Aerosol Chemical Speciation Monitor Data Research Articles

A novel probabilistic source apportionment approach: Bayesian auto-correlated matrix factorization

Abstract. The concentrations of atmospheric particulate matter and many of its constituents are temporally auto-correlated. However, this information has not been utilized in source apportionment methods. Here, we present a Bayesian matrix factorization model (BAMF) that considers the temporal auto-correlation of the components (sources) and provides a direct error estimation. The performance of BAMF is compared with positive matrix factorization (PMF) using synthetic Time-of-Flight Aerosol Chemical Speciation Monitor data, representing different urban environments from typical European towns to megacities. We find that BAMF resolves sources with overall higher factorization performance (temporal behavior and bias) than PMF on all datasets with temporally auto-correlated components. Highly correlated components continue to be challenging and ancillary information is still required to reach good factorizations. However, we demonstrate that adding even partial prior information about the chemical composition of the components to BAMF improves the factorization. Overall, BAMF-type models are promising tools for source apportionment and merit further research.

Real-Time Source Apportionment of Organic Aerosols in Three European Cities.

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.

Eight years of sub-micrometre organic aerosol composition data from the boreal forest characterized using a machine-learning approach

Abstract. The Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II, located within the boreal forest of Finland, is a unique station in the world due to the wide range of long-term measurements tracking the Earth–atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date. Similarly to other previously reported efforts in OA source apportionment from multi-seasonal or multi-annual data sets, we approached the OA characterization challenge through positive matrix factorization (PMF) using a rolling window approach. However, the existing methods for extracting minor OA components were found to be insufficient for our rather remote site. To overcome this issue, we tested a new statistical analysis framework. This included unsupervised feature extraction and classification stages to explore a large number of unconstrained PMF runs conducted on the measured OA mass spectra. Anchored by these results, we finally constructed a relaxed chemical mass balance (CMB) run that resolved different OA components from our observations. The presented combination of statistical tools provided a data-driven analysis methodology, which in our case achieved robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012–2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, and 2.6 µg m−3) into three sub-categories – low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA) – proving that the tested methodology was able to provide results consistent with literature. LV-OOA was the most dominant OA type (organic mass fraction IQR: 49 %, 62 %, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ng m−3 h−1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19 %, 31 %, and 43 %). Due to SV-OOA's clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. Two nearby sawmills also played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2 %, 6 %, and 13 %). Notably, the quantification of POA at SMEAR II using ACSM data was not possible following existing rolling PMF methodologies. Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. Similar wind direction and speed dependence was not observed among other OA types. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In the event of slower wind speeds, POA likely evaporated and/or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements 2 to 4 years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that short-term (POA) pollution plumes were averaged out due to the slow time resolution of the ACSM combined with the further 3 h data averaging needed to ensure good signal-to-noise ratios (SNRs). Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the 8-year measurement period. Due to the unique and realistic seasonal cycles and meteorology dependences of the independent OA subtypes complemented by the reasonably low degree of unexplained OA variability, we believe that the presented data analysis approach performs well. Therefore, we hope that these results encourage also other researchers possessing several-year-long time series of similar data to tackle the data analysis via similar semi- or unsupervised machine-learning approaches. This way the presented method could be further optimized and its usability explored and evaluated also in other environments.

Comparison of five methodologies to apportion organic aerosol sources during a PM pollution event

This study presents a comparison of five methodologies to apportion primary (POA) and secondary organic aerosol (SOA) sources from measurements performed in the Paris region (France) during a highly processed PM pollution event. POA fractions, estimated from EC-tracer method and positive matrix factorization (PMF) analyses, conducted on measurements from PM10 filters, aerosol chemical speciation monitor (ACSM) and offline aerosol mass spectrometry (AMS), were all comparable (2.2–3.7 μg m−3 as primary organic carbon (POC)). Associated relative uncertainties (measurement + model) on POC estimations ranged from 8 to 50%. The best apportionment of primary traffic OA was achieved using key markers (EC and 1-nitropyrene) in the chemical speciation-based PMF showing more pronounced rush-hour peaks and greater correlation with NOx than other traffic related POC factors. All biomass burning-related factors were in good agreement, with a typical diel profile and a night-time increase linked to residential heating. If PMF applied to ACSM data showed good agreement with other PMF outputs corrected from dust-related factors (coarse PM), discrepancies were observed between individual POA factors (traffic, biomass burning) and directly comparable SOA factors and highly oxidized OA. Similar secondary organic carbon (SOC) concentrations (3.3 ± 0.1 μg m−3) were obtained from all approaches, except the SOA-tracer method (1.8 μg m−3). Associated uncertainties ranged from 14 to 52% with larger uncertainties obtained for PMF-chemical data, EC- and SOA-tracer methods. This latter significantly underestimated total SOA loadings, even including biomass burning SOA, due to missing SOA classes and precursors. None of the approaches was able to identify the formation mechanisms and/or precursors responsible for the highly oxidized SOA fraction associated with nitrate- and/or sulfate-rich aerosols (35% of OA). We recommend the use of a combination of different methodologies to apportion the POC/SOC concentrations/contributions to get the highest level of confidence in the estimates obtained.

Multi-year ACSM measurements at the central European research station Melpitz (Germany) – Part 1: Instrument robustness, quality assurance, and impact of upper size cutoff diameter

Abstract. The aerosol chemical speciation monitor (ACSM) is nowadays widely used to identify and quantify the main components of fine particles in ambient air. As such, its deployment at observatory platforms is fully incorporated within the European Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS). Regular intercomparisons are organized at the Aerosol Chemical Monitoring Calibration Center (ACMCC; part of the European Center for Aerosol Calibration, Paris, France) to ensure the consistency of the dataset, as well as instrumental performance and variability. However, in situ quality assurance remains a fundamental aspect of the instrument's stability. Here, we present and discuss the main outputs of long-term quality assurance efforts achieved for ACSM measurements at the research station Melpitz (Germany) since 2012 onwards. In order to validate the ACSM measurements over the years and to characterize seasonal variations, nitrate, sulfate, ammonium, organic, and particle mass concentrations were systematically compared against the collocated measurements of daily offline high-volume PM1 and PM2.5 filter samples and particle number size distribution (PNSD) measurements. Mass closure analysis was made by comparing the total particle mass (PM) concentration obtained by adding the mass concentration of equivalent black carbon (eBC) from the multi-angle absorption photometer (MAAP) to the ACSM chemical composition, to that of PM1 and PM2.5 during filter weighing, as well as to the derived mass concentration of PNSD. A combination of PM1 and PM2.5 filter samples helped identifying the critical importance of the upper size cutoff of the ACSM during such exercises. The ACSM–MAAP-derived mass concentrations systematically deviated from the PM1 mass when the mass concentration of the latter represented less than 60 % of PM2.5, which was linked to the transmission efficiency of the aerodynamic lenses of the ACSM. The best correlations are obtained for sulfate (slope =0.96; R2=0.77) and total PM (slope =1.02; R2=0.90). Although, sulfate did not exhibit a seasonal dependency, total PM mass concentration revealed a small seasonal variability linked to the increase in non-water-soluble fractions. The nitrate suffers from a loss of ammonium nitrate during filter collection, and the contribution of organo-nitrate compounds to the ACSM nitrate signal make it difficult to directly compare the two methods. The contribution of m∕z 44 (f44) to the total organic mass concentration was used to convert the ACSM organic mass (OM) to organic carbon (OC) by using a similar approach as for the aerosol mass spectrometer (AMS). The resulting estimated OCACSM was compared with the measured OCPM1 (slope =0.74; R2=0.77), indicating that the f44 signal was relatively free of interferences during this period. The PM2.5 filter samples use for the ACSM data quality might suffer from a systematic bias due to a size truncation effect as well as to the presence of chemical species that cannot be detected by the ACSM in coarse mode (e.g., sodium nitrate and sodium sulfate). This may lead to a systematic underestimation of the ACSM particle mass concentration and/or a positive artifact that artificially decreases the discrepancies between the two methods. Consequently, ACSM data validation using PM2.5 filters has to be interpreted with extreme care. The particle mass closure with the PNSD was satisfying (slope =0.77; R2=0.90 over the entire period), with a slight overestimation of the mobility particle size spectrometer (MPSS)-derived mass concentration in winter. This seasonal variability was related to a change on the PNSD and a larger contribution of the supermicrometer particles in winter. This long-term analysis between the ACSM and other collocated instruments confirms the robustness of the ACSM and its suitability for long-term measurements. Particle mass closure with the PNSD is strongly recommended to ensure the stability of the ACSM. A near-real-time mass closure procedure within the entire ACTRIS–ACSM network certainly represents an optimal quality control and assurance of both warranting the quality assurance of the ACSM measurements as well as identifying cross-instrumental biases.

Gaining knowledge on source contribution to aerosol optical absorption properties and organics by receptor modelling

In this source apportionment study, an original approach based on receptor modelling was tested to relate primary and secondary organic aerosol (OA) contributions - estimated from ACSM (Aerosol Chemical Speciation Monitor) measurements - to their emission sources. Moreover, thanks to the coupling of optical and chemical variables as input to the receptor model, information such as the impact of mineral dust to the aerosol absorption in the atmosphere and estimates for the absorption Ångström exponent (α) of the sources were retrieved.An advanced source apportionment study using the Multilinear Engine (ME-2) was performed on data collected during February 2017 in Rome (Italy), in the frame of the CARE (Carbonaceous Aerosol in Rome and Environs) experiment. A complete chemical characterisation (elements, non-refractory components, and carbonaceous components) was carried out, and the aerosol absorption coefficients bap(λ) at 7 wavelengths (370, 470, 520, 590, 660, 880, and 950 nm) were retrieved by an Aethalometer AE33; all these variables (chemical + optical) were used as input to the receptor model. The final constrained solution consisted of nine factors which were assigned to major sources impacting on the investigated site (hereafter sources are referred to as: biomass burning, nitrate and aged aerosol, traffic exhaust, sulphate, mineral dust, marine aerosol, traffic non-exhaust, local source, and polluted marine aerosol), comprising both local urban sources and contributions from long-range transport. The bootstrap analysis supported the goodness of the solution.Total OA concentration from ACSM was apportioned by our receptor model and afterwards compared with HOA (hydrocarbon-like organic aerosol), BBOA (biomass burning-like organic aerosol), and OOA (oxygenated organic aerosol) concentrations obtained as results from an independent source apportionment study previously performed. As an original result of this work, insights on OA contributions were thus retrieved: (1) the contribution of organic aerosol assigned by ME-2 to the traffic exhaust source was fully comparable to HOA assessed by ACSM data analysis; (2) our source apportionment results gave the relevant indication that the OOA apportionment made on ACSM data likely includes a secondary OA contribution due to biomass burning.Other relevant results came from bap apportionment obtained by our multi-variable source apportionment approach: traffic exhaust was the main contributor to aerosol absorption in the atmosphere, but mineral dust contribution was also notable when a not negligible mineral dust transport episode was registered at the measurement site. In addition, source dependent optical absorption parameters (i.e. the absorption Ångström exponent - α - and the mass absorption cross section at different wavelengths) were retrieved without any a-priori assumption.In perspective, our modelling approach paves the way to more powerful source apportionment approaches which have the potential of providing much more insights on aerosol properties and sources.

Large-scale particulate air pollution and chemical fingerprint of volcanic sulfate aerosols from the 2014–2015 Holuhraun flood lava eruption of Bárðarbunga volcano (Iceland)

Abstract. Volcanic sulfate aerosols play a key role in air quality and climate. However, the rate of oxidation of sulfur dioxide (SO2) precursor gas to sulfate aerosols (SO42-) in volcanic plumes is poorly known, especially in the troposphere. Here we determine the chemical speciation as well as the intensity and temporal persistence of the impact on air quality of sulfate aerosols from the 2014–2015 Holuhraun flood lava eruption of Icelandic volcano Bárðarbunga. To do so, we jointly analyse a set of SO2 observations from satellite (OMPS and IASI) and ground-level measurements from air quality monitoring stations together with high temporal resolution mass spectrometry measurements of an Aerosol Chemical Speciation Monitor (ACSM) performed far from the volcanic source. We explore month/year long ACSM data in France from stations in contrasting environments, close and far from industrial sulfur-rich activities. We demonstrate that volcanic sulfate aerosols exhibit a distinct chemical signature in urban/rural conditions, with NO3:SO4 mass concentration ratios lower than for non-volcanic background aerosols. These results are supported by thermodynamic simulations of aerosol composition, using the ISORROPIA II model, which show that ammonium sulfate aerosols are preferentially formed at a high concentration of sulfate, leading to a decrease in the production of particulate ammonium nitrate. Such a chemical signature is however more difficult to identify at heavily polluted industrial sites due to a high level of background noise in sulfur. Nevertheless, aged volcanic sulfates can be distinguished from freshly emitted industrial sulfates according to their contrasting degree of anion neutralization. Combining AERONET (AErosol RObotic NETwork) sunphotometric data with ACSM observations, we also show a long persistence over weeks of pollution in volcanic sulfate aerosols, while SO2 pollution disappears in a few days at most. Finally, gathering 6-month long datasets from 27 sulfur monitoring stations of the EMEP (European Monitoring and Evaluation Programme) network allows us to demonstrate a much broader large-scale European pollution, in both SO2 and SO4, associated with the Holuhraun eruption, from Scandinavia to France. While widespread SO2 anomalies, with ground-level mass concentrations far exceeding background values, almost entirely result from the volcanic source, the origin of sulfate aerosols is more complex. Using a multi-site concentration-weighted trajectory analysis, emissions from the Holuhraun eruption are shown to be one of the main sources of SO4 at all EMEP sites across Europe and can be distinguished from anthropogenic emissions from eastern Europe but also from Great Britain. A wide variability in SO2:SO4 mass concentration ratios, ranging from 0.8 to 8.0, is shown at several stations geographically dispersed at thousands of kilometres from the eruption site. Despite this apparent spatial complexity, we demonstrate that these mass oxidation ratios can be explained by a simple linear dependency on the age of the plume, with a SO2-to-SO4 oxidation rate of 0.23 h−1. Most current studies generally focus on SO2, an unambiguous and more readily measured marker of the volcanic plume. However, the long persistence of the chemical fingerprint of volcanic sulfate aerosols at continental scale, as shown for the Holuhraun eruption here, casts light on the impact of tropospheric eruptions and passive degassing activities on air quality, health, atmospheric chemistry and climate.

Characterization of aerosol mass spectra responses to temperature over a forest site in Lithuania

The chemical composition of submicron aerosol particles was characterized using an aerosol chemical speciation monitor (ACSM) at a mixed-mature forest site at the Aukštaitija Integrated Monitoring Station in the eastern part of Lithuania. Four organic aerosol (OA) factors were determined by positive matrix factorization (PMF) analysis applied for the ACSM data: semi-volatile oxygenated OA (SVOOA, the contribution to the total OA mass concentration was 33%), low-volatile oxygenated OA (LVOOA, 39%), cooking related OA (COA, 15%) and biomass burning OA (BBOA, 13%). In our study secondary organic aerosol (SOA) has been identified as one of the most important contributors to the submicron particle (PM1) mass concentration. A comparison between SVOOA and submicron forest organic aerosol mass (SFOM) was performed, and a good correlation of 0.75 between them was found. In our study the SOA concentration dependence on temperature was explored in 3 means: firstly, by investigating aerosol mass spectra measured during hot and cold days. It has been found that during hot days events signal intensities of m/z 42, 43, 45, 53 and 59 were about 2.1–2.7 times higher compared to those during cold days. Secondly, by investigating the temperature dependence of SVOOA and estimated SFOM. It has been found that SFOM and SVOOA concentrations increase with temperature. Thirdly, by assessing the influence of temperature related stomatal conduction on SVOOA concentration: no direct influence on SVOOA concentration was observed.

Aerosol hygroscopic growth, contributing factors, and impact on haze events in a severely polluted region in northern China

Abstract. This study investigates the impact of the aerosol hygroscopic growth effect on haze events in Xingtai, a heavily polluted city in the central part of the North China Plain (NCP), using a large array of instruments measuring aerosol optical, physical, and chemical properties. Key instruments used and measurements made include the Raman lidar for atmospheric water vapor content and aerosol optical profiles, the PC-3016A GrayWolf six-channel handheld particle and mass meter for atmospheric total particulate matter (PM) that has diameters less than 1 and 2.5 µm (PM1 and PM2.5, respectively), the aerosol chemical speciation monitor (ACSM) for chemical components in PM1, and the hygroscopic tandem differential mobility analyzer (H-TDMA) for aerosol hygroscopicity. The changes in PM1 and PM2.5 agreed well with that of the water vapor content due to the aerosol hygroscopic growth effect. Two cases were selected to further analyze the effects of aerosol hygroscopic growth on haze events. The lidar-estimated hygroscopic enhancement factor for the aerosol backscattering coefficient during a relatively clean period (Case I) was lower than that during a pollution event (Case II) with similar relative humidity (RH) levels of 80 %–91 %. The Kasten model was used to fit the aerosol optical hygroscopic growth factor (GF) whose parameter b differed considerably between the two cases, i.e., 0.1000 (Case I) versus 0.9346 (Case II). The aerosol acidity value calculated from ACSM data for Case I (1.35) was less than that for Case II (1.50) due to different amounts of inorganics such as NH4NO3, NH4HSO4, and (NH4)2SO4. Model results based on H-TDMA data showed that aerosol hygroscopic growth factors in each size category (40, 80, 110, 150, and 200 nm) at different RH levels (80 %–91 %) for Case I were lower than those for Case II. For similar ambient RH levels, the high content of nitrate facilitates the hygroscopic growth of aerosols, which may be a major factor contributing to heavy haze episodes in Xingtai.

Spatial Distribution of Carbonaceous Aerosol in the Southeastern Baltic Sea Region (Event of Grass Fires)

The aerosol chemical composition in air masses affected by large vegetation fires transported from the Kaliningrad region (Russia) and southeast regions (Belarus and Ukraine) during early spring (March 2014) was characterized at the remote background site of Preila, Lithuania. In this study, the chemical composition of the particulate matter was studied by high temporal resolution instruments, including an Aerosol Chemical Speciation Monitor (ACSM) and a seven-wavelength aethalo-meter. Air masses were transported from twenty to several hundred kilometres, arriving at the measurement station after approximately half a day of transport. The concentration-weighted trajectory analysis suggests that organic aerosol particles are mainly transported over the Baltic Sea and the continent (southeast of Belarus). Results show that a significant fraction of the vegetation burning organic aerosol is transformed into oxidised forms in less than a half-day. Biomass burning aerosol (BBOA) was quantified from the ACSM data using a positive matrix factorization (PMF) analysis, while its spatial distribution was evaluated using air mass clustering approach.

Characterization and source apportionment of organic aerosol using offline aerosol mass spectrometry

Abstract. Field deployments of the Aerodyne Aerosol Mass Spectrometer (AMS) have significantly advanced real-time measurements and source apportionment of non-refractory particulate matter. However, the cost and complex maintenance requirements of the AMS make its deployment at sufficient sites to determine regional characteristics impractical. Furthermore, the negligible transmission efficiency of the AMS inlet for supermicron particles significantly limits the characterization of their chemical nature and contributing sources. In this study, we utilize the AMS to characterize the water-soluble organic fingerprint of ambient particles collected onto conventional quartz filters, which are routinely sampled at many air quality sites. The method was applied to 256 particulate matter (PM) filter samples (PM1, PM2.5, and PM10, i.e., PM with aerodynamic diameters smaller than 1, 2.5, and 10 µm, respectively), collected at 16 urban and rural sites during summer and winter. We show that the results obtained by the present technique compare well with those from co-located online measurements, e.g., AMS or Aerosol Chemical Speciation Monitor (ACSM). The bulk recoveries of organic aerosol (60–91 %) achieved using this technique, together with low detection limits (0.8 µg of organic aerosol on the analyzed filter fraction) allow its application to environmental samples. We will discuss the recovery variability of individual hydrocarbon ions, ions containing oxygen, and other ions. The performance of such data in source apportionment is assessed in comparison to ACSM data. Recoveries of organic components related to different sources as traffic, wood burning, and secondary organic aerosol are presented. This technique, while subjected to the limitations inherent to filter-based measurements (e.g., filter artifacts and limited time resolution) may be used to enhance the AMS capabilities in measuring size-fractionated, spatially resolved long-term data sets.

Two years of near real-time chemical composition of submicron aerosols in the region of Paris using an Aerosol Chemical Speciation Monitor (ACSM) and a multi-wavelength Aethalometer

Abstract. Aerosol mass spectrometer (AMS) measurements have been successfully used towards a better understanding of non-refractory submicron (PM1) aerosol chemical properties based on short-term campaigns. The recently developed Aerosol Chemical Speciation Monitor (ACSM) has been designed to deliver quite similar artifact-free chemical information but for low cost, and to perform robust monitoring over long-term periods. When deployed in parallel with real-time black carbon (BC) measurements, the combined data set allows for a quasi-comprehensive description of the whole PM1 fraction in near real time. Here we present 2-year long ACSM and BC data sets, between mid-2011 and mid-2013, obtained at the French atmospheric SIRTA supersite that is representative of background PM levels of the region of Paris. This large data set shows intense and time-limited (a few hours) pollution events observed during wintertime in the region of Paris, pointing to local carbonaceous emissions (mainly combustion sources). A non-parametric wind regression analysis was performed on this 2-year data set for the major PM1 constituents (organic matter, nitrate, sulfate and source apportioned BC) and ammonia in order to better refine their geographical origins and assess local/regional/advected contributions whose information is mandatory for efficient mitigation strategies. While ammonium sulfate typically shows a clear advected pattern, ammonium nitrate partially displays a similar feature, but, less expectedly, it also exhibits a significant contribution of regional and local emissions. The contribution of regional background organic aerosols (OA) is significant in spring and summer, while a more pronounced local origin is evidenced during wintertime, whose pattern is also observed for BC originating from domestic wood burning. Using time-resolved ACSM and BC information, seasonally differentiated weekly diurnal profiles of these constituents were investigated and helped to identify the main parameters controlling their temporal variations (sources, meteorological parameters). Finally, a careful investigation of all the major pollution episodes observed over the region of Paris between 2011 and 2013 was performed and classified in terms of chemical composition and the BC-to-sulfate ratio used here as a proxy of the local/regional/advected contribution of PM. In conclusion, these first 2-year quality-controlled measurements of ACSM clearly demonstrate their great potential to monitor on a long-term basis aerosol sources and their geographical origin and provide strategic information in near real time during pollution episodes. They also support the capacity of the ACSM to be proposed as a robust and credible alternative to filter-based sampling techniques for long-term monitoring strategies.

An Aerosol Chemical Speciation Monitor (ACSM) for Routine Monitoring of the Composition and Mass Concentrations of Ambient Aerosol

We present a new instrument, the Aerosol Chemical Speciation Monitor (ACSM), which routinely characterizes and monitors the mass and chemical composition of non-refractory submicron particulate matter in real time. Under ambient conditions, mass concentrations of particulate organics, sulfate, nitrate, ammonium, and chloride are obtained with a detection limit <0.2 μg/m3 for 30 min of signal averaging. The ACSM is built upon the same technology as the widely used Aerodyne Aerosol Mass Spectrometer (AMS), in which an aerodynamic particle focusing lens is combined with high vacuum thermal particle vaporization, electron impact ionization, and mass spectrometry. Modifications in the ACSM design, however, allow it to be smaller, lower cost, and simpler to operate than the AMS. The ACSM is also capable of routine stable operation for long periods of time (months). Results from a field measurement campaign in Queens, NY where the ACSM operated unattended and continuously for 8 weeks, are presented. ACSM data is...