{
  "abstract": "Air quality forecasting is critical for mitigating the health impacts of fine particulate matter (PM2.5). This study introduces a hybrid deep learning model, the bidirectional enhanced quantile gated recurrent unit (BEQ-GRU), for short-term forecasting of PM2.5 concentrations with explicit emphasis on extreme pollution occurrences. This model was applied to forecast PM2.5 levels in two high-risk Sri Lankan cities, Colombo and Kandy, delivering 24-hour and 48-hour ahead forecasts. Unlike conventional point-estimation models that optimise for mean accuracy, the BEQ-GRU targets the upper tail of the conditional distribution through an asymmetric quantile objective engineered to capture the heavy-tailed distribution of pollution exceedances. Beyond the architecture, this paper contributes a formal mathematical treatment of the method: (i) it is proved that the quantile loss function recovers the conditional quantile, and its 19:1 gradient asymmetry at τ = 0.95 is quantified; (ii) the “smoothing effect” of mean-optimal training is characterised through a peaks-over-threshold (POT) model, and it is proved that mean-trained detectors systematically miss an identifiable band of exceedances; and (iii) a decision-theoretic result (Theorem 1) is established, showing that thresholding a τ-quantile forecast is Bayes-optimal for cost-sensitive exceedance alarms, with the optimal level fixed by the public-health cost ratio as τ* = CFN/(CFN+CFP). This identity explains, rather than merely reports, the observed high-sensitivity, zero-false-alarm regime. Hourly data from October 2022 to December 2023 were analysed, incorporating meteorological variables and co-pollutant concentrations as predictors. Statistical, machine learning, and deep learning models were compared. Deep learning consistently outperformed conventional approaches, capturing more than 70% of 24-hour and 60% of 48-hour extreme spikes at both stations. The proposed BEQ-GRU achieved the best performance for Colombo, while a standard Bidirectional GRU was most effective for Kandy. The framework can be adapted, with minor modifications, for proactive air-quality management and public-health early warning.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Anjana Jayasinghe"
    },
    {
      "name": "Chandima Tilakaratne"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6j0v1pkcdwx3f.bib",
  "cited_by": 0,
  "doi": "10.31223/x5r219",
  "fulltext_url": null,
  "id": "6j0v1pkcdwx3f",
  "issns": [],
  "landing_url": "https://eartharxiv.org/repository/view/14766/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/legalcode",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Deep Learning"
    ],
    [
      "bepress",
      "Environmental Sciences"
    ],
    [
      "bepress",
      "Gated Recurrent Unit (GRU)"
    ],
    [
      "bepress",
      "PM2.5 forecasting"
    ],
    [
      "bepress",
      "decision theory"
    ],
    [
      "bepress",
      "early-warning systems"
    ],
    [
      "bepress",
      "extreme pollution events"
    ],
    [
      "bepress",
      "quantile regression"
    ],
    [
      "openalex",
      "2305|Air Quality Monitoring and Forecasting"
    ]
  ],
  "openalex": "W7206217862",
  "pdf_url": "https://eartharxiv.org/repository/object/14766/download/25672/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6j0v1pkcdwx3f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "14766",
      "source": "eartharxiv",
      "url": "https://eartharxiv.org/repository/view/14766/"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "bepress:Environmental Sciences",
      "subfield": "environmental-science"
    }
  ],
  "title": "Bidirectional enhanced quantile gated recurrent unit for short-term forecasting of PM2.5 with emphasis on extreme events in Sri Lanka",
  "title_text": "Bidirectional enhanced quantile gated recurrent unit for short-term forecasting of PM2.5 with emphasis on extreme events in Sri Lanka",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/6j0v1pkcdwx3f",
  "venue": "EarthArXiv",
  "versions": []
}