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Four correction models with differing forms were developed on a training dataset of 32 PurpleAir–Federal Equivalent Method (FEM) hourly fine particulate matter (PM 2.5) observation colocation sites across North America (NA). These were evaluated in comparison with four existing models from external sources using the data from 15 additional NA colocation sites. Colocation sites were determined automatically based on proximity and a novel quality control process. The Canadian Air Quality Health Index Plus (AQHI +) system was used to make comparisons across the range of concentrations common to NA, as well as to provide operational and health-related context to the evaluations. The model found to perform the best was our Model 2, PM2.5-corrected=PM2.5-cf-1/(1+0.24/(100/RH%-1)), where RH is limited to the range [30%,70%] , which is based on the RH growth model developed by Crilley et al. (2018). Corrected concentrations from this model in the moderate to high range, the range most impactful to human health, outperformed all other models in most comparisons. Model 7 (Barkjohn et al., 2021) was a close runner-up and excelled in the low-concentration range (most common to NA). The correction models do not perform the same at different locations, and thus we recommend testing several models at nearby colocation sites and utilizing that which performs best if possible. If no nearby colocation site is available, we recommend using our Model 2. This study provides a robust framework for the evaluation of low-cost PM 2.5 sensor correction models and presents an optimized correction model for North American PurpleAir (PA) sensors. [ABSTRACT FROM AUTHOR]

Four correction models with differing forms were developed on a training data set of 32 PurpleAir-Federal Equivalent Method (FEM) hourly fine particulate matter (PM2.5) observation colocation sites across North America (NA). These were evaluated in comparison with four existing models from external sources using the data from 15 additional NA colocation sites. Colocation sites were determined automatically based on proximity and a novel quality control process. The Canadian AQHI+ system was used to make comparisons across the range of concentrations common to NA, as well as to provide operational and health-related context to the evaluations. The model found to perform the best was our Model 2, PM2.5-corrected = PM2.5-cf-1 / (1 + 0.24 / (100 / RH% - 1)) - RH is limited to the range [30 %, 70 %], which is based on the RH growth model developed by Crilley et al. (2018). Corrected concentrations from this model in the moderate to high range, the range most impactful to human health, outperformed all other models in most comparisons. Model 7 (Barkjohn et al., 2020) was a close runner up and excelled in the low concentration range (most common to NA). The correction models do not perform the same at different locations, so we recommend testing several models at nearby colocation sites and utilizing that which performs best if possible. If no nearby colocation site is available, then we recommend using our Model 2. This study provides a robust framework for the evaluation of low-cost PM2.5 sensor correction models and presents an optimized correction model for North American PA sensors. [ABSTRACT FROM AUTHOR]

The amount of ice present in clouds can affect cloud lifetime, precipitation and radiative properties. The formation of ice in clouds is facilitated by the presence of airborne ice-nucleating particles. Sea spray is one of the major global sources of atmospheric particles, but it is unclear to what extent these particles are capable of nucleating ice. Sea-spray aerosol contains large amounts of organic material that is ejected into the atmosphere during bubble bursting at the organically enriched sea-air interface or sea surface microlayer. Here we show that organic material in the sea surface microlayer nucleates ice under conditions relevant for mixed-phase cloud and high-altitude ice cloud formation. The ice-nucleating material is probably biogenic and less than approximately 0.2 micrometres in size. We find that exudates separated from cells of the marine diatom Thalassiosira pseudonana nucleate ice, and propose that organic material associated with phytoplankton cell exudates is a likely candidate for the observed ice-nucleating ability of the microlayer samples. Global model simulations of marine organic aerosol, in combination with our measurements, suggest that marine organic material may be an important source of ice-nucleating particles in remote marine environments such as the Southern Ocean, North Pacific Ocean and North Atlantic Ocean. [ABSTRACT FROM AUTHOR]