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ChinaHighPM2.5 is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from MODIS/Terra+Aqua MAIAC AOD products together with other auxiliary data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution.
\n\nThis is the MODIS/Terra+Aqua Level 3 (L3) yearly 0.01 degree (≈ 1 km) gridded ground-level PM2.5 products in Eastern China (ECHAP_PM2.5_Y1K) from 2000 to 2020, which are averaged from the Level 2 daily products. Note that the data for the year 2000 is averaged from March 2000 to December 2000. The annual PM2.5 estimates are highly related to ground-based measurements (R2 = 0.94) with an average root-mean-square error (RMSE) of 5.07 µg m-3.
\n\nNote that this dataset is closed since a new version is published at 10.5281/zenodo.3539349.
", "languages": [ { "id": "eng", "title": { "en": "English" } } ], "publication_date": "2019-11-12", "publisher": "Zenodo", "references": [ { "reference": "Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136" }, { "reference": "Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y. Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees, Atmospheric Chemistry and Physics, 2020, 20(6), 3273-3289. https://doi.org/10.5194/acp-20-3273-2020" } ], "related_identifiers": [ { "identifier": "10.1016/j.rse.2020.112136", "relation_type": { "id": "isreferencedby", "title": { "de": "Wird referenziert von", "en": "Is referenced by" } }, "resource_type": { "id": "publication-article", "title": { "de": "Zeitschriftenartikel", "en": "Journal article" } }, "scheme": "doi" } ], "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "subjects": [ { "subject": "PM2.5" }, { "subject": "1 km resolution" }, { "subject": "Remote Sensing" }, { "subject": "Artificial intelligence" } ], "title": "ChinaHighPM2.5 (Version 3)", "version": "3" }, "parent": { "access": { "owned_by": { "user": 82631 }, "settings": { "accept_conditions_text": null, "allow_guest_requests": false, "allow_user_requests": false, "secret_link_expiration": 0 } }, "communities": { "default": "87b4209c-8b19-4f11-9ff3-a9253eed4236", "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_policy": "open", "review_policy": "closed", "visibility": "public" }, "children": { "allow": false }, "created": "2023-12-17T17:10:52.682810+00:00", "custom_fields": {}, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "87b4209c-8b19-4f11-9ff3-a9253eed4236", "links": {}, "metadata": { "description": "Long-term, full-coverage (gapless), high-resolution and high-quality datasets of ambient air pollutants for China (i.e., ChinaHighAirPollutants, CHAP).", "organizations": [ { "id": "047s2c258" } ], "title": "CHAP: High-resolution and High-quality Ambient Air Pollutants Dataset for China", "type": { "id": "project" }, "website": "https://weijing-rs.github.io/product.html" }, "revision_id": 6, "slug": "chap", "updated": "2023-12-17T17:37:06.090776+00:00" } ], "ids": [ "87b4209c-8b19-4f11-9ff3-a9253eed4236" ] }, "id": "3539349", "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.3539349", "provider": "datacite" } } }, "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.4660858", "provider": "datacite" }, "oai": { "identifier": "oai:zenodo.org:4660858", "provider": "oai" } }, "revision_id": 54, "stats": { "all_versions": { "data_volume": 327883362975079.0, "downloads": 216784, "unique_downloads": 49989, "unique_views": 31311, "views": 38500 }, "this_version": { "data_volume": 550105897470.0, "downloads": 19004, "unique_downloads": 4490, "unique_views": 5608, "views": 6776 } }, "status": "published", "updated": "2023-09-27T03:23:16.074374+00:00", "versions": { "index": 3, "is_latest": false } }