Predicting indoor heat exposure risk during extreme heat events
Introduction
Heat waves are typically defined as prolonged periods of elevated temperature and humidity (D'Ippoliti et al., 2010, Smith et al., 2013). These events are associated with increases in morbidity and mortality, and extreme heat waves can cause public health emergencies. In summer 2003, a 9-day heat wave in Western Europe caused between 50,000 and 70,000 excess deaths (Larsen, 2006, Robine et al., 2008). Although most analyses of historical and future heat-related mortality have focused on increases in outdoor ambient temperature, the majority of fatal heat exposures in the developed world occur indoors. In New York City (NYC), over 80% of heat strokes citywide have been attributed to exposure at home (New York City Department of Health and Mental Hygiene, 2013), and during the 2003 European heat wave, 50% of the observed fatalities in France occurred in homes (a figure that does not include deaths in hospitals that may have resulted from residential heat exposure) (Fouillet et al., 2006). For the United States (US) and Europe, climate models predict increases in the frequency and duration of extreme summertime temperatures (Duffy and Tebaldi, 2012, Karl et al., 2008), heat wave intensity and frequency (IPCC, 2007, Meehl and Tebaldi, 2004), and heat-associated morbidity and mortality (Hayhoe et al., 2010, Huang et al., 2011, Knowlton et al., 2007, Lin et al., 2012). An “analog city” approach has estimated, for example, that a heat wave analogous to the 2003 European event would lead to a tenfold increase in annual heat-related deaths in the city of Chicago (Hayhoe et al., 2010). Given this background, a thorough evaluation of key heat exposure environments is imperative; yet our understanding of heat and humidity conditions in the indoor residential environment is extremely limited.
The few studies that have attempted to characterize summertime conditions in the indoor residential environment (Arena et al., 2010, Franck et al., 2013, Mavrogianni et al., 2010, Mirzaei et al., 2012, Nguyen et al., 2013, Tamerius et al., 2013, White-Newsome et al., 2012, Wright et al., 2005) have demonstrated that temperature and humidity vary significantly across homes, despite similar outdoor conditions. Indoor environments are influenced by stable attributes (such as building type, window placement, and socioeconomic status) as well as behavioral factors such as cooking, bathing, and use of air conditioning (Tamerius et al., 2013, Yik et al., 2004). Like other health risks, heat stress is more likely to have adverse effects, including fatalities, among residents at the lower end of the socioeconomic spectrum (Harlan et al., 2006, Klinenberg, 2002). Improving public health measures designed to mitigate the effects of extreme heat necessitates accurate characterization of the range of heat and humidity conditions experienced in residential environments.
In this study, we analyze the association between indoor heat and humidity measurements recorded in 285 low- and middle-income New York City homes during the summer (June–September) and concurrent outdoor conditions. We use these observed relationships to build models to predict the response of indoor temperature and humidity to a range of outdoor conditions. Employing these models, we simulate expected indoor conditions during two extreme heat events: the 10-day 2006 NYC heat wave and a 9-day event that is an NYC analog of the 2003 Paris heat wave. In these simulations we employ a heat index (HI) measure that is commonly used to issue heat advisories in many US cities, including NYC (US Department of Commerce, 2010). The heat index is an important indicator of health risk because it combines temperature and humidity, both of which modulate the human body's ability to dissipate heat (Havenith, 2005).
Section snippets
Residences in the study sample
The residences monitored for indoor temperature and humidity in this study were the homes of participants in two recruitment research studies in the New York City area: 1) Endotoxin, Obesity, and Asthma in NYC Head Start (Head Start); and 2) New York City Neighborhood Allergy and Asthma Study (NAAS) (Olmedo et al., 2011, Rotsides et al., 2010). Briefly, the Head Start cohort is a cohort of children from low-income families who attended Head Start programs serving neighborhoods with high asthma
Results
We quantified the association between summertime outdoor and indoor temperature (T), dew point temperature (DP), and heat index (HI) in 285 low- and middle-income homes in New York City. The homes are typical of those inhabited by NYC families in this socioeconomic category, though not a random sample of all low- and middle-income housing in the city. We found that indoor and outdoor DPs were the most strongly associated of the three pairs of variables, with indoor hourly DP increasing by 0.66
Discussion
Heat waves are a regular feature of the current climate system in temperate countries. We expect climate change will exacerbate summertime heat exposure risk by increasing the frequency, severity, and/or duration of heat waves (IPCC, 2007). Our response to this public health threat needs to be informed by a thorough evaluation of indoor conditions and their association with specific health outcomes. Currently, building owners in New York City are required by law to provide a minimum level of
Conclusions
Should heat wave frequency and intensity increase in the coming decades, exposure to dangerous heat and humidity levels will increase. The majority of heat-related fatalities occur at home, yet indoor conditions and their association with specific health outcomes have not been characterized. Indeed, heat exposure warnings are based on outdoor conditions, where people spend little time. Our findings on summertime heat and humidity levels in a subset of low- and middle-income New York City homes
Conflict of interest
The authors assert that they have no actual or potential conflicts of interest in the publication of this research article.
Acknowledgments
Funding provided by US NIH grant GM100467 (JS, JT) and NIEHS Center grant ES009089 (JS). Funding for the Head Start study was provided by NHLBI grant HL068236 and NIEHS grant P30 ES 009. Funding for the NAAS study was provided by NIEHS grants ES014400 and P30 ES09089 and HUD grant NYHHHU0003-3. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute Of General Medical Sciences or the National Institutes of Health.
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