English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A growing body of research on statistical applications for characterizationof atmospheric aerosol Fourier transform infrared (FT-IR) samples collectedon polytetrafluoroethylene (PTFE) filters (e.g., Russell et al., 2011; Ruthenburget al., 2014) and a rising interest in analyzing FT-IR samples collected byair quality monitoring networks call for an automated PTFE baselinecorrection solution. The existing polynomial technique (Takahama et al., 2013)is not scalable to a project with a large number of aerosol samples because itcontains many parameters and requires expert intervention. Therefore, thequestion of how to develop an automated method for baseline correctinghundreds to thousands of ambient aerosol spectra given the variability inboth environmental mixture composition and PTFE baselines remains. This studyapproaches the question by detailing the statistical protocol, which allowsfor the precise definition of analyte and background subregions, appliesnonparametric smoothing splines to reproduce sample-specific PTFEvariations, and integrates performance metrics from atmospheric aerosol andblank samples alike in the smoothing parameter selection. Referencing 794atmospheric aerosol samples from seven Interagency Monitoring of PROtectedVisual Environment (IMPROVE) sites collected during 2011, we start byidentifying key FT-IR signal characteristics, such as non-negative absorbanceor analyte segment transformation, to capture sample-specific transitionsbetween background and analyte. While referring to qualitative properties ofPTFE background, the goal of smoothing splines interpolation is to learn thebaseline structure in the background region to predict the baseline structurein the analyte region. We then validate the model by comparing smoothingsplines baseline-corrected spectra with uncorrected and polynomialbaseline (PB)-corrected equivalents via three statistical applications: (1) clusteringanalysis, (2) functional group quantification, and (3) thermal opticalreflectance (TOR) organic carbon (OC) and elemental carbon (EC) predictions.The discrepancy rate for a four-cluster solution is 10 %. For all functionalgroups but carboxylic COH the discrepancy is  ≤ 10 %. Performance metricsobtained from TOR OC and EC predictions (R2 ≥  0.94 %, bias ≤ 0.01 µg m−3, and error  ≤ 0.04 µg m−3)are on a par with those obtained from uncorrected and PB-corrected spectra.The proposed protocol leads to visually and analytically similar estimates asthose generated by the polynomial method. More importantly, the automatedsolution allows us and future users to evaluate its analyticalreproducibility while minimizing reducible user bias. We anticipate theprotocol will enable FT-IR researchers and data analysts to quickly andreliably analyze a large amount of data and connect them to a variety ofavailable statistical learning methods to be applied to analyte absorbancesisolated in atmospheric aerosol samples

An automated baseline correction protocol for infrared spectra of atmospheric aerosols collected on polytetrafluoroethylene (Teflon) filters