15 research outputs found

    Investigation of four-year chemical composition and organic aerosol sources of submicron particles at the ATOLL site in northern France

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    This study presents the first long-term online measurements of submicron (PM1) particles at the ATOLL (ATmospheric Observations in liLLe) platform, in northern France. The ongoing measurements using an Aerosol Chemical Speciation Monitor (ACSM) started at the end of 2016 and the analysis presented here spans through December 2020. At this site, the mean PM1 concentration is 10.6 μg m-3, dominated by organic aerosols (OA, 42.3%) and followed by nitrate (28.9%), ammonium (12.3%), sulfate (8.6%), and black carbon (BC, 8.0%). Large seasonal variations of PM1 concentrations are observed, with high concentrations during cold seasons, associated with pollution episodes (e.g. over 100 μg m-3 in January 2017). To study OA origins over this multiannual dataset we performed source apportionment analysis using rolling positive matrix factorization (PMF), yielding two primary OA factors, a traffic-related hydrocarbon-like OA (HOA) and biomass-burning OA (BBOA), and two oxygenated OA (OOA) factors. HOA showed a homogeneous contribution to OA throughout the seasons (11.8%), while BBOA varied from 8.1% (summer) to 18.5% (winter), the latter associated with residential wood combustion. The OOA factors were distinguished between their less and more oxidized fractions (LO-OOA and MO-OOA, on average contributing 32% and 42%, respectively). During winter, LO-OOA is identified as aged biomass burning, so at least half of OA is associated with wood combustion during this season. Furthermore, ammonium nitrate is also a predominant aerosol component during cold-weather pollution episodes - associated with fertilizer usage and traffic emissions. This study provides a comprehensive analysis of submicron aerosol sources at the recently established ATOLL site in northern France from multiannual observations, depicting a complex interaction between anthropogenic and natural sources, leading to different mechanisms of air quality degradation in the region across different seasons

    Real-time source apportionment of organic aerosols in three European cities.

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    97% of the urban population in the EU in 2019 were exposed to an annual fine particulate matter level higher than the World Health Organization (WHO) guidelines (5 μg/m3). Organic aerosol (OA) is one of the major air pollutants, and the knowledge of its sources is crucial for designing cost-effective mitigation strategies. Positive matrix factorization (PMF) on aerosol mass spectrometer (AMS) or aerosol chemical speciation monitor (ACSM) data is the most common method for source apportionment (SA) analysis on ambient OA. However, conventional PMF requires extensive human labor, preventing the implementation of SA for routine monitoring applications. This study proposes the source finder real-time (SoFi RT, Datalystica Ltd.) approach for efficient retrieval of OA sources. The results generated by SoFi RT agree remarkably well with the conventional rolling PMF results regarding factor profiles, time series, diurnal patterns, and yearly relative contributions of OA factor on three year-long ACSM data sets collected in Athens, Paris, and Zurich. Although the initialization of SoFi RT requires a priori knowledge of OA sources (i.e., the approximate number of factors and relevant factor profiles) for the sampling site, this technique minimizes user interactions. Eventually, it could provide up-to-date trustable information on timescales useful to policymakers and air quality modelers

    Two-stroke scooters are a dominant source of air pollution in many cities.

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    Fossil fuel-powered vehicles emit significant particulate matter, for example, black carbon and primary organic aerosol, and produce secondary organic aerosol. Here we quantify secondary organic aerosol production from two-stroke scooters. Cars and trucks, particularly diesel vehicles, are thought to be the main vehicular pollution sources. This needs re-thinking, as we show that elevated particulate matter levels can be a consequence of 'asymmetric pollution' from two-stroke scooters, vehicles that constitute a small fraction of the fleet, but can dominate urban vehicular pollution through organic aerosol and aromatic emission factors up to thousands of times higher than from other vehicle classes. Further, we demonstrate that oxidation processes producing secondary organic aerosol from vehicle exhaust also form potentially toxic 'reactive oxygen species'.This work was supported by the Swiss Federal Office for the Environment (FOEN), the Federal Roads Office (FEDRO), the Swiss National Science Foundation (Ambizione PZ00P2_131673, SAPMAV 200021_13016), the EU commission (FP7, COFUND: PSI-Fellow, grant agreement n.° 290605), the UK Natural Environment Research Council (NERC), the French Environment and Energy Management Agency (ADEME, Grant number 1162C00O2) and the Velux Foundation.This is the accepted manuscript version. The final version is available from http://www.nature.com/ncomms/2014/140513/ncomms4749/full/ncomms4749.html

    A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data

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    A new methodology for performing long-term source apportionment (SA) using positive matrix factorization (PMF) is presented. The method is implemented within the SoFi Pro software package and uses the multilinear engine (ME-2) as a PMF solver. The technique is applied to a 1-year aerosol chemical speciation monitor (ACSM) dataset from downtown Zurich, Switzerland. The measured organic aerosol mass spectra were analyzed by PMF using a small (14 d) and rolling PMF window to account for the temporal evolution of the sources. The rotational ambiguity is explored and the uncertainties of the PMF solutions were estimated. Factor–tracer correlations for averaged seasonal results from the rolling window analysis are higher than those retrieved from conventional PMF analyses of individual seasons, highlighting the improved performance of the rolling window algorithm for long-term data. In this study four to five factors were tested for every PMF window. Factor profiles for primary organic aerosol from traffic (HOA), cooking (COA) and biomass burning (BBOA) were constrained. Secondary organic aerosol was represented by either the combination of semi-volatile and low-volatility organic aerosol (SV-OOA and LV-OOA, respectively) or by a single OOA when this separation was not robust. This scheme led to roughly 40 000 PMF runs. Full visual inspection of all these PMF runs is unrealistic and is replaced by predefined user-selected criteria, which allow factor sorting and PMF run acceptance/rejection. The selected criteria for traffic (HOA) and BBOA were the correlation with equivalent black carbon from traffic (eBCtr) and the explained variation of 60, respectively. COA was assessed by the prominence of a lunchtime concentration peak within the diurnal cycle. SV-OOA and LV-OOA were evaluated based on the fractions of 43 and 44 in their respective factor profiles. Seasonal pre-tests revealed a non-continuous separation of OOA into SV-OOA and LV-OOA, in particular during the warm seasons. Therefore, a differentiation between four-factor solutions (HOA, COA, BBOA and OOA) and five-factor solutions (HOA, COA, BBOA, SV-OOA and LV-OOA) was also conducted based on the criterion for SV-OOA. HOA and COA contribute between 0.4–0.7 µg m−3 (7.8 %–9.0 %) and 0.7–1.2 µg m−3 (12.2 %–15.7 %) on average throughout the year, respectively. BBOA shows a strong yearly cycle with the lowest mean concentrations in summer (0.6 µg m−3, 12.0 %), slightly higher mean concentrations during spring and fall (1.0 and 1.5 µg m−3, or 15.6 % and 18.6 %, respectively), and the highest mean concentrations during winter (1.9 µg m−3, 25.0 %). In summer, OOA is separated into SV-OOA and LV-OOA, with mean concentrations of 1.4 µg m−3 (26.5 %) and 2.2 µg m−3 (40.3 %), respectively. For the remaining seasons the seasonal concentrations of SV-OOA, LV-OOA and OOA range from 0.3 to 1.1 µg m−3 (3.4 %–15.9 %), from 0.6 to 2.2 µg m−3 (7.7 %–33.7 %) and from 0.9 to 3.1 µg m−3 (13.7 %–39.9 %), respectively. The relative PMF errors modeled for this study for HOA, COA, BBOA, LV-OOA, SV-OOA and OOA are on average ±34 %, ±27 %, ±30 %, ±11 %, ±25 % and ±12 %, respectively

    Enhanced light absorption by mixed source black and brown carbon particles in UK winter

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    Black carbon (BC) and light-absorbing organic carbon (brown carbon, BrC) play key roles in warming the atmosphere, but the magnitude of their effects remains highly uncertain. Theoretical modelling and laboratory experiments demonstrate that coatings on BC can enhance BC’s light absorption, therefore many climate models simply assume enhanced BC absorption by a factor of ∼1.5. However, recent field observations show negligible absorption enhancement, implying models may overestimate BC’s warming. Here we report direct evidence of substantial field-measured BC absorption enhancement, with the magnitude strongly depending on BC coating amount. Increases in BC coating result from a combination of changing sources and photochemical aging processes. When the influence of BrC is accounted for, observationally constrained model calculations of the BC absorption enhancement can be reconciled with the observations. We conclude that the influence of coatings on BC absorption should be treated as a source and regionally specific parameter in climate models
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