Ultrafine particles (UFPs, <100 nm) are associated with health and environmental risks. Reducing exposure to these particles requires identifying their origin. This study classified days into five categories based on dominant sources of UFP during the day: Strong Nucleation, Midday Pollution, Traffic Pollution, Baseline, and Mixed. The primary focus was on Midday Pollution days; days that showed higher UFP concentrations around noon, without evidence of particle formation. The classification was performed manually using particle size distribution data (6-523 nm) collected from 2006 to 2021 at the Southern Ontario Center for Atmospheric Aerosol Research (SOCAAR). The same types of days were also identified based on total particle number concentration data using ensemble learning models at three additional sites with differing concentrations of UFP: Hanlan's Point (a background site with no local traffic), Downsview (an urban background site 250 meters from the nearest roadway), and Highway 401 (a site located near a major highway). The classified days at these locations had patterns consistent with those at SOCAAR. The noon-to-morning ratio of UFP at Hanlan's Point was roughly twice that of SOCAAR and Highway 401, indicating more intense particle amplification at the background site. Midday Pollution days occurred more frequently during colder months. Higher sodium to chloride molar ratios on Midday Pollution days indicated that the atmospheric chemistry was different compared to other types of days. These results indicate that concentrations of UFP may rise midday due to a process other than new particle formation events.

The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite provides hourly air quality measurements over North America. This study evaluates TEMPO observations over snow-covered surfaces, highlighting its ability to capture sharp spatial and temporal gradients in vertical column densities and surface concentrations, key parameters for assessing air quality and public health impacts. Data from the 2024 Study of Winter Air Pollution in Toronto, including in situ and mobile Multi-Axis Differential Optical Absorption Spectroscopy measurements, are used to assess TEMPO's precision and accuracy. Additional evaluations are performed at Pandora sites across North America to examine wintertime performance. Comparisons show strong correlations between TEMPO and surface observations, with significant improvements in bias after applying corrections to air mass factors, cloud fraction, and surface albedo (from -32% to -9% over snow).

Hourly concentrations of PM2.5-bound elements were continuously monitored in Windsor, Canada, from April 2021 to April 2023. Health risk assessment methods of the USEPA were utilized to quantify lifetime cumulative cancer risks (CRs) using six PM2.5-bound elements, and chronic non-cancer hazard quotients (HQs) using 11 elements, for each season, each source factor, and each hour of day. The two-year average PM2.5 mass concentration was 9.2 mu g/m(3), slightly exceeding Ontario's Ambient Air Quality Criteria of 8.8 mu g/m(3). A discernible diurnal concentration pattern was noted for most elements, peaking during morning rush hours and tapering during the daytime, largely attributed to local human activities and changes in atmospheric mixing heights. Despite this, both the total lifetime cumulative CR (4.1 & times; 10(-5)) and non-cancer total HQ (0.82) from exposure to ambient elements remained below the corresponding USEPA-acceptable levels. The seasonal variation in CRs and HQs was minimal. However, the diurnal variation was strong, with higher risks during morning rush hours (6:00-8:00) when traffic volume peaks, and lower risks during the daytime (12:00-20:00) when atmospheric mixing height is enhanced. Metal processing emerged as the most significant contributor to the total CR (52%) and HQ (60%), followed by coal/heavy oil burning (19% and 16%, respectively), and vehicular exhaust (19% and 12%, respectively). The remaining two source factors accounted for 10% of CR and 12% of HQ. Cd (62%) was the largest contributor to CRs, followed by Cr(VI) (25%), Co (6%), As (5%), Ni (2%), and Pb (<0.1%). Similarly, Cd dominated HQs (73%), followed by Mn (11%), Ni (6.3%), with the remaining eight elements collectively contributing 9.7%. Although levels of CRs and HQs are low, efforts to mitigate ambient Cd emissions from metal processing sources will help reduce exposure and protect the environment and human health, given Cd is the primary contributor to the total CR and HQ during the study period.

Ultrafine particles (UFP, less than 100 nm in aerodynamic diameter) pose significant health and environmental risks. These particles can originate from anthropogenic sources such as traffic emissions and fuel combustion, or form through atmospheric reactions. To reduce human exposure to UFP, it is important to understand the processes that can increase UFP concentrations. Current methods typically use particle size data to identify processes such as nucleation events. Recent studies have applied machine learning, particularly the transfer learning method, to this task. However, particle size distribution data is not widely available at many monitoring sites. In this study, we aimed to identify UFP sources using machine learning classification models only based on high time-resolution total number concentration data, rather than size distribution data, making the approach applicable to locations where only total UFP measurements are available. We have previously analyzed particle size data ranging from 6 to 520 nm collected from 2006 to the end of 2021 at the Southern Ontario Centre for Atmospheric Aerosol Research (SOCAAR) near a busy roadway in downtown Toronto, Canada. The days were classified into five categories: Strong Nucleation, Midday Pollution, Traffic Pollution, Baseline, and Mixed. Two machine learning approaches were tried: (1) ensemble learning models, and (2) transfer learning models. Both approaches performed well, achieving average accuracies of 80% and 75%, respectively. While transfer learning models were more robust to missing values, the ensemble learning method was slightly more robust to moderate noise and was also less computationally demanding. The ensemble learning models were then applied to a second monitoring location with a lower UFP level. The predicted classes at this site had similar characteristics to the classified days at the first site, with 78%, 58%, and 50% of the Strong Nucleation, Baseline, and Traffic Pollution days identified at both sites being classified into the same respective categories. These results suggest that the ensemble learning models could be transferred to other locations.

Air pollution is associated with mortality and morbidity worldwide. The health effects are primarily associated with the inhalation of particulate matter (PM), which can lead to oxidative stress through the excessive production of reactive oxygen species (ROS) in the respiratory, cardiovascular, and neurological systems. The influence of PM size and gaseous oxidants on the production of ROS in the lung is understudied, as most studies to date have focused on assessing this aspect broadly in fine or coarse PM. In this work, we used a kinetic model of redox chemistry in the lung epithelial lining fluid (KM-SUB-ELF) to study the production of ROS (including hydroxyl radical; (OH)-O-center dot) from the inhalation of size-resolved PM (spanning eight size bins between 0.18 and 10 mu m), ozone (O-3), and nitrogen dioxide (NO2) at three sites located at different distances from roadways in Toronto. The highest ROS production was found at the site located next to a major highway, with particulate pollutants being the primary contributor. The total ROS production peaked in the PM accumulation mode at all sites (up to 41% associated with submicrometer PM), following closely the size distribution of trace metals, especially Cu. The (OH)-O-center dot production was more site-specific; it peaked in the accumulation mode with highway traffic emissions, and in quasi-ultrafine (<0.18 mu m) mode in areas with moderate traffic, following the trends of soluble Fe and PM (up to 60% associated with submicron PM). ROS production in the lung was also induced by gaseous oxidants. Ozone played the dominant role, particularly at sites with moderate traffic but high O-3 levels where up to 52% of ROS and 14% of (OH)-O-center dot produced in the lung were attributed to inhaled O-3. The results highlight the importance of traffic emissions, submicrometer PM, and gaseous oxidants in deriving the health effects of air pollutants in urban environments.

The purpose of this work is to derive new nitrogen dioxide (NO2) vertical profiling data products from Pandora spectrometers and investigate the factors contributing to the bias of this dataset relative to established ground-based and spaceborne datasets. Possible applications of the NO2 vertical profile dataset include air quality monitoring and satellite validation studies. We explore the application of the optimal estimation method to Pandora multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements to retrieve vertical profile information for NO2. We use the Heidelberg Profile (HeiPro) retrieval algorithm to derive, for the first time, NO2 profiles and partial columns (0–4 km) from Pandora MAX-DOAS measurements from 2018–2020 from Downsview, a suburban neighbourhood in the northern end of Toronto, Canada, that is subject to local traffic emissions and urban influences. Validation of the new dataset was done via comparison with official Pandora direct-Sun measurements, in situ observations, satellite data, and an air quality forecasting model. We find that, for tropospheric partial column comparisons, the HeiPro dataset has a positive mean relative bias to Pandora direct-Sun (61 ± 9.7 %) and TROPOspheric Monitoring Instrument (TROPOMI; 37 ± 51 %) observations, as well as the Global Environmental Multiscale–Modelling Air quality and Chemistry (GEM-MACH) model output (67 ± 7.1 %), with similar seasonal and diurnal cycles in the bias with Pandora direct-Sun and GEM-MACH data. Contributing factors to the large bias of HeiPro-to-Pandora direct-Sun measurements were investigated, and NO2 heterogeneity, combined with differences between direct-Sun and multi-axis viewing geometries, was found to contribute a maximum of 52 % of the total relative bias during morning measurement times. For surface NO2 comparisons, we find that HeiPro measurements capture the magnitude and diurnal variability in surface NO2 reasonably well (mean relative bias to in situ surface NO2 of −9.7 ± 7.5 %) but are biased low compared to GEM-MACH (mean relative bias of −37 ± 2.4 %). Compared to HeiPro, the GEM-MACH model profiles are biased high in the lower boundary layer and biased low in the free troposphere.

This study investigates the impacts of wildfires on nanoparticle characteristics and exposure disparities in Toronto, integrating data from a large-scale mobile monitoring campaign and fixed-site measurements during the unprecedented 2023 wildfire season. Our results reveal changes in particle characteristics during wildfire days, with particle number concentrations decreasing by 60% and particle diameter increasing by 30% compared to nonwildfire days. Moreover, the median lung deposited surface area (LDSA) levels rose by 31% during wildfire events. We employed gradient boosting models to estimate near-road LDSA levels on both wildfire and nonwildfire days. The LDSA ratio (wildfire/nonwildfire) exceeded 2.0 in certain areas along highways and in downtown Toronto. Furthermore, our findings show that marginalized communities faced greater LDSA increases than less marginalized ones. Under wildfire conditions, the LDSA ratio difference between the most and least marginalized groups was 16% for recent immigrants and visible minorities and 7% for seniors and children, both statistically significant. This study delivers critical insights into the spatiotemporal variations of nanoparticle characteristics during wildfire and nonwildfire periods, demonstrating the substantial health risks posed by increased LDSA levels and the inequitable distribution of these risks among Toronto's diverse population.

BACKGROUND:During the 2023 wildfire season, Ontario, Canada, had unprecedented wildfire smoke, but the health impact on the population is unknown. We aimed to quantify the acute impact of the wildfire smoke on respiratory and cardiovascular outcomes across Ontario. METHODS:We conducted a quasi-experimental study by leveraging the timing of 2 consecutive wildfire smoke episodes in June 2023. Heavy wildfire smoke blanketed much of Ontario on 2 occasions, in early June and again in late June, causing severely degraded daily air quality. Following the epidemiologic triangulation framework, we collected health data on emergency department visits for 4 outcomes (asthma-related causes, other respiratory causes, ischemic heart disease, and non-cardiorespiratory causes) from Ontario's real-time syndromic surveillance system and the National Ambulatory Care Reporting System. We also employed different epidemiologic methodologies, including interrupted time-series and case-crossover analyses. RESULTS:After the initial heavy wildfire smoke in early June 2023, daily asthma-related visits increased substantially across Ontario, peaking at a 23.6% increase (95% confidence interval 13.2%-34.9%) at a 1-day lag and lasting up to a lag of 5 days after the start of the smoke episode. The later episode of heavy smoke, despite causing higher exposures, had a reduced effect on asthma-related visits. We did not detect any effect on other outcomes in either episode. These findings were consistent across different methodologies and data sources. Post hoc analysis revealed that asthma-related visits were briefly elevated after the wildfire smoke among children (40% higher), but we observed a more sustained effect among adults (48% higher, lasting 1 week). INTERPRETATION:The 2023 wildfires substantially increased asthma-related emergency department visits in Ontario, with age and timing of exposure being important factors influencing the impact. As wildfires emerge as one of the fastest-growing environmental risk factors globally, future research should identify and evaluate measures to effectively mitigate the acute health impacts of wildfire smoke.

To accurately study the characteristics of an air pollution emitter, it is necessary to isolate the contribution of that emitter to total measured pollution concentrations. A variety of published methods exist to complete this task, like placing measurements upwind the emitter, employing a distant background measurement station, or algorithmic methods that extract a background from the time series of measured concentrations (e.g. wavelet decomposition). In this study, we measured nitrogen oxides (NOx), carbon monoxide (CO), carbon dioxide (CO2), and fine particulate matter (PM2.5) at four sites spanning Toronto, Ontario, Canada. We first characterized the spatial variability of background concentrations across the city and then tested the accuracy of seven different algorithmic methods of estimating true measured upwind-of-emitter backgrounds near Toronto's Highway 401 by using the data collected at a downwind site. These methods included time-series and regression methods, including machine learning (XGBoost). We observed background concentrations had notable spatial variability, except for PM2.5. When predicting backgrounds upwind the highway, we found a distant measurement station provided an accurate background only during some times of day and was least accurate during rush hours. When testing algorithmic predictions of upwind-of-highway backgrounds, we found that regression models surpassed the performance of time-series methods, with best predictions having R2 exceeding 0.8 for all four pollutants. Despite the better performance of regression models, time-series methods still provided reasonable estimates. We also found that emitter-specific covariates (e.g. traffic counts, on-site dispersion modelling) did not play an important role in regressions, suggesting backgrounds can be well characterized by time of day, meteorology, and distant measurement stations. Based on our results, we provide ranked recommendations for choosing background estimation methods. We suggest future air pollution research characterizing individual emitters includes careful consideration of how background concentrations are estimated.

Abstract. The purpose of this work is to derive new NO2 vertical profiling data products from Pandora spectrometers and investigate the factors contributing to the bias of this dataset relative to established ground-based and spaceborne datasets. Possible applications of the NO2 vertical profile dataset include air quality monitoring and satellite validation studies. We explore the application of the optimal estimation method to Pandora multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements to retrieve vertical profile information for nitrogen dioxide (NO2). We use the Heidelberg Profile (HeiPro) retrieval algorithm to derive, for the first time, NO2 profiles and partial columns (0–4 km) from Pandora MAX-DOAS measurements from 2018–2020 at Downsview, a suburban neighbourhood in the north end of Toronto, Canada that is subject to local traffic emissions and urban influences. Validation of the new dataset was done via comparison with official Pandora direct-Sun measurements, in situ observations, satellite data, and an air quality forecasting model. We find that, for tropospheric partial column comparisons, the HeiPro dataset has a positive mean relative bias to Pandora direct-Sun (61 % ± 9.7 %) and TROPOMI (41 % ± 47 %) observations, as well as the GEM-MACH model output (61 % ± 7.5 %), with similar seasonal and diurnal cycles in the bias with Pandora direct-Sun and GEM-MACH. Contributing factors to the large bias of HeiPro to Pandora direct-Sun were investigated, and NO2 heterogeneity, combined with differences between direct-Sun and multi-axis viewing geometries, was found to contribute a maximum of 52 % of the total relative bias during morning measurement times. For surface NO2 comparisons, we find that HeiPro measurements capture the magnitude and diurnal variability of surface NO2 reasonably well (mean relative bias to in situ surface NO2: −8.9 % ± 7.6 %) but are low-biased compared to GEM-MACH (mean relative bias: −36 % ± 2.4 %). Compared to HeiPro, the GEM-MACH model profiles are high-biased in the lower boundary layer and low-biased in the free troposphere.

Polycyclic aromatic compounds (PACs) continue to demand attention due to their widespread presence and well-established health implications. Given that incomplete combustion is a major contributor to PACs and inhalation constitutes a crucial human exposure pathway, a comprehensive understanding of the concentrations, spatial distributions, and fates of a broad range of PACs in the atmosphere is important. Passive air samplers (PASs) are a commonly utilized technique for PAC sampling and monitoring. In this study, we present the results from two 1-year calibration experiments, one starting in summer and the other in winter, using a passive air sampler equipped with XAD resin as the sorbent (XAD-PAS). Throughout both experiments, PACs were consistently sorbed during the initial 6-month period. However, the sorbed amounts for many PACs exhibited a decrease after half a year of deployment. Three hypotheses to explain this phenomenon were explored, including the uptake of atmospheric particles, evaporation from the sorbent, and reactions with photo-oxidants. All had to be rejected based on the obtained data, additional laboratory experiments, and model results. Model simulations were further used to (i) confirm that a loss process must be invoked to explain the observed uptake behavior and (ii) estimate the kinetics of that loss process for different PACs. Sampling rates (SRs) for 28 PACs derived from the linearized uptake curves during the first 6 months of deployment were comparable to those of other semi-volatile organic compounds obtained during the same calibration experiment, and they also demonstrate a consistent negative correlation with volatility.

Abstract. To accurately study the characteristics of an air pollution emitter, it is necessary to isolate the contribution of that emitter to total measured pollution concentrations. A variety of published methods exist to complete this task, like placing measurements upwind the emitter, employing a distant background measurement station, or algorithmic methods that extract a background from the time-series of measured concentrations (e.g., wavelet decomposition). In this study, we measured nitrogen oxides (NOx), carbon monoxide (CO), carbon dioxide (CO2), and fine particulate matter (PM2.5) at four sites spanning Toronto, Ontario, Canada. We first characterized the spatial variability of background concentrations across the city, and then tested the accuracy of seven different algorithmic methods of estimating true measured upwind-of-emitter backgrounds near Toronto’s Highway 401 by using the data collected at a downwind site. These methods included time-series and regression methods, including machine learning (XGBoost). We observed background concentrations had notable spatial variability, except for PM2.5. When predicting backgrounds upwind the highway, we found a distant measurement station provided an accurate background only during some times of day and was least accurate during rush hours. When testing algorithmic predictions of upwind-of-highway backgrounds, we found that regression models outperformed time-series methods, with best predictions having R2 exceeding 0.75 for all four pollutants. Despite the better performance of regression models, time-series methods still provided reasonable estimates; we also found that emitter-specific covariates (e.g. traffic counts, onsite dispersion modelling) did not play an important role in regressions, suggesting backgrounds can be well-characterized by time of day, meteorology, and distant measurement stations. Based on our results, we provide ranked recommendations for choosing background estimation methods. We suggest future air pollution research characterizing individual emitters include careful consideration of how background concentrations are estimated.

This study characterizes cancer and non-cancer risks due to inhalation exposure to volatile organic compounds (VOCs) in a border city of Windsor in southern Ontario, Canada, using hourly ambient concentrations collected from 17 November 2021 to 17 March 2023. The total incremental lifetime cancer risk (CR) due to benzene and ethylbenzene is 4.33 × 10−6, which is in the acceptable risk range of 1 × 10−6 to 1 × 10−4 used by the USEPA. The CR was higher in winter (5.20 × 10−6), followed by fall (4.32 × 10−6), spring (3.86 × 10−6), and summer (2.96 × 10−6), all in the acceptable range. The total chronic non-cancer risk (Hazard Quotient, HQ) of inhalation exposure to 16 VOCs was 0.0488, with a higher value in fall (0.0571), followed by winter (0.0464), and lower in spring (0.0454) and summer (0.0451), all in the safe level of below HQ = 1 used by the USEPA. The hazard index (HI) by organs was higher for the nervous system (0.0213), followed by the hematologic system and immune system (0.0165 each), but much lower for the other five target organs, i.e., the liver/kidney (1.52 × 10−4), developmental system (3.38 × 10−4), endocrine system and urinary system (2.82 × 10−4 each), and respiratory system (9.70 × 10−5). Similar hour-of-day trends were observed in the total CR, total HQ, and HI by organs with higher values in the early morning hours of 5:00–8:00 and lower values during 12:00 to 15:00. Benzene was the major contributor to both total CR (89%) and total HQ (34%) due to its high toxicity and high concentrations. Benzene, toluene, ethylbenzene, and xylenes (BTEX) contributed 100% of the total CR and 51% of the total HQ. Further, BTEX is the sole contributor to the HI for the hematologic system and immune system and the major contributor to the HI for the nervous system (39%) and developmental system (55%). Higher cancer and non-cancer risks were associated with the airmass from the east, southeast, and southwest of Windsor.

The Michigan–Ontario Ozone Source Experiment (MOOSE) is an international air quality field study that took place at the US–Canada Border region in the ozone seasons of 2021 and 2022. MOOSE addressed binational air quality issues stemming from lake breeze phenomena and transboundary transport, as well as local emissions in southeast Michigan and southern Ontario. State-of-the-art scientific techniques applied during MOOSE included the use of multiple advanced mobile laboratories equipped with real-time instrumentation; high-resolution meteorological and air quality models at regional, urban, and neighborhood scales; daily real-time meteorological and air quality forecasts; ground-based and airborne remote sensing; instrumented Unmanned Aerial Vehicles (UAVs); isotopic measurements of reactive nitrogen species; chemical fingerprinting; and fine-scale inverse modeling of emission sources. Major results include characterization of southeast Michigan as VOC-limited for local ozone formation; discovery of significant and unaccounted formaldehyde emissions from industrial sources; quantification of methane emissions from landfills and leaking natural gas pipelines; evaluation of solvent emission impacts on local and regional ozone; characterization of the sources of reactive nitrogen and PM2.5; and improvements to modeling practices for meteorological, receptor, and chemical transport models.

This study investigated the sensitivity of the positive matrix factorization (PMF) model using concentrations of PM2.5-bound elements in Windsor, Ontario, Canada. Five scenarios were devised to assess impacts of input data on source identification, source contributions, and model performance. The study found that the model outcomes and performance were not sensitive to data below method detection limits (MDLs) being replaced with ½ MDLs, nor whether brown carbons (BrCs) data were excluded. By analyzing two episodic events individually, unique factors of fireworks and mineral dust were identified for each of the two episodes. Moreover, PMF model performance was improved greatly for event markers of the episodes and elements with less variability in concentration when compared with the base case scenario. Excluding the two episodes from the entire dataset had little impact on factor identification and source contributions but improved the model performance for three out of twelve elements unique to the two episodes. Overall, the PMF model outcomes and performance were sensitive to percentages of concentrations below MDLs and element concentrations with large variability due to high concentrations observed in episodes. Our findings are useful for dealing with data below MDLs and episodic events in conducting future PMF source apportionment of PM2.5-bound elements.

While transportation emissions have declined over the past several decades, volatile organic compound (VOC) emissions from solvent use applications have increased as urban areas expand. In this work, the Canadian air quality model (GEM-MACH-TEB) is used to assess the importance of solvent emissions during the Michigan Ontario Ozone Source Experiment (MOOSE). Model predictions are compared to ozone and total mono-substituted aromatics (TOLU) observations collected in Windsor, Ontario. For summer 2018, model estimates of TOLU from solvent emissions are smaller (30% for an 8 h daytime average) in Windsor than estimates from positive matrix factorization (44% for a 24 h average). The use of updated U.S. solvent emissions from the EPA’s VCPy (Volatile Chemical Product framework) for summer 2021 simulations increases the solvent use source contribution over Detroit/Windsor (30–50% for an 8 h daytime average). This also provides a more uniform spatial distribution across the U.S./Canada border (30–50% for an 8 h daytime average). Long-chain alkanes are the dominant speciation in the model’s air pollutant emission inventory and in the observation-derived solvent use factor. Summertime 8 h daytime ozone decreased by 0.4% over Windsor for a 10% solvent use VOC emission reduction scenario. A 10% mobile NOx emission reduction scenario resulted in a 0.6% O3 decrease over Windsor and more widespread changes over the study region.

This study deployed 59 passive air samplers (PAS) across the province of Ontario, Canada (and eight additional deployments in bordering states of northern USA) to assess the influence of local/regional sources, temporal differences, and possible transformations in the atmospheric gaseous elemental mercury (GEM) pool. The spatial mapping achieved with the PASs allowed for the observation of differences in GEM and its isotopic composition over the region. Concentrations of GEM were higher with more negative δ 202 Hg values near urban/industrial areas and suspected emission sources in southern Ontario. In northern Ontario, far from industrial influences, lower concentrations of GEM with isotopically more positive δ 202 Hg values were found at the boreal forest sites compared to sites within the Hudson Bay Lowlands. The differences in northern Ontario may be from greater uptake of GEM by the boreal forest removing isotopically light Hg and lowering concentrations. Lower GEM and a shift toward more positive δ 202 Hg were also observed during the summer relative to other times of year supporting vegetation uptake as an important sink for GEM. PASs were also deployed along a transect of increasing distance from a Hg recycling facility. The Hg emissions from the facility had unique positive Δ 199 Hg and Δ 200 Hg signatures, but GEM concentrations and isotopic compositions returned to background within 400 m of the facility. The province‐wide variations in atmospheric GEM and isotopic compositions show that both sources (e.g., urban/industrial emissions) and sinks (e.g., vegetation uptake) contribute to the spatial and temporal patterns of the residual atmosphere GEM pool.

Ambient fine particulate matter (PM2.5) levels in Windsor, Ontario, Canada, are impacted by local emissions and regional/transboundary transport input and also attributable to secondary formation. PM2.5-bound elements were monitored hourly in Windsor from April to October 2021. Observed concentrations of the elements were generally comparable to historical measurements at urban sites in Ontario. A clear diurnal pattern was observed for most of the elements, i.e., high in the morning and low in the afternoon, mostly related to evolution of atmospheric mixing heights and local anthropogenic activities. Conversely, sulfur showed elevated levels in the afternoon, suggesting conversion of gaseous sulfur dioxide to particulate sulphate was enhanced by increased ambient temperatures. Five source factors were resolved using the US EPA positive matrix factorization model, including three traffic-related sources (i.e., vehicular exhaust, crustal dust, and vehicle tire and brake wear factors) and two industrial sources (i.e., coal/heavy oil burning and metal processing factors). Overall, the three traffic-related sources were mostly local and contributed to 47% of the total elemental concentrations, while the two industrial sources may originate from regional/transboundary sources and contributed to 53%. Measures to control both local traffic emissions and regional/transboundary industrial sources would help reduce levels of PM2.5-bound elements in Windsor.

We present tropospheric nitrogen dioxide (NO2) changes observed by the Canadian Pandora measurement program in the Greater Toronto Area (GTA), Canada, and compare the results with surface NO2 concentrations measured via in situ instruments to assess the local emission changes during the first two years of the COVID-19 pandemic. In the City of Toronto, the first lockdown period started on 15 March 2020, and continued until 24 June 2020. ECMWF Reanalysis v5 (ERA-5) wind information was used to facilitate the data analysis and reveal detailed local emission changes from different areas of the City of Toronto. Evaluating seven years of Pandora observations, a clear NO2 reduction was found, especially from the more polluted downtown Toronto and airport areas (e.g., declined by 35% to 40% in 2020 compared to the 5-year mean value from these areas) during the first two years of the pandemic. Compared to the sharp decline in NO2 emissions in 2020, the atmospheric NO2 levels in 2021 started to recover, but are still below the mean values in pre-pandemic time. For some sites, the pre-pandemic NO2 local morning rush hour peak has still not returned in 2021, indicating a change in local traffic and commuter patterns. The long-term (12 years) surface air quality record shows a statistically significant decline in NO2 with and without April to September 2020 observations (trend of −4.1%/yr and −3.9%/yr, respectively). Even considering this long-term negative trend in NO2, the observed NO2 reduction (from both Pandora and in situ) in the early stage of the pandemic is still statistically significant. By implementing the new wind-based validation method, the high-resolution satellite instrument (TROPOMI) can also capture the local NO2 emission pattern changes to a good level of agreement with the ground-based observations. The bias between ground-based and satellite observations during the pandemic was found to have a positive shift (5–12%) than the bias during the pre-pandemic period.