EDIAQI IAQ Simulation Tool
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The EDIAQI IAQ Simulation Tool is an open-access, web-based platform engineered to estimate outdoor air pollution and predict indoor air contaminant concentrations for any building across Europe.[1] By combining satellite observations, high-resolution urban map features, and building physics, the platform calculates indoor exposure to fine particulate matter (PM2.5), coarse particulate matter (PM10), and nitrogen dioxide (NO2) without requiring the installation of physical monitoring hardware.[1][2]
European citizens spend up to 90% of their daily time inside enclosed microenvironments, where pollutants accumulate through outdoor infiltration and indoor routines like cooking, heating, and cleaning.[3] The EDIAQI Simulation Tool bridges the gap between atmospheric science and practical facility management. It converts building attributes and household habits into clear exposure metrics, helping families, school administrators, and property managers understand environmental risks and select optimal ventilation or filtration interventions.[4]
Jump to: How It Works • Decision Tree • Actions • Technical Details • Target Audiences • Limitations • Related Pages
How the Tool Works
The simulation engine operates directly within modern web browsers without requiring local software installation or user registration, processing inputs via a three-stage evaluation workflow:[1]
- Geographical Location and Outdoor Ambient Baseline: The user specifies a site in Europe by typing a street address or selecting coordinates on an interactive map. The platform queries European atmospheric databases and spatial map layers to determine outdoor PM10, PM2.5, and NO2 concentrations at the target location, while evaluating surrounding coordinates to reflect local spatial variation.[1]
- Building Envelope and Behavioral Configuration: The user configures property attributes and occupancy routines using intuitive sliders and inputs:
- Building envelope characteristics: Building typologies (residential, educational, office), construction era (pre-1960, 1960 to 1990, 1990 to 2010, post-2010), floor area (m²), floor elevation level, window glazing (single, double, triple), and ventilation strategy (natural window airing, mechanical balanced system, exhaust-only extraction).[1][2]
- Indoor environmental state: Representative room temperature (°C), relative humidity (%), and typical occupant density.[1]
- Indoor emission sources and routines: Indoor smoking intensity, gas stove use, presence of wall-to-wall carpets, active cooking routines, candle or incense burning, application of cleaning chemicals, pet presence, office printers, fresh paint, and newly unpacked composite wood furniture.[3][4]
- Instant Exposure Prediction: Clicking the Predict Indoor Air Quality button triggers the machine learning and mass transfer calculations. The interface outputs outdoor ambient versus indoor occupied concentrations side by side, accompanied by qualitative health-based air quality bands (such as Good, Moderate, or Poor).[5][2]
Applying the EDIAQI Decision Tree with the Simulator
The EDIAQI Simulation Tool serves as a practical diagnostic foundation for the EDIAQI Decision Tree, guiding users through a structured four-stage evaluation process:[4]
1. Why Measure and Simulate?
Indoor air can contain microscopic combustion particles and chemical compounds that cause eye irritation, reduce cognitive attention in classrooms, and trigger childhood asthma.[4][6] Because sensory perception cannot identify dangerous concentrations of fine particles or nitrogen dioxide, predictive simulation provides an objective screening alternative for building occupants who do not possess physical air monitors.[5][7]
2. How to Configure Your Assessment?
Users should configure parameters based on representative, daily operating habits rather than best-case scenarios.[2] For example, in classrooms or residential living rooms, enter actual student or occupant counts, account for window closure during cold months, and record regular frequencies of cleaning, printing, or cooking.[4] When low-cost physical sensors are deployed, place them in central breathing zones between 1.0 m and 1.5 m above floor level, away from open windows and heating elements, to compare live data against model outputs.[2]
3. What is the Issue? Identifying Indoor vs. Outdoor Origins
Comparing outdoor concentrations with simulated indoor exposure helps identify the dominant pollution pathway:[4][2]
- Outdoor-Driven Contamination: When indoor pollutant levels rise in parallel with high outdoor concentrations, ambient traffic or industrial emissions are penetrating through envelope cracks or open windows.[4] Under these conditions, the indoor/outdoor (I/O) concentration ratio remains below or near 1.0.
- Indoor-Driven Contamination: When indoor levels exceed ambient outdoor air, internal sources are driving exposure: unvented gas burners, indoor smoking, candle soot, cleaning sprays, or material off-gassing trapped by inadequate ventilation.[3] Here, the I/O ratio significantly exceeds 1.0.
Practical Actions for Indoor Mitigation
Based on the simulated sources and ventilation performance, building operators can implement targeted interventions:[4][2]
Eliminate or substitute high-emission activities. Operate kitchen range hoods ducted directly outdoors during gas cooking, eliminate unvented indoor burning (candles, incense, tobacco), and replace harsh chemical cleaners and high-emission furnishings with eco-certified, low-emission alternatives.[6]
In naturally ventilated buildings, practice short, intensive window airing periods during off-peak traffic hours when ambient air is cleanest.[4] In mechanically ventilated properties, verify outdoor air supply flow rates and maintain continuous air exchange matched to occupancy in compliance with EN 16798 standards.[4]
In urban locations subject to road traffic or seasonal combustion smoke, install stand-alone air purifiers with certified HEPA (EN 1822) or ISO 16890 ePM1 filters to remove airborne particulate matter.[4] For gaseous contaminants such as NO2 and volatile organic compounds, utilize units equipped with substantial activated carbon adsorbent stages.[3]
When simulated exposures indicate persistent high risk, verify indoor concentrations using accredited continuous monitors or certified laboratory sorbent protocols before undertaking major structural renovations.[7][2]
Test your building scenario online
Launch the EDIAQI IAQ Simulation Tool → or consult the EDIAQI Decision Tree → for systematic building guidance.
[+] Click here to view technical modeling and data interoperability details
The simulation architecture combines physical modeling and machine learning algorithms to compute localized air quality without physical hardware:[1]
- Copernicus Reanalysis Fields: The ambient baseline layer extracts background pollutant fields from the Copernicus Atmosphere Monitoring Service (CAMS) pan-European regional ensemble.[1]
- Geospatial Land-Use Regression (LUR): Automated geospatial routines query OpenStreetMap (OSM) to extract road network classification (motorways, primary, secondary), structural building density, distance to industrial infrastructure, and buffer areas of green spaces and waterways within 1 km and 5 km radii.[1]
- Ground Monitoring Data Fusion: Near-real-time observations from the nearest European Environment Agency (EEA) Air Quality e-Reporting stations are integrated alongside temporal indicators (hour of day, day of week, seasonal cycles).[1]
- LightGBM Regression Engine: A gradient boosting framework (LightGBM) models non-linear spatial dependencies and urban dispersion patterns, predicting baseline outdoor ambient levels (PM10, PM2.5, NO2) at the point of interest and surrounding coordinates.[1]
To convert ambient outdoor levels into occupied indoor exposure concentrations, the simulator applies a dynamic mass-balance model parameterized with data from EDIAQI pilot campaigns:[2][4]
$$\frac{dC_{in}}{dt} = a \cdot P \cdot C_{out} - (a + k) \cdot C_{in} + \frac{S}{V}$$
Where:
- $C_{in}$ and $C_{out}$ represent indoor and outdoor pollutant concentrations ($\mu\text{g/m}^3$).
- $a$ is the building air exchange rate ($\text{h}^{-1}$), parameterized by building era, glazing type, and ventilation configuration.
- $P$ is the dimensionless envelope penetration factor for the contaminant.
- $k$ is the indoor deposition and loss rate coefficient ($\text{h}^{-1}$), accounting for surface loss mechanisms.
- $S$ is the internal emission source rate ($\mu\text{g/h}$), derived from user-selected cooking, heating, smoking, and cleaning routines.
- $V$ is the indoor air volume ($\text{m}^3$), calculated from entered floor area and standard room ceiling heights.[4]
Under Deliverables D4.3 and D4.7, modeled parameters and metadata structures conform to Open Geospatial Consortium (OGC) SensorThings API specifications and European environmental registries:[1]
| Parameter Name | ObservedProperty Code | Controlled Vocabulary URI | Standard Unit |
|---|---|---|---|
| Particulate matter < 2.5 µm | PM2.5 | https://dd.eionet.europa.eu/vocabulary/aq/pollutant/6001 |
µg/m³ |
| Particulate matter < 10 µm | PM10 | https://dd.eionet.europa.eu/vocabulary/aq/pollutant/5 |
µg/m³ |
| Nitrogen dioxide | NO2 | https://dd.eionet.europa.eu/vocabulary/aq/pollutant/8 |
µg/m³ |
- Metadata Integration: Outputs contain geospatial reference attributes, calculation timestamps, and building classifications aligned with ISO 16000 and European building directives.[1]
Target Audience Practical Application Matrix
| Target Audience | Practical Value and Action |
|---|---|
| Homeowners and Tenants | Evaluate residential exposure risks, test how eliminating unvented gas cooking or smoking lowers particle levels, and establish whether natural airing improves air quality based on nearby traffic.[4] |
| Schools and Kindergartens | Screen classroom air quality without hardware investments, examine ventilation needs under high student occupancy, and define airing routines to protect children vulnerable to asthma.[4] |
| Building and Facility Managers | Conduct preliminary indoor climate assessments across multi-building portfolios, simulate the exposure improvements achievable via ventilation upgrades, and justify air filter investments.[4] |
| Municipalities and Urban Planners | Identify residential neighborhoods and educational zones subject to significant pollution penetration, supporting low-emission zoning and municipal energy renovation schemes.[8][1] |
| Researchers and Public Health Officers | Apply standardized mass-balance transfer functions to evaluate population exposure and assess environmental health inequalities across European building typologies.[1] |
Current Limitations and Future Developments
While the simulation platform provides an accessible screening tool, users should recognize its operational parameters:[7][2]
- Screening Tool vs. Certified Compliance: The platform generates statistical and physical estimations rather than direct empirical sensor measurements. It is intended for awareness and risk identification; formal legal verification requires certified reference sampling or accredited monitoring methods.[7]
- Target Pollutant Boundaries: The machine learning models focus on PM10, PM2.5, and NO2. Specific contaminants such as radon gas, airborne microplastics, formaldehyde, or mold spores are not currently simulated and require physical testing.[7][3]
- Ongoing Consortium Enhancements: As empirical data continue to be gathered from EDIAQI pilot buildings in Ferrara, Tallinn, and Zagreb, the platform undergoes regular development:[1][2]
- Ingesting live data from deployed low-cost sensor networks to calibrate indoor transfer functions dynamically.[2]
- Incorporating local meteorological forecast feeds to deliver 24-hour predictive indoor alerts and smart window-opening schedules.[1]
- Linking with physics-informed hybrid digital twins to evaluate room-scale airflow distribution and optimal air cleaner positioning.[1]
How to Access
The simulator is publicly accessible free of charge through any web browser:
- Web Application: https://iaqsim.ascalia.io/
Related wiki pages
References
- ↑ 1.00 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10 1.11 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19
- ↑ 2.00 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09 2.10 2.11
- ↑ 3.0 3.1 3.2 3.3 3.4 Halios, C. H., Landeg-Cox, C., Lowther, S. D., Middleton, A., Marczylo, T., & Dimitroulopoulou, S. (2022). Chemicals in European residences: Part I: A review of emissions, concentrations and health effects of volatile organic compounds (VOCs). Science of the Total Environment, 839, 156201.
- ↑ 4.00 4.01 4.02 4.03 4.04 4.05 4.06 4.07 4.08 4.09 4.10 4.11 4.12 4.13 4.14 4.15 4.16 Sadrizadeh, S., et al. (2022). Indoor air quality and health in schools: A critical review for developing the roadmap for the future school environment. Journal of Building Engineering, 57, 104908.
- ↑ 5.0 5.1 World Health Organization. (2021). WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide.
- ↑ 6.0 6.1 World Health Organization. (2010). WHO guidelines for indoor air quality: selected pollutants.
- ↑ 7.0 7.1 7.2 7.3 7.4
- ↑ Settimo, G., Manigrasso, M., & Avino, P. (2020). Indoor air quality: A focus on the European legislation and state-of-the-art research in Italy. Atmosphere, 11(4), 370.