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These data models describe the main entities involved with smart applications that deal with environmental issues.

List of data models

The following entity types are available:

  • AeroAllergenObserved. An observation of pollen levels at a certain place and time.

  • AirQualityForecast. A forecast of air quality conditions valid during a period

  • AirQualityObserved. An observation of air quality conditions at a certain place and time.

  • AirQualityMonitoring. Air Quality Monitoring (AQM) Data Model.

  • ElectroMagneticObserved. The Data Model is intended to measure excessive electric and magnetic fields (EMFs), or radiation in a work or public environment according to the level of exposure to electromagnetic fields on the air. The frequency of the hertzian waves is conventionally lower than 300 GHz, propagating in space without artificial guide. They are between 9 kHz and 300 GHz.

  • EnvironmentObserved. This entity contains a harmonised description of the environmental conditions observed at a particular location and time. This entity is primarily associated with the vertical segment of the environment and agriculture but may also be used in smart home, smart cities, industry and related IoT applications.

  • FloodMonitoring. Flood Sensor Data Model intended to represent the level of flooding w.r.t water flow/level at a certain water mass(river, lake,etc.)..

  • IndoorEnvironmentObserved. An observation of air and climate conditions for indoor environments.

  • MosquitoDensity. A Data Model for density of mosquitoes in cities.

  • NightSkyQuality. Data regarding the observed sky quality and the status of the measuring device.

  • NoiseLevelObserved. An observation of those acoustic parameters that estimate noise pressure levels at a certain place and time.

  • NoisePollution. Noise Pollution data model merges specific and punctual noise measurements (coming, e.g. from NoiseLevelObservation entities) into average parameters referred to city areas, providing a more city-related data about noise pollution status and evolution.

  • NoisePollutionForecast. Noise Pollution forecast stores the expectation about noise pollution based on some input elements and the noise elements present.

  • WaterObserved. Water observation data model is intended to represent the parameters of flow, level and volume of water observed, as well as the swell information, over a fixed or variable area. This observation also includes the masses of floating objects on this area. The data collected is provided by Sensors, Cameras,Water stations positioned at specific or sensitive locations for rivers, streams, torrent, lakes, seas, etc.

  • PhreaticObserved. The Data Model is intended to measure, observe and control the level and quality of groundwater at a given time (T), by a fixed or mobile monitoring system. Depending on the device used, it is also possible to measure the quality of water such as its electrical conductivity, its salt content, its temperature, etc. In this case, the values measured are processed by the Data Model WaterObserved and WaterQualityObserved. Additional Information about Attributes: For attributes dedicated to water, a MetaData attribute can also be used. it contains the TimeStamp in seconds, the qualification and control status of the measurement.

  • RainFallRadarObserved. The Data Model is intended to measure the water slides on a predefined area by a set of 4 Location represented by a Geo property format.

  • TrafficEnvironmentImpact. Environmental Impact of traffic based on the vehicles traffic and their emission characteristics

  • TrafficEnvironmentImpactForecast. Environmental Impact of traffic based on the vehicles traffic expectations and their emission characteristics


Link to the 11 current contributors of the data models of this Subject.


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