Air pollutants in the particulate (PM2.5 species, PAHs) and gaseous phases (VOCs) collected between 2013 and 2019 once every three or six days for a period of 24 hours in an industrialized city in Ontario were analyzed to apportion their common sources. The consequences of using these species jointly for receptor modelling were assessed via combined-phase source apportionment that used the data as is , and in a protocol that factored in the potential for photochemical losses of gas-phase species. Thus, photochemically corrected initial concentrations (PIC) were calculated. Analyses of the inputs followed either with positive matrix factorization or its dispersion- normalized variant (DN-PMF). Comparisons of applying PMF to the originally observed input data (BASE) and DN-PMF on data with PIC corrections were made. When the inputs consisted only of VOCs, three factors were resolved with BASE PMF: natural gas, vehicular emissions, and industrial emissions co-emitted with summertime gasoline evaporation. A fourth factor was obtained, representing reactive VOCs when DN-PIC PMF was used. When the combined phase input data were analyzed, nine factors were resolved for both BASE and DN-PIC PMF. These factors in order of diminishing average PM mass contributions were: particulate sulphate, secondary organic aerosol (SOA), particulate nitrate (pNO3), biomass burning with natural gas, crustal matter, winter blend of gasoline, coking/coal combustion, steelmaking, and summer blend/light duty vehicular emissions. When BASE and DN-PIC PMF results are compared, the average PM mass contribution of the summer gasoline fuel factor increased from 2% in BASE case to 5%, suggesting severe underestimation of this source's contributions without DN-PIC. Also, substantial increases of reactive VOCs in the SOA factor, and PAHs with >= four rings in the pNO3 and steelmaking factors were observed with DN-PIC PMF compared to the BASE PMF case, indicating that for SOA, reactive VOCs at this location contributed to SOA sources.

Advanced receptor models can leverage the information derived from optical and chemical variables as input by a variety of instruments at different time resolutions to extract the source specific absorption Ångström exponent (AAE) from aerosol absorption. The multilinear engine (ME-2), a Positive Matrix Factorization (PMF) solver, serves as a proficient tool for performing such analyses, thereby overcoming the constraints imposed by the assumptions in current optical source apportionment methods such as the Aethalometer approach since the use of a-priori AAE values introduces additional uncertainty into the results of optical methods. Comprehensive PM2.5 chemical speciation datasets, and aerosol absorption coefficients (babs, λ) at seven wavelengths measured by an Aethalometer (AE33), were used in multi-time source apportionment (MT-PMF). The study focused on two locations in the Los Angeles (LA) Basin: Central LA (CELA, Main St.), an urban area surrounded by major freeways, and Rubidoux (RIVR), a residential area surrounded by local roads. Factor profiles and temporal variations of their contributions were obtained. Additionally, factor displacements (DISP) and profile constraints were applied. Five-factor solutions were obtained at both sites. At CELA, the resolved factors included traffic + crustal matter (traffic+ Cr_M), secondary sulfate + nitrate (SSN), biomass burning (BB), diesel emissions (DIE) and aged sea salt (ASS). Moreover, source-dependent AAE values at CELA were obtained without a-priori assumption, with values of 1.46 for traffic+ Cr_M, 1.45 for DIE and 2.37 for BB. At RIVR, the resolved factors were traffic+ Cr_M (AAE = 1.24), particulate sulfate, particulate nitrate, BB (AAE = 3.00) and aged sea salt. PM2.5 composition differed at both locations. SSN accounted for the largest fraction of the ambient PM2.5 mass concentration, their sum at the CELA site averaged 40 % of the PM2.5 mass while the same species represented 77 % at RIVR.

PM2.5 was sampled over a seven-year period (2013-2019) at two locations similar to 50 km apart in Southern Ontario (concurrently for five years: 2015-2019). One is a heavily industrialized site (Hamilton), while the other was a rural site (Simcoe). To assess the impact of industrialization on the composition and sources of PM affecting air quality in these two locations, positive matrix factorization coupled with dispersion normalization (DN-PMF) was used to identify six and eight factors at Simcoe and Hamilton, respectively. The Simcoe factors in order of diminishing PM mass contribution were: particulate sulphate (pSO4), secondary organic aerosol (SOA), crustal matter, particulate nitrate (pNO3), biomass burning, and vehicular emissions. At Hamilton, the effects of industrialization were observed by the similar to 36% higher average ambient PM2.5 concentration for the study period as well as the presence of factors unique to metallurgy, i.e., coking and steelmaking, compared to Simcoe. The coking and steelmaking factors contributed similar to 15% to the PM mass at Hamilton. Seasonal variants of appropriate nonparametric trend tests with the associated slopes (Sen's) were used to assess statistically significant changes in the factor contributions to PM2.5 over time. Specifically at Hamilton, a significant decline in PM contributions was noted for coking (-0.03 mu g/m(3)/yr or -4.1%/yr) while steelmaking showed no statistically significant decline over the study period. Other factors at Hamilton that showed statistically significant declines over the study period were: pSO4 (-0.27 mu g/m(3)/yr or -12.6%/yr), biomass burning (-0.05 mu g/m(3)/yr or -9.02%/yr), crustal matter (-0.03 mu g/m(3)/yr or -5.28%/yr). These factors mainly accounted for the significant decline in PM2.5 over the study period (-0.35 mu g/m(3)/yr or -4.24%/yr). This work shows the importance of long-term monitoring in assessing the unique contributions and temporal changes of industrialization on air quality in Ontario and similarly affected locations.

Ambient fine size fraction particulate matter (PM2.5) sources were resolved by positive matrix factorization at two Canadian cities on the Atlantic and Pacific coast over the 2010-2016 period, corresponding to implementation of the North American Emissions Control Area (NA ECA) low-sulphur marine fuel regulations. Source types contributing to local PM2.5 concentrations were: ECA regulation-related (residual oil, anthropogenic sulphate), urban transportation and residential (gasoline, diesel, secondary nitrate, biomass burning, road dust/soil), industry (refinery, Pb-enriched), and largely natural (biogenic sulphate, sea salt). Anthropogenic sources accounted for approximately 80 % of PM2.5 mass over 2010-2016. Anthropogenic and biogenic sources of PM2.5-sulphate were separated and apportioned. Anthropogenic PM2.5-sulphate was approximately 2-3 times higher than biogenic PM2.5-sulphate prior to implementation of the NA ECA low-S marine fuel regulations, decreasing to 1-2 times higher after regulation implementation. Non-marine anthropogenic sources (gasoline, road dust, local industry factors) were shown to together contribute 38 % - 45 % of urban PM2.5. At both coastal cities, the residual oil and anthropogenic sulphate factors clearly reflected the effects of the low-S fuel regulations at reducing primary and secondary sulphur-related PM2.5 emissions. Comparing a pre-regulation and post-regulation period, residual oil combustion PM2.5 decreased by 0.24-0.25 μg/m3 (94%-95 % decrease) in both cities and anthropogenic sulphate PM2.5 decreased by 0.78 μg/m3 in Halifax (47 % decrease) and 0.71 μg/m3 in Burnaby (58 % decrease). Regulation-related PM2.5 across these factors decreased by approximately 1 μg/m3 after regulation implementation, providing a quantified lower estimate of the beneficial influence of the regulations on urban ambient PM2.5 concentrations. Further reductions in coastal city ambient PM2.5 may best consider air quality strategies that include multiple sources, including marine shipping and non-marine anthropogenic source types given this analysis found that marine vessel emissions remain an important source of urban ambient PM2.5.

Over the past decade, Positive Matrix Factorization (PMF) has become the most commonly used tool for source apportionment of air pollutants with a large fraction of those studies using the implementation developed by the U.S. Environmental Protection Agency (EPA-PMF) and its very useful diagnostic tools. During the same period, more studies are utilizing monitoring tools that provide hourly or even higher time resolution data leading to large data sets (millions of data points). Because of the nature of the graphic user interface in EPA-PMF, it cannot handle data sets more than approximately half a million data points. Thus, alternative approaches are needed to permit these larger data sets to be analyzed and still be able to provide the diagnostic outputs that permit better interpretations of the results. We have developed a protocol to use the multilinear engine (ME-2) that is the solver used in EPA-PMF to make such analyses. Here we report how to use this approach and present results for a representative data set of particle size distributions obtained in Rochester, New York between 2008 and 2019.

Ambient fine particulate matter (PM2.5) data were collected in the lower City of Hamilton, Ontario to apportion the sources of this pollutant over an 18-month period. Hamilton has complex topographical features that may result in worsened air pollution within the lower city, thus, dispersion-normalized, multi-time resolution factor analysis (DN-MT-FA) was used to identify and quantify contributions of factors in a manner that reduced the influence of local meteorology. These factors were secondary organic aerosols type 1 (SOA_1), particulate nitrate (pNO3), particulate sulphate (pSO4), primary traffic organic matter (PTOM), Steel/metal processing and vehicular road dust emissions (Steel & Mobile) and, secondary organic aerosols type 2 (SOA_2) with origins ranging from mainly regional to mainly local. Factors that were mainly local (PTOM, Steel & Mobile, SOA_2) contributed up to 17% of the average PM2.5 mass while mixed local/regional factors (pNO3, pSO4) made up 43% on average, indicating the potential for further reduction of harmful PM concentrations locally. Of particular interest from a health protection perspective, was the composition of PM2.5 on days when an exceedance of the 24-hr WHO air quality guideline for this pollutant was observed. In general, SOA_1 was found to drive summer exceedances while pNO3 dominated in the winter. During the summer period, SOA_1 was attributable to wildfires in the northern parts of Canada while local traffic sources in winter contributed to the high levels of pNO3. While local, industrial factors only had minor relative mass contributions during exceedances, they are high in highly oxidized organic species (SOA_2) and toxic metals (Steel & Mobile). Thus, they are likely to have more impacts on human health. The methods and results described in this work will be useful in understanding prevalent sources of particulate matter pollution in the ambient air in the presence of complex topography and meteorological effects.

Ambient fine size fraction particulate matter (PM2.5) sources in the western Canada city of Calgary, Alberta were identified and their contributions quantified for 2015–2019 based on elemental and ion composition data from sampling at a central monitoring site and the US Environmental Protection Agency Positive Matrix Factorization (v5.0) receptor model. Meaningful source apportionment results were obtained by using reconstructed PM2.5 mass and an estimated unmeasured mass in absence of carbonaceous component measurements. Auxiliary analyses identified source regions. Identified source types and average contribution to PM2.5 were secondary organic aerosol (24%), wood smoke (16%), soil/crustal matter (15%), traffic/rail (14%), particulate sulfate (13%), particulate nitrate (10%), and road salt/aged road salt (9% combined). Calgary PM2.5 contributions of secondary organic aerosol, particulate sulfate, and traffic/rail sources were similar to the next-nearest western city of Edmonton while particulate nitrate contributions were lower, wood smoke impacts greater and other factors showed greater inter-city differences. Secondary PM2.5 contributions in Calgary were consistent with estimates for medium and large cities in central Canada for SOA and particulate nitrate but lower for particulate sulfate. Primary wood smoke PM2.5 contributions were consistent with other Canadian cities and confirmed strong influence of winter season biomass burning. Combined on-road vehicle and rail transportation activity were found to contribute comparably more to ambient PM2.5 in Calgary than in central Canadian cities. Soil/crustal re-suspension/transport and intensive winter-time use of road salts were also important local PM2.5 sources. Several factors were characterized by local sources (traffic/rail, soil/crustal matter, road salt, aged road salt, wood smoke) with episodic regional source contributions (wood smoke, soil/crustal matter) and secondary aerosol factors (SOA, particulate sulfate, particulate nitrate) also demonstrated influence from local sources alongside regional source contributions, indicating that Calgary ambient PM2.5 concentrations are likely strongly influenced by local source emissions. These findings present positive opportunities for multiple air quality management and source mitigation strategies, including at the local/municipal level.

Traffic-related air pollutants (TRAP) including nitric oxide (NO), nitrogen oxide (NOx), carbon monoxide (CO), ultrafine particles (UFP), black carbon (BC), and fine particulate matter (PM2.5) were simultaneously measured at near-road sites located at 10 m (NR10) and 150 m (NR150) from the same side of a busy highway to provide insights into the influence of winter time meteorology on exposure to TRAP near major roads. The spatial variabilities of TRAP were examined for ambient temperatures ranging from -11 °C to +19 °C under downwind, upwind, and stagnant air conditions. The downwind TRAP concentrations at NR10 were higher than the upwind concentrations by a factor of 1.4 for CO to 13 for NO. Despite steep downwind reductions of 38 % to 75 % within 150 m, the downwind concentrations at NR150 were still well above upwind concentrations. Near-road concentrations of NOx and UFP increased as ambient temperatures decreased due to elevated emissions of NOx and UFP from vehicles under colder temperatures. Traffic-related PM2.5 sources were identified using hourly PM2.5 chemical components including organic/inorganic aerosol and trace metals at both sites. The downwind concentrations of primary PM2.5 species related to tailpipe and non-tailpipe emissions at NR10 were substantially higher than the upwind concentrations by a factor of 4 and 32, respectively. Traffic-related PM2.5 sources accounted for almost half of total PM2.5 mass under downwind conditions, leading to a rapid change of PM2.5 chemical composition. Under stagnant air conditions, the concentrations of most TRAP and related PM2.5 including tailpipe emissions, secondary nitrate, and organic aerosol were comparable to, or even greater than, the downwind concentrations under windy conditions, especially at NR150. This study demonstrates that stagnant air conditions further widen the traffic-influenced area and people living near major roadways may experience increased risks from elevated exposure to traffic emissions during cold and stagnant winter conditions.

Industrial emissions of volatile organic compounds (VOCs) directly impact air quality downwind of facilities and contribute to regional ozone and secondary organic aerosol production. Positive matrix factorization (PMF) is often used to apportion VOCs to their respective sources using measurement data collected at fixed sites, for example air quality monitoring stations. Here, we apply PMF analysis to high time-resolution VOC measurement data collected both while stationary and while moving using a mobile monitoring platform. The stationary monitoring periods facilitated the extraction of representative industrial VOC source profiles while the mobile monitoring periods were critical for the spatial identification of VOC hotspots. Data were collected over five days in a heavily industrialized region of southwestern Ontario containing several refineries, petrochemical production facilities and a chemical waste disposal facility. Factors associated with petroleum, chemical waste and rubber production were identified and ambient mixing ratios of selected aromatic, unsaturated and oxygenated VOCs were apportioned to local and background sources. Fugitive emissions of benzene, highly localized and predominantly associated with storage, were found to be the dominant local contributor to ambient benzene mixing ratios measured while mobile. Toluene and substituted aromatics were predominantly associated with refining and traffic, while methyl ethyl ketone was linked to chemical waste handling. The approach described here facilitates the apportionment of VOCs to their respective local industrial sources at high spatial and temporal resolution. This information can be used to identify problematic source locations and to inform VOC emission abatement strategies.

Chemical speciation data for PM10, collected for annual trend analyses of health-relevant species, at three receptor sites in a highly industrialized area (IJmond) in the Netherlands were used in a multi-time resolution receptor model (ME-2) to identify the PM10 sources in this area. Despite the available data not being optimized for receptor modelling, five-factor solutions were obtained for all sites based on independent PMF analysis on PM10 data from the three sites (IJM, WAZ and BEV). Four factors were common to all three sites: nitrate-sulphate (average percentage contributions to PM10: IJM: 35.3 %, WAZ: 37.7 %, and BEV: 36.3 %); sea salt (20.2 %, 23.7 %, 15.2 %); industrial (8.1 %, 11.0 %, 18.1 %) and brake wear/traffic (31.4 %, 21.2 %, 20.6 %). At WAZ, a local/site-specific factor containing most of the PAH measurements was found (6.4 %) while a crustal matter factor was resolved at IJM (7.6 %) and BEV (9.8 %). Additionally, sludge-drying was a potential source of the marker species in the industrial factor at WAZ. Bootstrapping (BS) and factor displacement (DISP) were applied to the factor profiles in this work for error estimation. In general, the factor profiles at all three sites had very small intervals from both BS and DISP methods. To our knowledge, this is the first time DISP was applied in a complex model such as the multi-time resolution model. Most of the measured metal and PAH concentrations found in the IJmond area during the 2017-2019 period had local sources, with significant contributions from several processes related to the steel industry. This study shows that available detailed PM10 chemical speciation data, although primarily collected for annual trend analyses of health-relevant species, could also be used in receptor modelling by applying a multi-time framework. We propose general recommendations for the optimization of the measurement strategy for source apportionment of PM in areas with similar urban-industrial land use.

Large mineral deposits have been discovered in Ontario's Far North and are being considered for further development. Particulate matter and trace elements can be emitted from potential mining activities and these air pollutants are associated with health risks and harmful to the sensitive ecosystem. An air monitoring station, powered by solar panels and a wind turbine, was established in this near-pristine area to monitor baseline levels of fine particulate matter (PM2.5) and trace elements downwind of a proposed mine site. Levels of PM2.5 and trace elements observed from 2015 to 2018 were much lower than measurements observed in southern Ontario, suggesting minimal influence of primary emissions in the study area. One episodic PM2.5 event in July 2015 was attributable to wildfire emissions in northern Ontario. Only 8 out of the 31 target elements were detected in 25% or more of the samples. Good correlations among As, Se, Pb, and Sb, between Mn and Fe, as well as between Ce and La indicated they originated from long-range atmospheric transport from the south. Ontario's Ambient Air Quality Criteria were not exceeded for any target air pollutants. Four years of air measurements filled the data gap of baseline information in this near-pristine study area and can be used to assess impacts of potential mining activities in the future. Field operations during this study period indicated that the battery-powered air instruments and meteorological sensors worked well in the harsh environment of Ontario's Far North even in cold winter months. The field experiences gained in this study can be applied to future air monitoring activities in harsh environments where no direct power supply is available and site access is limited.

Large marine vessels have historically used high-sulphur (S) residual fuel oil (RFO), with substantial airborne releases of sulphur dioxide (SO₂) and fine particulate matter (PM2.5) enriched in vanadium (V), nickel (Ni) and other air pollutants. To address marine shipping air pollution, Canada and the United States have jointly implemented a North American Emissions Control Area (NA ECA) within which ships are regulated to use lower-sulphur marine fuel or equivalent SO2 scrubbers (i.e., 3.5% maximum fuel S reduced to 1% S in 2012 and 0.1% S in 2015). To investigate the effects of these regulations on local air quality, we examined changes in air pollutant (SO₂, PM2.5, NO₂, O₃), and related PM2.5 components (V, Ni, sulphate) concentrations over 2010–2016 at the Canadian port cities of Halifax, Vancouver, Victoria, Montreal, and Quebec City. SO2 concentrations showed large statistically significant decreases at all sites (−28% to −83% mean hourly change), with the largest improvements in the coastal cities when the 0.1% fuel S regulation took effect. Statistically significant PM2.5 but smaller fractional reductions were also observed (−7% to −37% mean hourly change), reflecting the importance of non-marine PM sources. RFO marker species V and Ni in PM2.5 dramatically declined following regulation implementation, consistent with decreased RFO use likely indicating the switch to low-S distillate fuel oil rather than exhaust scrubbers for initial compliance. Significant changes in other pollutants with non-marine sources (NO2, O3) were not contemporaneous with the regulatory timeline. The large SO2 improvements in the port cities have reduced 1-h concentrations to <30 ppb, comparable to Canadian urban locations with few local SO2 sources and likely reducing health risks to susceptible populations such as asthmatics and the elderly. Our findings indicate that the implementation of the NA ECA improved air quality at Canadian port cities immediately following the requirement for lower-S fuel. These air quality improvements suggest that large-scale international benefits can result from implementation of the 2020 global low-S marine fuel regulations.

Passive samplers have proven to be effective for continuous monitoring of volatile organic compounds (VOCs) in ambient air in remote, urban and industrial environments. Thermal desorption tubes fitted with endcaps that facilitate passive uptake through diffusion are now routinely used for monitoring fugitive benzene emissions from refineries and petrochemical facilities across North America (EPA Method 325A/B). However, deployment periods of 14 days are typically employed to minimize the risk of poor retention, requiring 26 deployments per year to return an annual average concentration for comparison with chronic exposure health-based standards. Here, we explore extending the deployment duration of these passive samplers to one, two and three months by limiting VOC uptake rates using an alternative diffusive endcap featuring a smaller cross-sectional area. Field testing was performed beside a major highway during two separate three-month campaigns. Uptake rates for benzene, toluene, ethylbenzene and xylenes (BTEX) were observed to remain linear for deployments of up to three months when using the low-uptake endcaps. Application of the low-uptake endcaps and the uptake rates determined here will enable annual average concentrations of BTEX to be calculated using only four tube deployments per year. The cost savings associated with this decreased deployment frequency will facilitate increased spatial resolution during future exposure assessment studies. The stability of selected air toxics within the tubes was also assessed and the results suggest that while aromatic VOCs are stable for storage times of at least 70 days, chloroform and trichloroethylene begin to degrade within two weeks of sampling.

Road traffic emissions are an increasingly important source of particulate matter in urban and non-road environments, where non-tailpipe emissions can contribute substantially to elevated levels of metals associated with adverse health effects. Thus, better characterization and quantification of traffic-emitted metals is warranted. In this study, real-world emission factors for fine particulate metals were determined from hourly x-ray fluorescence measurements over a three-year period (2015–2018) at an urban roadway and busy highway. Inter-site differences and temporal trends in real-world emission factors for metals were explored. The emission factors at both sites were within the range of past studies, and it was found that Ti, Fe, Cu, and Ba emissions were 2.2–3.0 times higher at the highway site, consistent with the higher proportion of heavy-duty vehicles. Weekday emission factors for some metals were also higher by 2.0–3.5 times relative to Sundays for Mn, Zn, Ca, and Fe, illustrating a dependence on fleet composition and roadway activity. Metal emission factors were also inversely related to relative humidity and precipitation, due to reduced road dust resuspension under wetter conditions. Correlation analysis revealed groups of metals that were co-emitted by different traffic activities and sources. Determining emission factors enabled the isolation of traffic-related metal emissions and also revealed that human exposure to metals in ambient air can vary substantially both temporally and spatially depending on fleet composition and traffic volume.

The contribution of traffic-related particulate matter (PM2.5, particles smaller than 2.5 mu m in diameter) sources can vary temporally and spatially, which may disproportionately contribute to health outcomes. Furthermore, non-exhaust emissions are a growing concern due to the high concentrations of redox active metals that can be present. The temporal and spatial variabilities of traffic-related PM2.5 sources were investigated in this study by comparing source contributions between two near-road sites. In order to identify local PM2.5 sources with greater temporal and spatial resolution, receptor modeling was performed for hourly-resolved organics, inorganic ions, trace elements, and black carbon in PM2.5 simultaneously measured at downtown and highway sites located within 15 m of a major roadway and highway, respectively, in Toronto. The source apportionment study revealed that traffic-related PM2.5 sources were mainly from exhaust emissions (9%-19% of PM2.5) and non-exhaust emissions including brake wear (2%-6%) and resuspension of road dust (3%-4%). The traffic-related sources exhibited strong diurnal and spatial variabilities, whereas no spatial and temporal differences were observed for the largest PM2.5 contributors, oxidized organic aerosol and secondary sulphate. During morning rush hours, the overall contribution of traffic exhaust and non-exhaust emissions were elevated up to 35%-48% of total PM2.5 mass, which was found to be the largest PM2.5 source at the highway site and the second largest contributor in the downtown area. Furthermore, the contribution of traffic-related sources at the highway site was higher than at the downtown site by a factor of 2-3, suggesting that exposure to traffic-related emissions varies greatly in space and time. Nearly one-third of the traffic-related source contributions were associated with non-exhaust emissions from brake wear and road dust resuspension in the urban environment. Elevated levels of non-exhaust sources were correlated with the number of heavy-duty vehicles, rather than total traffic volume. Although the contribution of brake wear and road dust sources to total PM2.5 mass was relatively low, non-exhaust emissions contributed a substantial fraction of trace elements, especially for Ba (74-79%), Cu (66-71%), and Mn (53-65%) in the urban atmosphere.

Exposure to wildfire smoke is a public health issue of increasing prominence in North America, particularly in western states and provinces. In this study, Aethalometer data collected at six sites in the Lower Fraser Valley (LFV), British Columbia, from September 2016 through August 2017 were analyzed to investigate the relative importance of fossil fuel and biomass burning contributions to black carbon (BC) in the region. Annual mean BC mass concentrations were in the range 0.3-0.8 mu g m(-3), and BC was apportioned to fossil fuel and biomass burning sources at hourly resolution using the dual-wavelength Aethalometer model, applying a typical a priori assumed biomass burning Absorption Angstrom Exponent (AAE) value close to 2. However, this approach was found to underestimate biomass contributions to BC mass by up to a factor of three during wildfire events that impacted air quality throughout the region in July and August 2017. Hourly resolution PM2.5 concentrations in excess of 100 mu g m(-3) were recorded at multiple sites and mean ambient PM2.5 concentrations for the wildfire smoke periods were up to nine times higher than mean concentrations for the remainder of the summer. A background extraction approach was applied to optimize BC source apportionment during these events and to enable the calculation of wildfire aerosol AAE, with similar values for the latter determined across all six sites (1.35 +/- 0.09). The relatively low AAE values observed suggest that evaporation and/or photo-bleaching of brown carbon occurred during transport of wildfire aerosol to the receptor sites. From an exposure perspective, for the sites investigated, up to 69 +/- 20% (+/- 1 sigma) of ambient BC is attributed to wildfires during these events. On an annual scale, however, fossil fuel combustion and residential/agricultural biomass burning are estimated to be more important contributors to ambient BC mass concentrations at every site.

The present study proposes an advanced methodology to refine the source apportionment of organic aerosol (OA). This methodology is based on the combination of offline and online datasets in a single Positive Matrix Factorization (PMF) analysis using the multilinear engine (ME-2) algorithm and a customized time synchronization procedure. It has been applied to data from measurements conducted in the Paris region (France) during a PM pollution event in March 2015. Measurements included OA ACSM (Aerosol Chemical Speciation Monitor) mass spectra and specific primary and secondary organic molecular markers from PM10 filters on their original time resolution (30 min for ACSM and 4 h for PM10 filters). Comparison with the conventional PMF analysis of the ACSM OA dataset (PMF-ACSM) showed very good agreement for the discrimination between primary and secondary OA fractions with about 75% of the OA mass of secondary origin. Furthermore, the use of the combined datasets allowed the deconvolution of 3 primary OA (POA) factors and 7 secondary OA (SOA) factors. A clear identification of the source/origin of 54% of the total SOA mass could be achieved thanks to specific molecular markers. Specifically, 28% of that fraction was linked to combustion sources (biomass burning and traffic emissions). A clear identification of primary traffic OA was also obtained using the PMF-combined analysis while PMF-ACSM only gave a proxy for this OA source in the form of total hydrocarbon-like OA (HOA) mass concentrations. In addition, the primary biomass burning-related OA source was explained by two OA factors, BBOA and OPOA-like BBOA. This new approach has showed undeniable advantages over the conventional approaches by providing valuable insights into the processes involved in SOA formation and their sources. However, the origins of highly oxidized SOA could not be fully identified due to the lack of specific molecular markers for such aged SOA.

Accurate, continuous long-term measurements of aromatic volatile organic compounds with known health impacts are needed for comprehensive assessments of air quality in the vicinity of industrial and vehicular emission sources. Historically, evacuated canister sampling and on-line gas chromatography (GC) have been the most popular approaches applied by government institutions in North America to measure benzene, toluene, ethylbenzene and xylenes (BTEX) in ambient air. Canister sampling is labour-intensive and typically does not provide the continuous data needed for epidemiological studies, while on-line GCs are costly and require shelter and power. In this work, the accuracy and suitability of three techniques for generating long-term ambient BTEX datasets are assessed. The first is a passive sampling method involving sorbent-packed thermal desorption tubes, the second is an on-line miniature gas chromatograph (miniGC) coupled with a photoionization detector (PID) and the third is a more traditional on-line gas chromatograph coupled with a flame ionization detector (GC-FID). These three techniques were deployed at an air quality monitoring station located adjacent to a busy highway for six weeks in summer 2018 to compare method performance and accuracy. Ambient mean concentrations of BTEX determined from the three methods for the entire study period were found to agree within 7% for benzene and within 30% for the other species. Interestingly, a temperature dependent positive bias previously identified for 14-day passive tube sampling for ethylbenzene and xylenes under cold wintertime conditions was found to be negligible under summertime conditions.

Adverse health outcomes related to exposure to air pollution have gained much attention in recent years, with a particular emphasis on traffic-related pollutants near roadways, where concentrations tend to be most severe. As such, many projects around the world are being initiated to routinely monitor pollution near major roads. Understanding the extent to which local on-road traffic directly affects these measurements, however, is a challenging problem, and a more thorough comprehension of it is necessary to properly assess its impact on near-road air quality. In this study, a set of commonly measured air pollutants (black carbon; carbon dioxide; carbon monoxide; fine particulate matter, PM2.5; nitrogen oxides; ozone; and ultrafine particle concentrations) were monitored continuously between 1 June 2015 and 31 March 2017 at six stations in Canada: two near-road and two urban background stations in Toronto, Ontario, and one near-road and one urban background station in Vancouver, British Columbia. Three methods of differentiating between local and background concentrations at near-road locations were tested: (1) differences in average pollutant concentrations between near-road and urban background station pairs, (2) differences in downwind and upwind pollutant averages, and (3) interpolation of rolling minima to infer background concentrations. The last two methods use near-road data only, and were compared with method 1, where an explicit difference was measured, to assess accuracy and robustness. It was found that method 2 produced average local concentrations that were biased high by a factor of between 1.4 and 1.7 when compared with method 1 and was not universally feasible, whereas method 3 produced concentrations that were in good agreement with method 1 for all pollutants except ozone and PM2.5, which are generally secondary and regional in nature. The results of this comparison are intended to aid researchers in the analysis of data procured in future near-road monitoring studies. Lastly, upon determining these local pollutant concentrations as a function of time, their variability with respect to wind speed (WS) and wind direction (WD) was assessed relative to the mean values measured at the specific sites. This normalization allowed generalization across the pollutants and made the values from different sites more comparable. With the exception of ozone and PM2.5, local pollutant concentrations at these near-road locations were enhanced by a factor of 2 relative to their mean in the case of stagnant winds and were shown to be proportional to WS−0.6. Downwind conditions enhanced local concentrations by a factor of ∼2 relative to their mean, while upwind conditions suppressed them by a factor of ∼4. Site-specific factors such as distance from roadway and local meteorology should be taken into consideration when generalizing these factors. The methods used to determine these local concentrations, however, have been shown to be applicable across pollutants and different near-road monitoring environments.

Six heavy metals - Mn, Fe, Cu, Zn, Se, and Pb among other elemental species were monitored in ambient PM2.5 at three near-road ambient air monitoring locations in the Greater Toronto and Hamilton Area (GTHA) with semi-continuous X-ray fluorescence (XRF) instrumentation over a period spanning January 1st, 2014 to June 30th, 2017. Land use in these air monitoring locations includes residential, institutional and industrial, thus, air monitoring is representative of typical urban areas. Ambient metal concentrations were found below Ontario's ambient air quality criteria. Temporal trends however indicated that high concentrations of Fe and Cu correlated with peak commuting and working hours on weekdays. To further understand the potential sources of these metals, scatterplots of metal concentrations and criteria pollutant gases were made on weekdays and weekends. These scatterplots reveal edges that are due to multiple sources of these metals. When these scatterplots are colour-coded by the hour of day, edges associated with the morning rush hour on weekdays for Fe and Cu (also Mn and Zn to a lesser extent) likely due to traffic-related emissions are more clearly-delineated from other edges arising from industrial or regional sources that were prevalent during other times of the day. Finally, an auxiliary receptor model was used to explore the potential source regions of these metals. It was observed that Mn, Fe and Cu had intense potential source regions within the GTHA on weekdays that diminished on the weekends, and in the case of Fe, the potential source regions in the GTHA were sensitive to the morning rush hour period, indicating that traffic-related emissions are a major source of Fe. Other metals, especially Zn, Se and Pb have source regions that are less sensitive to the morning rush hour period and are usually situated outside the GTHA.