{"doi":"10.1029/2023gh000798","title":"Satellite‐Based Long‐Term Spatiotemporal Trends in Ambient NO <sub>2</sub> Concentrations and Attributable Health Burdens in China From 2005 to 2020","abstract":"Abstract Despite the recent development of using satellite remote sensing to predict surface NO 2 levels in China, methods for estimating reliable historical NO 2 exposure, especially before the establishment of NO 2 monitoring network in 2013, are still rare. A gap‐filling model was first adopted to impute the missing NO 2 column densities from satellite, then an ensemble machine learning model incorporating three base learners was developed to estimate the spatiotemporal pattern of monthly mean NO 2 concentrations at 0.05° spatial resolution from 2005 to 2020 in China. Further, we applied the exposure data set with epidemiologically derived exposure response relations to estimate the annual NO 2 associated mortality burdens in China. The coverage of satellite NO 2 column densities increased from 46.9% to 100% after gap‐filling. The ensemble model predictions had good agreement with observations, and the sample‐based, temporal and spatial cross‐validation (CV) R 2 were 0.88, 0.82, and 0.73, respectively. In addition, our model can provide accurate historical NO 2 concentrations, with both by‐year CV R 2 and external separate year validation R 2 achieving 0.80. The estimated national NO 2 levels showed a increasing trend during 2005–2011, then decreased gradually until 2020, especially in 2012–2015. The estimated annual mortality burden attributable to long‐term NO 2 exposure ranged from 305 thousand to 416 thousand, and varied considerably across provinces in China. This satellite‐based ensemble model could provide reliable long‐term NO 2 predictions at a high spatial resolution with complete coverage for environmental and epidemiological studies in China. Our results also highlighted the heavy disease burden by NO 2 and call for more targeted policies to reduce the emission of nitrogen oxides in China.","journal":"GeoHealth","year":2023,"id":325777,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":30,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8645,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":760022,"name":"Qingyang Zhu","orcid":"0000-0001-7205-5199","position":1,"is_corresponding":false},{"id":708985,"name":"Xiangfeng Lu","orcid":"0000-0003-4088-2338","position":2,"is_corresponding":false},{"id":451070,"name":"Dongfeng Gu","orcid":"0000-0002-2781-7825","position":3,"is_corresponding":false},{"id":23140,"name":"Yang Liu","orcid":"0000-0001-5477-2186","position":4,"is_corresponding":false},{"id":708978,"name":"Keyong Huang","orcid":"0000-0002-3494-1744","position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T01:08:28.266998Z","pmid":"37206379","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}