Human Survival Research Articles

Polymer-modified nanofillers for enhancing CO2 separation performance of MMMs: A comparative study on the role of bridging ligands

Advanced CO2 separation technology is one of the key technologies for reducing carbon emissions and can make significant contributions to reducing the negative impact of the greenhouse effect on human survival. Membrane separation technology is regarded as a new generation of carbon capture technology because of its great potential to reduce carbon capture energy consumption and improve net carbon capture efficiency. Mixed matrix membranes (MMMs) can break through the performance limit of traditional polymer membranes, but the influence of bridging agents for post-modifying nanofillers on membrane structure and properties needs further systematic investigation. In this work, four types of ligands were selected for synthesizing polymer-modified nanofillers and then incorporated into poly (vinylamine) (PVAm) to prepare MMMs. The comparative analysis results indicate that bridging ligands with superior flexibility are conducive to enhancing the gas permeability within the pores by diminishing the compactness of the polymer shell. The constructed phase interface transition area with uniformly enhanced mechanical strength demonstrates the improved interface compatibility between the matrix and nanofillers bridged via the flexible ligand. Techno-economic evaluation verifies the industrial application potential of the optimal MMMs targeting CO2 capture from post-combustion gas.

Femtosecond laser technology: A new approach for water disinfection

Water makes up around 60 % of the body, making it essential to all body systems and crucial to human health and survival. The lack of clean drinking water is currently a problem in many different countries. While there are several approaches for disinfecting drinking water, including the widely used ozonation, UV irradiation, and chlorination processes, each has some drawbacks. The purpose of this study is to evaluate the effectiveness of femtosecond laser technology in disinfecting water polluted with Escherichia coli (E. coli), for the first time. Bacterial cells were exposed to a femtosecond laser light with different wavelengths, intensities, and exposure times. E. coli was subjected to femtosecond laser irradiation for 5, 10, 15, 30, and 45 min at different powers of 50, 100, and 200 mW. Femtosecond laser light, ranging from 400 to 800 nm in wavelengths, was used to irradiate E. coli bacteria. These wavelengths cover the ultraviolet, visible, and infrared spectra. Under these experimental settings, the concentration of E. coli bacteria (in CFU/ml) was instantly determined using the MTT assay. Bacterial growth was dramatically decreased to 45.2 CFU/ml (%) after 45 min of exposure to 400 nm wavelength and 200 mW averaged power. The results obtained in this study provide evidence to conclude that femtosecond laser irradiation dramatically suppresses E. coli growth. Without making direct chemical contact, femtosecond laser irradiation offers a possible remote water disinfection approach. Therefore, the femtosecond laser light could be used for water disinfection as a promising eco-friendly technique.

Multiscale spatiotemporal characteristics and influencing factors of ecosystem service value of groundwater in China

Ecosystem services are fundamental to human survival on Earth, but studies on ecosystem service value of groundwater (ESV-G) are rare. The multiscale characteristics and influencing factors of ESV-G in China from 2000 to 2020 were analyzed in this study. The results showed that ESV-G decreased first and then increased, the average ESV-G was 130.30 thousand yuan/km2, and ESV-G tended to shift towards middle level (second to fourth class). The Hu Line was the dividing line between the first class (more than half area) and the others. The AI and FRAC values indicated that the patches of ESV-G were more concentrated, with simpler shapes that were more amenable to governance at the province scale. Hot spots and cold spots were mainly located in the eastern and western parts of Hu Line, respectively. The ESV-G of the cold spots per unit area at the province scale was higher than that at the city scale, which indicated that the province scale had the potential for higher ESV-G per unit area and cost advantage. Precipitation and temperature were the main factors affecting ESV-G; the influence of human activities on ESV-G increased on a larger scale as time went by. Combination of precipitation and Digital Elevation Model (DEM) had the greatest influence on ESV-G among the combinational influencing factors. The province scale was the optimal scale to manage ESV-G. Climate change had led to the expansion of hot and cold spots of ESV-G, northern and southern areas should combine existing policies to carry out differentiated governance. This study extended the scope of ecosystem service value studies from land surface to underground, providing a scientific basis for the management of groundwater ecosystem.

African Accounts of Religious Conversations and Interventions in Mental Healthcare

The importance of healthcare has attracted conversations from healthcare professionals, as well as other groups like the United Nations, academic institutions, governments, the private sector, and religious organisations, all contributing to healthcare conversations because healthcare is foundational to human survival. However, there are questions on the quality and gaps of conversations and interventions regarding mental health among religious groups. This is because the quality of healthcare delivery may be rooted in the quality of conversations and interventions. This study explores the quality of mental healthcare (MHC) conversations and interventions in Traditional Religions, Christianity, and Islam in Africa. It interrogates past and recent conversations and interventions within the frameworks of spiritual care (SC) as a form of compassion science, interreligious collaborations, spiritual healings, and allopathic collaborations. Lastly, it places a searchlight on the loopholes of spiritual care in MHC and recommends closure where possible in the pursuit of improved healthcare and well-being in Africa.

What is Artificial General Intelligence and Why Could It Be a Threat as Serious as Climate Change?: An Urgent Call for Medical Education

This article not only explains Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI) concisely in a manner that improves understanding among medical educators and professionals, but also contrasts the emphasis on climate change in medical education with the comparatively less attention paid to the threat of AGI and ASI. Awareness is called for about this technology, which could potentially lead to a prosperous age or the extinction of humanity.

AI-Based Visual Early Warning System

Facial expressions are a universally recognised means of conveying internal emotional states across diverse human cultural and ethnic groups. Recent advances in understanding people’s emotions expressed through verbal and non-verbal communication are particularly noteworthy in the clinical context for the assessment of patients’ health and well-being. Facial expression recognition (FER) plays an important and vital role in health care, providing communication with a patient’s feelings and allowing the assessment and monitoring of mental and physical health conditions. This paper shows that automatic machine learning methods can predict health deterioration accurately and robustly, independent of human subjective assessment. The prior work of this paper is to discover the early signs of deteriorating health that align with the principles of preventive reactions, improving health outcomes and human survival, and promoting overall health and well-being. Therefore, methods are developed to create a facial database mimicking the underlying muscular structure of the face, whose Action Unit motions can then be transferred to human face images, thus displaying animated expressions of interest. Then, building and developing an automatic system based on convolution neural networks (CNN) and long short-term memory (LSTM) to recognise patterns of facial expressions with a focus on patients at risk of deterioration in hospital wards. This research presents state-of-the-art results on generating and modelling synthetic database and automated deterioration prediction through FEs with 99.89% accuracy. The main contributions to knowledge from this paper can be summarized as (1) the generation of visual datasets mimicking real-life samples of facial expressions indicating health deterioration, (2) improvement of the understanding and communication with patients at risk of deterioration through facial expression analysis, and (3) development of a state-of-the-art model to recognize such facial expressions using a ConvLSTM model.

Landscape Design Planning and Research of Renewable Energy Application Projects from a Sustainable Perspective

The substantial emission of carbon exacerbates climate change, posing a threat to human survival and sustainable development. The primary inducement for the carbon emission issue is the excessive dependence of human activities on fossil energy. The construction industry is among the major sectors contributing to carbon emissions, and the low-carbon development within this industry serves as the cyclical engine for the path towards carbon reduction. Currently, there is limited research that integrates low-carbon development with the landscape design of renewable energy projects. Most studies emphasize the application of relevant low-carbon measures, overlooking the landmark effects of building decarbonization. Addressing how to truly achieve landscape design for renewable energy projects from a sustainable perspective based on relevant carbon emission quantification indicators during the architectural design phase is an urgent problem to be solved. This paper takes a sustainable perspective as the starting point and systematically summarizes landscape design concepts from this perspective. It proposes three major design approaches: ecological sustainability, cultural integration, and the application of advanced technology. Using these approaches, the paper tackles the balance between economics, energy, and the environment in landscape design, recognizing it as essentially a high-dimensional multi-objective optimization problem from a methodological perspective. The paper introduces an energy-economy-environment coupling model and utilizes big data. After low-carbon planning and design, both the net carbon footprint and total carbon footprint of the landscape significantly decrease. The Greenfield carbon sequestration per kilogram increases from 301,023.21 to the current 347,856.02, with an increase of 46,832.81.

Sustainable Synthesis of MIL-53(Al)-Cys Based Chemosensor for the Fluoro-Chromogenic Detection of Copper

Copper is the third most abundant heavy metal in the environment important for human survival. However, in excess it can have deleterious effects on aquatic as well as terrestrial beings. The detection of Cu2+ has huge significance in food safety and environment monitoring. The conventional detection methods are costly and complex. Therefore, we have designed a waste derived material for Cu2+ determination in complex samples. Our work is based on the eco-friendly synthesis of waste derived Al-MOF with remarkable properties grafted with L-Cysteine to develop the novel MIL-53(Al)-Cys sensor. The formation of the sensing probe was verified by various characterization techniques including FE-SEM, HR-TEM, PXRD, FTIR, UV-Vis and Photoluminescence spectroscopies. The developed waste-derived probe showed multifunctional properties with the visual and fluorescent detection of Cu2+ ions with high sensitivity and fast response time. The proposed waste-derived fluoro-chromogenic sensor exhibited a “turn of” detection response mechanism with a visual color change only the presence of Cu2+ ions. The results showed reproducible, selective and stable response with a LOD of 0.2 ng/mL, which is lower than the permissible levels. Therefore, the observed results highlight the potential of developed waste-derived MIL-53(Al)-Cys chemosensor as a promising platform in water remediation applications.

Prediction of PM2.5 concentration based on a CNN-LSTM neural network algorithm.

Fine particulate matter (PM2.5) is a major air pollutant affecting human survival, development and health. By predicting the spatial distribution concentration of PM2.5, pollutant sources can be better traced, allowing measures to protect human health to be implemented. Thus, the purpose of this study is to predict and analyze the PM2.5 concentration of stations based on the integrated deep learning of a convolutional neural network long short-term memory (CNN-LSTM) model. To solve the complexity and nonlinear characteristics of PM2.5 time series data problems, we adopted the CNN-LSTM deep learning model. We collected the PM2.5data of Qingdao in 2020 as well as meteorological factors such as temperature, wind speed and air pressure for pre-processing and characteristic analysis. Then, the CNN-LSTM deep learning model was integrated to capture the temporal and spatial features and trends in the data. The CNN layer was used to extract spatial features, while the LSTM layer was used to learn time dependencies. Through comparative experiments and model evaluation, we found that the CNN-LSTM model can achieve excellent PM2.5 prediction performance. The results show that the coefficient of determination (R2) is 0.91, and the root mean square error (RMSE) is 8.216µg/m3. The CNN-LSTM model achieves better prediction accuracy and generalizability compared with those of the CNN and LSTM models (R2 values of 0.85 and 0.83, respectively, and RMSE values of 11.356 and 14.367, respectively). Finally, we analyzed and explained the predicted results. We also found that some meteorological factors (such as air temperature, pressure, and wind speed) have significant effects on the PM2.5 concentration at ground stations in Qingdao. In summary, by using deep learning methods, we obtained better prediction performance and revealed the association between PM2.5 concentration and meteorological factors. These findings are of great significance for improving the quality of the atmospheric environment and protecting public health.

Spatiotemporal Evolution and Driving Forces of Production-Living-Ecological Space in Arid Ecological Transition Zone Based on Functional and Structural Perspectives: A Case Study of the Hexi Corridor

The rational allocation of land resources is crucial to ensuring human well-being, livelihood, and survival. The study of Production-Living-Ecological Space (PLES) provides new perspectives on land resource allocation. However, few studies have assessed the feasibility of PLES optimization in ecological transition zones. For this study, using the composite functional space classification method, a classification and functional utility scoring system were constructed. Various methods, including dynamic attitude, transfer matrix, and spatial autocorrelation, were employed to characterize the evolution of the quantity and quality of PLES in the Hexi Corridor. Moreover, the mechanisms driving these changes were explored using a geodetector. Our findings revealed that: (1) The distribution of Production-Ecological Space (PES) is higher in the west and south and lower in the east and north. Production-Living Space (PLS) is scattered. Ecological-Production Space (EPS) is mostly distributed in the south or west, whereas Ecological Space (ES) is mainly located in the north and west of the Hexi Corridor. (2) From 1980 to 2020, the area of PES and PLS increased by 2037.84 km2 and 673 km2, respectively; the area of EPS was relatively stable, and the area of ES decreased by 2523.06 km2. (3) The evolution of PLES quality indicated that the high functional utility area of PES and PLS was roughly the same as the expanded functional utility area, whereas the expanded functional utility area of EPS and ES is similar to the median functional utility area. (4) The spatiotemporal evolution of PLES is closely linked to natural, economic, and social factors.

Relationship among micro-pollutants, economic growth, renewable energy, nanotechnology and ecological foot prints of China: evidence from panel data analysis.

An important concern is the availability of clean drinking water, which is an essential need for human survival. This issue arises due to the existence of hazardous micropollutants originating from various emission sources. Nanotechnology aids in the mitigation of micropollutants by assimilating and counteracting their effects, hence diminishing their influence on water and other ecosystems. The study investigates the relationship between nanotechnological progress, the adoption of renewable energy, environmental consequences, and economic growth in China, using the Environmental Kuznets Curve theory as a conceptual framework. The study employs panel cointegration tests to analyze structural breaks from 2000 to 2020. Nanotechnology is expected to reduce environmental degradation and the presence of micro-pollutants by increasing the use of renewable energy and promoting energy conservation. Nanotechnology is crucial for mitigating micro-pollutants and advancing sustainable development in this specific context. However, the literature also highlights the harmful consequences of nanoparticle emissions caused by nanotechnology on human and environmental health for a long duration, requiring more examination. This research is the first empirical inquiry into the relationship between improvements in nanotechnology, the use of renewable energy, economic growth, and ecological effect, all within the context of the Environmental Kuznets Curve theory. The results confirm the successful incorporation of all components with a focus on long-term outcomes. The findings suggest that the EKC hypothesis is relevant in China. In China, advancements in nanotechnology have a moderating effect on environmental degradation. The use of renewable energy sources in China enhances environmental circumstances. Given the offered empirical evidence, it is advisable for the government to have a leading role in the development of innovative nanotechnologies that have low emissions of nanoparticles. By using this approach, it will be possible to encourage the conservation of energy and the use of renewable sources in a more secure way, hence improving the effectiveness of sustainable development initiatives.

Towards neuroadaptive navigation assistance to reduce spatial de-skilling

Maps have been invaluable navigation aids for millennia and thus have been critical for human survival. The increasing popularity of and high dependence on digital, location-aware assistive navigation technology, however, has been shown to divert our attention from the environment and to negatively influence innate spatial abilities. To mitigate this, neuroadaptive mobile geographic information displays (namGIDs) are proposed that respond in real-time to navigators’ cognitive task demands and wayfinder’s situated visuo-spatial attention needs. In doing so, namGIDs may not only help navigators maintain navigation efficiency but more importantly, also continuously scaffold spatial learning. To do this, the proposed navigation assistance must strike the appropriate balance between welcomed mobility efficiency gains while limiting human spatial deskilling. Leveraging neuroadaptive cartography, we can ensure to remain effective navigators, empowered to explore the world with confidence.

The Effect of Green Bonds on Natural Capital Protection: Worldwide Evidence

The continued destruction of nature, including the degradation of natural capital and biodiversity, threatens ecosystem services and human well-being. Natural capital is crucial for providing the conditions necessary for human survival and ecosystem services. Without protecting natural capital, this degradation will have lasting effects on current and future generations. Existing studies increasingly show that Green Bonds can finance environmental projects effectively, but few have examined their impact on natural capital protection. This study investigates the effect of Green Bonds on natural capital protection globally, using IMF Climate Data and World Bank data from 2019 to 2022. By applying the Green Investment theory and quantile-panel regression analysis, the results show that Green Bonds significantly enhance natural capital protection at the 25th and 50th percentiles. This indicates that countries can improve their natural capital protection through the issuance of Green Bonds. Moreover, the study finds that renewable energy positively correlates with natural capital protection, while population growth and total natural resource rents have negative impacts. Hence, this study recommends that governments prioritize the issuance of Green Bonds to fund environmental initiatives.

Religiosity and Carbon Emissions: International Evidence

ABSTRACT Informal institutions can exert important effects on individual behavior, corporate decisions and social outcomes. Against the background in which global warming poses the critical menace to the world economy and human survival, this study draws on the role theory and adopts the OLS regression and IVREG model to examine the impact of religiosity (an informal institution) on carbon emissions at the country level. Our findings reveal that religiosity, proxied by the percentage of religious population within a country, is significantly negatively associated with carbon emissions, validating the important role of religious social norms in carbon emissions reduction. Moreover, the Kyoto Protocol attenuates the negative effect of religiosity on carbon emissions. Our study calls on policy makers and stakeholders to attach the importance to the impact of religiosity on environmental protection and the trade-off between economic development and carbon emissions reduction.

Nonindustrial pretreatment and enzymes can yield sufficient calories from lignocellulosic biomass for human survival.

Following a global catastrophe causing reduced sunlight, the environment would become unfavorable for crop growth. Under such conditions, people might need to convert inedible plant biomass into food to meet their daily nutritional requirements. However, the possibility of converting biomass into food under low-resource conditions has not been thoroughly studied. To address this uncertainty, we evaluated the potential for using resources available in a typical household to extract sugars from willow biomass and meet the carbohydrate needs of an adult. Grinding willow biomass in a household blender for 24 min produced willow particles similar to those produced in a laboratory-scale Wiley mill. Thermal treatments of these particles with hot water extraction, pressure cooking, or microwaving only extracted 0.5%-0.8% (w/w) glucose from the biomass. Household acid or alkali treatments yielded only 0.5% (w/w) glucose. These sugar yields would be insufficient to provide nutrition to an adult. In contrast, enzymatic hydrolysis of pretreated willow at 50°C for 72 h yielded 2%-8% (w/w) glucose, and pretreating willow with sodium hydroxide and pressure before enzymatic treatment increased glucose yields to 28% (w/w). With this pretreatment approach and subsequent enzymatic conversion, ~1.4 kg of biomass/day could potentially fulfill the energy needs of an adult under post-catastrophic conditions. We posit that while biomass can be successfully pretreated for enzymatic deconstruction at a household level, producing sufficient enzymes for efficient sugar extraction from inedible plant biomass in a post-catastrophic environment might not be feasible at the household scale, thus requiring community-scale infrastructure and coordination.

Research on Teaching Planning and Teacher Evaluation Integrating Sustainable Development and Renewable Energy Concepts

Energy is an essential basis for human survival, social progress and economic development. With the rapid development of China's economy and the acceleration of urbanization, the problem of energy shortage is increasingly serious, and the problem of energy security is prominent. Based on stochastic power flow algorithm, this paper proposes the safety assessment and teaching planning of wind power generation in renewable energy. Firstly, the dynamic safety domain and thermal stability safety domain of wind power systems are proposed in this paper. Within the selected security domain, the key factors that are most likely to lead to the risk of a distribution network are selected by means of entropy-modified analytic hierarchy process (AHP). The incompleteness of the single weighting method is solved, and the total evaluation score of each layer of the index system is calculated from the subjective and objective perspectives to determine the weak link in the occurrence of accidents in the distribution network. The total score is less than 90 points, which is prone to failure risk. Finally, based on the safety index system, PLF is introduced into the uncertainty safety assessment of wind power systems to overcome the inefficiency of traditional uncertainty assessment methods. Methods PLF was used as a means of system state analysis to replace a lot of conventional power flow analysis in uncertain safety assessment, and related safety indexes were defined based on PLF results. The test system ST-PLF is taken as an example to verify the rationality of the proposed method. The results show that the proposed method can effectively realize the static security assessment of the system and identify the potentially vulnerable elements of the system. The evaluation time of the proposed method is 0.73 s, while that of the traditional method is 48.54 s. The evaluation speed of the proposed method is greatly improved, and the time is only 1.55% of that of Monte Carlo method.

Aloe vera Cuticle: A Promising Organic Water-Retaining Agent for Agricultural Use

Water is an important resource for both human and environmental survival. However, due to current human practices, we are facing a serious crisis in accessing water. Thus, solutions must be explored to optimize the use of this resource. In the search for an organic water-retaining agent for agricultural use, the techno-functional properties of Aloe vera (Aloe barbadensis Miller) cuticle, an agro-industrial residue generated after gel extraction, were evaluated. The residue was dried and ground. The effects of particle size (180 µm and 250 µm), temperature (10 °C, 20 °C, 30 °C, and 40 °C), and pH (4.5, 6.0, and 7.0) on the solubility and water-holding capacity (WHC) of the obtained product (i.e., hydrogel) were then evaluated. The treatment with the highest WHC was selected and compared with the WHC of a commercial synthetic polyacrylamide gel widely used in agriculture. The effects of KNO3 and Ca(NO3)2 at different concentrations (10 g L−1, 20 g L−1, 30 g L−1, and 40 g L−1) on the WHC of the gels were assessed. Particle size, temperature, and pH interactions had statistically significant effects on solubility, while the WHC was affected by particle size × temperature and pH × temperature interactions. The highest product solubility (75%) was obtained at the smallest particle size (i.e., 180 µm), pH 4.5, and 20 °C. Meanwhile, the highest WHC (18 g g−1) was obtained at the largest particle size (i.e., 250 µm), pH 6.0, and 20 °C. This optimized gel kept its WHC across both salts and their concentrations. In contrast, the commercial gel significantly decreased its WHC with salt concentration. The product elaborated with A. vera cuticle could have bioeconomic potential as a water-retention agent for agricultural use, with the advantage that it is not affected by the addition of salts used for plant fertilization.

Girlhood in <em>What Use Are Flowers?</em>: Lorraine Hansberry, the Postapocalyptic Earth, Nurturing

The earliest date ascribed to the manuscript of Lorraine Hansberry’s posthumously published play What Use Are Flowers? is December 19, 1961. However, there is no conclusive evidence showing when Hansberry first conceived the idea of what became Flowers, a play about child survivors of an unnamed (but obviously) nuclear holocaust. Yet, given the context of President John F. Kennedy’s speech during the Berlin Crisis, which became a flashpoint for the national debate on nuclear war, shelters, and the survivability of the ideas of home and family, attributing it to 1961 would be unsurprising. In Flowers, an aging hermit, the only remaining adult survivor, plays the role of father and teacher, creating the simulacrum of a patriarchal family without a woman (mother) to care for the children of postapocalyptic Earth. Through readings of Flowers and its precursor text “Gedachtnis,” both of which feature the female child survivor named after a wild plant that is neither edible nor useful to human survival, I examine the question of girlhood and its human–plant conditions as a contested terrain through which patriarchy is both nurtured and challenged. What ultimately explodes in Flowers may not be a technological atomic bomb, but rather a nuclear device in human history, which has been fueling sustainable, renewable energy in a male-dominated world, namely, girls’ use by, and value to, men.

Prediction of Dynamics of Riverbank Erosion: A Tale of the Riverine Town Chandpur Sadar

River channel migration often occurs inside the floodplain areas of the river. The structural changes occurring in rivers within floodplain zones serve as a significant ecological signal of their tremendous impact on both ecological and human survival. The aim of the research is to assess the spatial and temporal fluctuations of the Meghna River's bankline from 1990 to 2020 (for 30 years), and to predict the future bankline position. The Landsat imageries from USGS were used for this study. The bankline was extracted from satellite imageries (Landsat) using Modified Normalized Difference Water Index (MNDWI) indices. The variations in bankline dynamics were assessed through the application of Net Shoreline Movement (NSM) and End Point Rate (EPR) analyses using the Digital Shoreline Analysis System (DSAS). Predictive modeling was conducted utilizing the Modified EPR Model, with validation performed via the Root Mean Square Error (RMSE) technique. Adjustment of errors was achieved by employing error coefficients tailored to each individual transect. Over the past three decades, the cumulative river erosion in Chandpur Sadar amounted to 18.03 square kilometers, with the highest Net Shoreline Movement (NSM) recorded at −1979.62 m during the study period. The forecasted maximum Net Shoreline Movement (NSM) is anticipated to be −495.79 m, signifying that the transect is associated with erosion, resulting in landward movement. Conversely, the minimum NSM is projected to be −1.01 m. The most significant movement is expected to occur in Puran Bazar, while the least movement is foreseen in Bakharpur. On average, the NSM across all transects is predicted to be −76.25 m. This study presents a cost-effective approach for evaluating the changes in river erosion over time. This technique may be valuable for policymakers in developing effective plans to reduce the negative impacts of erosion-related risks.

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

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 (Tair) predicts heat-related mortality well in cities with a robust negative Tair-relative humidity correlation (CT-RH). However, in cities with near-zero or weak positive CT-RH, HSIs considering humidity provide enhanced predictive power compared to Tair. Furthermore, the magnitude and timing of heat-related mortality measured by HSIs could differ largely from those associated with Tair 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.