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26 results for “heat risk”
Data of paper "Global supply chains amplify economic costs of future extreme heat risk"
<p>This is the database of articles "Global supply chains amplify economic costs of future extreme heat risk". The database contains the number of deaths caused by future heat waves in regions around the world under different SSP scenarios (e.g. SSP119, SSP245, SSP585), as well as global health losses, labor losses, and indirect losses as a percentage of regional or sectoral value added under different SSP scenarios. The regions of the database are aggregated using the GTAP 141 aggregating schema.</p>
Valuation of heat related mortality risk and tick-borne diseases
<p>Monetary impacts of premature mortality due to heat waves</p> <p>Preferences for public programmes against spread of ticks due to climate change and a new vaccine against Lyme disease, prevalence of tick-borne diseases and exposure to ticks</p>
Pediatric Heat Risk Index Project
<p>The pediatric risk index project attempts to create a risk index specifically for pediatric populations using socioeconomic and health data across the United States. This new index is then overlayed with heat warming anomaly trends in this decade and future climate projections to better understand which vulnerable populations have the greatest exposure to extreme heat events and will only continue to be exposed to these events in the future. This repository contains the climate normal data, Google Earth Engine and R code for data processing, and the final data products. MODIS Land Surface Temperature Dataset and the NOAA NCEI Climate Grid Daily Temperature dataset can be accessed via Google Earth Engine with the code in this respository. This repository also has a GitHub link with the Google Earth Engine code availabile. </p>
Impact of urban heat islands on human mortality risk in European cities
<p>These data contain estimates of temperature-related human mortality, as well as the associated economic assessments, related to urban heat islands for 85 European cities over the years 2015-2017. They are based on temperature-mortality relationships from Masselot et al. 2023 and 100m resolution UrbClim urban climate model simulations of near-surface air temperature (De Ridder et al. 2015, Hooyberghs et al. 2019), re-gridded to 500m resolution.</p> <p> </p> <p>Details of the methodology are provided in the associated paper:</p> <p>Huang, W.T.K. et al. Economic valuation of temperature-related mortality attributed to urban heat islands in European cities. <em>Nat Commun</em> <strong>14</strong>, 7438 (2023). <a href="https://doi.org/10.1038/s41467-023-43135-z">https://doi.org/10.1038/s41467-023-43135-z</a></p> <p>And associated core analysis code is available on GitHub at https://github.com/hkatty/Paper_UHI_mortality_Europe (doi:10.5281/zenodo.8429209). </p> <p> </p> <p> </p> <p>The content of the files are as follows:</p> <p><strong>spatial_timeseries</strong> zip files: These contain the most unprocessed attributable fraction estimates, with the exposure-response relationships applied to the modelled temperature, prior to any further processing.</p> <p><strong>uhi</strong> csv files: These are tables of the average mortality and years of life lost, as well as associated economic assessment, related to urban heat islands for each city. They are identical to Tables S4-S11 in the supplementary materials of the above paper.</p> <p><strong>spatial_maps_time_averaged_diff_from_rural.zip</strong>: Spatial maps showing the difference from the rural average for each day and grid box, then averaged over time. </p> <p><strong>data_urbanruralavg_timeseries.nc</strong>: Time series of urban and rural averages, as well as the difference between the two (i.e. the urban heat island effect).</p> <p><strong>avg_diff_from_rural_urbanrural.nc</strong>: The above timeseries file temporally aggregated.</p> <p><strong>simulated_urbanruraldiff_timeseries.zip</strong>: Time series of urban-rural difference in attributable fraction for 1000-member ensembles representing uncertainties in the exposure-response relationships as captured by Monte Carlo simulations.</p> <p><strong>simulated_urbanruraldiff_averaged.zip</strong>: The above simulated timeseries temporally aggregated.</p> <p> </p> <p><strong>Some variables explained:</strong></p> <p>fAF = forward attributable fraction (i.e. fraction of total mortality associated with a single day's temperature, cumulative over lag time)</p> <p>fAD = forward attributable deaths (i.e. equivalent to fAF but for number of deaths)</p> <p>tas = temperature</p> <p>heat_ex = average over heat extreme days (i.e. the warmest 2% days in 2015-2017 for the city)</p> <p>cold_ex = average over cold extreme days (i.e. as heat_ex but for the coldest 2% days)</p> <p>heat = average over days warmer than the age-dependent optimal temperature</p> <p>cold = average over days colder than the age-dependent optimal temperature</p> <p>heat_count = number of days warmer than the optimal for the age group, note that for combined 2085.1 and 2085.5 age groups, days are counted if it is considered warm for at least one age group (therefore heat_count + cold_count ≠ total days over period)</p> <p>cold_count = number of days colder than the optimal for the age group</p> <p>rural = rural average</p> <p>imd = land imperviousness</p> <p>popden = population density</p> <p>age groups: </p> <p>20 = 20 to 44<br>45 = 45 to 64<br>65 = 65 to 74<br>75 = 75 to 84<br>85 = 85 and over<br>2085.1 = all above age groups combined, weighted by the local population age structure<br>2085.5 = all above age groups combined, weighted by the 2013 European standard population age structure</p> <p> </p> <p>References:</p> <p>De Ridder, K., Lauwaet, D., and Maiheu, B., (2015): UrbClim – A fast urban boundary layer climate model. Urban Climate, 12, 21–48. <a href="https://doi.org/10.1016/J.UCLIM.2015.01.001">https://doi.org/10.1016/J.UCLIM.2015.01.001</a>.</p> <p>Hooyberghs, H., Berckmans, J., Lauwaet, D., Lefebre, F., and De Ridder, K., (2019): Climate variables for cities in Europe from 2008 to 2017. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). <a href="https://doi.org/10.24381/cds.c6459d3a">https://doi.org/10.24381/cds.c6459d3a</a>.</p> <p>Masselot et al. (2023): Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe, The Lancet Planetary Health, <a href="https://doi.org/10.1016/S2542-5196(23)00023-2">https://doi.org/10.1016/S2542-5196(23)00023-2</a>. </p>
Data from: It's cool to be dominant: social status alters short-term risks of heat stress
Climate change has potential to trigger social change. As a first step towards understanding mechanisms determining the vulnerability of animal societies to rising temperatures, we investigated interactions between social rank and thermoregulation in three arid-zone bird species: fawn-coloured lark (Mirafra africanoides, territorial); African red-eyed bulbul (Pycnonotus nigricans, loosely social) and sociable weaver (Philetairus socius, complex cooperative societies). We assessed relationships between body temperature (Tb), air temperature (Ta) and social rank in captive groups in the Kalahari Desert. Socially dominant weavers and bulbuls had lower mean Tb than subordinate conspecifics, and dominant individuals of all species maintained more stable Tb as Ta increased. Dominant bulbuls and larks tended to monopolise available shade, but dominant weavers did not. Nevertheless, dominant weavers thermoregulated more precisely, despite expending no more behavioural effort on thermoregulation than subordinates. Increasingly unequal risks associated with heat stress may have implications for stability of animal societies in warmer climates.
Heat risk map of Riyadh
<p>The dataset is used to assess the urban heat risk in the city of Riyadh using proxy variables to evaluate the environmental, infrastructural, and social dimensions of the city.</p><p>The environmental component was evaluated using the mean values of land surface temperature (LST), air temperature (T2m), and discomfort index (DI) across the districts of Riyadh. These factors, derived from data like MODIS LST and available WRF simulations, represented the degree of heat exposure in different regions. </p><p>The infrastructural component of heat risk was evaluated by looking at the city's infrastructure, that is the building density per district. Buildings can act as "heat traps," thus higher building density suggests increased heat risk.</p><p>The social component considered demographic factors such as the percentage of the population over 65 old (OP) and under 14 years old (YP), which can indicate sensitivity to extreme heat conditions. </p><p>To map the heat risk, these components were combined into a composite heat risk indicator. For this to be achieved, each parameter was reclassified into three categories (1-less, 2-moderate, and 3-high) using the quantile classification which is a data classification method that distributes a set of values into groups that contain an equal number of values. </p><p> </p><p> LST (°C) DI T2m (°C) <14 y.o. (%) >65 y.o (%) Buildings per sq. m.(BD)</p><p>1-Less risk <47.2 <28 <40.6 <23 <1 <66</p><p>2-Moderate risk 47.2 ≤ LST ≤ 47.9 28≤ DI ≤ 28.2 40.6 ≤ T2m ≤ 40.8 23≤ YP ≤28 1≤ OP ≤ 3 66≤ BD ≤ 109</p><p>3-High risk >47.9 >28.2 >40.8 >28 >3 >109</p><p>LST: Land Surface Temperature; DI: Discomfort Index; T2m: Air temperature at 2m height; YP<14 y.o.: People under 14 years old; OP y.o.: Older people over 65 years old; </p><p>Since the relative importance of each parameter is unknown, we considered that all parameters contributed equally to the composite heat risk index and the arithmetic values were aggregated. The final value for each district was then reclassified into three categories using the quantile classification method resulting in the final three categories of Urban Heat Risk (Less heat risk, Moderate heat risk, High heat risk)</p>
Data from: Seeking temporal refugia to heat stress: Increasing nocturnal activity despite predation risk
<p>Flexibility in activity timing may enable organisms to quickly adapt to environmental changes. Under global warming, diurnally adapted endotherms may achieve a better energy balance by shifting their activity towards cooler nocturnal hours. However, this shift may expose animals to new or increased environmental challenges (e.g., increased predation risk, reduced foraging efficiency). We analysed a large dataset of activity data from 47 ibex (<em>Capra ibex</em>) in two protected areas, characterized by varying levels of predation risk (presence vs absence of the wolf – <em>Canis lupus</em>). We found that ibex increased nocturnal activity following warmer days and during brighter nights. Despite the considerable sexual dimorphism typical of this species and the consequent different predation-risk perception, males and females demonstrated consistent responses to heat in both predator-present and predator-absent areas. This supports the hypothesis that shifting activity towards nighttime may be a common strategy adopted by diurnal endotherms in response to global warming. As nowadays different pressures are pushing mammals towards nocturnality, our findings emphasize the urgent need to integrate knowledge of temporal behavioural modifications into management and conservation planning.</p>
Extreme heat and heatwaves are linked to the risk of unintentional child injuries in Guangzhou city
<p>This repository holds source data for the manuscript figures, which are available as Excel files.</p>
Data from: Heat stress increases risk taking in foraging shorebirds
<p>1. Animals often face a trade-off between food acquisition and predation/disturbance avoidance. Yet the extent to which this trade-off is affected by modulating factors such as thermal risk and foraging opportunities has been largely overlooked.</p> <p>2. Here, we examined the influence of temporal and environmental gradients on the flight initiation distance (FID, the distance at which animals flee from an approaching human-simulated predator) and escape mode (flying/low risk versus running/low cost) in 16 species of shorebirds foraging on tidal flats of the Bijagós Archipelago, Guinea-Bissau. We measured escape responses throughout the low tide period during wet and dry seasons and simultaneously recorded microclimate variables and occurrence of heat-reduction behaviour (ptiloerection). Furthermore, we measured corticosterone metabolites (CORTm) from droppings in red knots <em>Calidris canutus</em> to assess whether ptiloerection is associated to a physiological stress response to hot conditions.</p> <p>3. Overall, birds tolerated a closer approach at higher environmental temperatures and when showing ptiloerection. They also had shorter FIDs during the dry season and towards the start/end of the low tide period. FIDs also increased with body mass and decreased in areas with more human presence. In red knots, individuals showing ptiloerection had higher levels of CORTm, demonstrating a link between physiological and behavioural stress coping responses to heat events.</p> <p>4. Our results suggest that heat-stressed shorebirds take greater risks, supporting the idea of a thermoregulation–predation risk trade-off. They also indicate that shorebirds adjust risk taking to tidal and seasonal cycles, generally reducing FIDs when the energetic costs of escape are expected to be large. Finally, they suggest that shorebirds habituate to non-lethal human presence and respond to perceived predation risk in accordance with the predictions of optimal escape theory.</p> <p>5. These results are relevant to many animals that face a tight window for foraging activity while being exposed to predation/disturbance and heat during the day. We discuss management implications of our results in the context of global change.</p>
Data from: Larger pollen loads increase risk of heat stress in foraging bumble bees
<p>Global declines in bumblebee populations are linked to climate change, but specific mechanisms imposing thermal stress on these species are poorly known. Here we examine the potential for heat stress in workers foraging for pollen, an essential resource for colony development. Laboratory studies have shown that pollen foraging causes increased thoracic temperatures (T<sub>th</sub>) in bees, but this effect has not been examined in bumblebees nor in real-world foraging situations. We examine the effects of increasing pollen load size on T<sub>th</sub> of <em>Bombus impatiens</em> workers in the field while accounting for body size and microclimate. We found that T<sub>th</sub> increased by 0.07°C for every mg of pollen carried (p = 0.007), resulting in a 2°C increase across the observed range of pollen load sizes. Bees carrying pollen were predicted to have a T<sub>th</sub> 1.7–2.2°C hotter than those without pollen, suggesting that under certain conditions, pollen loads could cause <em>B. impatiens</em> workers to heat from a safe T<sub>th</sub> to one within the range of critical thermal limits that we measured (41.3°C to 48.4°C). Bumblebees likely adopt behavioral or physiological strategies to counteract the thermal stress induced by pollen foraging and may have limited foraging opportunities as environmental temperatures continue to increase.</p>
Data from: It's cool to be dominant: social status alters short-term risks of heat stress
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Data from: Larger pollen loads increase risk of heat stress in foraging bumble bees
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Data from: Seeking temporal refugia to heat stress: Increasing nocturnal activity despite predation risk
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A heat-sensitive songbird’s risk of lethal hyperthermia increases with humidity
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Data from: Heat stress increases risk taking in foraging shorebirds
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Pilot Randomized Controlled Trial To Assess the Effectiveness of a Heat Risk Reduction Decision Support Platform and Barriers and Facilitators of Its Implementation
ClinicalTrials.gov study NCT06971978. IPD Sharing: NO. Countries: 0. Publications: 1.
Effect of Rapid Heat Stress on Firefighters Musculoskeletal Injury Risk
ClinicalTrials.gov study NCT06442956. IPD Sharing: NO. Countries: 1. Publications: 24.
Physiological Responses to Heat Stress During High-risk Events
ClinicalTrials.gov study NCT06907225. IPD Sharing: NO. Countries: 1. Publications: 2.
3-Monochloropropandiol and Glycidyl Esters in Heat-Processed Oil-Based Food Products: Exposure and Risk
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Risk Factors for Exertional Heat Illness
ClinicalTrials.gov study NCT04979455. IPD Sharing: NO. Countries: 1. Publications: 0.
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