Influence of semi- and intermediate-volatile organic compounds (S/IVOC) parameterizations, volatility distributions and aging schemes on organic aerosol modelling in winter conditions
Introduction
Atmospheric pollution from particulate matter (PM) represents one of the major environmental and social concern for human health and it poses several challenges in terms of management and mitigation of harmful impacts. According to the latest European Environment Agency report (EEA, 2017), approximately 53% of the EU-28 population was exposed to PM concentrations exceeding the WHO Air Quality Guidance value for PM10 (WHO, 2006) in 2015. Premature deaths resulting from such exposure are estimated to be around 400 000 in the EU-28 countries. Nevertheless, the trend of mean PM concentration in Europe is rather flat during the most recent years (Guerreiro et al., 2014; Barmpadimos et al., 2012). Development of cost– effective mitigation policies depends heavily upon reliable air quality models results (Harrison et al., 2008) which can give insights about the impact of a given control strategy on PM concentrations.
A relevant fraction of submicron particulate matter is given by organic aerosol (OA), which accounts for 20–90% of total PM2.5 (Zhang et al., 2007). However, the large complexity of OA chemical composition, with thousands of organic chemical species found in the ambient aerosol (Goldstein and Galbally, 2007), as well as the complex atmospheric processing of organic compounds strongly limited scientific progress in the OA modelling area (Hallquist et al., 2009; Fuzzi et al., 2015). Within the atmospheric modelling community, there is mounting evidence that – despite an overall good agreement in gaseous pollutants – OA mass is in most applications underestimated mainly because of the not well reproduced secondary (SOA) fraction (Meroni et al., 2017; Ciarelli et al., 2016; Woody et al., 2016; Zhang et al., 2013; Bergström et al., 2012; Hodzic et al., 2010).
The traditional scheme for OA modelling in Chemical Transport Models (CTMs) is based on the so called “Two-product approach” by Odum et al. (1996). This approach considers primary organic aerosol (POA) that is directly emitted from various combustion sources (e.g. vehicles exhaust, biomass burning) as a non–volatile species that does not chemically evolve. SOA is formed from the early generation oxidation of gaseous organic volatile (VOC) precursors, which produces two nonreactive semi-volatile products that are partitioned between gas and aerosol phases depending on temperature and OA mass concentration. However, recent experimental studies highlighted that this approach presents two main limitations. First, Robinson et al. (2007) suggested that POA species should be treated as semi-volatile compounds that can evaporate from the particulate phase, react in the gas-phase and repartition as SOA, as pointed out also in other works (Jimenez et al., 2009; Grieshop et al., 2009). In the conceptual model of Robinson et al. (2007), POA emission is associated with semi–volatile (SVOC) and intermediate–volatile (IVOC) compounds emissions. SVOC compounds are characterized by a relatively low volatility (effective saturation concentration C* between 10−1 and 103 μg m−3) and are in the substantial partitioning with the particulate phase whereas IVOC compounds (C* between 103 and 106 μg m−3) are highly volatile and they partition preferentially to the gas-phase in atmospheric conditions. The second main issue of Odum et al. (1996) approach is related to the further oxidation of SOA in the atmosphere (i.e., the so-called aging process), which is traditionally neglected as the products of VOC oxidation were considered non–reactive. These two limitations led to the development of a new framework for the description of all OA components and their reactions. This new framework – in literature referred to as VBS (Volatility Basis Set) – rethinks the distinction between the traditional primary and secondary OA by grouping organic species into surrogates according to their volatility and degree of oxidation, thus providing a more realistic picture of the behavior of atmospheric organic aerosol. Details about theoretical aspects of VBS framework are provided in Donahue et al. (2006); Donahue et al. (2011); Donahue et al. (2012b).
Several applications of the VBS scheme to CTMs in different case studies can be found in the recent scientific literature (Fountoukis et al., 2011; Tsimpidi et al., 2011; Bergström et al., 2012; Zhang et al., 2013; Fountoukis et al., 2014; Koo et al., 2014; Ciarelli et al., 2016; Woody et al., 2016; Fountoukis et al., 2016; Meroni et al., 2017). The general conclusion stemming from these works is that the VBS scheme enhances the prediction of both OA levels and degree of oxidation, although the high number of parameters to be constrained in the VBS scheme causes a large uncertainty in the models results. For instance, all the studies cited above scaled the IVOC emissions, which are traditionally neglected in official emission inventories (Ots et al., 2016; Hodzic et al., 2010), on POA emissions using a factor between 1.5 and 3, as suggested by Robinson et al. (2007). However, this assumption historically derives from chassis dynamometer tailpipe measurements performed two decades ago on two diesel vehicles (Schauer et al., 1999) and – whilst it might hold true for vehicles exhaust emissions (Kim et al., 2016) – it is likely to be incorrect for other emissions sources (e.g. biomass burning, Ciarelli et al., 2017b). Very recent experimental works presented more detailed and source-specific parametrizations for IVOC emissions, which might be implemented in CTMs to provide more accurate results. As an example, Jathar et al. (2014) performed smog chamber experiments to investigate SOA formation from gasoline vehicles, diesel vehicles and biomass burning, and they reported that unspeciated organics – which are not appropriately included in current emission inventories and, in turn, chemical transport models – account for 10–20% of total non-methane organic gases (NMOG). Zhao et al. (2015) and Zhao et al. (2016) characterized emissions of IVOC from on-road and off-road diesel and gasoline vehicles during dynamometer testing, respectively, reporting both new volatility distributions of the organics emissions and new parametrizations for IVOC emissions calculation. Ciarelli et al. (2017b) performed novel smog chamber experiments for wood combustion emissions, and their result suggest an average ratio of non–traditional VOCs (i.e. IVOC) to POA emissions of 4.75, much higher compared to the widely adopted 1.5, which however was based on diesel vehicles measurements.
Recent European modelling studies attempted to integrate these new parametrizations into CTMs. Ciarelli et al. (2017a) constrained a modified VBS scheme to treat biomass burning OA and evaluated the implementation of this scheme in CAMx. Ots et al. (2016) and Sartelet et al. (2018) investigated different parametrizations for traffic-related S/IVOC emissions for the UK and the greater Paris area, respectively. Chrit et al. (2018) addressed both biomass burning and traffic-related S/IVOC emission parametrizations, volatility distributions and aging by performing a set of sensitivity simulations over western Mediterranean region during winter time. The overall outcome of these works is that updating S/IVOC emission parametrizations and volatility distributions helps in closing the gap between observed and predicted OA concentrations.
Here, we present a new set of sensitivity simulations with CAMx that, differently from previous studies, aims to evaluate model performances in conditions where high OA levels are measured. Following the most recent European studies, we investigate the impact of volatility distributions of organics emissions, S/IVOC emission parametrizations, SOA yields from gaseous precursors and different aging schemes, by implementing the latest experimental information available in the scientific literature. The study area is the Po Valley (Northern Italy) during wintertime (February–March 2013), which is a well-known hotspot where PM levels remain problematic despite the air quality remediation plans intended to get in compliance with current EU air quality standards, mainly because of adverse meteorological conditions (Caserini et al., 2017; Perrino et al., 2014; Pernigotti et al., 2012; Ferrero et al., 2011). We evaluate our model results against two OA–specific datasets, available for both an urban site (Bologna, February 2013) and a rural one (Ispra, March 2013). These two datasets are derived from Positive Matrix Factorization (PMF) analysis of Aerosol Mass Spectrometer (AMS) and Aerosol Chemical Speciation Monitor (ACSM) measurements (DeCarlo et al., 2006; Ng et al., 2011), which allow a thorough comparison of each fraction of organic aerosol (i.e. primary and secondary). We also point out how the development of different meteorological condition can influence the overall model performance as well as, more specifically, the reconstruction of the organic fraction.
Section snippets
The modelling setup
CAMx v6.40 (ENVIRON, 2016) was used to calculate the concentrations of both gaseous and particulate pollutants over the Po Valley domain, for a two-month long period covering February and March 2013. OA concentrations can be computed by CAMx v6.40 with three different schemes: (i) a traditional two–product model, which is called SOAP in CAMx (Strader et al., 1999), (ii) the same two–product model with revised yields for SOA production (SOAP2), based on new aerosol yield data that accounts for
Results
The validation of both meteorological variables and gaseous precursors for our case study is reported in Meroni et al. (2017). It is worth noting that our set of simulations differ only for OA-related features with respect to Meroni et al. (2017), and therefore a new validation of both meteorological variables and gaseous precursors was not needed. OA concentrations were validated by means of several model performance metrics. Mean Bias (MB) and Mean Fractional Bias (MFB) aim to assess the
Discussion
Table 6, Table 7 summarize the main performance metrics for OA at Bologna and Ispra sites, respectively. The same validation indices for each fraction of OA (i.e. POA, SOA, BBOA and HOA) are reported in the supplementary material (Table S1 to Table S8). At Bologna urban site, mean fractional bias for OA ranges from −80.1% in the worst case run (00_vbs_meroni) to −10.1% in the best one (06_vbs_bioaging) and IOA from 0.52 to 0.75. Notable improvements, though with overall poorer metrics, are
Conclusions
We presented a high–resolution (5 km) set of new simulations performed with CAMx v6.40 over the Po Valley area (Northern Italy), aimed to enhance OA levels prediction and to gain insight into the sensitivity of CAMx to different uncertain features of the input setup. In particular, we investigated the role of volatility distributions of organics emissions, S/IVOC emissions parametrizations, SOA yields from S/IVOC precursors and different aging schemes by exploiting the latest experimental
Declaration of interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
RSE contribution was funded by the Research Fund for the Italian Electrical System under the Contract Agreement between RSE S.p.A. and the Ministry of Economic Development - General Directorate for Nuclear Energy, Renewable Energy and Energy Efficiency in compliance with the Decree of March 8, 2006. The aerosol characterization in Bologna was funded by Regione Emilia Romagna as part of the “Supersito” project (DRG of Emilia-Romagna Region 428/10 and 1971/13). Authors are very grateful to
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2022, Journal of Environmental Sciences (China)Citation Excerpt :On the other hand, IVOC emissions are generally higher than SVOC emissions. For example, IVOC emissions in China for 2016, the US for 2008 and Northern Italy for February 2013 were 1.3, 0.9 and 2.7 times higher than SVOC emissions, respectively (Giani et al., 2019; Jathar et al., 2014; Wu et al., 2020). In the gas-particle partitioning theory, instantaneous reversible gas-particle equilibration is typically assumed in current air quality models without considering mass transport limitations inside the particles under low relative humidity (RH) (Zaveri et al., 2018).
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2021, Environmental PollutionCitation Excerpt :They reported that, when progressing from no aftertreatment system and diesel oxidation catalyst (DOC) systems to combinations of DOC + selective catalytic reduction (SCR) and DOC + DPF + SCR systems, the total PM including SOA (refractory BC (rBC), organic matter, sulfate, nitrate, and ammonium) was reduced by up to 100% (from 106.2 mg kWh−1 to almost 0 mg kWh−1). The research results of gasoline and diesel vehicles based on various other parameters, such as the type of vehicle, model year, and driving mode, form an important basis for assessing the air pollution levels of countries or metropolitan areas (Giani et al., 2019; Jathar et al., 2014; Karjalainen et al., 2019; Platt et al., 2017; Zhao et al., 2017). However, the main types of fuel for vehicles operating in Korea are gasoline (46.3%), diesel (42.1%), and LPG (8.5%; Table S1), which lack research data that could be used as the basis for identifying the primary and secondary air pollution effects caused by the combinations of different emissions.
- 1
Now at Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN, USA.
- 2
Now at Institute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas (FORTH/ICE-HT), Patras, Greece.