eprintid: 10159900 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/15/99/00 datestamp: 2022-11-21 11:57:51 lastmod: 2022-11-21 11:57:51 status_changed: 2022-11-21 11:57:51 type: article metadata_visibility: show sword_depositor: 699 creators_name: Chang, Victor creators_name: Ni, Pin creators_name: Li, Yuming title: K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F44 keywords: Science & Technology, Technology, Computer Science, Information Systems, Computer Science, Software Engineering, Telecommunications, Computer Science, PM2.5 CONCENTRATIONS, POLLUTION, NUMBER note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: Air pollution has caused environmental and health hazards across the globe, particularly in emerging countries such as China. In this article, we propose the use of air quality index and the development of advanced data processing, analysis, and visualization techniques based on the AI-based k-clustering method. We analyze the air quality data based on seven key attributes and discuss its implications. Our results provide meaningful values and contributions to the current research. Our future work will include the use of advanced AI algorithms and big data techniques to ensure better performance, accuracy and real-time checks. date: 2020-07-01 date_type: published publisher: IEEE COMPUTER SOC official_url: https://doi.org/10.1109/MITP.2020.2993851 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1946861 doi: 10.1109/MITP.2020.2993851 lyricists_name: Ni, Pin lyricists_id: PNIXX47 actors_name: Ni, Pin actors_id: PNIXX47 actors_role: owner funding_acknowledgements: VCR 000011 [] full_text_status: public publication: IT Professional volume: 22 number: 4 pagerange: 28-34 pages: 7 citation: Chang, Victor; Ni, Pin; Li, Yuming; (2020) K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index. IT Professional , 22 (4) pp. 28-34. 10.1109/MITP.2020.2993851 <https://doi.org/10.1109/MITP.2020.2993851>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10159900/1/ITPro_Air_quality_submission_final.pdf