Humidity's Role in Heat-Related Health Outcomes: A Heated Debate

Abstract

Background: As atmospheric greenhouse gas concentrations continue to rise, temperature and humidity will increase further, causing potentially dire increases in human heat stress. On physiological and biophysical grounds, exposure to higher levels of humidity should worsen heat stress by decreasing sweat evaporation. However, population-scale epidemiological studies of heat exposure and response often do not detect associations between high levels of humidity and heat-related mortality or morbidity. These divergent, disciplinary views regarding the role of humidity in heat-related health risks limit confidence in selecting which interventions are effective in reducing health impacts and in projecting future heat-related health risks.

Objectives: Via our multidisciplinary perspective we seek to a) reconcile the competing realities concerning the role of humidity in heat-related health impacts and b) help ensure robust projections of heat-related health risks with climate change. These objectives are critical pathways to identify and communicate effective approaches to cope with present and future heat challenges.

Discussion: We hypothesize six key reasons epidemiological studies have found little impact of humidity on heat-health outcomes: a) At high temperatures, there may be limited influence of humidity on the health conditions that cause most heat-related deaths (i.e., cardiovascular collapse); b) epidemiological data sets have limited spatial extent, a bias toward extratropical (i.e., cooler and less humid), high-income nations, and tend to exist in places where temporal variations in temperature and humidity are positively correlated; c) analyses focus on older, vulnerable populations with sweating, and thus evaporative, impairments that may be further aggravated by dehydration; d) extremely high levels of temperature and humidity (seldom seen in the historical record) are necessary for humidity to substantially impact heat strain of sedentary individuals; e) relationships between temperature and humidity are improperly considered when interpreting epidemiological model results; and f) sub-daily meteorological phenomena, such as rain, occur at high temperatures and humidity, and may bias epidemiological studies based on daily data. Future research must robustly test these hypotheses to advance methods for more accurate incorporation of humidity in estimating heat-related health outcomes under present and projected future climates. https://doi.org/10.1289/EHP11807.

Figure 1.

Flowchart illustrating the central problems proposed in this commentary. (A) With increasing atmospheric greenhouse gasses, temperature (top, red projection) and humidity (moisture in the atmosphere; bottom, blue projection) increase, with nonlinear, growing increases for humidity. (B) According to epidemiologists (darker outline, turquoise arrow), heat-related health outcomes, such as excess all-cause mortality including cardiovascular collapse, should primarily follow daily temperature exposures; but according to physiologists (lighter outline, purple arrow), heat strain and stroke should follow shorter-duration exposures to both temperature and humidity (among other variables).^, (C) We propose that the physiologists’ perspective with a strong role for humidity would result in faster increases in adverse heat–health outcomes with warming (lighter, purple projection) compared with the epidemiologists’ perspective (darker, turquoise projection). Note that this is an illustration and does not incorporate actual data—for example, the light colored shading schematically illustrates uncertainty in climate change projections. Hypothesized resolutions to this discrepancy are summarized in Tables 2 and 3.

Figure 2.

Changes in moist heat stress metrics following global warming are strongly correlated with changes in specific humidity. (A) Multi-model mean change in the 98th percentile of daily equivalent potential temperature (which scales with wet-bulb temperature). (B) Multi-model mean change in air temperature, conditioned on the 98th percentile of daily equivalent potential temperature. (C) Multi-model mean change in specific humidity (converted to degrees Celsius), conditioned on the 98th percentile of equivalent potential temperature. Output from transient warming simulations (${\mathrm{CO}}_2$ concentrations are increased at 1%/y) of 14 CMIP6 models is analyzed; each panel plots the annual mean comparing years 71–80 and years 1–10 of near-surface ($2-\text{m}$) atmospheric quantities. This figure uses data from Lutsko, and methods and particular model simulations used are described in detail in that paper. Note: CMIP6, Coupled Model Intercomparison Project Phase 6; ${\mathrm{CO}}_2$, carbon dioxide.

Figure 3.

Historical temperature and humidity conditions compared to locations of available mortality data. (A) Time mean of annual maximum wet-bulb temperature from stations in HadISD (version 3.1.2.202104p), overlaid with locations of mortality data (blue circles) used in a recent study attributing heat-related mortality from climate change, (B) Same data sources as (A)^, but instead plotting correlation between daily anomalies of $2-\text{m}$ air temperature and specific humidity. Anomalies are calculated as deviations from the daily resolution seasonal cycle for each station. In both (A and B), gray dashed lines demarcate latitude bounds of the tropics. Note: max, maximum; q, specific humidity; $T_a$, air temperature; $T_d$, dew point; $T_w$, wet-bulb temperature.

Figure 4.

Environmental constraints on sweating efficiency compared to historical extreme heat data. Threshold combinations of temperature and humidity for a sedentary man (blue) and woman (orange) at which further increases in humidity will theoretically increase thermoregulatory strain because of reductions in sweating efficiency (i.e., the proportion of sweat that evaporates). For context, the hottest single 1-h temperature and accompanying ambient water vapor pressure from the airport weather stations of 108 global cities across a 13-y period (1 January 2007 to 31 December 2019) are plotted (solid black circles). Five cities with the largest populations were selected in specific countries across all six habitable continents (North America, Europe, Asia, South America, Oceania, and Africa) to represent a wide range of hot weather conditions. Data underlying this figure can be found in Excel Tables S1 and S2. Note: F, female; M, male.