Regional Estimates of Chemical Composition of Fine Particulate

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Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors Aaron van Donkelaar, Randall V Martin, Chi Li, and Richard T Burnett Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06392 • Publication Date (Web): 30 Jan 2019 Downloaded from http://pubs.acs.org on January 31, 2019

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Environmental Science & Technology

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Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-

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Statistical Method with Information from Satellites, Models, and Monitors

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Aaron van Donkelaar*, Randall V. Martin, Chi Li, and Richard T. Burnett

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*corresponding author: Aaron van Donkelaar, Department of Physics and Atmospheric Science,

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Dalhousie University, 6300 Coburg Road, Halifax, Nova Scotia, Canada, B3H 3J5

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[email protected]

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Abstract

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An accurate fine-resolution surface of the chemical composition of fine particulate matter (PM2.5) would

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offer valuable information for epidemiological studies and health impact assessments. We develop

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geoscience-derived estimates of PM2.5 composition from a chemical transport model (GEOS-Chem) and

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satellite observations of aerosol optical depth, and statistically fuse these estimates with ground-based

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observations using a geographically weighted regression over North America to produce a spatially

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complete representation of sulfate, nitrate, ammonium, black carbon, organic matter, mineral dust, and

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sea-salt over 2000-2016. Significant long-term agreement is found with cross-validation sites over North

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America (R2=0.57—0.96), with the strongest agreement for sulfate (R2=0.96), nitrate (R2=0.90), and

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ammonium (R2=0.86). We find that North American decreases in population-weighted fine particulate

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matter (PM2.5) concentrations since 2000 have been most heavily influenced by regional changes in

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sulfate and organic matter. Regionally, the relative importance of several chemical components are

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found to change with PM2.5 concentration, such as higher PM2.5 concentrations having a larger

ACS Paragon Plus Environment


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proportion of nitrate and a smaller proportion of sulfate. This dataset offers information for research

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into the health effects of PM2.5 chemical components.

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Introduction

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Numerous associations have been found between negative health endpoints and human exposure to

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fine particulate matter (PM2.5) mass concentration1-5, such that PM2.5 exposure is increasingly recognized

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as the leading environmental risk for global burden of disease6. The effects of chemical composition of

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PM2.5 on those associations, however, is less well known in part due insufficient information about PM2.5

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composition7, 8. Combining multiple information sources of satellite remote sensing, chemical transport

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modelling, and ground-based observations could improve estimates of PM2.5 composition.

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Ground-based monitoring of PM2.5 mass and composition has been integral to understand PM2.5

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sources9, 10, for exposure assessment11, and for epidemiological studies12, 13. Satellite retrievals of

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aerosol optical depth (AOD) provide a measure of the extinction of solar radiation due to the presence

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of aerosol in the atmospheric column, and are therefore related to PM2.5 at the surface14-17. A powerful

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suite of retrievals are now available (MISR18, MODIS Dark Target19, 20, MODIS and SeaWiFS Deep Blue21-23,

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and MODIS MAIAC 24, 25). The additional spatial coverage and resolution of this information source

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provides valuable insight beyond what is possible with ground-based monitors alone, leading numerous

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studies to incorporate satellite retrievals of AOD in their methods to represent a continuous PM2.5

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surface17, 26, 27.

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The geoscience-based approach of relating satellite AOD retrievals to PM2.5 using chemical transport

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model simulations in combination with a statistical fusion to ground-based observations is an effective

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method to represent the distribution of PM2.5 across North America28, and around the world29. This

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approach combines the strengths of each data source to provide spatially continuous coverage spanning

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almost two decades: two features of great value for epidemiological research. Chemical Transport

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Models (CTMs) offer valuable information about PM2.5 chemical composition30, 31. Previous studies have

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found that ground-based measurements of PM2.5 chemical composition were more consistent with



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satellite-derived PM2.5 partitioned into PM2.5 chemical composition using a CTM than with pure CTM

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simulations of PM2.5 alone, implying CTM skill in representing the relative abundance of PM2.5 chemical

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composition, and benefits from satellite remote-sensing in representing the local PM2.5 mass

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concentration32-34. Here we combine and extend these methods to integrate satellite, simulated and

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ground-based observations of both total PM2.5 mass and PM2.5 component mass to produce estimates of

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sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), organic matter (OM), black carbon (BC), mineral dust

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(DUST), and sea-salt (SS) over North America.

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Data Sources and Methods

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Ground-based Monitors

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Ground-based observations of PM2.5 mass and composition from 2000 to 2016 were obtained from the

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United States’ Environmental Protection Agency’s Air Quality System (AQS) and Environment Canada’s

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National Air Pollution Surveillance (NAPS) program. Federal Reference Method and non-Federal

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Reference Methods PM2.5 were included. Sources of compositional observations included the Clean Air

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Status and Trends Network (CASTNET), the Interagency Monitoring of Protected Visual Environments

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(IMPROVE) network, the Chemical Speciation Network (CSN), the National Core Network (NCORE), and

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NAPS. Spatially and seasonally varying factors are used to convert organic carbon to organic matter35.

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Observations of chloride mass were scaled by the molar ratio of sodium-chloride and chloride to

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represent sea-salt. Mineral dust mass is calculated following IMPROVE protocols, based on observations

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of aluminum, silicon, calcium, iron and titanium. The sampling frequencies of between one and six days

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provided by these ground-based sources are treated as sufficient to represent monthly averages.

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GEOS-Chem Chemical Transport Model


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We used the North American nested GEOS-Chem chemical transport model (http://geos-chem.org; v9-

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01-03; 1/2° x 2/3° resolution) described in van Donkelaar et al. 29 as a data source for AOD, and to

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simulate the spatiotemporally varying geophysical relationship between AOD and PM2.5. This simulation

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includes the sulfate-nitrate-ammonium system36, 37, primary38-40 and secondary carbonaceous aerosols41-

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43,

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log-normal size distributions, growth factors and refractive indices, based on the Global Aerosol Data Set

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(GADS) and aircraft measurements46-48. Biomass burning emissions were from the GFED-3 inventory49, 50.

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For consistency with ground-based measurements of PM2.5, simulated PM2.5 and compositional mass

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were calculated at 35% relative humidity using modeled composition-dependent hygroscopicity29, 51.

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Population estimates are based on the Gridded Population of the World (GPW v4) database52.

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Satellite-derived PM2.5 Mass

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We first produce satellite-derived PM2.5 mass estimates. We then partition these mass estimates into

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chemical composition using a chemical transport model. We then statistically fuse these PM2.5

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components with ground-based measurements. This methods yields an accurate continuous surface

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despite sparse composition monitor density.

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We combined AOD from multiple satellite products (MISR18, MODIS Dark Target19, 20, MODIS and

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SeaWiFS Deep Blue21-23, and MODIS MAIAC 24, 25) with simulation (GEOS-Chem) based upon their relative

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uncertainties as determined using ground-based sun photometer (AERONET53; V3) observations for

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2000-2016, following van Donkelaar et al.29. The relative contribution of each satellite- and simulation-

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based product to the combined AOD is shown in Supporting Material, Figure S1. We related these AOD

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to near-surface PM2.5 concentrations using the spatially and temporally-varying, geoscience-based

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relationship that results from the ratio of simulated AOD and PM2.5 from the GEOS-Chem chemical

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transport model to produce monthly mean geoscience-based PM2.5 surfaces. Geographically Weighted

mineral dust44, and sea-salt45. Aerosol optical properties were determined from Mie calculations of



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Regression (GWR) was then used at 1 km resolution to predict the bias between these initial, monthly

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PM2.5 estimates (SAT) and ground-based monitor (GM) observations, following the form:

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(GM PM2.5 – SAT PM2.5) = ΣβiSPECi + βED×DUED×DU

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where βi represented the spatially-varying predictor coefficients associated with species i, and SPECi

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represented the mass concentration of each component (e.g. SO42-,NO3-,NH4+,OM, BC, DUST and

[1]

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seasalt). Component mass concentrations used in [1] were estimated by applying the simulated relative

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contribution to the initially derived PM2.5 mass concentration further developing the approach of Philip

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et al.32. ED is the log of the elevation difference between the local elevation and the mean elevation

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within the simulation grid cell, according to the 1'×1' ETOPO1 Global Relief Model available from the

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National Geophysical Data Center (http://www.ngdc.noaa.gov/mgg/global/seltopo.html). DU is the

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inverse distance to the nearest urban land surface, based upon the 1' resolution MODIS Land Cover Type

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Product (MCD12Q1) 54. Uncertainty in component-specific emissions and chemistry, and its potential for

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impact on the simulated AOD to PM2.5 relationship used to produce the geoscience-based estimates,

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make the temporal and spatial structure of component masses a valuable predictor of bias in the

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geoscience-based values. ED and DU are combined within equation [1] to represent urban impacts at

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finer resolution than the GEOS-Chem simulation, which are amplified in regions with sub-grid

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topographic variability.

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Monthly GWR parameter coefficients were calibrated based upon comparison with coincident

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observations, in contrast to the temporally-invariant parameter coefficients used in van Donkelaar et

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al.29. Components were included that comprised at least 10% of total PM2.5 mass at monitor locations,

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and whose contribution to PM2.5 mass varied at least 10% across observed values at site locations. The

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bias predicted by the GWR calculations was used to adjust the initial PM2.5 mass estimate, and the slope

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and offset compared to GM observations applied as a final calibration.


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PM2.5 Composition Estimates

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Simulated relative composition was then applied to this hybrid PM2.5 mass estimate to produce

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estimates of SO42-, NO3-, NH4+, OM, BC, DUST, and SS. GWR was used to predict the monthly bias in the

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resultant estimate of compositional mass concentration following a similar methodology:

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(GM SPEC – SAT SPEC) = Σβi’SPECi’ + βED×DU’ED×DU

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where GM SPEC is the ground-based monitor observation of each speciated component and SAT SPEC is

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the initial, derived estimate. βi’ represented the spatially-varying predictor coefficients associated with

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species i, and SPECi’ represented the mass concentration of each component based on the hybrid total-

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mass PM2.5 estimate. PM2.5 chemical composition was adjusted to include aerosol water at 35% RH, for

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closure with PM2.5 mass observations. The biases predicted by the GWR calculations were used to

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adjust the initial compositional mass estimates, with the slope and offset compared to GM observations

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applied as a final calibration.

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Evaluation and Sampling Effects

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Performance was evaluated using a ten-fold cross-validation, where a random 10% of the ground-based

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observations are withheld during both the PM2.5 mass and compositional statistical fusions, and the

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remaining 90% used to constrain parameter coefficients. This procedure is performed ten times using

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different, random, hold back locations. The withheld ground-based observations are then used for a

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cross-validated performance evaluation.

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Combining neighboring pixels from the derived estimates provides a means to reduce the random

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component of their uncertainty, albeit at the expense of resolution. We tested the impact of such

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spatial averaging by averaging the nearest ground-based monitor-hosting pixels and comparing the


[2]


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result with the average of the corresponding ground monitor observations. Combining ground monitor-

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hosting pixels in this way additionally improves the representation of the broader area by including

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multiple ground-based point observations, providing a more compatible measurement with which to

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evaluate the area-mean concentrations inherent to the geoscience-based and geoscience-statistical

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hybrid estimates.

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Of the >2000 ground-based total-mass PM2.5 monitor locations used in this study between 2000 and

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2016, only 19% were active at the same location over this entire time period. Compositional monitoring

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also had large changes in monitored locations, with only between 2 - 22% of each component monitored

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at a consistent location over the whole time period. Such changes to ground monitor density and

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placement over time have the potential to introduce spatial variation in GWR parameter coefficients,

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affecting the consistency of the fused dataset across years. We used the change in GWR-based

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adjustment when using the subset of ground-based observations available in alternative years to

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quantify this impact. An adjustment is made to the hybrid values based on this evaluation for temporal

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consistency, but the adjustment is generally small. Details are provided in Supporting Information,

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Section 1.0.

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Results and Discussion

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Figure 1 shows across North America the derived and observed, total and compositional PM2.5 for 2000-

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2016, before (geoscience-based) and after (hybrid) statistical fusion. The hybrid estimates exhibit a

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broad maximum across the eastern United States driven by sulfate (SO42-), nitrate (NO3-), ammonium

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(NH4+), and organics (OM). Mineral dust (DUST) is primarily confined to the southwest and sea-salt (SS)

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to the coast. Across all components, average cross-validated agreement over this period is significantly


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improved by statistical fusion over North America (e.g. R2=0.30—0.80 versus R2=0.57—0.96;

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slope=0.58—1.74 versus slope=0.85—1.05). A similar level of improvement is obtained for total PM2.5

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mass (R2=0.49 versus R2=0.70). Such improvements in agreement show the influence of incorporated

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ground-based observations on the geoscience-based estimates. Hybrid agreement is highest for SO42-

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(R2=0.96; slope=1.01), NH4+ (R2=0.90; slope=1.01), and NO3- (R2=0.86; slope=0.99). OM, BC, DUST and SS

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underperform these species in both geoscience (R2=0.30—0.54; slope=0.85—1.74) and hybrid

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geoscience-statistical (R2=0.57—0.80; slope=0.85—1.05) values, but maintain significant agreement.

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The uncertainty of OM is impacted by biomass burning events that often occur in areas of low monitor

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density which reduces the observational constraint. Similarly, regions with the highest DUST and SS

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concentrations tend to be less densely monitored. Lower annual R2 of BC, DUST, and SS are also

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influenced by the smaller range of concentrations of these components, as evident from similar normal

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distribution of errors to other most components.

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Table 1 summarizes the cross-validated annual performance for individual years over this period.

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Relatively reduced performance are present over North America for total and component PM2.5 (mean

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R2=0.43—0.90; slope=0.88—1.21) compared to the multi-year averages in Figure 1, in part reflecting

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increased representiveness differences with fewer observations. The bias and variance of the hybrid

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values compared to cross-validated ground-based observations remain fairly stable over a range of

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regions. Some metrics can, however, change given a different range of PM2.5, as demonstrated in lower

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R2 found in cleaner regions such as Canada or the North-Western United States, despite comparable bias

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and variance to other regions.

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Figure 2 shows mean seasonality of both total and compositional PM2.5 over 2000-2016. Table 2

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summarizes the cross-validated seasonal agreement, and typical values at ground-monitored locations.

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Overall performances remain similar across seasons. Summertime highs in PM2.5 of 10.8 μg/m3 for

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2000-2016 are driven primarily by SO42- (3.4 μg/m3) and OM (3.4 μg/m3). The strongest contributors to



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wintertime PM2.5 of 9.9 μg/m3 are NO3- (2.1 μg/m3), SO42- (1.9 μg/m3) and OM (1.9 μg/m3). DUST is

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regionally important over parts of the Southern and Southwestern United States during spring and

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summer.

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Figure 3 shows the population-weighted average total and compositional PM2.5 mass for 2000-2004,

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2006-2010, and 2012-2016. Figure 4 and Table 3 provide regional, population-weighted mean

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perspectives of these data. Population-weighted PM2.5 in the United States decreased from 11.5 μg/m3

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in 2000-2004 to 8.3 μg/m3 in 2012-2016. Reductions in population-weighted SO42- of 1.6 μg/m3

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dominated overall, with the largest changes across the Southern, Midwestern and Northeastern United

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States. Reductions in population-weighted OM of 1.2 μg/m3 reflect large changes over the

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Southwestern and Midwestern United States. Population-weighted PM2.5 in Canada decreased from 7.6

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μg/m3 in 2000-2004 to 6.4 μg/m3 in 2012-2016, driven largely by changes in SO42- in regions such as

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Eastern Canada, Western Canada and Atlantic Canada and OM in Western Canada. Changes to SO42-

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have reduced the seasonality of PM2.5 by reducing the summertime peak in recent years55. The impact

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of wildfires is most visible over Northern and Western Canada, with the magnitude of seasonal OM

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enhancements varying from year to year56 .

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Total PM2.5 mass concentrations estimated from fusion with PM2.5 monitors, can differ from the

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summed mass concentration of all components within these estimates, as discernible from Figure 4 and

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Table 3. This difference in total mass reflects uncertainties in the representation of both total mass and

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component concentrations. Applications that require complete closure of PM2.5 component mass with

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PM2.5 mass may benefit from the relative contribution of each component to the sum of all components,

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rather than direct use of component masses, or the application of that relative contribution to the total

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PM2.5 mass surface.


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Figure 5 shows the regional variation of composition with population-weighted PM2.5 mass between

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2012 and 2016, based on component totals. Over both Canada and the United States the relative

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contribution of NO3- is smallest at low PM2.5 concentrations, but is a major contributor over most regions

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at high PM2.5. By contrast, the relative contribution of DUST and SS generally decreases with increasing

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PM2.5, and contributes only minimally in areas with the highest PM2.5 concentrations. The impact of

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wildfires can again be seen over Northern Canada, where OM contributions to PM2.5 increase with PM2.5

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mass, even driving a small proportion of the population over the Canadian PM2.5 guideline during some

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years.

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Figure 6 shows relative PM2.5 composition for populations above current national PM2.5 limits based on

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component totals, as well as the percentage of population above those limits, as they change from 2000

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through 2016. North American populations exhibit significant improvement in meeting national limits.

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The Southwestern United States is the only region not meeting local standards for a large proportion

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(around 25%) of its population after 2012, with national and continental above-standard population-

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weighted PM2.5 composition largely representative of this region after this time. Similarly, changes to

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the continent-wide, population-weighted, above-standard relative composition is driven predominately

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by changes to which regions have the largest populations above these standards, rather than changes in

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relative contribution within these regions themselves, although some regional changes in relative

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composition are visible.

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Table 4 shows the impact of spatial averaging on the variance of the difference between cross-validated

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ground monitor and hybrid values. Larger areas are represented with much higher accuracy, as shown

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in a reduction of mean error variance of approximately two-thirds when averaging over an area of about

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25 km2 as compared to an individual pixel with an area of about 1 km2. This reduction in variance

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represents the effects of an improved area-representation of both ground monitor and hybrid values.



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Overall, the combination of information from satellites, simulations and ground-based measurements

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enabled estimates PM2.5 composition with promising accuracy (R2=0.57 - R2=0.96). This analysis offered

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insight into the large spatiotemporal changes in PM2.5 composition over this period, driven by reductions

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in sulfate and organic matter. The approach presented here could be readily adapted to other regions

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with PM2.5 ground monitoring networks, such as Europe or China.

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Annual PM2.5 composition estimates resulting from this effort are freely available as a public good from

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the Dalhousie University Atmospheric Composition Analysis Group Website as version V4.NA.02 (North

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America) at: http://fizz.phys.dal.ca/~atmos/martin/?page_id=140, or by contacting the authors.

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Acknowledgements

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This work was supported by Health Canada contract #4500358772. The authors would also like to thank

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the teams responsible for collecting and making available the ground-based observations (in-situ and

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AERONET) used in this work.

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Supporting Information

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GWRwSPEC-Supplemental.pdf: This file contains the supplemental figures and tables mentioned within the main text of this manuscript.

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Table 1: Mean cross-validated, all-species and compositional agreement between annual derived and in-situ PM2.5 for 2000-2016 for years with at least 5 coincident data pairs. Bias and variance define the normal-fit distribution of differences between annual derived and in-situ PM2.5. Slope and offset refer to the line of best fit. Bracketed terms denote 5th and 95th percentile of the annual values. Nyears provides the number of datasets. N describes the number of comparison pairs used within these datasets. Value corresponds to the mean of in-situ observations. Region North America

Source Geoscience

Component PM2.5 SO42NH4+ NO3OM BC Dust SS Hybrid PM2.5 SO42NH4+ NO3OM BC Dust SS United Geoscience PM2.5 States SO42NH4+ NO3OM BC Dust SS Hybrid PM2.5 SO42NH4+ NO3OM BC Dust SS SouthGeoscience PM2.5 SO4Western NH4+ United NO3States OM BC Dust SS Hybrid PM2.5 SO4NH4+ NO3OM BC Dust SS Southern Geoscience PM2.5

R2 bias variance slope offset 0.39 (0.24,0.54) 0.53 (-1.70,2.10) 2.2 (1.9,2.9) 0.99 (0.75,1.36) -0.2 (-2.6,1.3) 0.66 (0.46,0.83) 0.41 (-0.29,0.83) 0.7 (0.3,0.9) 0.69 (0.44,1.30) 0.4 (-0.1,0.7) 0.50 (0.32,0.61) 0.14 (-0.27,0.44) 0.3 (0.1,0.4) 0.67 (0.52,1.03) 0.3 (0.1,0.4) 0.59 (0.37,0.68) 0.28 (0.12,0.44) 0.4 (0.3,0.5) 0.65 (0.46,0.74) 0.1 (0.0,0.2) 0.28 (0.07,0.52) -0.83 (-1.40,-0.01) 1.3 (0.9,1.7) 1.24 (0.88,1.76) -0.2 (-1.6,0.7) 0.33 (0.20,0.48) 0.06 (-0.01,0.18) 0.2 (0.1,0.3) 1.14 (0.86,1.58) -0.1 (-0.2,-0.0) 0.23 (0.08,0.43) -0.02 (-0.57,0.28) 0.4 (0.3,0.7) 1.57 (0.98,2.48) -0.4 (-1.1,-0.1) 0.33 (0.06,0.58) -0.13 (-0.25,0.03) 0.2 (0.1,0.3) 1.43 (1.12,2.15) -0.0 (-0.2,0.1) 0.60 (0.39,0.76) 0.21 (0.04,0.46) 1.6 (1.4,1.8) 0.96 (0.88,1.05) 0.2 (-0.4,1.0) 0.90 (0.81,0.95) 0.03 (-0.01,0.16) 0.3 (0.2,0.4) 1.01 (0.94,1.12) -0.0 (-0.1,0.0) 0.76 (0.61,0.83) -0.00 (-0.03,0.03) 0.2 (0.1,0.2) 1.03 (0.92,1.08) -0.0 (-0.1,0.1) 0.75 (0.62,0.81) 0.00 (-0.03,0.07) 0.3 (0.2,0.4) 0.99 (0.81,1.11) 0.0 (-0.0,0.1) 0.48 (0.36,0.62) 0.11 (-0.03,0.20) 0.8 (0.6,1.2) 0.88 (0.75,1.02) 0.2 (-0.1,0.5) 0.64 (0.49,0.74) 0.01 (-0.01,0.02) 0.1 (0.1,0.2) 0.98 (0.89,1.10) 0.0 (-0.0,0.1) 0.43 (0.33,0.56) 0.01 (-0.01,0.04) 0.2 (0.2,0.3) 0.94 (0.88,1.00) 0.0 (-0.0,0.1) 0.53 (0.15,0.73) -0.02 (-0.11,0.00) 0.1 (0.1,0.3) 1.21 (0.89,2.12) -0.0 (-0.2,0.0) 0.40 (0.24,0.54) 0.49 (-1.71,2.22) 2.2 (1.8,2.8) 0.94 (0.71,1.26) 0.2 (-1.7,1.6) 0.66 (0.46,0.83) 0.42 (-0.29,0.84) 0.7 (0.3,0.9) 0.69 (0.44,1.30) 0.4 (-0.0,0.7) 0.49 (0.32,0.60) 0.15 (-0.29,0.46) 0.3 (0.1,0.4) 0.66 (0.52,0.99) 0.3 (0.1,0.4) 0.62 (0.39,0.70) 0.29 (0.11,0.47) 0.4 (0.2,0.5) 0.64 (0.44,0.75) 0.1 (0.0,0.1) 0.44 (0.27,0.55) -1.03 (-1.57,-0.09) 1.0 (0.7,1.3) 1.72 (1.03,2.15) -1.1 (-2.1,0.4) 0.34 (0.21,0.48) 0.07 (-0.01,0.21) 0.2 (0.1,0.2) 1.09 (0.76,1.60) -0.1 (-0.2,0.0) 0.22 (0.06,0.45) -0.02 (-0.56,0.27) 0.4 (0.3,0.6) 1.58 (1.00,2.48) -0.4 (-1.1,-0.1) 0.36 (0.06,0.62) -0.12 (-0.23,0.04) 0.1 (0.1,0.3) 1.32 (1.02,1.92) 0.0 (-0.1,0.1) 0.61 (0.41,0.76) 0.15 (-0.03,0.50) 1.5 (1.4,1.7) 0.93 (0.88,0.99) 0.6 (0.1,1.1) 0.90 (0.81,0.96) 0.03 (-0.01,0.15) 0.3 (0.2,0.4) 1.01 (0.94,1.12) -0.0 (-0.1,0.0) 0.77 (0.61,0.84) 0.00 (-0.02,0.04) 0.2 (0.1,0.2) 1.03 (0.92,1.09) -0.0 (-0.1,0.1) 0.78 (0.66,0.83) 0.01 (-0.02,0.07) 0.3 (0.2,0.4) 0.98 (0.83,1.09) 0.0 (-0.0,0.1) 0.55 (0.37,0.74) -0.01 (-0.08,0.11) 0.6 (0.5,0.7) 1.10 (0.93,1.29) -0.2 (-0.6,0.1) 0.68 (0.57,0.77) 0.01 (-0.00,0.03) 0.1 (0.1,0.2) 0.98 (0.88,1.18) 0.0 (-0.1,0.1) 0.42 (0.34,0.55) 0.00 (-0.01,0.04) 0.2 (0.2,0.3) 0.95 (0.89,1.00) 0.0 (-0.0,0.1) 0.56 (0.18,0.77) -0.01 (-0.10,0.01) 0.1 (0.1,0.2) 1.17 (0.82,2.04) -0.0 (-0.2,0.0) 0.32 (0.22,0.47) 0.80 (-0.87,2.05) 3.3 (2.4,4.3) 0.45 (0.21,0.94) 3.9 (0.6,5.8) 0.17 (0.06,0.34) -0.14 (-0.28,0.00) 0.3 (0.2,0.5) 0.95 (0.62,1.40) 0.1 (-0.3,0.4) 0.34 (0.10,0.66) 0.19 (-0.11,0.47) 0.5 (0.2,0.9) 0.41 (0.30,0.68) 0.4 (0.2,0.6) 0.71 (0.49,0.85) 0.49 (0.31,0.74) 0.6 (0.4,0.9) 0.35 (0.25,0.48) 0.0 (-0.0,0.1) 0.45 (0.14,0.73) -0.50 (-1.27,0.47) 0.9 (0.6,1.5) 1.16 (0.77,1.97) 0.0 (-1.6,1.0) 0.32 (0.10,0.54) 0.01 (-0.11,0.11) 0.2 (0.1,0.3) 1.66 (0.88,2.66) -0.2 (-0.6,-0.0) 0.16 (0.02,0.40) -0.49 (-1.26,0.07) 0.6 (0.4,1.0) 1.72 (0.99,3.22) -0.9 (-3.7,0.1) 0.37 (0.03,0.77) -0.12 (-0.24,0.07) 0.1 (0.1,0.3) 1.54 (0.89,2.94) -0.0 (-0.4,0.1) 0.60 (0.36,0.77) -0.56 (-0.87,-0.20) 2.4 (2.1,2.9) 0.97 (0.91,1.04) 0.7 (-0.1,1.3) 0.59 (0.32,0.79) 0.07 (0.03,0.12) 0.2 (0.1,0.3) 0.87 (0.67,1.04) 0.0 (-0.1,0.2) 0.75 (0.34,0.91) 0.09 (-0.05,0.31) 0.3 (0.1,0.8) 0.85 (0.51,1.15) 0.1 (-0.0,0.5) 0.78 (0.66,0.89) -0.01 (-0.09,0.07) 0.4 (0.3,0.6) 1.02 (0.74,1.21) -0.0 (-0.1,0.1) 0.52 (0.10,0.86) 0.04 (-0.08,0.19) 0.8 (0.4,1.4) 1.16 (0.74,1.77) -0.3 (-1.4,0.4) 0.42 (0.09,0.75) 0.02 (-0.01,0.05) 0.1 (0.1,0.2) 0.95 (0.62,1.34) 0.0 (-0.1,0.1) 0.21 (0.07,0.37) -0.01 (-0.06,0.09) 0.4 (0.3,0.5) 1.02 (0.89,1.19) -0.0 (-0.2,0.1) 0.40 (0.02,0.71) -0.01 (-0.07,0.02) 0.1 (0.1,0.2) 1.54 (0.89,2.88) -0.1 (-0.4,0.0) 0.16 (0.01,0.36) 0.77 (-1.78,2.85)ACS Paragon 1.6 (1.1,2.1) 0.90 (0.62,1.24) 0.7 (-2.5,3.0) Plus Environment

RMSD 2.6 (1.9,3.4) 0.8 (0.3,1.2) 0.4 (0.2,0.6) 0.5 (0.3,0.6) 1.5 (1.0,2.0) 0.2 (0.2,0.3) 0.5 (0.3,0.8) 0.2 (0.2,0.4) 1.6 (1.4,1.8) 0.3 (0.2,0.4) 0.2 (0.1,0.2) 0.3 (0.2,0.4) 0.8 (0.6,1.2) 0.1 (0.1,0.2) 0.2 (0.2,0.3) 0.1 (0.1,0.3) 2.5 (1.9,3.4) 0.8 (0.3,1.2) 0.4 (0.2,0.6) 0.5 (0.3,0.6) 1.5 (1.0,1.9) 0.2 (0.2,0.3) 0.5 (0.3,0.8) 0.2 (0.1,0.3) 1.6 (1.4,1.7) 0.3 (0.2,0.4) 0.2 (0.1,0.2) 0.3 (0.2,0.4) 0.6 (0.5,0.7) 0.1 (0.1,0.2) 0.2 (0.2,0.3) 0.1 (0.1,0.3) 3.5 (2.5,4.4) 0.4 (0.3,0.5) 0.5 (0.2,1.0) 0.7 (0.5,1.2) 1.1 (0.8,1.7) 0.2 (0.1,0.3) 0.8 (0.4,1.5) 0.2 (0.1,0.3) 2.5 (2.1,3.0) 0.2 (0.1,0.3) 0.3 (0.1,0.8) 0.4 (0.3,0.6) 0.8 (0.4,1.4) 0.1 (0.1,0.2) 0.4 (0.3,0.5) 0.1 (0.1,0.2) 2.2 (1.2,3.3)

N Nyears 884 (755,944) 17 275 (159,319) 17 141 (50,185) 17 267 (159,302) 17 121 (65,192) 17 121 (77,187) 17 228 (125,268) 17 104 (43,121) 17 884 (755,944) 17 275 (159,319) 17 141 (50,185) 17 267 (159,302) 17 121 (65,192) 17 121 (77,187) 17 228 (125,268) 17 104 (43,121) 17 772 (700,808) 17 263 (157,304) 17 132 (50,174) 17 256 (157,289) 17 112 (65,181) 17 112 (76,175) 17 225 (123,262) 17 95 (42,110) 17 772 (700,808) 17 263 (157,304) 17 132 (50,174) 17 256 (157,289) 17 112 (65,181) 17 112 (76,175) 17 225 (123,262) 17 95 (42,110) 17 145 (123,169) 17 56 (39,62) 17 20 (14,23) 16 56 (39,62) 17 39 (30,47) 17 38 (30,46) 17 53 (36,58) 17 36 (22,43) 17 145 (123,169) 17 56 (39,62) 17 20 (14,23) 16 56 (39,62) 17 39 (30,47) 17 38 (30,46) 17 53 (36,58) 17 36 (22,43) 17 231 (216,249)

Value [μg/m3] 9.6 (7.3,12.0) 2.0 (1.0,3.0) 1.0 (0.4,1.5) 1.0 (0.7,1.3) 2.5 (2.0,3.1) 0.5 (0.4,0.6) 0.7 (0.6,0.8) 0.2 (0.2,0.3) 9.6 (7.3,12.0) 2.0 (1.0,3.0) 1.0 (0.4,1.5) 1.0 (0.7,1.3) 2.5 (2.0,3.1) 0.5 (0.4,0.6) 0.7 (0.6,0.8) 0.2 (0.2,0.3) 9.9 (7.5,12.4) 2.1 (1.0,3.0) 1.1 (0.4,1.5) 1.0 (0.7,1.3) 2.3 (1.8,2.7) 0.5 (0.4,0.6) 0.7 (0.6,0.8) 0.2 (0.2,0.3) 9.9 (7.5,12.4) 2.1 (1.0,3.0) 1.1 (0.4,1.5) 1.0 (0.7,1.3) 2.3 (1.8,2.7) 0.5 (0.4,0.6) 0.7 (0.6,0.8) 0.2 (0.2,0.3) 8.8 (7.4,10.8) 0.8 (0.6,1.1) 0.9 (0.4,1.6) 1.0 (0.6,1.6) 2.2 (1.5,2.9) 0.4 (0.3,0.5) 1.0 (0.8,1.2) 0.2 (0.1,0.2) 8.8 (7.4,10.8) 0.8 (0.6,1.1) 0.9 (0.4,1.6) 1.0 (0.6,1.6) 2.2 (1.5,2.9) 0.4 (0.3,0.5) 1.0 (0.8,1.2) 0.2 (0.1,0.2) 17 10.8 (8.1,13.7)

Environmental Science & Technology SO4NH4+ NO3OM BC Dust SS PM2.5 SO4NH4+ NO3OM BC Dust SS

United States

Hybrid

MidWestern United States

Geoscience

Hybrid

NorthEastern United States

Geoscience

Hybrid

NorthWestern United States

Geoscience

PM2.5 SO4NH4+ NO3OM BC Dust SS PM2.5 SO4NH4+ NO3OM BC Dust SS PM2.5 SO4NH4+ NO3OM BC Dust SS PM2.5 SO4NH4+ NO3OM BC Dust SS PM2.5 SO4NH4+ NO3OM BC Dust SS

0.30 (0.05,0.57) 0.72 (-0.38,1.40) 0.4 (0.3,0.6) 0.81 (0.50,1.38) 0.2 (-0.5,0.8) 0.44 (0.06,0.66) 0.07 (-0.36,0.38) 0.2 (0.1,0.2) 0.83 (0.51,1.19) 0.1 (-0.1,0.5) 0.29 (0.02,0.55) 0.16 (-0.12,0.35) 0.2 (0.2,0.4) 0.79 (0.44,1.12) 0.0 (-0.1,0.1) 0.40 (0.05,0.73) -1.94 (-2.83,-0.46) 0.9 (0.6,1.3) 1.62 (0.66,2.29) -1.0 (-3.1,1.4) 0.15 (0.00,0.35) 0.17 (-0.00,0.34) 0.2 (0.1,0.3) 0.54 (0.24,0.92) 0.2 (0.0,0.3) 0.36 (0.08,0.67) 0.18 (-0.28,0.51) 0.3 (0.2,0.4) 0.89 (0.42,1.23) -0.0 (-0.3,0.2) 0.72 (0.26,0.89) -0.23 (-0.34,-0.00) 0.3 (0.1,0.4) 1.97 (0.99,3.06) -0.0 (-0.2,0.1) 0.41 (0.14,0.62) 0.28 (0.03,0.65) 1.1 (1.0,1.3) 0.85 (0.78,0.97) 1.5 (0.2,2.5) 0.50 (0.12,0.80) 0.07 (-0.02,0.30) 0.3 (0.2,0.4) 0.96 (0.77,1.10) 0.0 (-0.4,0.7) 0.56 (0.20,0.78) 0.03 (0.01,0.07) 0.1 (0.1,0.2) 0.95 (0.65,1.19) -0.0 (-0.2,0.3) 0.42 (0.09,0.63) 0.05 (0.02,0.08) 0.2 (0.1,0.3) 0.95 (0.83,1.10) -0.0 (-0.1,0.1) 0.36 (0.07,0.72) -0.25 (-0.38,0.05) 0.6 (0.5,1.0) 0.94 (0.55,1.35) 0.3 (-1.0,1.4) 0.38 (0.06,0.69) -0.03 (-0.07,0.04) 0.1 (0.1,0.2) 0.83 (0.57,1.20) 0.1 (-0.1,0.3) 0.53 (0.30,0.73) -0.00 (-0.05,0.04) 0.2 (0.2,0.3) 0.88 (0.60,1.06) 0.1 (-0.0,0.3) 0.75 (0.20,0.93) -0.01 (-0.03,0.02) 0.1 (0.1,0.2) 0.95 (0.63,1.16) 0.0 (-0.0,0.1) 0.50 (0.33,0.69) 0.24 (-2.05,2.46) 1.7 (1.3,2.3) 1.00 (0.77,1.35) -0.1 (-3.2,2.0) 0.49 (0.12,0.81) 0.50 (-0.34,1.01) 0.6 (0.3,0.9) 0.70 (0.40,1.29) 0.5 (-0.2,1.1) 0.18 (0.00,0.52) 0.19 (-0.32,0.56) 0.2 (0.1,0.4) 0.63 (0.40,0.86) 0.4 (0.2,0.7) 0.60 (0.44,0.77) 0.51 (0.30,0.86) 0.4 (0.2,0.6) 0.63 (0.51,0.75) 0.2 (-0.0,0.6) 0.54 (0.19,0.82) -1.06 (-1.51,0.26) 1.0 (0.6,1.4) 2.34 (1.24,3.86) -2.6 (-7.8,-0.1) 0.67 (0.36,0.81) 0.17 (0.05,0.35) 0.1 (0.1,0.2) 1.16 (0.82,1.84) -0.2 (-0.4,-0.1) 0.06 (0.00,0.20) 0.13 (-0.44,0.44) 0.2 (0.2,0.4) 0.71 (0.29,1.70) 0.0 (-0.7,0.2) 0.18 (0.00,0.50) -0.10 (-0.16,0.08) 0.1 (0.0,0.1) 1.57 (0.52,2.50) -0.1 (-0.2,0.1) 0.69 (0.51,0.85) 0.16 (-0.30,0.73) 1.2 (1.0,1.5) 1.00 (0.94,1.09) -0.1 (-0.6,0.4) 0.84 (0.71,0.93) -0.06 (-0.12,0.07) 0.3 (0.2,0.4) 1.03 (0.97,1.12) 0.0 (-0.2,0.2) 0.65 (0.42,0.87) -0.02 (-0.05,0.02) 0.2 (0.1,0.2) 0.98 (0.86,1.07) 0.0 (-0.1,0.3) 0.71 (0.54,0.84) 0.04 (-0.04,0.17) 0.3 (0.2,0.4) 0.92 (0.73,1.07) 0.1 (-0.1,0.4) 0.49 (0.18,0.73) 0.04 (-0.14,0.27) 0.5 (0.4,0.6) 1.39 (0.96,2.06) -0.9 (-2.5,-0.0) 0.82 (0.50,0.95) 0.04 (0.01,0.07) 0.1 (0.0,0.1) 1.01 (0.84,1.22) -0.0 (-0.1,0.1) 0.21 (0.03,0.47) -0.01 (-0.09,0.05) 0.2 (0.1,0.3) 0.66 (0.46,0.94) 0.2 (0.1,0.4) 0.41 (0.12,0.70) 0.02 (0.01,0.04) 0.0 (0.0,0.1) 1.19 (0.86,1.76) -0.0 (-0.1,0.0) 0.27 (0.12,0.46) -0.90 (-4.47,1.49) 1.7 (1.3,2.5) 0.91 (0.71,1.37) 1.0 (-3.1,2.8) 0.73 (0.56,0.90) 0.93 (-0.32,1.71) 0.4 (0.2,0.6) 0.72 (0.44,1.48) 0.1 (-0.3,0.5) 0.53 (0.27,0.74) 0.34 (-0.25,0.73) 0.2 (0.1,0.3) 0.64 (0.45,1.41) 0.2 (0.1,0.4) 0.59 (0.34,0.72) 0.24 (0.01,0.48) 0.3 (0.2,0.5) 0.53 (0.36,0.80) 0.2 (0.1,0.3) 0.29 (0.02,0.69) -1.77 (-2.36,-0.89) 0.8 (0.4,1.0) 1.79 (1.11,2.38) -1.6 (-3.1,0.5) 0.28 (0.02,0.55) -0.09 (-0.20,0.10) 0.3 (0.2,0.3) 1.58 (1.02,2.89) -0.3 (-1.0,-0.0) 0.09 (0.00,0.25) 0.15 (-0.18,0.28) 0.1 (0.1,0.2) 0.30 (0.12,0.71) 0.1 (0.0,0.3) 0.66 (0.09,0.94) 0.01 (-0.09,0.25) 0.2 (0.1,0.3) 0.74 (0.41,1.12) 0.1 (0.0,0.2) 0.51 (0.30,0.68) 0.02 (-0.29,0.63) 1.3 (1.0,1.5) 0.91 (0.80,0.98) 0.9 (0.2,2.1) 0.83 (0.75,0.93) 0.01 (-0.05,0.17) 0.3 (0.1,0.4) 0.97 (0.89,1.13) 0.1 (-0.1,0.3) 0.63 (0.47,0.74) -0.06 (-0.12,0.05) 0.2 (0.1,0.3) 0.95 (0.76,1.37) 0.2 (-0.1,0.4) 0.77 (0.60,0.85) -0.06 (-0.14,0.05) 0.2 (0.2,0.3) 1.00 (0.80,1.11) 0.1 (-0.0,0.1) 0.44 (0.03,0.78) -0.08 (-0.36,0.32) 0.4 (0.3,0.6) 0.93 (0.33,1.43) 0.2 (-0.7,1.5) 0.55 (0.25,0.84) 0.00 (-0.03,0.06) 0.1 (0.1,0.2) 0.82 (0.42,1.39) 0.1 (-0.2,0.3) 0.48 (0.27,0.81) 0.03 (-0.01,0.07) 0.1 (0.1,0.1) 1.05 (0.69,1.37) -0.0 (-0.1,0.1) 0.51 (0.14,0.85) 0.05 (-0.10,0.11) 0.3 (0.1,0.4) 0.46 (0.25,0.94) 0.2 (0.0,0.4) 0.04 (0.00,0.14) 1.53 (0.81,2.38) 2.5 (1.9,3.0) 0.30 (0.19,0.43) 3.6 (2.8,4.4) 0.12 (0.01,0.41) -0.28 (-0.43,-0.18) 0.2 (0.1,0.4) 1.08 (0.64,1.82) 0.0 (-0.3,0.4) - (-,-) - (-,-) - (-,-) - (-,-) - (-,-) 0.38 (0.13,0.56) 0.01 (-0.04,0.09) 0.2 (0.1,0.3) 0.72 (0.50,1.02) 0.1 (0.0,0.2) 0.20 (0.00,0.49) -0.33 (-0.92,0.78) 0.7 (0.4,1.0) 0.84 (0.34,1.58) 0.5 (-0.8,1.2) 0.06 (0.00,0.16) 0.11 (0.02,0.23) 0.2 (0.1,0.3) 0.74 (0.34,1.37) 0.0 (-0.1,0.1) 0.13 (0.00,0.36) -0.25 (-0.98,0.06) 0.3 (0.2,0.5) 1.54 (0.69,2.97) -0.4 (-2.1,0.2) 0.39 (0.07,0.66) -0.19 (-0.46,0.00) 0.2 (0.1,0.3) 2.55 (0.87,7.68) -0.2 (-1.4,0.1)


Page 14 of 31 1.0 (0.3,1.5) 0.3 (0.1,0.4) 0.3 (0.2,0.4) 2.2 (1.1,3.0) 0.2 (0.1,0.4) 0.4 (0.2,0.6) 0.3 (0.1,0.5) 1.2 (1.0,1.5) 0.3 (0.2,0.5) 0.1 (0.1,0.2) 0.2 (0.1,0.3) 0.7 (0.5,1.0) 0.1 (0.1,0.2) 0.2 (0.2,0.3) 0.1 (0.1,0.2) 2.2 (1.4,3.1) 0.9 (0.4,1.3) 0.4 (0.2,0.7) 0.6 (0.4,0.9) 1.5 (0.7,1.9) 0.2 (0.1,0.4) 0.4 (0.2,0.6) 0.1 (0.1,0.2) 1.3 (1.0,1.6) 0.3 (0.2,0.4) 0.2 (0.1,0.2) 0.3 (0.2,0.4) 0.5 (0.4,0.6) 0.1 (0.1,0.1) 0.2 (0.1,0.3) 0.0 (0.0,0.1) 2.4 (1.5,5.1) 1.1 (0.2,1.8) 0.5 (0.1,0.8) 0.4 (0.2,0.6) 2.0 (1.2,2.5) 0.3 (0.2,0.4) 0.2 (0.2,0.3) 0.2 (0.1,0.4) 1.3 (1.1,1.6) 0.3 (0.1,0.4) 0.2 (0.1,0.3) 0.2 (0.2,0.3) 0.5 (0.3,0.6) 0.1 (0.1,0.2) 0.1 (0.1,0.1) 0.3 (0.1,0.4) 2.9 (2.3,3.6) 0.4 (0.2,0.5) - (-,-) 0.2 (0.1,0.3) 0.9 (0.6,1.2) 0.2 (0.1,0.3) 0.4 (0.2,1.1) 0.3 (0.1,0.6)

74 (43,96) 46 (17,74) 69 (42,83) 21 (12,40) 21 (15,40) 61 (31,81) 18 (9,20) 231 (216,249) 74 (43,96) 46 (17,74) 69 (42,83) 21 (12,40) 21 (15,40) 61 (31,81) 18 (9,20) 176 (164,190) 55 (24,64) 38 (24,42) 54 (24,61) 16 (6,38) 15 (6,37) 47 (16,54) 13 (10,14) 176 (164,190) 55 (24,64) 38 (24,42) 54 (24,61) 16 (6,38) 15 (6,37) 47 (16,54) 13 (10,14) 119 (109,126) 49 (28,56) 26 (20,31) 49 (28,56) 17 (8,33) 17 (9,31) 38 (17,46) 14 (5,18) 119 (109,126) 49 (28,56) 26 (20,31) 49 (28,56) 17 (8,33) 17 (9,31) 38 (17,46) 14 (5,18) 102 (65,122) 29 (21,33) 7 (6,9) 28 (21,32) 19 (9,23) 21 (16,24) 27 (20,29) 18 (8,21)

17 17 17 17 17 17 15 17 17 17 17 17 17 17 15 17 17 16 17 17 17 17 14 17 17 16 17 17 17 17 14 17 17 16 17 17 17 17 17 17 17 16 17 17 17 17 17 17 17 15 17 17 17 17 17

2.9 (1.3,4.3) 1.0 (0.3,1.5) 0.7 (0.5,1.0) 2.9 (2.1,3.4) 0.7 (0.5,0.9) 0.8 (0.6,0.9) 0.2 (0.2,0.3) 10.8 (8.1,13.7) 2.9 (1.3,4.3) 1.0 (0.3,1.5) 0.7 (0.5,1.0) 2.9 (2.1,3.4) 0.7 (0.5,0.9) 0.8 (0.6,0.9) 0.2 (0.2,0.3) 10.8 (7.8,13.4) 2.5 (1.3,3.6) 1.3 (0.6,1.9) 1.7 (1.2,2.2) 2.3 (1.8,2.9) 0.6 (0.4,0.7) 0.6 (0.5,0.7) 0.1 (0.1,0.2) 10.8 (7.8,13.4) 2.5 (1.3,3.6) 1.3 (0.6,1.9) 1.7 (1.2,2.2) 2.3 (1.8,2.9) 0.6 (0.4,0.7) 0.6 (0.5,0.7) 0.1 (0.1,0.2) 10.4 (7.5,13.1) 2.6 (1.1,3.9) 1.2 (0.4,1.7) 1.0 (0.7,1.2) 2.3 (1.6,2.8) 0.6 (0.4,0.9) 0.4 (0.3,0.5) 0.4 (0.3,0.7) 10.4 (7.5,13.1) 2.6 (1.1,3.9) 1.2 (0.4,1.7) 1.0 (0.7,1.2) 2.3 (1.6,2.8) 0.6 (0.4,0.9) 0.4 (0.3,0.5) 0.4 (0.3,0.7) 6.8 (5.4,8.3) 0.6 (0.3,0.7) 0.3 (0.1,0.5) 0.4 (0.2,0.5) 2.1 (1.4,3.1) 0.4 (0.2,0.5) 0.4 (0.3,0.6) 0.1 (0.1,0.2)

Page 15 of 31

Environmental Science & Technology Hybrid

Canada

Geoscience

Hybrid

PM2.5 SO4NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS

0.22 (0.05,0.45) 0.31 (0.16,0.60) 0.04 (0.00,0.08) 0.50 (0.24,0.66) 0.35 (0.03,0.69) 0.29 (0.00,0.76) 0.19 (0.01,0.47) 0.52 (0.14,0.81) 0.21 (0.06,0.42) 0.36 (0.01,0.72) 0.52 (0.07,0.85) 0.22 (0.02,0.61) 0.21 (0.00,0.67) 0.16 (0.00,0.58) - (-,-) 0.26 (0.00,0.41) 0.30 (0.14,0.51) 0.52 (0.03,0.93) 0.61 (0.00,0.94) 0.50 (0.03,0.92) 0.31 (0.00,0.76) 0.18 (0.00,0.46) - (-,-) 0.22 (0.14,0.33)

0.40 (0.10,0.64) -0.02 (-0.07,0.05) -0.00 (-0.02,0.01) -0.02 (-0.09,0.04) 0.17 (-0.18,0.51) 0.04 (0.00,0.10) 0.02 (-0.04,0.09) -0.01 (-0.08,0.01) 1.18 (-1.09,2.25) 0.08 (-0.43,0.65) -0.07 (-0.22,0.03) -0.14 (-0.34,-0.00) 2.09 (0.90,3.16) -0.11 (-0.25,-0.01) - (-,-) -0.17 (-0.22,-0.12) 0.80 (0.37,1.17) -0.17 (-0.32,0.02) -0.12 (-0.31,0.09) -0.33 (-0.49,-0.15) 2.04 (1.23,3.73) -0.14 (-0.22,-0.02) - (-,-) -0.00 (-0.04,0.03)

2.1 (1.8,2.5) 0.2 (0.1,0.3) 0.1 (0.1,0.1) 0.2 (0.1,0.3) 0.6 (0.3,1.1) 0.1 (0.0,0.3) 0.2 (0.1,0.3) 0.1 (0.0,0.2) 2.3 (1.6,4.3) 0.3 (0.2,0.6) 0.1 (0.1,0.2) 0.3 (0.2,0.5) 1.1 (0.8,1.3) 0.2 (0.2,0.3) - (-,-) 0.2 (0.1,0.3) 1.8 (1.5,2.2) 0.3 (0.2,0.5) 0.2 (0.0,0.3) 0.3 (0.2,0.4) 0.9 (0.3,1.2) 0.2 (0.1,0.3) - (-,-) 0.1 (0.1,0.1)

0.65 (0.51,0.85) 0.76 (0.45,1.18) 2.81 (1.87,3.75) 0.86 (0.55,1.17) 0.72 (0.25,1.43) 0.57 (0.26,1.01) 0.88 (0.62,1.43) 1.37 (0.78,2.83) 1.25 (0.70,2.15) 0.83 (0.26,1.40) 0.94 (0.70,1.23) 0.84 (0.49,1.82) 1.94 (0.80,3.25) 1.50 (1.21,2.15) - (-,-) 5.60 (2.72,11.18) 1.03 (0.79,1.26) 1.16 (0.79,1.65) 1.49 (1.01,2.21) 1.45 (0.89,2.16) 2.09 (0.81,3.70) 0.88 (0.65,1.24) - (-,-) 2.34 (1.57,3.65)


2.0 (1.0,2.5) 0.1 (-0.1,0.4) -0.3 (-0.3,-0.2) 0.1 (-0.0,0.2) 0.3 (-1.0,1.1) 0.1 (-0.0,0.2) 0.0 (-0.2,0.2) -0.1 (-0.5,0.0) -2.5 (-9.6,1.1) 0.3 (-0.2,1.0) 0.1 (-0.1,0.3) 0.2 (-0.6,0.4) -3.7 (-14.0,2.0) -0.3 (-0.8,-0.1) - (-,-) -1.4 (-3.2,-0.4) -0.6 (-2.2,1.0) -0.1 (-0.7,0.4) -0.3 (-0.9,0.1) -0.1 (-0.5,0.3) -6.1 (-15.2,0.5) 0.1 (-0.0,0.2) - (-,-) -0.3 (-0.6,-0.1)

2.2 (1.9,2.5) 0.2 (0.1,0.3) 0.1 (0.1,0.1) 0.2 (0.1,0.3) 0.6 (0.3,1.1) 0.1 (0.1,0.3) 0.2 (0.1,0.3) 0.1 (0.0,0.2) 2.8 (2.3,4.4) 0.5 (0.3,0.8) 0.2 (0.1,0.2) 0.3 (0.2,0.5) 2.3 (1.3,3.3) 0.3 (0.2,0.4) - (-,-) 0.2 (0.2,0.3) 2.0 (1.7,2.4) 0.3 (0.2,0.5) 0.2 (0.1,0.4) 0.5 (0.3,0.6) 2.2 (1.3,3.8) 0.2 (0.1,0.3) - (-,-) 0.1 (0.1,0.1)

102 (65,122) 29 (21,33) 7 (6,9) 28 (21,32) 19 (9,23) 21 (16,24) 27 (20,29) 18 (8,21) 97 (43,120) 9 (5,12) 9 (6,11) 9 (5,12) 9 (6,11) 9 (6,11) - (-,-) 9 (6,11) 97 (43,120) 9 (5,12) 9 (6,11) 9 (5,12) 9 (6,11) 9 (6,11) - (-,-) 9 (6,11)

17 17 15 17 17 17 17 17 17 14 13 14 13 13 0 13 17 14 13 14 13 13 0 13

6.8 (5.4,8.3) 0.6 (0.3,0.7) 0.3 (0.1,0.5) 0.4 (0.2,0.5) 2.1 (1.4,3.1) 0.4 (0.2,0.5) 0.4 (0.3,0.6) 0.1 (0.1,0.2) 7.5 (6.3,8.6) 1.5 (0.8,2.2) 0.6 (0.3,0.9) 0.8 (0.5,1.2) 6.1 (4.1,8.3) 0.5 (0.4,0.7) - (-,-) 0.2 (0.1,0.2) 7.5 (6.3,8.6) 1.5 (0.8,2.2) 0.6 (0.3,0.9) 0.8 (0.5,1.2) 6.1 (4.1,8.3) 0.5 (0.4,0.7) - (-,-) 0.2 (0.1,0.2)


Page 16 of 31

Table 2: Mean cross-validated, all-species and compositional agreement between seasonal derived hybrid and in-situ PM2.5 over North America for 2000-2016 for seasons with at least 5 coincident data pairs. Bias and variance define the normal-fit distribution of differences between annual derived and in-situ PM2.5. Slope and offset refer to the line of best fit. Bracketed terms denote 5th and 95th percentile. Nyears provides the number of datasets. N describes the number of comparison pairs used within these datasets. Value corresponds to the mean of in-situ observations. Region/ Source North America Hybrid

Season MAM

JJA

SON

DJF

Component PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS

R2 0.63 (0.46,0.78) 0.85 (0.73,0.93) 0.70 (0.44,0.86) 0.71 (0.53,0.80) 0.52 (0.35,0.65) 0.63 (0.48,0.73) 0.50 (0.27,0.69) 0.49 (0.00,0.69) 0.70 (0.47,0.84) 0.85 (0.72,0.93) 0.72 (0.45,0.86) 0.48 (0.34,0.56) 0.34 (0.16,0.54) 0.58 (0.42,0.72) 0.45 (0.22,0.59) 0.43 (0.09,0.68) 0.51 (0.29,0.70) 0.84 (0.72,0.91) 0.58 (0.27,0.77) 0.62 (0.48,0.74) 0.44 (0.28,0.62) 0.47 (0.29,0.66) 0.25 (0.18,0.32) 0.47 (0.10,0.69) 0.43 (0.27,0.55) 0.84 (0.78,0.88) 0.65 (0.56,0.79) 0.75 (0.69,0.80) 0.52 (0.31,0.65) 0.56 (0.34,0.69) 0.28 (0.15,0.38) 0.45 (0.15,0.69)

bias 0.13 (-0.01,0.37) 0.03 (-0.02,0.21) -0.01 (-0.08,0.03) 0.01 (-0.04,0.09) 0.09 (0.00,0.19) 0.01 (-0.01,0.04) 0.00 (-0.02,0.04) -0.06 (-0.36,0.00) 0.20 (-0.02,0.50) 0.03 (-0.04,0.20) -0.02 (-0.13,0.01) 0.01 (-0.01,0.06) 0.09 (-0.15,0.24) 0.00 (-0.01,0.03) 0.02 (-0.01,0.05) -0.01 (-0.01,-0.00) 0.21 (-0.02,0.54) 0.02 (-0.02,0.15) 0.01 (-0.04,0.11) 0.00 (-0.03,0.07) 0.08 (-0.06,0.19) 0.01 (-0.01,0.03) -0.00 (-0.03,0.03) -0.05 (-0.38,-0.00) 0.17 (0.01,0.56) 0.02 (-0.05,0.17) -0.03 (-0.05,0.01) -0.03 (-0.08,0.08) 0.09 (-0.01,0.16) 0.01 (-0.01,0.02) 0.00 (-0.01,0.03) -0.02 (-0.14,0.00)

variance 1.6 (1.3,1.9) 0.4 (0.3,0.6) 0.2 (0.2,0.4) 0.4 (0.3,0.5) 0.8 (0.5,1.1) 0.1 (0.1,0.2) 0.3 (0.2,0.3) 0.2 (0.1,0.8) 1.7 (1.5,2.0) 0.6 (0.3,0.9) 0.3 (0.1,0.4) 0.2 (0.2,0.3) 1.1 (0.7,1.4) 0.1 (0.1,0.2) 0.4 (0.3,0.5) 0.1 (0.1,0.2) 1.8 (1.6,2.2) 0.4 (0.2,0.7) 0.3 (0.1,0.4) 0.4 (0.3,0.5) 1.0 (0.7,1.3) 0.2 (0.1,0.2) 0.3 (0.2,0.3) 0.1 (0.1,0.3) 2.5 (2.3,3.0) 0.4 (0.3,0.5) 0.4 (0.2,0.4) 0.7 (0.5,0.9) 0.8 (0.6,1.0) 0.2 (0.1,0.2) 0.2 (0.1,0.2) 0.2 (0.1,0.4)

slope 0.95 (0.86,1.00) 1.01 (0.91,1.19) 1.02 (0.90,1.11) 0.99 (0.85,1.15) 0.94 (0.82,1.12) 0.99 (0.84,1.10) 0.97 (0.87,1.07) 1.37 (0.88,3.29) 0.95 (0.90,0.99) 1.02 (0.95,1.08) 1.06 (0.96,1.22) 0.97 (0.70,1.08) 0.86 (0.69,1.15) 0.98 (0.80,1.10) 0.96 (0.87,1.13) 1.06 (0.92,1.23) 0.95 (0.87,1.03) 1.02 (0.97,1.11) 1.02 (0.85,1.19) 0.99 (0.81,1.11) 0.91 (0.77,1.03) 0.98 (0.92,1.08) 0.96 (0.88,1.03) 1.29 (0.94,3.24) 0.97 (0.89,1.03) 1.02 (0.91,1.16) 1.02 (0.90,1.15) 1.01 (0.90,1.10) 0.95 (0.81,1.09) 0.99 (0.86,1.18) 0.97 (0.90,1.05) 1.52 (0.89,6.74)


offset 0.4 (-0.0,1.3) -0.0 (-0.2,0.1) -0.0 (-0.1,0.2) 0.0 (-0.1,0.1) 0.1 (-0.2,0.3) 0.0 (-0.0,0.0) 0.0 (-0.1,0.1) -0.1 (-0.8,0.0) 0.4 (-0.0,0.9) -0.0 (-0.1,0.1) -0.0 (-0.2,0.1) 0.0 (-0.0,0.1) 0.4 (-0.4,0.9) 0.0 (-0.1,0.1) 0.0 (-0.1,0.1) -0.0 (-0.0,0.0) 0.4 (-0.3,1.1) -0.0 (-0.1,0.0) 0.0 (-0.1,0.1) 0.0 (-0.0,0.1) 0.2 (-0.1,0.5) 0.0 (-0.0,0.0) 0.0 (-0.0,0.1) -0.1 (-0.9,0.1) 0.2 (-0.3,1.0) -0.0 (-0.1,0.1) 0.0 (-0.1,0.2) 0.0 (-0.1,0.1) 0.0 (-0.2,0.2) 0.0 (-0.1,0.0) 0.0 (-0.0,0.0) -0.1 (-1.1,0.0)

RMSD 1.6 (1.3,1.9) 0.4 (0.3,0.6) 0.2 (0.2,0.4) 0.4 (0.3,0.5) 0.8 (0.5,1.1) 0.1 (0.1,0.2) 0.3 (0.2,0.3) 0.2 (0.1,0.8) 1.8 (1.5,2.1) 0.6 (0.3,0.9) 0.3 (0.1,0.5) 0.2 (0.2,0.3) 1.1 (0.7,1.4) 0.1 (0.1,0.2) 0.4 (0.3,0.5) 0.1 (0.1,0.2) 1.8 (1.6,2.3) 0.4 (0.2,0.7) 0.3 (0.1,0.4) 0.4 (0.3,0.5) 1.0 (0.7,1.3) 0.2 (0.1,0.2) 0.3 (0.2,0.3) 0.1 (0.1,0.5) 2.5 (2.3,3.1) 0.4 (0.3,0.6) 0.4 (0.2,0.4) 0.7 (0.5,0.9) 0.8 (0.6,1.0) 0.2 (0.1,0.2) 0.2 (0.1,0.2) 0.2 (0.1,0.4)

N 824 (635,910) 262 (128,311) 130 (29,178) 254 (128,295) 113 (55,167) 114 (65,168) 216 (96,263) 92 (16,120) 819 (639,914) 264 (145,312) 131 (39,179) 257 (142,296) 114 (63,167) 116 (74,166) 219 (112,263) 94 (6,120) 827 (667,907) 267 (154,309) 134 (45,180) 259 (151,295) 117 (65,174) 117 (77,170) 220 (117,260) 94 (14,120) 819 (661,897) 273 (174,313) 139 (59,185) 265 (172,300) 116 (69,172) 116 (78,165) 227 (134,265) 97 (24,118)

Nyears 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 16 17 17 17 17 17 17 17 17 16 16 16 16 16 16 16 16

Value [μg/m3] 8.7 (6.7,11.1) 2.4 (1.2,3.3) 1.2 (0.5,1.7) 1.1 (0.7,1.6) 2.1 (1.5,2.7) 0.4 (0.3,0.5) 0.8 (0.6,1.0) 0.3 (0.2,0.4) 10.8 (7.6,13.7) 3.4 (1.4,5.3) 1.3 (0.4,2.0) 0.5 (0.4,0.8) 3.4 (2.7,4.3) 0.6 (0.5,0.8) 1.0 (0.8,1.1) 0.2 (0.1,0.6) 9.1 (6.9,11.7) 2.3 (1.0,3.6) 1.1 (0.3,1.8) 1.0 (0.6,1.5) 2.6 (2.0,3.1) 0.5 (0.4,0.7) 0.6 (0.5,0.7) 0.2 (0.2,0.3) 9.9 (7.8,13.1) 1.9 (1.1,2.5) 1.5 (0.7,2.1) 2.1 (1.5,2.6) 1.9 (1.5,2.4) 0.4 (0.3,0.5) 0.4 (0.3,0.5) 0.3 (0.2,0.4)

Page 17 of 31


Table 3: Temporal change in mean population-weighted, all-species and compositional PM2.5. Mean population-weighted component percentage over, relative to the total sum of components, is given in parenthesis for each time period. Region1 North America

United States

Southwestern United States

Southern United States

Midwestern United States

Northeastern United States

Northwestern United States

Canada

Eastern Canada

Component PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+

2000-2004 2006-2010 2012-2016 Trend [95% C.I.] [μg/m3] [μg/m3] [μg/m3] [μg/m3/yr] 11.5 10.0 8.3 -0.27 [-0.30,-0.25] 3.0 (25%) 2.4 (23%) 1.4 (18%) -0.14 [-0.16,-0.11] 1.3 (11%) 1.1 (10%) 0.5 (7%) -0.07 [-0.08,-0.06] 1.6 (14%) 1.3 (12%) 1.0 (13%) -0.05 [-0.06,-0.05] 4.2 (35%) 3.8 (36%) 3.0 (40%) -0.10 [-0.12,-0.08] 0.8 (7%) 0.9 (9%) 0.7 (9%) -0.02 [-0.03,-0.01] 0.6 (5%) 0.7 (6%) 0.6 (8%) -0.00 [-0.01,0.00] 0.5 (4%) 0.4 (3%) 0.4 (5%) -0.01 [-0.02,-0.01] 12.0 10.3 8.5 -0.29 [-0.32,-0.26] 3.1 (25%) 2.4 (23%) 1.4 (18%) -0.14 [-0.17,-0.12] 1.3 (11%) 1.1 (10%) 0.5 (7%) -0.07 [-0.08,-0.06] 1.7 (14%) 1.3 (12%) 1.0 (13%) -0.06 [-0.06,-0.05] 4.3 (34%) 3.8 (36%) 3.0 (39%) -0.11 [-0.12,-0.09] 0.9 (7%) 1.0 (9%) 0.7 (9%) -0.02 [-0.03,-0.01] 0.7 (5%) 0.7 (6%) 0.7 (9%) -0.00 [-0.01,0.00] 0.6 (4%) 0.4 (3%) 0.4 (5%) -0.01 [-0.02,-0.01] 13.0 10.8 9.7 -0.28 [-0.34,-0.22] 1.6 (10%) 1.3 (9%) 0.9 (9%) -0.06 [-0.07,-0.05] 1.3 (8%) 1.0 (7%) 0.5 (5%) -0.06 [-0.07,-0.06] 3.3 (19%) 2.3 (17%) 1.6 (17%) -0.14 [-0.16,-0.12] 7.2 (42%) 6.3 (45%) 4.2 (43%) -0.27 [-0.32,-0.22] 1.5 (9%) 1.4 (10%) 1.0 (10%) -0.05 [-0.06,-0.04] 1.1 (6%) 1.0 (8%) 1.0 (10%) -0.01 [-0.01,0.00] 0.9 (6%) 0.6 (4%) 0.6 (6%) -0.03 [-0.04,-0.02] 11.7 10.4 8.4 -0.29 [-0.33,-0.25] 3.6 (32%) 2.9 (29%) 1.6 (23%) -0.17 [-0.20,-0.14] 1.2 (10%) 1.0 (10%) 0.4 (5%) -0.07 [-0.08,-0.06] 0.8 (7%) 0.6 (6%) 0.5 (7%) -0.02 [-0.03,-0.02] 3.8 (34%) 3.3 (33%) 2.9 (40%) -0.08 [-0.10,-0.06] 0.7 (6%) 0.9 (9%) 0.6 (8%) -0.01 [-0.02,0.00] 0.7 (6%) 0.9 (9%) 0.8 (11%) 0.01 [-0.00,0.02] 0.5 (5%) 0.4 (4%) 0.4 (5%) -0.01 [-0.02,-0.00] 12.1 10.8 8.8 -0.29 [-0.33,-0.24] 3.3 (27%) 2.6 (25%) 1.6 (21%) -0.14 [-0.17,-0.12] 1.6 (13%) 1.4 (13%) 0.7 (9%) -0.08 [-0.09,-0.06] 2.3 (19%) 1.9 (18%) 1.5 (19%) -0.07 [-0.09,-0.05] 3.4 (28%) 3.3 (30%) 2.7 (34%) -0.06 [-0.07,-0.04] 0.7 (6%) 0.8 (8%) 0.6 (8%) -0.01 [-0.02,-0.00] 0.6 (5%) 0.5 (5%) 0.5 (7%) -0.00 [-0.01,0.00] 0.3 (3%) 0.2 (2%) 0.2 (2%) -0.01 [-0.02,-0.01] 12.3 10.3 8.2 -0.35 [-0.40,-0.30] 3.9 (31%) 2.9 (28%) 1.5 (21%) -0.20 [-0.23,-0.16] 1.6 (13%) 1.3 (13%) 0.6 (8%) -0.09 [-0.10,-0.07] 1.4 (12%) 1.1 (11%) 1.0 (14%) -0.03 [-0.04,-0.03] 3.7 (30%) 3.2 (31%) 2.8 (39%) -0.07 [-0.09,-0.05] 0.8 (7%) 1.0 (10%) 0.6 (9%) -0.02 [-0.03,-0.01] 0.4 (3%) 0.4 (4%) 0.3 (5%) -0.01 [-0.01,-0.00] 0.5 (4%) 0.4 (3%) 0.3 (4%) -0.01 [-0.02,-0.01] 7.6 6.4 6.1 -0.13 [-0.18,-0.09] 0.9 (12%) 0.6 (10%) 0.4 (9%) -0.04 [-0.04,-0.03] 0.4 (6%) 0.3 (5%) 0.2 (3%) -0.02 [-0.03,-0.02] 0.7 (9%) 0.6 (9%) 0.5 (9%) -0.02 [-0.03,-0.02] 4.1 (52%) 3.4 (54%) 2.8 (55%) -0.11 [-0.14,-0.08] 0.6 (8%) 0.6 (10%) 0.5 (10%) -0.01 [-0.02,-0.01] 0.4 (6%) 0.4 (6%) 0.3 (6%) -0.01 [-0.01,-0.01] 0.6 (7%) 0.4 (6%) 0.4 (7%) -0.02 [-0.02,-0.01] 7.6 7.1 6.4 -0.10 [-0.13,-0.07] 1.9 (23%) 1.6 (20%) 1.0 (17%) -0.08 [-0.09,-0.06] 0.8 (10%) 0.7 (9%) 0.4 (7%) -0.03 [-0.04,-0.03] 1.0 (12%) 0.9 (11%) 0.8 (13%) -0.02 [-0.03,-0.01] 3.5 (41%) 3.5 (45%) 2.8 (47%) -0.05 [-0.08,-0.02] 0.5 (6%) 0.6 (7%) 0.4 (7%) -0.01 [-0.01,0.00] 0.4 (4%) 0.3 (4%) 0.3 (6%) -0.00 [-0.01,0.00] 0.3 (4%) 0.2 (3%) 0.2 (4%) -0.01 [-0.01,-0.00] 8.6 7.9 7.3 -0.12 [-0.17,-0.07] 2.5 (26%) 2.0 (23%) 1.3 (19%) -0.10 [-0.13,-0.08] 1.0 (11%) 0.9 (10%) 0.5 (7%) -0.04 [-0.06,-0.03] ACS Paragon Plus Environment

Trend [95% C.I.] [%/yr] -2.7 [-3.0,-2.5] -6.0 [-7.1,-4.8] -7.0 [-8.0,-5.9] -4.1 [-4.7,-3.5] -2.7 [-3.2,-2.3] -2.2 [-3.4,-1.0] -0.2 [-0.9,0.5] -3.4 [-4.5,-2.3] -2.8 [-3.1,-2.5] -6.0 [-7.1,-4.9] -7.1 [-8.1,-6.1] -4.2 [-4.8,-3.6] -2.8 [-3.3,-2.4] -2.3 [-3.4,-1.1] -0.2 [-0.9,0.5] -3.4 [-4.5,-2.3] -2.5 [-3.0,-2.0] -4.9 [-5.7,-4.1] -6.8 [-7.7,-5.8] -5.7 [-6.7,-4.8] -4.6 [-5.4,-3.7] -3.7 [-4.7,-2.7] -0.6 [-1.4,0.3] -4.0 [-5.4,-2.7] -2.8 [-3.3,-2.4] -6.0 [-7.1,-5.0] -7.8 [-9.0,-6.6] -3.5 [-4.3,-2.6] -2.3 [-2.9,-1.7] -1.5 [-3.3,0.3] 0.7 [-0.6,2.0] -2.5 [-3.9,-1.1] -2.7 [-3.1,-2.3] -5.6 [-6.7,-4.5] -6.0 [-7.3,-4.7] -3.5 [-4.6,-2.5] -1.8 [-2.3,-1.4] -1.2 [-2.3,-0.2] -0.7 [-1.6,0.1] -5.4 [-7.1,-3.7] -3.4 [-3.8,-2.9] -7.1 [-8.2,-5.9] -7.5 [-8.6,-6.4] -2.9 [-3.6,-2.1] -2.2 [-2.8,-1.5] -2.3 [-3.5,-1.1] -1.8 [-2.9,-0.7] -3.7 [-4.9,-2.5] -2.0 [-2.7,-1.3] -5.9 [-6.6,-5.1] -7.7 [-8.9,-6.5] -3.5 [-4.4,-2.6] -3.2 [-4.2,-2.3] -2.4 [-3.6,-1.2] -2.6 [-3.7,-1.4] -3.5 [-5.2,-1.9] -1.4 [-1.9,-0.9] -5.0 [-6.1,-3.9] -5.3 [-6.6,-3.9] -2.5 [-3.5,-1.5] -1.6 [-2.5,-0.8] -1.0 [-2.0,0.0] -0.6 [-1.5,0.3] -3.2 [-4.3,-2.1] -1.5 [-2.1,-0.8] -5.2 [-6.5,-4.0] -5.3 [-6.8,-3.8]


Western Canada

Atlantic Canada

Northern Canada

1Regions

NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS PM2.5 SO42NH4+ NO3OM BC Dust SS

1.2 (12%) 3.8 (39%) 0.5 (6%) 0.4 (4%) 0.3 (3%) 6.4 0.9 (14%) 0.5 (8%) 0.9 (14%) 3.1 (48%) 0.4 (6%) 0.4 (6%) 0.2 (4%) 4.3 1.3 (31%) 0.2 (4%) 0.1 (2%) 1.7 (40%) 0.2 (5%) 0.2 (4%) 0.6 (14%) 2.7 0.5 (23%) 0.1 (7%) 0.2 (8%) 1.0 (42%) 0.1 (6%) 0.2 (10%) 0.1 (6%)

1.0 (12%) 3.7 (42%) 0.6 (7%) 0.3 (4%) 0.2 (2%) 6.2 0.9 (14%) 0.5 (7%) 0.8 (12%) 3.4 (51%) 0.5 (7%) 0.4 (6%) 0.2 (3%) 4.0 1.1 (28%) 0.2 (5%) 0.1 (2%) 1.8 (43%) 0.3 (7%) 0.1 (3%) 0.5 (12%) 3.0 0.6 (21%) 0.1 (5%) 0.1 (2%) 1.4 (50%) 0.2 (8%) 0.2 (8%) 0.2 (6%)

0.9 (13%) 3.1 (45%) 0.5 (7%) 0.4 (5%) 0.2 (3%) 5.4 0.5 (11%) 0.2 (5%) 0.6 (13%) 2.5 (52%) 0.4 (8%) 0.3 (7%) 0.2 (3%) 3.7 0.7 (21%) 0.1 (4%) 0.1 (4%) 1.5 (46%) 0.2 (7%) 0.1 (4%) 0.5 (15%) 3.5 0.5 (14%) 0.1 (3%) 0.1 (4%) 2.0 (61%) 0.2 (6%) 0.2 (7%) 0.2 (6%)

-0.02 [-0.04,-0.01] -0.06 [-0.09,-0.03] -0.01 [-0.01,0.00] -0.00 [-0.00,0.00] -0.01 [-0.01,-0.00] -0.08 [-0.12,-0.03] -0.03 [-0.04,-0.02] -0.02 [-0.03,-0.01] -0.02 [-0.04,-0.01] -0.05 [-0.09,-0.01] -0.00 [-0.01,-0.00] -0.01 [-0.01,0.00] -0.01 [-0.01,-0.00] -0.06 [-0.09,-0.03] -0.05 [-0.07,-0.04] -0.01 [-0.01,-0.00] 0.00 [-0.00,0.00] -0.01 [-0.04,0.01] -0.00 [-0.00,0.00] -0.00 [-0.01,-0.00] -0.01 [-0.02,-0.00] 0.07 [-0.02,0.16] -0.01 [-0.02,0.01] -0.00 [-0.01,0.00] -0.00 [-0.01,0.00] 0.09 [-0.01,0.19] 0.00 [-0.00,0.01] 0.00 [-0.00,0.01] 0.01 [0.00,0.01]

are defined in Supplemental Figure S3.


Page 18 of 31 -2.3 [-3.6,-1.0] -1.6 [-2.5,-0.8] -1.0 [-2.1,0.0] -0.0 [-1.0,1.0] -3.2 [-4.6,-1.8] -1.3 [-2.0,-0.5] -3.6 [-4.7,-2.6] -5.1 [-6.7,-3.6] -3.0 [-4.5,-1.5] -1.8 [-3.1,-0.5] -1.1 [-2.1,-0.1] -1.6 [-3.2,0.1] -3.5 [-4.8,-2.2] -1.5 [-2.3,-0.6] -5.1 [-6.5,-3.6] -3.8 [-6.3,-1.3] 1.0 [-1.6,3.5] -0.9 [-2.2,0.5] -0.3 [-1.7,1.2] -3.0 [-4.9,-1.1] -2.0 [-3.5,-0.5] 2.4 [-0.6,5.4] -1.1 [-3.7,1.6] -3.4 [-8.1,1.4] -1.9 [-7.4,3.6] 6.2 [-0.6,13.1] 2.0 [-0.7,4.7] 1.3 [-2.0,4.5] 3.4 [1.2,5.6]

Page 19 of 31


Table 4: Effect of spatial averaging on mean error variance of annual mean all-species and compositional PM2.5. Bracketed terms provide 5th and 95th percentile. Region North America

Component PM2.5 SO42NH4+ NO3OM BC Dust SS

1 km2 1.62 (1.43,1.79) 0.30 (0.19,0.40) 0.18 (0.10,0.23) 0.31 (0.23,0.40) 0.83 (0.61,1.16) 0.13 (0.08,0.19) 0.23 (0.20,0.26) 0.12 (0.07,0.28)

Variance [μg/m3] 9 km2 25 km2 0.74 (0.64,0.90) 0.54 (0.42,0.75) 0.14 (0.10,0.22) 0.09 (0.05,0.16) 0.07 (0.04,0.11) 0.05 (0.02,0.07) 0.13 (0.10,0.17) 0.09 (0.06,0.11) 0.40 (0.24,0.71) 0.20 (0.09,0.28) 0.06 (0.04,0.10) 0.04 (0.01,0.06) 0.10 (0.08,0.15) 0.06 (0.04,0.08) 0.06 (0.04,0.09) 0.02 (0.01,0.03)


100 km2 0.35 (0.22,0.54) - (-,-) - (-,-) - (-,-) - (-,-) - (-,-) - (-,-) - (-,-)


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TOC Art 84x47mm (600 x 600 DPI)



Figure 1: Mean PM2.5 mass and composition for 2000-2016. The left column contains the initial, purely geoscience-based estimates. The right column contains hybrid geoscience-statistical estimates. Map dots indicate monitor locations used in scatterplots. Annotations include the coefficient of variation (R2), line of best fit (y), normal-fit distribution of differences between derived and in-situ PM2.5, N(bias, variance) and number of comparison points (N). Black text/points refer to comparison at all points. Grey text/points refer to cross-validation comparison. Table 1 provides a summary of annual comparisons for 2000 to 2016. 1164x1862mm (72 x 72 DPI)


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Figure 2: Seasonal average total and compositional PM2.5 concentrations for 2000-2016. Regional, population-weighted mean concentrations, in μg/m3, are given to the left of the color bar. Points correspond to monitor locations active during each time period. 1354x1778mm (72 x 72 DPI)



Figure 3: Average total and compositional PM2.5 mass for 2000-2004, 2006-2010, and 2012-2016.

Regional, population-weighted mean concentrations, in μg/m3, and given to the left of the color bar. Points correspond to monitor locations active during each time period. 1016x1778mm (72 x 72 DPI)


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Figure 4: Regional variation in population-weighted composition versus time from 2000-2016. Left column shows a stacked bar plot, with the black line denoting PM2.5. Right column plots individual components.

SO42- (Red), NO3- (Blue), NH4+ (Magenta), BC (Black), OM (Green), Mineral Dust (Yellow), and SS (Cyan) are denoted by color. Regions are defined in Supplemental Figure S3. 203x254mm (300 x 300 DPI)



Figure 5: Regional variation in population-weighted composition versus population-weighted PM2.5 mass for 2012-2016. Stacked bar plots show percentage per component relative to component totals. SO42- (Red),

NO3- (Blue), NH4+ (Magenta), BC (Black), OM (Green), Mineral Dust (Yellow), SS (Cyan) are denoted by color. Each bin represents one percent of the regional population. Grey line indicates percentage of regional population at, or below, each PM2.5 level. Total regional populations are given in the top right of each panel. Regions are defined in Supplemental Figure S3. The vertical black line indicates the long-term PM2.5 U.S. standard of 12 μg/m3 and Canadian Guideline of 10 μg/m3. 139x254mm (300 x 300 DPI)


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Figure 6: Annual population-weighted composition of PM2.5 above national annual limits (12 μg/m3 for U.S. regions and 10 μg/m3 for Canadian regions), based on component totals. The U.S. standard is applied to North America. Regions with an average of 10% of the population below local standards are not shown. Grey line correspond to the percentage of the population above the local standard. Regions are defined in Supplemental Figure S3. 127x254mm (300 x 300 DPI)


Regional Estimates of Chemical Composition of Fine Particulate

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