Comment on amt-2021-304

doi:10.5194/amt-2021-304-rc3

Abstract

Wildfires have increased in frequency, duration and size in the western United States (U.S.) over the past decades. These trends are projected to continue, with negative consequences for air quality across the U.S. Wildfires emit large quantities of particles and gases that serve as air pollutants and their precursors, and can lead to severe air quality conditions over large spatial and long temporal scales. Characterization of the chemical constituents in smoke as a function of combustion conditions, fuel type, and fuel component is an important step towards improving the prediction of air quality effects from fires and evaluating mitigation strategies. Building on the comprehensive characterization of gaseous non-methane organic compounds (NMOCs) identified in laboratory and field studies, a supervised pattern recognition algorithm was developed that successfully identified unique chemical speciation profiles among similar fuel types common in western coniferous forests. The algorithm was developed using laboratory data from single fuel species and tested on simplified synthetic fuel mixtures. The fuel types in the synthetic mixtures were differentiated but as the relative mixing proportions became more similar, the differentiation became poorer. Using the results from the pattern recognition algorithm, a classification model based on linear discriminant analysis was trained to differentiate smoke samples based on the contribution(s) of dominant fuel type(s). The classification model was applied to field data and despite the complexity of contributing fuels, and the presence of fuels "unknown" to the classifier, the dominant sources/fuel types were identified correctly. The pattern recognition and classification algorithms are a promising approach for identifying the types of fuels contributing to smoke samples and facilitating selection of appropriate chemical speciation profiles for predictive air quality modeling, using a highly reduced suite of measured NMOCs. Utility and performance of the pattern recognition and classification algorithms can be improved by expanding the training and test sets to include data from a broader range of single and mixed fuel types.

Highlights

While CO2 and CH4 are important greenhouse gases, non-methane organic compounds (NMOCs) are of particular importance in the context of air quality because they serve as precursors to secondary air pollutants including photochemical ozone (O3) and secondary organic aerosol (SOA) (Alvarado and Prinn (2009))
The pattern recognition (PR) algorithm was applied to the FIREX FL16 data set to identify a group of marker compounds that could be used to 140 differentiate fuel types
Because the BFRS data span a wide range of complexity in the fuels sampled, a synthetic data set was generated to test the performance of the PR and classification algorithms on mixed fuel samples prior to application on the BFRS data

Summary

Introduction

Wildfire emissions are dependent on a number of factors such as combustion conditions (e.g., flaming vs smoldering), fuel conditions (e.g., moisture content), and fuel type (e.g., species and component) (Goode et al (2000), Urbanski (2013), Liu et al (2017), Stockwell et al (2014), Stockwell et al (2015), Koss et al (2018), Sekimoto et al 35 (2018), Hatch et al (2019), Prichard et al (2020)) Differences in these factors can affect the total flux of emissions as well as the profile of emissions, i.e., the identities and quantities of individual constituents. The relatively rapid expansion in available NMOC data provides opportunities for developing more detailed speciation profiles (in which a 55 higher fraction of the detected mass is assigned to unique compounds or formulas) and for applying statistical data analysis methods, including to identify unique sets of compounds that allow differentiation of fuel type(s) and estimation of their contributions to smoke samples. Hatch et al (2019) demonstrated that the variability in NMOC composition could not be attributed entirely to MCE, and that chemical speciation was highly correlated among some 105 fuel types across a range of MCE values, conifers; within conifers, clear differences in monoterpenoid emissions were observed as a function of fuel species

Pattern recognition algorithm

Preprocessing and feature selection

Principal component analysis and k-means clustering PCA (step 3), as described in

Sample and fuel type selection for pattern recognition and classification

PCA and k-means clustering

Blodgett samples

Conclusions