The B’more Cool initiative is exploring patterns of heat exposure and impact across Baltimore, Maryland, åÊand evaluating active and proposed mitigation and adaptation interventions.
Extreme heat is the deadliest form of climate hazard in the United States today, killing an average of 600 people per year . These deaths are almost entirely preventable; however, the frequency and intensity of heat waves is increasing over most of the country [2, 3]. Health impacts of heat waves are particularly severe in cities [4, 5], a pattern that is exacerbated by the urban heat island (UHI) effectÛÓ cities tend to be several degrees warmer than surrounding areas due to a combination of low evaporative fraction, stagnant winds, anthropogenic heat release, air pollution, large surface area for heat storage, and geometric urban canyon effects that enhance absorption of short-wave radiation and inhibit long-wave radiative heat loss . Mesoscale atmospheric circulations can further intensify this effect . Many city governments are attuned to the negative impacts that heat waves and the UHI have on health and well-being, and major cities, including Baltimore, have identified heat stress management as a top priority for disaster preparedness and climate change adaptation [8, 9].
Both heat wave preparedness and UHI mitigation in U.S. cities is hampered by a lack of detailed and scientifically rigorous information on the spatial structure of the UHI and the influence that fine-scale landscape structure has on heat island intensity. We know, for example, that downtown areas with limited vegetation cover tend to be warmer than tree-lined neighborhoods on the urban periphery, and this general pattern is captured by thermal satellite imagery (Figure 1).
But the correlation between satellite-retrieved radiative skin temperature and sensible air temperature at street level (Tair) is mediated by a number of surface and atmospheric properties [10, 11], such that a satellite snapshot does not offer an adequate basis for monitoring and responding to extreme heat events in a spatially explicit manner. Note, for example, the intensely localized skin temperature features in Figure 1 associated with contrasts in surface properties. These satellite-derived skin temperature patterns are sometimes referred to as a ÛÏsurface UHI,Û but they differ spatially and temporally from the near-surface air temperature UHIÛÓsometimes called the ÛÏcanopy UHIÛÛÓthat is of more relevance to human health and is the focus of our analysis.
The disconnect between satellite-derived skin temperature and Tair means that cities usually rely on National Weather Service alerts from synoptic weather stations located outside of the urban core to define Tair thresholds for heat wave warnings. These warnings can be issued from stations that are known to be 5-10 degrees cooler than urban areas. The alerts are issued for too broad a region, and so they do not take local conditions into account. There is a need for heat monitoring systems that capture heterogeneities in the UHI at neighborhood and sub-neighborhood scale, such that city offices with responsibility for health, emergency management, housing, and sustainability can effectively target acute interventions for vulnerable populations during heat wave events. Further, the relationship between heat conditions and health impacts is most often quantified at city-wide or regional scale on account of limited spatially distributed information on air temperature and humidity conditions and on health response, and the potential for inaccuracy when interpolating data to fine scale . There is a need to quantify these relationships across the city to assess vulnerability and prioritize interventions.
Secondly, as temperatures rise under global warming, some cities are taking action to reduce the UHI as a climate change adaptation measure. According to some studies, full mitigation of the UHI, were it possible, has the potential to fully offset the projected local increase in surface temperature due to global warming in the 21st century under a moderate emissions scenario in many cases . To the first order, we know that large-scale greening and surface brightening (e.g., highly reflective ÛÏcool surfacesÛ) have the potential to moderate the UHI due to well-known energy partitioning and radiation balance effects . In practice, however, UHI mitigation activities currently proceed in piecemeal fashion; cities, neighborhoods, and individual property owners undertake different actions in different neighborhoods, leading to a mosaic of greening and other heat mitigation activities of irregular scale and connectivity. This presents a significant challenge for planning optimal UHI mitigation activities and for tracking their efficacy.
These challenges have motivated a new collaboration on UHI monitoring, mitigation, and adaptation in Baltimore, devoted to closing the loop between urban design, scientifically-informed heat management, and implementation of environment and health management efforts: the BÛªmore Cool initiative. BÛªmore Cool includes planners and community resilience experts in the Baltimore city government, climate scientists and health experts from Johns Hopkins University, and designers from the Maryland College Institute of Art (MICA). Together, we are exploring patterns of heat exposure and impact across the city and evaluating active and proposed mitigation and adaptation interventions. At the heart of the effort is the BÛªmore Cool network of low-cost temperature and humidity sensors (Figure 2) that are deployed in summer across heat vulnerable neighborhoods (Figure 3). These sensors, composed of an iButton hygrometer and a radiation shield designed at MICA, are used to monitor local variations in heat. The first full scale deployment took place in the summer of 2015. The BÛªmore Cool network joins a small but growing family of in situ UHI measurement networks in U.S. cities, each of which is optimized to specific monitoring needs and research questions of the target city [14, 15].
The BÛªmore Cool network measurements are combined with satellite-derived Earth observations including skin temperature from Landsat, ASTER and MODIS sensors, high-resolution satellite estimates of vegetation cover, albedo, and topography, and GIS information on infrastructure and human demographics. Merging these datasets allows us to quantify the relationship between satellite-derived temperature estimates and air temperature and to define regression models that predict local air temperature as a function of the natural and built environment. Importantly, the iButton monitors can be combined with indoor air temperature measurements made in the homes of people suffering from asthma or chronic obstructive pulmonary disease who have participated in studies conducted by the Center for Childhood Asthma in the Urban Environment. This allows us to quantify the relative health burden of localized UHI relative to indoor temperature conditions.
In parallel, the BÛªmore Cool network is being used to monitor locations where Baltimore is planting additional street trees, creating pocket parks, focusing community outreach and education efforts, and installing ÛÏcool roofs.Û These actions are intended to reduce energy use and potentially to temper localized UHI features, and the monitoring network allows us to track baseline conditions and changes in the UHI over time as these interventions are put into place. Numerous studies have been published that promote or question the impact that these types of improvement projects have on the UHI, but the majority of studies rely on models or satellite data with only sporadic in situ measurements at urban scale . The BÛªmore Cool network offers uniquely detailed data to evaluate the efficacy of these measures for UHI reduction.
Moving forward, BÛªmore Cool will inform plans by the city of Baltimore related to sustainability, resiliency and greening. The project also aims to develop an operational spatially-varying extreme heat warning system that utilizes satellite-derived measurements in combination with the BÛªmore Cool network and information on the distribution of vulnerable populations. Finally, BÛªmore Cool has already begun to deploy similar networks in other cities, including Birmingham, Alabama, and Nairobi, Kenya. Through partnerships like this we hope to advance application of Earth observations to heat warning, emergency response, and collaborative planning in multiple cities contending with the burden of a UHI in a warming climate.
 J. Berko, D. D. Ingram, S. Saha, and J. D. Parker, “Deaths attributed to heat, cold, and other weather events in the United States, 2006-2010,” National Center for Health Statistics, Hyattsville, MD2014.
 T. C. Peterson, R. R. Heim, R. Hirsch, D. P. Kaiser, H. Brooks, N. S. Diffenbaugh, R. M. Dole, J. P. Giovannettone, K. Guirguis, T. R. Karl, R. W. Katz, K. Kunkel, D. Lettenmaier, G. J. McCabe, C. J. Paciorek, K. R. Ryberg, S. Schubert, V. B. S. Silva, B. C. Stewart, A. V. Vecchia, G. Villarini, R. S. Vose, J. Walsh, M. Wehner, D. Wolock, K. Wolter, C. A. Woodhouse, and D. Wuebbles, “Monitoring and Understanding Changes in Heat Waves, Cold Waves, Floods, and Droughts in the United States: State of Knowledge,” Bulletin of the American Meteorological Society, vol. 94, pp. 821-834, 2013/06/01 2013.
 T. T. Smith, B. F. Zaitchik, and J. M. Gohlke, “Heat waves in the United States: definitions, patterns and trends,” Climatic change, vol. 118, pp. 811-825, 2013.
 G. B. Anderson and M. L. Bell, “Heat waves in the United States: mortality risk during heat waves and effect modification by heat wave characteristics in 43 US communities,” Environmental health perspectives, vol. 119, p. 210, 2011.
 M. J. Heaton, S. R. Sain, T. A. Greasby, C. K. Uejio, M. H. Hayden, A. J. Monaghan, J. Boehnert, K. Sampson, D. Banerjee, V. Nepal, and O. V. Wilhelmi, “Characterizing urban vulnerability to heat stress using a spatially varying coefficient model,” Spatial and Spatio-temporal Epidemiology, vol. 8, pp. 23-33, 2014.
 L. Kleerekoper, M. van Esch, and T. B. Salcedo, “How to make a city climate-proof, addressing the urban heat island effect,” Resources, Conservation and Recycling, vol. 64, pp. 30-38, 2012.
 D. Li and E. Bou-Zeid, “Synergistic Interactions between Urban Heat Islands and Heat Waves: The Impact in Cities Is Larger than the Sum of Its Parts*,” Journal of Applied Meteorology and Climatology, vol. 52, pp. 2051-2064, 2013/09/01 2013.
 C. o. Baltimore, “City of Baltimore Disaster Preparedness and Planning Project (DP3),” City of Baltimore Office of Sustainability, Baltimore, MD2013.
 N. Y. City, “Climate Risk Information 2013: Observations, Climate Change Projections, and Maps,” New York City Panel on Climate Change, New York City2013.
 Y. Kestens, A. Brand, M. Fournier, S. Goudreau, T. Kosatsky, M. Maloley, and A. Smargiassi, “Modelling the variation of land surface temperature as determinant of risk of heat-related health events,” International Journal of Health Geographics, vol. 10, p. 7, 2011.
 J. L. White-Newsome, S. J. Brines, D. G. Brown, J. T. Dvonch, C. J. Gronlund, K. Zhang, E. M. Oswald, and M. S. OÛªNeill, “Validating Satellite-Derived Land Surface Temperature with in Situ Measurements: A Public Health Perspective,” Environmental health perspectives, vol. 121, p. 925, 2013.
 M. Georgescu, P. E. Morefield, B. G. Bierwagen, and C. P. Weaver, “Urban adaptation can roll back warming of emerging megapolitan regions,” Proceedings of the National Academy of Sciences, February 10, 2014 2014.
 Y. Zhou and J. M. Shepherd, “AtlantaÛªs urban heat island under extreme heat conditions and potential mitigation strategies,” Natural Hazards, vol. 52, pp. 639-668, 2010.
 J. B. Basara, H. G. Basara, B. G. Illston, and K. C. Crawford, “The impact of the urban heat island during an intense heat wave in Oklahoma City,” Advances in Meteorology, vol. 2010, 2010.
 B. V. Smoliak, P. K. Snyder, T. E. Twine, P. M. Mykleby, and W. F. Hertel, “Dense Network Observations of the Twin Cities Canopy-Layer Urban Heat Island*,” Journal of Applied Meteorology and Climatology, vol. 54, pp. 1899-1917, 2015.
 D. E. Bowler, L. Buyung-Ali, T. M. Knight, and A. S. Pullin, “Urban greening to cool towns and cities: A systematic review of the empirical evidence,” Landscape and urban planning, vol. 97, pp. 147-155, 2010.