Predicting ambient aerosol thermal–optical reflectance (TOR) measurements from infrared spectra: extending the predictions to different years and different sites

[摘要] Organic carbon (OC) and elemental carbon (EC) are major components ofatmospheric particulate matter (PM), which has been associated with increasedmorbidity and mortality, climate change, and reduced visibility. Typically OCand EC concentrations are measured using thermal–optical methods such asthermal–optical reflectance (TOR) from samples collected on quartz filters.In this work, we estimate TOR OC and EC using Fourier transform infrared(FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE Teflon) filtersusing partial least square regression (PLSR) calibrated to TOR OC and ECmeasurements for a wide range of samples. The proposed method can beintegrated with analysis of routinely collected PTFE filter samples that, inaddition to OC and EC concentrations, can concurrently provide informationregarding the functional group composition of the organic aerosol. We haveused the FT-IR absorbance spectra and TOR OC and EC concentrations collectedin the Interagency Monitoring of PROtected Visual Environments (IMPROVE)network (USA). We used 526 samples collected in 2011 at seven sites tocalibrate the models, and more than 2000 samples collected in 2013 at 17 sites to test the models. Samples from six sites are present both in thecalibration and test sets. The calibrations produce accurate predictions bothfor samples collected at the same six sites present in the calibration set(R² = 0.97 and R² = 0.95 for OC and EC respectively), and for samples from9 of the 11 sites not included in the calibration set (R² = 0.96 andR² = 0.91 for OC and EC respectively). Samples collected at the other two sites require a different calibration model to achieve accurate predictions.We also propose a method to anticipate the prediction error; we calculate thesquared Mahalanobis distance in the feature space (scores determined by PLSR)between new spectra and spectra in the calibration set. The squaredMahalanobis distance provides a crude method for assessing the magnitude ofmean error when applying a calibration model to a new set of samples.

[学科分类] 几何与拓扑