Table 1. Advantages and Disadvantages of the Four NO₂ Data Sets from This Study and Potentially Appropriate Applications.

NO2 data set	strengths	weaknesses	potentially appropriate applications for policy-relevant end-users
monitors	direct measurements	sparsely and unevenly distributed	monitoring attainment of National Ambient Air Quality Standards
	many monitoring sites have a long-term data record	prone to biases based on measurement techniques	ground-truthing satellite and model data sets
	relatively low technical barriers to the usage of data	costly (>$10,000)
satellites	semiempirical observations	retrievals are prone to interference from clouds and surface albedo	characterizing relative differences in NO2 levels across population groups or regions when surface-level concentrations are not needed
	high spatial resolution (∼1 km) achieved through oversampling techniques	temporal resolution sacrificed through oversampling	guiding placement of future AQS monitors or measurement campaigns (e.g., targeted mobile measurements)
	valuable tool to understand recent shocks to air pollution (e.g., COVID-19 pandemic)	measures total tropospheric columnar rather than surface-level concentrations, so satellite-derived NO2 cannot be used in conventional health studies	identifying potential hotspots and new emission sources
	(for OMI only) record since 2005 enables long-term trend analysis	technical skills need to preprocess and analyze data	estimating urban NOX emissions
		presently most satellites are polar-orbiting, providing ∼one snapshot per day in the early afternoon
		the spatial resolution of satellite instruments prior to TROPOMI (launched in 2017) is nearly 1 order of magnitude coarser than TROPOMI’s resolution
photochemical and statistical models	increasingly high spatial resolution (statistical models: 50 m to 1 km; photochemical models: 10 km)	computationally expensive to generate	reporting long-term trends
	estimates surface-level concentrations, useable for health impact assessments	ability to model recent year(s) may be hampered by lag to acquire up-to-date model inputs	estimating NO2-attributable disease burdens and associated disparities
	useful for assessing long-term trends	technical skills need to preprocess and analyze data	(for photochemical models only) quantifying source sector contributions to ambient NO2
		(statistical models only) driven by monitor placement