Sources Of Ambient Particulate Matter Research Articles

Physicochemical and toxicological properties of wood smoke particulate matter as a function of wood species and combustion condition

Wood burning is a major source of ambient particulate matter (PM) and has been epidemiologically linked to adverse pulmonary health effects, however the impact of fuel and burning conditions on PM properties has not been investigated systematically. Here, we employed our recently developed integrated methodology to characterize the physicochemical and biological properties of emitted PM as a function of three common hardwoods (oak, cherry, mesquite) and three representative combustion conditions (flaming, smoldering, incomplete). Differences in PM and off-gas emissions (aerosol number/mass concentrations; carbon monoxide; volatile organic compounds) as well as inorganic elemental composition and organic carbon functional content of PM0.1 were noted between wood types and combustion conditions, although the combustion scenario exerted a stronger influence on the emission profile. More importantly, flaming combustion PM0.1 from all hardwoods significantly stimulated the promoter activity of Sterile Alpha Motif (SAM) pointed domain containing ETS (E-twenty-six) Transcription Factor (SPDEF) in human embryonic kidney 293 (HEK-293 T) cells, a biomarker for mucin gene expression associated with mucus production in pulmonary diseases. However, no bioactivity was observed for smoldering and incomplete combustion, which was likely driven by differences in the organic composition of PM0.1. Detailed chemical speciation of organic components of wood smoke is warranted to identify the individual compounds that drive specific biological responses.

Review of Road Dust Resuspension Modelling Approaches and Comparisons Analysis for a UK Case Study

This report provides a review of several measurement studies and campaigns focusing on road dust resuspension (RDS), which is becoming an important source of ambient particulate matter (PM) concentrations. There is a growing requirement from stakeholders, policy makers and scientists for gathering additional data around RDS, since there is a lack of well-established methodologies or those developed for measuring or modelling this source of emissions are not very recent. Moreover, conventional inventory methodologies are not available for this source of emissions, and it is not yet covered in the UK’s National Atmospheric Emissions Inventory (NAEI) neither in the inventory guidebooks at European level. Based on the available literature and models, this paper also seeks to provide some preliminary considerations whether an approach can be tailored for application in air quality models used for modelling ambient PM concentrations in the UK in a consistent manner and demonstrated using a state-of-art air quality modelling tool, namely RapidAir®. The impact of including resuspension emissions on roadside concentrations was modelled in a UK case study with the greatest increases in modelled road PM10 concentrations observed on or near the road surface. The paper concludes with recommendations for longer-term implementation of the proposed improvements such as investigation of the temporal effects of RDS on PM levels; of the influence of different type of tyre materials and mixture (e.g., composition) and designs (e.g., shape, dimensions and tread); and influence of different existing pavements or of new road surface types.

Influence of New Year's fireworks on air quality – A case study from 2010 to 2021 in Augsburg, Germany

Fireworks have been shown to contribute short-term but potent source of ambient particulate matter (PM). Here we present a source apportionment-based approach to estimate the quantitative contributions of fireworks in releasing black carbon (eBC), polycyclic aromatic hydrocarbons (PAHs) and metals into urban ambient air on six New Year's Day fireworks events from the period 2010 to 2021. Simplified PMF analyses were performed to assign PAHs, eBCs, and metals to major contributors (building heating, traffic, and fireworks) of ambient PM. The trends of PM10 and PM2.5 concentrations clearly showed the drastic increase of the concentrations on New Year's Days. The PMF analyses showed that, on average, about 35% (20–80% for individual years) of the PAHs and about 45% of eBC (10–100%) were associated with the fireworks. Metals presented in high concentrations in pyrotechnic sets, namely Ba, Cu, K, Mg, and Sr were attributed to fireworks about 90%, while Al was attributed to fireworks by 86%. Other metals (Ca, Cr, Fe, Na, Pb, Ti, and Zn) were attributed to fireworks by variable proportions averaging at 67%, 77%, 44%, 59%, 64%, 75%, and 33%, respectively. Overall, these findings complement future monitoring programs and regulations for fireworks emissions.

Long-term exposure to ambient fine particulate matter originating from traffic and residential wood combustion and the prevalence of depression

IntroductionAir pollution has been suggested to be associated with depression. However, current evidence is conflicting, and no study has considered different sources of ambient particulate matter with an aerodynamic diameter...

Approaches to reducing rotational ambiguity in receptor modeling of ambient particulate matter

Rotational ambiguity is a major problem in the application of factor analysis methods to the mixture resolution problem. However, self-modeling curve resolution of spectrochemical data is often able to achieve good separations given fixed spectral characteristics including true zero absorbance values and typically smaller measurement uncertainties. For air pollution mixture problems such as the identification and quantification of sources of ambient particulate matter (PM), there are problems of the lack of true zero values in either the source profiles or their contributions to the ambient concentrations, variability in the source profiles, and typically much larger measurement uncertainties. Recent work has provided several approaches to reducing rotational ambiguity and providing improved resolution including preprocessing the data to remove the meteorologically induced covariance, applying constraints based on a priori knowledge, and analysis of multiple site data. Examples of these applications to airborne PM have shown improved results.

Sources, variability and parameterizations of intra-city factors obtained from dispersion-normalized multi-time resolution factor analyses of PM2.5 in an urban environment

Ambient fine particulate matter (PM2.5) data of similar continuously monitored species at two air monitoring sites with different characteristics within the City of Toronto were used to gauge the intra-city variations in the PM composition over a largely concurrent period spanning two years. One location was <8 m from the side of a major highway while the other was an urban background location. For the first time, multi-time resolution factor analysis was applied to dispersion-normalized concentrations to identify and quantify source contributions while reducing the influence of local meteorology. These factors were particulate sulphate (pSO4), particulate nitrate (pNO3), secondary organic aerosols (SOA), crustal matter (CrM) that were common to both sites, a hydrocarbon-like organic matter (HOM) exclusive to the urban background site, three black carbon related factors (BC, BC-HOM at the highway site, and a brown carbon rich factor (BC-BrC) at the urban background site), biomass burning organic matter (BBOM) and brake dust (BD) factors exclusive to the highway site. The PM2.5 composition was different between these two locations, over only a 10 km distance. The sum of SOA, pSO4 and pNO3 at the urban background site averaged 57% of the PM2.5 mass while the same species represented 43% of the average PM2.5 mass at the highway site. Local or site-specific factors may be of greater interest for control policy design. Thus, regression analyses with potential explanatory, site-specific variables were performed for results from the highway site. Three model approaches were explored: multiple linear regression (MLR), regression with a generalized reduced gradient (GRG) algorithm, and a generalized additive model (GAM). GAM gave the largest fraction of variance for the locally-found factors at the highway site. Heavy-duty vehicles were most important for explaining the black carbon (BC and BC-HOM) factors. Light-duty vehicles were dominant for the brake dust (BD) factor. The auxiliary modelling for the local factors showed that the traffic-related factors likely originated along the main roadways at their respective sites while the more regional factors, − pSO4, pNO3, SOA, − had sources that were both regional and local in origin and with contributions that varied seasonally. These results will be useful in understanding ambient particulate matter sources on a city scale that will support air quality management planning.

Ambient particulate matter source apportionment using receptor modelling in European and Central Asia urban areas

This work presents the results of a PM2.5 source apportionment study conducted in urban background sites from 16 European and Asian countries. For some Eastern Europe and Central Asia cities this was the first time that quantitative information on pollution source contributions to ambient particulate matter (PM) has been performed. More than 2200 filters were sampled and analyzed by X-Ray Fluorescence (XRF), Particle-Induced X-Ray Emission (PIXE), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to measure the concentrations of chemical elements in fine particles. Samples were also analyzed for the contents of black carbon, elemental carbon, organic carbon, and water-soluble ions. The Positive Matrix Factorization receptor model (EPA PMF 5.0) was used to characterize similarities and heterogeneities in PM2.5 sources and respective contributions in the cities that the number of collected samples exceeded 75. At the end source apportionment was performed in 11 out of the 16 participating cities. Nine major sources were identified to have contributed to PM2.5: biomass burning, secondary sulfates, traffic, fuel oil combustion, industry, coal combustion, soil, salt and “other sources”. From the averages of sources contributions, considering 11 cities 16% of PM2.5 was attributed to biomass burning, 15% to secondary sulfates, 13% to traffic, 12% to soil, 8.0% to fuel oil combustion, 5.5% to coal combustion, 1.9% to salt, 0.8% to industry emissions, 5.1% to “other sources” and 23% to unaccounted mass. Characteristic seasonal patterns were identified for each PM2.5 source. Biomass burning in all cities, coal combustion in Krakow/POL, and oil combustion in Belgrade/SRB and Banja Luka/BIH increased in Winter due to the impact of domestic heating, whereas in most cities secondary sulfates reached higher levels in Summer as a consequence of the enhanced photochemical activity. During high pollution days the largest sources of fine particles were biomass burning, traffic and secondary sulfates.

Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017

Abstract. Based on published literature and typical profiles from the Nankai University source library, a total of 3326 chemical profiles of the main primary sources of ambient particulate matter (PM) across China from 1987 to 2017 are investigated and reviewed to trace the evolution of their main components and identify the main influencing factors concerning their evolution. In general, the source chemical profiles are varied with respect to their sources and are influenced by different sampling methods. The most complicated profiles are likely attributed to coal combustion (CC) and industrial emissions (IE). The profiles of vehicle emissions (VE) are dominated by organic carbon (OC) and elemental carbon (EC), and vary due to the changing standards of sulfur and additives in gasoline and diesel as well as the sampling methods used. In addition to the sampling methods used, the profiles of biomass burning (BB) and cooking emissions (CE) are also impacted by the different biofuel categories and cooking types, respectively. The variations of the chemical profiles of different sources, and the homogeneity of the subtype source profiles within the same source category are examined using uncertainty analysis and cluster analysis. As a result, a relatively large variation is found in the source profiles of CC, VE, IE, and BB, indicating that these sources urgently require the establishment of local profiles due to their high uncertainties. The results presented highlight the need for further investigation of more specific markers (e.g., isotopes, organic compounds, and gaseous precursors), in addition to routinely measured components, in order to properly discriminate sources. Although the chemical profiles of the main sources have been previously reported in the literature, it should be noted that some of these chemical profiles are currently out of date and need to be updated immediately. Additionally, in the future, specific focus should be placed on the source profile subtypes, especially with respect to local IE in China.

Estimating the Contribution of Local Primary Emissions to Particulate Pollution Using High‐Density Station Observations

AbstractLocal primary emission, transport, and secondary formation of aerosols constitute the major atmospheric particulate matter (PM) over a certain region. To identify and quantify major sources of ambient PM is important for pollution mitigation strategies, especially on a city scale. We developed two source apportionment methods to make the first‐order estimates of local primary contribution ratio (LCR) of PM2.5 (PM with diameter less than 2.5 μm) using the high‐density (about 1/km2) network observations with high sampling frequency (about 1 hr). Measurements of PM2.5 mass concentration from 169 sites within a 20 km × 20 km domain are analyzed. The two methods developed here are mainly based on the spatial and temporal variations of PM2.5 within an urban area. The accuracy of our developed methods is subject to the assumptions on the spatial heterogeneity of primary and secondary formed aerosols as well as those from long‐range transport to a city. We apply these two methods to a typical industrial city in China in winter of 2015 with frequent severe haze events. The local primary pollution contributions calculated from the two methods agree with each other that they are often larger than 0.4. The LCR range is from 0.4 to 0.7, with an average value of 0.63. Our study indicates the decisive role of locally emitted aerosols in the urban severe haze formation during the winter time. It further suggests that reductions of local primary aerosol emissions are essential to alleviate the severe haze pollution, especially in industrial cities.

Spatial and temporal distribution, chemical characteristics, and sources of ambient particulate matter in the Beijing-Tianjin-Hebei region

Particulate matter (PM) pollution is severe in the Beijing-Tianjin-Hebei (BTH) region. Although the air quality has improved, the average PM2.5 and PM10 concentrations in 2016 were still higher than the National Ambient Air Quality Standard by 2.0 and 1.7 times, respectively. Using the empirical orthogonal function (EOF) method to analyze the spatial characteristics of its 13 cities, it was found that the BTH region could be categorized into four districts. The first district included Xingtai, Shijiazhuang, and Baoding; the second district included Handan, Hengshui, and Langfang; the third district included Beijing, Tangshan, Cangzhou, and Tianjin; and the fourth district included Qinhuangdao, Chengde, and Zhangjiakou. PM2.5 samples were collected synchronously in five typical cities, and it was shown that the major chemical constituents of PM included organic carbon (OC), nitrate (NO3−), sulfate (SO42−), ammonium (NH4+), elemental carbon (EC), Si, Cl−, Fe, Al, and Mg. The species with the highest contents were OC in the winter, SO42− and NH4+ in the summer, and NO3− in the spring. The highest concentrations of OC, NO3−, EC, Si, Cl−, Al, and Mg were found in Baoding, and the highest concentrations of SO42−, NH4+, and Fe were found in Shijiazhuang. The sources of PM2.5 were analyzed using the positive matrix factorization model. The major sources of PM2.5 in the BTH region included coal combustion (10.9%–18.6%), secondary inorganic aerosols (35.4%–42.4%), vehicle emissions (10.6%–18.6%), soil/road dust (10.6%–23.6%), and industrial emissions (8.6%–18.2%).

Evaluation of traffic exhaust contributions to ambient carbonaceous submicron particulate matter in an urban roadside environment in Hong Kong

Abstract. Road traffic has significant impacts on air quality particularly in densely urbanized and populated areas where vehicle emissions are a major local source of ambient particulate matter. Engine type (i.e., fuel use) significantly impacts the chemical characteristics of tailpipe emission, and thus the distribution of engine types in traffic impacts measured ambient concentrations. This study provides an estimation of the contribution of vehicles powered by different fuels (gasoline, diesel, LPG) to carbonaceous submicron aerosol mass (PM1) based on ambient aerosol mass spectrometer (AMS) and elemental carbon (EC) measurements and vehicle count data in an urban inner city environment in Hong Kong with the aim to gauge the importance of different engine types to particulate matter burdens in a typical urban street canyon. On an average per-vehicle basis, gasoline vehicles emitted 75 and 93 % more organics than diesel and LPG vehicles, respectively, while EC emissions from diesel vehicles were 45 % higher than those from gasoline vehicles. LPG vehicles showed no appreciable contributions to EC and thus overall represented a small contributor to traffic-related primary ambient PM1 despite their high abundance (∼ 30 %) in the traffic mix. Total carbonaceous particle mass contributions to ambient PM1 from diesel engines were only marginally higher (∼ 4 %) than those from gasoline engines, which is likely an effect of recently introduced control strategies targeted at commercial vehicles and buses. Overall, gasoline vehicles contributed 1.2 µg m−3 of EC and 1.1 µ m−3 of organics, LPG vehicles 0.6 µg m−3 of organics and diesel vehicles 2.0 µg m−3 of EC and 0.7 µg m−3 of organics to ambient carbonaceous PM1.

Chemical characterization and sources of PM2.5 at 12-hr resolution in Guiyang, China.

The increasing emission of primary and gaseous precursors of secondarily formed atmospheric particulate matter due to continuing industrial development and urbanization are leading to an increased public awareness of environmental issues and human health risks in China. As part of a pilot study, 12-hr integrated fine fraction particulate matter (PM2.5) filter samples were collected to chemically characterize and investigate the sources of ambient particulate matter in Guiyang City, Guizhou Province, southwestern China. Results showed that the 12-hr integrated PM2.5 concentrations exhibited a daytime average of 51 ± 22μg·m-3 (mean ± standard deviation) with a range of 17-128μg·m-3 and a nighttime average of 55 ± 32μg·m-33 with a range of 4-186 μg·m-3. The 24-hr integrated PM2.5 concentrations varied from 15 to 157 μg·m-3, with a mean value of 53 ± 25 μg·m-3, which exceeded the 24-hr PM2.5 standard of 35μg·m-3 set by USEPA, but was below the standard of 75μg·m-3, set by China Ministry of Environmental Protection. Energy-dispersive X-ray fluorescence spectrometry (XRF) was applied to determine PM2.5 chemical element concentrations. The order of concentrations of heavy metals in PM2.5 were iron (Fe) > zinc (Zn) > manganese (Mn) > lead (Pb) > arsenic (As) > chromium (Cr). The total concentration of 18 chemical elements was 13 ± 2 μg·m-3, accounting for 25% in PM2.5, which is comparable to other major cities in China, but much higher than cities outside of China.

The relative importance of tailpipe and non-tailpipe emissions on the oxidative potential of ambient particles in Los Angeles, CA.

This study examines the associations between the oxidative potential of ambient PM2.5 and PM0.18, measured by means of the dithiothreitol (DTT) assay, and their chemical constituents and modeled sources. Particulate matter (PM) samples were collected from 2012-2013 in Central Los Angeles (LA) and 2013-2014 in Anaheim, California, USA. Detailed chemical analyses of the PM samples, including carbonaceous species, inorganic elements and water-soluble ions, were conducted. Univariate analysis indicated a high correlation (R > 0.60) between the DTT activity and the concentrations of carbonaceous species at both sites. The strongest correlations were observed between DTT and organic tracers of primary vehicle tailpipe emissions including polycyclic aromatic hydrocarbons (PAHs) and hopanes as well as EC, with higher correlations for PM0.18versus PM2.5 components. Moreover, metals and trace elements (e.g., Ba, Cu, Fe, Mn, Pb and Sb) in both size ranges were also associated with DTT activity. Multiple linear regression (MLR) analysis was performed on DTT activity and PM sources identified by a Molecular Marker-Chemical Mass Balance (MM-CMB) model (i.e. major carbonaceous sources: vehicle tailpipe emissions, wood smoke, primary biogenic and secondary organic carbon) together with other typical sources of ambient PM (i.e. crustal material, vehicular abrasion, secondary ions and sea salt). Overall, our findings illustrate the relative importance of different traffic sources on the oxidative potential of ambient PM. Despite major reductions of tailpipe emissions, the lack of similar reductions (and possibly an increase) in non-tailpipe emissions makes them an important source of traffic-related PM in Los Angeles and their increasing role in the overall PM toxicity raises concerns for public health.

Neurotoxicity of traffic-related air pollution

The central nervous system is emerging as an important target for adverse health effects of air pollution, where it may contribute to neurodevelopmental and neurodegenerative disorders. Air pollution comprises several components, including particulate matter (PM) and ultrafine particulate matter (UFPM), gases, organic compounds, and metals. An important source of ambient PM and UFPM is represented by traffic-related air pollution, primarily diesel exhaust (DE). Human epidemiological studies and controlled animal studies have shown that exposure to air pollution, and to traffic-related air pollution or DE in particular, may lead to neurotoxicity. In particular, air pollution is emerging as a possible etiological factor in neurodevelopmental (e.g. autism spectrum disorders) and neurodegenerative (e.g. Alzheimer's disease) disorders. The most prominent effects caused by air pollution in both humans and animals are oxidative stress and neuro-inflammation. Studies in mice acutely exposed to DE (250–300μg/m3 for 6h) have shown microglia activation, increased lipid peroxidation, and neuro-inflammation in various brain regions, particularly the hippocampus and the olfactory bulb. An impairment of adult neurogenesis was also found. In most cases, the effects of DE were more pronounced in male mice, possibly because of lower antioxidant abilities due to lower expression of paraoxonase 2.

Emissions of particulate matter from animal houses in the Netherlands

In the Netherlands, emissions from animal houses represent a major source of ambient particulate matter (PM). The objective of the present paper was to provide accurate and up to date concentrations and emission rates of PM10 and PM2.5 for commonly used animal housing systems, under representative inside and outside climate conditions and ventilation rates. We set up a national survey which covered 13 housing systems for poultry, pigs, and dairy cattle, and included 36 farms. In total, 202 24-h measurements were carried out, which included concentrations of inhalable PM, PM10, PM2.5, and CO2, ventilation rate, temperature, and relative humidity. On an animal basis, geometric mean emission rates of PM10 ranged from 2.2 to 12.0 mg h−1 in poultry and from 7.3 to 22.5 mg h−1 in pigs. The mean PM10 emission rate in dairy cattle was 8.5 mg h−1. Geometric mean emission rates of PM2.5 ranged from 0.11 to 2.41 mg h−1 in poultry and from 0.21 to 1.56 mg h−1 in pigs. The mean PM2.5 emission rate in dairy cattle was 1.65 mg h−1. Emissions are also reported per Livestock Unit and Heat Production Unit. PM emission rates increased exponentially with increasing age in broilers and turkeys and increased linearly with increasing age in weaners and fatteners. In laying hens, broiler breeders, sows, and dairy cattle, emission levels were variable throughout the year.

Spatiotemporal Variations and Possible Sources of Ambient PM10from 2003 to 2012 in Luzhou, China

Descriptive statistics methods were used to study the spatiotemporal variations and sources of ambient particulate matter (PM10) in Luzhou, China. The analyzed datasets were collected from four national air quality monitoring stations: Jiushi (S1), Xiaoshi (S2), Zhongshan (S3), Lantian (S4) over the period of 2003-2012. This city was subjected serious PM10 pollution, and the long-term annual average PM10 concentrations varied from 76 to 136 μg/m 3 . The maximum concentration was more than 3-fold of the annual average (40 μg/m 3 ) issued by EPA-China for the ambient air quality. General temporal pattern was characterized by high concentrations in winter and low concentrations in summer, and general spatial gradient was in the reduction order of S2 > S4 > S3 > S1, which were both due to different particulate contributors and special meteorological conditions. The source apportionment indicated that vehicular emissions, road dusts, coal burning and chemical dusts were the major contributors of the identified PM10 pollution, and the vehicular emissions and the road wear re-suspended particles dominated the heavy PM10 pollution in recent years. Two other potential sources, agricultural and celebration activities could decrease the air quality in a short term. Finally, some corresponding suggestions and measures were provided to improve the air quality.

Source apportionment of particulates by receptor models over Bay of Bengal during ICARB campaign

Source apportionment study of aerosols over Bay of Bengal (BOB) were investigated during Integrated Campaign on Aerosol Radiation Budget (ICARB) in the pre–monsoon (March–April 2006) and winter (December–January 2008–09) seasons. Positive matrix factorization (PMF) was applied to identify sources of ambient particulate matter using daily chemical composition data collected in the pre–monsoon (total suspended particles, TSP) and winter season (particles with a diameter < 10μm, PM10). Sea salt (SS), secondary aerosol (SA), Si–dust, fossil fuel combustion (FFC), biomass burning (BB) sources have been identified in both seasons, however their relative contributions were different. The combined contribution of Si–dust, secondary aerosol and fossil fuel combustion, constitute ~67% of particulate matter in pre–monsoon, whereas, secondary aerosols and biomass burning were the major contributors (63.2%) to particulate matter in winter. The identified sources effectively predict the measured particulate concentration in the pre–monsoon (r2=0.74) and winter season (r2=0.82). Another receptor model, principal component analysis (PCA) was done to increase the plausibility of the results obtained by PMF. PCA resulted in the identification of the sources that were comparable to the PMF outputs. PCA of TSP in the pre–monsoon season resulted in the extraction of three components (crustal dust + secondary aerosol, biomass burning, fossil fuel combustion + industrial emissions) that explained the 83% of the variance in the data. Similarly, in winter season, PCA resulted in the extraction of four components (biomass burning + secondary aerosol, industrial emission, crustal dust, sea salt) that explained the 86% of the variance of the data.

Source apportionment of PM&amp;lt;sub&amp;gt;10&amp;lt;/sub&amp;gt; in a north-western Europe regional urban background site (Lens, France) using positive matrix factorization and including primary biogenic emissions

Abstract. In this work, the source of ambient particulate matter (PM10) collected over a one-year period at an urban background site in Lens (France) was determined and investigated using a positive matrix factorization receptor model (US EPA PMF v3.0). In addition, a potential source contribution function (PSCF) was performed by means of the Hybrid Single-Particle Lagrangian Integrated Trajectory (Hysplit) v4.9 model to assess prevailing geographical origins of the identified sources. A selective iteration process was followed for the qualification of the more robust and meaningful PMF solution. Components measured and used in the PMF included inorganic and organic species: soluble ionic species, trace elements, elemental carbon (EC), sugar alcohols, sugar anhydride, and organic carbon (OC). The mean PM10 concentration measured from March 2011 to March 2012 was about 21 μg m−3 with typically OM, nitrate and sulfate contributing to most of the mass and accounting respectively for 5.8, 4.5 and 2.3 μg m−3 on a yearly basis. Accordingly, PMF outputs showed that the main emission sources were (in decreasing order of contribution) secondary inorganic aerosols (28% of the total PM10 mass), aged marine emissions (19%), with probably predominant contribution of shipping activities, biomass burning (13%), mineral dust (13%), primary biogenic emissions (9%), fresh sea salts (8%), primary traffic emissions (6%) and heavy oil combustion (4%). Significant temporal variations were observed for most of the identified sources. In particular, biomass burning emissions were negligible in summer but responsible for about 25% of total PM10 and 50% of total OC in wintertime. Conversely, primary biogenic emissions were found to be negligible in winter but to represent about 20% of total PM10 and 40% of total OC in summer. The latter result calls for more investigations of primary biogenic aerosols using source apportionment studies, which quite usually disregard this type of source. This study further underlines the major influence of secondary processes during daily threshold exceedances. Finally, apparent discrepancies that could be generally observed between filter-based studies (such as the present one) and aerosol mass spectrometer-based PMF analyses (organic fractions) are also discussed.

Emissions from commercial-grade charbroiling meat operations induce oxidative stress and inflammatory responses in human bronchial epithelial cells

Commercial charbroiling emissions are a significant source of ambient particulate matter (PM) in urban settings. The objective of this study was to determine whether organic extract of PM emissions from commercial charbroiling meat operations could induce an inflammatory response in human bronchial epithelial cells and whether this effect was mediated by oxidative stress. PM samples were collected during cooking hamburgers on a commercial-grade under-fired charbroiler and sequentially extracted with water and methanol to obtain the aqueous PM suspension (AqPM) and organic extract (OE). The pro-oxidative and pro-inflammatory effects of OE were assessed using human bronchial epithelial cell line BEAS-2B. While AqPM did not have any effect, OE effectively induced the expression of heme oxygennase-1 and cyclooxygenase-2 in BEAS-2B cells. OE also up-regulated the levels of IL-6, IL-8, and prostaglandin E2. OE-induced cellular inflammatory response could be effectively suppressed by the antioxidant N-acetyl cysteine, nuclear factor (erythroid-derived 2)-like 2 activator sulforaphane and p38 MAPK inhibitor SB203580. In conclusion, organic chemicals emitted from commercial charbroiling meat operations could induce an inflammatory response in human bronchial epithelial cells, which was mediated by oxidative stress and p38 MAPK.

Quantitative determination of carbonaceous particle mixing state in Paris using single-particle mass spectrometer and aerosol mass spectrometer measurements

Abstract. Single-particle mixing state information can be a powerful tool for assessing the relative impact of local and regional sources of ambient particulate matter in urban environments. However, quantitative mixing state data are challenging to obtain using single-particle mass spectrometers. In this study, the quantitative chemical composition of carbonaceous single particles has been determined using an aerosol time-of-flight mass spectrometer (ATOFMS) as part of the MEGAPOLI 2010 winter campaign in Paris, France. Relative peak areas of marker ions for elemental carbon (EC), organic aerosol (OA), ammonium, nitrate, sulfate and potassium were compared with concurrent measurements from an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS), a thermal–optical OCEC analyser and a particle into liquid sampler coupled with ion chromatography (PILS-IC). ATOFMS-derived estimated mass concentrations reproduced the variability of these species well (R2 = 0.67–0.78), and 10 discrete mixing states for carbonaceous particles were identified and quantified. The chemical mixing state of HR-ToF-AMS organic aerosol factors, resolved using positive matrix factorisation, was also investigated through comparison with the ATOFMS dataset. The results indicate that hydrocarbon-like OA (HOA) detected in Paris is associated with two EC-rich mixing states which differ in their relative sulfate content, while fresh biomass burning OA (BBOA) is associated with two mixing states which differ significantly in their OA / EC ratios. Aged biomass burning OA (OOA2-BBOA) was found to be significantly internally mixed with nitrate, while secondary, oxidised OA (OOA) was associated with five particle mixing states, each exhibiting different relative secondary inorganic ion content. Externally mixed secondary organic aerosol was not observed. These findings demonstrate the range of primary and secondary organic aerosol mixing states in Paris. Examination of the temporal behaviour and chemical composition of the ATOFMS classes also enabled estimation of the relative contribution of transported emissions of each chemical species and total particle mass in the size range investigated. Only 22% of the total ATOFMS-derived particle mass was apportioned to fresh, local emissions, with 78% apportioned to regional/continental-scale emissions.