2021-09-30, 09:30–10:00, Academic
Lakes ecosystems are exposed to growing threats due to climate change and other anthropic pressures. For example, water warming is predicted to favour harmful algal blooms (HABs) that are toxic to peoples and animals. In addition, warming tends to increase the thermal stratification of lakes and reduce turnovers, which can lead to oxygen depletion in deep layers and release of toxic gases (methane, hydrogen sulphide) from sediments. Similarly, the increased use of plastics has produced nano- and micro-plastics pollution which, together with anthropogenic micropollutants, is posing a new emerging risk factor to lake biota.
To effectively study and manage those issues, researchers and managers need monitoring data (observations) to derive effective data-driven management policies. Observations have traditionally been collected from limnological vessels through periodic (often monthly) monitoring campaigns, during which water samples are collected for further analyses in the laboratory and various measurements are performed using on-board instruments (e.g. CTD sonde measuring Conductivity, temperature and Depth or Secchi disk to observe turbidity). However, environmental issues including HABs and changes in lake stratification due to warming, call for a shift towards monitoring approaches that allows higher-frequency (e.g. hourly od daily) automatic collection of key water-quality properties (e.g. phytoplankton concentration, temperature, dissolved oxygen). Therefore, to match current challenges, leverage better phenomena understanding and activate proactive measures, monitoring systems have to be updated to provide a better temporal and spatial resolution. At the same time, this development should not increase the costs of monitoring, which are often a limiting factor in lake management.
The combination of geospatial open source software, open standards and low cost microcomputer may constitute a sustainable solution. To test and evaluate this opportunity, in the framework of the SIMILE project a novel system based on istSOS and Raspberrypi that adopts cutting edge technologies and approaches, like edge computing and LoRa, has been implemented and deployed for testing. The designed system is based on a two tier approach: one tier located in-situ and one tier located at data-center. The In-situ tier is delegated to data sensing and edge computing, while the data-center tier is dedicated to data analytics and sharing.
In-situ tier consists of a mooring platform installed with sensors, monitoring water-quality and meteorological parameters, and connected to a data gateway. The gateway is composed of a Raspberrypi microcomputer hosting an istSOS service and a LoRa communication device. Collected data are in real-time checked for soundness and gross-error detection and registered in the local istSOS with associated its evaluated quality index. At specific time intervals data are aggregated and associated with a new quality index resulting from a number of data checks, related for example to time consistency or step test. Aggregated and quality checked data are successfully transmitted to the data-center tier using the LoRaWAN open protocol. The proposed approach permits to locally store data collected at the high frequency of 1 minutes, elaborate these data at the edge with quality checks and cleanings and communicate in real-time 30 minutes aggregated data, which is the selected adequate frequency to analyse and study the phenomena. Nevertheless, also high-frequency data are transmitted at fixed intervals (weekly) for row data backup. This proposed solution permits to reduce battery consumption, bandwidth usage and transmission costs while moving data processing and filtering at the sensor reducing computational efforts of the data-center (edge computing).
The data-center tier is composed of a number of containerized Web services dedicated to data collection, protection and serving. All the web services are based on open source software, in particular Keycloack is used for authentication and authorization, istSOS for standard data management according to the OCG Sensor Observation Service, Grafana for time series data plotting, VerneMQ for high-performance and light weight data publish-subscribe thanks to the MQTT open industry standard protocol. Specifically for this project, istSOS software has been expanded to support two specific data types: profile of observations and specimens. Specimens have been implemented on the base of the OGC samplingSpecimen while profiles have been considered as a collection of sensors located at different heights and characterized by identical sampling time. These enhancements have been included in the latest istSOS release 2.4-RC2 accessible on github (https://github.com/istSOS/istsos2). Graphical user interface to visualise these data is under development and will be released soon.
proffessor of geomatics at the Institue of Earth Science, SUPSI, Switzerland.
2 - Basic. General basic knowledge is required.Language of the Presentation –
Massimiliano is professor of geomatics at the Institue of Earth Science, SUPSI, Switzerland.