Towards a better understanding of fine PM sources: Online and offline datasets combination in a single PMF.

Source apportionment (SA) techniques allocate the measured ambient pollutants with their potential source origin; thus, they are a powerful tool for designing air pollution mitigation strategies. Positive Matrix Factorization (PMF) is one of the most widely used SA approaches, and its multi-time resolution (MTR) methodology, which enables mixing different instrument data in their original time resolution, was the focus of this study. One year of co-located measurements in Barcelona, Spain, of non-refractory submicronic particulate matter (NR-PM1), black carbon (BC) and metals were obtained by a Q-ACSM (Aerodyne Research Inc.), an aethalometer (Aerosol d.o.o.) and fine offline quartz-fibre filters, respectively. These data were combined in a MTR PMF analysis preserving the high time resolution (30 min for the NR-PM1 and BC, and 24 h every 4th day for the offline samples). The MTR-PMF outcomes were assessed varying the time resolution of the high-resolution data subset and exploring the error weightings of both subsets. The time resolution assessment revealed that averaging the high-resolution data was disadvantageous in terms of model residuals and environmental interpretability. The MTR-PMF resolved eight PM1 sources: ammonium sulphate + heavy oil combustion (25%), ammonium nitrate + ammonium chloride (17%), aged secondary organic aerosol (SOA) (16%), traffic (14%), biomass burning (9%), fresh SOA (8%), cooking-like organic aerosol (5%), and industry (4%). The MTR-PMF technique identified two more sources relative to the 24 h base case data subset using the same species and four more with respect to the pseudo-conventional approach mimicking offline PMF, indicating that the combination of both high and low TR data is significantly beneficial for SA. Besides the higher number of sources, the MTR-PMF technique has enabled some sources disentanglement compared to the pseudo-conventional and base case PMF as well as the characterisation of their intra-day patterns.

Highly Time-Resolved Apportionment of Carbonaceous Aerosols from Wildfire Using the TC-BC Method: Camp Fire 2018 Case Study.

The Camp Fire was one of California's deadliest and most destructive wildfires, and its widespread smoke threatened human health over a large area in Northern California in November 2018. To analyze the Camp Fire influence on air quality on a 200 km distant site in Berkeley, highly time-resolved total carbon (TC), black carbon (BC), and organic carbon (OC) were measured using the Carbonaceous Aerosol Speciation System (CASS, Aerosol Magee Scientific), comprising two instruments, a Total Carbon Analyzer TCA08 in tandem with an Aethalometer AE33. During the period when the air quality was affected by wildfire smoke, the BC concentrations increased four times above the typical air pollution level presented in Berkeley before and after the event, and the OC increased approximately ten times. High-time-resolution measurements allow us to study the aging of OC and investigate how the characteristics of carbonaceous aerosols evolve over the course of the fire event. A higher fraction of secondary carbonaceous aerosols was observed in the later phase of the fire. At the same time, the amount of light-absorbing organic aerosol (brown carbon) declined with time.

Characterization of fresh PM deposits on calcareous stone surfaces: Seasonality, source apportionment and soiling potential.

Particulate matter (PM) pollution is one of the major threats to cultural heritage outdoors. It has been recently implied that organic aerosols will prevail over inorganic carbon particulates in the future, changing the main mechanisms of damage caused by poor air quality to calcareous heritage in particular. We studied fresh particulate deposits on marble and limestone surfaces exposed to urban air in sheltered and unsheltered configurations. Due to different air pollution sources in different seasons, the amount and composition of surface deposits varied throughout the year. The main and most constant contributor to PM2.5 (particles smaller than 2.5 µm) were primary traffic emissions (30 %), followed by secondary formation of acidic inorganic aerosols, such as sulphate in summer and nitrate in winter (33 % altogether), and seasonal biomass-burning emissions (14 %). Although biomass burning is the major source of primary organic aerosols including the light-absorbing fraction that prevailed over black carbon (BC) in colder months (up to 60 % carbonaceous aerosol mass), we show that surface darkening causing the soiling effect is still governed by the minor BC fraction of atmospheric aerosols, which remained below 20 % of the carbonaceous aerosol mass throughout the year. This, however, can change in remote environments affected by biomass-burning emissions, such as winter resorts, or by rigorous BC mitigation measures in the future. In the short run, sheltered positions were less affected by different removal processes, but we show that surface deposits are not simply additive when considering longer periods of time. This must be taken into account when extrapolating surface accumulation to longer time scales.

Two-year-long high-time-resolution apportionment of primary and secondary carbonaceous aerosols in the Los Angeles Basin using an advanced total carbon-black carbon (TC-BC(λ)) method.

In recent years, carbonaceous aerosols (CA) have been recognized as a significant contributor to the concentration of particles smaller than 2.5 µm (i.e., PM2.5), with a negative impact on public health and Earth's radiative balance. In this study, we present a method for CA apportionment based on high-time-resolution measurements of total carbon (TC), black carbon (BC), and spectral dependence of absorption coefficient using a recently developed Carbonaceous Aerosol Speciation System (CASS). Two-year-long CA measurements at two different locations within California's Los Angeles Basin are presented. CA was apportioned based on its optical absorption properties, organic or elemental carbon composition, and primary or secondary origin. We found that the secondary organic aerosols (SOA), on average, represent >50 % of CA in the study area, presumably resulting from the oxidation of anthropogenic and biogenic volatile organic components. Remarkable peaks of SOA in summer afternoons were observed, with a fractional contribution of up to 90 %. On the other hand, the peak of primary emitted CA, consisting of BC and primary organic aerosol (POA), contributed >80 % to the CA during morning rush hours on winter working days. The light absorption of BC dominated over the brown carbon (BrC), which contributed to 20 % and 10 % of optical absorption at the lower wavelength of 370 nm during winter nights and summer afternoons, respectively. The highest contribution of BrC, up to 50 %, was observed during the wildfire periods. Although the uncertainty levels can be high for some CA components (such as split between primary emitted and secondary formed BrC during winter nights), further research focused on the optical properties of CA at different locations may help to better constrain the parameters used in CA apportionment studies. We believe that the CASS system combined with the apportionment method presented in this study can offer simplified and cost-effective insights into the composition of carbonaceous aerosols.

Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks.

Subdiffusive reflectance captured at short source-detector separations provides increased sensitivity to the scattering phase function and hence allows superficial probing of the tissue ultrastructure. Consequently, estimation of subdiffusive optical parameters has been the subject of many recent studies focusing on lookup-table-based (LUT) inverse models. Since an adequate description of the subdiffusive reflectance requires additional scattering phase function related optical parameters, the LUT inverse models, which grow exponentially with the number of estimated parameters, become excessively large and computationally inefficient. Herein, we propose, to the best of our knowledge, the first artificial-neural-network-based inverse Monte Carlo model that overcomes the limitations of the LUT inverse models and thus allows efficient real-time estimation of optical parameters from subdiffusive spatially resolved reflectance. The proposed inverse model retains the accuracy, is about four orders of magnitude faster than the LUT inverse models, grows only linearly with the number of estimated optical parameters, and can be easily extended to estimate additional optical parameters.

Virtually increased acceptance angle for efficient estimation of spatially resolved reflectance in the subdiffusive regime: a Monte Carlo study.

Light propagation in biological tissues is frequently modeled by the Monte Carlo (MC) method, which requires processing of many photon packets to obtain adequate quality of the observed backscattered signal. The computation times further increase for detection schemes with small acceptance angles and hence small fraction of the collected backscattered photon packets. In this paper, we investigate the use of a virtually increased acceptance angle for efficient MC simulation of spatially resolved reflectance and estimation of optical properties by an inverse model. We devise a robust criterion for approximation of the maximum virtual acceptance angle and evaluate the proposed methodology for a wide range of tissue-like optical properties and various source configurations.