Mapping Heat-Health Vulnerability Based on Remote Sensing: A Case Study in Karachi

Abstract

As a result of global climate change, the frequency and intensity of heat waves have increased significantly. According to the World Meteorological Organization (WMO), extreme temperatures in southwestern Pakistan have exceeded 54 °C in successive years. The identification and assessment of heat-health vulnerability (HHV) are important for controlling heat-related diseases and mortality. At present, heat waves have many definitions. To better describe the heat wave mortality risk, we redefine the heat wave by regarding the most frequent temperature (MFT) as the minimum temperature threshold for HHV for the first time. In addition, different indicators that serve as relevant evaluation factors of exposure, sensitivity and adaptability are selected to conduct a kilometre-level HHV assessment. The hesitant analytic hierarchy process (H-AHP) method is used to evaluate each index weight. Finally, we incorporate the weights into the data layers to establish the final HHV assessment model. The vulnerability in the study area is divided into five levels, high, middle-high, medium, middle-low and low, with proportions of 3.06%, 46.55%, 41.85%, 8.53% and 0%, respectively. Health facilities and urbanization were found to provide advantages for vulnerability reduction. Our study improved the resolution to describe the spatial heterogeneity of HHV, which provided a reference for more detailed model construction. It can help local government formulate more targeted control measures to reduce morbidity and mortality during heat waves.

Mapping Heat Wave Hazard in Urban Areas: A Novel Multi-Criteria Decision Making Approach

“…This finding calls into question the fidelity of current heat wave risk mapping and classification methods. In addition, it echoes a need for more regional and finer scale extreme events studies, particularly for heat waves [47,103].…”

“…A study across seven US cities found that increased risk of mortality during a heat wave was associated with lower income and housing value, higher percentage of elderly residents, Asian and Pacific Islander ethnicity, young children, and residents with only primary education [46]. Studies also suggest the importance of communication skills and language barriers in determining population vulnerability to heat waves [16,47].…”

“…Liu et al [56] used mean land surface temperature to map heat wave hazard in China. Similarly, Wu et al [47] argue the mean temperature level across 24 h during a heat wave predicts mortality and morbidity rates better than maximum and minimum temperatures; yet, most studies focus on maximum daytime or nighttime temperature as an indicator of heat wave hazard [71][72][73].…”

Spatiotemporal Distribution of Heatwave Hazards in the Chinese Mainland for the Period 1990–2019

“…Some researchers have directly used the temperature index to define hazards [ 29 , 42 ]. However, the scientific literature shows that a heatwave hazard is a measure of the severity of heatwave events, usually determined by the intensity, duration, frequency, and extent of heatwaves [ 32 , 44 , 45 , 46 , 47 , 48 , 49 ] and calculated using the graphic overlay method [ 50 , 51 , 52 , 53 ].…”

Spatiotemporal Distribution of Heatwave Hazards in Chinese Mainland for the Period 1990-2019

“…Some researchers have directly used the temperature index to define hazards (Dong et al, 2020;Estoque et al, 2020); however, the scientific literature shows that a heatwave hazard is a measure of the severity of heatwave events, usually determined by the intensity, duration, frequency, and extent of heatwaves (Anderson & Bell, 2011;Kang & Eltahir, 2018;Raei et al, 2018;Savić et al, 2018;S. Wang, 2021;Yao & Wang, 2021;Yin et al, 2020) and calculated using the graphic overlay method Dai, Liu, Wu, et al, 2021;Ian L. McHarg, 1969;Wu et al, 2022).…”