environMAP
the Mapping Application Process for environmental datasets
As part of the environMENTAL project, environMAP – the Mapping Application Process for environmental datasets – integrates spatial environmental data with large-scale cohort studies by linking participants’ residential locations or movement patterns to geospatial variables. The infrastructure and methodology are designed to be scalable and applicable to additional countries worldwide.
Our platform presents a curated selection of geospatial products spanning urbanicity and natural space, climate, weather extremes, air pollution, and regional socioeconomic status. The platform is currently in a preliminary stage, and we are actively expanding the database to include a broader range of environmental variables for Europe and beyond.
Figure: environMAP Focus Area and Cohort Locations
| Variable Name | Spatial Resolution | Neighbourhood (Diameter) [m] | Temporal Coverage | Units |
Grey Spaces: World Settlement Footprint (WSF) 3D 1 |
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| Average Building Height | 90 m | None | static with data from 2010–2015 | dm |
| Building Fraction | 90 m | None | static with data from 2010–2015 | % |
| Total Building Area | 90 m | None | static with data from 2010–2015 | m² |
| Total Building Volume | 90 m | 90 / 1000 / 2000 | static with data from 2010–2015 | m³ |
| Total Building Volume Variance | 90 m | 1000 / 2000 | static with data from 2010–2015 | m³ |
| Total Building Volume Standard Deviation | 90 m | 1000 / 2000 | static with data from 2010–2015 | m³ |
Green Spaces: Tree Cover 2 and NDVI 3 |
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| Normalised Difference Vegetation Index (NDVI) | 300 m | None | Every 10 days (2013–2021) | |
| Tree Cover | 30 m | 30 / 300 / 500 / 1000 / 3000 / 5000 | 2010 | % |
Blue Spaces: Distance to Waterbodies 4 |
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| Surrounding Waterbodies | 30 m | 300 / 500 / 1000 / 3000 / 5000 | Yearly (1984–2021) | |
| Surrounding Waterbodies Gaussian Filter | 30 m | 300 / 500 / 1000 / 3000 / 5000 | Yearly (1984–2021) | % |
Light Exposure: Night-time Lights 5 and Solar Radiation 6 7 |
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| Night-time Lights (NTL) | 1000 m | None | Yearly (1992-2018) | dm |
| SARAH-2 | 0.05° | None | Monthly (2005-2020) | W/m2 |
| Solar Radiation | 300 m | None | Weekly (static) | W/m² |
Elevation: Copernicus Digital Elevation Model (DEM) 8 |
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| Elevation | 30 m | 30/300/500/1000/3000/5000 | static with data from 2010-2015, 2019 | m |
| Elevation Range | 30 m | 30/300/500/1000/3000/5000 | static with data from 2010-2015, 2019 | m |
| Elevation Standard Deviation | 30 m | 30/300/500/1000/3000/5000 | static with data from 2010-2015, 2019 | m |
| Slope | 30 m | 30/300/500/1000/3000/5000/7500 | static with data from 2010-2015, 2019 | ° |
| Aspect | 30 m | None | static with data from 2010-2015, 2019 | ° |
| Terrain Ruggedness Index (TRI) | 30 m | 90 | static with data from 2010-2015, 2019 | |
| Roughness | 30 m | 90 | static with data from 2010-2015, 2019 | |
Weather Patterns and Extreme Weather Data 9 |
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| Cloud cover (total, high, medium and low clouds) | 0.25° | None | Hourly; Monthly means and standard deviation | |
| Temperature, 2 m | 0.25° | None | Hourly; Diurnal range; Daily and monthly mean, maximum and minimum | °C |
| Temperature, dewpoint | 0.25° | None | Hourly; Daily and monthly diurnal range, mean, maximum and minimum; Monthly diurnal and mean standard deviation | °C |
| Wet-bulb globe temperature (WBGTmax) | 0.25° | None | Daily and monthly mean; Monthly standard deviation | °C |
| Precipitation volume | 0.25° | None | Hourly; Daily and monthly mean; Monthly standard deviation | mm/day |
| Precipitation duration | 0.25° | None | Daily and monthly duration; Monthly standard deviation; Daily duration of maximum events [h] | h/day |
| Total number of wet days (≥1 mm) | 0.25° | None | Monthly total number of wet days (≥1 mm) | days |
| Simple Precipitation Intensity Index (mean on wet days ≥1 mm during the month) | 0.25° | None | Monthly | mm/day |
| TXx: Monthly maximum value of daily maximum temperature | 0.25° | None | Monthly | °C |
| TNx: Monthly maximum value of daily minimum temperature | 0.25° | None | Monthly | °C |
| TNn: Monthly minimum value of daily minimum temperature | 0.25° | None | Monthly | °C |
| TX90p: Percentage of warm days (>90th percentile) | 0.25° | None | Monthly | % |
| TN90p: Percentage of warm nights (>90th percentile) | 0.25° | None | Monthly | % |
| TX10p: Percentage of cold days (<10th percentile) | 0.25° | None | Monthly | % |
| TN10p: Percentage of cold nights (<10th percentile) | 0.25° | None | Monthly | % |
| Warm spell duration index | 0.25° | None | Yearly | days |
| Cold spell duration index | 0.25° | None | Yearly | days |
| WBGTmax90p: Percentage of days with extreme WBGTmax (>90th percentile) | 0.25° | None | Monthly | % |
| CWD: Maximum number of consecutive wet days |
0.25° | None | Monthly | days |
| Rx5D: Monthly highest 5-day precipitation amount | 0.25° | None | Monthly | mm |
| R10mm: Heavy precipitation days (≥10 mm) | 0.25° | None | Monthly | days |
| R20mm: Very heavy precipitation days (≥20 mm) | 0.25° | None | Monthly | days |
| R75pTOT: Moderate wet days per month (≥75th percentile) | 0.25° | None | Monthly | days |
| R95pTOT: Very wet days per month (≥95th percentile) | 0.25° | None | Monthly | days |
Atmospheric Composition Data: Air Quality and Pollution 10 |
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| Sea salt aerosol mixing ratios | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/kg-1 |
| Dust aerosol mixing ratios | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/kg-1 |
| Hydrophilic organic matter aerosol mixing ratio | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/kg-1 |
| Hydrophobic organic matter aerosol mixing ratio | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/kg-1 |
| Hydrophilic black carbon aerosol mixing ratio | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/kg-1 |
| Hydrophobic black carbon aerosol mixing ratio | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/kg-1 |
| Sulphate aerosol mixing ratio | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/kg-1 |
| Nitrogen dioxide mass mixing ratio (NO₂) | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/kg-1 |
| Sulphur dioxide mass mixing ratio (SO₂) | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/kg-1 |
| Total column carbon monoxide (CO) | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/m-2 |
| Total column formaldehyde (CH₂O) | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/m-2 |
| Total column methane (CH₄) | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/m-2 |
| Total column ozone (O₃) | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/m-2 |
| Black carbon aerosol optical depth (AOD) at 550 nm | 0.75° | None | 3-hourly; Monthly mean and standard deviation | |
| Dust AOD at 550 nm | 0.75° | None | 3-hourly; Monthly mean and standard deviation | |
| Organic matter AOD at 550 nm | 0.75° | None | 3-hourly; Monthly mean and standard deviation | |
| Sea salt AOD at 550 nm | 0.75° | None | 3-hourly; Monthly mean and standard deviation | |
| Sulphate AOD at 550 nm | 0.75° | None | 3-hourly; Monthly mean and standard deviation | |
| Total AOD at 550 nm | 0.75° | None | 3-hourly; Monthly mean and standard deviation | |
| Particulate matter d < 1 μm (PM₁) | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/m-3 |
| Particulate matter d < 2.5 μm (PM₂.₅) | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/m-3 |
| Particulate matter d < 10 μm (PM₁₀) | 0.75° | None | 3-hourly; Monthly mean and standard deviation | kg/m-3 |
- Esch T, Brzoska E, Dech S, et al. World Settlement Footprint 3D – A first three-dimensional survey of the global building stock. Remote Sensing of Environment 2022; 270: 112877. https://doi.org/10.1016/j.rse.2021.112877.
- Hansen MC, Potapov PV, Moore R, et al. High-resolution global maps of 21st-century forest cover change. Science 2013; 342: 850–53. https://doi.org/10.1126/science.1244693.
- Sterckx S, Benhadj I, Duhoux G, et al. The PROBA-V mission: image processing and calibration. International Journal of Remote Sensing 2014; 35: 2565–88. https://doi.org/10.1080/01431161.2014.883094.
- Pekel J-F, Cottam A, Gorelick N, Belward AS. High-resolution mapping of global surface water and its long-term changes. Nature 2016; 540: 418–22. https://doi.org/10.1038/nature20584.
- Li X, Zhou Y, Zhao M, Zhao X. A harmonized global nighttime light dataset 1992-2018. Sci Data 2020; 7: 168. https://doi.org/10.1038/s41597-020-0510-y.
- Muellejans H, Pavanello D, Sample T, et al. State-of-the-art assessment of solar energy technologies. 1831-9424 2022. https://doi.org/10.2760/735053.
- Solar Radiation Product by Julien Radoux (https://maps.elie.ucl.ac.be/lifewatch/geoviewer.html?tab=sun#).
- European Union, ESA, DLR e.V., Airbus Defence and Space GmbH. Copernicus DEM, 2022.
- Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. Quart J Royal Meteoro Soc 2020; 146: 1999–2049. https://doi.org/10.1002/qj.3803.
- Inness A, Ades M, Agustí-Panareda A, et al. The CAMS reanalysis of atmospheric composition. Atmos. Chem. Phys. 2019; 19: 3515–56. https://doi.org/10.5194/acp-19-3515-2019.
