Evaluation of a Passive Sampling Method for Long-Term Continuous Monitoring of Volatile Organic Compounds in Urban Environments.

Environmental Protection Agency Method 325 was developed for continuous passive monitoring of volatile organic compounds (VOCs), particularly benzene, at petroleum refinery fencelines. In this work, a modified version of the method was evaluated at an Ontario near-road research station in winter to assess its suitability for urban air quality monitoring. Samples were collected at 24 hour and 14 day resolution to investigate accuracy for different exposure times. Tubes were analyzed by thermal desorption-gas chromatography-mass spectrometry, and 11 VOCs were quantified, including aromatic air toxics. The same VOCs were simultaneously monitored using traditional canister sampling for comparison, and a subset of four were also monitored using a novel miniature gas chromatograph. Good agreement (within 10%) was observed between the 14 day passive tube samples and the canister samples for benzene. However, field-calibrated uptake rates were required to correct passive tube concentrations for less volatile aromatics. Passive tube deployment and analysis is inexpensive; sampling does not require power, and accurate measurements of benzene are demonstrated here for an urban environment. The method is expected to be advantageous for the generation of long-term continuous benzene datasets suitable for epidemiological research with greater spatial coverage than is currently available using traditional monitoring techniques.

Near-Road Air Pollutant Measurements: Accounting for Inter-Site Variability Using Emission Factors.

A daily integrated emission factor (EF) method was applied to data from three near-road monitoring sites to identify variables that impact traffic related pollutant concentrations in the near-road environment. The sites were operated for 20 months in 2015-2017, with each site differing in terms of design, local meteorology, and fleet compositions. Measurement distance from the roadway and local meteorology were found to affect pollutant concentrations irrespective of background subtraction. However, using emission factors mostly accounted for the effects of dilution and dispersion, allowing intersite differences in emissions to be resolved. A multiple linear regression model that included predictor variables such as fraction of larger vehicles (>7.6 m in length; i.e., heavy-duty vehicles), vehicle speed, and ambient temperature accounted for intersite variability of the fleet average NO, NO x, and particle number EFs (R2:0.50-0.75), with lower model performance for CO and black carbon (BC) EFs (R2:0.28-0.46). NO x and BC EFs were affected more than CO and particle number EFs by the fraction of larger vehicles, which also resulted in measurable weekday/weekend differences. Pollutant EFs also varied with ambient temperature and because there were little seasonal changes in fleet composition, this was attributed to changes in fuel composition and/or post-tailpipe transformation of pollutants.

Improving the correlations of ambient tapered element oscillating microbalance PM2.5 data and SHARP 5030 Federal Equivalent Method in Ontario: a multiple linear regression analysis.

UNLABELLED: Tapered element oscillating microbalances equipped with sample equilibration system (TEOM-SES) used by the province of Ontario for the ambient monitoring of PM2.5 (particulate matter with an aerodynamic diameter < or = 2.5 microm) in its air quality index (AQI) network were collocated with the Synchronized Hybrid Ambient Real-time Particulate monitor (SHARP 5030) at two monitoring sites for a period spanning approximately 2 years to determine the similarities and differences between the measurement outputs of both instrumental systems. Due mainly to mass loss observed with the TEOM-SES in cooler months, the province has recently switched its PM2.5 instrumentation at all stations in its monitoring network from the TEOM-SES to the SHARP 5030, which has the U.S. Environmental Protection Agency (EPA) Federal Equivalent Method (FEM) Class III designation. Thus, it has become imperative to develop corrections for historical and future TEOM measurements for the purpose of making them more agreeable to the new FEM method. This work details the authors' multiple linear regression analyses (MLRAs) of particulate matter data from both instrumental monitors, with the inclusion of operational parameters of physicochemical relevance for both cases of transformations of historical TEOM and TEOM measurements to be made in the future. For historical TEOM data, it was observed that the transformations only benefited winter and fall months. Furthermore, comparisons of the transformed historical TEOM data with PM2.5 concentrations determined from the Federal Reference Method (FRM) sampler at seven locations within the province showed marked improvements over the observed TEOM-FRM comparisons. IMPLICATIONS: This work provides a path to correcting the historically observed underreporting of particulate mass in winter and fall in Ontario by making the TEOM-based continuous data resemble the new FEM outputs (in this case, more SHARP-like). It is possible that the transformation of mainly winter TEOM data as detailed in this work may potentially lead to revisions in historical annual composite mean PM2.5 concentrations and total annual number of days PM2.5 exceeded the Canada-wide Standard (CWS) metric across the province.

Effect of industrialization on the differences in sources and composition of ambient PM2.5 in two Southern Ontario locations.

PM2.5 was sampled over a seven-year period (2013-2019) at two locations ∼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 ∼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 ∼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 µg/m³/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 µg/m³/yr or -12.6%/yr), biomass burning (-0.05 µg/m³/yr or -9.02%/yr), crustal matter (-0.03 µg/m³/yr or -5.28%/yr). These factors mainly accounted for the significant decline in PM2.5 over the study period (-0.35 µg/m³/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.

Source apportionment of ambient PM2.5 in an industrialized city using dispersion-normalized, multi-time resolution factor analyses.

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.

Evaluating the effectiveness of low-sulphur marine fuel regulations at improving urban ambient PM2.5 air quality: Source apportionment of PM2.5 at Canadian Atlantic and Pacific coast cities with implementation of the North American Emissions Control Area.

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.

Source apportionment of ambient PM10 collected at three sites in an urban-industrial area with multi-time resolution factor analyses.

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.

Characterization of winter air pollutant gradients near a major highway.

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.

Quantifying metal emissions from vehicular traffic using real world emission factors.

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.

Air quality in Canadian port cities after regulation of low-sulphur marine fuel in the North American Emissions Control Area.

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.

Evaluation of PAH diagnostic ratios as source apportionment tools for air particulates collected in an urban-industrial environment.

A variety of polycyclic aromatic hydrocarbon (PAH) diagnostic ratios were examined as source apportionment tools in the analysis of a PAH data set associated with atmospheric particulate matter collected in an urban-industrial environment. Seventy-six PM(10) samples were collected concurrently at 4 sampling sites over a one-month period in Hamilton, Ontario, Canada, a city of 500 000 people that is home to two integrated steel companies, associated industries and a network of roadways and major highways. Samples collected under well defined meteorological conditions were categorized as being 'upwind' or 'downwind' of the industrial sector. All sample extracts were analyzed for 48 parent PAH, methylphenanthrenes and sulfur-containing aromatics and showed a thousand-fold range of total PAH concentrations (0.23-172 ng m(-3)). Of all PAH diagnostic ratios examined, the two most useful were the anthracene/(anthracene+phenanthrene) and benz[a]anthracene/(benz[a]anthracene+chrysene/triphenylene) ratios. These afforded the best discrimination of samples that had significant industrial impacts. This work is the first example of the use of a linear combination of PAH ratios, coupled with total PAH data and well defined local samples to determine the relative impacts of mobile and industrial emissions in an urban-industrial environment. Use of a linear combination of PAH ratios allowed us to categorize 95% of the data as 'upwind' or 'downwind' of the industrial sector. It is important to determine PAH ratio threshold values based on data from well defined local samples rather than relying on literature values alone.

A comparative study of two factor analytic models applied to PAH data from inhalable air particulate collected in an urban-industrial environment.

Two factor analysis (FA)-based receptor modeling methods were applied to a polycyclic aromatic hydrocarbon (PAH) dataset from extracts of 75 PM(10) air particulate samples collected concurrently at 4 sampling sites proximate to the urban-industrial area in Hamilton, Ontario, Canada. The total PAH concentrations of 48 target compounds ranged from 0.23 to 172 ng m(-3). Principal component analysis (PCA) and positive matrix factorization (PMF) analysis were followed by multilinear regression analyses to identify and quantify PAH source contributions, together with spatial and temporal trends. The correlations between predicted and observed total PAH levels were excellent in both models (R(2) > 0.98). The PCA afforded large negative contributions in a number of samples, so further analysis was abandoned. The PMF analysis showed 3 factors which were identified as gasoline emissions, diesel emissions and coke oven emissions. Contributions of gasoline emissions and diesel emissions factors were surprisingly similar at all 4 sites indicative of a background of vehicle emissions across the city. The PMF coke oven emission factor showed the greatest variability in total loadings, consistent with the large PAH emissions from the steel industries and the large influence of wind direction on PAH concentrations. The highest coke oven contributions were observed at sites closest to the industrial area on days when these sites were downwind of the industries. The PMF coke oven impact factor showed good correlations with two commonly used PAH diagnostic ratios when the ratios were combined into a single ratio. This integrated approach allowed us to categorize >90% of the samples based on the wind direction of the impacting source.

Source apportionment of PAH in Hamilton Harbour suspended sediments: comparison of two factor analysis methods.

A total of 26 suspended sediment samples collected over a 5-year period in Hamilton Harbour, Ontario, Canada and surrounding creeks were analyzed for a suite of polycyclic aromatic hydrocarbons and sulfur heterocycles. Hamilton Harbour sediments contain relatively high levels of polycyclic aromatic compounds and heavy metals due to emissions from industrial and mobile sources. Two receptor modeling methods using factor analyses were compared to determine the profiles and relative contributions of pollution sources to the harbor; these methods are principal component analyses (PCA) with multiple linear regression analysis (MLR) and positive matrix factorization (PMF). Both methods identified four factors and gave excellent correlation coefficients between predicted and measured levels of 25 aromatic compounds; both methods predicted similar contributions from coal tar/coal combustion sources to the harbor (19 and 26%, respectively). One PCA factor was identified as contributions from vehicular emissions (61%); PMF was able to differentiate vehicular emissions into two factors, one attributed to gasoline emissions sources (28%) and the other to diesel emissions sources (24%). Overall, PMF afforded better source identification than PCA with MLR. This work constitutes one of the few examples of the application of PMF to the source apportionment of sediments; the addition of sulfur heterocycles to the analyte list greatly aided in the source identification process.