Iron-driven secondary brown carbon formation reactions from water-soluble organics in cloud droplets and aerosols create insoluble and soluble products of emerging atmospheric importance. This work shows, for the first time, results on dark iron-catalyzed polymerization of catechol forming insoluble black polycatechol particles and colored water-soluble oligomers under conditions characteristic of viscous multicomponent aerosol systems with relatively high ionic strength (I = 1–12 m) and acidic pH (∼2). These systems contain ammonium sulfate (AS)/nitrate (AN) and C3–C5 dicarboxylic acids, namely, malonic, malic, succinic, and glutaric acids. Using dynamic light scattering (DLS) and ultra high pressure liquid chromatography-mass spectrometry (UHPLC-MS), we show results on the rate of particle growth/agglomeration and identity of soluble oligomeric reaction products. We found that increasing I above 1 m and adding diacids with oxygen-to-carbon molar ratio (O:C > 1) significantly reduced the rate of polycatechol formation/aggregation by a factor of 1.3 ± 0.4 in AS solution in the first 60 min of reaction time. Using AN, rates were too slow to be quantified using DLS, but particles formed after 24 h reaction time. These results were explained by the relative concentration and affinity of ligands to Fe­(III). We also report detectable amounts of soluble and colored oligomers in reactions with a slow rate of polycatechol formation, including organonitrogen compounds. These results highlight that brown carbon formation from iron chemistry is efficient under a wide range of aerosol physical states and chemical composition.

Preliminary analysis of satellite measurements from around the world showed drops in nitrogen dioxide (NO2) with lockdowns due to the COVID-19 pandemic. 1 A number of studies have found these drops to be correlated with local decreases in transportation and/or industry. None of these studies, however, has rigorously quantified the statistical significance of these drops relative to natural meteorological variability and other factors that influence pollutant levels during similar time periods in previous years. Here, we develop a novel statistical testing framework that accounts for seasonal variability, transboundary influences, and new factors such as COVID-19 restrictions in explaining trends in several pollutant levels at 16 ground-based measurement sites in Southern Ontario, Canada. We find statistically significant and temporary drops in NO2 (11 out 16 sites) and CO (all 4 sites) in April-June 2020, with pollutant levels 20% lower than in the previous three years.Much fewer sites (2-3 out of 16) experienced statistically significant drops in O3 and PM2.5. The statistical testing framework developed here is the first of its kind applied to air quality data, and highlights the need for rigorous assessment of statistical significance, should analyses of pollutant level changes post COVID-19 lockdowns be used to inform policy decisions in Ontario, Canada.

Nitrogen-containing organic carbon (NOC) in atmospheric particles is an important class of brown carbon (BrC). Redox active NOC like aminophenols received little attention in their ability to form BrC. Here we show that iron can catalyze dark oxidative oligomerization of o- and p-aminophenols under simulated aerosol and cloud conditions (pH 1–7, and ionic strength 0.01–1 M). Homogeneous aqueous phase reactions were conducted using soluble Fe(III), where particle growth/agglomeration were monitored using dynamic light scattering. Mass yield experiments of insoluble soot-like dark brown to black particles were as high as 40%. Hygroscopicity growth factors (κ) of these insoluble products under sub- and super-saturated conditions ranged from 0.4–0.6, higher than that of levoglucosan, a prominent proxy for biomass burning organic aerosol (BBOA). Soluble products analyzed using chromatography and mass spectrometry revealed the formation of ring coupling products of o- and p-aminophenols and their primary oxidation products. Heterogeneous reactions of aminophenol were also conducted using Arizona Test Dust (AZTD) under simulated aging conditions, and showed clear changes to optical properties, morphology, mixing state, and chemical composition. These results highlight the important role of iron redox chemistry in BrC formation under atmospherically relevant conditions.

The Region of Waterloo is the third fastest growing region in Southern Ontario in Canada with a population of 619,000 as of 2019. However, only one air quality monitoring station, located in a city park in Kitchener, Ontario, is currently being used to assess the air quality of the region. In September 2020, a network of AQMesh Multisensor Mini Monitoring Stations (pods) were installed near elementary schools in Kitchener located near different types of emission source. Data analysis using a custom-made long-distance scaling software showed that the levels of nitrogen oxides (NO and NO2), ground level ozone (O3), and fine particulate matter (PM2.5) were traffic related. These pollutants were used to calculate the Air Quality Health Index-Plus (AQHI+) at each location, highlighting the inability of the provincial air quality monitoring station to detect hotspot areas in the city. The case study presented here quantified the impact of the 2021 summer wildfires on the local air quality at a high time resolution (15-min). The findings in this article show that these multisensor pods are a viable alternative to expensive research-grade equipment. The results highlight the need for networks of local scale air quality measurements, particularly in fast-growing cities in Canada.

Preliminary analysis of satellite measurements from around the world showed drops in nitrogen dioxide (NO₂) with lockdowns due to the COVID-19 pandemic. A number of studies have found these drops to be correlated with local decreases in transportation and/or industry. None of these studies, however, has rigorously quantified the statistical significance of these drops relative to natural meteorological variability and other factors that influence pollutant levels during similar time periods in previous years. Here, we develop a novel statistical testing framework that accounts for seasonal variability, transboundary influences, and new factors such as COVID-19 restrictions in explaining trends in several pollutant levels at 16 ground-based measurement sites in Southern Ontario, Canada. We find statistically significant and temporary drops in NO₂ (11 out 16 sites) and CO (all 4 sites) in April-June 2020, with pollutant levels 20% lower than in the previous three years. Much fewer sites (2-3 out of 16) experienced statistically significant drops in O₃ and PM2.5.<b> </b>The statistical testing framework developed here is the first of its kind applied to air quality data, and highlights the need for rigorous assessment of statistical significance, should analyses of pollutant level changes post COVID-19 lockdowns be used to inform policy decisions in Ontario, Canada. See Methods section in the manuscript.