Table 2.

Data sources and statistical methods of O₃ exposure assignment

Study	Data sources	Methods	Resolution	Rating	Metrics	Level of incremental risk ratio
Abbey et al. 199938	monitoring station observations	IDW interpolation	N/Ra	low	ADMA8	12.03 ppbV
Lipfert et al. 200639	monitoring station observations	nearest matching (assumed)b	N/R	low	ADMA1	40 ppbV
Jerrett et al. 200940	monitoring station observations	nearest matching (assumed)	N/R	low	6mDMA1	10 ppbV
Krewski et al. 200941	monitoring station observations	ordinary kriging interpolation	N/R	low	6mDMA1	10 ppbV
Smith et al. 200942	monitoring station observations	nearest matching (assumed)	N/R	low	6mDMA1	1 μg/m³
Lipsett et al. 201143	monitoring station observations	IDW interpolation	250 m	low	ADA24	22.96 ppbV
Zanobetti et al. 201144	monitoring station observations	nearest matching (assumed)	N/R	low	6mDMA8	5 ppbV
Carey et al. 201345	monitoring station observations	interpolation (IDW assumed)	1 km	low	ADA24	3.0 μg/m3
Jerrett et al. 201346	monitoring station observations	IDW interpolation	N/R	low	ADA24	24.1782 ppbV
Bentayeb et al. 201547	monitoring station observations, model simulation, other auxiliary predictors	universal kriging-embedded land use regression	2 km	good	6mDMA8	12.3 μg/m3
Crouse et al. 201548	monitoring station observations, model simulation	linear data assimilation	21 km	good	6mDMA8	9.5 ppbV
Tonne et al. 201649	KCLurban air dispersion model simulation	N/Ac	20 m	moderate	ADA24	5.3 μg/m3
Turner et al. 201650	monitoring station observations, CMAQ model simulation	hierarchical Bayesian space-time data assimilation	12 km	high	ADMA8 6mDMA8	10 ppbV
Di et al. 201751	monitoring station observations, model simulation, satellite remote sensing observations, other auxiliary predictors	ensemble machine learning	1 km	high	6mDMA8	10 ppbV
Weichenthal et al. 201752	monitoring station observations, model simulation	linear data assimilation	21 km	good	6mDMA8	10.503 ppbV
Cakmak et al. 201853	monitoring station observations, model simulation	linear data assimilation	21 km	good	6mDMA8	10 ppbV
Hvidtfeldt et al. 201954	AirGIS dispersion model simulation	N/A	1 km	moderate	ADA24	10 μg/m3
Kazemiparkouhi et al. 201955	monitoring station observations	nearest matching (assumed)	6 km	low	6mDMA1 6mDMA8 6mDA24	10 ppbV
Lim et al. 201956	monitoring station observations, CMAQ model simulation	Bayesian space-time downscaling	12 km	high	6mDMA8	10 ppbV
Paul et al. 202057	monitoring station observations, model simulation	linear data assimilation	21 km	good	6mDMA8	6.4 ppbV
Shi et al. 202158	monitoring station observations, model simulation, satellite remote sensing observations, other auxiliary predictors	ensemble machine learning	1 km	high	6mDMA8	10 ppbV
Strak et al. 202159	monitoring station observations, model simulation, satellite remote sensing observations, other auxiliary predictors	universal kriging-embedded land use regression	100 m	high	6mDMA8	10 μg/m3
Yazdi et al. 202160	monitoring station observations, model simulation, satellite remote sensing observations, other auxiliary predictors	ensemble machine learning	1 km	high	6mDMA8	1 ppbV
Bauwelinck et al. 202261	monitoring station observations, model simulation, satellite remote sensing observations, other auxiliary predictors	land use regression	100 m	high	6mDMA8	10 μg/m3
Stafoggia et al. 202262	monitoring station observations, model simulation, satellite remote sensing observations, other auxiliary predictors	universal kriging-embedded land use regression	100 m	high	6mDMA8	10 μg/m3

Methodological ratings were based on spatial interpolation and multi-data assimilation approaches. Spatial resolutions, exposure metrics, and levels of incremental risk ratio were also listed.

^aN/R, not reported.

^bThe statistical methods were not clearly stated in literature, so the most basic method was assumed. The nearest neighborhood matching shall be the simplest way to assign spatially sparse observations onto cohort participants, and the inverse distance weighting (IDW) is the simplest spatial interpolation approach.

^cN/A, not applicable. The chemical transport model simulations were directly used for individual exposure assignment without further statistical processing.