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Both a well-designed on-board monitoring campaign and an adequate data-driven statistical modeling method are required to accurately characterize the building's overall heat transfer coefficient (HTC).
We describe and evaluate the body-temperature monitoring campaign and the SARS fever hotline.
Ninety-five percent (n = 1,060) and 71% (n = 791) of respondents had heard about the body-temperature monitoring campaign and the fever hotline, respectively.
A telephone survey of Taipei city residents was performed to assess knowledge of the body-temperature monitoring campaign and use of the fever hotline.
Both the body-temperature monitoring campaign and the hotline were publicized through television, posters, fliers, radio, the Internet, magazines, and newspapers.
In the population-based survey, almost all respondents knew about the body-temperature monitoring campaign, and 71% knew about the fever hotline.
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The local contribution was estimated from land-use regressions incorporating monitoring data from field monitoring campaigns and a variety of traffic variables as predictors.
Investigators conducting epidemiological studies of long-term PM2.5 components need to be mindful of the features of the monitoring data and incorporate this understanding into the design of their monitoring campaigns and the development of their exposure prediction models.
The quality of LUR-based exposure estimation of outdoor air pollution concentrations relies largely on coverage and quality of specific monitoring campaigns and the geographic data to support them.
Future studies of long-term average concentrations of PM2.5 components and health need to assess exposure data characteristics before designing their own monitoring campaigns and developing exposure prediction models.
Land use regression (LUR) models based on pollution data collected via short-term intensive monitoring campaigns and Geographic Information Systems GISS) information on pollution sources and meteorology are one example [ 33, 34].
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