68 datasets found
  1. Number of deaths due to heat stroke in India 2010-2022

    • statista.com
    Updated Jul 10, 2025
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    Statista (2025). Number of deaths due to heat stroke in India 2010-2022 [Dataset]. https://www.statista.com/statistics/1007647/india-number-of-deaths-due-to-heat-stroke/
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    Dataset updated
    Jul 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    India
    Description

    Heat stroke caused about 730 deaths across India in 2022, almost double the number of deaths recorded in the previous year. During the period in consideration, the highest number of deaths due to heat stroke occurred in 2015.

  2. Number of ambulance patients heat stroke fatalities Japan summer 2014-2023

    • statista.com
    Updated Jul 10, 2025
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    Statista (2025). Number of ambulance patients heat stroke fatalities Japan summer 2014-2023 [Dataset]. https://www.statista.com/statistics/883357/japan-summer-heatstrokes-occurrence-ambulance-fatalities/
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    Dataset updated
    Jul 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Japan
    Description

    From May to September 2023, *** people who were taken to the hospital by an ambulance due to heat strokes died, indicating no change compared to the previous year. Within the surveyed period, the highest number of *** heatstroke fatalities was recorded in the summer of 2018.

  3. d

    All India, Year wise Deaths due to Heatwaves as reported by NDMA, MoSPI,...

    • dataful.in
    Updated Jun 4, 2025
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    Dataful (Factly) (2025). All India, Year wise Deaths due to Heatwaves as reported by NDMA, MoSPI, NCRB, MoES, WMO and IMD [Dataset]. https://dataful.in/datasets/451/
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    xlsx, csv, application/x-parquetAvailable download formats
    Dataset updated
    Jun 4, 2025
    Dataset authored and provided by
    Dataful (Factly)
    License

    https://dataful.in/terms-and-conditionshttps://dataful.in/terms-and-conditions

    Area covered
    India
    Variables measured
    Number of deaths
    Description

    This dataset consists of the number of deaths due to heatwaves reported by different agencies/organizations. These are listed below: 1. MoSPI or MoES: Ministry of Statistics and Programme Implementation (MoSPI) published data on heat wave deaths in its annual Envistats report until 2021. Since 2022, the data has been collated from the Ministry of Earth Sciences since in the Envistats report, the source is mentioned as the India Meteorological Department (IMD), Ministry of Earth Sciences. 2. National Disaster Management Authority (NDMA): The data reported by this organization in some of its reports and workshop content has been collated. Values shared by Ministry of Health in the Parliament , which started recording the figures since 2015, is same as this until 2022. 3. World Meteorological Organization (WMO) 4. National Crime Records Bureau (NCRB)'s Accidental Deaths and Suicides India report: Data on heat stroke deaths reported by police departments at state level is presented in the report, which has been collated in the dataset. 5. IMD: Data on heatwave deaths reported by the IMD in its annual reports has been collated separately since the figures are slightly different from that reported by MoSPI/MoES.

  4. f

    Mortality attributable to hot and cold ambient temperatures in India: a...

    • plos.figshare.com
    docx
    Updated May 30, 2023
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    Sze Hang Fu; Antonio Gasparrini; Peter S. Rodriguez; Prabhat Jha (2023). Mortality attributable to hot and cold ambient temperatures in India: a nationally representative case-crossover study [Dataset]. http://doi.org/10.1371/journal.pmed.1002619
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    docxAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOS Medicine
    Authors
    Sze Hang Fu; Antonio Gasparrini; Peter S. Rodriguez; Prabhat Jha
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    India
    Description

    BackgroundMost of the epidemiological studies that have examined the detrimental effects of ambient hot and cold temperatures on human health have been conducted in high-income countries. In India, the limited evidence on temperature and health risks has focused mostly on the effects of heat waves and has mostly been from small scale studies. Here, we quantify heat and cold effects on mortality in India using a nationally representative study of the causes of death and daily temperature data for 2001–2013.Methods and findingsWe applied distributed-lag nonlinear models with case-crossover models to assess the effects of heat and cold on all medical causes of death for all ages from birth (n = 411,613) as well as on stroke (n = 19,753), ischaemic heart disease (IHD) (n = 40,003), and respiratory diseases (n = 23,595) among adults aged 30–69. We calculated the attributable risk fractions by mortality cause for extremely cold (0.4 to 13.8°C), moderately cold (13.8°C to cause-specific minimum mortality temperatures), moderately hot (cause-specific minimum mortality temperatures to 34.2°C), and extremely hot temperatures (34.2 to 39.7°C). We further calculated the temperature-attributable deaths using the United Nations’ death estimates for India in 2015. Mortality from all medical causes, stroke, and respiratory diseases showed excess risks at moderately cold temperature and hot temperature. For all examined causes, moderately cold temperature was estimated to have higher attributable risks (6.3% [95% empirical confidence interval (eCI) 1.1 to 11.1] for all medical deaths, 27.2% [11.4 to 40.2] for stroke, 9.7% [3.7 to 15.3] for IHD, and 6.5% [3.5 to 9.2] for respiratory diseases) than extremely cold, moderately hot, and extremely hot temperatures. In 2015, 197,000 (121,000 to 259,000) deaths from stroke, IHD, and respiratory diseases at ages 30–69 years were attributable to moderately cold temperature, which was 12- and 42-fold higher than totals from extremely cold and extremely hot temperature, respectively. The main limitation of this study was the coarse spatial resolution of the temperature data, which may mask microclimate effects.ConclusionsPublic health interventions to mitigate temperature effects need to focus not only on extremely hot temperatures but also moderately cold temperatures. Future absolute totals of temperature-related deaths are likely to depend on the large absolute numbers of people exposed to both extremely hot and moderately cold temperatures. Similar large-scale and nationally representative studies are required in other low- and middle-income countries to better understand the impact of future temperature changes on cause-specific mortality.

  5. d

    Year, Organization wise Deaths due to Heatwaves reported at State Level

    • dataful.in
    Updated Jun 4, 2025
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    Dataful (Factly) (2025). Year, Organization wise Deaths due to Heatwaves reported at State Level [Dataset]. https://dataful.in/datasets/20847
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    csv, xlsx, application/x-parquetAvailable download formats
    Dataset updated
    Jun 4, 2025
    Dataset authored and provided by
    Dataful (Factly)
    License

    https://dataful.in/terms-and-conditionshttps://dataful.in/terms-and-conditions

    Area covered
    India
    Variables measured
    Number of deaths
    Description

    This dataset consists of the number of deaths due to heatwaves reported by different agencies/organizations. These are listed below: 1. MoES: Ministry of Earth Sciences gives data on deaths due to extreme weather events every year, including deaths due to heatwaves. This data is available since 2010. For years lacking data, it suggests that no heat wave-related deaths occurred during that period. 2. National Crime Records Bureau (NCRB)'s Accidental Deaths and Suicides India report: Data on heat stroke deaths reported by police departments at state level is presented in the report, which has been collated in the dataset. This data has been included in the dataset since 2013. 3. MoHFW: The Ministry of Health and Family Welfare started recording the figures since 2015.

  6. Number of deaths due to heat waves across India 2008-2021

    • statista.com
    Updated Jul 10, 2025
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    Statista (2025). Number of deaths due to heat waves across India 2008-2021 [Dataset]. https://www.statista.com/statistics/1006983/india-deaths-due-to-heat-waves/
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    Dataset updated
    Jul 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    India
    Description

    In 2021, no deaths were caused due to heat waves in India. This was a significant decrease from the previous year's number of 27. The highest number of deaths was recorded in 2015 when over two thousand people died due to heat waves across the nation.

  7. a

    Full Range Heat Anomalies - USA 2023

    • keep-cool-global-community.hub.arcgis.com
    • hub.arcgis.com
    • +1more
    Updated Apr 23, 2024
    + more versions
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    The Trust for Public Land (2024). Full Range Heat Anomalies - USA 2023 [Dataset]. https://keep-cool-global-community.hub.arcgis.com/datasets/e89a556263e04cb9b0b4638253ca8d10
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    Dataset updated
    Apr 23, 2024
    Dataset authored and provided by
    The Trust for Public Land
    Area covered
    Description

    Notice: this is not the latest Heat Anomalies image service.This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the contiguous United States, Alaska, Hawaii, and Puerto Rico. The Heat Anomalies is also reclassified into a Heat Severity raster also published on this site. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.To explore previous versions of the data, visit the links below:Full Range Heat Anomalies - USA 2022Full Range Heat Anomalies - USA 2021Full Range Heat Anomalies - USA 2020Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter or cooler than the average temperature for that same city as a whole. This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

  8. Heat-related mortality rate in Europe Summer 2022, by country

    • statista.com
    Updated Sep 2, 2024
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    Statista (2024). Heat-related mortality rate in Europe Summer 2022, by country [Dataset]. https://www.statista.com/statistics/1401185/heat-related-mortality-rate-europe-summer-2022/
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    Dataset updated
    Sep 2, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    Europe
    Description

    As climate change causes average temperatures to rise across the European continent, this will inevitably lead to an increasing number of heat-related deaths, or deaths as a result of excess exposure to high temperatures. This is particularly an issue for southern European countries, such as Italy, Greece, Spain, and Portugal, who are already experiencing a massive uptick in the number of extreme heat days per year, with temperatures often exceeding 40 degrees Celsius in these countries during the Summer. In the Summer of 2022, Italy recorded 295 heat deaths per million inhabitants, resulting in a total of over 18,000 people dying due to excess heat exposure. Europe-wide, this rate was 114 heat deaths per million inhabitants.

  9. G

    Extreme heat: heat waves

    • open.canada.ca
    html
    Updated Oct 5, 2020
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    Health Canada (2020). Extreme heat: heat waves [Dataset]. https://open.canada.ca/data/en/dataset/fd244e9e-26bb-43e5-aac8-0762ccfed285
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    htmlAvailable download formats
    Dataset updated
    Oct 5, 2020
    Dataset provided by
    Health Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    Many places in Canada have a high number of extreme heat events, often called heat waves. Extreme heat can put your health at risk, causing illnesses like heat stroke and even death.

  10. Understanding Heat Health in Toledo, Ohio

    • urban-heat-health-toledo-demo-sandbox.hub.arcgis.com
    Updated Aug 3, 2023
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    Esri PS Natural Resources, Environment and Geodesign (2023). Understanding Heat Health in Toledo, Ohio [Dataset]. https://urban-heat-health-toledo-demo-sandbox.hub.arcgis.com/datasets/understanding-heat-health-in-toledo-ohio
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    Dataset updated
    Aug 3, 2023
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri PS Natural Resources, Environment and Geodesign
    Area covered
    Toledo
    Description

    As warmer temperatures continue to trend in Toledo, we must prepare for future heat events to keep our neighbors safe from heat-related deaths and illnesses such as heat stroke, heat exhaustion and respiratory difficulties. These risks are particularly troubling where there are extensive areas of concrete and pavement because those materials absorb and re-emit the sun’s heat, which can increase daytime and nighttime temperatures. These pockets of extreme heat are called Heat Islands and are areas of special concern in our community.Our goal is to protect our citizens from extreme heat. We have identified the intersection of heat islands and our most vulnerable populations who are at greater risk of heat-related illnesses due to their current housing, working, or health conditions.

  11. a

    Heat Severity

    • impactmap-smudallas.hub.arcgis.com
    Updated May 3, 2024
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    SMU (2024). Heat Severity [Dataset]. https://impactmap-smudallas.hub.arcgis.com/datasets/heat-severity/about
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    Dataset updated
    May 3, 2024
    Dataset authored and provided by
    SMU
    Area covered
    Description

    This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, patched with data from 2021 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.

  12. f

    Table_2_Clinical Characteristics and Risk Factors Associated With Acute...

    • frontiersin.figshare.com
    pdf
    Updated Jun 1, 2023
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    Ming Wu; Conglin Wang; Zheying Liu; Li Zhong; Baojun Yu; Biao Cheng; Zhifeng Liu (2023). Table_2_Clinical Characteristics and Risk Factors Associated With Acute Kidney Injury Inpatient With Exertional Heatstroke: An Over 10-Year Intensive Care Survey.pdf [Dataset]. http://doi.org/10.3389/fmed.2021.678434.s002
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    pdfAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Frontiers
    Authors
    Ming Wu; Conglin Wang; Zheying Liu; Li Zhong; Baojun Yu; Biao Cheng; Zhifeng Liu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Background: Exertional heat stroke (EHS) is a life-threatening injury that can lead to acute kidney injury (AKI). The clinical characteristics of and risk factors for EHS complicated with AKI have been poorly documented.Methods: A retrospective study with EHS admitted to the intensive care unit (ICU) from January 2008 to June 2019 was performed. Data including baseline clinical information at admission, main organ dysfunction, 90-day mortality and total cost of hospitalization were collected.Results: A total of 187 patients were finally included, of which 82 (43.9%) had AKI. AKI patients had more severe organ injury and higher total hospitalization costs than non-AKI patients. Multivariate logistic analysis showed that lymphocyte, neutrophil, D-dimer and myoglobin (MB) ≥ 1,000 ng/ml were independent risk factors for AKI caused by EHS. In addition, SOFA score [hazard ratio (HR) 4.1, 95% confidence interval (95% CI) 1.6–10.8, P = 0.004] and GCS score (HR 3.2, 95% CI 1.2–8.4 P = 0.017) were the risk factor for 90-day mortality in patients with EHS complicated with AKI, with an area under the curve (AUC) of 0.920 (95% CI 0.842–0.998, P < 0.001) and 0.851 (95% CI 0.739–0.962, P < 0.001), respectively. Survival analysis showed that the 90-day mortality in AKI patients was significantly high (P < 0.0001) and the mortality rate of patients with AKI stage 2 was the highest than other stages.Conclusions: EHS complicated with AKI is associated with higher hospitalization costs and poorly clinical outcomes. MB ≥1,000 ng/ml, Inflammation, coagulation were associated with the occurrence and development of AKI. Early treatment strategies based reducing the SOFA and GCS score may be pivotal for improving the prognosis of EHS.

  13. a

    Heat Severity in LA 2023

    • citysurvey-lacs.opendata.arcgis.com
    • visionzero.geohub.lacity.org
    • +2more
    Updated Jul 22, 2024
    + more versions
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    eva.pereira_lahub (2024). Heat Severity in LA 2023 [Dataset]. https://citysurvey-lacs.opendata.arcgis.com/datasets/lahub::heat-severity-in-la-2023/about
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    Dataset updated
    Jul 22, 2024
    Dataset authored and provided by
    eva.pereira_lahub
    Area covered
    Description

    Data pulled from 2023 Trust for Public Lands' image service.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset.

  14. Heat Stress Wbgt Meters Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
    + more versions
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    Dataintelo (2025). Heat Stress Wbgt Meters Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/heat-stress-wbgt-meters-market
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    pdf, pptx, csvAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Heat Stress WBGT Meters Market Outlook



    The global Heat Stress WBGT (Wet Bulb Globe Temperature) Meters market size was estimated at USD 180 million in 2023 and is expected to reach USD 300 million by 2032, growing at a compound annual growth rate (CAGR) of 6.5% during the forecast period. This growth can be attributed to the increasing awareness about workplace safety due to rising temperatures and stringent government regulations on occupational health and safety standards. Additionally, the surge in industrial activities and the growing importance of monitoring heat stress conditions in various sectors are significant growth factors driving this market.



    One of the primary growth factors for the Heat Stress WBGT Meters market is the growing global focus on worker safety and health. With climate change leading to more extreme weather conditions, employers are increasingly required to monitor and manage the heat stress experienced by their workers, particularly in industries such as construction, manufacturing, and agriculture. Heat stress can lead to serious health issues such as heat stroke, exhaustion, and even fatalities, thereby necessitating the use of WBGT meters to ensure safe working environments. Furthermore, the introduction of stringent regulations by occupational safety and health administrations in various countries is propelling the demand for these devices.



    The advancement in technology is another significant factor contributing to the market growth. Modern WBGT meters are equipped with advanced features such as real-time data monitoring, wireless connectivity, and integration with other health monitoring systems. These technological advancements not only provide more accurate data but also facilitate easier and more efficient monitoring of heat stress conditions. This has made these devices indispensable in sectors where maintaining optimal working conditions is critical, thus driving the market forward.



    Increasing industrial activities, particularly in developing regions, further bolster the Heat Stress WBGT Meters market. As industries such as construction, oil & gas, and mining expand in regions like Asia Pacific and Latin America, the demand for effective heat stress management tools is rising. The growth of the industrial sector in these regions, combined with the rising awareness about the health impacts of heat stress, is expected to significantly drive the market during the forecast period. Moreover, the growing adoption of these meters in sports and athletics to monitor the environmental conditions of athletes adds another dimension to the market's expansion.



    In the realm of occupational safety, Heat Index Monitors have emerged as crucial tools for assessing and managing heat stress in various work environments. These devices provide a comprehensive measure of heat stress by considering factors such as temperature, humidity, and radiant heat. Unlike traditional thermometers, Heat Index Monitors offer a more accurate representation of the environmental conditions that can affect worker safety and productivity. Their integration into safety protocols helps organizations proactively address heat-related risks, ensuring that workers are protected from potential heat illnesses. With the increasing focus on workplace safety and the need for precise monitoring tools, the role of Heat Index Monitors is becoming increasingly significant across industries.



    Regionally, North America and Europe are expected to dominate the market due to their established industrial bases and stringent occupational health and safety regulations. Asia Pacific is anticipated to exhibit the fastest growth, driven by increasing industrialization, rising awareness about worker safety, and supportive government initiatives to ensure safer working environments. Latin America and the Middle East & Africa are also expected to show considerable growth due to the expansion of industrial activities and the growing importance of occupational health and safety measures in these regions.



    Product Type Analysis



    The Heat Stress WBGT Meters market is segmented by product type into portable and fixed devices. Portable WBGT meters are designed for on-the-go measurements and are commonly used in industries where workers are constantly moving, such as construction and agriculture. These meters are favored for their ease of use, versatility, and the ability to provide instant readings, making them essential for dynamic and variable working environments. The growi

  15. u

    Extreme heat: heat waves - Catalogue - Canadian Urban Data Catalogue (CUDC)

    • data.urbandatacentre.ca
    • beta.data.urbandatacentre.ca
    Updated Sep 30, 2024
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    (2024). Extreme heat: heat waves - Catalogue - Canadian Urban Data Catalogue (CUDC) [Dataset]. https://data.urbandatacentre.ca/dataset/gov-canada-fd244e9e-26bb-43e5-aac8-0762ccfed285
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    Dataset updated
    Sep 30, 2024
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Area covered
    Canada
    Description

    Many places in Canada have a high number of extreme heat events, often called heat waves. Extreme heat can put your health at risk, causing illnesses like heat stroke and even death.

  16. d

    Forces of Nature from NCRB: Year and Gender-wise Deaths due to Natural...

    • dataful.in
    Updated Jul 3, 2025
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    Dataful (Factly) (2025). Forces of Nature from NCRB: Year and Gender-wise Deaths due to Natural Calamities by Type at All India level [Dataset]. https://dataful.in/datasets/460
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    application/x-parquet, csv, xlsxAvailable download formats
    Dataset updated
    Jul 3, 2025
    Dataset authored and provided by
    Dataful (Factly)
    License

    https://dataful.in/terms-and-conditionshttps://dataful.in/terms-and-conditions

    Area covered
    India
    Variables measured
    Deaths
    Description

    This dataset consists of data on the deaths due to different forces of nature/natural calamities as recorded in the Accidental Deaths and Suicides India report which records data for the calendar year. Data has also been compiled on the basis of gender. Data is available since 1990 or since the year when data recording for certain forces of nature began.

    Note: Forces of Nature includes Avalanche, Cyclone, Earthquake, Epidemic, Exposure to cold, Flood, Floods, Forest Fire, Forest Fires, Heat or Sun Stroke, Heatstroke, Landslide, Landslides, Lightning, Other natural causes, Tornado, Torrential Rain, Torrential Rains and Tsunami etc.

  17. f

    Table 1_Elevated NLR and PCT levels and reduced GCS score predict 90-day...

    • frontiersin.figshare.com
    docx
    Updated Jun 27, 2025
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    Huili Guo; Jingjing Ji; Leifang Ouyang; Conglin Wang; Jinxin Jia; Zhifeng Liu (2025). Table 1_Elevated NLR and PCT levels and reduced GCS score predict 90-day mortality in heatstroke: findings from a 13-year retrospective cohort study.docx [Dataset]. http://doi.org/10.3389/fmed.2025.1599592.s001
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    docxAvailable download formats
    Dataset updated
    Jun 27, 2025
    Dataset provided by
    Frontiers
    Authors
    Huili Guo; Jingjing Ji; Leifang Ouyang; Conglin Wang; Jinxin Jia; Zhifeng Liu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Background and objectiveHeatstroke is the most severe heat-related illness and is associated with high mortality rate. Inflammation and immune dysfunction are considered the key pathophysiological processes of heatstroke. The neutrophil-to-lymphocyte ratio (NLR) can reflect the states of innate and adaptive immune systems. The aim of the present study was to explore the predictive role of the NLR in heatstroke patients.MethodsThis single-center retrospective cohort study included all patients with exertional-heatstroke (EHS) admitted to the intensive-care-unit (ICU) of the General Hospital of Southern Theater Command of PLA from June 2009 to May 2022. The dynamic changes in the main immune cell counts and ratios were recorded.ResultsA total of 232 patients were enrolled. Survivors had decreased NLRs 24 h after admission, while nonsurvivors had continuously increased NLRs after admission. The AUC for the 24-h NLR was 0.928, with a cutoff of 11.981. The patients were divided into NLR-high (NLR > 11) and NLR-low (NLR ≤ 11) groups based on their 24-h NLRs. Patients in the NLR-high group had increased 90-day mortality. According to the multivariate analysis, an increased PCT level and decreased GCS score were independent risk factors for death in heatstroke patients with an NLR over 11, with odds ratios of 1.0999 (95% CI: 1.0050–1.2038, p value: 0.03863) and 0.6836 (95% CI: 0.5246–0.8908, p value: 0.00486), respectively.ConclusionAn NLR greater than 11 in the early phase could be an independent predictor of prognosis in heatstroke patients, and an increased PCT level and decreased GCS score were risk factors for a poor prognosis.

  18. a

    Full Range Heat Anomalies - USA 2020

    • hrtc-oc-cerf.hub.arcgis.com
    Updated Mar 4, 2023
    + more versions
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    The Trust for Public Land (2023). Full Range Heat Anomalies - USA 2020 [Dataset]. https://hrtc-oc-cerf.hub.arcgis.com/datasets/TPL::full-range-heat-anomalies-usa-2020
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    Dataset updated
    Mar 4, 2023
    Dataset authored and provided by
    The Trust for Public Land
    Area covered
    Description

    Notice: this is not the latest Heat Island Anomalies image service. For 2023 data visit https://tpl.maps.arcgis.com/home/item.html?id=e89a556263e04cb9b0b4638253ca8d10.This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2019 and 2020.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter or cooler than the average temperature for that same city as a whole. This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

  19. Urban Heat Islands

    • climate-center-lincolninstitute.hub.arcgis.com
    • hub.arcgis.com
    Updated Feb 13, 2020
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    Urban Observatory by Esri (2020). Urban Heat Islands [Dataset]. https://climate-center-lincolninstitute.hub.arcgis.com/maps/cdffeabb1b62410d8ef8dc8ae66917f9
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    Dataset updated
    Feb 13, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Description

    This scene contains the relative heat severity for every pixel for every city in the United States, from this source layer. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2018 and 2019.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this scene is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource Center: https://www.epa.gov/heat-islands/heat-island-resourcesDr. Ladd Keith, University of Arizona: https://www.laddkeith.com/ Dr. Ben McMahan, University of Arizona: https://www.climas.arizona.edu/about/people/ben-mcmahan Dr. Jeremy Hoffman, Science Museum of Virginia: https://jeremyscotthoffman.com/about-me-shift#about Dr. Hunter Jones, NOAA: https://cpo.noaa.gov/News/News-Article/ArtMID/6226/ArticleID/971/CPOs-Hunter-Jones-delivers-keynote-on-Climate-and-Extreme-Heat-at-Design-for-Risk-Reduction-Symposium-in-NYC Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and Resiliency: https://youtu.be/sAHlqGDU0_4 Disclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Pete.Aniello@tpl.org with feedback.

  20. a

    Urban heat island severity for U.S. cities

    • keep-cool-global-community.hub.arcgis.com
    Updated Sep 12, 2019
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    The Trust for Public Land (2019). Urban heat island severity for U.S. cities [Dataset]. https://keep-cool-global-community.hub.arcgis.com/datasets/TPL::urban-heat-island-severity-for-u-s-cities-1
    Explore at:
    Dataset updated
    Sep 12, 2019
    Dataset authored and provided by
    The Trust for Public Land
    Area covered
    United States
    Description

    This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset.Methodology: This dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource Center: https://www.epa.gov/heat-islands/heat-island-resourcesDr. Ladd Keith, University of Arizona: https://www.laddkeith.com/ Dr. Ben McMahan, University of Arizona: https://www.climas.arizona.edu/about/people/ben-mcmahan Dr. Jeremy Hoffman, Science Museum of Virginia: https://jeremyscotthoffman.com/about-me-shift#about Dr. Hunter Jones, NOAA: https://cpo.noaa.gov/News/News-Article/ArtMID/6226/ArticleID/971/CPOs-Hunter-Jones-delivers-keynote-on-Climate-and-Extreme-Heat-at-Design-for-Risk-Reduction-Symposium-in-NYC Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and Resiliency: https://youtu.be/sAHlqGDU0_4 Disclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

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Statista (2025). Number of deaths due to heat stroke in India 2010-2022 [Dataset]. https://www.statista.com/statistics/1007647/india-number-of-deaths-due-to-heat-stroke/
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Number of deaths due to heat stroke in India 2010-2022

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Dataset updated
Jul 10, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
India
Description

Heat stroke caused about 730 deaths across India in 2022, almost double the number of deaths recorded in the previous year. During the period in consideration, the highest number of deaths due to heat stroke occurred in 2015.

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