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Plume rise is commonly parameterized based on ambient atmospheric conditions and emission source metrics (e.g. stack effluent temperature and exit momentum), with empirical formulae (e.g., Briggs, 1984) employed in large-scale air-quality models (e.g. Environment and Climate Change Canada’s GEM-MACH model). Past evaluations against observed plume heights emitted from industrial sources (e.g., Canadian Oil Sands) have attributed the discrepancies between observed and predicted plume heights to various causes, such as spatial variability of meteorological fields between observation and stack locations and/or inaccuracies in model meteorological predictions. It has been shown that stack-location-specific meteorology and layered (vertical) calculation of plume buoyancy can improve predicted plume heights (Akingunola et al. 2018). However, more recent observations have shown that predicted plume heights remain biased low relative to aircraft observations of well-characterized SO2 plumes, particularly under colder winter conditions, and demonstrate the need for further improvements to plume rise predictions. We introduce a new algorithm for plume rise calculation, which incorporates thermodynamic effects of the emitted water vapour from industrial stack combustion sources on the resulting calculation of plume height. The high temperature effluent from these stacks usually contain significant amounts of combustion-generated water. As the plume rises and cools, this water vapour condenses, increasing plume temperature and buoyancy through the release of latent heat, which can result in additional plume rise. We have developed a revised plume rise algorithm for implementation within the regional models, through combining the Briggs’ empirical parameterization with concepts of cloud parcel thermodynamic effects for the release or uptake of latent heat associated with the phase change of water. Our results show significant improvement in model plume rise prediction, through evaluation against SO2 plumes observed during a 2018 aircraft campaign over the Canadian Oil Sands. We also discuss results from long-term (15-month duration) model simulations with the new versus the original algorithm, along with evaluations against aircraft-based and surface monitoring network observed concentrations. The potential impact of the condensed in-plume liquid water on aqueous phase chemistry will also be discussed. This work is the first plume rise algorithm to incorporate the effects of latent heat release of both combustion-emitted and in-plume ambient-entrained water, for implementation in air quality models. ReferencesAkingunola, A., Makar, P. A., Zhang, J., Darlington, A., Li, S.-M., Gordon, M., Moran, M. D., and Zheng, Q.: A chemical transport model study of plume-rise and particle size distribution for the Athabasca oil sands, Atmos. Chem. Phys., 18, 8667–8688, https://doi.org/10.5194/acp-18-8667-2018, 2018. Briggs, G. A.: Plume rise and buoyancy effects, atmospheric sciences and power production, in: DOE/TIC-27601 (DE84005177), edited by: Randerson, D., TN, Technical Information Center, US Dept. of Energy, Oak Ridge, USA, 327–366, 1984.

Air quality monitoring networks provide invaluable data for studying human health, environmental impacts, and the effects of policy changes, but obtaining high quality data can be costly, with each site in a monitoring network requiring instrumentation and skilled operator time. It is therefore important to ensure that each monitor in the network is providing unique data to maximize the value of the entire network. Differences in measurement approaches for the same chemical between monitoring stations may also result in discontinuities in the network data. Both of these factors suggest the need for objective, machine-learning methodologies for monitoring network data analysis. Air quality models are another valuable tool to augment monitoring networks. The models simulate air quality over a large region where monitoring may be sparse. The gridded output from air-quality models thus contain inherent information on the similarity of sources, chemical oxidation pathways and removal processes for chemicals of interest, provided appropriate tools are available to identify these similarities on a gridded basis. The output from these models can be immense, again requiring the use of special, highly optimized tools for post-processing analysis.Spatiotemporal clustering is a family of techniques that have seen widespread use in air quality, whereby time-series taken at different locations are grouped based on the level of similarity between time-series data within the dataset. Hierarchical clustering is one such algorithm, which has the advantage of not requiring an a priori assumption about how many clusters there might be (unlike K-means). However, traditional approaches for hierarchical clustering become computationally expensive as the number of time-series increases in size, resulting in prohibitive computational costs when the total number of time-series to be compared rises above 30,000, even on a supercomputer. Similarly, the comparison and clustering of large numbers of discrete data (such as multiple mass spectrometer data sampled at high time resolution from a moving laboratory platform) becomes computationally prohibitive using conventional methods. In this study we present a high-performance hierarchical clustering algorithm which is able to run in parallel over many nodes on massively parallel computer systems, thus allowing for efficient clustering for very large monitoring network and model output datasets. The new high-performance program is able to cluster 290,000 annual time series (from either monitoring network data or gridded model output) in 13 hours on 800 nodes. We present here some example results showing how the algorithm can be used to analyse very large datasets, providing new insights into “airsheds” depicting regions of similar chemical origin and history, different spatial regimes for nitrogen, sulphur, and base cation deposition, . These analyses show how different processes control each species at different potential monitoring site locations, via cluster-generated airshed maps for each species. The efficiency and flexibility of the algorithm allows for extremely large datasets to be analysed in hours of wall-clock time instead of weeks or months. The new algorithm is being used as the numerical engine for a new tool for the analysis of EU monitoring network data.

Inorganic heterogeneous chemistry (the reactions taking place between inorganic components of the gas-particle system) is one of the most complex and computationally demanding parts of atmospheric chemistry models. Accurate and highly computationally efficient algorithms for carrying out these calculations are essential for these models. Here we present a revised and updated approach for carrying out these calculations, called HETV2.HETV2 updates the original HETV metastable state subroutines (Makar et al., 2003) expanding the aerosol system to include base cations (Mg2+, K+, Ca2+, Na+), and partitioning between chlorine, ammonium, and nitrate ions and HCl, NH3 and HNO3 gases. HETV2 is based on the algorithms of ISORROPIA II (Fountoukis and Nenes, 2007), with several key improvements for accuracy and computational efficiency of the calculations. First, the accuracy and stability of polynomial roots have been improved by using a Taylor series expansion of the quadratic formula, for times when the coefficients differ by orders of magnitude. Second, the new algorithms in HETV2 enforce mass conservation for cases where all species are present and the ratio of total base cations to sulfate is between 1.0 and 2.0. Third, the code has been optimized using a “vectorization by gridpoint” approach, allowing a single call to each subroutine for n sets of input conditions, reducing the subroutine call factor overhead. Fourth, the code has been optimized to remove unnecessary calculations, and the programming language has been updated from Fortran 77 to Fortran 90. Fifth, all subroutines that require bisection to obtain an equilibrium solution (i.e., the ‘major systems’) have had their root-finding method updated to the ‘Interpolate, Truncate and Project (ITP)’ method (Oliveria et al., 2021); the ITP method can obtain superlinear convergence, and therefore may significantly reduce the number of iterations, and hence the computational time, required to obtain the same result as ISORROPIA II. The new algorithms significantly improve both the computational speed and accuracy for inorganic heterogeneous chemistry calculations relative to ISORROPIA II. In this talk, we will describe the inorganic heterogeneous chemistry systems that are solved, the improvements to the algorithms, and compare the computational speed of ISORROPIA II to the new HETV2 code (depending on the chemical subspace examined, the new code is up to 2x faster than ISORROPIA II).References Fountoukis, C., & Nenes, A., 2007. ISORROPIA II: A computationally efficient thermodynamic equilibrium model for Aerosols. Atmospheric Chemistry and Physics, 7(17), 4639–4659.Makar, P. A., Bouchet, V. S., & Nenes, A., 2003. Inorganic Chemistry calculations using HETV—a vectorized solver for the SO42−–NO3−–NH4+ system based on the ISORROPIA algorithms. Atmospheric Environment, 37(16), 2279–2294.Oliveira, I. F., & Takahashi, R. H., 2021. An enhancement of the bisection method average performance preserving Minmax optimality. ACM Transactions on Mathematical Software, 47(1), 1–24.

Below-cloud scavenging is the process of aerosol removal from the atmosphere between cloud-base and the ground by precipitation (e.g. rain or snow), and affects aerosol number/mass concentrations, lifetime and distributions. An accurate representation of precipitation phases is important in treating below-cloud scavenging as the efficiency of aerosol scavenging differs significantly between liquid and solid precipitation. To study cloud processes and precipitation chemistry, we examined representation of below-cloud aerosol scavenging of in the current GEM-MACH model, including a revised approach in precipitation phase partitioning and implementing a new aerosol below-cloud scavenging scheme (from Wang et al., 2014) and comparing with the GEM-MACH’s existing scavenging scheme, based on Slinn (1984). Overall, the multi-phase partitioning and Wang et al. (2014) scavenging scheme improve GEM-MACH performance as compared with observations. Including multi-phase approach leads to a decrease on SO42- scavenging and impacts the below-cloud scavenging of SO2 into the aqueous phase. The impact of the new scheme on wet deposition of NO3- and NH4+ varies, with both increases and decreases in wet scavenging, and is more important at specific cloud locations. The two aerosol scavenging rates differ during liquid precipitation in the 0.1-1 µm size range mostly at high precipitation intensity. The two aerosol scavenging schemes diverge for aerosols smaller than 1 µm for solid precipitation at lower intensity (R=0.01 mm/h), while at higher precipitation intensities (R=10 mm/h), the two schemes show larger differences for aerosols larger than 1 µm. The changes on the speciated particles (sulphate, nitrate and ammonium) are consistent with the changes in the wet scavenging, leading to higher modelled concentrations of particulate sulphate in the atmosphere.

Air quality monitoring networks provide invaluable data for studying human health, environmental impacts, and the effects of policy changes. In a European legislative context, the data collected constitutes the basis for reporting air quality status and exceedances under the Ambient Air Quality Directives (AAQD) following specific requirements. Consequently, the network's representativity and ability to accurately assess the air pollution situation in European countries become a key issue. The combined use of models and measurements is currently understood as the most robust way to map the status of air pollution in an area, allowing it to quantify both the spatial and temporal distribution of pollution. This spatial-temporal information can be used to evaluate the representativeness of the monitoring network and support air quality monitoring design using hierarchical clustering techniques.The hierarchical clustering methodology applied in this context can be used as a screening tool to analyse the level of similarity or dissimilarity of the air concentration data (time-series) within a monitoring network. Hierarchical clustering assumes that the data contains a level of (dis)similarity and groups the station records based on the characteristics of the actual data. The advantage of this type of clustering is that it does not require an a priori assumption about how many clusters there might be, but it can become computationally expensive as the number of time-series increases in size. Three dissimilarity metrics are used to establish the level of similarity (or dissimilarity) of the different air quality measurements across the monitoring network: (1) 1-R, where R is the Pearson linear correlation coefficient, (2) the Euclidean distance (EuD), and (3) multiplication of metric (1) and (2). The metric based on correlation assesses dissimilarities associated with the changes in the temporal variations in concentration. The metric based on the EuD assesses dissimilarities based on the magnitude of the concentration over the period analysed. The multiplication of these two metrics (1-R) x EuD assesses time variation and pollution levels correlations, and it has been demonstrated to be the most useful metric for monitoring network optimization.This study presents the MoNET webtool developed based on the hierarchical clustering methodology. This webtool aims to provide an easy solution for member states to quality control the data reported as a tier-2 level check and evaluate the representativeness of the air quality network reporting under the AAQD. Some examples from the ongoing evaluation of the monitoring site classification carried out as a joint exercise under the Forum for Air Quality Modeling (FAIRMODE) and the National Air Quality Reference Laboratories Network (AQUILA) are available to show the usability of the tool. MoNet should be able to identify outliers, i.e., issues with the data or data series with very specific temporal-magnitude profiles, and to distinguish, e.g., pollution regimes within a country and if it resembles the air quality zones required by the AAQD and set by the member states; stations monitoring high-emitting sources; background regimes vs. a local source driving pollution regime in cities.

Emissions from wildfires are a significant source of air pollution, which can adversely impact air quality and ecosystems thousands of kilometers downwind. These emissions can be estimated by a bottom-up approach, using inputs such fuel type, burned area, and standardized emission factors. Emissions are also commonly derived with a top-down approach, using satellite observed fire radiative power (FRP) as proxy for fuel consumption. More recently, wildfire emissions have been demonstrated to be estimated directly from satellite observations, including carbon monoxide (CO). Here, we explore the potential of satellite-derived CO emission rates from wildfires and provide new insights into the understanding of satellite-derived fire CO emissions globally, with respect to differences in regions and vegetation type. Specifically, we use the TROPOMI (Tropospheric Monitoring Instrument) high spatial-resolution satellite datasets to create a global inventory database of burning emissions CO emissions between 2019 and 2021. Our retrieval methodology includes an analysis of conditions under which emission estimates may be inaccurate and filters these accordingly. Additionally, we determine biome specific emission coefficients (emissions relative to FRP) and show how combining the satellite derived CO emissions with satellite observed FRP from the Moderate Resolution Imaging Spectrometer (MODIS) establishes an annual CO emission budget from wildfires. The resulting emissions totals are compared to other top-down and bottom-up emission inventories over the past two decades. In general, the satellite-derived emissions inventory values and bottom-up emissions inventories have similar CO emissions totals across different global regions, though the discrepancies may be large for some regions (Southern Hemisphere South America, Southern Hemisphere Africa, Southeast Asia) and for some bottom-up inventories (e.g. FINN2.5, where CO emissions are a factor of 2 to 5 higher than other inventories). Overall, these estimates can help to validate emission inventories and predictive air quality models, and help to identify limitations present in existing bottom-up emissions inventory estimates.

Investigation of the cloud microphysics is carried out by using a fully coupled version of GEM-MACH, the Environment and Climate Change Canada’s (ECCC) online air quality forecast model, (Global Environmental Multiscale–Modelling Air quality and Chemistry) for the Arctic domain during summer 2014. Simulation results indicate that model is capable of simulating the low clouds prevailing in summertime Arctic, particularly thin water clouds (or clouds with liquid water path < 50 g m-2), which have a significant effect on cloud radiative forcing in the Arctic.Model simulations are also compared with the July 2014 NETCARE field campaign aircraft observations based from Resolute NU. The field campaign consisted of two periods with distinct metrological conditions: relatively pristine and relatively polluted Arctic atmosphere with the influence of transport from lower latitudes. For the relatively polluted period, simulations of cloud’s microphysics suggested more and smaller droplets with higher liquid water content (LWC), and hence lower precipitation and longer cloud lifetime. The model agrees well with the observation results showing that aerosols in the size range of 50-100 nm are commonly activated in the summer Arctic, with even smaller aerosols (< 50 nm) being activated during the pristine period.

Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from twelve state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, the U.S., August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within one day are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by FRP-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019 mainly over the transported smoke plumes, owing to the underestimated emissions on the 7th. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center’s Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper with the day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported one-day-old smoke. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

Cases of the transport of NA biomass burning plumes into the Canadian Arctic were identified based on existing model simulations, using the Environment and Climate Change Canada’s on-line air quality forecast model (GEM-MACH). Analysis shows that, in one case, the pollutants from wild fires in northern Canada were first transported northwards rising against the polar dome then eastwards descending into the study area in the Canadian Arctic archipelago as the polar dome weakened and retreated out of the region. Precipitation associated with the uplifting of the air mass over the polar dome was responsible for scavenging aerosols in the initial fire plume but CO, unaffected by wet scavenging, remained in the air mass and hence impacted the study area. In another case, the study area is shown to be affected by the transport of an air mass up Baffin Bay within the marine boundary layer influenced by both biomass burning in northern Canada and anthropogenic pollution of eastern North America. Model simulations were also conducted using the fully coupled version of GEM-MACH to investigate the impact of biomass burning aerosols on the Arctic marine boundary layer clouds. The study shows that, during the transient period, there is an enhancement in average droplet number concentration and decrease in average droplet diameter of liquid water clouds in the Arctic due to the influence of northern Canada’s biomass burning aerosols.

Using a fully coupled air quality prediction model, simulations were carried out to investigate the impact of aerosol indirect effect on air pollution-meteorology interaction in an urban environment. We found that the aerosol indirect effect results in an increase in cloud droplet number concentration, a reduction in cloud droplet size, and an increase in cloud water. While, as a result, precipitation production is suppressed in low-level clouds, we found that, in a case of deep convective clouds, there is an enhancement of cloud ice and precipitation production at higher levels due to the increase in abundance of smaller drops being carried up in the updraft. There is also an indication of enhanced convective activity due to urban heating.

Experimental air-quality forecasts for the Canadian provinces of Alberta and Saskatchewan have been carried out since 2012, using a 10 km/2.5 km nested resolution version of Environment and Climate Change Canada’s Global Environmental Multiscale-Modelling Air-quality and Chemistry (GEM-MACH) on-line air-quality model. We describe here some of the main results of that work, and a major upgrade of this forecasting system (based on work carried out following a 2013 monitoring intensive field campaign in the Athabasca oil sands region of Canada). The new forecasting system has been designed in preparation for a follow-up field campaign, taking place during April and June of 2018.

Before the launch of TROPOMI, only two other satellite instruments were able to observe aerosol plume heights globally, MISR and CALIOP. The TROPOMI aerosol layer height is a potential game changer, since it has daily global coverage and the aerosol layer height retrieval is available in near-real time. The aerosol layer height can be useful for aviation and air quality alerts, as well as for improving air quality forecasting related to wildfires. Here, TROPOMI's aerosol layer height product is evaluated with MISR and CALIOP observations for wildfire plumes in North America for the 2018 fire season (June to August). Further, observing system simulation experiments were performed to interpret the fundamental differences between the different products. The results show that MISR and TROPOMI are, in theory, very close for aerosol profiles with single plumes. For more complex profiles with multiple plumes, however, different plume heights are retrieved: the MISR plume height represents the top layer, and the plume height retrieved with TROPOMI tends to be an average altitude of several plume layers. The comparison between TROPOMI and MISR plume heights shows, that on average, the TROPOMI aerosol layer heights are lower, by approximately 600 m, compared to MISR which is likely due to the different measurement techniques. From the comparison to CALIOP, our results show that the TROPOMI aerosol layer height is more accurate for thicker plumes and plumes below approximately 4.5 km. MISR and TROPOMI are further used to evaluate the plume height of Environment and Climate Change Canada's operational forecasting system FireWork with fire plume injection height estimates from the Canadian Forest Fire Emissions Prediction System (CFFEPS). The modelled plume heights are similar compared to the satellite observations, but tend to be slightly higher with average differences of 270–580 m and 60–320 m compared to TROPOMI and MISR, respectively.

Polycyclic aromatic hydrocarbons (PAHs) are toxic compounds that are ubiquitous in the atmospheric environment. The input for an emissions processing system that was originally configured forthe study of criteria air pollutants was updated to calculate emissions of six semivolatile PAHs. The goal of the work was to produce emissions estimates with the spatial and temporal resolution needed to serve as input to a regional air quality model for southern Canada and the U.S. Such modeling is helpful in determining reductions in PAH emissions that may be necessary to protect human and ecosystem health. The total annual emission of the six PAHs (sigma6PAH) for both countries was estimated at 18 273 Mg/year. A total of 90% of these emissions arise from U.S. sources. The top six source types account for 73% of emissions and are related to metal production, open burning, incineration, and forest fires. The emission factors used in this study were derived from published compilations. Although this approach has the advantage of quality control during the compilation process, some compilations include factors from older studies that may overestimate emissions since they do not account for recent improvements in emission control technology. When compared to estimates published in the National Emissions Inventory (NEI) for 2002, the U.S. emissions in this study are higher by a factor of 4 (16 424 vs 4102 Mg/year). The cause of this difference has been investigated, and much of it is likely due to our use of data unavailable in the 2002 NEI but inferred here on the basis of the PAH emissions literature. Augmenting the 2002 NEI with this additional information would bring its reported annual emissions to 8213 Mg/year, which is within a factor of 2 of the estimates herein. The results presented for southern Canada are the first published values for all known PAH sources in that country.

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