Pan-Arctic surface ozone: modelling vs. measurements

Within the framework of the International Arctic Systems for Observing the Atmosphere (IASOA), we report a modelling-based study on surface ozone across the Arctic. We use surface ozone from six sites – Summit (Greenland), Pallas (Finland), Barrow (USA), Alert (Canada), Tiksi (Russia), and Villum Research Station (VRS) at Station Nord (North Greenland, Danish realm) – and ozone-sonde data from three Canadian sites: Resolute, Eureka, and Alert. Two global chemistry models – a global chemistry transport model (parallelised-Tropospheric Offline Model of Chemistry and Transport, p-TOMCAT) and a global chemistry climate model (United Kingdom Chemistry and Aerosol, UKCA) – are used for model data comparisons. Remotely sensed data of BrO from the GOME-2 satellite instrument and ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) at Eureka, Canada, are used for model validation. The observed climatology data show that spring surface ozone at coastal sites is heavily depleted, making ozone seasonality at Arctic coastal sites distinctly different from that at inland sites. Model simulations show that surface ozone can be greatly reduced by bromine chemistry. In April, bromine chemistry can cause a net ozone loss (monthly mean) of 10–20 ppbv, with almost half attributable to open-ocean-sourced bromine and the rest to sea-ice-sourced bromine. However, the open-ocean-sourced bromine, via sea spray bromide depletion, cannot by itself produce ozone depletion events (ODEs; defined as ozone volume mixing ratios, VMRs, < 10 ppbv). In contrast, sea-ice-sourced bromine, via sea salt aerosol (SSA) production from blowing snow, can produce ODEs even without bromine from sea spray, highlighting the importance of sea ice surface in polar boundary layer chemistry. Modelled total inorganic bromine (BrY) over the Arctic sea ice is sensitive to model configuration; e.g. under the same bromine loading, BrY in the Arctic spring boundary layer in the p-TOMCAT control run (i.e. with all bromine emissions) can be 2 times that in the UKCA control run. Despite the model differences, both model control runs can successfully reproduce large bromine explosion events (BEEs) and ODEs in polar spring. Model-integrated tropospheric-column BrO generally matches GOME-2 tropospheric columns within ∼ 50 % in UKCA and a factor of 2 in p-TOMCAT. The success of the models in reproducing both ODEs and BEEs in the Arctic indicates that the relevant parameterizations implemented in the models work reasonably well, which supports the proposed mechanism of SSA production and bromide release on sea ice. Given that sea ice is a large source of SSA and halogens, changes in sea ice type and extent in a warming climate will influence Arctic boundary layer chemistry, including the oxidation of atmospheric elemental mercury. Note that this work dose not necessary rule out other possibilities that may act as a source of reactive bromine from the sea ice zone

A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises

The performance and the uncertainty of receptor models (RMs) were assessed in intercomparison exercises employing real-world and synthetic input datasets. To that end, the results obtained by different practitioners using ten different RMs were compared with a reference. In order to explain the differences in the performances and uncertainties of the different approaches, the apportioned mass, the number of sources, the chemical profiles, the contribution-to-species and the time trends of the sources were all evaluated using the methodology described in Belis et al. (2015). In this study, 87% of the 344 source contribution estimates (SCEs) reported by participants in 47 different source apportionment model results met the 50% standard uncertainty quality objective established for the performance test. In addition, 68% of the SCE uncertainties reported in the results were coherent with the analytical uncertainties in the input data. The most used models, EPA-PMF v.3, PMF2 and EPA-CMB 8.2, presented quite satisfactory performances in the estimation of SCEs while unconstrained models, that do not account for the uncertainty in the input data (e.g. APCS and FA-MLRA), showed below average performance. Sources with well-defined chemical profiles and seasonal time trends, that make appreciable contributions (>10%), were those better quantified by the models while those with contributions to the PM mass close to 1% represented a challenge. The results of the assessment indicate that RMs are capable of estimating the contribution of the major pollution source categories over a given time window with a level of accuracy that is in line with the needs of air quality management

Evaluation of receptor and chemical transport models for PM10 source apportionment

In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs varied depending on the source: traffic/exhaust and industry were the source categories with the best results in the RMSEu tests while the most critical ones were soil dust and road dust. The differences between RMs and CTMs source reconstructions confirmed the importance of cross validating the results of these two families of models

Results of the first European Source Apportionment intercomparison for Receptor and Chemical Transport Models

In this study, the performance of the source apportionment model applications were evaluated by comparing the model results provided by 44 participants adopting a methodology based on performance indicators: z-scores and RMSEu, with pre-established acceptability criteria. Involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), provided a unique opportunity to cross-validate them. In addition, comparing the modelled source chemical profiles, with those measured directly at the source contributed to corroborate the chemical profile of the tested model results. The most used RM was EPA- PMF5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) and more difficulties are observed with SCE time series (72% of RMSEu accepted). Industry resulted the most problematic source for RMs due to the high variability among participants. Also the results obtained with CTMs were quite comparable to their ensemble reference using all models for the overall average (>92% of successful z-scores) while the comparability of the time series is more problematic (between 58% and 77% of the candidates’ RMSEu are accepted). In the CTM models a gap was observed between the sum of source contributions and the gravimetric PM10 mass likely due to PM underestimation in the base case. Interestingly, when only the tagged species CTM results were used in the reference, the differences between the two CTM approaches (brute force and tagged species) were evident. In this case the percentage of candidates passing the z-score and RMSEu tests were only 50% and 86%, respectively. CTMs showed good comparability with RMs for the overall dataset (83% of the z-scores accepted), more differences were observed when dealing with the time series of the single source categories. In this case the share of successful RMSEu was in the range 25% - 34%.JRC.C.5-Air and Climat

Assessing volatile organic compound sources in a boreal forest using positive matrix factorization (PMF)

Ambient volatile organic compound (VOC) concentrations including individual monoterpenoids, sesquiterpenes (SQTs), isoprene, 2-methyl-3-buten-1-ol (MBO), methacrolein (MACR), C5-C10 aldehydes, benzene and toluene were measured in a coniferous forest in Hyytia center dot la center dot, southern Finland, in April-November 2016 with 1-2 h time resolution. Positive matrix factorization (PMF) was used to resolve the major sources which were responsible for the observed ambient VOC concentrations. The most reliable results were obtained with a 10 factor solution including four anthropogenic and six biogenic sources. Three of the biogenic factors were induced by light. They were either light dependent emissions or products of photochemical reactions. Three factors appeared to be temperature dependent emissions. Biogenic emissions were clearly the most important source of the measured VOCs, but the contribution from a local sawmill was also significant. About half of the monoterpenes (MTs) could be appointed to Scots pine emissions, but the influence from the activities at a near-by sawmill and Norway spruce emissions were also found. In the case of some individual MTs (e.g. limonene), spruce emissions dominated. Spruce emissions were also mainly responsible for ambient SQTs, aldehydes, and isoprene. For anthropogenic compounds, benzene and toluene, background, and local activities were the main sources. PMF was useful in resolving the sources of ambient VOCs.Peer reviewe

Long-term volatility measurements of submicron atmospheric aerosol in Hyytiälä, Finland

The volatility of submicron atmospheric aerosol particles was investigated at a boreal forest site in Hyytiälä, Finland from January 2008 to May 2010. These long-term observations allowed for studying the seasonal behavior of aerosol evaporation with a special focus on compounds that remained in the aerosol phase at 280 °C. The temperature-response of evaporation was also studied by heating the aerosol sample step-wise to six temperatures ranging from 80 °C to 280 °C. The mass fraction remaining after heating (MFR) was determined from the measured particle number size distributions before and after heating assuming a constant particle density (1.6 g cm−3). On average 19% of the total aerosol mass remained in the particulate phase at 280 °C. The particles evaporated less at low ambient temperatures during winter as compared with the warmer months. Black carbon (BC) fraction of aerosol mass correlated positively with the MFR at 280 °C, but could not explain it completely: most of the time a notable fraction of this non-volatile residual was something other than BC. Using additional information on ambient meteorological conditions and results from an Aerodyne aerosol mass spectrometer (AMS), the chemical composition of MFR at 280 °C and its seasonal behavior was further examined. Correlation analysis with ambient temperature and mass fractions of polycyclic aromatic hydrocarbons (PAHs) indicated that MFR at 280 °C is probably affected by anthropogenic emissions. On the other hand, results from the AMS analysis suggested that there may be very low-volatile organics, possibly organonitrates, in the non-volatile (at 280 °C) fraction of aerosol mass

Atmospheric aerosols local–regional discrimination for a semi-urban area in India

In the European Integrated project on Aerosol Cloud Climate and Air Quality interactions (EUCAARI), measure- ments were carried out with a sequential filter-based aerosol sampler and on-line instruments for aerosol composition and behaviour at Gual Pahari, close to New Delhi. In fine mode (PM2.5), the secondary organic carbon (SOC) to total organic carbon ratio was 46%. This indicated that condensation of SOC on fine size particles could occur rapidly which may be related to the growth of aerosols and the potential to the size of cloud conden- sation nuclei in the region. Source region discrimination was improved significantly through coupling conditional probability functions with receptor modelling, and validation through volume size distribution. The air masses from industrial and dense populated regions show a mix of local as well as regional emissions to fine mode aerosols. The back-trajectory analysis captured the long-range transport of sea-salt aerosols enriched with mineral dust. The surface wind directions identified the influence of local emission activities.JRC.H.2-Air and Climat

Elucidating the present-day chemical composition, seasonality and source regions of climate-relevant aerosols across the Arctic land surface.

The Arctic is warming two to three times faster than the global average, and the role of aerosols is not well constrained. Aerosol number concentrations can be very low in remote environments, rendering local cloud radiative properties highly sensitive to available aerosol. The composition and sources of the climate-relevant aerosols, affecting Arctic cloud formation and altering their microphysics, remain largely elusive due to a lack of harmonized concurrent multi-component, multi-site, and multi-season observations. Here, we present a dataset on the overall chemical composition and seasonal variability of the Arctic total particulate matter (with a size cut at 10 mu m, PM10, or without any size cut) at eight observatories representing all Arctic sectors. Our holistic observational approach includes the Russian Arctic, a significant emission source area with less dedicated aerosol monitoring, and extends beyond the more traditionally studied summer period and black carbon/sulfate or fine-mode pollutants. The major airborne Arctic PM components in terms of dry mass are sea salt, secondary (non-sea-salt, nss) sulfate, and organic aerosol (OA), with minor contributions from elemental carbon (EC) and ammonium. We observe substantial spatiotemporal variability in component ratios, such as EC/OA, ammonium/nss-sulfate and OA/nss-sulfate, and fractional contributions to PM. When combined with component-specific back-trajectory analysis to identify marine or terrestrial origins, as well as the companion study by Moschos et al 2022 Nat. Geosci. focusing on OA, the composition analysis provides policy-guiding observational insights into sector-based differences in natural and anthropogenic Arctic aerosol sources. In this regard, we first reveal major source regions of inner-Arctic sea salt, biogenic sulfate, and natural organics, and highlight an underappreciated wintertime source of primary carbonaceous aerosols (EC and OA) in West Siberia, potentially associated with the oil and gas sector. The presented dataset can assist in reducing uncertainties in modelling pan-Arctic aerosol-climate interactions, as the major contributors to yearly aerosol mass can be constrained. These models can then be used to predict the future evolution of individual inner-Arctic atmospheric PM components in light of current and emerging pollution mitigation measures and improved region-specific emission inventories

European intercomparison for receptor models using a synthetic database

Establishing to what extent a methodology for identifying pollution sources is appropriate for a specific purpose and expressing the reliability of the results quantitatively is complex. In order to assess model performances and estimate their uncertainties, intercomparison exercises (IE) have been conducted within the framework of the JRC Initiative on Harmonization of Source Apportionment with Receptor Models (RM). The present IE involved 20 expert groups from Europe and 2 from South America and was performed using a synthetic database (DB) developed on purpose. The test DB consisted of 364 PM2,5 daily samples including total mass and 38 inorganic and organic species deriving from a simulation of the CAMx PSAT tool for the calendar year 2005 and extracted for a cell corresponding to the city of Milan. A total of 26 solutions obtained using the following model versions were reported for evaluation: EPA PMF 3.0 (12), EPA PMF 4.1 (1), EPA PMF 5.0 (1), PMF2 (3), EPA CMB 8.2 (4), EPA CMB ROBOTIC (1), FA MLR (1), COPREM (1) and ME-2 (1). Participants provided the number and label of the identified sources, their contribution estimation (SCE) and uncertainty. In addition, the source/factor chemical profiles, the contribution of the sources to each species and the contribution of each source/factor in each sample were also reported. Source/factors identified by participants were classified into 9 different source categories: biomass burning, traffic exhaust, road dust, sulphates, nitrates, crustal material, industry and secondary. An 85% of participants reported a number of source/factors close to the \u201ctrue\u201d number of sources in the synthetic database (\ub12). The inclusion of every source/factor into a category was checked by comparing its chemical profile and time trend with all the other members of the same category and with the reference source. The SCEs of the different solutions were compared with the reference source contributions in the synthetic database using the z-score and z\u2019-score indicators (ISO 5725-5) according to the methodology described by Karagulian & Belis, (2012). More than 80% of the 200 assessed source/factor contribution estimations met the acceptability criterion when compared to the source contributions used in the creation of the synthetic database. Even though, these results are about 10% lower than those obtained in a previous intercomparison using real-world data, a quite satisfactory ability of RM to retrieve the \u201ctrue\u201d source contributions comes out from this IE. Karagulian, F., and Belis, C. A., 2012. Enhancing Source Apportionment with Receptor Models to Foster the Air Quality Directive Implementation. International Journal of Environmental Pollution 50, 190-19