Observation Instance

HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
    "ob_id": 45156,
    "uuid": "2440f838ab92489d8aa09fa1cf39bd1b",
    "title": "Modelled West Midlands Air Quality Maps from 2016 - 2030",
    "abstract": "This dataset contains high-resolution spatial mapping layers of ambient air quality across the West Midlands, produced as part of the West Midlands Air Quality Improvement Programme (WMAQIP) from 2016 - 2030. \r\nThe data provide gridded concentration outputs for key atmospheric pollutants, specifically nitrogen dioxide (NO₂) and fine particulate matter (PM2.5), to support regional environmental planning and public health assessments. The data is organized into pollutant-specific directories (NO2 and PM2.5), each subdivided by strategic environmental simulation scenarios. These include baseline conditions, targeted traffic mitigation pathways (Traffic_Reduction), and projected future emissions compliance models (2030_NetZero). Within these directories, data is delivered as georeferenced raster layers. Each raster consists of a standard image file paired with spatial data sidecar files: core raster data (.tif), GIS world georeferencing coordinates (.tfw), rendering optimization pyramids (.ovr), and structured XML metadata (.xml). While this dataset represents the finalized empirical outputs of the WMAQIP predictive modeling framework, full details regarding the underlying chemical transport models, input emissions inventories, and meteorological validation methods are comprehensively documented in the accompanying technical literature linked in the metadata references. \r\n\r\nHigh resolution air quality models combining emissions, chemical processes, dispersion and dynamical treatments are necessary to develop effective policies for clean air in urban environments. Task farming was applied to reduce runtime for ADMS-Urban, a quasi-Gaussian plume air dispersion model. The model represents the full range of source types (point, road and grid sources) occurring in an urban area at high resolution. The option to automatically split up a large model domain into smaller sub-regions, each of which can then be executed concurrently on multiple cores of a HPC or across a PC network, a technique known as task farming was implemented and evaluated. The approach has been tested for a large model domain covering the West Midlands, UK, as part of modelling work in the WM-Air (West Midlands Air Quality Improvement Programme) project. Annual air quality maps (at 10m x 10 m resolution) for the baseline and modelling scenarios have been generated.",
    "keywords": "Air quality modelling,ADMS-Urban,High Performance Computing,West Midlands",
    "publicationState": "published",
    "dataPublishedTime": "2026-07-09T08:22:55",
    "doiPublishedTime": null,
    "updateFrequency": "notPlanned",
    "status": "pending",
    "result_field": {
        "ob_id": "https://catalogue.ceda.ac.uk/api/v2/observations/46015/?format=api",
        "dataPath": "/badc/deposited2026/WM-AirQualityMaps/",
        "oldDataPath": [],
        "storageLocation": "internal",
        "storageStatus": "online",
        "volume": 2274323648,
        "numberOfFiles": 89,
        "fileFormat": "GEOTIFF and TIFF files"
    },
    "timePeriod": "https://catalogue.ceda.ac.uk/api/v2/times/12831/?format=api",
    "geographicExtent": "https://catalogue.ceda.ac.uk/api/v2/bboxes/4949/?format=api",
    "nonGeographicFlag": false,
    "phenomena": [],
    "dataLineage": "Air quality data compiled, processed, and georeferenced into GeoTIFF (.tif) format. The main spatial data (.tif) is accompanied by auxiliary spatial information including World Files (.tfw) for spatial reference, overview files (.ovr) for rapid rendering, and associated XML metadata profiles (.xml) compliant with standard GIS software requirements.",
    "removedDataTime": null,
    "removedDataReason": "",
    "language": "English",
    "identifier_set": [],
    "projects": [
        "https://catalogue.ceda.ac.uk/api/v2/projects/45144/?format=api"
    ],
    "observationcollection_set": [],
    "responsiblepartyinfo_set": [
        "https://catalogue.ceda.ac.uk/api/v2/rpis/219947/?format=api",
        "https://catalogue.ceda.ac.uk/api/v2/rpis/216099/?format=api",
        "https://catalogue.ceda.ac.uk/api/v2/rpis/216100/?format=api",
        "https://catalogue.ceda.ac.uk/api/v2/rpis/216101/?format=api",
        "https://catalogue.ceda.ac.uk/api/v2/rpis/216102/?format=api",
        "https://catalogue.ceda.ac.uk/api/v2/rpis/216103/?format=api",
        "https://catalogue.ceda.ac.uk/api/v2/rpis/216104/?format=api",
        "https://catalogue.ceda.ac.uk/api/v2/rpis/216098/?format=api",
        "https://catalogue.ceda.ac.uk/api/v2/rpis/216105/?format=api",
        "https://catalogue.ceda.ac.uk/api/v2/rpis/216106/?format=api"
    ],
    "procedureAcquisition": null,
    "procedureCompositeProcess": null,
    "procedureComputation": "https://catalogue.ceda.ac.uk/api/v2/computations/45155/?format=api",
    "permissions": [
        {
            "ob_id": "https://catalogue.ceda.ac.uk/api/v2/observations/2526/?format=api",
            "useLimitation": null,
            "accessConstraints": null,
            "accessCategory": "public",
            "accessRoles": null,
            "label": "public: None group",
            "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/",
            "licenceClassifications": "any, attribution required"
        }
    ],
    "discoveryKeywords": []
}