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Environmental Modeling

Estimated Long-term (1981-2016) Concentrations of Ambient Fine Particulate Matter across North America from Chemical Transport Modeling, Satellite Remote Sensing and Ground-based Measurements Jun Meng, Chi Li, Randall V Martin, Aaron van Donkelaar, Perry Hystad, and Michael Brauer Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06875 • Publication Date (Web): 17 Apr 2019 Downloaded from http://pubs.acs.org on April 18, 2019

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Environmental Science & Technology

Estimated Long-term (1981-2016) Concentrations of Ambient Fine Particulate Matter across North America from Chemical Transport Modeling, Satellite Remote Sensing and Ground-based Measurements

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Jun Meng1*, Chi Li1, Randall V. Martin1,2, Aaron van Donkelaar1, Perry Hystad3, Michael Brauer4

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Affiliations

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1Department

of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada

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2Smithsonian

Astrophysical Observatory, Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA

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3College

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4School

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Columbia, V6T 1Z3, Canada

of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA

of Population and Public Health, The University of British Columbia, 2206 East Mall, Vancouver, British

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*Corresponding

author:

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Address: 6310 Coburg road, Sir James Dunn bldg., Halifax, NS. Canada B3H 4J5

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Phone: (902) 932-3886; E-mail: [email protected];

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ACS Paragon Plus Environment


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Abstract Art

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Abstract

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Accurate data concerning historical fine particulate matter (PM2.5) concentrations are needed

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to assess long-term changes in exposure and associated health risks. We estimated historical

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PM2.5 concentrations over North America from 1981-2016 for the first time by combining

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chemical transport modeling, satellite remote sensing and ground-based measurements. We

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constrained and evaluated our estimates with direct ground-based PM2.5 measurements when

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available and otherwise with historical estimates of PM2.5 from PM10 measurements or total

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suspended particles (TSP) measurements. The estimated PM2.5 concentrations were generally

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consistent with direct ground-based PM2.5 measurements over their duration from 1988

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onward (R2 = 0.6-0.85) and to a lesser extent with PM2.5 inferred from PM10 measurements

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from 1985 to 1998 (R2 =0.5-0.6). The collocated comparison of the trends of population-

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weighted annual average PM2.5 from our estimates and ground-based measurements were

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highly consistent (RMSD = 0.66 μg m-3). The population-weighted annual average PM2.5 over

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North America decreased from 22  6.4 μg m-3 in 1981, to 12  3.2 μg m-3 in 1998, and to 7.9 

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2.1 μg m-3 in 2016, with an overall trend of -0.33 μg m-3 yr-1 (95% CI: -0.35 -0.30).

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1 Introduction Ambient fine particulate matter with aerodynamic diameter less than 2.5 microns

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(PM2.5) is recognized as the leading environmental risk factor for the global burden of disease

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with an estimated 4.1 million [3.6 million to 4.6 million] attributable deaths in 20161. Long-term

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exposure to high PM2.5 adversely affects human health2–8. Several epidemiological studies

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reported adverse effects from long-term exposure at levels of PM2.5 concentrations9–12 below

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the World Health Organization (WHO) guideline (10 μg m-3 annual average), the U.S. standard

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(12 μg m-3 annual average) and the Canadian standard (10 μg m-3 annual average, to be

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reduced to 8.8 μg m-3 in 2020). However, the shape of the concentration-response function at

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these low PM2.5 concentrations remains uncertain. Information about historical PM2.5

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concentrations across Canada and the United States is needed to understand long-term

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changes in exposure and their implications for health effects research.

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Understanding historical long-term exposure is complicated by the paucity of PM2.5

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monitoring sites across North America before the late 1990s and by the spatial variation of

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monitoring sites over time. Ground-based monitoring provides historical time series at specific

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points for PM2.5, PM10, and total suspended particles (TSP). Several cohort studies have

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attempted to infer historical PM estimates using monitoring data for urban areas in later

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years4,13,14. A recent study by Kim et al.15 demonstrated that historical measurements of PM10

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and TSP offer valuable information for prediction of historical PM2.5 concentrations across the

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continental United States.

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Additional sources of data are available to inform estimates of historical PM2.5 spatial and temporal variations to improve the overall representativeness. Chemical transport 3



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modeling offers additional valuable information about historical PM2.5 concentrations through

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the representation of atmospheric processes with historical emission inventories16–18. Satellite

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remote sensing offers a powerful additional constraint on PM2.5 spatial distributions19,20

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especially after 2002 when both the Terra and Aqua satellites were in orbit. Some studies21,22

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have developed prediction models to estimate historical PM2.5 by back-casting using the ratio

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between PM2.5 and PM10 or TSP observations. Other studies19,23–25 use land-use regression,

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which included predictor variables derived from geographic information systems, or combine

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information from other PM measurements or satellite data. However, those studies either

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focused on smaller regions21,25 or shorter durations19.

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In this paper, we present historical estimates of PM2.5 across North America by

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combining information from chemical transport modeling, satellite-derived PM2.5 estimates and

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ground-based monitoring from 1981-2016. These estimates can be used to assess long-term

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health impacts associated with low levels of PM2.5 throughout North America.

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2 Materials and Methods Figure 1 provides an overview of our method to develop estimates of historical PM2.5

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concentrations across North America by incorporating information from ground-based

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monitoring, chemical transport modeling, and satellite-derived PM2.5. We started with a fine

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resolution chemical transport model (GEOS-Chem) simulation across North America for 1989–

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2016. We downscaled the simulation to 0.01 x 0.01 using a satellite-derived PM2.5 dataset20.

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We applied geographically weighted regression (GWR) to the downscaled simulation to

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incorporate information from ground-based measurements into the estimates. For the years 4


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1981-1988, we relied on information on interannual variation from ground-based

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measurements to backcast the gridded PM2.5 concentrations. Each step is described further

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below.

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2.1 Historical Particulate Matter Monitoring Data

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We collected ground-based measurements for 1981-2016 across Canada and the United

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States. Canadian particulate matter (PM) data were obtained from the National Air Pollutant

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Surveillance (NAPS) (http://maps-cartes.ec.gc.ca/rnspa-naps/data.aspx?lang=en). This database

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includes continuous PM measurement data, dichotomous sampler (dichot, PM10 and PM2.5)

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data and total suspended particulate (TSP) data. Instrument-specific calibrations were applied

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as recommended by the Canadian Council of Ministers of the Environment (CCME)26. Daily PM

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data for the United States were obtained from the US Air Quality System Data Mart for PM10

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and PM2.5 (https://aqsdr1.epa.gov/aqsweb/aqstmp/airdata/download_files.html). In addition,

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data from the inhalable particle network (IPN) which consisted of PM2.5 measurements in the

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early 1980’s were included. Table S1 in the supporting information summarizes available

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monitoring data by measurement type in selected years (1981-2016). In Canada, dichot PM2.5

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and PM10 sampling began in the mid 1980s, followed by continuous PM2.5 monitoring in the late

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1990s. In the United States, most PM10 sampling began in the late 1980s, followed by

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widespread PM2.5 monitoring in 1999. Limited PM2.5 measurements were available prior to

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1999. Separate predictive models based on uniform method were created for Canada and US

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monitoring data since the larger number of monitoring stations in the US would overwhelm the

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Canadian dataset. Detailed information about the predictive models of inferring monthly PM2.5

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concentrations from the historical PM10 and TSP measurements is provided in the supporting

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information SI.1

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2.2 Estimated Historical Gridded PM2.5 Data

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GEOS-Chem Chemical Transport Model

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We use the GEOS-Chem chemical transport model (version 11-01, http://www.geos-

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chem.org), with updated historical emissions inventories and meteorological data to

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consistently simulate PM2.5 concentrations across North America for 1989-2016. GEOS-Chem

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includes detailed aerosol-oxidant chemistry27,28. The simulation of concentrations of PM2.5

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components includes the sulfate-nitrate-ammonium (SNA) aerosol system28,29, mineral dust30,

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sea salt31, and carbonaceous aerosol32 with updates to black carbon33, and SOA34,35 including an

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aqueous-phase mechanism for SOA from isoprene35.Our simulation used a relative humidity

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dependent and composition dependent fixed size distribution following Martin et al.36 with

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updates to organics37and mineral dust38. We drove the simulation using MERRA-2

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meteorological data from NASA’s Global Modeling and Assimilation Office (GMAO) with a

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nested resolution at 0.5˚ × 0.625˚ over North America for 1989-2016 for which updated

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historical emissions were available. Anthropogenic emissions over North America were from

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the 2011 National Emissions Inventory (NEI2011, http://ww.epa.gov/air-emissions-inventories)

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for the US and the Criteria Air Contaminants (CAC, http://www.ec.gc.ca/inrp-npri/) for Canada

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with historical scale factors applied to each simulating year. Black carbon (BC) and organic

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carbon (OC) emissions were calculated by applying sector-specific OC and BC to PM2.5 emission

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ratios18,39,40. Open fire emissions were from GFED441 for years 1997-2016 and from the RETRO

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fire emission inventory42 for earlier years.

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Creation of Historical Gridded PM2.5 Dataset

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Given our objective of a consistent dataset over the entire 1989-2016 period, and the

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lack of satellite aerosol optical depth (AOD) for the entire period, we used the 5-year average

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from near the middle of the period (2004-2008) of geophysical satellite-based PM2.5 estimates

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(referred to as PMsat)20, derived from both the Terra and Aqua satellites, to downscale GEOS-

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Chem model simulation (1989-2016) to a resolution relevant for exposure at 0.01˚ × 0.01˚

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following C. Li et al.18 We calculated the ratio between PMsat and the 5-yr average (2004-2008)

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of GEOS-Chem simulations. Then, we used this ratio to downscale simulations in all the years

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from 1989 to 2016. The downscaling process does not change the simulated relative temporal

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variation of PM2.5, since the same scale factor was applied to all years. This downscaled

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estimate (referred to as PMscl) contained fine-scale spatial information from satellite-derived

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PM2.5 estimates (PMsat) and long-term temporal information from the GEOS-Chem simulation.

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We evaluate the approach by excluding the satellite-based estimates.

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Ground-based monitoring offers reliable information on PM2.5 when and where

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available. We used this information to constrain our estimates. We included monitor

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information across both the US and Canada to produce a continuous surface for North America.

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Following van Donkelaar et al.20, we applied geographically weighted regression (GWR) to PMscl

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over 1989-2016 using available PM2.5 observations, and PM2.5 concentrations inferred from

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PM10 observations. Geographically weighted regression (GWR)43 is a multiple regression, an

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extension of least-squares regression, to allow predictor coefficients to vary by choosing

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different spatial weighting function at several geographic locations according to their inverse-

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squared distance from individual observation sites. We used GWR to regress the spatial

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relationship between multiple predictors and the bias between PM2.5 estimates and PM2.5

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measurements. Our predictors in GWR include urban land cover, sub-grid elevation difference,

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and aerosol chemical composition from GEOS-Chem simulation. We fit the GWR model at the

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same resolution (1o x 1o) as the downscaled PM2.5 estimates, which was scaled by satellite-

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driven PM2.5, following the equation (1) below:

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(Measured PM2.5 – Estimated PM2.5) = 1ULC + 2SED + 3SUL + 4NIT + 5PrC + 6SOA + 7DST + 

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(1)

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where 1 to 7 represented the spatial weighted predictor coefficients for each

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predictor, and  is the error. ULC was the percent of urban land cover from the 500-m spatial

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resolution MODIS land cover type product44. SED was the sub-grid elevation difference, which is

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the difference between the site elevation, which is from the ETOPO1 Global Relief Model of the

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National Geophysical Data Center45, and the annual mean elevation of the GEOS-Chem grid cell.

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SUL, NIT, PrC, SOA and DST are sulfate, nitrate, primary carbon, secondary carbon and dust

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respectively as simulated with GEOS-Chem. We conducted sensitivity tests by changing the

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weight of PM10 observations in the GWR regression and found that a reduction by 75% of the

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weight of PM10 best represented its uncertainty compared to direct PM2.5 measurements from

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ground-based measurements, GEOS-Chem transport model simulations and satellite remote

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sensing.

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For years 1981-1988, reliable emission inventories were not available for GEOS-Chem

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simulation. Instead we used the information on inter-annual variation from ground-based

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measurements to back-cast the gridded PM2.5 concentrations following previous studies21,22.

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Ground-based measurements include TSP measurements, PM10 measurements and PM2.5

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measurements. Ground-based PM2.5 concentrations inferred from TSP measurements were

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included for this time period since fewer than 200 PM10 sites existed before 1986 and even

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fewer PM2.5 monitoring sites existed. For each year (e.g. 1988), we calculated the ratio between

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the annual mean PM2.5 of this year and the following 3-year mean PM2.5 (e.g. 1989-1991) for

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each ground-based monitoring site. We used the ratios from TSP sites as the basis, which were

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overwritten by the ratios from PM10 sites, and then by the ratios from PM2.5 sites. This ratio

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field from ground-based measurements was then interpolated to other grids using distance

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weighted interpolation. Finally, we applied this gridded ratio field to the following 3-year mean

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PM2.5 estimates to get the estimated PM2.5 for each year. The process is described by equation

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(2) below:

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Y(t)=  [Y(t+1) + Y(t+2) + Y(t+3)]/3

(2)

where Y(t) represents the PM2.5 estimates in year t, and  is the gridded ratio field. We evaluated the backcasting method by repeating the procedure for the years 2001-

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2008 using measurements for the years 2001-2011, for comparison with our estimates for

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2001-2008 (SI Table S5).

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We calculated the overall root mean square difference (RMSD) between the estimates and measurements for each year over 1981-2016 as a measure of uncertainty.

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3 Results and Discussion We first evaluated the approach in the years when only PM2.5 stations were used for

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GWR adjustment to statistically incorporate information from ground-based observations into

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the downscaled model results. Figure 2 shows scatter plots of 2004-2008 mean PM2.5 from the 9



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downscaled simulation (PMscl) before and after GWR adjustment, versus in situ PM2.5. As found

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by van Donkelaar et al.20, the GWR model significantly reduces the mean bias (MB) and root

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mean square difference (RMSD) over both Canada and the US. Out-of-sample cross validation

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using 50% of randomly selected sites to train the GWR model exhibits significantly improved

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performance (R2 = 0.69; RMSD = 2.3 μg m-3) (bottom left panel) compared with the base case

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(R2 = 0.52; RMSD = 3.1 μg m-3). In such a holdback analysis, GWR parameter coefficients are

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trained using only 50% of available ground-based monitors. The withheld sites provide an

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independent dataset with which to evaluate the quality of fused PM2.5 estimates in areas

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without ground-based observation. The improvement at these independent sites suggests

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improvement in the GWR adjusted surface even at locations away from ground-based

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observation. The bottom right panel of Figure 2 shows the 2004-2008 mean performance of

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GWR-adjusted values made using only the PM2.5 sites that were also available before 1998 (< 70

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sites in total), consisting mostly of remote and rural U.S.-based sites. Limiting the GWR-based

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adjustment to only these earlier-available PM2.5 sites provided no improvement in agreement

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compared to the initial estimates without GWR. The negative MB in PMscl (-1.00 μg m-3) (top

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left panel) is not corrected in the adjusted estimates (-0.83 μg m-3) (bottom right panel), due to

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a lack of representative urban and eastern sites which generally have higher PM2.5 levels.

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Complementary information from PM10 sites that are representative of urban environments is

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necessary for early years.

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Figure 3 shows scatter plots for the 1992-1996 time period to evaluate the performance

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of PM2.5 inferred from PM10. The top panels show that the performance of the scaled

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geophysical estimate is promising with an R2 versus PM2.5 monitors of 0.69 that increases to

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0.86 after GWR adjustment. The RMSD decreases from 2.7 to 1.5 μg m-3 over ~100 PM2.5 sites in

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the adjusted estimates. For ~2000 PM10 sites, significantly improved agreement is also found

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after GWR adjustment. Cross validation with 50% out-of-sample sites (bottom left) further

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confirms the overall robustness of the approach. As found in the 2004-2008 period, using only

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PM2.5 sites for GWR modeling does not improve the overall representation of the estimates,

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especially for PM10 sites in urban areas.

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Figure 4 shows the R2 and RMSD for each year (1981-2016) of the estimates versus

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ground-based measurements to provide an overall assessment of uncertainty. Only PM2.5 data

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are used over 1999-2016 since sufficient PM2.5 measurements are available after 1999. Since

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the number of PM10 sites reduces significantly prior to 1989 (~1000 in 1989, ~600 in 1988, ~400

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sites in 1986 and < 50 sites in 1984), the back-casting from 1985 to 1981 is based primarily on

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the trend information from TSP-based estimates, and expected to be more uncertain. The R2

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increases with the increase of PM10 sites for years 1985-1990. The R2 is around 0.8 for years

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1989-2005 compared to PM2.5 sites. The relative RMSD at only PM2.5 sites drops from 30% in

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the early 1990s to below 20% prior to 1999 when the PM2.5 measurements became more

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widespread. The decrease in R2 after 2008 reflects weaker spatial PM2.5 gradients in recent

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years as PM2.5 levels decline across North America. Higher RMSD errors are expected before

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1999 due to more uncertainties in emission inventories as well as larger uncertainties in the

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monitor data used in GWR adjustments. Overall, the GWR-adjusted PM2.5 estimates yield an

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estimated error of less than 20% since 1999 and, less than 30% from 1981-1998.

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We tested how the satellite-derived PM2.5 data used for downscaling affected the performance of the estimated dataset. Supporting information SI.3 describes sensitivity

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estimates of PM2.5 data without satellite remote sensing. The R2 of these sensitivity estimates

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are between 0.1 - 0.2 lower than our base estimate across all years, with larger differences in

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years preceding 1999 when fewer PM2.5 measurements were available. The relative RMSD of

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the sensitivity estimates at direct observed PM2.5 sites are higher than the base estimates by

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10% - 20%. This analysis indicates the significance of the constraints on PM2.5 spatial

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distributions offered by satellite remote sensing.

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Figure 5 shows the distribution of PM2.5 estimates and ground-based measurements for

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1985, 1995, 2005 and 2015 from this study. Enhancements in both the GWR adjusted estimates

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and ground-based measurements are apparent across eastern US and California. The estimated

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PM2.5 is generally consistent with ground-based measurements (Figure 4), especially with the

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direct PM2.5 measurements. PM2.5 concentrations decrease dramatically during the last three

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decades, especially in eastern United States.

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Figure 6 shows the time series of population-weighted annual average PM2.5

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concentrations across North America. We used gridded population estimates from the

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Socioeconomic Data and Applications Center46,47 for calculating population-weighted average

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(SI.3). The population-weighted annual average PM2.5 over North America decreased from 22 

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6.4 μg m-3 in the year 1981 to 7.9  2.1 μg m-3 in the year 2016. The linear tendency over this

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period is -0.33 μg m-3 yr-1  0.2 μg m-3 yr-1. Both time series of the in-situ measurements and

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estimates of population-weighted annual mean PM2.5 exhibit minor peaks in 2005 and 2007.

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The collocated comparison of the trends of population-weighted annual average PM2.5 from our

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estimates and ground-based measurements are highly consistent (RMSD=0.66 μg m-3) over

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1985-1995. Population-weighted annual average PM2.5 calculated from direct PM2.5 sites is 20%

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lower than that calculated from all in-situ sites, illustrating the effects of changes in monitor

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placement over time when assessing long-term changes in ambient PM2.5, and the value of

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spatiotemporally continuous PM2.5 estimates from this work. Larger errorbars prior to 1990

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reflect greater uncertainty in the TSP dataset.

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Figure S5 in the supporting information shows regional time series of population-

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weighted annual average PM2.5. Figure S6 in the supporting information shows regional time

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series of relative percentage change of population-weighted annual average PM2.5

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concentrations using 2016 as the reference year. Northwestern North America has the most

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dramatical decline for population-weighted average PM2.5 concentrations with a factor of 2.7

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decrease over 1981-2016, followed by southeastern and northeastern North America with a

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factor of 2.4 decrease over 1981-2016. The relative changes in northcentral, southcentral and

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southwestern North America are similar with a factor of 1.6–2.0 decrease in population-

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weighted PM2.5 over 1981-2016. Overall the spatially resolved historical PM2.5 dataset across

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North America reveals a factor of 1.7 decrease in population-weighted PM2.5 over 1981-2016.

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Comparison with previous estimates of historical PM2.5 concentrations is instructive. Our

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estimated historical PM2.5 concentrations during 1982-1991 in the southeastern US indicate a

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decrease of 3.9 μg m-3, similar to the reported decline of 3-5 μg m-3 found by Parhurst et al.21

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We find similar large-scale reductions in historical PM2.5 concentrations during 1981-2000 as

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Lall et al.22, albeit with smoother temporal trends in the present study that are more consistent

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with Kim et al.15 The primary difference with our prior historical PM2.5 estimates48–50 is that our

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current study spans a time period (1981-2016) about twice as long as our prior work by

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including more trend information from our GEOS-Chem simulation, and includes historical

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ground-based measurements prior to 1999. Nonetheless the population-weighted trends from

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our current dataset remain within 0.03 μg m-3 yr-1 of our prior work, indicating overall

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consistency as further discussed in the supporting information (SI.4).

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Data availability. The annual mean estimated PM2.5 for 1981-2016 across North America

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dataset has been deposited in the Zenodo Digital Repository (DOI: 10.5281/zenodo.2616769)51.

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Supplemental Information. Detailed description of prediction of monitoring historical PM2.5

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from measured PM10 and TSP, sensitivity test of estimated PM2.5 dataset without satellite

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remote sensing information, population data and further discussion of population-weighted

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PM2.5 trends. Supporting figures for section 3 are provided.

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Acknowledgements. This research was supported by research agreement 4952-RFA14-3/16-3

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with the Health Effects Institute (HEI), an organization jointly funded by the United States

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Environmental Protection Agency (EPA) (Assistance Award No. R-82811201) and certain motor

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vehicle and engine manufacturers. The contents of this article do not necessarily reflect the

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views of HEI, or its sponsors, nor do they necessarily reflect the views and policies of the EPA or

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motor vehicle and engine manufacturers. We are grateful to the Atlantic Computational

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Excellence Network and Compute Canada for computing resources.

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5. Figures and tables

Figure 1. Overview of estimation method.

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Figure 2. Comparison over 2004-2008 of mean PM2.5 estimates with in situ measurements before (top left) and after GWR adjustment using all sites (top right), using cross validation sites using 50% random holdout (bottom left), and using PM2.5 sites present over 1989-1997 (bottom right). Open circles are Canadian sites and crosses are US sites. Number of sites are shown in brackets. Statistics shown are mean bias (MB, in μg m-3), coefficient of determination (R2) and root mean square difference (RMSD, in μg m-3).

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Figure 3 Comparison over 1992-1996 of mean PM2.5 estimates with in situ measurements before (top left) and after GWR adjustment using all sites (top right), using cross validation using 50% random holdout (bottom left), and using only PM2.5 sites (bottom right). Open circles are Canadian sites and crosses are US sites. No. of sites are shown in brackets. Comparison for PM2.5 (black) and PM10 (red) sites are shown separately. Statistics shown are mean bias (MB, in μg m-3), coefficient of determination (R2) and root mean square difference (RMSD, in μg m-3).

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Figure 4. Statistics (R2 and RMSD) of estimated PM2.5 against ground-based measurements from year 1981 to 2016. Solid lines indicate performance of base estimates. Dashed lines indicate performance of sensitivity estimates that exclude satellite remote sensing information (no blue dashed line). Numbers at the top of each figure indicate the number of monitors of direct PM2.5 (black), PM2.5 inferred from PM10 (green) and PM2.5 inferred from TSP (blue).

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Figure 5. Estimated fine particulate matter annual means in 1985, 1995, 2005 and 2015 over North America. Left panels are estimated PM2.5. Inset values in the left panel are the population-weighted average PM2.5 mass. Right panels indicate PM2.5 derived from ground-based measurements of PM2.5, PM10 and TSP.

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Figure 6. Time series of population-weighted average annual PM2.5 concentrations across North America. Error bars are included for population-weighted annual mean estimated PM2.5 concentrations.

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Estimated Long-term (1981-2016) Concentrations ... - ACS Publications

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