Table 1. Performance of PM_2.5 Composition Models over Europe^a.

		component	Cu	Fe	K	Ni	S	Si	V	Zn
	inclusion of X, Y coordinates	no. of sites	414	413	414	402	404	400	402	413
Model Building
SLR	one-step	model r2	0.56	0.55	0.61	0.62	0.79	0.52	0.70	0.48
		model RMSEb	3.3	65.5	64.6	0.9	146.5	59.7	1.7	11.8
	two-step, step1	model r2	0.52	0.53	0.52	0.56	0.80	0.48	0.66	0.47
		model RMSE	3.4	67.4	71.1	1.0	142.2	61.9	1.8	11.9
	two-step, step2	model r2	0.56	0.53	0.60	0.60	0.82	0.50	0.69	0.48
		model RMSE	3.3	67.4	65.0	0.9	135.4	61.1	1.7	11.8
RFc	one-step	model r2	0.95	0.95	0.97	0.95	0.98	0.95	0.97	0.95
		model RMSE	1.1	20.8	16.8	0.3	40.2	19.9	0.5	3.5
	two-step, step1	model r2	0.95	0.95	0.97	0.95	0.98	0.94	0.97	0.96
		model RMSE	1.1	20.9	17.4	0.3	41.8	20.3	0.5	3.4
	two-step, step2	model r2	0.98	0.98	0.99	0.98	0.99	0.98	0.99	0.99
		model RMSE	0.6	12.4	9.5	0.2	27.0	12.2	0.3	1.8
HOV
SLR	one-step	HOV r2	0.47	0.48	0.58	0.57	0.76	0.50	0.63	0.41
		HOV RMSE	3.6	70.5	66.4	1.0	156.4	60.8	1.8	12.5
	two-step, step1	HOV r2	0.44	0.46	0.50	0.51	0.76	0.46	0.60	0.42
		HOV RMSE	3.7	71.7	72.6	1.0	154.9	63.4	1.9	12.4
	two-step, step2	HOV r2	0.48	0.48	0.59	0.56	0.79	0.46	0.63	0.41
		HOV RMSE	3.6	70.5	66.1	1.0	147.0	62.9	1.8	12.5
RF	one-step	HOV r2	0.60	0.60	0.82	0.74	0.91	0.62	0.85	0.68
		HOV RMSE	3.2	61.7	44.1	0.7	97.0	52.9	1.2	9.3
	two-step, step1	HOV r2	0.59	0.59	0.79	0.74	0.90	0.60	0.84	0.68
		HOV RMSE	3.2	62.4	47.4	0.7	102.1	54.2	1.2	9.2
	two-step, step2	HOV r2	0.59	0.61	0.80	0.76	0.90	0.62	0.86	0.71
		HOV RMSE	3.2	61.3	45.8	0.7	99.5	53.1	1.1	8.7

^aSLR = supervised linear regression; RF = random forest; r² = squared Pearson correlation; RMSE = root-mean-square error; HOV = fivefold hold-out validation.

^bUnit of RMSE: ng/m³.

^cPerformance of RF on training set cannot be interpreted.