{
  "abstract": "Permanent air-quality networks are expensive to install and maintain, yet many decisions depend on one future regional exposure rather than the complete pollution field. We introduce Target-Aware Permanent Sampling (TAPS), a graph-diffusion framework for selecting permanent locations observed repeatedly over time, that can reduce unnecessary sensor installations, maintenance, and cost while preserving the information needed for future air-quality decisions, subject to validation with the estimator that will use the network. We formulate the permanent space-time sampling problem and show that, in the noiseless model, recovery of one prescribed target can require less information than full-state identification, for which we give an explicit lower bound on the permanent-location count. We also derive a greedy rule whose marginal gain factors into raw target response and a finite-update correction. We evaluate TAPS on regulatory air-quality networks in California, Canada, and England. In each case the prescribed regional target becomes numerically recoverable at permanent-location budgets well below those required to identify the retained state, and TAPS attains lower regularized target risk than target-weight, geometric, and design-based placements. A blind prospective study further shows that the criterion can be applied before candidate sites have any ground-monitor history, using exogenous environmental covariates alone.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.37247",
  "author_total": 2,
  "authors": [
    {
      "name": "Michelle Lin"
    },
    {
      "name": "Laura P. Schaposnik"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6pkp07ecgf9d5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.37247",
  "fulltext_url": null,
  "id": "6pkp07ecgf9d5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.37247",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2305|Air Quality Monitoring and Forecasting"
    ]
  ],
  "openalex": "W7214924574",
  "pdf_url": "https://arxiv.org/pdf/2609.37247",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6pkp07ecgf9d5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:35Z",
      "native_id": "2609.37247",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.37247"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "TAPS: Target-Aware Permanent Sampling for Graph Diffusion",
  "title_text": "TAPS: Target-Aware Permanent Sampling for Graph Diffusion",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:35Z",
  "url": "https://research.draconitic.com/paper/6pkp07ecgf9d5",
  "venue": "arXiv",
  "versions": []
}