Chemical Mass Balance Model Research Articles

Development of a spatially resolved linked hydrodynamic and exposure model (LOTOX2) for PCBs in Lake Ontario

A linked hydrodynamic–hydrophobic organic chemical mass balance and food chain bioaccumulation model, LOTOX2, was developed to support the Lake Ontario Lakewide Management Plan (LaMP) for establishing contaminant load reduction strategies. This paper describes the development of LOTOX2, including the linkage with a relatively finer-scale hydrodynamic model (the Princeton Ocean Model, POM). An important component of this development was the reconstruction of PCB loading history (1930–2005), which was used to understand historic trends and to conduct model calibration/confirmation for total PCBs (tPCBs) in the lake water, sediments, and adult lake trout using data for the last 25years. A separate mass balance was conducted for the radioisotope cesium-137 (137Cs) in order to develop a sorbent mass balance model for the system. Following calibration and confirmation, a diagnostic application of the model showed that the lake is not yet at steady-state with current loads. It will take more than 50years for tPCB concentration in lake trout to decrease to a steady-state value of about 0.4ppm if year 2005 loads remain constant. If all external loads were instantaneously eliminated in 2005, it would take approximately 40years for the adult lake trout tPCB concentration to reach 0.05ppm (the uniform Great Lakes protocol value for unrestricted consumption) from its current level of 0.74ppm.

Vertical characteristics of carbonaceous species and their source contributions in a Chinese mega city

In this work, two new chemical mass balance (CMB) models were applied to estimate the possible source contributions to organic carbon (OC) and the secondary OC (SOC) concentrations in PM10 at 10, 40, 120 and 220 m heights on a meteorological tower in Tianjin. The OC and EC (elemental carbon) were simultaneously measured at the four heights from 24 August to 12 September 2009. The vertical characteristics of OC and EC showed a general decreasing trend with the increasing height. The SOC concentrations were then estimated by the minimum OC/EC ratio method, the NCPCRCMB model and the CMB-Iteration method. The results suggest increasing trends for SOC/TOC and SOC/PM10 up the tower. Additionally, wind rose maps and hourly 72-h back trajectory cluster maps were combined with the source apportionment results to discuss the vertical source contributions to OC, indicating that the percentage contributions of long-range transport sources (such as soil dust and SOC sources) increased with height. SOC formation and transportation by wind affected OC more strongly than each primary source at greater heights. Finally, the levels and sources of the excess OC (the difference between OC concentrations at lower heights to that at 220 m) are discussed. The results suggest that it is more effectively managed for reducing OC to control vehicle and coal sources at the ground level in Tianjin. Information about the more significant sources of excess OC can be used for the development of effective control strategies.

Source characterization of PM10 and PM2.5 mass using a chemical mass balance model at urban roadside

The 24-h average ambient particulate matter (PM10 and PM2.5) concentrations are sampled concurrently during November 2008–April 2009 at a busy roadside in Chennai City, India. The elemental (Ag, Al, As, B, Ba, Be, Bi, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Se, Sr, Te, Tl, V and Zn) and ionic (Na+, NH4+, K+, Ca2+, Mg2+, F−, Cl−, NO2−, NO3− and SO42−) composition of PM10 and PM2.5 are determined using an inductively coupled plasma-optical emission spectrometer (ICP-OES) and an ion chromatograph (IC), respectively. The emission inventory at the study area is also carried out to identify the likely PM emission sources.The U.S. EPA's-CMB (chemical mass balance) version 8.2 is applied to identify the source contribution of ambient PM10 and PM2.5 concentrations at the study area. Results indicated that diesel exhausts (43–52% in PM10 and 44–65% in PM2.5) and gasoline exhausts (6–16% in PM10 and 3–8% in PM2.5) are found to be the major source contributors at the study site followed by the paved road dusts (PM10=PM2.5=0.–2.3%), brake lining dusts (0.1% in PM10 and 0.2% in PM2.5), brake pad wear dusts (0.1% in PM10 and 0.01% in PM2.5), marine aerosols (PM10=PM2.5=0.1%) and cooking (~0.8% in PM10 and ~1.5% in PM2.5).

Concentrations and source apportionment of PM10 and associated major and trace elements in the Rhodes Island, Greece

Ambient concentrations of PM10 and associated major and trace elements were measured over the cold and the warm season of 2007 at two sites located in the Rhodes Island (Greece), in Eastern Mediterranean, aimed at source apportionment by Chemical Mass Balance (CMB) receptor modeling. Source chemical profiles, necessary in CMB modeling, were obtained for a variety of emission sources that could possibly affect the study area, including sea spray, geological material, soot emissions from the nearby oil-fuelled thermal power plant, and other anthropogenic activities, such as vehicular traffic, residential oil combustion, wood burning, and uncontrolled open-air burning of agricultural biomass and municipal waste. Source apportionment of PM10 and elemental components was carried out by employing an advanced CMB version, the Robotic Chemical Mass Balance model (RCMB). Vehicular emissions were found to be major PM10 contributor accounting, on average, for 36.8% and 31.7% during the cold period, and for 40.9% and 39.2% in the warm period at the two sites, respectively. The second largest source of ambient PM10, with minor seasonal variation, was secondary sulfates (mainly ammonium and calcium sulfates), with total average contribution around 16.5% and 18% at the two sites. Soil dust was also a remarkable source contributing around 22% in the warm period, whereas only around 10% in the cold season. Soot emitted from the thermal power plant was found to be negligible contributor to ambient PM10 (<1%), however it appeared to appreciably contribute to the ambient V and Ni (11.3% and 5.1%, respectively) at one of the sites during the warm period, when electricity production is intensified. Trajectory analysis did not indicate any transport of Sahara dust; on the contrary, long range transport of soil dust from arid continental regions of Minor Asia and of biomass burning aerosol from the countries surrounding the Black Sea was considered possible.

PM2.5 source apportionment in the southeastern U.S.: Spatial and seasonal variations during 2001–2005

The seasonal and spatial variations of source contributions of 112 composite fine particulate matter (PM2.5) samples collected in the Southeastern Aerosol Research and Characterization Study (SEARCH) monitoring network during 2001–2005 using molecular marker‐based chemical mass balance (CMB‐MM) model were determined. The lowest PM2.5 concentration occurs in January with higher values in warm months (maxima in July at four inland sites versus October at the coastal sites). Sulfate shows a similar pattern and plays a primary role in PM2.5 seasonality. Carbonaceous material (organic matter plus EC) exhibits less seasonality, but more spatial variations between the inland and coastal sites. Compared with the data at coastal sites, source attributions of diesel exhaust, gasoline exhaust, other organic matter (other OM), secondary sulfate, nitrate, and ammonium in PM2.5 mass at inland sites are higher. The difference in source attributions of wood combustion, meat cooking, vegetative detritus, and road dust among the eight sites is not significant. Contributions of eight primary sources to fine OC are wood burning (17 ± 19%), diesel exhaust (9 ± 4%), gasoline exhaust (5 ± 7%), meat cooking (5 ± 5%), road dust (2 ± 3%), vegetative detritus (2 ± 2%), cigarette smoke (2 ± 2% at four urban sites), and coke production (2 ± 1% only at BHM). Primary and secondary sources explain 82–100% of measured PM2.5 mass at the eight sites, including secondary ionic species (SO42−, NH4+, and NO3−; 41.4 ± 5.7%), identified OM (24.9 ± 11.3%), “other OM” (unexplained OM, 23.3 ± 10.3%), and “other mass” (11.4 ± 9.6%). Vehicle exhaust from both diesel and gasoline contributes the lowest fraction to PM2.5 mass in July and higher fractions at BHM and JST than other sites. Wood combustion, in contrast, contributes significantly to a larger fraction in winter than in summer. Road dust shows relatively high levels in July and April across the eight sites, while minor sources such as meat cooking and other sources (e.g., vegetative detritus, coke production, and cigarette smoke) show relatively small seasonal and spatial variations in the SEARCH monitoring network.

Source Apportionment of PM2.5 in a Subarctic Airshed - Fairbanks, Alaska

ABSTRACTFairbanks, Alaska has some of the highest measured ambient PM2.5 concentrations in the United States, with wintertime levels often exceeding the 24-hour PM2.5 National Ambient Air Quality Standard (NAAQS) of 35 µg/m3. In an effort to understand the sources of PM2.5 in the Fairbanks airshed, source apportionment using Chemical Mass Balance (CMB) modeling was conducted at four locations in Fairbanks over a three-winter period (2008/2009, 2009/2010, and 2010/2011). At each of the four sites, PM2.5 concentrations averaged between 22.5 ± 12.0 µg/m3 and 26.5 ± 18.9 µg/m3, with frequent exceedances of the 24-hour NAAQS on the scheduled sample days. The results of the CMB modeling revealed that wood smoke (likely residential wood combustion) was the major source of PM2.5 throughout the winter months in Fairbanks, contributing between 60% and nearly 80% of the measured PM2.5 at the four sites. The other sources of PM2.5 identified by the CMB model were secondary sulfate (8–20%), ammonium nitrate (3–11%), diesel exhaust (not detected-10%), and automobiles (not detected-7%). Approximately 1% of the PM2.55 was unexplained by the CMB model. Additional research is needed to confirm the woodsmoke results of the CMB model, as well as determine which sources (fuel oil residential heating, coal combustion, etc.) contribute to the measured secondary sulfate.

Source Apportionment of PM10 in Four Cities of Northeastern China

Ambient particulate matter with the aerodynamics diameter less than 10 μm (PM10) was sampled in four northeastern Chinese cities (Shenyang, Anshan, Fushun and Huludao) from August 2001 to August 2005. Chemical compositions including 20 elements, SO4 2− , organic and total carbon were determined. In addition, chemical source profiles consisting of the same particulate components were obtained from a number of naturally occurring geological sources (soil dust from exposed lands and marine salt) and sources of atmospheric particulates resulting from human activities (construction derived dust, coal combustion fly ash, iron and steel manufacturing dust, zinc dust, vehicle exhaust, and sulfate). Chemical mass balance modeling (CMB) was applied to determine the particulate matter (PM) sources and their contributions to PM10 in these four cities. The results showed that soil dust and coal fly ash were the major sources of ambient PM10 in all four cities. The construction derived dust, iron and steel manufacturing dust also was the major source of PM10 in Huludao, Anshan, respectively. Higher contribution of iron and steel manufacturing dust (33.3 μg/m 3 , 20.9%) in Anshan was observed in this study, compared with previous studies. The zinc dust was a special PM source of Huludao, with a contribution of 19.1 μg/m 3 (7.0%) to PM10.

Sources for PM air pollution in the Po Plain, Italy: II. Probabilistic uncertainty characterization and sensitivity analysis of secondary and primary sources

Very high levels of ambient particulate matter (PM) are frequently encountered in the north of Italy and air quality limits are regularly exceeded. To obtain quantitative information on the pollution sources and to gain understanding of the dynamics of pollution episodes in this populated area PM10 and/or PM2.5 samples were collected daily at nine urban to regional sites distributed over the central Po Plain and one site in the Valtelline Valley. In total, 23 five-week winter campaigns and one comparative summer/autumn campaign (2007–2009) were organized. The PM was analyzed for 61 chemical constituents and a data-base was built up consisting of approx. 70000 records of the concentrations and their associated uncertainty. In addition 14C/12C ratios were determined in PM10 from four sites. Primary and secondary sources were quantified using macro-tracer methods in combination with chemical mass balance modelling and positive matrix factorization and the combined results were computed by probability- and sensitivity analysis. Monte Carlo simulations yielded probability distributions for seven source categories contributing to the carbonaceous fraction of PM and five major source categories contributing to the PM10 and PM2.5 mass. Despite large uncertainties in the combined source contribution estimates the paper demonstrates that secondary aerosol formed simultaneously over the Po Plain is the main responsible for the typical, rapid build-up of air pollution after clean-air episodes. Next to secondary sources, the most important sources are primary emissions from road transport followed by biomass burning (BB). In the Valtelline Valley, higher contributions from BB and lower contributions from secondary sources were observed.

Characterization, sources and redox activity of fine and coarse particulate matter in Milan, Italy

Abstract The correlation between health effects and exposure to particulate matter (PM) has been of primary concern to public health organizations. An emerging hypothesis is that many of the biological effects derive from the ability of PM to generate reactive oxygen species (ROS) within affected cells. Milan, one of the largest and most polluted urban areas in Europe, is afflicted with high particle levels. To characterize its ambient PM, fine and coarse PM (PM2.5 and PM2.5–10, respectively) samples were collected on a weekly basis for a year-long period. Samples were analyzed for their chemical properties and ROS-activity. A molecular marker chemical mass balance (MM-CMB) model was also applied to apportion primary and secondary sources to fine organic carbon (OC) and PM. Findings revealed that PM2.5 is a major contributor to ambient particle levels in Milan, averaging 34.5 ± 19.4 μg m−3 throughout the year. Specifically, secondary inorganic ions and organic matter were the most dominant fine PM species contributing to 36 ± 7.1% and 34 ± 6.3% of its mass on a yearly-based average, respectively. Highest PM2.5 concentrations occurred during December–February and were mainly attributed to poor atmospheric dispersion. On the other hand, PM2.5–10 exhibited an annual average of 6.79 ± 1.67 μg m−3, with crustal elements prevailing. Source apportionment results showed that wood-smoke and secondary organic aerosol sources contribute to 4.6 ± 2.6% and 9.8 ± 11% of fine OC on a yearly-based average, respectively. The remaining OC is likely associated with petroleum-derived material that is not adequately represented by existing source profiles used in this study. Lastly, ROS-activity measurements indicated that PM2.5-induced redox activity expressed per m3 of air volume is greatest during January (837 μg Zymosan equivalents m−3) and February (920 μg Zymosan equivalents m−3). Conversely, intrinsic (per PM mass) ROS-activity peaked in July (22,587 μg Zymosan equivalents mg−1 PM) and August (25,161 μg Zymosan equivalents mg−1 PM), suggesting the influence of specific components on oxidant properties of PM. A correlation analysis between ROS-activity and select PM chemical components showed that Ni, Cr, Cu and water-soluble OC are strongly associated with ROS.

Sources of high PM2.5 concentrations in Milan, Northern Italy: Molecular marker data and CMB modelling

In Milan (MI), the largest city in Northern Italy, the annually average PM2.5 concentration is above 25μgm−3, the value that the EU established as a target for 2010, and the upper limit from 2015 onwards (2008/30/CE).Over a three-year period (2006–2009) PM concentrations and chemical compositions were measured in an urban site (MI), a rural site (OB) and a remote site (ASC) in Northern Italy. Chemical characterization (EC/OC, inorganic ions, elements, C20–C32 n-alkanes, C2–C5 mono and dicarboxylic acids, levoglucosan and PAHs) was carried out on PM2.5 samples from the three sites, and PM10 from MI.Molecular markers were used in Chemical Mass Balance (CMB) modelling to estimate the contributions of primary sources to OC, and then PM mass from each source was reconstructed in MI, OB and ASC for different seasons. Estimates of the traffic (TR) source contribution to PM2.5 mass ranged from 4.1 (±2.0) μgm−3 during the summer, to 13.3 (±6.7) μgm−3 during the winter in MI. TR was the main primary source for PM2.5 concentrations in MI (17–24%). Its contribution was lower at the OB site (7–9%) and at the remote ASC site (3–4%). TR is a local source, while biomass burning (BB) is a diffuse regional source in Northern Italy: during fall and winter, BB was 25–30% and 27–31% of PM2.5 at MI and OB respectively. Other primary sources accounted for a small amount of the PM2.5, i.e. natural gas combustion (0–1%), plant debris (0–4%), road dust (RD=0–4%; but 15% at ASC during winter and 10% of PM10 at MI during summer) and sea salt (0–1%).Secondary inorganic+organic aerosol constituted the major part of the PM2.5 mass during spring and summer (50–65%) at the three sites.

Critical review of receptor modelling for particulate matter: A case study of India

Abstract India is used as a case study in reviewing the application of receptor models for source apportionment. India has high concentrations of airborne particulate matter, and the application of effective abatement measures is a high priority, and demands confidence in the results of source apportionment studies. The many studies conducted are reviewed, and reveal a very wide range of conclusions, even for the same city. To some degree these divergences may be the result of using different sampling locations and/or seasons, but to a large extent differences probably arise from methodological weaknesses. The assignment of factors from multivariate receptor models to specific source categories is in many cases highly questionable as factors often include combinations of chemical constituents that are of low plausibility. This ambiguity in terms of presence of tracer elements may be the result of genuine collinearity of diverse sources, or more probably arises from methodological problems. Few studies have used either organic molecular markers or chemical mass balance (CMB) models, and there is a shortage of data on locally-derived emission source profiles, although recent work has begun to remedy this weakness. The conclusions include a number of recommendations for use in design of future studies.

Chemical Characterization and Source Apportionment of Fine and Coarse Particulate Matter Inside the Refectory of Santa Maria Delle Grazie Church, Home of Leonardo Da Vinci’s “Last Supper”

The association between exposure to indoor particulate matter (PM) and damage to cultural assets has been of primary relevance to museum conservators. PM-induced damage to the "Last Supper" painting, one of Leonardo da Vinci's most famous artworks, has been a major concern, given the location of this masterpiece inside a refectory in the city center of Milan, one of Europe's most polluted cities. To assess this risk, a one-year sampling campaign was conducted at indoor and outdoor sites of the painting's location, where time-integrated fine and coarse PM (PM(2.5) and PM(2.5-10)) samples were simultaneously collected. Findings showed that PM(2.5) and PM(2.5-10) concentrations were reduced indoors by 88 and 94% on a yearly average basis, respectively. This large reduction is mainly attributed to the efficacy of the deployed ventilation system in removing particles. Furthermore, PM(2.5) dominated indoor particle levels, with organic matter as the most abundant species. Next, the chemical mass balance model was applied to apportion primary and secondary sources to monthly indoor fine organic carbon (OC) and PM mass. Results revealed that gasoline vehicles, urban soil, and wood-smoke only contributed to an annual average of 11.2 ± 3.7% of OC mass. Tracers for these major sources had minimal infiltration factors. On the other hand, fatty acids and squalane had high indoor-to-outdoor concentration ratios with fatty acids showing a good correlation with indoor OC, implying a common indoor source.

Determining spatial variability in PM2.5 source impacts across Detroit, MI

Abstract Intra-urban variability in air pollution source impacts was investigated using receptor modeling of daily speciated PM2.5 measurements collected at residential outdoor locations across Detroit, MI (Wayne County) as part of the Detroit Exposure and Aerosol Research Study (DEARS) during summer and winter from 2004 to 2006. Six areas were selected for the residential monitoring in the DEARS to capture impacts from different sources including local industry, motor vehicles, and upwind regional sources. PM2.5 measurements were also collected at the Allen Park, MI Chemical Speciation Network (CSN) site for comparison with the residential outdoor sites. Sources impacting PM2.5 were quantified using the EPA Chemical Mass Balance Model (CMB 8.2). Published source profiles were used as input to CMB along with a mixed industrial profile and a steel manufacturing profile obtained by applying the EPA Positive Matrix Factorization Model (PMF 4.0) to CSN data from a Midwestern U.S. site with industrial sources similar to Detroit. Major PM2.5 sources impacting the Allen Park and residential monitoring areas during DEARS included motor vehicles (24–36% by mass), secondary sulfate/coal combustion (17–35%), secondary nitrate (16–37%) and organic matter (17–21%). Road dust, steel manufacturing, and mixed industrial sources contributed less than 11% by mass. CMB source contribution estimates for Allen Park during the DEARS generally compared well to CMB estimates from the collocated year-long CSN measurements using the same source profiles. CMB source contributions during DEARS showed similar contributions across the residential monitoring areas for secondary sulfate/coal combustion and secondary nitrate consistent with regional impacts for these sources. Contributions from motor vehicles, steel manufacturing, and mixed industrial sources varied across the DEARS monitoring areas, indicating impacts from local sources within the Detroit airshed that may not be well characterized by the Allen Park monitoring location.

PM2.5 Source Apportionment: Reconciling Receptor Models for U.S. Nonurban and Urban Long-Term Networks

ABSTRACT Chemical mass balance (CMB) and trajectory receptor models were applied to speciated particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) measurements from Speciation Trends Network (STN; part of the Chemical Speciation Network [CSN]) and Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network across the state of Minnesota as part of the Minnesota PM2.5 Source Apportionment Study (MPSAS). CMB equations were solved by the Unmix, positive matrix factorization (PMF), and effective variance (EV) methods, giving collective source contribution and uncertainty estimates. Geological source profiles developed from local dust materials were either incorporated into the EV-CMB model or used to verify factors derived from Unmix and PMF. Common sources include soil dust, calcium (Ca)-rich dust, diesel and gasoline vehicle exhausts, biomass burning, secondary sulfate, and secondary nitrate. Secondary sulfate and nitrate aerosols dominate PM2.5 mass (50–69%). Mobile sources outweigh area sources at urban sites, and vice versa at rural sites due to traffic emissions. Gasoline and diesel contributions can be separated using data from the STN, despite significant uncertainties. Major differences between MPSAS and earlier studies on similar environments appear to be the type and magnitude of stationary sources, but these sources are generally minor (<7%) in this and other studies. Ensemble back-trajectory analysis shows that the lower Midwestern states are the predominant source region for secondary ammoniated sulfate in Minnesota. It also suggests substantial contributions of biomass burning and soil dust from out-of-state on occasions, although a quantitative separation of local and regional contributions was not achieved in the current study. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of the Air & Waste Management Association for a summary of input data, Unmix and PMF factor profiles, and additional maps. IMPLICATIONS The IMPROVE and STN data have been widely used for source apportionment by receptor models. Applying more than one model to available PM2.5 data reveals similarities and differences in source contribution estimates. The inter-model discrepancies identify areas for more focused investigation. Together, the information could lead to better-defined emission reduction strategies.

Traffic-related air toxics and preterm birth: a population-based case-control study in Los Angeles county, California

BackgroundNumerous studies have associated air pollutant exposures with adverse birth outcomes, but there is still relatively little information to attribute effects to specific emission sources or air toxics. We used three exposure data sources to examine risks of preterm birth in Los Angeles women when exposed to high levels of traffic-related air pollutants - including specific toxics - during pregnancy.MethodsWe identified births during 6/1/04-3/31/06 to women residing within five miles of a Southern California Air Quality Management District (SCAQMD) Multiple Air Toxics Exposure Study (MATES III) monitoring station. We identified preterm cases and, using a risk set approach, matched cases to controls based on gestational age at birth. Pregnancy period exposure averages were estimated for a number of air toxics including polycyclic aromatic hydrocarbons (PAHs), source-specific PM2.5 (fine particulates with aerodynamic diameter less than 2.5 μm) based on a Chemical Mass Balance model, criteria air pollutants based on government monitoring data, and land use regression (LUR) estimates of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOx). Associations between these metrics and odds of preterm birth were estimated using conditional logistic regression.ResultsOdds of preterm birth increased 6-21% per inter-quartile range increase in entire pregnancy exposures to organic carbon (OC), elemental carbon (EC), benzene, and diesel, biomass burning and ammonium nitrate PM2.5, and 30% per inter-quartile increase in PAHs; these pollutants were positively correlated and clustered together in a factor analysis. Associations with LUR exposure metrics were weaker (3-4% per inter-quartile range increase).ConclusionsThese latest analyses provide additional evidence of traffic-related air pollution's impact on preterm birth for women living in Southern California and indicate PAHs as a pollutant of concern that should be a focus of future studies. Other PAH sources besides traffic were also associated with higher odds of preterm birth, as was ammonium nitrate PM2.5, the latter suggesting potential importance of secondary pollutants. Future studies should focus on accurate modeling of both local and regional spatial and temporal distributions, and incorporation of source information.

Characteristics of fine particle carbonaceous aerosol at two remote sites in Central Asia

Abstract Central Asia is a relatively understudied region of the world in terms of characterizing ambient particulate matter (PM) and quantifying source impacts of PM at receptor locations, although it is speculated to have an important role as a source region for long-range transport of PM to Eastern Asia, the Pacific Ocean, and the Western United States. PM is of significant interest not only because of its adverse effect on public health but also due to its more recently realized role in climate change. To investigate the sources and characteristics of PM in the region, a series of PM2.5 and PM10 samples were collected on an every-other-day basis at two sites (termed “Bishkek” and “Teploklyuchenka”) in the Central Asian nation of the Kyrgyz Republic (also known as Kyrgyzstan) for a full year from July 2008 to July 2009. These samples were analyzed using standard methods for mass, organic carbon (OC), elemental carbon (EC), water-soluble organic carbon (WSOC), water-insoluble organic carbon by difference (OC minus WSOC) and a variety of molecular marker chemical species to be used in a chemical mass balance (CMB) model to apportion the sources of OC. These analyses indicate that approximately 19 ± 6.4% of the PM2.5 mass at both sites throughout the year consists of OC. The carbonaceous component of PM2.5 is dominated by OC, with OC/Total Carbon (TC) ratios being around 0.8 in the winter to almost 0.95 in the summer months. The CMB analysis indicated that mobile sources, i.e., gasoline and diesel engine exhaust, biomass combustion, and biogenic secondary organic aerosol (SOA) formation from isoprene and α-pinene precursors in the summer months were the dominant sources of OC. A strong positive correlation was observed between non-biomass burning WSOC and the un-apportioned OC from the CMB analysis, indicating that some of this un-apportioned OC is WSOC and likely the result of SOA-forming atmospheric processes that were not estimated by the CMB analysis performed. In addition, a comparison of the predominant contributors to OC between the two sites indicates that biomass combustion is a stronger relative source of OC at the Teploklyuchenka site, particularly in the winter, while contributions of isoprene- and α-pinene-derived SOA to the measured OC was relatively similar between the sites.

Sources for PM air pollution in the Po Plain, Italy: I. Critical comparison of methods for estimating biomass burning contributions to benzo(a)pyrene

Abstract Particle-bound benzo(a)pyrene (B(a)P) constitutes an air pollution problem in many areas of Europe and has been linked to biomass burning (BB). The present study, conducted in 2007 and 2009 at ten stations in the North Italian Po Plain and Valtelline Valley, examines four methods for the quantification of BB contributions to particle-bound B(a)P using data for 61 predictor compounds in more than 700 ambient PM10 and PM2.5 samples. The study was carried out during the heating season – a period of the year with minimal volatilization and atmospheric degradation of B(a)P, which favour source apportionment by receptor modelling. The lowest estimates of the source contribution (SCE) from BB were obtained with the levoglucosan tracer method and multi-linear regression analysis of daily variations in B(a)P concentrations using levoglucosan as the main predictor in combination with a few other predictors including gaseous pollutants and meteorological data. The standard uncertainty of these methods was driven by the uncertainty in the BB emission factor for levoglucosan and mounted to 90% (1 σ). Positive matrix factorization (PMF), using only PAH congeners as predictors, did not produce factors interpretable as emission sources. However, PMF utilizing a broad range of predictor compounds afforded five factors with compositions similar to emission sources. The yielded B(a)P SCEs for BB agreed well with results of chemical mass balance modelling (CMB). Both receptor models gave good predictions (p) of the observed (o) B(a)P concentrations (PMF: p/o = 89 ± 9%, CMB: p/o = 114 ± 17%) with lower uncertainties than the tracer methods (CMB 60%; PMF 54%; 1 σ). The average BB SCEs (mean ± 95% confidence interval) from these models were: 1.0 ± 0.4 ng m−3 at a kerbside in Milan, 1.0 ± 0.2 ng m−3 at six urban background stations in the Po Plain, 0.7 ± 0.3 ng m−3 at two rural background stations in the Po Plain, and 2.1 ± 1.1 ng m−3 at an urban background station in the Valtelline Valley representing 74 ± 32%, 79 ± 18%, 85 ± 33%, and 84 ± 46% of all modelled B(a)P sources, respectively.

The study on vertical variability of PM10 and the possible sources on a 220 m tower, in Tianjin, China

Abstract In this study, PM10 samples were collected at a meteorological tower in Tianjin, China. Four height levels (10 m, 40 m, 120 m and 220 m) were selected as the sampling sites. During the measurement campaign, the highest PM10 and species concentrations were obtained at 10 m, while lower concentrations were obtained at higher sampling sites. According to the vertical variability analysis of species concentrations and fractions (%), significant differences between different heights were found for certain species, such as Al, Si, Ca, OC and EC, while such differences for NO 3 − and SO 4 2 − were insignificant. In addition, the source contributions at each sampling site were calculated by a chemical mass balance (CMB) model. In all sampling sites, secondary sulfate accounted for the largest contributions (24.11–30.96%). The other estimated contributions were secondary nitrate (16.19–20.95%), crustal dust (10.74–11.37%), coal combustion (12.47–14.39%), vehicle exhaust (13.92–14.78%) and cement dust (4.60–9.89%). Finally, the conditional probability function (CPF) plots and potential source contribution function (PSCF) maps show that the ambient samples might be from local potential sources at lower sampling heights as well as regional potential sources at higher sampling heights during this measurement campaign.

Traffic-Related Air Toxics and Term Low Birth Weight in Los Angeles County, California

Background: Numerous studies have linked criteria air pollutants with adverse birth outcomes, but there is less information on the importance of specific emission sources, such as traffic, and air toxics.Objectives: We used three exposure data sources to examine odds of term low birth weight (LBW) in Los Angeles, California, women when exposed to high levels of traffic-related air pollutants during pregnancy.Methods: We identified term births during 1 June 2004 to 30 March 2006 to women residing within 5 miles of a South Coast Air Quality Management District (SCAQMD) Multiple Air Toxics Exposure Study (MATES III) monitoring station. Pregnancy period average exposures were estimated for air toxics, including polycyclic aromatic hydrocarbons (PAHs), source-specific particulate matter < 2.5 μm in aerodynamic diameter (PM2.5) based on a chemical mass balance model, criteria air pollutants from government monitoring data, and land use regression (LUR) model estimates of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOx). Associations between these metrics and odds of term LBW (< 2,500 g) were examined using logistic regression.Results: Odds of term LBW increased approximately 5% per interquartile range increase in entire pregnancy exposures to several correlated traffic pollutants: LUR measures of NO, NO2, and NOx, elemental carbon, and PM2.5 from diesel and gasoline combustion and paved road dust (geological PM2.5).Conclusions: These analyses provide additional evidence of the potential impact of traffic-related air pollution on fetal growth. Particles from traffic sources should be a focus of future studies.

Source apportionment of particulate matter in the ambient air of Hyderabad city, India

Source apportionment of particulate matter (PM) has been carried out for the city of Hyderabad using the chemical mass balance model (CMB8, Ver. 8.0) in PM10 and PM2.5 size modes. Urban particles were collected using Continuous Particulate Matter Analyzer (TEOM) during different seasons conducted in Punjagutta site, a critical traffic corridor, during June 2004–May 2005. The measurement of PM10 & PM2.5 at the site is measured throughout the day. Samples were collected in every 15 min; additionally instrument computes the total mass accumulation for every 30 min, 1-h, 8-h and 24 h average mass concentrations. Chemical characterization of PM10 & PM2.5 was done by ICP-MS. Source apportionment studies were carried out to quantify the possible sources affecting region using CMB Model Ver. 8.o. The CMB8 executed separately for both coarse and fine sizes. Results obtained by CMB indicate the dominance of resuspended dust (40%), followed by vehicular pollution (22%), combustion (12%), industrial (9%) and refuse burning (7%) in PM10; while in PM2.5 vehicular pollution (31%) dominated over resuspended dust (26%), combustion (9%), industrial (7%) and refuse burning (6%).