The oil sands in Alberta, Canada, are a significant source of air pollution. Observations from the Ozone Monitoring Instrument (OMI) on the NASA Aura satellite have been used to quantify NOx emissions from the surface mining region of the oil sands. Two related emissions methods were utilized, one for point and one for area sources, where OMI vertical column densities of NO2 were combined with winds from a meteorological reanalysis and a two-dimensional exponentially modified Gaussian (EMG) plume model. This work better connects the two (point and area) emissions methods and discusses the interpretation of fit parameters and the ability of OMI (and other sensors) to resolve emissions between neighbouring sources. The two methods employed, in good agreement with each other, indicated an increase in emissions from about 55 to 80 kt [NO2] yr−1 between 2005–2011 and a flat trend thereafter. Reported emissions were within 15 % of reported emissions, consistent to within uncertainties. In an extension of this methodology, OMI observations were combined with reported point source emissions to derive the more uncertain emissions component from the large off-road mining fleet. These were found to make up about 60 % of total NOx emissions, also consistent with reported emissions. The OMI-derived 0.9 % yr−1 increase in fleet emissions and the 5.5 % yr−1 increase in bitumen mined, generally a good proxy for fleet emissions, can be reconciled by considering the evolution of the mine fleet over this period. OMI is therefore able to track the transition from US EPA Tier 1 standards, through Tier 4 standards, to the present and in doing so demonstrates the efficacy of this policy. Furthermore, this analysis shows that had the fleet remained at Tier 1, this source would currently be emitting an additional 40 kt [NO2] yr−1.

Fine particulate matter (PM2.5) concentrations in Red Deer, Alberta, Canada, exceeded the national Canadian Ambient Air Quality Standards (CAAQS) for 2011-2013. In response, a monitoring campaign was deployed to measure PM2.5 composition and precursor gases. The objective of the study was to assess PM2.5 sources, and ultimately to support management actions to improve air quality. The elevated PM2.5 concentrations were regional scale events, with similar and correlated concentrations observed at two stations in Red Deer, and at a site located 9.4 km upwind. Receptor source apportionment identified 9 factors, with secondary organics/aged smoke, sulphate and nitrate factors making up 60% of the PM2.5 mass. When wildfire-impacted samples are removed, seasonal average PM2.5 mass concentrations are largest in the spring, and are dominated by the sulphate and nitrate factors (74%). The relationship between the sulphate factor and meteorological conditions is consistent with a regional source, which could include the coal-fired power plants and the smaller upstream oil and gas operations in the area. Alberta's power plants are switching from coal to natural gas, sulphate levels are expected to decrease. The elevated nitrate factors appeared to be affected by local emissions sources that differed at each monitoring site, and could include urban and industrial sources. The elevated nitrate factors occurred under regional meteorological conditions that promote nitrate formation, with higher contributions observed for sample days with relative humidity >65%, temperature between 0 to -10 degrees C, and snow cover. The nitrate factor enhancements support a non-negligible contribution from the heterogeneous reactions forming nitric acid. A thermodynamic analysis showed that the ammonia-sulphate-nitrate system is nitric acid limited for this study, suggesting that decreases in nitrogen oxide precursors would help to manage PM2.5 concentrations. This study demonstrates the complexity of managing PM2.5 concentrations in an urban environment with nearby industrial sources.

To combat global warming, Canada has committed to reducing greenhouse gases to be (GHGs) 40 %–45 % below 2005 emission levels by 2025. Monitoring emissions and deriving accurate inventories are essential to reaching these goals. Airborne methods can provide regional and area source measurements with small error if ideal conditions for sampling are met. In this study, two airborne mass-balance box-flight algorithms were compared to assess the extent of their agreement and their performance under various conditions. The Scientific Aviation's (SciAv) Gaussian algorithm and the Environment and Climate Change Canada's top-down emission rate retrieval algorithm (TERRA) were applied to data from five samples. Estimates were compared using standard procedures, by systematically testing other method fits, and by investigating the effects on the estimates when method assumptions were not met. Results indicate that in standard scenarios the SciAv and TERRA mass-balance box-flight methods produce similar estimates that agree (3 %–25 %) within algorithm uncertainties (4 %–34 %). Implementing a sample-specific surface extrapolation procedure for the SciAv algorithm may improve emission estimation. Algorithms disagreed when non-ideal conditions occurred (i.e., under non-stationary atmospheric conditions). Overall, the results provide confidence in the box-flight methods and indicate that emissions estimates are not overly sensitive to the choice of algorithm but demonstrate that fundamental algorithm assumptions should be assessed for each flight. Using a different method, the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) independently mapped individual plumes with emissions 5 times larger than the source SciAv sampled three days later. The range in estimates highlights the utility of increased sampling to get a more complete understanding of the temporal variability of emissions and to identify emission sources within facilities. In addition, hourly on-site activity data would provide insight to the observed temporal variability in emissions and make a comparison to reported emissions more straightforward.

Satellite-derived and reported sulfur dioxide (SO2) emissions from the Canadian oil sands are shown to have been consistent up to 2013. Post-2013, these sources of emissions data diverged, with reported emissions dropping by a factor of two, while satellite-derived emissions for the region remained relatively constant, with the discrepancy (satellite-derived emissions minus reported emissions) peaking at 50 kt(SO2) yr(-1) around 2016. The 2013-2014 period corresponds to when new flue-gas desulfurization units came on-line. Previous work has established a high level of consistency between at-stack SO2 emissions observations and satellite estimates, and surface monitoring network SO2 concentrations over the same multi-year period show similar trends as the satellite data, with a slight increase in concentrations post-2013. No clear explanation for this discrepancy currently exists. The implications of the discrepancy towards estimated total sulfur deposition to downwind ecosystems were estimated relative to 2013 emissions levels, with the satellite-derived values leaving the area of regional critical load exceedances of aquatic ecosystems largely unchanged from 2013 values, 335 000 km(2), and reported values potentially decreasing this area to 185 000 km(2).

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.

Smoke from wildfires are a significant source of air pollution, which can adversely impact ecosystems and the air quality in downwind populated areas. With increasing severity of wildfires over the years, these are a significant threat to air quality in densely populated areas. Emissions from wildfires are most commonly estimated by a bottom-up approach, using proxies such fuel type, burn area, and emission factors. Emissions are also commonly derived with a top-down approach, using satellite observed Fire Radiative Power. Furthermore, wildfire emissions can also be estimated directly from satellite-borne measurements. Here, we present advancements and improvements of direct emission estimates of forest fire NOx emissions by using TROPOMI (Tropospheric Monitoring Instrument) high-resolution satellite datasets, including NO2 vertical column densities (VCDs) and information on plume height and aerosol scattering. The effect of smoke aerosols on the sensitivity of TROPOMI to NO2 (via air mass factors) is estimated with recalculated VCDs, and validated with aircraft observations. Different top-down emission estimation methods are tested on synthetic data to determine the accuracy, and the sensitivity to parameters, such as wind fields, satellite sampling, instrument noise, NO2:NOx conversion ratio, species atmosphere lifetime and plume spread. Lastly, the top-down, bottom-up and direct emission estimates of fire emissions are quantitatively compared.

In this work, a computer-aided design and finite element analysis methodology has been developed for the caravan chassis. 3-D models are created by software ANSYS to predict the stress and deflection of the chassis under various working conditions. The optimal design techniques developed by the group helps to find the best shapes and sizes of the chassis elements. Computer-aided manufacturing has been employed for the components and assembly procedures. The computational techniques have been applied for both the traditional and the new generation chassis. For the traditional chassis, the weak parts of the structure have been discovered with improvements proposed to enhance the four ground beams and reducing the material in other beams. Pre-stress has been reduced for the manufacturing process. For the new generation chassis, a versatile, high quality and cost effective chassis has been successfully designed and launched into UK market. It is envisaged that benefits will gain from the improved products for better performance and less cost and the computational techniques will play a key role in the product design.

In May 2016, the Horse River wildfire led to the evacuation of ∼ 88 000 people from Fort McMurray and surrounding areas and consumed ∼ 590 000 ha of land in Northern Alberta and Saskatchewan. Within the plume, satellite instruments measured elevated values of CO, NH3, and NO2. CO was measured by two Infrared Atmospheric Sounding Interferometers (IASI-A and IASI-B), NH3 by IASI-A, IASI-B, and the Cross-track Infrared Sounder (CrIS), and NO2 by the Ozone Monitoring Instrument (OMI). Daily emission rates were calculated from the satellite measurements using fire hotspot information from the Moderate Resolution Imaging Spectroradiometer (MODIS) and wind information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis, combined with assumptions on lifetimes and the altitude range of the plume. Sensitivity tests were performed and it was found that uncertainties of emission estimates are more sensitive to the plume shape for CO and to the lifetime for NH3 and NOx. The satellite-derived emission rates were ∼ 50–300 kt d−1 for CO, ∼ 1–7 kt d−1 for NH3, and ∼ 0.5–2 kt d−1 for NOx (expressed as NO) during the most active fire periods. The daily satellite-derived emission estimates were found to correlate fairly well (R∼0.4–0.7) with daily output from the ECMWF Global Fire Assimilation System (GFAS) and the Environment and Climate Change Canada (ECCC) FireWork models, with agreement within a factor of 2 for most comparisons. Emission ratios of NH3∕CO, NOx∕CO, and NOx∕NH3 were calculated and compared against enhancement ratios of surface concentrations measured at permanent surface air monitoring stations and by the Alberta Environment and Parks Mobile Air Monitoring Laboratory (MAML). For NH3∕CO, the satellite emission ratios of ∼ 0.02 are within a factor of 2 of the model emission ratios and surface enhancement ratios. For NOx∕CO satellite-measured emission ratios of ∼0.01 are lower than the modelled emission ratios of 0.033 for GFAS and 0.014 for FireWork, but are larger than the surface enhancement ratios of ∼0.003, which may have been affected by the short lifetime of NOx. Total emissions from the Horse River fire for May 2016 were calculated and compared against total annual anthropogenic emissions for the province of Alberta in 2016 from the ECCC Air Pollutant Emissions Inventory (APEI). Satellite-measured emissions of CO are ∼1500 kt for the Horse River fire and exceed the total annual Alberta anthropogenic CO emissions of 992.6 kt for 2016. The satellite-measured emissions during the Horse River fire of ∼30 kt of NH3 and ∼7 kt of NOx (expressed as NO) are approximately 20 % and 1 % of the magnitude of total annual Alberta anthropogenic emissions, respectively.

AIM-North is a proposed satellite mission that would provide observations of unprecedented frequency and density for monitoring northern greenhouse gases (GHGs), air quality (AQ) and vegetation. AIM-North would consist of two satellites in a highly elliptical orbit formation, observing over land from ∼40°N to 80°N multiple times per day. Each satellite would carry a near-infrared to shortwave infrared imaging spectrometer for CO2, CH4, and CO, and an ultraviolet-visible imaging spectrometer for air quality. Both instruments would measure solar-induced fluorescence from vegetation. A cloud imager would make near-real-time observations, which could inform the pointing of the other instruments to focus only on the clearest regions. Multiple geostationary (GEO) AQ and GHG satellites are planned for the 2020s, but they will lack coverage of northern regions like the Arctic. AIM-North would address this gap with quasi-geostationary observations of the North and overlap with GEO coverage to facilitate intercomparison and fusion of these datasets. The resulting data would improve our ability to forecast northern air quality and quantify fluxes of GHG and AQ species from forests, permafrost, biomass burning and anthropogenic activity, furthering our scientific understanding of these processes and supporting environmental policy.

Zenith-Sky scattered light Differential Optical Absorption Spectroscopy (ZS-DOAS) has been used widely to retrieve total column ozone (TCO). ZS-DOAS measurements have the advantage of being less sensitive to clouds than direct-sun measurements. However, the presence of clouds still affects the quality of ZS-DOAS TCO. Clouds are thought to be the largest contributor to random uncertainty in ZS-DOAS TCO, but their impact on data quality still needs to be quantified. This study has two goals: (1) to investigate whether clouds have a significant impact on ZS-DOAS TCO, and (2) to develop a cloud-screening algorithm to improve ZS-DOAS measurements in the Arctic under cloudy conditions. To quantify the impact of weather, 8 years of measured and modelled TCO have been used, along with information about weather conditions at Eureka, Canada (80.05∘ N, 86.41∘ W). Relative to direct-sun TCO measurements by Brewer spectrophotometers and modelled TCO, a positive bias is found in ZS-DOAS TCO measured in cloudy weather, and a negative bias is found for clear conditions, with differences of up to 5 % between clear and cloudy conditions. A cloud-screening algorithm is developed for high latitudes using the colour index calculated from ZS-DOAS spectra. The quality of ZS-DOAS TCO datasets is assessed using a statistical uncertainty estimation model, which suggests a 3 %–4 % random uncertainty. The new cloud-screening algorithm reduces the random uncertainty by 0.6 %. If all measurements collected during cloudy conditions, as identified using the weather station observations, are removed, the random uncertainty is reduced by 1.3 %. This work demonstrates that clouds are a significant contributor to uncertainty in ZS-DOAS TCO and proposes a method that can be used to screen clouds in high-latitude spectra.

Following the large volcanic eruptions of Pinatubo in 1991 and El Chichón in 1982, decreases in stratospheric NO2 associated with enhanced aerosol were observed. The Optical Spectrograph and Infrared Imaging Spectrometer (OSIRIS) measured the widespread enhancements of stratospheric aerosol following seven volcanic eruptions between 2002 and 2014, although the magnitudes of these eruptions were all much smaller than the Pinatubo and El Chichón eruptions. In order to isolate and quantify the relationship between volcanic aerosol and NO2, NO2 anomalies were calculated using measurements from OSIRIS and the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). In the tropics, variability due to the quasi-biennial oscillation was subtracted from the time series. OSIRIS profile measurements indicate that the strongest anticorrelations between NO2 and volcanic aerosol extinction were for the 5 km layer starting ∼ 3 km above the climatological mean tropopause at the given latitude. OSIRIS stratospheric NO2 partial columns in this layer were found to be smaller than background NO2 levels during these aerosol enhancements by up to ∼ 60 % with typical Pearson correlation coefficients of R ∼ −0. 7. MIPAS also observed decreases in NO2 partial columns during periods affected by volcanic aerosol, with percent differences of up to ∼ 25 % relative to background levels. An even stronger anticorrelation was observed between OSIRIS aerosol optical depth and MIPAS N2O5 partial columns, with R ∼ −0. 9, although no link with MIPAS HNO3 was observed. The variation in OSIRIS NO2 with increasing aerosol was found to be consistent with simulations from a photochemical box model within the estimated model uncertainty.

The lack of PSCs should not be a large factor here since only latitudes north of 55S are considered in the updated analysis (see Response to Reviewer 3, General Comment A). Moreover, the intent of the model is to demonstrate that the ‘average’ relationship seen in the OSIRIS NO2 and aerosol extinction observations can be captured by the model, thereby demonstrating that the two are consistent with our current understanding of the impact of aerosol on NOy partitioning. It is possible that one or more OSIRIS monthly/zonal means might be contain some PSC signal, and this would lead to some departure from the non-PSC expected relationship, but there is no evidence of this in the observations at 70◦ or 80◦N and at the high end of the AOD scale (the last two

Ozone depletion events in the polar troposphere have been linked to extremely high concentrations of bromine, known as bromine explosion events (BEE). However, the optimum meteorological conditions for the occurrence of these events remain uncertain. On 4–5 April 2011, a combination of both blowing snow and a stable shallow boundary layer was observed during a BEE at Eureka, Canada (86.4°W, 80.1°N). Measurements made by a Multi‐Axis Differential Optical Absorption Spectroscopy spectrometer were used to retrieve BrO profiles and partial columns. During this event, the near‐surface BrO volume mixing ratio increased to ~20 parts per trillion by volume, while ozone was depleted to ~1 ppbv from the surface to 700 m. Back trajectories and Global Ozone Monitoring Experiment‐2 satellite tropospheric BrO columns confirmed that this event originated from a bromine explosion over the Beaufort Sea. From 30 to 31 March, meteorological data showed high wind speeds (24 m/s) and elevated boundary layer heights (~800 m) over the Beaufort Sea. Long‐distance transportation (~1800 km over 5 days) to Eureka indicated strong recycling of BrO within the bromine plume. This event was generally captured by a global chemistry‐climate model when a sea‐salt bromine source from blowing snow was included. A model sensitivity study indicated that the surface BrO at Eureka was controlled by both local photochemistry and boundary layer dynamics. Comparison of the model results with both ground‐based and satellite measurements confirmed that the BEE observed at Eureka was triggered by transport of enhanced BrO from the Beaufort Sea followed by local production/recycling under stable atmospheric shallow boundary layer conditions.

Do the authors mean a 4 km thick layer above the tropopause, in tropics and midlatitudes, and against what conditions is the change? Please define NOx in line 9 of page 2. Please improve wording in line 13 of page 3, it contains contradictions. From the data version number, it looks like that Höpfner et al (2015) is used (line 9ff, page 4), here also the SO2 data prior to 2005 are OK. In these data, especially if the 5day means are used, all important volcanic events should be identified with significance (see also lines 15 and 25, page 8). This is not the case for the older dataset presented in Höpfner et al (2013) which had the focus on the middle and upper stratosphere. For the crude assumptions on the Mie scattering efficiency the wavelength should be repeated (line 2, page 5). The statements on particle size (line 5, page 5) are confusing, more details please, give at least a range for the effective particle size. If you model particle size from aerosol formation from injected SO2, you get in increase in effective particle size for both volcanoes. What is the basis for the crude error assumptions (factor 3)? Isn’t there also an averaging kernel for OSIRIS (line 19ff, page 5)? In Fig. 1 an additional panel with the zonal wind at 20 km (?) might be useful (line 21, page 6). Why are different partial columns given in section 3.3 (line 23, page 7) and earlier in the text (including abstract)? C2 In Table 2 at least the eruption of Rabaul in Oct. 2006 is missing. There appear to be contradictions between Fig. 3 and 4. Improve Fig. 3 concerning SO2 with the Höpfner (2015) 5-day dataset. Place the symbols for the volcanoes at the correct latitude. It might be better to use volume mixing ratios at 19 km (or 3 km above the tropopause) instead of the partial columns to reduce data gaps. The results are also sensitive to the treatment of negatives in the individual data. I don’t understand the statement on tropospheric water vapor (line 22, page 8). The current understanding is that for explosive eruptions SO2 is directly injected into the stratosphere, in the plume only water from the volcano might matter, but the satellite sees only what comes out of the plume. Section 4.2: In Figs. 1, 6 and 7 appear often extinction ratios < 1. Please explain or correct, from definition this should not happen. Please adjust color bars to reasonable range, Don’t use formulations like ’somewhat linear’ (line 4, page 10; line 3, page 12). 3 Technical corrections Line 20ff, page 1: Better ’anticorrelation’ instead of ’relationship’. Line 6, page 5: typo and bad wording. Figure 3, caption line 4: Do you mean a 4 km thick layer 3 km above the tropopause? Please improve text. Truncate Figs. 6 and 7 at 12 km, the data below are not relyable. Say ’aerosol extinction ratio’ also in captions. The black contours are superfluous. The colorbars should have the same steps as the colors in the figures (less is more!).

A variant of the limb–nadir matching technique for deriving tropospheric NO2 columns is presented in which the stratospheric component of the NO2 slant column density (SCD) measured by the Ozone Monitoring Instrument (OMI) is removed using non-coincident profiles from the Optical Spectrograph and InfraRed Imaging System (OSIRIS). In order to correct their mismatch in local time and the diurnal variation of stratospheric NO2, OSIRIS profiles, which were measured just after sunrise, were mapped to the local time of OMI observations using a photochemical box model. Following the profile time adjustment, OSIRIS NO2 stratospheric vertical column densities (VCDs) were calculated. For profiles that did not reach down to the tropopause, VCDs were adjusted using the photochemical model. Using air mass factors from the OMI Standard Product (SP), a new tropospheric NO2 VCD product – referred to as OMI-minus-OSIRIS (OmO) – was generated through limb–nadir matching. To accomplish this, the OMI total SCDs were scaled using correction factors derived from the next-generation SCDs that improve upon the spectral fitting used for the current operational products. One year, 2008, of OmO was generated for 60° S to 60° N and a cursory evaluation was performed. The OmO product was found to capture the main features of tropospheric NO2, including a background value of about 0.3 × 1015 molecules cm−2 over the tropical Pacific and values comparable to the OMI operational products over anthropogenic source areas. While additional study is required, these results suggest that a limb–nadir matching approach is feasible for the removal of stratospheric NO2 measured by a polar orbiter from a nadir-viewing instrument in a geostationary orbit such as Tropospheric Emissions: Monitoring of Pollution (TEMPO) or Sentinel-4.