Applications of an advanced clustering tool for EU AQ monitoring network data analysis
Notice bibliographique
Résumé
Air quality monitoring networks provide invaluable data for studying human health, environmental impacts, and the effects of policy changes. In a European legislative context, the data collected constitutes the basis for reporting air quality status and exceedances under the Ambient Air Quality Directives (AAQD) following specific requirements. Consequently, the network's representativity and ability to accurately assess the air pollution situation in European countries become a key issue. The combined use of models and measurements is currently understood as the most robust way to map the status of air pollution in an area, allowing it to quantify both the spatial and temporal distribution of pollution. This spatial-temporal information can be used to evaluate the representativeness of the monitoring network and support air quality monitoring design using hierarchical clustering techniques.The hierarchical clustering methodology applied in this context can be used as a screening tool to analyse the level of similarity or dissimilarity of the air concentration data (time-series) within a monitoring network. Hierarchical clustering assumes that the data contains a level of (dis)similarity and groups the station records based on the characteristics of the actual data. The advantage of this type of clustering is that it does not require an a priori assumption about how many clusters there might be, but it can become computationally expensive as the number of time-series increases in size. Three dissimilarity metrics are used to establish the level of similarity (or dissimilarity) of the different air quality measurements across the monitoring network: (1) 1-R, where R is the Pearson linear correlation coefficient, (2) the Euclidean distance (EuD), and (3) multiplication of metric (1) and (2). The metric based on correlation assesses dissimilarities associated with the changes in the temporal variations in concentration. The metric based on the EuD assesses dissimilarities based on the magnitude of the concentration over the period analysed. The multiplication of these two metrics (1-R) x EuD assesses time variation and pollution levels correlations, and it has been demonstrated to be the most useful metric for monitoring network optimization.This study presents the MoNET webtool developed based on the hierarchical clustering methodology. This webtool aims to provide an easy solution for member states to quality control the data reported as a tier-2 level check and evaluate the representativeness of the air quality network reporting under the AAQD. Some examples from the ongoing evaluation of the monitoring site classification carried out as a joint exercise under the Forum for Air Quality Modeling (FAIRMODE) and the National Air Quality Reference Laboratories Network (AQUILA) are available to show the usability of the tool. MoNet should be able to identify outliers, i.e., issues with the data or data series with very specific temporal-magnitude profiles, and to distinguish, e.g., pollution regimes within a country and if it resembles the air quality zones required by the AAQD and set by the member states; stations monitoring high-emitting sources; background regimes vs. a local source driving pollution regime in cities.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».