# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 1 Introduction  
Many applications require detailed knowledge about environmental conditions to incorporate in different decision-making tasks. An example in the field of public health is the prediction of the impact that environmental variables such as sea and air temperature, rainfall and humidity have on the behavior of diseases such as Cholera [1] and Influenza [2]. Other examples include the prediction of landslides from rainfall and soil conditions [3] and the study of the interactions between atmosphere and biosphere, such as heat and hydrological transfer and their impact on agriculture [4]. Apart from the above applications in science and engineering, environmental conditions are very important also in decision making related to the daily activities of citizens. Examples of this are the impact of weather, sea conditions and air pollution in many open-air activities such as running, hiking, yachting, surfing, sightseeing, and sea bathing. More precisely, the air quality information generated in the scope of the TRAFAIR project ([[http://trafair.eu/](http://trafair.eu/)]([http://trafair.eu/](http://trafair.eu/))) may be used by running and sightseeing applications to help in the elaboration of relevant routes inside the cities. Similarly, the sea surface conditions, including currents and wave heights, generated by the high-frequency radar (HF Radar) infrastructure of the project RADAR-ON-RAIA project ([[http://radaronraia.eu/](http://radaronraia.eu/)]([http://radaronraia.eu/](http://radaronraia.eu/))) may be incorporated in decision support for yachting and surfing applications. In general however, to leverage the existence of environmental datasets in such applications, currently available open data infrastructures shouldenable IT practitioners to discover and access them in an effective and efficient manner. Unfortunately, as it is illustrated in Figure 1, the models, standards and technologies involved in the construction of the geospatial and environmental data infrastructures used by scientific and engineering applications, are not aligned with the semantic technologies on which general purpose open data infrastructures are based. If we focus on the data acquisition layer in Figure 1, both environmental and conventional applications incorporate data generated by sensors, models and people. The main difference among them is that environmental data use to have a spatio-temporal component that is much more complex than that of conventional data. In particular, the data that is required for environmental management tasks is collected and generated at a large scale by different authorities and geoscientific communities. Conventional observation systems include large and heterogeneous networks of static in-situ environmental stations (such as meteorological and air quality stations and sea buoys), mobile in-situ platforms such as radiosondes, gliders, and drifters, static remote sensing systems such as meteorological and coast high-frequency radars, and mobile remote sensing systems on board aircraft and satellites. Besides, environmental models help both in understanding related physical processes and in estimating past, present and future environmental conditions [5]. All the above-advanced data acquisition infrastructures and models generate data with complex spatio-temporal structure and semantics. Due to this, specific data and metadata models and service interfaces have been proposed to achieve integrated data acquisition from such complex and heterogeneous data sources (see the bottom left part of Figure 1). Contrary to the above, conventional data acquisition infrastructures as those used in internet of things (IoT) and smart city applications rely on more simple models and interfaces in which the geospatial component restricts to basic geometries such as points and where provenance metadata is not generally provided (see the bottom right part of Figure 1).