Spatiotemporal modeling of occupational particulate matter using personal low-cost sensor and indoor location tracking data.
Occupational exposure to particulate matter (PM) can result in multiple adverse health effects and should be minimized to protect workers' health. PM exposure at the workplace can be complex with many potential sources and fluctuations over time, making it difficult to control. Dynamic maps that vis...
| Published in: | Journal of Occupational & Environmental Hygiene Vol. 21; no. 10; pp. 696 - 709 |
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| Main Authors: | , , , , , , , , |
| Format: | CEU pictorial research tables/charts Journal Article |
| Published: |
Taylor & Francis Ltd
Oct2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180649925&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180649925 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15459624 V1L jtl: Journal of Occupational & Environmental Hygiene issn: 15459624 maglogo: Y pubinfo: dt: Oct2024 vid: 21 iid: 10 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 180649925 179310836 180649925 180649925 10.1080/15459624.2024.2389279 180649925 ppf: 696 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Spatiotemporal modeling of occupational particulate matter using personal low-cost sensor and indoor location tracking data. aug: au: Ruiter, Sander Franken, Remy Krone, Tanja Le Feber, Maaike Gunnink, Jan Kuijpers, Eelco Peters, Susan Vermeulen, Roel Pronk, Anjoeka affil: Unit Healthy Living and Work, Department Risk Assessment for Products in Development, Netherlands Organization for Applied Scientific Research (TNO), Utrecht, The Netherlands sug: subj: Models, Theoretical Occupational Exposure Analysis Particulate Matter Analysis Environmental Monitoring Equipment and Supplies Geographic Information Systems Air Pollutants, Occupational Analysis Air Pollution, Indoor Analysis Human Pilot Studies Feedback Robotics Work Environment Adverse Health Care Event Environmental Monitoring Methods Descriptive Statistics Comparative Studies Education, Continuing (Credit) ab: Occupational exposure to particulate matter (PM) can result in multiple adverse health effects and should be minimized to protect workers' health. PM exposure at the workplace can be complex with many potential sources and fluctuations over time, making it difficult to control. Dynamic maps that visualize how PM is distributed throughout a workplace over time can help in gaining better insights into when and where exposure occurs. This study explored the use of spatiotemporal modeling followed by kriging for the development of dynamic PM concentration maps in an experimental setting and a workplace setting. Data was collected using personal low-cost PM sensors and an indoor location tracking system, mounted on a moving robot or worker. Maps were generated for an experimental study with one simulated robot worker and a workplace study with four workers. Cross-validation was performed to evaluate the performance and robustness of three types of spatiotemporal models (metric, separable, and summetric) and, as an additional external validation, model estimates were compared with measurements from sensors that were placed stationary in the laboratory or workplace. Spatiotemporal models and maps were generated for both the experimental and workplace studies, with average root mean squared error (RMSE) from 10-fold cross-validation ranging from 7–12 and 73–127 µg/m3, respectively. Workplace models were relatively more robust compared to the experimental study (relative SD ranging from 8–14% of the average RMSE vs. 27–56%, respectively), presumably due to the larger number of parallel measurements. Model estimates showed low to moderate fits compared to stationary sensor measurements (R2 ranging from 0.1–0.5), indicating maps should be interpreted with caution and only used indicatively. Together, these findings show the feasibility of using spatiotemporal modeling for generating dynamic concentration maps based on personal data. The described method could be applied for exposure characterization within comparable study designs or can be expanded further, for example by developing real-time, location-based worker feedback systems, as efficient tools to visualize and communicate exposure risks. pubtype: Academic Journal doctype: CEU pictorial research tables/charts Journal Article ougenre: Unknown language: English refInfo: holdings: @attributes: islocal: N |
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