{
  "abstract": "Air quality networks measure pollutant concentrations only at instrumented sites, leaving most locations unobserved. Graph-based virtual-node methods increasingly fill this gap, but are usually summarised by a single leave-one-station-out (LOO) R2 that leaves unstated where the model can be trusted and why it fails. Here, the operating envelope is the object of study. A graph attention network (GAT) combining multi-pollutant lags, ERA5 meteorology, MODIS aerosol optical depth, and land-use composition predicts daily-mean PM10 at unmonitored locations on the 18-station Styrian (Austria) network for 2020–2023. Under LOO cross-validation with disjoint years, the GAT reaches a median R2 of 0.52, seed-stable and ahead of Lagged-IDW (0.26), Lagged-Kriging (0.22), and Ridge-AR (0.45). At two stations whose PM10 distributions lie outside the training support, performance degrades sharply. Ablation and attribution trace the failure to one component: inverse-distanceweighted imputation of held-out lag features interacting with the persistence-residual head; graph topology is immaterial. An outlier-gated imputation policy raises pooled mean R2 from 0.31 to 0.38, node-masking training cuts per-station variance fourfold, and zeroing all auxiliary inputs lifts both outlier stations to positive R2: the partition is induced by the feature pipeline, not the architecture. The partition reproduces on NO2, and the model transfers to the 33-station Bavarian network (median R2 0.39, no negative folds). The outof-scope stations are clean-air sites under-represented in an urban-industrial network, and the worst is flagged in advance by a static-feature outlier detector: a deployable signal of where virtual-node estimates can be trusted and where the network must grow in regime diversity.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Dejan Strbad"
    },
    {
      "name": "Valentino Petrić"
    },
    {
      "name": "Nikolina Račić"
    },
    {
      "name": "Francesco Mureddu"
    },
    {
      "name": "Mario Lovrić"
    },
    {
      "name": "Mario Lovric",
      "orcid": "0000-0002-3541-9624"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9w2bcwtsmrfmb.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008048/v1",
  "fulltext_url": null,
  "id": "9w2bcwtsmrfmb",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008048/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2305|Air Quality Monitoring and Forecasting"
    ]
  ],
  "openalex": "W7204780510",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008048/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-30",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/9w2bcwtsmrfmb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008048",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008048/v1"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Air Quality Monitoring and Forecasting",
      "subfield": "environmental-science"
    }
  ],
  "title": "Graph attention networks for daily PM10 prediction at unmonitored European monitoring sites: operational scope, diagnostics, and outlier-robust training",
  "title_text": "Graph attention networks for daily PM10 prediction at unmonitored European monitoring sites: operational scope, diagnostics, and outlier-robust training",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/9w2bcwtsmrfmb",
  "venue": "ChemRxiv",
  "versions": []
}