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High-altitude remote sites are unique places to study aerosol-cloud interactions since they are located at the altitude where clouds may form. At these remote sites, organic aerosols (OA) are the main constituents of the overall aerosol population, playing a crucial role in defining aerosol hygroscopicity (κ). To estimate the CCN budget at OA dominated sites, it is crucial to accurately characterize OA hygroscopicity (κOA) and how its temporal variability affects the CCN activity of the aerosol population since κOA is not well established due to complex nature of ambient OA. In this study, we performed CCN closures at a high-altitude remote site during summer season to investigate the role of κOA in predicting CCN concentrations under different atmospheric conditions. In addition, we performed an OA source apportionment using Positive Matrix Factorization (PMF). Three OA factors were identified from the PMF analysis: hydrocarbon-like OA (HOA), less-oxidized oxygenated OA (LO-OOA) and more-oxidized oxygenated OA (MO-OOA), with average contributions of 5 %, 36 % and 59 % of the total OA, respectively. This result highlights the predominance of secondary organic aerosol with high degree of oxidation at this high-altitude site. To understand the impact of each OA factor on the overall OA hygroscopicity we defined three κOA schemes that assume different hygroscopicity values for each OA factor. Our results show that the different κOA schemes lead to similar CCN closure results between observations and predictions (slope and correlation ranging between 1.08–1.40 and 0.89–0.94, respectively). However, the predictions were not equally accurate across the day. During nighttime, CCN predictions underestimated observations by 6–16 %, while during morning and midday hours, when the aerosol was influenced by vertical transport of particles and/or new particle formation events, CCN concentrations were overestimated by 0–20 %. To further evaluate the role of κOA in CCN predictions, we established a new OA scheme that uses the OA oxidation level (parameterized by the f44 factor) to calculate κOA and predict CCN. This method also shows a large bias, especially during midday hours (up to 40 %), indicating that diurnal information about the oxygenation degree does not improve CCN predictions. Finally, we used a neural network model with four inputs: N80 (number concentration of particles with diameter >80 nm), OA fraction, f44 and surface global radiation) to predict CCN. This model matched the observations better than the previous approaches, with a bias within ±10 % and with no daily variation, reproducing the CCN variability along the day. Therefore, neural network models seem to be an appropriate tool to estimate CCN concentrations using ancillary parameters accordingly. [ABSTRACT FROM AUTHOR]

Light-absorbing aerosols (LAAs) are short-lived climate forcers with a significant impact on Earth's radiative balance. LAAs include dust aerosols, black carbon (BC) and organic light-absorbing carbonaceous aerosol (collectively termed brown carbon, BrC), which have also been proven to be highly toxic. In this study, aerosol absorption at five wavelengths (ranging from ultraviolet to infrared) was monitored continuously using filter-based photometers during two winter seasons in 2020 and 2021 in the city of Modena (southern central Po Valley, northern Italy), at two regulatory air quality monitoring sites, along with other pollutants (coarse particulate matter, PM 10 ; fine particulate matter, PM 2.5 ; O 3 ; NO; NO 2 ; and C 6 H 6) and the vehicular traffic rate. The aerosol optical depth (AOD) and other column aerosol optical properties were concurrently monitored at four wavelengths by an AErosol RObotic NETwork (AERONET) sun photometer under urban background conditions within Modena. In situ absorption levels were apportioned to both sources (fossil fuel and biomass burning) and species (BC and BrC), while columnar absorption was apportioned to BC, BrC and mineral dust. The combined analysis of the atmospheric aerosol and gas measurements and of the meteorological conditions (in situ and from the ERA5 reanalysis) identified the location of potential urban sources of BC and BrC, most likely related to traffic and biomass burning. In situ data show different diurnal/weekly patterns for BrC from biomass burning and BC from traffic, with minor differences between the background and the urban traffic conditions. AERONET version 3 absorption aerosol optical depth (AAOD) retrievals at four wavelengths allowed the estimation of the absorptive direct radiative effect due to LAAs over the same period under the reasonable assumption that the AOD signal is concentrated within the mixing layer. AERONET retrievals showed a modest correlation of columnar absorption with planetary boundary layer (PBL)-scaled in situ observations, although the correlation improved significantly during a desert dust transport event that affected both in situ aerosol and columnar absorption, particularly in the blue spectrum range. A low correlation occurred between the contribution of BrC to aerosol absorption for the in situ and the columnar observations, with the BrC contribution being generally larger for in situ observations. Finally, evidence of a highly layered atmosphere during the study period, featuring significant spatial mixing and modest vertical mixing, was shown by ERA5-based atmospheric temperature profiles and by the large correlation of concurrent AERONET AOD retrievals in Modena and in Ispra (on the northwestern side of the Po Valley, ca. 225 km from Modena). [ABSTRACT FROM AUTHOR]

Between April 2002 and June 2017, the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) made continuous measurements of a suite of in-situ aerosol optical properties at a long-term monitoring site near Trinidad Head (THD), California. In addition to aerosol optical properties, between 2002–2006 a scanning humidograph system was operated and inorganic ion and total aerosol mass concentrations were obtained from filter measurements. Combined analysis of these datasets demonstrates consistent patterns in aerosol climatology and highlights changes in sources throughout the year. THD is predictably dominated by sea salt aerosols, however, marine biogenic aerosols are the largest contributor to PM1 in the warmer months. Additionally, a persistent combustion source appears in the winter, likely a result of wintertime home heating. While the influences of local anthropogenic sources from vehicular and marine traffic are visible in the optical aerosol data, their influence is largely dictated by wind direction at the site. Comparison of the THD aerosol climatology to that reported for other marine sites shows that the location is representative of clean marine measurements, even with the periodic influence of anthropogenic sources. [ABSTRACT FROM AUTHOR]

Light Absorbing Aerosols (LAA) are short-lived climate forcers with a significant impact on Earth radiative balance and include dust aerosols, Black Carbon (BC) and organic light-absorbing carbonaceous aerosol (collectively termed as Brown Carbon, BrC), which have been also proven to be highly toxic. Aerosol absorption at 5 wavelengths (UV, B, G, R, IR) by filter photometer was monitored continuously during two winter seasons in 2020 and 2021 in the city of Modena (South-central Po valley, Northern Italy) at the two regulatory air quality monitoring sites, along with other pollutants (PM10, PM2.5, O3, NO, NO2, C6H6) and vehicular traffic rate. Columnar levels of AOD and of other aerosol optical properties were concurrently monitored at multiple wavelengths by a local sun-photometer at urban background conditions and within the AERONET network. In-situ absorption levels were apportioned both to sources (fossil fuel, biomass burning) and to species (BC, BrC), while columnar absorption was apportioned to BC, BrC and mineral dust. The combined analysis of the atmospheric aerosol and gas levels and of the meteorological conditions (in-situ and by ERA5 reanalysis) identified the location of potential urban sources for BC and BrC, most likely traffic and biomass burning. In-situ data shown different diurnal/weekly patterns for BrC by biomass burning and BC by traffic, with minor differences among the background and the traffic urban conditions. AERONET version 3 Absorption Aerosol Optical Depth (AAOD) retrievals at 4 wavelengths allowed the estimate of the Absorptive Direct Radiative Effect by LAA over the same period under the reasonable assumption that the AOD signal is built up within the mixing layer. AERONET retrievals showed a modest correlation of columnar absorption with PBL-scaled in-situ observations, although the correlation improves significantly during a desert dust transport event, affecting both in-situ aerosol and columnar absorption, particularly in the Blue spectrum range. Low correlation occurred between the contribution of BrC aerosol absorption for the in-situ and the columnar observations, with this contribution being generally larger at the former. Finally, evidences of a strongly layered atmosphere during the study period, featured by large spatial mixing and modest vertical mixing, were shown by ERA5-based atmospheric temperature profiles and by the large correlation of concurrent AERONET AOD retrievals in Modena and in Ispra (on the NW side of the Po valley, ca. 225 km distant from Modena). [ABSTRACT FROM AUTHOR]

PurpleAir sensors (PASs) are low-cost tools to measure fine particulate matter (PM) concentrations and are now widely used, especially in regions with few regulatory monitors. However, the raw PAS data have significant biases, so the sensors must be calibrated to generate accurate data. The U.S. EPA recently developed a national correction equation and has integrated corrected PAS data onto its AirNow website. This integration results in much better spatial coverage for PM 2.5 (particulate matter with diameters less than 2.5 µ m) across the US. The goal of our study is to evaluate the EPA correction equation for three different types of aerosols: typical urban wintertime aerosol, smoke from biomass burning, and mineral dust. We identified 50 individual pollution events, each having a peak hourly PM 2.5 concentration of at least 47 µ g m -3 and a minimum of 3 h over 40 µ g m -3 and characterized the primary aerosol type as either typical urban, smoke, or long-range transported dust. For each event, we paired a PAS sampling outside air with a nearby regulatory PM 2.5 monitor to evaluate the agreement. All 50 events show statistically significant correlations (R values between 0.71–1.00) between the hourly PAS and regulatory data but with varying slopes. We then corrected the PAS data using either the correction equation from Barkjohn et al. (2021) or a new equation that is now being used by the U.S. EPA for the AirNow Fire and Smoke Map (U.S. EPA, 2022b). Both equations do a good job at correcting the data for smoke and typical pollution events but with some differences. Using the Barkjohn et al. (2021) equation, we find mean slopes of 1.00 and 0.99 for urban and smoke aerosol events, respectively, for the corrected data versus the regulatory data. For heavy smoke events, we find a small change in the slope at very high PM 2.5 concentrations (> 600 µ g m -3), suggesting a ∼ 20 % underestimate in the corrected PAS data at these extremely high concentrations. Using the new EPA equation, we find slopes of 0.95 and 0.88 for urban and smoke events, respectively, indicating a slight underestimate in PM 2.5 using this equation, especially for smoke events. For dust events, while the PAS and regulatory data still show significant correlations, the PAS data using either correction equation underestimate the true PM 2.5 by a factor of 5–6. We also examined several years of co-located regulatory and PAS data from a site near Owens Lake, California (CA), which experiences high concentrations of PM 2.5 due to both smoke and locally emitted dust. For this site, we find similar results as above; the corrected PAS data are accurate in smoke but are too low by a factor of 5–6 in dust. Using these data, we also find that the ratios of PAS-measured PM 10 / PM 1 mass and 0.3 µ m / 5 µ m particle counts are significantly different for dust compared to smoke. Using this difference, we propose a modified correction equation that improves the PAS data for some dust events, but further work is needed to improve this algorithm. [ABSTRACT FROM AUTHOR]

PurpleAir Sensors (PASs) are low-cost tools to measure fine particulate matter (PM) concentrations and are now widely used, especially in regions with few regulatory monitors. However, the raw PAS data have significant biases, so the sensors must be calibrated to generate accurate data. The U.S. EPA recently developed a national correction equation and have integrated corrected PAS data onto its AirNow website. This integration results in much better spatial coverage for PM2.5 across the U.S. The goal of our study is to evaluate the EPA correction equation for three different types of aerosols: typical urban wintertime aerosol, smoke from biomass burning, and mineral dust. We identified 50 individual pollution events, each having a peak hourly PM2.5 concentration of at least 47 µg m-3 and a minimum of 3 hours over 40 µg m-3 and characterize the primary aerosol type as either typical urban, smoke or dust. For each event, we paired an PAS sampling outside air with a nearby regulatory PM2.5 monitor to evaluate the agreement. All 50 events show statistically significant correlations (R values between 0.71-1.00) between the hourly PAS and regulatory data, but with varying slopes. Using the standard EPA correction for the typical urban and smoke aerosols, we find average slopes of 1.00 and 0.99, respectively. This means that the standard EPA correction is highly effective at generating accurate data for these aerosol types. For heavy smoke events, we find a small change in the slope at very high PM2.5 concentrations (>600µg m-3), suggesting a ~20% under-estimate in the corrected PAS data at these extremely high concentrations. For dust events, while the PAS and regulatory data still show significant correlations, the PAS data using the standard correction underestimates the true PM2.5 by a factor of 5-6. We also examined several years of co-located regulatory and PAS data from a site near Owens Lake, CA, which experiences high concentrations of PM2.5 due to both smoke and dust. For this site we find similar results as above; the PAS corrected data are accurate in smoke, but are too low by a factor of 5-6 in dust. Using these data we also find that the ratios of PAS measured PM10 to PM1 mass and 0.3 µm to 5 µm particle counts are significantly different for dust compared to smoke. Given the ability of the PAS data to identify dust aerosols, we propose a modified correction algorithm that significantly improves the PAS data for dust events. [ABSTRACT FROM AUTHOR]