# Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens
## 4 Data Acquisition and Integration
### Earth Observation and Modeling Data Sources
The former requiring the active involvement of the people and the latter being more autonomous and automatic. The term mobile crowdsensing is coined in [59] to denote both participatory and opportunistic sensing approaches. Mobile crowdsensing applications are also classified in [59] according to the phenomena that they try to measure into environmental, infrastructure and social. Examples of phenomena that have already been measured in the environmental domain include air quality [60; 61] and noise [62]. An important kind of mobile crowdsensing applications are those supported by social media analytics, due to the huge amount of data that they are able to generate on a daily basis. Text content generated by citizens, such as tweets, is analyzed with the help of natural language processing tools to extract relevant data. Such data use to include specific keywords related to the application that enables the filtering. It is also common to extract information about the feelings of the user (sentiment data). In environmental applications is very important to collect reference geographic data. Some applications like Tweeter may already provide a location for each message. However, geographic references to local and also remote locations may also be present in the text that may be extracted with specific technologies already developed in the area of geographic information retrieval [63; 64], to provide appropriate geographic context related to observed entities. Many examples of environmental mobile crowdsensing applications based on social media analysis have been reported in the literature, including sentiment analysis to estimate the impact of hurricanes [65], the construction of earthquake reporting systems [66] and the monitoring and prediction of floodings [67; 68; 69]. A survey on these kinds of environmental data generation applications classifies the approaches based on the type of data they produce [58]. Some of them produce data about the interaction between people and nature. Examples of these are those in the scope of tourism and recreation such as hiking, walking, etc. Some other applications directly generate data on nature, such as land cover description, water, and air quality monitoring, etc. A final type of applications collect data useful for planning and governance. They contribute to solve problems such as hazard preparedness, urban planning, etc. It is pointed out in [58] that social media offer huge volumes of real time data, and that current large scale data analytics technologies are ready to process them in real time, however, important challenges have to be faced. Such challenges include data heterogeneity, data quality, ethics in data collection and use and uncertainty about future data availability. One of the critical areas of work is the integration of these crowdsensing data with other more traditional sources. Figure 8: Observations obtained from environmental sensors of different types. Environmental modeling enables on the one hand a better understanding of environmental processes and on the other hand to generate predictions on the impacts that changes of some variables will produce on some other. Examples of the above are weather monitoring and forecast, human impact in climate change or land degradation, flooding monitoring and prediction, air quality monitoring and prediction and ecosystem analysis. Model outputs may be used to predict future conditions (forecasting), to understand better past situations (reanalysis) and also to have a better coverage of what is hopping now (nowcasting). Environmental models may be classified as either mathematical or physical [5]. Physical or hardware models are versions of real world systems at an scale that enables their implementation. Examples are wind tunnels and channel flumes, and they are useful when mathematical models are not appropriate due to high complexity and/or uncertainty, or due to the lack of conceptual knowledge. Mathematical models try to express the relationships between the system variables in terms of mathematical rules.