{"data":{"id":"10.5281/zenodo.4009308","type":"dois","attributes":{"doi":"10.5281/zenodo.4009308","prefix":"10.5281","suffix":"zenodo.4009308","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Runmei Ma","affiliation":["China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention"],"nameIdentifiers":[]},{"name":"Ban, Jie","nameType":"Personal","givenName":"Jie","familyName":"Ban","affiliation":["China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention"],"nameIdentifiers":[]},{"name":"Wang, Qing","nameType":"Personal","givenName":"Qing","familyName":"Wang","affiliation":["China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention"],"nameIdentifiers":[]},{"name":"Yayi Zhang","affiliation":["China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention"],"nameIdentifiers":[]},{"name":"Tiantian Li","affiliation":["China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention"],"nameIdentifiers":[]}],"titles":[{"title":"Full-coverage 1 km daily ambient PM2.5 and O3 concentrations of China in 2005-2017 based on multi-variable random forest model"}],"publisher":"Zenodo","container":{},"publicationYear":2021,"subjects":[{"subject":"PM2.5"},{"subject":"O3"},{"subject":"Random forest"},{"subject":"Simulation"},{"subject":"China"}],"contributors":[],"dates":[{"date":"2021-09-03","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.4009307","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"},{"rights":"Open Access","rightsUri":"info:eu-repo/semantics/openAccess"}],"descriptions":[{"description":"The aim of our study was to construct random forest models with high-performance, and estimate daily average PM\u003csub\u003e2.5\u003c/sub\u003e concentration and O\u003csub\u003e3\u003c/sub\u003e daily maximum 8h average concentration (O\u003csub\u003e3\u003c/sub\u003e-8hmax) of China in 2005-2017 at a spatial resolution of 1km×1km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on ten-fold cross validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly simulations of PM\u003csub\u003e2.5\u003c/sub\u003e gave average model fitting R\u003csup\u003e2\u003c/sup\u003e values of 0.85, 0.88 and 0.90, respectively; these R\u003csup\u003e2\u003c/sup\u003e values were 0.77, 0.77, and 0.69 for O\u003csub\u003e3\u003c/sub\u003e-8hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e-8hmax simulations. During 2005-2017, PM\u003csub\u003e2.5\u003c/sub\u003e exhibited an overall downward trend, while ambient O\u003csub\u003e3\u003c/sub\u003e experienced an upward trend. Whilst the spatial patterns of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e-8hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristic. Each dataset is the annual mean concentration of PM\u003csub\u003e2.5\u003c/sub\u003e or O\u003csub\u003e3\u003c/sub\u003e-8hmax based on the standard grid (Grid.csv) for that year. The coordinate system of the grid is WGS-84.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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