Dataset Information

Quantifying elemental and organic carbon in diesel particulate matter by mid-infrared spectrometry.

ABSTRACT: A method for the quantification of airborne organic carbon (OC) and elemental carbon (EC) within aerosolized diesel particulate matter (DPM) is described in this article. DPM is a known carcinogen encountered in many industrial workplaces (notably mining) and in the ambient atmosphere. The method described here collects DPM particles onto a quartz fiber filter, after which reflection-mode infrared spectra are measured on a mid-infrared Fourier transform (FT-IR) spectrometer. Several infrared absorption bands are investigated for their efficacy in quantifying OC and EC. The thermo-optical (T-O) method is used to calibrate a linear regression model to predict OC and EC from the infrared spectra. The calibrated model, generated from laboratory DPM samples, is then utilized to quantify OC and EC in mine samples obtained from two metal mine locations under a variety of operating conditions. The feasibility of further improving these results by partial least squares (PLS) regression was investigated. A single calibration that is broadly applicable would be considered an improvement over currently available portable instruments, which require aerosol-specific calibration.

SUBMITTER: Parks DA

PROVIDER: S-EPMC8563026 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Quantifying elemental and organic carbon in diesel particulate matter by mid-infrared spectrometry.

Parks David A DA Griffiths Peter R PR Weakley Andrew T AT Miller Arthur L AL

Aerosol science and technology : the journal of the American Association for Aerosol Research 20210501 9

A method for the quantification of airborne organic carbon (OC) and elemental carbon (EC) within aerosolized diesel particulate matter (DPM) is described in this article. DPM is a known carcinogen encountered in many industrial workplaces (notably mining) and in the ambient atmosphere. The method described here collects DPM particles onto a quartz fiber filter, after which reflection-mode infrared spectra are measured on a mid-infrared Fourier transform (FT-IR) spectrometer. Several infrared abs ...[more]

PMID: 34732970

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Project description:Emerging evidence indicates that near-roadway pollution (NRP) in ambient air has adverse health effects. However, specific components of the NRP mixture responsible for these effects have not been established. A major limitation for health studies is the lack of exposure models that estimate NRP components observed in epidemiological studies over fine spatial scale of tens to hundreds of meters. In this study, exposure models were developed for fine-scale variation in biologically relevant elemental carbon (EC). Measurements of particulate matter (PM) and EC less than 2.5 μm in aerodynamic diameter (EC2.5) and of PM and EC of nanoscale size less than 0.2 μm were made at up to 29 locations in each of eight Southern California Children's Health Study communities. Regression-based prediction models were developed using a guided forward selection process to identify traffic variables and other pollutant sources, community physical characteristics and land use as predictors of PM and EC variation in each community. A combined eight-community model including only CALINE4 near-roadway dispersion-estimated vehicular emissions accounting for distance, distance-weighted traffic volume, and meteorology, explained 51% of the EC0.2 variability. Community-specific models identified additional predictors in some communities; however, in most communities the correlation between predicted concentrations from the eight-community model and observed concentrations stratified by community were similar to those for the community-specific models. EC2.5 could be predicted as well as EC0.2. EC2.5 estimated from CALINE4 and population density explained 53% of the within-community variation. Exposure prediction was further improved after accounting for between-community heterogeneity of CALINE4 effects associated with average distance to Pacific Ocean shoreline (to 61% for EC0.2) and for regional NOx pollution (to 57% for EC2.5). PM fine spatial scale variation was poorly predicted in both size fractions. In conclusion, models of exposure that include traffic measures such as CALINE4 can provide useful estimates for EC0.2 and EC2.5 on a spatial scale appropriate for health studies of NRP in selected Southern California communities.

Dataset Information

Quantifying elemental and organic carbon in diesel particulate matter by mid-infrared spectrometry.

Publications

Quantifying elemental and organic carbon in diesel particulate matter by mid-infrared spectrometry.

Similar Datasets

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