Estimates of exceedances of critical loads for acidifying deposition in Alberta and Saskatchewan

Abstract

Abstract. Estimates of potential harmful effects on ecosystems in the Canadian provinces of Alberta and Saskatchewan due to acidifying deposition were calculated, using a 1-year simulation of a high-resolution implementation of the Global Environmental Multiscale-Modelling Air-quality and Chemistry (GEM-MACH) model, and estimates of aquatic and terrestrial ecosystem critical loads. The model simulation was evaluated against two different sources of deposition data: total deposition in precipitation and total deposition to snowpack in the vicinity of the Athabasca oil sands. The model captured much of the variability of observed ions in wet deposition in precipitation (observed versus model sulfur, nitrogen and base cation R2 values of 0.90, 0.76 and 0.72, respectively), while being biased high for sulfur deposition, and low for nitrogen and base cations (slopes 2.2, 0.89 and 0.40, respectively). Aircraft-based estimates of fugitive dust emissions, shown to be a factor of 10 higher than reported to national emissions inventories (Zhang et al., 2018), were used to estimate the impact of increased levels of fugitive dust on model results. Model comparisons to open snowpack observations were shown to be biased high, but in reasonable agreement for sulfur deposition when observations were corrected to account for throughfall in needleleaf forests. The model–observation relationships for precipitation deposition data, along with the expected effects of increased (unreported) base cation emissions, were used to provide a simple observation-based correction to model deposition fields. Base cation deposition was estimated using published observations of base cation fractions in surface-collected particles (Wang et al., 2015).Both original and observation-corrected model estimates of sulfur, nitrogen, and base cation deposition were used in conjunction with critical load data created using the NEG-ECP (2001) and CLRTAP (2017) methods for calculating critical loads, using variations on the Simple Mass Balance model for terrestrial ecosystems, and the Steady State Water Chemistry and First-order Acidity Balance models for aquatic ecosystems. Potential ecosystem damage was predicted within each of the regions represented by the ecosystem critical load datasets used here, using a combination of 2011 and 2013 emissions inventories. The spatial extent of the regions in exceedance of critical loads varied between 1 × 104 and 3.3 × 105 km2, for the more conservative observation-corrected estimates of deposition, with the variation dependent on the ecosystem and critical load calculation methodology. The larger estimates (for aquatic ecosystems) represent a substantial fraction of the area of the provinces examined.Base cation deposition was shown to be sufficiently high in the region to have a neutralizing effect on acidifying deposition, and the use of the aircraft and precipitation observation-based corrections to base cation deposition resulted in reasonable agreement with snowpack data collected in the oil sands area. However, critical load exceedances calculated using both observations and observation-corrected deposition suggest that the neutralization effect is limited in spatial extent, decreasing rapidly with distance from emissions sources, due to the rapid deposition of emitted primary dust particles as a function of their size. We strongly recommend the use of observation-based correction of model-simulated deposition in estimating critical load exceedances, in future work.

A Measurement‐Model Fusion Approach for Improved Wet Deposition Maps and Trends

“…Measurement-model fusion methods have been widely used to improve air quality model output for ambient concentrations (e.g., Berrocal et al, 2010aBerrocal et al, , 2010bBerrocal et al, 2012;Robichaud et al, 2016;Robichaud & Ménard, 2014), with more limited application to WD data (e.g., Makar et al, 2018;Sahu et al, 2010). In 2017 the World Meteorology Organization hosted a 3-day workshop on measurement-model fusion for global total atmospheric deposition.…”

Water chemistry, landscape, and spatial controls of δ13C and δ15N of zooplankton taxa in boreal lakes: One size does not fit all

“…We performed variance partitioning by multiple regression to quantify the unique and shared fractions of variation in carbon/nitrogen isotopic composition of zooplankton taxa and DOM explained by CHEM, HYDR_LAND, and SPATIAL. Only the subsets of significant predictors from each of the variable sets (identified by the above regression analyses) were used in this analysis (Peres-Neto, Legendre, Dray, & Borcard, 2006 (Shewchuk, 1982;Whitfield & Watmough, 2012) and potential atmospheric nitrogen deposition from nearby Athabasca oil sands operations (Makar et al, 2018) across the region. Given that exact spatial patterns of these factors are largely unknown, our spatial modelling approach has an advantage over the conventional approach of using a priori-defined, fixed regional-scale unit to successfully capture the variation induced by potential regional factors that may be operating at varying spatial scales.…”

“…Finally, we assumed that pure spatial variation (i.e. spatially structured isotopic variation independent of influence of local environmental factors [CHEM and HYDR_LAND]) represents the effects of unmeasured regional‐scale factors such as spatial variations in bedrock geology, soil properties (Shewchuk, ; Whitfield & Watmough, ) and potential atmospheric nitrogen deposition from nearby Athabasca oil sands operations (Makar et al., ) across the region. Given that exact spatial patterns of these factors are largely unknown, our spatial modelling approach has an advantage over the conventional approach of using a priori‐defined, fixed regional‐scale unit to successfully capture the variation induced by potential regional factors that may be operating at varying spatial scales.…”

“…independent of its effects through lake/catchment‐specific factors, can be attributable to the effects of regional‐scale factors that were not explicitly measured in the present study (Anas, Scott, Cooper, & Wissel, ; Beisner et al., ). For instance, spatial variations in bedrock geology, soil properties (Shewchuk, ; Whitfield & Watmough, ) and potential atmospheric nitrogen deposition from nearby Athabasca oil sands operations (Makar et al., ) across the region may influence among‐lake SI variability at the food‐web base (Aravena, Schiff, Trumbore, Dillon, & Elgood, ; Elliott et al., ).…”

Supplementary material to "How much does traffic contribute to benzene and PAH air pollution? Results from a high-resolution North American air quality model centered on Toronto, Canada"

“…Aside from partitioning with the gas-phase, particulate PAH mass may be reduced through particulate deposition, which is a function of the size of the particles within which the PAHs reside. Gas-phase PAH deposition follows the approach taken for other gases in GEM-MACH (see the SI of Makar et al, 2018); a multiple resistance, temperature-dependent "big-leaf" approach, employing parameters based on Henry's law constants and water solubility. Gaseous PAHs and benzene may also be scavenged by cloud water and rain via Henry's Law and by snow surface adsorption (Franz and Eisenreich, 1998).…”