SENZAIR - Sensor networks for monitoring

Project facts

Project promoter:
VSB - Technical University of Ostrava(CZ)
Project Number:
CZ-ENVIRONMENT-0162
Status:
Completed
Final project cost:
€101,927
Other Project Partners
Regional Association of Silesia in Teschen(CZ)

Description

VŠB TU Ostrava has been involved in the field of sensors for more than 5 years. One of the first achievements was the successful completion of a project to create a sensor network for the identification of air pollution sources. The proposed project would build on the experience of previously implemented projects and enable several municipalities on the Czech-Polish border to carry out regular monitoring in accordance with the needs of the state administration. In terms of content, this project would also build on the i-AIRP''s project, which identifies sources of pollution and then, on the basis of the underlying causes, establishes action plans to address local problems in the participating municipalities. Considering the fact that VSB is an experienced operator that is capable of operating a network with a high number of sensors (in the order of hundreds), the proposed network will include 30 PMx sensors (+6 for QC) and 3 meteo units to monitor wind direction and speed, temperature and atmospheric pressure. The system will monitor 3 areas at the same time. All data will be transferred to a MySQL database and displayed as points, areas and graphs. The data will be exportable for further processing. Each measured site will have a card ( with location, description, photos, etc.). The system will also store other necessarry information about the operation of sensors, particularly the validation factors set at the beginning and during the monitoring. The quality of the measurements will  be monitored both automatically (by the system) and by the operator. Outputs for the public will be easy to understand i.e. colour traffic lights, simple interpretations. The monitoring will continue within the framework of sustainability.

Summary of project results

The project focused on the acquisition, operation, and evaluation of sensor measurement technology. The primary aim was to verify the suitability of sensor technology for measuring air quality in municipalities, particularly concerning local heating. A fundamental issue is the growing popularity of using sensors for measuring almost anything, including air quality, without basic accountability for the accuracy of the measurements. The market is literally filled with sensors lacking laboratory validation. However, municipalities are part of public administration and need air quality measurements for their citizens. The results are used to implement measures such as boiler replacements, traffic optimization, or industrial interventions. Therefore, it is necessary to approach measurements responsibly. A database was prepared in the project for further use by the Czech Hydrometeorological Institute (ČHMÚ) and to transfer experiences to other projects. The database is native, meaning it contains all events that occurred on the sensors, such as outages, extremes, high or low sensitivity, etc. This is to understand the correct selection and ensure responsible operation of the sensors. Additionally, the project found that municipalities are very heterogeneous areas, and air pollution by PMx is uneven. This could be due to local influences or sensor errors, which will need to be further analysed in the data. However, the project provides extensive experience that can be used to adequately set the quality of measurements. The measurements also proved that sensors cannot replace reference measurements, which are much more robust and under continuous control. Data collected from sensors can be used for interpretations, though some caution is needed.

The sensors were procured through a public tender, with calibration performed at the Czech Hydrometeorological Institute (ČHMÚ) using the FIDAS dust monitor as a reference. The measurement period lasted 40 days, resulting in a validation protocol. Before the heating season, 30 sensor locations were selected across three municipalities on the Czech-Polish border, and the sensors were installed. Data from the sensors was continuously monitored and transmitted online to the Smartenvi.eu server, with results shared with ČHMÚ for further analysis.

The validated sensors provide a useful tool for air quality protection, identifying emission sources and monitoring PM10 levels, which often exceed the annual limit during the heating season. The highest concentrations were recorded in Chotěbuz-Karvinská, Bystřice-Harcov, and Horní Suchá, with a maximum of 604 µg/m³. While the sensors correlate well with each other, they may overestimate extreme values compared to reference stations. The project produced a supervised database, which will assist with future sensor deployment and air quality management. The municipality plans to use the data for citizen engagement and future air protection measures, especially as EU limits tighten by 2030.

Air quality is a key concern for our citizens, and public administration prioritizes its protection. The SENZAIR project focused on continuous monitoring of PM10 and PM2.5 particles. While municipalities understand the need to tackle dust pollution, they often struggle to identify the primary pollution sources. Combined with the AIRP''S (TROMSO) project, these measurements support Action Plans that guide activities like green space maintenance, street cleaning, and energy and transport projects. Action Plans require continuous monitoring, especially with the EU Green Deal''s 2030 deadline approaching, which will bring significant air quality legislation.

While the sensors correlate well within the network, their alignment with reference methods varies, with differences caused by location or sensor type. Validation checks during the project did not significantly alter PMx measurements. Though sensor trends align with reference data, they often show deviations or outliers that should ideally be filtered. The resulting database remains unchanged to better understand sensor performance and guide their future use.

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