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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
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For AAS measurements, samples must be introduced as clear solutions, often requiring extensive preliminary treatment to dissolve materials like soils, animal tissues, and minerals. Common methods for sample preparation include treatment with hot mineral acids, wet ashing, combustion in closed containers, high-temperature ashing, or fusion with reagents.
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Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
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Estimating elemental composition of personal PM

Na Li1, Chunyu Xu1, Dongqun Xu1

  • 1China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.

The Science of the Total Environment
|June 9, 2023
PubMed
Summary

Personal exposure to fine particulate matter (PM2.5) and its elements differs significantly from outdoor measurements. Indoor and outdoor levels, along with lifestyle factors, strongly predict personal exposure to PM2.5-bound elements.

Keywords:
Elemental compositionInfluencing factorsMixed-effects modelPM(2.5)Personal exposure

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Area of Science:

  • Environmental Health Sciences
  • Atmospheric Chemistry
  • Exposure Science

Background:

  • Personal exposure to fine particulate matter (PM2.5) and its elemental constituents can differ substantially from fixed monitoring site data.
  • Understanding these variations is crucial for accurately assessing health risks associated with PM2.5 exposure.

Purpose of the Study:

  • To characterize the discrepancies between personal, indoor, and outdoor concentrations of PM2.5-bound elements.
  • To develop predictive models for personal exposure to 21 PM2.5-bound elements.

Main Methods:

  • Collected personal, indoor, and outdoor PM2.5 filter samples over five consecutive days across two seasons from 66 non-smoking adults in Beijing and Nanjing, China.
  • Developed personal element-specific models using linear mixed-effects models, evaluating them with R-squared and root mean square error (RMSE).

Main Results:

  • Personal exposures to PM2.5 and most elements varied by element and city, with significant correlations to indoor and outdoor measurements.
  • Indoor and outdoor PM2.5 elemental concentrations were the primary determinants of personal exposures, explaining substantial variance.
  • Home ventilation, time-activity patterns, and season also significantly influenced personal exposure levels.

Conclusions:

  • Personal PM2.5 elemental exposures are distinct from ambient measurements and influenced by a combination of indoor/outdoor sources and personal behaviors.
  • The developed modeling approach effectively predicts personal exposures, improving the association between PM2.5 composition and health outcomes.