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A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques
Research article (npj Climate and Atmospheric Science, 2024) · cited 16× · AI/ML
A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques
Summary
A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques is a scholarly article[1].
Key Facts
A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques. Retrieved May 24, 2026, from https://4ort.xyz/entity/a-novel-spatiotemporal-prediction-approach-to-fill-air-pollution-data-gaps-using-mobile-sensors-machine-learning-and-cit
MLA“A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/a-novel-spatiotemporal-prediction-approach-to-fill-air-pollution-data-gaps-using-mobile-sensors-machine-learning-and-cit.
BibTeX@misc{4ortxyz_a-novel-spatiotemporal-prediction-approach-to-fill-air-pollution-data-gaps-using-mobile-sensors-machine-learning-and-cit_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques}}, year = {2026}, url = {https://4ort.xyz/entity/a-novel-spatiotemporal-prediction-approach-to-fill-air-pollution-data-gaps-using-mobile-sensors-machine-learning-and-cit}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques — https://4ort.xyz/entity/a-novel-spatiotemporal-prediction-approach-to-fill-air-pollution-data-gaps-using-mobile-sensors-machine-learning-and-cit (retrieved 2026-05-24)
