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67 results for “heat exposure”
Individually experienced temperatures: a heat exposure study in five greater Phoenix, AZ area neighborhoods (2014)
Urban environmental health hazards, including exposure to extreme heat, have become increasingly important to understand in light of ongoing climate change and urbanization. Most current knowledge about heat-health risks is based on measurements of outdoor air temperatures. Further, neighborhoods are often considered a homogenous and appropriate unit with which to assess risk and implement intervention strategies. Little is known about temperatures individuals actually experience within neighborhoods and cities, given differential access to cooling resources, complex activity patterns, and heterogeneous thermal and social environments. This dataset contains information collected during a study about individually experienced temperatures (IETs) within and between neighborhoods in Phoenix, Arizona. In September 2014, 80 research participants were recruited from 5 Phoenix-area neighborhoods and equipped with air temperature sensors that recorded IETs as they went about their daily lives. Surveys, activity log phone calls, and exit interviews were used to collect additional information from participants about demographics (age, race, gender), housing status, activities during the week, lifestyle, occupation, orientation toward the neighborhood, uses of indoor and outdoor spaces as well as public and private cooling resources. 86% of participants (69 out of 80) filled out background surveys, 89% of participants (71 out of 80) filled out daily surveys, 31% of participants (25 out of 80) engaged in activity log calls, and 48% of participants (39 out of 80) participated in exit interviews. The research team found that 1) variance in mean IET was relatively equal within each neighborhood and 2) significant differences existed in average mean IETs between neighborhoods. Data collected in this study help explain how intra-city differences in outdoor temperatures manifest themselves into IETs of urban residents. Individual differences are an overlooked determinant of heat expos
Dataset from: "Global labor loss due to humid heat exposure underestimated for outdoor workers"
<p>Data from Environmental Research Letters manuscript 'Global labor loss due to humid heat exposure underestimated for outdoor workers', DOI: https://doi.org/10.1088/1748-9326/ac3dae.</p> <p>Associated Python (Jupyter Lab) scripts and working environment to load and plot data can be found at: https://github.com/LukeAParsons/erfs_comparison</p> <p>Abstract:</p> <p>'Humid heat impacts a large portion of the world’s population that works outdoors. Previous studies have quantified humid heat impacts on labor productivity by relying on exposure response functions that are based on uncontrolled experiments under a limited range of heat and humidity. Here we use the latest empirical model, based on a wider range of temperatures and humidity, for studying the impact of humid heat and recent climate change on labor productivity. We show that globally, humid heat may currently be associated with over 650 billion hours of annual lost labor (148 million full time equivalent jobs lost), 400 billion hours more than previous estimates. These differences in labor loss estimates are comparable to losses caused by the COVID-19 pandemic. Globally, annual heat-induced labor productivity losses are estimated at 2.1 trillion in 2017 PPP$, and in several countries are equivalent to more than 10% of gross domestic product. Over the last four decades, global heat-related labor losses increased by at least 9% (>60 billion hours annually using the new empirical model) highlighting that relatively small changes in climate (<0.5 ◦C) can have large impacts on global labor and the economy.'</p>
# Replication code and data for: Global projections of heat exposure of older adults
<p># Replication code and data for: Global projections of heat exposure of older adults<br>By Giacomo Falchetta, Enrica De Cian, Ian Sue Wing and Deborah Carr<br>Nature Communications. DOI: 10.1038/s41467-024-47197-5</p> <p>An output data file, containing grid-cell level counts of people by age group, of climate hazard indicators, and of heat exposure metrics for both the historical climate and current demography and for future scenarios and projections is contained in the repository ("aging_climate_output_data.csv"). </p> <p>Software requirements:<br>- R v4.3+: https://cran.r-project.org/bin/windows/base/<br>- RStudio: v2023.06.0+: https://posit.co/download/rstudio-desktop/<br>- Package dependencies: raster, sf, tidyverse, rasterVis, rgdal, maptools, pbapply, terra, knitr, kableExtra, modelsummary, openxlsx, xtable, ggforce, maptools, weights, spatstat, rworldmap, scales, patchwork, stars, viridis, devtools, stargazer, readxl, nominatimlite, urbnmapr</p> <p>To replicate the analysis:<br>- Clone the replication code repository from https://github.com/giacfalk/aging_climate<br>- Download input data from this Zenodo data repository<br>- Download all the 1km age and gender-stratified global population counts rasters from the following WorldPop page https://hub.worldpop.org/geodata/summary?id=24798 and put them in a subdirectory of the working directory called "AGEPOP"<br>- Run the "project_pop.R" script to generate gridded age-stratified population data for each SSP scenario<br>- Run the "compare_pop_projections.R" file to compare the generated gridded age-stratified population data with an array of pre-existing sources from different countries and produce a summary comparison table (NOTE: before running the script, decompress the "new_comparison_data.zip" folder into the working directory)<br>- Run "projections_exposure_m.R" to quantify heat exposure and generate the figures and tables reported in the paper</p> <p>To process the data and run succesfully, the script requires a computer with at least 32GB RAM. The running time varies based on CPU characteristics, but a runtime of at least 2 hours should be expected to generate all the output data, figures, and tables. All output files are saved in the working directory.</p> <p>___</p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.</p> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p> </p> <p> </p> </div> </div> </div> </div> </div>
Fig. 4 in Filtration and respiration responses of mussels (Mytilus edulis) to trematode parasite infections (Renicola roscovita) and transient heat exposure
Fig. 4. Filtration rate in relation to infection intensity (metacercariae mussel– 1) for small (A) and large (B) mussels. Generalized Additive Mixed Models (GAMMs) predictions (lines) and 95 % CIs (shaded area) are conditioned on the average time points. Each point represents filtration measurement per minute and each stratum shows temporal filtration of one mussel.
Fig. 3 in Filtration and respiration responses of mussels (Mytilus edulis) to trematode parasite infections (Renicola roscovita) and transient heat exposure
Fig. 3. Linear mixed models of filtration and respiration rates of small (A, C) and large (B, D) M. edulis, either uninfected (light blue) or infected (light pink) with Renicola roscovita under a constant temperature of 17 ◦C. The bottom red line in the subplot B indicates the interval of significant difference between smoothers. The shaded area represents 95 % CIs. Each point represents the filtration or respiration rate measured minutely. The sample size for each group of small or large mussels was 9–18 and 9–16 for infected and uninfected, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Filtration and respiration responses of mussels (Mytilus edulis) to trematode parasite infections (Renicola roscovita) and transient heat exposure
Fig. 2. Post-warming scaled mussel filtration (A) and respiration (B) in relation to infection intensity. Generalized Additive Mixed Models (GAMMs) predictions (lines) and 95 % CIs (shaded area) are conditioned on the average post-warming time points. Individual points represent filtration or respiration measured every 5 min and each stratum represents measurements of one mussel.
Fig. 1. Mussel filtration and respiration responses during Experiment 1 in Filtration and respiration responses of mussels (Mytilus edulis) to trematode parasite infections (Renicola roscovita) and transient heat exposure
Fig. 1. Mussel filtration and respiration responses during Experiment 1. Generalized Additive Mixed Models (GAMMs) of responses of small size mussels uninfected and infected with Renicola roscovita during exposure to a constant mild temperature (for 5 h) followed by a 24-h thermal fluctuation. Each point represents filtration or respiration measurement per 5 min (shaded areas represent 95 % CIs). Sample size for each group was 8 and 11 for infected and uninfected, respectively. The negative values recorded during the metabolic depression phase are due to extra random variation in the measurement, variability between individuals and the white noise of oximeter device.
Temporal and spatial heat exposure in Colombo - dataset
<p>The dataset consists of two shapefiles that provide information on outdoor heat stress and anthropogenic heat flux in Colombo, Sri Lanka in 500 m resolution.</p> <p>1. "heat_stress_indicator.shp" contains the outdoor heat stress indicators, including heat index (HI), humidex (HD), and discomfort index (DI). These indicators were calculated using the modelled variables of urban land surface model SUEWS, and the values represent the averages at 7:00, 14:00, 19:00, and 23:00 during a heatwave (23-28, 2020) in Colombo, Sri Lanka. The results are for each 500 m grid across Colombo.</p> <p>2. "QF.shp" contains the calculated anthropogenic heat flux for each 500 m grid in Colombo, Sri Lanka. Published as: Blunn, L., Xie, X., Grimmond, S., Luo, Z., Sun, T., Perera, N., Ratnayake, R. and Emmanuel, R., 2024. Spatial and temporal variation of anthropogenic heat emissions in Colombo, Sri Lanka. Urban Climate, 54, p.101828. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.uclim.2024.101828" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.uclim.2024.101828</span></span></a></p> <p> </p>
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Berlin, Germany
<p><strong>Berlin heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045).</strong></p> <p>Heat stress exposure maps for Berlin representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p><strong>Scenario: </strong>Urban planning scenario (situation LULC today + integrated urban planning projects 2030)</p> <p><strong>Exposure mapping variable: </strong><br /> Total population 2030<br /> Population density inhabitants per hectare 2030</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Almada, Portugal
<p><strong>Average number of heatwave days per year versus socio economic data - base scenario</strong></p> <p>Heat stress exposure maps for the city of Almada representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modelled over the reference period 1986-2005 using the present land use / cover situation for the city.</p> <p><strong>Exposure mapping variable include the following: </strong><br /> Total population 2011<br /> Population density inhabitants per hectare 2011<br /> Number of inhabitants aged 0 to 19 years 2011<br /> Number of inhabitants aged 20 to 65 years 2011<br /> Number of inhabitants aged +65 years 2011<br /> Number of childcare centres 2014<br /> Number of hospitals 2014<br /> Number of schools 2014<br /> Number of schools and universities 2014<br /> Number of universities 2014<br /> Number of resthomes 2014</p>
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Antwerp, Belgium
<p><strong>Antwerp heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045)</strong></p> <p>Heat stress exposure maps for Antwerp representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p><strong>Exposure mapping variable include:</strong><br /> * Total population 2030<br /> * Population density inhabitants per hectare 2030</p>
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Almada, Portugal
<p><strong>Almada heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045)</strong></p> <p>Heat stress exposure maps for the city of Almada representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p>Exposure mapping variable include:<br /> * Total population 2011<br /> * Population density inhabitants per hectare 2011</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Antwerp, Belgium
<p>Average number of heatwave days per year versus socio economic data - base scenario (1986-2005)</p> <p>Heat stress exposure maps for the city of Antwerp representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modelled over the reference period 1986-2005 using the present land use / cover situation for the city.</p> <p><strong>Exposure mapping variable include the following: </strong></p> <p>Total population 2014</p> <p>Population density inhabitants per hectare 2014</p> <p>Number of inhabitants aged 0 to 4 years 2014</p> <p>Number of inhabitants aged 0 to 17 years 2014</p> <p>Number of inhabitants aged 18 to 65 years 2014</p> <p>Number of inhabitants aged +65 years 2014</p> <p>Number of schools 2014</p> <p>Number of childcare centers 2014</p> <p>Number of hospitals 2014</p> <p>Number of elderly stay facilities 2014</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany (Map-2 & Map-3)
<p>Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany</p> <p>Map-2 & Map-3 (zip.file) ref. to DOI: 10.5281/zenodo.45015</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany
<p><strong>Average number of heatwave days per year versus socio economic data - base scenario (1986-2005)</strong></p> <p>Heat stress exposure maps for the city of Berlin representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modelled over the reference period 1986-2005 using the present land use / cover situation for the city.</p> <p><strong>Exposure mapping variable include the following:</strong></p> <p>Total population 2013</p> <p>Population density inhabitants per hectare 2013</p> <p>Number of inhabitants aged 0 to 17 years 2013</p> <p>Number of inhabitants aged 18 to 65 years 2013</p> <p>Number of inhabitants aged +65 years 2013</p> <p>Number of schools 2014</p> <p>Number of childcare centers 2014</p> <p>Number of hospitals 2014</p> <p>Number of elderly stay facilities 2014</p>
El Niño Enhances Exposure to Humid Heat Extremes with Regionally Varying Impacts during Eastern vs Central Pacific Events
<p><span>Humid heat extremes, characterized by high wet bulb temperature (Tw), pose significant health risks. While strong El Niño events are known to affect the frequency of extreme Tw days, the distinct impacts of Central Pacific (CP) and Eastern Pacific (EP) El Niño events remain understudied. Using a 12-member CMIP6 ensemble at discrete global warming targets (+1.5˚C, 2˚C, 3˚C, 4˚C), this study shows progressively enhanced humid heat exposure during EP events primarily in Mainland Southeast Asia, while South Asia experiences regionally opposing effects from EP and CP events. EP and CP events expose distinct, regionally varying areas to dangerous Tw, yet both significantly amplify population and area exposure to humid heat extremes at all global warming levels. This amplification surpasses the impact of an additional degree of global warming, highlighting El Niño’s compounding effect on heat stress threats across warmer climates.</span> <br><a name="_msocom_1"></a></p>
Dataset for 'Heat and Humidity Exposure in Mega-cities - an Applied Tool for Energy and Water Harvesting Technologies'
<p>This is the dataset for the article named '<strong>Heat and Humidity Exposure in Mega-cities - an Applied Tool for Energy and Water Harvesting Technologies </strong>'</p>
Data from: Within- and across-generational effects of temperature: Exposure of Manduca sexta larvae to heat stress impacts future reproduction and offspring development
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Data from: Embryonic exposure to heat impacts development time, adult morphology, and fecundity in pea aphids
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Data and code from: Early developmental decline in HSP expression affects subsequent response to transient heat exposure
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