The development and application of a new method for quantifying total atmospheric sulfur in the Alberta Oil Sands.

Continuous ambient sulfur measurements are routinely conducted around the globe at numerous monitoring sites impacted by industrial sources such as gas and oil processing facilities, pulp and paper mills, smelters, sewage treatment facilities, or concentrated animal feeding operations, as well as natural sources such as volcanoes. Various jurisdictions have or plan to establish Air Ambient Quality Objectives/Guidelines/Standards for Total Reduced Sulfur (TRS) based on odor perception and/or health effects. A conventional TRS monitoring technique is widely used, but few studies have looked at potential biases in the resulting TRS measurements.

Capturing Exposure Disparities with Chemical Transport Models: Evaluating the Suitability of Downscaling Using Land Use Regression.

High resolution exposure surfaces are essential to capture disparities in exposure to traffic-related air pollution in urban areas. In this study, we develop an approach to downscale Chemical Transport Model (CTM) simulations to a hyperlocal level (∼100m) in the Greater Toronto Area (GTA) under three scenarios where emissions from cars, trucks and buses are zeroed out, thus capturing the burden of each transportation mode. This proposed approach statistically fuses CTMs with Land-Use Regression using machine learning techniques.

Aircraft and satellite observations reveal historical gap between top-down and bottom-up CO 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 CO emissions that combines aircraft-measured CO/NO emission ratios (ERs) with inventory and satellite-derived NO 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.

Characterizing long-term NO 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 (NO) 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 NO concentrations derived from the previously used method of back-extrapolation. The optimal spatiotemporal LUR model (R = 0.

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

Unlabelled: 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 (NO). We present the first retrievals of TROPOMI NO 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.

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.

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).

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.