Application of machine learning approaches in the analysis of mass absorption cross-section of black carbon aerosols: Aerosol composition dependencies and sensitivity analyses

Figures & data

Figure 1. Hourly averaged AAE vs. SAE with points colored by the ratio of the SVM-predicted MAC_BC to the value derived from empirical observations for (a) training data; (b) test data; and (c) independent validation data. In panels (a)–(c), the square marker and error bars indicate the mean and standard deviation of the dataset; the filled circles represent over-predictions, and the filled triangles represent under-predictions. The classification scheme presented in Cappa et al. (2016) is shown in panel (d); this is overlaid onto panels (a)–(c).

Figure 2. The ratio of the SVM-predicted MAC_BC to the value derived from empirical observations of MAC_BC as a function of (a) OA concentration; (b) BC concentration; and (c) SSA at 870 nm for the TCAP and CACTI data. The dashed line represents that the model fits MAC_BC.true without error.

Figure 3. Association between the ratio of the SVM-predicted MAC_BC to the value derived from empirical observations to aerosol properties from the FIREX study. (a) The “AAE-SAE” space as in Figure 1; (b) SSA at 528 nm; (c) SSA at 870 nm; (d) the OC/EC ratio; (e) estimated E_abs; and (f) estimated mixing state. The dashed line in panels (b)–(f) represents a perfect agreement.

Figure 4. Statistical results of sensitivity analysis. The results of CNN applied to Case 3(a) and Case 3(b) for CACTI result in extreme prediction errors, so they fall outside of axis limits. The detailed results of R², MSE, and fractional bias and error can be found in Tables S1 and S2. Note the different scales in panels (a) and (b).

Table 1. Input variables considered in our sensitivity analyses. Multiple-λ includes information at 467, 528, and 652 nm, while single-λ only includes information at 528 nm. Values within the N_i and V_i columns represent the bounds of the particle size range (as electrical mobility diameter).

	Babs & Bscat	N1 (nm)	N2 (nm)	N3 (nm)	V1 (nm)	V2 (nm)	V3 (nm)	Notes
Case 1(a)	Multiple λ	< 50	50 − 200	> 200	< 1000	1000 − 2500	> 2500	SMPS and APS: 11.3 − 18,747 nm
Case 1(b)	Single λ	Same as Case 1(a)
Case 2(a)	Multiple λ	< 100	100 − 300	> 300	< 500	500 − 1500	> 1500	Repartitioning of size bins from Case 1(a)
Case 2(b)	Single λ	Same as Case 2(a)
Case 3(a)	Multiple λ	< 100	100 − 1000	–	< 500	500 − 1000	–	Emulating only an SMPS for sizing (< 1000 nm)
Case 3(b)	Single λ	Same as Case 3(a)
Case 4(a)	Multiple λ	–	440 − 600	> 600	–	440 − 2500	> 2500	Emulating only an APS for sizing (> 440 nm)
Case 4(b)	Single λ	Same as Case 4(a)

Cappa, C. D., K. R. Kolesar, X. Zhang, D. B. Atkinson, M. S. Pekour, R. A. Zaveri, A. Zelenyuk, and Q. Zhang. 2016. Understanding the optical properties of ambient sub- and supermicron particulate matter: Results from the CARES 2010 field study in northern California. Atmos. Chem. Phys. 16 (10):6511–35. doi:10.5194/acp-16-6511-2016.

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