Enhancing non-refractory aerosol apportionment from an urban industrial site through receptor modelling of complete high time-resolution aerosol mass spectra

Receptor modelling was performed on quadrupole unit mass resolution aerosol mass spectrometer (Q-AMS) sub-micron particulate matter (PM) chemical speciation measurements from Windsor, Ontario, an industrial city situated across the Detroit River from Detroit, Michigan. Aerosol and trace gas measurements were collected on board Environment Canada’s CRUISER mobile laboratory. Positive matrix factorization (PMF) was performed on the AMS full particle-phase mass spectrum (PMFFull MS) encompassing both organic and inorganic components. This approach was compared to the more common method of analysing only the organic mass spectra (PMFOrg MS). PMF of the full mass spectrum revealed that variability in the non-refractory sub-micron aerosol concentration and composition was best explained by six factors: an amine-containing factor (Amine); an ammonium sulphate and oxygenated organic aerosol containing factor (Sulphate-OA); an ammonium nitrate and oxygenated organic aerosol containing factor (Nitrate-OA); an ammonium chloride containing factor (Chloride); a hydrocarbon like organic aerosol (HOA) factor; and a moderately oxygenated organic aerosol factor (OOA). PMF of the organic mass spectrum revealed three factors of similar composition to some of those revealed through PMFFull MS: Amine, HOA and OOA. Including both the inorganic and organic mass proved to be a beneficial approach to analysing the unit mass resolution AMS data for several reasons. First, it provided a method for potentially calculating more accurate sub-micron PM mass concentrations, particularly when unusual factors are present, in this case, an Amine factor. As this method does not rely on a priori knowledge of chemical species, it circumvents the need for any adjustments to the traditional AMS species fragmentation patterns to account for atypical species, and can thus lead to more complete factor profiles. It is expected that this method would be even more useful for HR-ToF-AMS data, due to the ability to better understand the chemical nature of atypical factors from high resolution mass spectra. Second, utilizing PMF to extract factors containing inorganic species allowed for the determination of extent of neutralization, which could have implications for aerosol parameterization. Third, subtler differences in organic aerosol components were resolved through the incorporation of inorganic mass into the PMF matrix. The additional temporal features provided by the inorganic aerosol components allowed for the resolution of more types of oxygenated organic aerosol than could be reliably re-solved from PMF of organics alone. Comparison of findings from the PMFFull MS and PMFOrg MS methods showed that for the Windsor airshed, the PMFFull MS method enabled additional conclusions to be drawn in terms of aerosol sources and chemical processes. While performing PMFOrg MS can provide important distinctions between types of organic aerosol, it is shown that including inorganic species in the PMF analysis can permit further apportionment of organics for unit mass resolution AMS mass spectra

First Results from the Oil Sands Passive Air Monitoring Network for Polycyclic Aromatic Compounds

Results are reported from an ongoing passive air monitoring study for polycyclic aromatic compounds (PACs) in the Athabasca oil sands region in Alberta, Canada. Polyurethane foam (PUF) disk passive air samplers were deployed for consecutive 2-month periods from November 2010 to June 2012 at 17 sites. Samples were analyzed for polycyclic aromatic hydrocarbons (PAHs), alkylated PAHs, dibenzothiophene and its alkylated derivatives (DBTs). Relative to parent PAHs, alkylated PAHs and DBTs are enriched in bitumen and therefore considered to be petrogenic markers. Concentrations in air were in the range 0.03–210 ng/m³, 0.15–230 ng/m³ and 0.01–61 ng/m³ for ∑PAHs, ∑alkylated PAHs and ΣDBTs, respectively. An exponential decline of the PAC concentrations in air with distance from mining areas and related petrogenic sources was observed. The most significant exponential declines were for the alkylated PAHs and DBTs and attributed to their association with mining-related emissions and near-source deposition, due to their lower volatility and greater association with depositing particles. Seasonal trends in concentrations in air for PACs were not observed for any of the compound classes. However, a forest fire episode during April to July 2011 resulted in greatly elevated PAH levels at all passive sampling locations. Alkylated PAHs and DBTs were not elevated during the forest fire period, supporting their association with petrogenic sources. Based on the results of this study, an “Athabasca PAC profile” is proposed as a potential source marker for the oil sands region. The profile is characterized by ∑PAHs/∑Alkylated PAHs = ∼0.2 and ∑PAHs/∑DBTs = ∼5

Robustness of Land-Use Regression Models Developed from Mobile Air Pollutant Measurements

Land-use regression (LUR) models are useful for resolving fine scale spatial variations in average air pollutant concentrations across urban areas. With the rise of mobile air pollution campaigns, characterized by short-term monitoring and large spatial extents, it is important to investigate the effects of sampling protocols on the resulting LUR. In this study a mobile lab was used to repeatedly visit a large number of locations (∼1800), defined by road segments, to derive average concentrations across the city of Montreal, Canada. We hypothesize that the robustness of the LUR from these data depends upon how many independent, random times each location is visited (<i>N</i>_vis) and the number of locations (<i>N</i>_loc) used in model development and that these parameters can be optimized. By performing multiple LURs on random sets of locations, we assessed the robustness of the LUR through consistency in adjusted <i>R</i>² (i.e., coefficient of variation, CV) and in regression coefficients among different models. As <i>N</i>_loc increased, <i>R</i>²_adj became less variable; for <i>N</i>_loc = 100 vs <i>N</i>_loc = 300 the CV in <i>R</i>²_adj for ultrafine particles decreased from 0.088 to 0.029 and from 0.115 to 0.076 for NO_2. The CV in the <i>R</i>²_adj also decreased as <i>N</i>_vis increased from 6 to 16; from 0.090 to 0.014 for UFP. As <i>N</i>_loc and <i>N</i>_vis increase, the variability in the coefficient sizes across the different model realizations were also seen to decrease

Biomass burning nitrogen dioxide emissions derived from space with TROPOMI: methodology and validation

Smoke from wildfires is a significant source of air pollution, which can adversely impact air quality and ecosystems downwind. With the recently increasing intensity and severity of wildfires, the threat to air quality is expected to increase. Satellite-derived biomass burning emissions can fill in gaps in the absence of aircraft or ground-based measurement campaigns and can help improve the online calculation of biomass burning emissions as well as the biomass burning emissions inventories that feed air quality models. This study focuses on satellite-derived NOx emissions using the high-spatial-resolution TROPOspheric Monitoring Instrument (TROPOMI) NO2 dataset. Advancements and improvements to the satellite-based determination of forest fire NOx emissions are discussed, including information on plume height and effects of aerosol scattering and absorption on the satellite-retrieved vertical column densities. Two common top-down emission estimation methods, (1) an exponentially modified Gaussian (EMG) and (2) a flux method, are applied to synthetic data to determine the accuracy and the sensitivity to different parameters, including wind fields, satellite sampling, noise, lifetime, and plume spread. These tests show that emissions can be accurately estimated from single TROPOMI overpasses. The effect of smoke aerosols on TROPOMI NO2 columns (via air mass factors, AMFs) is estimated, and these satellite columns and emission estimates are compared to aircraft observations from four different aircraft campaigns measuring biomass burning plumes in 2018 and 2019 in North America. Our results indicate that applying an explicit aerosol correction to the TROPOMI NO2 columns improves the agreement with the aircraft observations (by about 10 %–25 %). The aircraft- and satellite-derived emissions are in good agreement within the uncertainties. Both top-down emissions methods work well; however, the EMG method seems to output more consistent results and has better agreement with the aircraft-derived emissions. Assuming a Gaussian plume shape for various biomass burning plumes, we estimate an average NOx e-folding time of 2 ±1 h from TROPOMI observations. Based on chemistry transport model simulations and aircraft observations, the net emissions of NOx are 1.3 to 1.5 times greater than the satellite-derived NO2 emissions. A correction factor of 1.3 to 1.5 should thus be used to infer net NOx emissions from the satellite retrievals of NO2.</p

High-Resolution Mapping of Nitrogen Dioxide With TROPOMI : First Results and Validation Over the Canadian Oil Sands

TROPOspheric Monitoring Instrument (TROPOMI), on-board the Sentinel-5 Precurser satellite, is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared. From these spectra several important air quality and climate-related atmospheric constituents are retrieved, including nitrogen dioxide (NO2) at unprecedented spatial resolution from a satellite platform. We present the first retrievals of TROPOMI NO2 over the Canadian Oil Sands, contrasting them with observations from the Ozone Monitoring Instrument satellite instrument, and demonstrate TROPOMI's ability to resolve individual plumes and highlight its potential for deriving emissions from individual mining facilities. Further, the first TROPOMI NO2 validation is presented, consisting of aircraft and surface in situ NO2 observations, and ground-based remote-sensing measurements between March and May 2018. Our comparisons show that the TROPOMI NO2 vertical column densities are highly correlated with the aircraft and surface in situ NO2 observations, and the ground-based remote-sensing measurements with a low bias (15–30 %); this bias can be reduced by improved air mass factors.</p