Table 1 –

Cross-validation results by year

Year	Ensemble model						Neural Network	Random Forest	Gradient Boosting
	R2	RMSE (ppb)	Intercept	Slope	Spatial R2	Temporal R2	R2	R2	R2
2000	0.889	5.705	0.088	0.991	0.848	0.905	0.889	0.887	0.889
2001	0.892	5.517	0.254	0.992	0.845	0.911	0.889	0.889	0.892
2002	0.908	5.375	0.338	0.984	0.863	0.924	0.904	0.906	0.907
2003	0.897	5.244	0.126	0.988	0.837	0.917	0.894	0.895	0.896
2004	0.889	4.986	0.543	0.982	0.812	0.912	0.886	0.886	0.888
2005	0.901	5.090	0.228	0.991	0.845	0.921	0.898	0.898	0.900
2006	0.898	4.873	0.357	0.992	0.839	0.918	0.895	0.896	0.898
2007	0.903	4.731	0.284	0.998	0.889	0.916	0.902	0.900	0.902
2008	0.904	4.447	0.317	0.990	0.886	0.916	0.902	0.901	0.903
2009	0.899	4.196	0.032	0.996	0.862	0.915	0.897	0.897	0.899
2010	0.891	4.399	0.090	0.990	0.863	0.908	0.889	0.888	0.890
2011	0.902	4.296	0.009	0.997	0.847	0.921	0.901	0.899	0.902
2012	0.920	4.003	0.339	0.990	0.883	0.933	0.919	0.916	0.919
2013	0.907	3.787	1.049	0.973	0.879	0.921	0.904	0.904	0.907
2014	0.913	3.585	0.259	0.991	0.888	0.922	0.913	0.909	0.912
2015	0.919	3.538	0.447	1.005	0.894	0.926	0.914	0.915	0.918
2016	0.906	3.579	0.187	0.989	0.897	0.934	0.901	0.904	0.907
Overall (2000–2016)	0.905	4.668	0.654	0.985	0.862	0.916	0.904	0.896	0.900

Note: The slope and intercept were obtained from the linear regression model, which we regressed predicted O₃ against monitored O₃.