Source Contributions to Ambient Fine Particulate Matter for Canada

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Source Contributions to Ambient Fine Particulate Matter for Canada Jun Meng, Randall V Martin, Chi Li, Aaron van Donkelaar, Zitely A. TzompaSosa, Xu Yue, Jun-Wei Xu, Crystal L Weagle, and Richard T. Burnett Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b02461 • Publication Date (Web): 06 Aug 2019 Downloaded from pubs.acs.org on August 7, 2019

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Environmental Science & Technology

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Source Contributions to Ambient Fine Particulate Matter for Canada

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Jun Meng1*, Randall V. Martin1,2,3, Chi Li1, Aaron van Donkelaar1, Zitely A. Tzompa-Sosa4, Xu Yue5, Jun-Wei Xu1, Crystal L. Weagle1, Richard T. Burnett6

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2Smithsonian

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

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3Department

of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis,

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Missouri, 63130, USA

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4Department

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5School

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Nanjing, 210044, China

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6Health

Affiliations 1Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada

of Atmospheric Science, Colorado State University, Fort Collins, Colorado, 80523, USA

of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Canada, Ottawa, Ontario, K1A 0k9, Canada

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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) 223-1686; 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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Understanding the sectoral contribution of emissions to fine particulate matter (PM2.5)

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offers information for air quality management, and for investigation of association with health

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outcomes. This study evaluates the contribution of different emission sectors to PM2.5 in 2013 for

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Canada using the GEOS-Chem chemical transport model, downscaled with satellite-based PM2.5.

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Despite the low population-weighted PM2.5 concentrations of 5.5 g m-3 across Canada, we find

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that over 70% of population-weighted PM2.5 originates from Canadian sources followed by 30%

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from the contiguous United States. The three leading sectoral contributors to population

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weighted PM2.5 over Canada are wildfires with 1.0 g m-3 (17%), transportation with 0.96 g m-3

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(16%) and residential combustion with 0.91 g m-3 (15%). The relative contribution to

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population-weighted PM2.5 of different sectors varies regionally with residential combustion as

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the leading contributor in Central Canada (19%); while wildfires dominate over Northern Canada

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(59%), Atlantic Canada (34%) and Western Canada (18%). The contribution from U.S. sources

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is larger over Central Canada (33%) than over Western Canada (17%), Atlantic Canada (17%)

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and Northern Canada (< 2%). Sectoral trend analysis showed that the contribution from

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anthropogenic sources to population-weighted PM2.5 decreased from 7.1 g/m3 to 3.4 g/m3 over

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the last two decades.

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1 Introduction

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Fine particulate matter with aerodynamic diameter less than 2.5 microns (PM2.5)

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adversely affects human health1–5. PM2.5 is recognized as the leading environmental risk factor

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for the global burden of disease6 with recent estimates of annual attributable deaths worldwide of

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4 million7 to 9 million8. Air quality in North America has improved dramatically during the last

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three decades by reducing emissions9,10 with national mean PM2.5 concentrations across Canada

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below the WHO air quality guideline (10 g/m3)11,12. Thus Canada is attractive to study the

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health effects of low-level PM2.5. Several studies report adverse effects of long-term exposure to

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the low-level PM2.5 concentration environment13–16, but none have examined the relationship

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with specific sources. A better understanding of the sources contributing to PM2.5 could inform

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national air quality management, and inform studies on the association of health outcomes with

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PM2.5 concentrations at low levels11,15.

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A number of studies have investigated the relative contribution of multiple sectors to

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PM2.5 globally17–19. Most previous studies on sectoral contributions to ambient PM2.5 over

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Canada either focused on a single emission sector or a specific regional area. Additionally, most

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of those studies used a top-down source attribution method based on statistical analyses of

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measured chemical composition, including studies on the contribution of regional transport in

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southeast Canada and urban areas20–22, dust in British Columbia23, industry in Ontario24, coal-

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fired power plants25, wildfires26,27 and biomass burning28,29. A few studies have used a bottom-up

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approach based on emission inventories and chemical transport modeling to study the sector

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contributions to gas phase pollutants or to specific regions of Canada. For example, Pappin and 3



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Hakami30 examined the source attribution to nitrogen oxides and volatile organic compounds

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across North America. Cho et al.31 investigated the contribution of oil sands development to

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PM2.5 and ozone using an air quality model.

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In this study, we used the GEOS-Chem chemical transport model at its finest resolution

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of 0.25 x 0.31 to investigate the contributions of different emission sectors to PM2.5 across

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Canada from both Canada and the United States sources. We evaluated the performance of the

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baseline simulation and examine the contributions of different sectors to PM2.5 concentrations

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across Canada. We also investigated the trend of the sectoral contribution to PM2.5

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concentrations across Canada over the last two decades.

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2 Materials and Methods

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2.1 GEOS-Chem simulations GEOS-Chem (http://www.geos-chem.org) includes detailed aerosol-oxidant chemistry32–

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implemented into GEOS-Chem by Pye et al.36 The interaction between aerosols and gas-phase

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chemistry includes the effects of aerosol extinction on photolysis rates37, brown carbon38,

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heterogeneous chemistry39 with dinitrogen pentoxide (N2O5) uptake by aerosol40 and

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hydroperoxyl radical (HO2) uptake by aerosol41.

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Gas-aerosol partitioning is performed by the ISORROPIA II thermodynamic scheme35 as

The GEOS-Chem aerosol simulation includes the sulfate-nitrate-ammonium (SNA)

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aerosol system33,35, mineral dust42, sea salt43, and carbonaceous aerosol44 with updates to black

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carbon45 (BC), semi-volatile SOA formation from isoprene46 and SOA formed from isoprene

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with an irreversible aqueous scheme47. The ratio between organic mass and organic carbon

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(OM/OC) is spatially resolved48. Nitric acid concentrations are reduced following Heald et al.49 4


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The mineral dust simulation includes updates to the aerosol size distribution50 and dust optics51.

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We includes recent updates in dry and wet deposition45,52–54.

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GEOS-Chem uses assimilated meteorological data from the Goddard Earth Observation

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System (GEOS) of NASA’s Global Modeling and Assimilation Office (GMAO). We used

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GEOS Forward Processing (GEOS-FP) meteorological data archived at a native horizontal

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resolution of 0.25 x 0.3125, roughly 25km x 25km, with 72 vertical levels.

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We used the nested-grid capability55,56 of the GEOS-Chem chemical transport model

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(version 11-02c) at 0.25 x 0.3125, to simulate PM2.5 concentrations across North America. We

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first conducted global simulations at coarse resolution of 2 x 2.5 to archive boundary

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conditions. Then, we conducted regional (nested-grid) simulations at fine resolution of 0.25 x

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0.31 over North America. The operator time steps in the simulation followed previous

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recommendations57. We used a 1-month spin up to remove the effects of initial conditions. We

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used the lowest layer of the model to represent the ground-level aerosol concentrations. We

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calculated simulated PM2.5 concentrations at 35% relative humidity (RH) and chemical

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composition at dry conditions for consistency with the measurement protocols over North

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America. We downscaled our simulations to better represent spatial variation of population

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density using satellite-derived PM2.5 gridded at 1 km resolution across North America58. We

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calculated the ratio between the annual mean satellite-derived PM2.5 and baseline simulated

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PM2.5. We applied this ratio to both the baseline simulation and sector sensitivity simulations to

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downscale all simulations to 1 km resolution.

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We used population data from the National Aeronautics and Space Administration

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Socioeconomic Data and Application Center (SEDAC, version4)59 to calculate population

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weighted average PM2.5 concentrations regionally and provincially in Canada.

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2.2 North American emissions for baseline simulation GEOS-Chem emissions were configured via the HEMCO module60. Anthropogenic

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emissions were provided by regional emission inventories. We used the Criteria Air

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Contaminants (CAC) over Canada (http://www.ec.gc.ca/inrp-npri/), the 2011 U.S. National

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

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States and the Big Band Regional Aerosol and Visibility Observational study (BRAVO) over

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Mexico61. We used annual scale factors obtained from other available datasets to scale the

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emission inventories to our simulation year. For CAC, we calculated annual scale factors over

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1990 to 2016 from Canada’s Air Pollutant Emission Inventory (APEI)62. For NEI2011, we

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calculated annual scale factors for 2013 from the national annual total trends from EPA

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(http://www.epa.gov/ttnchie1/trends/). We calculated BC and OC emissions by applying the

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sector-specific OC and BC to PM2.5 emission ratios reported in the EPA SPECIATE database63

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following recent studies64,65.

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Other emissions were default in GEOS-Chem. Wildfire emissions at 3-hour resolution

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were from the fourth-generation global fire emissions database (GFED-4)66. Aircraft emissions

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were from the AEIC inventory67. Ship emissions were from ICOADS68. Lighting NOx emissions

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were also included69. Biogenic volatile organic compounds (VOC) emissions were from the

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MEGAN v2.1 inventory70–72. Biogenic soil NOx emissions were from Hudman et al.73 Volcano

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emissions were implemented by Fisher et al.52 Marine dimethyl sulfide (DMS) emissions were

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from Breider et al.74

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2.3 Sector sensitivity analyses

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We conducted a baseline simulation for the year 2013 using the emissions described above over North America. We quantified the impact of individual sectors by conducting 6


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sensitivity simulations that individually exclude each emission sector from the baseline

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simulation. This method had been extensively used in previous studies to evaluate the

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contribution of different sectors or regions to PM2.5 concentrations or health impacts17–19,75,76.

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This method may lead to uncertainty due to the non-linear response to emission changes. We

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studied five anthropogenic emission sectors (Power Generation, Agriculture, Transportation,

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Industry and Residential Combustion), as well as wildfires, sea salt, and other sources (mineral

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dust, biogenic secondary organic aerosol, DMS, volcanos, and long-range transport). We also

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investigated the diesel sector, which is a sub-sector of the transportation sector.

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The sectoral emissions for our sensitivity simulations were from APEI for Canada and

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from the U.S. NEI 2011 v6.377 for the United States. The power generation sector included coal

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burning for electric power generation. The agriculture sector primarily included ammonia from

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livestock and agricultural soils. The transportation sector contained mobile sources and dust from

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paved and unpaved roads. The diesel sector, which included emissions from diesel engine

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vehicles and trucks and off-road use of diesel, was a sub-sector of the transportation sector. The

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industry sector contained multiple industrial sources including the petroleum industry, chemical

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industry, mineral product industry, pulp and paper industry, and aluminum industry. The

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residential combustion sector included residential fuel and wood combustion emissions.

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We individually excluded each sectoral emission from the baseline simulation by

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applying the sectoral scale factor from APEI and the U.S. NEI 2011 v6.3 to baseline emission

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inventories (CAC and NEI2011) respectively. For the five major anthropogenic sectors, we also

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further separated the contribution from U.S emissions by performing five extra sensitivity

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simulations that only exclude the emissions from the Canadian sources.

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2.4 Sectoral contribution trend analysis We conducted sectoral sensitivity analyses for selected years (1990, 2000 and 2010) over

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the last two decades to investigate trends in the sectoral contributions across Canada. We

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conducted GEOS-Chem baseline and sectoral sensitivity simulations for the years 1990, 2000

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and 2010, driven by assimilated meteorology data from the Modern-Era Retrospective analysis

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for Research and Application, version 2 (MERRA-2), which included updates in both the

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Goddard Earth Observing System Model and the assimilation system back to 198078. We first

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conducted global simulations at coarse resolution of 2 x 2.5 to archive boundary conditions.

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Then, we conducted regional (nested-grid) simulations at the finest resolution available for

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MERRA-2 (0.5 x 0.625) over North America.

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For baseline simulations, we used the Criteria Air Contaminants (CAC) over Canada

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(http://www.ec.gc.ca/inrp-npri/) and the Community Emissions Data System (CEDS)79 historical

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emission inventory over the United States. We used annual scale factors obtained from other

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available datasets to scale the emission inventories to our simulation year. For CAC, we

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calculated annual scale factors from Canada’s Air Pollutant Emission Inventory (APEI)62, which

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had a time span from 1990 to 2016. We used the 3-hour resolution of GFED466 for wildfire

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emissions for 2000 and 2010 simulations. We implemented a ground-based North America fire

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emission database80 into GEOS-Chem for wildfire emissions in the 1990 simulation.

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For sectoral sensitivity simulations, we quantified the impact of individual sectors by

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conducting simulations that individually exclude each sectoral emission from the baseline

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simulation for each year (1990, 2000 and 2010). We grouped APEI into 5 sectors, which include

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the agricultural, power generation, surface transport, industrial and residential combustion

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sectors. CEDS had eight sectors, which included agricultural, energy transformation and

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extraction, industrial combustion and process, surface transport, residential combustion, solvents,

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waste disposal and handling, and international shipping sectors. We individually excluded each

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sector emission from the baseline simulations.

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2.5 Ground-based measurements of PM2.5 and its chemical components

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We collected ground-based measurements of PM2.5 concentrations and its chemical

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components from several networks across North America in 2013 to evaluate our baseline

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simulation. Detailed information on network selection and data processing are provided in the

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Supporting Information (SI.1). Monitor locations are in Figure S1. Numerous studies have

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described and evaluated these ground-based measurements81,82. We treated these ground-based

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measurements as “truth” to evaluate model performance, even though these datasets do have

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uncertainties82.

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We compared the baseline simulated PM2.5 and its chemical components with ground-

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based measurements using reduced major axis linear regression. We reported root mean square

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error (RMSE), correlation (r), intercept as well as slopes. Details about the performance of the

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baseline simulation in 2013 are provided in SI.2.

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3 Results and Discussion

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3.1 Sectoral contributions of emissions to PM2.5 concentrations over Canada.

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Figure 1 shows the annual mean contributions of individual emission sectors to PM2.5

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concentrations. For Canada, the wildfires sector is the leading contributor, responsible for 1.0 g

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m-3 of the total population-weighted PM2.5 with the largest influences across northern Canada.

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The transportation sector (0.96 g m-3) follows closely with large contributions across populated

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regions of southern Canada. The residential combustion sector (0.91 g m-3), the third largest

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contributor, is most important in rural British Columbia and southern Quebec where wood is

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used for residential heating. The industry (0.86 g m-3) and agriculture (0.63 g m-3) sectors are

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most important in Alberta. The biogenic SOA sector (0.54 g m-3) most influences Ontario and

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southern Quebec reflecting both isoprene and terpene sources. The power generation sector (0.44

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g m-3) is most important along the southern border of central Canada where advection from US

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sources is prevalent.

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The relative contribution of different emission sectors varies seasonally across Canada. In

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winter (Figure S4), residential combustion (1.6 g m-3) is the leading contributor due to home

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heating. Both of the transportation (1.4 g m-3) and agriculture (1.2 g m-3) sectors are important

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in winter due to the seasonal increase in ammonium nitrate. In summer (Figure S5), the

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importance of the wildfires and biogenic SOA sectors increases across both Canada and the

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United States. The contribution from the agriculture sector in summer is negative due to

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nonlinearities in which there is enhanced PM2.5 formation with decreasing aerosol acidity as

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ammonia emissions increase18,19,47,83.

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Figure 2 shows the fractional contribution of different sectors to population weighted

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annual mean PM2.5 concentrations over different regions (defined in Figure S6) of Canada. Over

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80% of the population-weighted PM2.5 of 5.5 g m-3 across Canada arises from the five

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anthropogenic sectors and the wildfires sector. The wildfires sector is the largest contributor

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across Canada, responsible for 17% of population-weighted PM2.5, followed closely by the

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transportation sector (16%), the residential combustion sector (15%) and the industry sector

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(14%). The agriculture and power generation sectors account for 10% and 7% of population-

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weighted PM2.5 respectively. Other sources, including sea salt, dust, DMS, volcanos, biogenic

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secondary organic aerosol and long-range transport, explain the remaining contribution. The

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leading anthropogenic contribution of the transportation sector in Canada is consistent with

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recent findings for the United States75, however, differs from regions such as India where

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residential burning dominates84, China where coal burning dominates83, and globally where

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residential burning dominates19,85.

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The diesel sector is a specified subsector of the transportation sector, which is the leading

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anthropogenic contributor across Canada in 2013 (Figure 2). Supplemental Table S1 shows the

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fractional annual contribution of diesel to the transportation sector from Canadian sources across

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different Canadian regions in 2013. The diesel sector accounts for 35% of population-weighted

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mean PM2.5 attributed to transportation sector with little variation ( 2%) regionally.

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The relative contribution of different emission sectors varies regionally across Canada. In

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Central Canada, with the highest population-weighted PM2.5 (5.9 g m-3) among all the Canadian

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regions, about 70% of population-weighted PM2.5 arises from five anthropogenic sectors. The

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residential sector is the leading contributor (19%) followed closely by the transportation sector

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(17%). In Western Canada, the wildfires sector is the largest contributor accounting for 18% of

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population-weighted PM2.5 followed by the agriculture (15%) and transportation (15%) sectors.

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In the Atlantic Canada region with low population-weighted PM2.5 (3.3 g m-3), the wildfires

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sector accounts for 34% of total population-weighted PM2.5 followed by secondary organic

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aerosol (13%) and then the transportation, industry, power generation and residential combustion

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sectors (~7% - 9%). Northern Canada has the lowest level of PM2.5 concentration with

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population-weighted PM2.5 of 1.4 g m-3 of which over half (59%) arises from the wildfires

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sector.

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The relative contribution of different emission sectors over each region in Canada also

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varies seasonally as shown in Figure S7 for winter and Figure S8 for summer. The percentage

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contribution from the five anthropogenic emission sectors increases in winter to 95% for Central

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Canada, 78% for Western Canada, 70% for Atlantic Canada and 30% for Northern Canada. In

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summer, the percentage contribution from wildfires is the largest over all regions (78% over

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Northern Canada, 55% over Atlantic Canada, 38% over Western Canada and 36% over Central

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Canada). The seasonality of dominant sources (i.e. anthropogenic emissions in winter vs.

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wildfires in summer) is similar across provinces (Figure S9 - S11).

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We separate the contributions from the United States out of the five primary

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anthropogenic emission sectors as shown in the hatched bars in Figure 2. We find that 27% of

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population-weighted PM2.5 in Canada is from anthropogenic U.S. sources. The U.S. agriculture,

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transportation and power generation sectors each account for 6% of population-weighted PM2.5

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across Canada followed by the U.S. residential combustion (4%) and industry (4%) sectors. The

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fractional contribution from the U.S. sources varies regionally. In Central Canada, 33% of

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population-weighted PM2.5 is from the U.S. with the U.S. power generation source accounting for

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9% of PM2.5 followed closely by the U.S. transportation sector (8%). In western Canada, 17% of

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population-weighted PM2.5 is from the U.S. with the U.S. agriculture sector as the largest

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contributor (6%). In Atlantic Canada, 17% of population-weighted PM2.5 is from U.S. sources

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with the power generation sector as the largest contributor among U.S. sectors accounting for 6%

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of PM2.5. The effects of U.S. emissions to PM2.5 in northern Canada is less than 2%. The

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fractional contribution from the U.S. sources also varies seasonally with 46% from the U.S.

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sources in winter and only 12% from the U.S. sources in summer.

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Figure 3 shows the Canadian population-weighted annual mean concentration of

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chemical components attributable to different sectors. PM2.5 from the wildfires sector is

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dominated by OM (94%). The main components of PM2.5 mass arising from the transportation

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sector are OM (0.46 g m-3) and nitrate (0.32 g m-3), reflecting the NOx and VOC emissions

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from traffic62. The contribution of sulfate attributable to the transportation sector is slightly

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negative due to nonlinear chemistry because the oxidation efficiency of SO2 to sulfate increases

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as the emission of NOx significantly decreases86,87 when eliminating the transportation sector.

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The residential combustion population-weighted mean PM2.5 concentration of 0.91 g m-3 is

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driven by OM (87%). PM2.5 from the industry sector includes contributions from OM (39%) and

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sulfate (31%). PM2.5 from the power generation sector is driven by sulfate (61%) followed by

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ammonium (24%). The population-weighted PM2.5 from the agriculture sector is dominated by

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nitrate (64%) and ammonium (34%). The concentration of organic matter increases when

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emissions from the agriculture sector are excluded due to the increase of aerosol acidity19,47,52.

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The remaining other sectors include biogenic SOA, sulfate from DMS and volcanos, and sea salt.

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Figure S12 shows the population weighted annual mean concentration of chemical components

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attributed to different sectors over different regions in Canada. The relative contributions of

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different chemical components remain similar across regions as the source magnitude varies.

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Figure 4 shows the sectoral contribution to population weighted average PM2.5 as a

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function of PM2.5 concentration levels in Canada. The importance of different sectors remains

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similar for different PM2.5 concentration levels. Wildfires have a larger fractional contribution at

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locations with smaller annual population weighted average PM2.5 concentrations. The percentage

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contribution from the five primary anthropogenic emission sectors increases with the ambient

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annual population weighted average PM2.5 concentrations. The sectoral contribution to PM2.5 in

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the U.S. is similar to that in Canada albeit with a weaker wildfire contribution and a stronger

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anthropogenic contribution in the US (Figure S13).

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3.2 The trend of sectoral contribution in the last two decades across Canada

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Figure 5 shows the contributions of different sectors to population-weighted average

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PM2.5 concentrations across Canada in 1990, 2000 and 2010. The population-weighted average

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PM2.5 concentration across Canada decreased from 8.9 g m-3 in 1990 to 7.4 g m-3 in 2000, and

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to 7.0 g m-3 in 2010. These values are close (within 16%) to observational constrained estimates

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for these years88, supporting the PM2.5 trends from the simulations. The power generation sector

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was the largest contributor to population-weighted average PM2.5 concentrations in 1990 (2.4 g

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m-3) and 2000 (1.9 g m-3); while the wildfires sector has the largest contribution in 2010 (1.6 g

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m-3) followed by the power generation sector (1.3 g m-3). The total contribution from the five

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anthropogenic sectors decreased from 7.1 g m-3 1990 to 3.4 g m-3 in 2013, reflecting the

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success of air quality control over the last three decades across North America. The contribution

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from the power generation sector decreased from 2.4 g m-3 in 1990 to 1.3 g m-3 in 2010, while

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the contribution from industry sector decreased from 1.4 g m-3 in 1990 to 0.7 g m-3 in 2010,

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both of which reflect the successful controls on emissions9,62 reductions in coal burning. PM2.5

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from the power generation sector continues to decrease dramatically after 2010 (Figure 2) driven

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by a decrease in total SO2 emissions over 2010 to 2013 of 20% for Canada and of 42% for the

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United States nationally. The relative sectoral contribution from the U.S. sources remains similar

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during the last two decades, with the largest fraction from U.S. source from power generation

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followed by agriculture.

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3.3 Variability in Wildfires contribution We examined how wildfire variability could affect the trends in section 3.2. Figure S14

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shows the annual total dry matter time series from 1990 to 2015 in our wildfires emission

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inventories across Canada. The total dry matter emitted in 2013 is 25 Tg, which is about the

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average during 1990 to 2015, while lower than average in recent years given that the total dry

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matter burnt time series from 1990 to 2015. Thus wildfires may play a proportionally larger role

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in other years, or in a warmer climate in the future89,90.

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Perspective. This assessment found that the contributions to PM2.5 at the low population-

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weighted PM2.5 concentrations across Canada of 5.5 g m-3 is primarily (81%) from five major

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anthropogenic sectors and the wildfires sector. The regionally-varying relative contribution of

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different emission sectors across Canada implies that mitigation strategies will benefit from

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regional policies. For example, across Central Canada, around 70% population-weighted PM2.5

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arises from five major anthropogenic sectors with the residential combustion sector as the

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leading contributor (19%) followed closely by the transportation sector (17%). The notable PM2.5

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contributions from the contiguous United States (~30%) implies benefits from international

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coordination. The leading U.S. contributors of the agricultural, transportation and power

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generation sectors are also important contributors to PM2.5 in the United States implying mutual

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benefits from reducing the emissions from these sources. The increasing contributions of

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anthropogenic sectors with increasing PM2.5 found here may have implications for the shape of

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the concentration-response function.

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Acknowledgement. This work was supported by Health Canada contract #4500358772. Jun

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Meng was partially supported a Nova Scotia Research and Innovation Graduate Scholarship. We

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are grateful to the Atlantic Computational Excellence Network and Compute Canada for

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computing resources. We thank four anonymous reviewers for invaluable comments and

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suggestions.

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Supporting Information. Detailed description of ground-based measurements, baseline

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simulation evaluation and supporting figures in this manuscript.

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Figures and tables

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Figure 1. Contribution of emission sectors to PM2.5 concentrations for 2013. Other Sources include volcano, dimethyl sulfide (DMS), and long-range transport (LRT) from Asia, Europe and Alaska. Inset values are population-weighted annual mean PM2.5 concentrations attributable to each sector across the U.S. (red) and Canada (blue). Absolute PM2.5 concentrations are in supporting information (SI) Figure S1. Figure was created using MATLAB_R2016b.

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Figure 2. Fractional contribution of different sectors to population-weighted average PM2.5 concentrations over different regions in Canada for 2013. The number under each bar represents the total population-weighted annual mean PM2.5 concentrations over that region. The hatched part in each sector represents the fractional contribution from the United States. Others include dimethyl sulfide (DMS), volcano, and long-range transport (LRT) from Asia, Europe and Alaska.

370

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Figure 3. Population-weighted annual mean concentrations of chemical components (g m-3) attributed to different sectors (including the contribution from U.S.) across Canada for 2013. Other includes dimethyl sulfide (DMS), volcano, biogenic secondary organic aerosol (SOA) and long-range transport (LRT) from Asia, Europe and Alaska. Figure was created using MATLAB_R2016b.

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Figure 4. Population-weighted sectoral fractional contribution versus population-weighted PM2.5 over Canada for 2013. Stacked bar plots show percentage of each sector in different PM2.5 levels. Other includes dimethyl sulfide (DMS), volcano, and long-range transport (LRT) from Asia, Europe and Alaska.

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Figure 5. Contribution of different sectors to population-weighted average PM2.5 concentrations over Canada in 1990, 2000 and 2010. The number under each bar represents the total population-weighted annual mean PM2.5 concentrations in that year. The hatched part in each sector represents the fractional contribution from the United States. Others include dimethyl sulfide (DMS), volcano, and long-range transport (LRT) from Asia, Europe and Alaska.

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Source Contributions to Ambient Fine Particulate Matter for Canada

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