Source-dependent absorption Ångström exponent in the Los Angeles Basin: Multi-time resolution factor analyses of ambient PM and aerosol optical absorption.

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 PM chemical speciation datasets, and aerosol absorption coefficients (b, λ) at seven wavelengths measured by an Aethalometer (AE33), were used in multi-time source apportionment (MT-PMF).

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

PM 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.

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

Ambient fine size fraction particulate matter (PM) 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 PM 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 PM mass over 2010-2016.

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

Ambient fine particulate matter (PM) 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.

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

Chemical speciation data for PM, 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 PM 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 PM data from the three sites (IJM, WAZ and BEV). Four factors were common to all three sites: nitrate-sulphate (average percentage contributions to PM: IJM: 35.

Characterization of winter air pollutant gradients near a major highway.

Traffic-related air pollutants (TRAP) including nitric oxide (NO), nitrogen oxide (NO), carbon monoxide (CO), ultrafine particles (UFP), black carbon (BC), and fine particulate matter (PM) 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.

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 (PM) 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 SO scrubbers (i.e.

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.

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.

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.

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.

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).

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.

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).