Regional variation in the role of humidity on city-level heat-related mortality

Abstract

The rising humid heat is regarded as a severe threat to human survivability, but the proper integration of humid heat into heat-health alerts is still being explored. Using state-of-the-art epidemiological and climatological datasets, we examined the association between multiple heat stress indicators (HSIs) and daily human mortality in 739 cities worldwide. Notable differences were observed in the long-term trends and timing of heat events detected by HSIs. Air temperature (T_air) predicts heat-related mortality well in cities with a robust negative T_air-relative humidity correlation (C_T-RH). However, in cities with near-zero or weak positive C_T-RH, HSIs considering humidity provide enhanced predictive power compared to T_air. Furthermore, the magnitude and timing of heat-related mortality measured by HSIs could differ largely from those associated with T_air in many cities. Our findings provide important insights into specific regions where humans are vulnerable to humid heat and can facilitate the further enhancement of heat-health alert systems.

Keywords: climate change; heat stress; humidity; mortality; urban climate.

Fig. 1.

Long-term trends of the extremes of six HSIs. a–c) The linear trends (per decade) of the T_air X₉₉ (99^th percentile of the annual values of each year) (a), and specific humidity (Q) (b) and RH (c) of the high-temperature days (daily T_air > T_air X₉₉) between 1980 and 2019. The results of a-c are based on the daily mean value. Stippling denotes the linear trend reaches the significant level (P < 0.05). d, e) The sum of the HSI vote of T_w, T_sWBG, H_x, APT, UTCI, and HI. The HSI vote is set as 1 when HSI X₉₉ shows a positive trend between 1980 and 2019 and is set as −1 when negative. Results based both on the daily mean (d) and daily maximum (e) values of HSIs are presented. Stippling denotes the linear trend of at least one HSI reaching the significant level (P < 0.05).

Fig. 2.

Intra-annual PT difference among air temperature (T_air) and HSIs. a) Averaged intra-annual PT of T_air (day of year when T_air reaches annual peak) for 1980–2019. b–g) Difference between averaged intra-annual PT of corresponding HSI and T_air (the former minus the latter) for 1980–2019. h–k) Occurrence frequency of the hottest 10 days measured by T_air and 8 HSIs for 4 cities: Austin (h), Brasilia (i), London (j), and Bangkok (k) for 1980–2019. The occurrence frequency is obtained by Gaussian kernel density estimation.

Fig. 3.

The BFI [including air temperature (T_air) and HSIs] in modeling/predicting daily human mortality for 739 MCC cities. a) The indicator with the minimum qAIC when fitting to the human mortality (defined as BFI). The color of the BFI is presented based on their sensitivity to the humidity (Fig. S1, e.g. T_air (zero sensitivity to humidity), T_w (maximum sensitivity to humidity)). The number in the bracket represents the rank in the sensitivity to humidity of the HSI. b) The number of cities and their locations under each BFI group. The results are based on the daily mean value of the indicators.

Fig. 4.

The factors that influence the lethal heat stress type (dry or humid) for city-level human mortality. a) The feature importance of 13 input features (Table S5) for the random forest algorithm classifying lethal heat stress type. The thick black line indicates the uncertainty in 500 times implementations. b) The Spearman correlation coefficient between daily mean air temperature and RH (C_T-RH) for 739 MCC cities. c) The distribution of the C_T-RH for cities versus their BFIs for predicting mortality. The distribution density is obtained by Gaussian kernel density estimation.

Fig. 5.

The seasonality of RR of heat stress for four cities (Miami, Bristol, Ho Chi Minh City, and Taipei). a, c, e, g) Exposure-response associations estimated by air temperature (T_air, black) and BFI (red) (with 95% confidence interval [CI], shaded area). The numbers indicate the optimum of T_air and BFI with the lowest RR = 1, and the vertical dotted lines indicate the 95^th percentile of local-specific warm-season indicator value. b, d, f, h) The averaged intra-annual variation of RR estimated by T_air (black) and BFI (red) during the warm season. The line represents the RR time series, and the shaded area represents the days under heat stress (indicator value > optimum). The numbers indicate the AF of death related to heat and the corresponding 95% CI. The intra-annual time series is the averaged results of 1980–2019.