage walking, bicycling, and transit use in order to simultaneously achieve public health and environmental sustainability goals. Under future scenarios that will see increasing global mean temperatures, trips that entail physical exertion outdoors are likely to account for substantial portions of some individuals׳ overall exposure to potentially dangerous air temperatures. This exposure pathway is poorly understood. In this study we develop a new method to assess outdoor heat exposure during non-motorized travel by combining simulated urban meteorology and transportation-activity data. We demonstrate its utility through application to several real-world planning issues using data from the San Francisco Bay Area. Specifically, we examine spatial and social disparities in heat exposure and find that socially disadvantaged (low-income people and zero-vehicle households) groups are disproportionately exposed to transport-related heat. Since the propensity to walk and bicycle tends to decrease with socioeconomic status (SES) and because lower SES groups are more vulnerable to heat-related health impacts, efforts to mitigate heat exposure can be of disproportionate benefit to these groups. Finally, we demonstrate how the results can be used to supplement more traditional indicators of heat vulnerability that to date do not directly account for actual length of outdoor exposure. The methodology developed here has the potential to guide healthy, sustainable, and equitable urban planning efforts.

The causes and consequences of indoor heat exposure are receiving growing attention as global temperature rises and people seek respite from the heat in indoor spaces. In this study, we measured indoor temperatures of 46 air-conditioned residences in Phoenix, Arizona, United States. Temperatures were collected concurrently at 5-min intervals from August 21 to September 19, 2016. Indoor temperatures exhibited significant heterogeneity across all residences, ranging from 16.5 to 37.2 °C with a mean (SD) of 26.4 °C (2.2 °C). On average, the 5-min indoor temperatures were moderately correlated with outdoor temperature (r = 0.421), although individual household correlations were highly variable, ranging from r = -0.244 to r = 0.924. Households were grouped into six clusters using K-means based on 19 temperature metrics. We tested for differences in demographic, behavior, and infrastructure indicators between those six clusters based on responses to a social survey. Nearly half the variance in preferred thermostat setting was explained by cluster (R2 = 0.455, p

This study investigated the real-time progression of heat strain in mountain hikers during time trials (TT). Participants (n = 12, 7M/5F, age 21.6 &plusmn, 2.47) attempted to climb Tempe Butte (~1.1 mi) four times in HOT and MOD trials (wet bulb globe temperature 31.6 &deg, C vs. 19.0 &deg, C). Performance, physiological outcomes, and fluid intake behavior were measured in real-time. Hot conditions significantly impaired hiking TT performance by 11%, reduced aerobic capacity by 7%, increased peak rate of perceived exertion (RPE) by 19%, and elevated core temperature (Tc) by 0.7 &deg, C compared to MOD (all p &lt, 0.03). Less-aerobically-fit participants were most negatively-affected by heat stress. Based on sweat lost, participants in HOT required 2.26 &plusmn, 0.91 L of fluids, brought 1.52 &plusmn, 0.83 L, and consumed 1.54 &plusmn, 0.49 L, losing an average of 1.1% &plusmn, 1.0%BM. Participants in MOD required 1.28 &plusmn, 0.39 L of fluids, brought 1.57 &plusmn, 1.09 L, and consumed 0.79 &plusmn, 0.57 L, losing an average of 1.0% &plusmn, 0.8%BM. Morning-after urine specific gravity (USG) values revealed 75% of hikers were hypohydrated (USG &ge, 1.020) after HOT, 67% after MOD. Heat stress impairs hiking TT performance while increasing RPE and Tc. Fitter participants showed less performance and physiological impairment from heat stress. Although hikers in both conditions lost similar body weight, hikers were limited in HOT by fluid availability, whereas in MOD, fluid was available and dehydration was voluntary.

We compared selected thermal indices in their ability to predict heat-related mortality in Prague, Czech Republic, during the extraordinary summer 2015. Relatively, novel thermal indices—Universal Thermal Climate Index and Excess Heat Factor (EHF)—were compared with more traditional ones (apparent temperature, simplified wet-bulb globe temperature (WBGT), and physiologically equivalent temperature). The relationships between thermal indices and all-cause relative mortality deviations from the baseline (excess mortality) were estimated by generalized additive models for the extended summer season (May–September) during 1994–2014. The resulting models were applied to predict excess mortality in 2015 based on observed meteorology, and the mortality estimates by different indices were compared. Although all predictors showed a clear association between thermal conditions and excess mortality, we found important variability in their performance. The EHF formula performed best in estimating the intensity of heat waves and magnitude of heat-impacts on excess mortality on the most extreme days. Afternoon WBGT, on the other hand, was most precise in the selection of heat-alert days during the extended summer season, mainly due to a relatively small number of “false alerts” compared to other predictors. Since the main purpose of heat warning systems is identification of days with an increased risk of heat-related death rather than prediction of exact magnitude of the excess mortality, WBGT seemed to be a slightly favorable predictor for such a system.

We tested mediation models using structural equation modeling (SEM) to better understand the pathways that lead to negative heat-health outcomes for socially vulnerable households and individuals in Phoenix, AZ. Extreme heat presents a significant risk to public health, and current theories of social vulnerability cannot explain the mechanistic pathways that lead to negative health outcomes. A better understanding of these pathways can be used to target more effective public health interventions. We find that self-reported indoor thermal discomfort was a strong and significant predictor of indoor heat illness outcomes (Std [Formula: see text], [Formula: see text]), suggesting that heat risk messaging could encourage the public to trust their personal sense of being too warm. Thermal discomfort (Std [Formula: see text], [Formula: see text]) and self-reported indoor heat illness (Std [Formula: see text], [Formula: see text]) were only predicted by socioeconomic status (SES); age, gender, and racialized group did not relate to heat illness or thermal discomfort based on our sample. However, specified mediators did not fully explain the relationship between SES and thermal discomfort or heat illness, suggesting that we do not fully understand how social vulnerability manifests as negative heat-health outcomes. In addition, results show that passive cooling techniques, like taking a cold shower, did not have a protective effect on heat-health outcomes, and these passive cooling behaviors were used in greater proportion by socially vulnerable individuals. This study concludes with 11 evidence-based hypotheses about how social vulnerability may produce negative heat-health outcomes.

The article responds to an issue article on heat-related mortality among U.S. citizens as compared with among U.S. non-citizen residents. Topics include problems with heat intervention measures among at-risk non-citizens, regional prevalence of heat deaths in the Southwestern U.S., and the need for more accurate reporting of heat-related deaths.

Background: Numerous studies have reported a strong association between temperature and mortality. Additional insights can be gained from investigating the effects of temperature on years of life lost (YLL), considering the life expectancy at the time of death. Objectives: The goal of this work was to assess the association between temperature and YLL at seven low-, middle-, and high-income sites. Methods: We obtained meteorological and population data for at least nine years from four Health and Demographic Surveillance Sites in Kenya (western Kenya, Nairobi), Burkina Faso (Nouna), and India (Vadu), as well as data from cities in the United States (Philadelphia, Phoenix) and Sweden (Stockholm). A distributed lag nonlinear model was used to estimate the association of daily maximum temperature and daily YLL, lagged 0–14 d. The reference value was set for each site at the temperature with the lowest YLL. Results: Generally, YLL increased with higher temperature, starting day 0. In Nouna, the hottest location, with a minimum YLL temperature at the first percentile, YLL increased consistently with higher temperatures. In Vadu, YLL increased in association with heat, whereas in Nairobi, YLL increased in association with both low and high temperatures. Associations with cold and heat were evident for Phoenix (stronger for heat), Stockholm, and Philadelphia (both stronger for cold). Patterns of associations with mortality were generally similar to those with YLL. Conclusions: Both high and low temperatures are associated with YLL in high-, middle-, and low-income countries. Policy guidance and health adaptation measures might be improved with more comprehensive indicators of the health burden of high and low temperatures such as YLL. https://doi.org/10.1289/EHP1745

Significant climate change in the Arctic has been observed by indigenous peoples and reported in scientific literature, but there has been little research comparing these two knowledge bases. In this study, Sami reindeer herder interviews and observational weather data were combined to provide a comprehensive description of climate changes in Northern Sweden. The interviewees described warmer winters, shorter snow seasons and cold periods, and increased temperature variability. Weather data supported three of these four observed changes; the only change not evident in the weather data was increased temperature variability. Winter temperatures increased, the number of days in cold periods was significantly reduced, and some stations displayed a 2 month-shorter snow cover season. Interviewees reported that these changes to the wintertime climate are significant, impact their identity, and threaten their livelihood. If consistency between human observations of changing weather patterns and the instrumental meteorological record is observed elsewhere, mixed methods research like this study can produce a clearer, more societally relevant understanding of how the climate is changing and the impacts of those changes on human well-being. Electronic supplementary material The online version of this article (10.1007/s11111-018-0302-x) contains supplementary material, which is available to authorized users.

Background Extreme heat is a public health challenge. The scarcity of directly comparable studies on the association of heat with morbidity and mortality and the inconsistent identification of threshold temperatures for severe impacts hampers the development of comprehensive strategies aimed at reducing adverse heat-health events. Objectives This quantitative study was designed to link temperature with mortality and morbidity events in Maricopa County, Arizona, USA, with a focus on the summer season. Methods Using Poisson regression models that controlled for temporal confounders, we assessed daily temperature–health associations for a suite of mortality and morbidity events, diagnoses, and temperature metrics. Minimum risk temperatures, increasing risk temperatures, and excess risk temperatures were statistically identified to represent different “trigger points” at which heat-health intervention measures might be activated. Results We found significant and consistent associations of high environmental temperature with all-cause mortality, cardiovascular mortality, heat-related mortality, and mortality resulting from conditions that are consequences of heat and dehydration. Hospitalizations and emergency department visits due to heat-related conditions and conditions associated with consequences of heat and dehydration were also strongly associated with high temperatures, and there were several times more of those events than there were deaths. For each temperature metric, we observed large contrasts in trigger points (up to 22°C) across multiple health events and diagnoses. Conclusion Consideration of multiple health events and diagnoses together with a comprehensive approach to identifying threshold temperatures revealed large differences in trigger points for possible interventions related to heat. Providing an array of heat trigger points applicable for different end-users may improve the public health response to a problem that is projected to worsen in the coming decades. Citation Petitti DB, Hondula DM, Yang S, Harlan SL, Chowell G. 2016. Multiple trigger points for quantifying heat-health impacts: new evidence from a hot climate. Environ Health Perspect 124:176–183; http://dx.doi.org/10.1289/ehp.1409119

Spatially targeted interventions may help protect the public when extreme heat occurs. Health outcome data are increasingly being used to map intra-urban variability in heat-health risks, but there has been little effort to compare patterns and risk factors between cities. We sought to identify places within large metropolitan areas where the mortality rate is highest on hot summer days and determine if characteristics of high-risk areas are consistent from one city to another. A Poisson regression model was adapted to quantify temperature-mortality relationships at the postal code scale based on 2.1 million records of daily all-cause mortality counts from seven U.S. cities. Multivariate spatial regression models were then used to determine the demographic and environmental variables most closely associated with intra-city variability in risk. Significant mortality increases on extreme heat days were confined to 12-44% of postal codes comprising each city. Places with greater risk had more developed land, young, elderly, and minority residents, and lower income and educational attainment, but the key explanatory variables varied from one city to another. Regression models accounted for 14-34% of the spatial variability in heat-related mortality. The results emphasize the need for public health plans for heat to be locally tailored and not assume that pre-identified vulnerability indicators are universally applicable. As known risk factors accounted for no more than one third of the spatial variability in heat-health outcomes, consideration of health outcome data is important in efforts to identify and protect residents of the places where the heat-related health risks are the highest.

The International Society of Biometeorology (ISB) has covered significant breadth and depth addressing fundamental and applied societal and environmental challenges in the last 60 years. Biometeorology is an interdisciplinary science connecting living organisms to their environment, but there is very little understanding of the existence and placement of this discipline within formal educational systems and institutions. It is thus difficult to project the ability of members of the biometeorological community-especially the biometeorologists of the future-to help solve global challenges. In this paper, we ask: At present, how we are training people to understand and think about biometeorology? We also ask: What are the current tools and opportunities in which biometeorologists might address future challenges? Finally, we connect these two questions by asking: What type of new training and skill development is needed to better educate "biometeorologists of the future" to more effectively address the future challenges? To answer these questions, we provide quantitative and qualitative evidence from an educationally focused workshop attended by new professionals in biometeorology. We identify four common themes (thermal comfort and exposures, agricultural productivity, air quality, and urbanization) that biometeorologists are currently studying and that we expect to be important in the future based on their alignment with the United Nations Sustainable Development Goals. Review of recent literature within each of these thematic areas highlights a wide array of skill sets and perspectives that biometeorologists are already using. Current and new professionals within the ISB have noted highly varying and largely improvised educational pathways into the field. While variability and improvisation may be assets in promoting flexibility, adaptation, and interdisciplinarity, the lack of formal training in biometeorology raises concerns about the extent to which continuing generations of scholars will identify and engage with the community of scholarship that the ISB has developed over its 60-year history.

Background: Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to “adaptation uncertainty” (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. Objectives: This study had three aims: a) Compare the range in projected impacts that arises from using different adaptation modeling methods; b) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c) recommend modeling method(s) to use in future impact assessments. Methods: We estimated impacts for 2070–2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. Results: The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Conclusions: Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634

Background: Environmental heat exposure is a public health concern. The impacts of environmental heat on mortality and morbidity at the population scale are well documented, but little is known abo...

Improvements in global sustainability, health, and equity will largely be determined by the extent to which cities are able to become more efficient, hospitable, and productive places. The development and evolution of urban areas has a significant impact on local and regional weather and climate, which subsequently affect people and other organisms that live in and near cities. Biometeorologists, researchers who study the impact of weather and climate on living creatures, are well positioned to help evaluate and anticipate the consequences of urbanization on the biosphere. Motivated by the 60th anniversary of the International Society of Biometeorology, we reviewed articles published in the Society’s International Journal of Biometeorology over the period 1974–2017 to understand if and how biometeorologists have directed attention to urban areas. We found that interest in urban areas has rapidly accelerated; urban-oriented articles accounted for more than 20% of all articles published in the journal in the most recent decade. Urban-focused articles in the journal span five themes: measuring urban climate, theoretical foundations and models, human thermal comfort, human morbidity and mortality, and ecosystem impacts. Within these themes, articles published in the journal represent a sizeable share of the total academic literature. More explicit attention from urban biometeorologists publishing in the journal to low- and middle-income countries, indoor environments, animals, and the impacts of climate change on human health would help ensure that the distinctive perspectives of biometeorology reach the places, people, and processes that are the foci of global sustainability, health, and equity goals.

Heat-related mortality remains a public health challenge in the United States. The objective of this study was to determine the temporal consistency of high-risk zones for heat-related mortality using historical georeferenced mortality data from seven US cities. A generalized additive model was used to identify city-specific threshold temperatures associated with increased mortality, and then the mortality rate on threshold-exceeding days was calculated for each postal code comprising each study city. This process was iterated by withholding subsets of data from the model and assessing predictability via cross-validation. In all cities, the average mortality rate in postal codes targeted for intervention by the statistical model was higher than that in non-targeted areas. Targeted areas for interventions in the study data accounted for 50 % of excess heat-related deaths despite only accounting for 25 % of total mortality. Focusing intervention measures at certain geographical zones within urban areas could be an effective means of combating heat-related mortality because there is temporal consistency in places where the death rate is most sensitive to heat.

Mortality rates increase immediately after periods of high air temperature. In the days and weeks after heat events, time series may exhibit mortality displacement-periods of lower than expected mortality. We examined all-cause mortality and meteorological data from 1980 to 2009 in the cities of Atlanta, Georgia; Boston, Massachusetts; Minneapolis-St. Paul, Minnesota; Philadelphia, Pennsylvania; Phoenix, Arizona; Seattle, Washington; and St. Louis, Missouri. We modeled baseline mortality using a generalized additive model. Heat waves were defined as periods of 3 or more consecutive days in which the apparent temperature exceeded a variable percentile. For each heat wave, we calculated the sum of excess and deficit mortality. Mortality displacement, which is the ratio of grand sum deficit to grand sum excess mortality, decreased as a function of event strength in all cities. Displacement was close to 1.00 for the weakest events. At the highest temperatures, displacement varied from 0.35 (95% confidence interval: 0.21, 0.55) to 0.75 (95% confidence interval: 0.54, 0.97). We found strong evidence of acclimatization across cities. Without consideration of displacement effects, the net impacts of heat-wave mortality are likely to be significant overestimations. A statistically significant positive relationship between the onset temperature of nondisplaced heat mortality and mean warm-season temperature (R(2) = 0.78, P < 0.01) suggests that heat mortality thresholds may be predictable across cities.

Societies are addressing increasingly complex governance challenges that necessitate collaboration between many organizations. Harnessing the emergent abilities of these collective efforts requires new administrative strategies and techniques, but if done well also provides promise for addressing important social challenges. In Maricopa County Arizona the Department of Public Health reports 632 confirmed heat-associated deaths from 2006 to 2013. In response, public health and other organizations coordinate across the County with a collection of public and private organizations and non-profit groups to provide services for heat relief as cooling centers during the summer. Here we show how participatory modeling can be used as a tool to enable this ad-hoc collaborative network to self-organize to provide more efficient service. The voluntary nature of the network imposes a structure on cooling service provision as the locations and open hours of centers are largely based on other ongoing operations. There are consequently both gaps and redundancies in spatial and temporal cooling center availability that exist when the network is examined from a system perspective. Over the last year, we engaged members of the heat relief community in central Arizona in a participatory modeling effort to help improve a simple prototype agent-based model that visualizes relevant components of the regional Heat Relief Network’s function. Through this process, the members developed systemic awareness of both the challenges and opportunities of coordination across the network. This effort helped network members begin to see cooling centers from a systems perspective, leverage their ability to see dynamic cooling center availability spatially and temporally and thus increase opportunities to align services along both dimensions. Our collaboration with the Heat Relief Network in central Arizona highlights participatory modeling as an innovative means for translating evidence to practice and facilitating knowledge dissemination, two important elements for successful applications on complexity governance.

Respiratory morbidity (particularly COPD and asthma) can be influenced by short-term weather fluctuations that affect air quality and lung function. We developed a model to evaluate meteorological conditions associated with respiratory hospital admissions in the Shenandoah Valley of Virginia, USA. We generated ensembles of classification trees based on six years of respiratory-related hospital admissions (64,620 cases) and a suite of 83 potential environmental predictor variables. As our goal was to identify short-term weather linkages to high admission periods, the dependent variable was formulated as a binary classification of five-day moving average respiratory admission departures from the seasonal mean value. Accounting for seasonality removed the long-term apparent inverse relationship between temperature and admissions. We generated eight total models specific to the northern and southern portions of the valley for each season. All eight models demonstrate predictive skill (mean odds ratio = 3.635) when evaluated using a randomization procedure. The predictor variables selected by the ensembling algorithm vary across models, and both meteorological and air quality variables are included. In general, the models indicate complex linkages between respiratory health and environmental conditions that may be difficult to identify using more traditional approaches.

Air-mass transitions are an important component of atmospheric circulation, but are dif- ficult to objectively identify and analyze in the context of decadal-scale climate change. Spatial syn- optic classification (SSC) uses surface weather observations of daily dew point temperature range, sea-level pressure range, and wind shift to identify days with a distinct change from one air mass to another ('transition days'). Thus, the SSC offers a unique opportunity to examine the climatology of synoptic-scale transition events. The frequency of the transition type has significantly declined across United States over the second half of the twentieth century, potentially revealing a climatic shift toward fewer abrupt air-mass changes, but the underlying causes have not been well-documented to date. We link the decline in winter transition frequency to decreasing intra-daily moisture and pres- sure variability by examining the largest within-day dew point temperature and sea-level pressure changes. Fewer transitions are being observed in the continental United States because of increasing daily minimum dew point temperatures in the upper Midwest and smaller pressure changes over 24 h periods in the western states. The frequency and intensity of air-mass transitions are closely tied to other components of the climate system; therefore, examination of transition trends serves as a novel framework for exploring climatic change.

In middle and high latitudes, climate change could impact the frequency and characteristics of frontal passages. Although transitions between air masses are significant features of the general circulation that influence human activities and other surface processes, they are much more difficult to objectively identify than single variables like temperature or even extreme events like fires, droughts, and floods. The recently developed Spatial Synoptic Classification (SSC) provides a fairly objective means of identifying frontal passages. In this research, we determine the specific meteorological patterns represented by the SSC’s Transition category, a “catch-all” group that attempts to identify those days that cannot be characterized as a single, homogeneous air mass type. The result is a detailed transition climatology for the continental USA. We identify four subtypes of the Transition category based on intra-day sea level pressure change and dew point temperature change. Across the contiguous USA, most transition days are identified as cold fronts and warm fronts during the winter season. Among the two less common subtypes, transition days in which the dew point temperature and pressure both rise are more frequently observed across the western states, and days in which both variables fall are more frequently observed in coastal regions. The relative frequencies of wintertime warm and cold fronts have changed over the period 1951–2007. Relative cold front frequency has significantly increased in the Northeast and Midwest regions, and warm front frequencies have declined in the Midwest, Rocky Mountain, and Pacific Northwest regions. The overall shift toward cold fronts and away from warm fronts across the northern USA arises from a combination of an enhanced ridge over western North America and a northward shift of storm tracks throughout the mid-latitudes. These results are consistent with projections of climate change associated with elevated greenhouse gas concentrations.

Back trajectory analysis is a commonly-used tool for understanding how short-term variability in surface ozone depends on transport into a given location. Lesser-used but equally effective methods are air-mass based approaches that are primarily driven by changes in temperature and humidity conditions. We compare and combine these two fundamentally different approaches by evaluating daily near-surface afternoon warm-season ozone concentrations from 2001 to 2006 in and around the Shenandoah Valley of Virginia. Analysis of variance is used to compare summer afternoon ozone levels between air masses as identified by the Spatial Synoptic Classification to clusters of 72-h back trajectories estimated by the HYSPLIT model. Ozone concentrations vary significantly across both air masses and trajectory clusters at all ozone monitors. Concentrations are highest for air masses characterized by dry, warm conditions and for air originating from the north and west of the study area or circulating over the mid-Atlantic region. In many cases, the interaction between synoptic types and back trajectory clusters produce results not evident from the examination of simple trajectories or air masses alone. For example, ozone concentrations on Moist Moderate days are 30 ppb higher when air parcels travel moderate distances into the Shenandoah Valley from the west than when they travel longer distances from the north or northeast. Combining air mass and trajectory approaches provides a more useful characterization of air quality conditions than either method alone.

Synoptic-scale air quality climatologies can often be categorized into trajectory methods based on air parcel movement or air mass approaches that are primarily dictated by thermal and moisture conditions. Here, we examine the relationship between these two essentially independent methods of synoptic-scale climate classification: 72-h Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model back-trajectories and the Spatial Synoptic Classification (SSC) of air masses for Martinsburg, West Virginia, located at the northern end of the Shenandoah Valley, from 1997 to 2006. Statistically significant differences exist between trajectory groups across the six SSC air mass types within each season and between seasons within each air mass type. Further examination of the trajectory distributions reveals a high degree of overlap between trajectories associated with different air mass types. In some cases, up to 50% of one air mass's trajectories are statistically closer to the trajectories of a different air mass type. Accordingly, air mass alone cannot be used to identify specific source regions of pollutants. This suggests that, while an air mass classification system can be used to identify general flow patterns, the integration of both thermodynamic and air-flow variables in a synoptic classification should provide information that is not discernible from either method independently. The inclusion of both back-trajectory and air mass information represents the suite of environmental conditions that may enhance or suppress pollutant concentrations in the Shenandoah Valley better than using either measure independently. Copyright © 2009 Royal Meteorological Society

Background: Most excess deaths that occur during extreme hot weather events do not have natural heat recorded as an underlying or contributing cause. This study aims to identify the specific individuals who died because of hot weather using only secondary data. A novel approach was developed in which the expected number of deaths was repeatedly sampled from all deaths that occurred during a hot weather event, and compared with deaths during a control period. The deaths were compared with respect to five factors known to be associated with hot weather mortality. Individuals were ranked by their presence in significant models over 100 trials of 10,000 repetitions. Those with the highest rankings were identified as probable excess deaths. Sensitivity analyses were performed on a range of model combinations. These methods were applied to a 2009 hot weather event in greater Vancouver, Canada. Results The excess deaths identified were sensitive to differences in model combinations, particularly between univariate and multivariate approaches. One multivariate and one univariate combination were chosen as the best models for further analyses. The individuals identified by multiple combinations suggest that marginalized populations in greater Vancouver are at higher risk of death during hot weather. Conclusions This study proposes novel methods for classifying specific deaths as expected or excess during a hot weather event. Further work is needed to evaluate performance of the methods in simulation studies and against clinically identified cases. If confirmed, these methods could be applied to a wide range of populations and events of interest.

Maricopa County, Arizona, anchor to the fastest growing megapolitan area in the United States, is located in a hot desert climate where extreme temperatures are associated with elevated risk of mortality. Continued urbanization in the region will impact atmospheric temperatures and, as a result, potentially affect human health. We aimed to quantify the number of excess deaths attributable to heat in Maricopa County based on three future urbanization and adaptation scenarios and multiple exposure variables. Two scenarios (low and high growth projections) represent the maximum possible uncertainty range associated with urbanization in central Arizona, and a third represents the adaptation of high-albedo cool roof technology. Using a Poisson regression model, we related temperature to mortality using data spanning 1983–2007. Regional climate model simulations based on 2050-projected urbanization scenarios for Maricopa County generated distributions of temperature change, and from these predicted changes future excess heat-related mortality was estimated. Subject to urbanization scenario and exposure variable utilized, projections of heat-related mortality ranged from a decrease of 46 deaths per year (− 95%) to an increase of 339 deaths per year (+ 359%). Projections based on minimum temperature showed the greatest increase for all expansion and adaptation scenarios and were substantially higher than those for daily mean temperature. Projections based on maximum temperature were largely associated with declining mortality. Low-growth and adaptation scenarios led to the smallest increase in predicted heat-related mortality based on mean temperature projections. Use of only one exposure variable to project future heat-related deaths may therefore be misrepresentative in terms of direction of change and magnitude of effects. Because urbanization-induced impacts can vary across the diurnal cycle, projections of heat-related health outcomes that do not consider place-based, time-varying urban heat island effects are neglecting essential elements for policy relevant decision-making.

*Introduction:* Most excess deaths that occur during extreme hot weather events do not have natural heat recorded as an underlying or contributing cause. This makes it challenging to identify exces...

Urban environmental health hazards, including exposure to extreme heat, have become increasingly important to understand in light of ongoing climate change and urbanization. In cities, neighborhoods are often considered a homogenous and appropriate unit with which to assess heat risk. This manuscript presents results from a pilot study examining the variability of individually experienced temperatures (IETs) within a single urban neighborhood. In July 2013, 23 research participants were recruited from the South End neighborhood of Boston and equipped with Thermochron iButtons that measured the air temperatures surrounding individuals as they went about their daily lives. IETs were measured during a heat wave period (July 17–20), which included 2 days with excessive heat warnings and 1 day with a heat advisory, as well as a reference period (July 20–23) in which temperatures were below seasonal averages. IETs were not homogeneous during the heat wave period; mean IETs were significantly different between participants (p < 0.001). The majority of participants recorded IETs significantly lower than outdoor ambient temperatures (OATs), and on average, the mean IET was 3.7 °C below the mean OAT. Compared with IETs during the reference period, IETs during the heat wave period were 1.0 °C higher. More than half of participants did not experience statistically different temperatures between the two test periods, despite the fact that the mean OAT was 6.5 °C higher during the heat wave period. The IET data collected for this sample and study period suggest that (1) heterogeneity in individual heat exposure exists within this neighborhood and that (2) outdoor temperatures misrepresent the mean experienced temperatures during a heat wave period. Individual differences in attributes (gender, race, socioeconomic status, etc.), behaviors (schedules, preferences, lifestyle, etc.), and access to resources are overlooked determinants of heat exposure and should be better integrated with group- and neighborhood-level characteristics. Understanding IETs for the population at large may lead to innovative advances in heat-health intervention and mitigation strategies.

Maricopa County, Arizona, anchor to the fastest growing megapolitan area in the United States, is located in a hot desert climate where extreme temperatures are associated with elevated risk of mortality. Continued urbanization in the region will impact atmospheric temperatures and, as a result, potentially affect human health. We aimed to quantify the number of excess deaths attributable to heat in Maricopa County based on three future urbanization and adaptation scenarios and multiple exposure variables. Two scenarios (low and high growth projections) represent the maximum possible uncertainty range associated with urbanization in central Arizona, and a third represents the adaptation of high-albedo cool roof technology. Using a Poisson regression model, we related temperature to mortality using data spanning 1983–2007. Regional climate model simulations based on 2050-projected urbanization scenarios for Maricopa County generated distributions of temperature change, and from these predicted changes future excess heat-related mortality was estimated. Subject to urbanization scenario and exposure variable utilized, projections of heat-related mortality ranged from a decrease of 46 deaths per year (− 95%) to an increase of 339 deaths per year (+ 359%). Projections based on minimum temperature showed the greatest increase for all expansion and adaptation scenarios and were substantially higher than those for daily mean temperature. Projections based on maximum temperature were largely associated with declining mortality. Low-growth and adaptation scenarios led to the smallest increase in predicted heat-related mortality based on mean temperature projections. Use of only one exposure variable to project future heat-related deaths may therefore be misrepresentative in terms of direction of change and magnitude of effects. Because urbanization-induced impacts can vary across the diurnal cycle, projections of heat-related health outcomes that do not consider place-based, time-varying urban heat island effects are neglecting essential elements for policy relevant decision-making.

Background: Extreme heat is a leading weather-related cause of illness and death in many locations across the globe, including subtropical Australia. The possibility of increasingly frequent and severe heat waves warrants continued efforts to reduce this health burden, which could be accomplished by targeting intervention measures toward the most vulnerable communities. Objectives: We sought to quantify spatial variability in heat-related morbidity in Brisbane, Australia, to highlight regions of the city with the greatest risk. We also aimed to find area-level social and environmental determinants of high risk within Brisbane. Methods: We used a series of hierarchical Bayesian models to examine city-wide and intracity associations between temperature and morbidity using a 2007–2011 time series of geographically referenced hospital admissions data. The models accounted for long-term time trends, seasonality, and day of week and holiday effects. Results: On average, a 10°C increase in daily maximum temperature during the summer was associated with a 7.2% increase in hospital admissions (95% CI: 4.7, 9.8%) on the following day. Positive statistically significant relationships between admissions and temperature were found for 16 of the city’s 158 areas; negative relationships were found for 5 areas. High-risk areas were associated with a lack of high income earners and higher population density. Conclusions: Geographically targeted public health strategies for extreme heat may be effective in Brisbane, because morbidity risk was found to be spatially variable. Emergency responders, health officials, and city planners could focus on short- and long-term intervention measures that reach communities in the city with lower incomes and higher population densities, including reduction of urban heat island effects. Citation: Hondula DM, Barnett AG. 2014. Heat-related morbidity in Brisbane, Australia: spatial variation and area-level predictors. Environ Health Perspect 122:831–836; http://dx.doi.org/10.1289/ehp.1307496

Extreme heat is a leading cause of weather-related mortality. Most research has considered the aggregate response of the populations of large metropolitan areas, but the focus of heat-related mortality and morbidity investigations is shifting towards a more fine-scale approach in which impacts are measured in smaller units such as postal codes. However, most existing statistical techniques to model the relationship between temperature and mortality cannot be directly applied to the intra-city scale because small sample sizes inhibit proper modelling of seasonality and long-term trends. Here we propose a time series technique based on local-scale mortality observations that can provide more reliable information about vulnerability within metropolitan areas. The method combines a generalised additive model with direct standardisation to account for changing death rates in intra-city zones. We apply the method to a 26-year time series of postal code-referenced mortality data from Philadelphia County, USA, where we find that heat-related mortality is unevenly spatially distributed. Fifteen of 46 postal codes are associated with significantly increased mortality on extreme heat days, most of which are located in the central and western portions of the county. In some cases the local death rate is more than double the county average. Identification of high-risk areas can enable targeted public health intervention and mitigation strategies.

This year marks the tenth anniversary of the development of the revised Spatial Synoptic Classification, the “SSC”, by Scott Sheridan. This daily weather-type classification scheme has become one of the key analytical tools implemented in a diverse range of climatological investigations, including analysis of air quality variability, human health, vegetation growth, precipitation and snowfall trends, and broader analyses of historical and future climatic variability and trends. The continued and expanding use of the SSC motivates a review and comparison of the system’s research and geographic foci to date, with the goal of identifying promising areas for future efforts, particularly within the context of human health and climate change. This review also assesses how the SSC has complemented and compares with other current environmental epidemiological studies in weather and health.

Background High temperature and humidity conditions are associated with short-term elevations in the mortality rate in many United States cities. Previous research has quantified this relationship in an aggregate manner over large metropolitan areas, but within these areas the response may differ based on local-scale variability in climate, population characteristics, and socio-economic factors. Methods We compared the mortality response for 48 Zip Code Tabulation Areas (ZCTAs) comprising Philadelphia County, PA to determine if certain areas are associated with elevated risk during high heat stress conditions. A randomization test was used to identify mortality exceedances for various apparent temperature thresholds at both the city and local scale. We then sought to identify the environmental, demographic, and social factors associated with high-risk areas via principal components regression. Results Citywide mortality increases by 9.3% on days following those with apparent temperatures over 34°C observed at 7:00 p.m. local time. During these conditions, elevated mortality rates were found for 10 of the 48 ZCTAs concentrated in the west-central portion of the County. Factors related to high heat mortality risk included proximity to locally high surface temperatures, low socioeconomic status, high density residential zoning, and age. Conclusions Within the larger Philadelphia metropolitan area, there exists statistically significant fine-scale spatial variability in the mortality response to high apparent temperatures. Future heat warning systems and mitigation and intervention measures could target these high risk areas to reduce the burden of extreme weather on summertime morbidity and mortality.

Time series of individual climate variables, such as air temperature and precipitation, have been thoroughly examined to evaluate climate change, but few studies have evaluated how air masses have varied over time. We use the Spatial Synoptic Classification air mass approach to classify multivariate meteorological surface variables into discrete groups and examine trends in air mass frequencies over the period 1948-2005 for the continental United States. We observe increases in warm, moist air masses at the expense of cold, dry air masses, consistent with expectations in an atmosphere with increasing greenhouse gas concentrations. Temporal variations in the North Atlantic Oscillation, Pacific/North American teleconnection pattern, Arctic Oscillation, and El Nino-Southern Oscillation partially explain some of these observed trends in winter.

Over the past 40 years residents of, and visitors to, the North Carolina coastal barrier islands have experienced the destructive forces of several 'named' extratropical storms. These storms have caused large-scale redistributions of sand and loss of coastal structures and infrastructure. While most of the population living on the islands are familiar with the wintertime storms, the damage and scars of the 'super northeasters'—such as the Ash Wednesday storm of 7 March 1962, and the Halloween storm of 1989—are slipping away from the public's memory. In this research we compared the damage zones of the 1962 Ash Wednesday storm, as depicted on aerial photographs taken after the storm, with photos taken of the same areas in 2003. With these high-resolution aerial photos we were able to estimate the extent of new development which has taken place along the Outer Banks of North Carolina since 1962. Three damage zones were defined that extend across the islands from the ocean landward on the 1962 aerial photos: (1) the zone of almost total destruction on the seaward edge of the islands where the storm waves break; (2) the zone immediately inland where moderate structural damage occurs during severe storms; and (3) the zone of flood damage at the landward margin of the storm surge and overwash. We considered the rate of coastal erosion, the rate of development, and increases in property values as factors which may contribute to changing the financial risk for coastal communities. In comparing the values of these four factors with the 1962 damage data, we produced a predicted dollar value for storm damage should another storm of the magnitude of the 1962 Ash Wednesday storm occur in the present decade. This model also provides an opportunity to estimate the rate of increase in the potential losses through time as shoreline erosion continues to progressively reduce the buffer between the development and the edge of the sea. Our data suggest that the losses along the North Carolina coast would rank amongst the all-time most costly natural disasters to have occurred in the United States, with up to $1 billion in losses in North Carolina alone.

Made available in DSpace on 2014-12-03T13:08:46Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-03-01Bitstream added on 2014-12-03T13:23:25Z : No. of bitstreams: 1 WOS000332324000017.pdf: 923617 bytes, checksum: eed2dd5aa03f0c820f24736dd64da227 (MD5) Here we present, for the first time, a glossary of biometeorological terms. The glossary aims to address the need for a reliable source of biometeorological definitions, thereby facilitating communication and mutual understanding in this rapidly expanding field. A total of 171 terms are defined, with reference to 234 citations. It is anticipated that the glossary will be revisited in coming years, updating terms and adding new terms, as appropriate. The glossary is intended to provide a useful resource to the biometeorology community, and to this end, readers are encouraged to contact the lead author to suggest additional terms for inclusion in later versions of the glossary as a result of new and emerging developments in the field. Univ Nottingham, Sch Geog, Nottingham NG7 2RD, England Univ Toronto, Dept Anthropol, Toronto, ON M5S 1A1, Canada Mississippi State Univ, Dept Geosci, Starkville, MS USA Univ Bremen, Dept Social Epidemiol, Inst Publ Hlth & Nursing Res, D-28359 Bremen, Germany Charite, Berlin Sch Publ Hlth, D-13353 Berlin, Germany Univ Loughborough, Sch Civil & Bldg Engn, Loughborough, Leics, England Univ Loughborough, Loughborough Design Sch, Loughborough, Leics, England Univ Wisconsin, Dept Geog, Milwaukee, WI 53201 USA Arizona State Univ, Sch Publ Affairs, Ctr Policy Informat, Phoenix, AZ USA Univ Kentucky, Dept Geog, Lexington, KY 40506 USA Univ Estadual Maringa UEM, Dept Anim Sci, Maringa, Parana, Brazil Univ Bern, Inst Phys, Bern, Switzerland Univ Bern, Oeschger Ctr Climate Change Res, Bern, Switzerland Sao Paulo State Univ UNESP, Fac Agr & Vet Sci, Dept Anim Sci, Sao Paulo, Brazil Univ Florence, Interdept Ctr Bioclimatol, Florence, Italy Univ Florence, Dept Agrifood Prod & Environm Sci, Florence, Italy Texas Tech Univ, Dept Geosci, Lubbock, TX 79409 USA Univ Munich, Munich, Germany Sao Paulo State Univ UNESP, Fac Agr & Vet Sci, Dept Anim Sci, Sao Paulo, Brazil