To design a method that uses preliminary hazard mapping data to optimize the number and location of sensors within a network for a long-term assessment of occupational concentrations, while preserving temporal variability, accuracy, and precision of predicted hazards.
Particle number concentrations (PNCs) and respirable mass concentrations (RMCs) were measured with direct-reading instruments in a large heavy-vehicle manufacturing facility at 80-82 locations during 7 mapping events, stratified by day and season. Using kriged hazard mapping, a statistical approach identified optimal orders for removing locations to capture temporal variability and high prediction precision of PNC and RMC concentrations. We compared optimal-removal, random-removal, and least-optimal-removal orders to bound prediction performance.
The temporal variability of PNC was found to be higher than RMC with low correlation between the two particulate metrics (ρ = 0.30). Optimal-removal orders resulted in more accurate PNC kriged estimates (root mean square error [RMSE] = 49.2) at sample locations compared with random-removal order (RMSE = 55.7). For estimates at locations having concentrations in the upper 10th percentile, the optimal-removal order preserved average estimated concentrations better than random- or least-optimal-removal orders (P < 0.01). However, estimated average concentrations using an optimal-removal were not statistically different than random-removal when averaged over the entire facility. No statistical difference was observed for optimal- and random-removal methods for RMCs that were less variable in time and space than PNCs.
Optimized removal performed better than random-removal in preserving high temporal variability and accuracy of hazard map for PNC, but not for the more spatially homogeneous RMC. These results can be used to reduce the number of locations used in a network of static sensors for long-term monitoring of hazards in the workplace, without sacrificing prediction performance.
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 November 2020.
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#11,109,863
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Outputs from Annals of Work Exposures and Health
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Outputs of similar age from Annals of Work Exposures and Health
#12
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Altmetric has tracked 33,779,198 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 583 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. This one has gotten more attention than average, scoring higher than 55% of its peers.
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We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
