The Hidden Cost of Heat Waves: Citizen Science and the Harlem Heat Project

In an average year, high temperatures kill more people in the United States than all other weather-related phenomena combined (NOAA 2016), and in New York City two-thirds of heat-related deaths occur at home. Those most at risk are the ill and elderly, who tend to be home throughout the day. Yet few studies capture indoor residential temperatures in non-air-conditioned homes. 

The Harlem Heat Project described here was created through collaboration between media partners and community groups. Interest in the health effects of heat waves was so high, and long-term scientific data on indoor temperatures of non-air-conditioned residences in New York City so sparse, that members of the local media started an indoor temperature measurement program on their own. Scientific advisors for data analysis were brought in once the basic outlines were in place. Stories about this project can be found on the websites of AdaptNY, WNYC, and ISeeChange.

The effects of high temperatures on human health vary between cities. Every city has an optimum outdoor temperature (daily averaged) that results in the fewest deaths. This optimum temperature is not just set by climatic conditions but also by human adaptations, such as home insulation, heating and cooling units, and physiological adaptation. As outdoor temperatures cool below this optimum, the mortality rate in cities such as New York increases relatively gently. When temperatures rise, however, the mortality rate climbs much more sharply. Unfortunately, metrics used by the National Weather Service are not tuned to urban areas, and may not create warnings for heat waves that can harm human health.

Within the borough of Manhattan in New York, the Harlem community is particularly prone to heat-related health impacts for both physical and socioeconomic reasons. Compared to the rest of the island, residential buildings in Harlem tend to be lower (average 4.6 floors vs 6.3 floors) and slightly wider spaced (average building plot fraction of 0.68 vs 0.73), so sunlight illuminates a larger fraction of the urban canyons. Meteorologically speaking, a 30–35-m-high ridge runs along the western side of Harlem, reducing prevailing surface winds; on such days air has more time to warm with sun-heated surfaces without cooler air being brought in. In addition, winds are generally from the south during a heat wave, so by the time surface air parcels reach Harlem, they have been heated by buildings in the southern portions of the city; these parcels tend to form a low-level inversion, cutting off convective mixing with cooler air from above. These combined effects make Harlem one of the warmer neighborhoods in the city.

Still, locally elevated temperatures are only a small contributor to heat-related health impacts. Socioeconomics plays a dominant role, and approximately 44% of Harlem residents are on assisted living. Air conditioners require expense not only at purchase, but also in installation, increased electrical usage, and even increases in rent. This may explain why residential air conditioning use in Harlem is among the lowest in the city. Harlem is a historically African American neighborhood, a demographic that—for reasons that are not entirely clear—is disproportionately susceptible to heat-related illness and mortality. Nearly 50% of heat wave–related deaths in New York City between 2000 and 2011 occurred within the African American community (CDC 2013). The combination of the physical and demographic reasons listed above and the presence of an environmental justice community action group called WE ACT, led to the selection of Harlem as an ideal choice for citizen scientist investigations into the effects of heat waves.

The Human Side of Data

A key aspect of the project was reporting the human experience—not simply recorded, but broadcast via media partners. In a series of nine radio stories, WNYC documented the science, interviewed residents about their summer experiences, explored the social and health issues of heat waves, and even produced a musical rendition of the summer by converting indoor and outdoor temperatures to pitch. 

Participants were encouraged to share their stories, observations, and insights on the ISeeChange web journal, capturing prose glimpses into the experience of Harlem heat waves. AdaptNY published a series of online stories documenting the progress of the Harlem Heat Project, going into more depth than afforded by radio or brief experiential reporting. The stories range from fact driven to descriptive storytelling, providing an overview of the project as a whole. 

Building Citizen Science

Working with citizen scientists, the project placed temperature and humidity sensors in 30 residences of northern Manhattan, with variable reporting periods (depending on recruitment and battery life) from early July through late September of 2016 (Fig. 1). Residents were recruited via the WE ACT membership, with an emphasis on those who had no or limited air conditioning; there were no other constraints other than willingness to participate. Half the residences were in tenement buildings (5–8 stories high, narrow walkways in between, typically made of brick) dating from the first half of the twentieth century, and the remainder were from row houses (3–4 stories high, stone or brick) or large public housing apartments (greater than 15 stories high, well spaced apart with greenery, brick construction). Half of the residences did not have air conditioning; for the rest of the residences all but one of the air conditioners were in a different room from the sensor.

Figure 1. (left) Sensor placement and (right) operational time chart. Bright red shows residences that do not use AC or window fans. Green shows use of window fans. Blue represents residences with AC in a room separate from the sensor, and purple indicates if the AC is in the same room. If the windows benefit from shade of other buildings or trees, the color is darkened. Only those residences used in the main analysis are listed. The inset map in the left panel shows the location of the rotated sensor map within the city (map courtesy of Google Earth; inset map courtesy of WikiCommons user PerryPlanet). 

Sensors were hand constructed both to save money and to invite community participation. They were composed of a temperature–RH sensor, battery, Micro Standard Digital (MicroSD) card, and datalogger at a total cost of about $60 per unit. Parts and construction are detailed online. 

Residents were asked to place the sensors exposed to ambient air against an interior wall that did not receive direct sunlight, preferably in a bedroom and definitely not in a kitchen or bathroom. They reported the positioning of the sensor, the floor of the building, the direction the windows faced, whether they benefited from shade, the use of fans and air conditioners, and the number of occupants.

Outdoor weather data were used for comparison to indoor conditions. Consistent weather data are available from the Central Park weather station (3 km from central Harlem) and the two airports: LaGuardia (10-km distance) and John F. Kennedy (20-km distance). Cloud cover was estimated from Geostationary Operational Environmental Satellite-East (GOES-East) geosynchronous satellite infrared imagery, by which clear and cloudy pixels were identified by spatial brightness temperature variability. 

Uncovering the Impact of Heat Waves

Figure 2 shows daily weather averages during the project period. Several heat waves occurred during the project period, defined as outdoor maximum temperatures above 90°F (32°C) on consecutive days. With outdoor diurnal ranges of 12° ± 3°F (6.7° ± 1.7°C) (quoted variabilities are standard deviations), heat waves typically occur when the diurnally averaged temperatures, shown in Fig. 2, are 85°F (29°C) or higher. 

This took place three times during the study: 22–24 and 26–28 July and 11–14 August. Since indoor diurnal variations are on the order of 3° ± 1.2°F (1.7° ± 0.7°C), the daily average indoor temperatures shown here reflect the indoor conditions throughout the day, with a typical 1-day lag from outdoor temperature peaks as a result of thermal inertia.

Figure 2. Daily weather averages from Central Park plus indoor temperatures (°F). Outdoor temperature (red solid line) and dewpoint (blue solid lines). The indoor temperatures are averaged across the residences and are denoted by boxes: without AC (red) and with AC (in another room; blue). Wind speed (blue; mph; right axis) and rainfall events (green). Cloud cover is indicated in the upper-bar color, with bright blue for clear skies and darkening shades of blue/gray for partially cloudy to overcast conditions.

This thermal inertia can be seen more clearly by zooming in on one of the heat waves. Figure 3 shows outdoor temperatures together with indoor temperatures from one residence with air conditioning and two non-air-conditioned residences: with and without window fans. The diurnal changes in indoor temperature are much smaller than outdoors. As the heat wave begins, the temperature of the AC residence tends to match the outdoor lows, while the temperature of each non-AC residence climbs closer to the peak each day. The temperature of the non-AC/nonfan residence actually ends up higher than the outdoor temperatures. As the heat wave ends, the non-AC temperatures drop more slowly than outdoors, so after the heat wave these indoor temperatures are higher than outdoors throughout the entire diurnal cycle for several days. 

In general the warmest residences lack AC and are on the upper floors with a southern exposure, receiving direct sunlight. The seasonal average summer temperatures indoors were always warmer than outdoors, even in residences that had air conditioning.

Figure 3. AC vs non-AC residences during a heat wave. Outdoor temperatures are shown in black, AC indoor temperatures are in green, window fan (non-AC) temperatures are in yellow, and non-AC (no window fan) temperatures are in red. 

The average dewpoint during the observation period was 65°F (18°C), so the apparent temperature normally deviated several degrees from the actual temperature. Since indoor temperatures are relatively flat compared to outdoors, and the absolute humidity typically varies little during a day, the indoor diurnal heat index also tends to be flat (Fig. 4). It is typically elevated above the outdoor average as a result of windows acting as a greenhouse. This means residents experience long-term exposure to average heat indices that are typically higher than outdoors: in our sample roughly two-thirds of the residences experience average elevated heat conditions compared to outdoors, compared to 100% of participants with higher indoor temperatures. 

Modeling Indoor Temperatures

In the absence of air conditioning, the indoor environment is forced entirely by the outdoor environment. It is possible to predict changes in indoor temperature by energy flows between the two systems, with adjustable constants tuned for each residence. Past researchers have concluded that human interventions like opening and closing windows or using fans cause dynamic changes to thermal parameters that can’t be modeled. But, if humans are consistent enough to be considered “part of the system” on the scale of days (rather than hours), such an approach may still work. 

Except for physically obvious exceptions, such as the case where an air conditioner was in the same room as the sensor, the modeling exhibited considerable skill in predicting the next day’s indoor temperature. So the results from this preliminary model demonstrate two significant points worth reporting:

1. The indoor temperatures are statistically consistent enough to be predictable.

2. Even with human intervention in the form of air conditioners in other rooms, curtain adjustments, etc., the indoor air temperature remains predictable.

The results are illustrated in Fig. 4, showing modeled day-to-day changes in indoor temperature (purple) and actual temperature changes (black). The left panel with no air conditioning is as predictable as the right panel with an air conditioner in another room of the apartment. This means that when an outdoor heat wave is predicted, an indoor heat wave can also be predicted. As the indoor heat wave is rather different from its outdoor cousin, and since people are typically indoors during these events, indoor prediction is arguably more impactful. A fully physical model with parameterized human adjustments may allow indoor forecasts several days in advance.

Figure 4. Observed changes in indoor air temperature from one day to the next (black) vs modeled changes in temperature (purple). (left) Residence has no air conditioning, and (right) residence has an air conditioner in different room than the sensor.

Future Plans and the Potential for Indoor Forecasting Tools

Based on our preliminary results we can envision a system of indoor sensors reporting real-time data and short-term forecasts via an online portal. The reporting would have anonymous identifiers so residents can track their own temperatures and predictions, but the ensemble of results would be available to all. City health and emergency management officials could use the ensemble of indoor temperature forecasts as a warning system to augment those based on outdoor weather prediction, while researchers would benefit from the automated dataset. But would such a system have an impact?

Research on state housing in Great Britain suggested that “soft” human interventions such as window and fan usage were as important as “hard” interventions in the form of housing construction, while recent research shows that New Yorkers rarely make window adjustments even when it would lead to cooler indoor conditions. It seems likely that if residents were reminded that they could reduce heat impacts by simple behaviors, such as turning on fans or pulling shades, they may be more inclined to do so. Going beyond this, however, New York City has launched its Cool Neighborhoods initiative, featuring the Be a Buddy program, in which residents will check on each other during heat waves, and will host workshops with media and community groups to improve communication during heat waves.

The Harlem Heat Project was a unique collaboration between media, community, and scientists that documented the human impact of indoor heat waves. The comparison between outdoor and indoor heat waves indicates a need to change the warning and classification systems to better serve the public. In the absence of air conditioning (or even with light use of it), indoor heatwaves are smoothed and lagged a day or so compared to outdoor heat waves because of thermal inertia of residences and are often elevated by the greenhouse effect of windows. The indoor conditions can be predicted separately for each residence based on outdoor conditions. We hope to assist in this by building an indoor weather/forecast system around a network of real-time residential sensors. Such a system may assist in building the social structures needed to address the effects of heat stress.