Aircraft and satellite observations reveal historical gap between top-down and bottom-up CO2 emissions from Canadian oil sands.

Measurement-based estimates of greenhouse gas (GHG) emissions from complex industrial operations are challenging to obtain, but serve as an important, independent check on inventory-reported emissions. Such top-down estimates, while important for oil and gas (O&G) emissions globally, are particularly relevant for Canadian oil sands (OS) operations, which represent the largest O&G contributor to national GHG emissions. We present a multifaceted top-down approach for estimating CO2 emissions that combines aircraft-measured CO2/NOx emission ratios (ERs) with inventory and satellite-derived NOx emissions from Ozone Monitoring Instrument (OMI) and TROPOspheric Ozone Monitoring Instrument (TROPOMI) and apply it to the Athabasca Oil Sands Region (AOSR) in Alberta, Canada. Historical CO2 emissions were reconstructed for the surface mining region, and average top-down estimates were found to be >65% higher than facility-reported, bottom-up estimates from 2005 to 2020. Higher top-down vs. bottom-up emissions estimates were also consistently obtained for individual surface mining and in situ extraction facilities, which represent a growing category of energy-intensive OS operations. Although the magnitudes of the measured discrepancies vary between facilities, they combine such that the observed reporting gap for total AOSR emissions is ≥(31 ± 8) Mt for each of the last 3 years (2018-2020). This potential underestimation is large and broadly highlights the importance of continued review and refinement of bottom-up estimation methodologies and inventories. The ER method herein offers a powerful approach for upscaling measured facility-level or regional fossil fuel CO2 emissions by taking advantage of satellite remote sensing observations.

Characterizing long-term NO2 concentration surfaces across a large metropolitan area through spatiotemporal land use regression modelling of mobile measurements.

A spatiotemporal land use regression (LUR) model optimized to predict nitrogen dioxide (NO2) concentrations obtained from on-road, mobile measurements collected in 2015-16 was independently evaluated using concentrations observed at multiple sites across Toronto, Canada, obtained more than ten years earlier. This spatiotemporal LUR modelling approach improves upon estimates of historical NO2 concentrations derived from the previously used method of back-extrapolation. The optimal spatiotemporal LUR model (R2 = 0.71 for prediction of NO2 data in 2002 and 2004) uses daily average NO2 concentrations observed at multiple long-term monitoring sites and hourly average wind speed recorded at a single site, along with spatial predictors based on geographical information system data, to estimate NO2 levels for time periods outside of those used for model development. While the model tended to underestimate samplers located close to the roadway, it showed great accuracy when estimating samplers located beyond 100 m which are probably more relevant for exposure at residences. This study shows that spatiotemporal LUR models developed from strategic, multi-day (30 days in 3 different months) mobile measurements can enhance LUR model's ability to estimate long-term, intra-urban NO2 patterns. Furthermore, the mobile sampling strategy enabled this new LUR model to cover a larger domain of Toronto and outlying suburban communities, thereby increasing the potential population for future epidemiological studies.

High resolution mapping of nitrogen dioxide with TROPOMI: First results and validation over the Canadian oil sands.

TROPOMI, on-board the Sentinel-5 Precursor satellite is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared spectral range. From these spectra several important air quality and climate-related atmospheric constituents are retrieved at an unprecedented high spatial resolution, including nitrogen dioxide (NO2). We present the first retrievals of TROPOMI NO2 over the Canadian Oil Sands, contrasting them with observations from the OMI satellite instrument, and demonstrate its 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, as well as 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 %) over the Canadian Oil Sands. PLAIN LANGUAGE SUMMARY: Nitrogen dioxide (NO2) is a pollutant that is linked to respiratory health issues and has negative environmental impacts such as soil and water acidification. Near the surface the most significant sources of NO2 are fossil fuel combustion and biomass burning. With a recently launched satellite instrument (TROPOspheric Monitoring Instrument; TROPOMI) NO2 can be measured with an unprecedented combination of accuracy, spatial coverage, and resolution. This work presents the first TROPOMI NO2 measurements near the Canadian Oil Sands and shows that these measurements have an outstanding ability to detect NO2 on a very high horizontal resolution that is unprecedented for satellite NO2 observations. Further, these satellite measurements are in excellent agreement with aircraft and ground-based measurements.

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 (Nvis) and the number of locations (Nloc) 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 R2 (i.e., coefficient of variation, CV) and in regression coefficients among different models. As Nloc increased, R2adj became less variable; for Nloc = 100 vs Nloc = 300 the CV in R2adj for ultrafine particles decreased from 0.088 to 0.029 and from 0.115 to 0.076 for NO2. The CV in the R2adj also decreased as Nvis increased from 6 to 16; from 0.090 to 0.014 for UFP. As Nloc and Nvis increase, the variability in the coefficient sizes across the different model realizations were also seen to decrease.

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/m(3), 0.15-230 ng/m(3) and 0.01-61 ng/m(3) 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.

Evaluating multipollutant exposure and urban air quality: pollutant interrelationships, neighborhood variability, and nitrogen dioxide as a proxy pollutant.

BACKGROUND: Although urban air pollution is a complex mix containing multiple constituents, studies of the health effects of long-term exposure often focus on a single pollutant as a proxy for the entire mixture. A better understanding of the component pollutant concentrations and interrelationships would be useful in epidemiological studies that exploit spatial differences in exposure by clarifying the extent to which measures of individual pollutants, particularly nitrogen dioxide (NO2), represent spatial patterns in the multipollutant mixture. OBJECTIVES: We examined air pollutant concentrations and interrelationships at the intraurban scale to obtain insight into the nature of the urban mixture of air pollutants. METHODS: Mobile measurements of 23 air pollutants were taken systematically at high resolution in Montreal, Quebec, Canada, over 34 days in the winter, summer, and autumn of 2009. RESULTS: We observed variability in pollution levels and in the statistical correlations between different pollutants according to season and neighborhood. Nitrogen oxide species (nitric oxide, NO2, nitrogen oxides, and total oxidized nitrogen species) had the highest overall spatial correlations with the suite of pollutants measured. Ultrafine particles and hydrocarbon-like organic aerosol concentration, a derived measure used as a specific indicator of traffic particles, also had very high correlations. CONCLUSIONS: Our findings indicate that the multipollutant mix varies considerably throughout the city, both in time and in space, and thus, no single pollutant would be a perfect proxy measure for the entire mix under all circumstances. However, based on overall average spatial correlations with the suite of pollutants measured, nitrogen oxide species appeared to be the best available indicators of spatial variation in exposure to the outdoor urban air pollutant mixture.