Consistent determination of the heating rate of light-absorbing aerosol using wavelength- and time-dependent Aethalometer multiple-scattering correction.

Accurate and temporally consistent measurements of light absorbing aerosol (LAA) heating rate (HR) and of its source apportionment (fossil-fuel, FF; biomass-burning, BB) and speciation (black and brown Carbon; BC, BrC) are needed to evaluate LAA short-term climate forcing. For this purpose, wavelength- and time-dependent accurate LAA absorption coefficients are required. HR was experimentally determined and apportioned (sources/species) in the EMEP/ACTRIS/COLOSSAL-2018 winter campaign in Milan (urban-background site). Two Aethalometers (AE31/AE33) were installed together with a MAAP, CPC, OPC, a low volume sampler (PM2.5) and radiation instruments. AE31/AE33 multiple-scattering correction factors (C) were determined using two reference systems for the absorption coefficient: 1) 5-wavelength PP_UniMI with low time resolution (12 h, applied to PM2.5 samples); 2) timely-resolved MAAP data at a single wavelength. Using wavelength- and time-independent C values for the AE31 and AE33 obtained with the same reference device, the total HR showed a consistency (i.e. reproducibility) with average values comparable at 95% probability. However, if different reference devices/approaches are used, i.e. MAAP is chosen as reference instead of a PP_UniMI, the HR can be overestimated by 23-30% factor (by both AE31/AE33). This became more evident focusing on HR apportionment: AE33 data (corrected by a wavelength- and time-independent C) showed higher HRFF (+24 ± 1%) and higher HRBC (+10 ± 1%) than that of AE31. Conversely, HRBB and HRBrC were -28 ± 1% and -29 ± 1% lower for AE33 compared to AE31. These inconsistencies were overcome by introducing a wavelength-dependent Cλ for both AE31 and AE33, or using multi-wavelength apportionment methods, highlighting the need for further studies on the influence of wavelength corrections for HR determination. Finally, the temporally-resolved determination of C resulted in a diurnal cycle of the HR not statistically different whatever the source- speciation- apportionment used.

Assessment of light extinction at a European polluted urban area during wintertime: Impact of PM1 composition and sources.

In this paper, results from receptor modelling performed on a well-characterised PM1 dataset were combined to chemical light extinction data (bext) with the aim of assessing the impact of different PM1 components and sources on light extinction and visibility at a European polluted urban area. It is noteworthy that, at the state of the art, there are still very few papers estimating the impact of different emission sources on light extinction as we present here, although being among the major environmental challenges at many polluted areas. Following the concept of the well-known IMPROVE algorithm, here a tailored site-specific approach (recently developed by our group) was applied to assess chemical light extinction due to PM1 components and major sources. PM1 samples collected separately during daytime and nighttime at the urban area of Milan (Italy) were chemically characterised for elements, major ions, elemental and organic carbon, and levoglucosan. Chemical light extinction was estimated and results showed that at the investigated urban site it is heavily impacted by ammonium nitrate and organic matter. Receptor modelling (i.e. Positive Matrix Factorization, EPA-PMF 5.0) was effective to obtain source apportionment; the most reliable solution was found with 7 factors which were tentatively assigned to nitrates, sulphates, wood burning, traffic, industry, fine dust, and a Pb-rich source. The apportionment of aerosol light extinction (bext,aer) according to resolved sources showed that considering all samples together nitrate contributed at most (on average 41.6%), followed by sulphate, traffic, and wood burning accounting for 18.3%, 17.8% and 12.4%, respectively.

Size-segregated aerosol in a hot-spot pollution urban area: Chemical composition and three-way source apportionment.

In this work, a comprehensive characterisation and source apportionment of size-segregated aerosol collected using a multistage cascade impactor was performed. The samples were collected during wintertime in Milan (Italy), which is located in the Po Valley, one of the main pollution hot-spot areas in Europe. For every sampling, size-segregated mass concentration, elemental and ionic composition, and levoglucosan concentration were determined. Size-segregated data were inverted using the program MICRON to identify and quantify modal contributions of all the measured components. The detailed chemical characterisation allowed the application of a three-way (3-D) receptor model (implemented using Multilinear Engine) for size-segregated source apportionment and chemical profiles identification. It is noteworthy that - as far as we know - this is the first time that three-way source apportionment is attempted using data of aerosol collected by traditional cascade impactors. Seven factors were identified: wood burning, industry, resuspended dust, regional aerosol, construction works, traffic 1, and traffic 2. Further insights into size-segregated factor profiles suggested that the traffic 1 factor can be associated to diesel vehicles and traffic 2 to gasoline vehicles. The regional aerosol factor resulted to be the main contributor (nearly 50%) to the droplet mode (accumulation sub-mode with modal diameter in the range 0.5-1 µm), whereas the overall contribution from the two factors related to traffic was the most important one in the other size modes (34-41%). The results showed that applying a 3-D receptor model to size-segregated samples allows identifying factors of local and regional origin while receptor modelling on integrated PM fractions usually singles out factors characterised by primary (e.g. industry, traffic, soil dust) and secondary (e.g. ammonium sulphate and nitrate) origin. Furthermore, the results suggested that the information on size-segregated chemical composition in different size classes was exploited by the model to relate primary emissions to rapidly-formed secondary compounds.

Implementing constrained multi-time approach with bootstrap analysis in ME-2: An application to PM2.5 data from Florence (Italy).

Advanced receptor models have been recently developed and tested in order to improve the resolution of apportionment problems reducing rotational ambiguity of results and aiming at identifying a larger number of sources. In particular, multi-time model is a factor analysis method able to compute source profiles and contributions using aerosol compositional data with different time resolutions. Unlike traditional factor analysis, each measured value can be inserted into multi-time model with its original time schedule, thus all temporal information can be effectively used in the modelling process. In this work, multi-time model was expanded in order to impose constraints on modelled factors aiming at improving the source identification. Moreover, as far as we know for the first time, a suitable bootstrap technique was implemented in the multi-time scheme to estimate the uncertainty of the final constrained solutions. These implemented approaches were tested on a PM2.5 (particulate matter with aerodynamic diameter <2.5 µm) dataset composed of 24-h samples collected during one year and hourly data sampled in parallel for two shorter periods in Florence (Italy). The daily samples were chemically characterised for elements, ions and carbonaceous components while elemental concentrations only were available for high-time resolved samples. The application of the advanced model revealed the major contribution from traffic (accounting for 37% of PM2.5 as annual average) and allowed an accurate characterisation of involved emission processes. In particular, exhaust and non-exhaust emissions were identified. The constraints imposed in the continuation run led to a better description of the factor associated to nitrates and also of biomass burning profile and the bootstrap results gave useful information to assess the reliability of source apportionment solutions. Finally, the comparison with the results computed by ME-2 base model applied to daily and hourly compositional data separately demonstrated the advantages provided by the multi-time approach.