{"doi":"10.1021/acs.est.3c07545","title":"Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment","abstract":null,"journal":"Environmental Science & Technology","year":2024,"id":612433,"datarank":0.5244761342199721,"base_score":3.4965075614664802,"endowment":3.4965075614664802,"self_citation_contribution":0.5244761342199721,"citation_network_contribution":0.0,"self_endowment_contribution":0.5244761342199721,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":32,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":104275,"name":"Shaojun Zhang","orcid":"0000-0001-5760-5797","position":1,"is_corresponding":false},{"id":583288,"name":"Yuan Wang","orcid":"0000-0002-7986-6880","position":2,"is_corresponding":false},{"id":217440,"name":"Jiani Yang","orcid":null,"position":3,"is_corresponding":false},{"id":1576874,"name":"Liyin He","orcid":null,"position":4,"is_corresponding":false},{"id":272380,"name":"Ye Wu","orcid":"0000-0002-9928-1177","position":5,"is_corresponding":false},{"id":1576876,"name":"Jiming Hao","orcid":null,"position":6,"is_corresponding":false},{"id":1576871,"name":"Yifan Wen","orcid":"0000-0003-1876-7990","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment","abstract":"Air pollution poses a critical public health threat around many megacities but in an uneven manner. Conventional models are limited to depict the highly spatial- and time-varying patterns of ambient pollutant exposures at the community scale for megacities. Here, we developed a machine-learning approach that leverages the dynamic traffic profiles to continuously estimate community-level year-long air pollutant concentrations in Los Angeles, U.S. We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (NO 2 ), maximum daily 8-h average ozone (MDA8 O 3 ), and fine particulate matter (PM 2.5 ) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM 2.5 levels exceeding limits due to heavy traffic activities and providing an “out-of-limit map” tool to identify exposure disparities within highly polluted communities. In contrast, the model without real-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents’ exposure to PM 2.5 . The underestimations are more severe for disadvantaged communities such as black and low-income groups, showing the significance of incorporating real-time traffic data in exposure disparity assessment.","is_dataset_classified":null,"base_score":3.4965075614664802,"endowment":3.4965075614664802,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38261755","pmcid":null,"openalex_id":"https://openalex.org/W4391130294","authors":[],"funders":[{"funder_name":"National Key Research and Development Program of China","grant_id":"2022YFC3703600","title":null},{"funder_name":"China Postdoctoral Science Foundation","grant_id":"BX20220179","title":null},{"funder_name":"Tsinghua University","grant_id":"2022SM011","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"42261160645","title":null},{"funder_name":"Alibaba Innovative Research Program","grant_id":"","title":null}],"total_grants":5,"fwci":7.6781,"citation_percentile":0.98189973,"influential_citations":0,"citation_trend":[{"year":2024,"count":9},{"year":2025,"count":16},{"year":2026,"count":7}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-029","oa_locations":[{"url":"https://pubs.acs.org/doi/pdf/10.1021/acs.est.3c07545","host_type":"publisher"},{"url":"https://doi.org/10.1021/acs.est.3c07545","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38261755","host_type":"repository"}],"fields_of_study":["Air Quality and Health Impacts","Air Quality Monitoring and Forecasting","Vehicle emissions and performance"],"mesh_terms":[],"keywords":["Air quality index","Quality (philosophy)","Computer science","Environmental justice","Environmental science","Environmental monitoring","Air pollution","Transport engineering","Engineering","Environmental engineering","Meteorology","Geography","Political science","Ecology","Air quality","Machine Learning","Population Exposure","On-road Traffic"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"No poverty"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-02T03:23:05.541901Z","pmid":null,"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":[]}