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Related Concept Videos

Atomic Emission Spectroscopy: Overview01:20

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Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...
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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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National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass

Provat K Saha1,2, Albert A Presto1,2, Steve Hankey3

  • 1Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.

Environmental Science & Technology
|September 26, 2022
PubMed
Summary
This summary is machine-generated.

National models can predict air pollutant levels like cooking organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA) across the US. These models highlight the importance of controlling cooking emissions for better air quality management.

Keywords:
aerosol mass spectrometryfine particulate matterspatial modeling

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Air Quality Modeling

Background:

  • Sparsely monitored air pollutants pose challenges for accurate spatial prediction.
  • Understanding the distribution of cooking organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA) is crucial for air quality management.
  • Existing land-use regression models often lack national scope and high spatial resolution.

Purpose of the Study:

  • To assess the feasibility of developing national empirical models for predicting ambient concentrations of COA and HOA.
  • To create high-resolution spatial models for these key air pollutants across the continental United States.
  • To evaluate the transferability and robustness of these national empirical models.

Main Methods:

  • Utilized aerosol mass spectrometry data for COA and HOA across the continental US.
  • Selected monitoring sites to represent diverse land-use and source-activity variables.
  • Developed land-use regression models and performed extensive cross-validation.
  • Compared empirical model predictions with national chemical transport model (CTM) simulations.

Main Results:

  • National empirical models explained approximately 60% of the spatial variability for both COA (R²=0.63) and HOA (R²=0.62).
  • Models demonstrated robustness and reasonable transferability through cross-validation.
  • Predicted concentrations revealed significant urban-rural and intra-urban variability, with hotspots in urban areas and along roadways.
  • COA concentrations were predicted to be approximately two times higher than HOA concentrations nationally.
  • Empirical predictions showed good spatial correlation with CTM simulations (R²=0.45 for COA, R²=0.4 for HOA).

Conclusions:

  • National empirical models are feasible for predicting ambient COA and HOA concentrations at high spatial resolution.
  • Commercial cooking emissions are a significant contributor to PM2.5 and warrant attention for air quality management.
  • The developed models provide valuable tools for understanding and managing air quality, particularly concerning primary organic aerosols.