Ular.palmiste: Created page with "{{Side box | bodystyle = width: 250px; float:right; clear:right; | title = EDIAQI Project | image = link= | below = '''Link''' * '''[https://ediaqi.eu/sites/default/files/materials/D3.1%20Indoor%20Air%20Pollution%20Observation%20Toolkit.pdf Report (pdf)]''' }} This deliverable presents the EDIAQI Indoor Air Pollution Observation Toolkit. Its purpose is to collect the scientific tools, measurement methods, validation procedu..."

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Created page with "{{Side box | bodystyle = width: 250px; float:right; clear:right; | title = EDIAQI Project | image = 200px|link= | below = '''Link''' * '''[https://ediaqi.eu/sites/default/files/materials/D3.1%20Indoor%20Air%20Pollution%20Observation%20Toolkit.pdf Report (pdf)]''' }} This deliverable presents the EDIAQI Indoor Air Pollution Observation Toolkit. Its purpose is to collect the scientific tools, measurement methods, validation procedu..."

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* '''[https://ediaqi.eu/sites/default/files/materials/D3.1%20Indoor%20Air%20Pollution%20Observation%20Toolkit.pdf Report (pdf)]'''
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This deliverable presents the EDIAQI Indoor Air Pollution Observation Toolkit. Its purpose is to collect the scientific tools, measurement methods, validation procedures, data analysis approaches and system set-up guidance used in EDIAQI for observing indoor air pollution. The toolkit supports EDIAQI pilots and campaigns, but is also intended to be useful for future indoor air quality researchers and practitioners.

The toolkit is organised into three main modules: sensor validation, indoor air pollutant measurement and analysis, and indoor air pollution system set-up. It combines laboratory and real-world sensor testing, offline sampling and laboratory analysis of pollutants not covered by low-cost sensor devices, and guidance for real-time monitoring systems, data visualisation and stakeholder communication.

== Why is this topic important? ==
Indoor air pollution is difficult to understand using one method alone. Low-cost sensors can provide real-time information about parameters such as particulate matter, CO2, temperature and relative humidity, but many important pollutants and health-relevant factors still require specialised sampling and laboratory analysis.

EDIAQI therefore uses a combined approach. Low-cost sensor devices are validated against reference or proxy-reference instruments, while additional methods are used to analyse pollutants such as VOCs, aldehydes, PAHs, microplastics, radon, black carbon, ultrafine particles and microbiome-related indicators.

This is important because indoor air quality data are used by different groups: researchers, pilot coordinators, technical partners, municipalities, building owners and, eventually, citizens. A shared toolkit helps ensure that data are collected, analysed, visualised and communicated in a more consistent and scientifically grounded way.

== Key messages ==
* '''The toolkit brings EDIAQI observation methods together:''' D3.1 collects the scientific and statistical tools used in EDIAQI into one practical toolkit. It covers measurement, sensor validation, laboratory analysis, data analysis, system architecture and visualisation.

* '''Low-cost sensors need validation:''' EDIAQI sensor devices are tested in laboratory, semi-real-world and real-world settings. Their performance is assessed using reference or proxy-reference instruments and statistical indicators such as accuracy, precision, correlation, RMSE, NRMSE and sensitivity to temperature and relative humidity.

* '''Not all indoor pollutants can be measured by sensors:''' Several important indoor air pollutants require offline sampling and laboratory analysis. The toolkit therefore includes protocols for passive and active sampling, dust collection and microplastics collection.

* '''Emerging pollutants are part of the EDIAQI approach:''' The toolkit includes methods for analysing microplastics, radon, microbiome, ultrafine particles and black carbon, in addition to more established chemical pollutants such as VOCs, aldehydes and PAHs.

* '''Real-time monitoring needs a full system set-up:''' Indoor air quality observation requires more than sensors. The toolkit describes IoT architecture, edge gateways, mist/fog/cloud computing, data storage, data tagging, dashboards and user-facing visualisations.

* '''Visualisation is essential for stakeholder uptake:''' The deliverable highlights the importance of understandable visual tools, including observatory dashboards for scientific users and simplified widgets for citizens and local stakeholders.

== What did the EDIAQI project do? ==
The EDIAQI team compiled a toolkit for indoor air pollution observation based on the work of WP3. The toolkit documents the methods and tools used in EDIAQI pilots and campaigns and links them to open-source code and templates made available through a GitHub repository.

For the sensor validation module, EDIAQI tested low-cost sensor devices from project sensor providers in different experimental conditions. These included chamber experiments, laboratory experiments, outdoor ambient intercomparison, a semi-real-world office experiment and a classroom field intercomparison in the Vilnius campaign.

For the indoor air pollutants module, EDIAQI described sampling and analytical methods for pollutants and parameters that are not fully covered by low-cost sensor devices. These include passive Radiello® sampling, active sampling, dust collection, microplastics collection, VOC and aldehyde analysis, PAH analysis, radon measurement and microbiome analysis.

For the system set-up module, EDIAQI reviewed real-time indoor air quality monitoring approaches and described technical requirements for observatories, IoT solutions and visual representation. The deliverable also presents an EDIAQI IoT approach using wireless devices, an Edge gateway, mesh networking, and mist/fog/cloud computing.

== What does this mean in practice? ==
The toolkit gives EDIAQI partners a shared methodological basis for indoor air pollution observation. It helps pilot teams decide how to validate sensors, what additional pollutants to sample, which laboratory methods to apply, and how to connect measurements to real-time monitoring and visualisation systems.

{| class="wikitable sortable" style="width:100%;"
! style="width:25%;" | User group
! style="width:75%;" | Practical relevance
|-
| '''Homeowners and tenants'''
| The deliverable is not written as a direct household guide, but it supports future tools that can communicate indoor air quality in a clearer and more understandable way. Citizen-facing widgets can help translate complex sensor data into simpler visual information.
|-
| '''Schools and kindergartens'''
| The toolkit is relevant for classroom and educational-building monitoring, including real-world sensor intercomparison, CO2 and particulate matter monitoring, and the interpretation of measurements in occupied rooms.
|-
| '''Commercial property owners'''
| Building owners and facility managers can benefit from structured monitoring systems that combine sensors, room-level data tagging, dashboards and alerts for indoor air quality conditions.
|-
| '''Local municipalities'''
| Municipalities responsible for public buildings can use the toolkit as a reference for planning more consistent monitoring campaigns across schools, offices or other municipal buildings.
|-
| '''EDIAQI pilot teams'''
| The toolkit provides practical protocols for sensor validation, filter sampling, laboratory analysis, pollutant characterisation and data visualisation across pilots and campaigns.
|-
| '''EDIAQI technical partners'''
| The deliverable provides guidance on IoT architecture, edge gateways, mist/fog/cloud computing, data flows, observatory design and simplified visual widgets.
|-
| '''Researchers'''
| The toolkit summarises methods and code templates that can support future indoor air pollution studies, including validation statistics, sampling protocols and bioinformatic analysis workflows.
|}

== Toolkit modules ==
{| class="wikitable" style="width:100%;"
! style="width:25%;" | Module
! style="width:75%;" | Main content
|-
| '''Sensor validation module'''
| Methods for validating low-cost sensor devices in laboratory, outdoor, semi-real-world and real-world settings. The module covers accuracy, precision, sensitivity to temperature and relative humidity, and statistical analysis of sensor performance.
|-
| '''Indoor air pollutants module'''
| Sampling and analytical methods for pollutants and parameters not fully measured by low-cost sensors. This includes VOCs, aldehydes, PAHs, PM1, microplastics, radon and microbiome analysis.
|-
| '''System architecture and visualisation module'''
| Guidance for setting up real-time indoor air pollution monitoring systems, including IoT architecture, Edge gateways, mesh networking, mist/fog/cloud computing, observatory dashboards and citizen-facing widgets.
|}

== Recommendations ==
* '''Validate low-cost sensors before relying on the data:''' Sensor data should be checked against reference or proxy-reference instruments wherever possible.
* '''Use both laboratory and real-world testing:''' Chamber and laboratory experiments help identify sensor behaviour under controlled conditions, while field tests show how devices perform in real indoor environments.
* '''Assess both accuracy and precision:''' Sensor evaluation should consider agreement with reference instruments as well as unit-to-unit variability between devices.
* '''Document temperature and humidity effects:''' Temperature and relative humidity can affect sensor response and should be included in validation and data interpretation.
* '''Combine sensors with offline sampling:''' Low-cost sensors alone do not cover all relevant indoor pollutants. Passive sampling, active sampling, dust collection and laboratory analysis should be used where needed.
* '''Use appropriate laboratory methods:''' VOCs, aldehydes, PAHs, microplastics, radon and microbiome samples require pollutant-specific analytical methods.
* '''Plan the monitoring system as a full architecture:''' Effective IAQ observation requires sensors, communication infrastructure, data storage, processing, visualisation and user-facing interpretation.
* '''Make visualisations understandable:''' Scientific dashboards are useful for researchers, but citizens and local stakeholders need simplified visual outputs that communicate indoor climate quality clearly.
* '''Use open-source code and templates where available:''' The GitHub resources linked to the deliverable support reproducible analysis and future reuse.

== Limitations ==
This deliverable is a toolkit and methodological reference. It does not provide final pilot results or a full interpretation of indoor air quality conditions in EDIAQI buildings. Some activities, such as the real-world low-cost sensor intercomparison in the Vilnius campaign, were still ongoing at the time of writing and are expected to be reported in later campaign deliverables.

The toolkit also notes that several important pollutant groups cannot yet be measured through simple low-cost online methods. For these pollutants, EDIAQI relies on offline sampling and specialised laboratory analysis. This makes the methods scientifically valuable but also more resource-intensive than simple sensor deployment.

No major updates were planned for this deliverable, although minor improvements may be implemented as regular project practice if considered useful by the management team.

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'''Related wiki pages'''
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* [[Sensors]]
* [[What is IAQ?|Indoor Air Quality]]
* [[Sensors#Low-cost sensors|Low-cost sensors]]
* [[IAQ Data Reporting and Visualization|Data visualisation]]

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Source deliverable
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This one-pager is based on:
* '''Deliverable:''' D3.1 – ''Indoor Air Pollution Observation Toolkit''
* '''Work Package:''' WP3 – SCIENCE
* '''Lead beneficiary:''' TROPOS
* '''Original deliverable type:''' DEM – Demonstrator, pilot, prototype
* '''Dissemination level:''' PU – Public
* '''Official submission date:''' 31 May 2024
* '''Version:''' Final

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D3.1 One-pager: Indoor Air Pollution Observation Toolkit - Revision history