# The dimensionality of MOX sensors in Air quality assessment

Luiz Miranda, Nathalie Redon, Caroline Duc, Marie Verriele, Jugurta Montalvão, Bernadette Dorizzi, Mossaab Hariz, Dijana Petrovska, Jerome Boudy

► To cite this version:

Luiz Miranda, Nathalie Redon, Caroline Duc, Marie Verriele, Jugurta Montalvão, et al.. The dimensionality of MOX sensors in Air quality assessment. Eurosensors 2025, Sep 2025, Wroclaw, Poland, Poland. 2025, 10.5162/EUROSENSORS2025/MP44. [hal-05300871](https://hal.science/hal-05300871v1)

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Submitted on 7 Oct 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# THE DIMENSIONALITY OF MOX SENSORS ARRAYS IN AIR QUALITY ASSESSMENT

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Institut Mines-Télécom

Luiz MIRANDA<fnref n="1" />, Nathalie REDON<fnref n="2" />, Caroline DUC<fnref n="2" />, Marie VERRIELE<fnref n="2" />, Jugurta MONTALVÃO<fnref n="3" />, Bernadette DORIZZI<fnref n="1" />, Mossaab HARIZ<fnref n="1" />, Dijana PETROVSKA<fnref n="1" />, Jerome BOUDY<fnref n="1" />

## Context

* Metal-oxide (MOX) gas sensors are widely used in various gas sensing despite their low selectivity

* Solution: use **sensor arrays** to benefit from an enhanced response

* Applications often use **more than 10 sensors, but in air quality monitoring only** 2 to 4 sensors are generally used [1]

→ **Why do small MOX sensor arrays work effectively for air quality monitoring?**

## What is Intrinsic Dimensionality (ID) ?

The **Dataset dimensionality** represents the number of parameters measured i.e. the apparent dimensionality, **D**. The **Intrinsic dimensionality (ID), d**, often lower than **D**, measures degrees of freedom and "perceived dimensionality".

<figure><img src="image_3.png" /><figcaption>ID estimation for different data sub-set</figcaption></figure>

**ID Estimation Methods** mainly used:

* PCA: Number of components explaining >90% variance. Tends to overestimate due to linearity.

* MLE [3]: Finds dimension *d* that best fits *k*-nearest neighbor distance distributions (*k*=20).

* GP [4]: Slope of log C(r) vs log r, where C(r) counts pairs within distance r. Multiple stable regions possible.

## Results and discussion

* Estimators show **higher ID for Dataset 1 than Dataset 2**, reflecting complexity differences in sensor count and measured phenomena.

* Relative response (Rs) captures **temporal information**, yielding **higher ID than average response** values.

* PCA **overestimated ID** except for **Rs in Dataset 1**. Further investigation needed.

* Air quality applications target **few specific gases** (like Dataset 2), resulting in **low complexity** requiring fewer sensors, explaining why 2-4 sensors suffice.

## Conclusions

* Few diverse sensors capture essential air quality info within constraints.

* Relative response yields higher ID than average response, indicating **temporal information** improves gas detection.

## Methodology

Study the **dimensionality (ID)** of two datasets with different complexities:

* Dataset 1 [22 MOX-sensors] is from an indoor activity detection experiment [2].

* Dataset 2 [4 MOX-sensors] is from a VOC metrological qualification set;

* Features from datasets:

* Average sensor measurement;

* Normalized relative response $R_s$

$$R_s = \frac{R_g - R_0}{R_0}$$

* i and iii: no activity / clean air

* ii: activity / target gas injection

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<table><thead><tr><th rowspan="2">Sensor reference</th><th colspan="2">Present in</th><th rowspan="2">Target pollutant<br>(according to datasheet)</th></tr><tr><th>Dataset 1</th><th>Dataset 2</th></tr></thead><tbody><tr><td>CCS 801</td><td>X</td><td>X</td><td>C<sub>2</sub>H<sub>5</sub>OH + CO + C<sub>7</sub>H<sub>8</sub></td></tr><tr><td>COM 31</td><td>X</td><td></td><td>CO</td></tr><tr><td>GM 102B</td><td>X</td><td></td><td>NO<sub>2</sub></td></tr><tr><td>GM 302B</td><td>X</td><td></td><td>Alcohol</td></tr><tr><td>GM 402B</td><td>X</td><td></td><td>C<sub>3</sub>H<sub>8</sub> + CH<sub>4</sub></td></tr><tr><td>GM 502B</td><td>X</td><td></td><td>VOCs</td></tr><tr><td>GM 602B</td><td>X</td><td></td><td>H<sub>2</sub>S</td></tr><tr><td>GM 702B</td><td>X</td><td></td><td>CO</td></tr><tr><td>GM 802B</td><td>X</td><td></td><td>NH<sub>3</sub></td></tr><tr><td>H2SM 31</td><td>X</td><td></td><td>H<sub>2</sub>S</td></tr><tr><td>MICS 2714</td><td>X</td><td></td><td>NO<sub>2</sub></td></tr><tr><td>MICS 5524</td><td>X</td><td>X</td><td>CO + C<sub>2</sub>H<sub>5</sub>OH + H<sub>2</sub> + NH<sub>3</sub> + CH<sub>4</sub></td></tr><tr><td>MICS 5914</td><td>X</td><td>X</td><td>NH<sub>3</sub> + C<sub>2</sub>H<sub>5</sub>OH + H<sub>2</sub> + C<sub>3</sub>H<sub>8</sub> + C<sub>4</sub>H<sub>10</sub></td></tr><tr><td>SB 5100</td><td>X</td><td></td><td>H<sub>2</sub>S</td></tr><tr><td>SBAQ 106</td><td>X</td><td></td><td>VOCs</td></tr><tr><td>TGS 2442</td><td>X</td><td></td><td>CO</td></tr><tr><td>TGS 2600</td><td>X</td><td></td><td>H<sub>2</sub> + C<sub>2</sub>H<sub>5</sub>OH + C<sub>4</sub>H<sub>10</sub></td></tr><tr><td>TGS 2602</td><td>X</td><td></td><td>VOCs (toluene + ethanol)</td></tr><tr><td>TGS 2603</td><td>X</td><td></td><td>C<sub>2</sub>H<sub>5</sub>OH + C<sub>3</sub>H<sub>9</sub>N + CH<sub>4</sub>S + H<sub>2</sub>S + H<sub>2</sub></td></tr><tr><td>TGS 2620</td><td>X</td><td></td><td>VOCs</td></tr><tr><td>TGS 8100</td><td>X</td><td>X</td><td>C<sub>2</sub>H<sub>5</sub>OH + H<sub>2</sub> + CO + C<sub>4</sub>H<sub>10</sub></td></tr><tr><td>VOCM 31</td><td>X</td><td></td><td>VOCs</td></tr></tbody></table>

<table><thead><tr><th rowspan="2">Estimator</th><th colspan="2">Dataset 1 (D = 21)</th><th colspan="2">Dataset 2 (D = 4)</th></tr><tr><th>Average</th><th>Rs</th><th>Average</th><th>Rs</th></tr></thead><tbody><tr><td>PCA (Linear)</td><td>6</td><td>5</td><td>3</td><td>4</td></tr><tr><td>MLE (Non-linear)</td><td>4,4</td><td>6,5</td><td>1,9</td><td>2,8</td></tr><tr><td>GP (Non-linear)</td><td>3,2</td><td>3.5 or 5.8</td><td>1.4 or 2.4</td><td>2,5</td></tr></tbody></table>

## References

[1] Z. Yuan et al., Adv. Eng. Mater. 26, 2400782 (2024), DOI: 10.1002/adem.202400782

[2] Miranda et al. (2025), Labelled dataset, DOI: 10.5281/zenodo.15462760

[3] E. Levina and P. Bickel, NIPS 17 (2004).

[4] P. Grassberger and I. Procaccia, Phys. Rev. Lett. 50, 346 (1983), DOI: 10.1103/PhysRevLett.50.346

## Further info:

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## Partners

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IMT Nord Europe
École Mines-Télécom
IMT-Université de Lille

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UNIVERSIDADE
FEDERAL DE
SERGIPE

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AMUSENS

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Funded by the European Union

Funded by the European Union under grant agreement no. 101130159. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

<footnote n="1">¹Samovar, Télécom SudParis, Institut Polytechnique de Paris, Palaiseau, France</footnote>

<footnote n="2">²IMT Nord Europe, Institut Mines-Télécom, Univ. Lille, Center for Energy and Environment, Douai, France</footnote>

<footnote n="3">³Universidade Federal de Sergipe, UFS, São Cristóvão, Brazil</footnote>