Chemical Mass Balance Results Research Articles

Atmospheric PM2.5 near an Urban-Industrial Complex during Air-pollution Episodes with Various Meteorological Conditions

This study investigates the atmospheric fine particulate matter (PM2.5) issue caused by the multi-effect of complicated sources, terrain, and meteorology at Southern Taiwan. Three sampling stations represent an urban, a rural, and a coastal sites near an urban-industrial complex. The atmospheric PM2.5 were measured during a pollution episode from November to February, when the reference samples were collected in April. The sample was collected with a constant-flow sampler with the Federal Reference Method performance. After determining the PM2.5 mass, their chemical compositions of ions, metals, and carbons were analyzed for the different properties caused by the multi-factors. The chemical mass balance (CMB) model was employed to evaluate the emission contributions. Additionally, an inverse trajectory model is used to analyze the pollutant transport and support the CMB results. The air-pollution episodes occurred within the winter to spring. The PM2.5 were composed of 51-69% ions, 18-31% carbonaceous species, and 1.5-3.0% metals. The SO42-, NH4+ and NO3- contributed 92-96% of the ions. Most of the organic/elemental carbon ratios were low, suggesting more primary carbon emissions. The metal contents were minor and dominated by Fe and Zn. The CMB model indicated the PM2.5 were dominated by 24% secondary SO42-, 14.7% traffics, 8.3% petrochemical emissions, 6.8% soil dust, and 4.5% sintering plant emission. For non-episode days, the PM2.5 were contributed by 34.9% traffics, 30% the secondary SO42-, 10.3% secondary NO3- , and 6.8% soil dust. Nevertheless, the frequent sea-land breeze might lead to more powerful wind eddies and bring the primary PM2.5 and the aerosol precursors from the emission areas. Consequently, the uncontrollable meteorological changings would lead to the pollution issues at the lowly convective area. Therefore, the averaging emissions of the PM2.5 and precursors should be lowered; meanwhile, the rapid controls of the primary emissions are suggested when the high-level PM2.5 are forecasted.

Fugitive Emissions of Volatile Organic Compounds from a Tailings Pond in the Oil Sands Region of Alberta.

Tailings ponds in the oil sands (OS) region in Alberta, Canada, have been associated with fugitive emissions of volatile organic compounds (VOCs) and other pollutants to the atmosphere. However, the contribution of tailings ponds to the total fugitive emissions of VOCs from OS operations remains uncertain. To address this knowledge gap, a field study was conducted in the summer of 2017 at Suncor's Pond 2/3 to estimate emissions of a suite of pollutants including 68 VOCs using a combination of micrometeorological methods and measurements from a flux tower. The results indicate that in 2017, Pond 2/3 was an emission source of 3322 ± 727 tons of VOCs including alkanes, aromatics, and oxygenated and sulfur-containing organics. While the total VOC emissions were approximately a factor of 2 higher than those reported by Suncor, the individual VOC species emissions varied by up to a factor of 12. A chemical mass balance (CMB) receptor model was used to estimate the contribution of the tailings pond to VOC pollution events in a nearby First Nations and Metis community in Fort McKay. CMB results indicate that Suncor Pond 2/3 contributed up to 57% to the total mass of VOCs measured at Fort McKay, reinforcing the importance of accurate VOC emission estimation methods for tailings ponds.

Source apportionment of fine organic carbon at an urban site of Beijing using a chemical mass balance model

Abstract. Fine particles were sampled from 9 November to 11 December 2016 and 22 May to 24 June 2017 as part of the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) field campaigns in urban Beijing, China. Inorganic ions, trace elements, organic carbon (OC), elemental carbon (EC), and organic compounds, including biomarkers, hopanes, polycyclic aromatic hydrocarbons (PAHs), n-alkanes, and fatty acids, were determined for source apportionment in this study. Carbonaceous components contributed on average 47.2 % and 35.2 % of total reconstructed PM2.5 during the winter and summer campaigns, respectively. Secondary inorganic ions (sulfate, nitrate, ammonium; SNA) accounted for 35.0 % and 45.2 % of total PM2.5 in winter and summer. Other components including inorganic ions (K+, Na+, Cl−), geological minerals, and trace metals only contributed 13.2 % and 12.4 % of PM2.5 during the winter and summer campaigns. Fine OC was explained by seven primary sources (industrial and residential coal burning, biomass burning, gasoline and diesel vehicles, cooking, and vegetative detritus) based on a chemical mass balance (CMB) receptor model. It explained an average of 75.7 % and 56.1 % of fine OC in winter and summer, respectively. Other (unexplained) OC was compared with the secondary OC (SOC) estimated by the EC-tracer method, with correlation coefficients (R2) of 0.58 and 0.73 and slopes of 1.16 and 0.80 in winter and summer, respectively. This suggests that the unexplained OC by the CMB model was mostly associated with SOC. PM2.5 apportioned by the CMB model showed that the SNA and secondary organic matter were the two highest contributors to PM2.5. After these, coal combustion and biomass burning were also significant sources of PM2.5 in winter. The CMB results were also compared with results from the positive matrix factorization (PMF) analysis of co-located aerosol mass spectrometer (AMS) data. The CMB model was found to resolve more primary organic aerosol (OA) sources than AMS-PMF, but the latter could apportion secondary OA sources. The AMS-PMF results for major components, such as coal combustion OC and oxidized OC, correlated well with the results from the CMB model. However, discrepancies and poor agreements were found for other OC sources, such as biomass burning and cooking, some of which were not identified in AMS-PMF factors.

An evaluation of source apportionment of fine OC and PM2.5 by multiple methods: APHH-Beijing campaigns as a case study.

This study aims to critically evaluate the source apportionment of fine particles by multiple receptor modelling approaches, including carbon mass balance modelling of filter-based radiocarbon (14C) data, Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) analysis on filter-based chemical speciation data, and PMF analysis on Aerosol Mass Spectrometer (AMS-PMF) or Aerosol Chemical Speciation Monitor (ACSM-PMF) data. These data were collected as part of the APHH-Beijing (Atmospheric Pollution and Human Health in a Chinese Megacity) field observation campaigns from 10th November to 12th December in winter 2016 and from 22nd May to 24th June in summer 2017. 14C analysis revealed the predominant contribution of fossil fuel combustion to carbonaceous aerosols in winter compared with non-fossil fuel sources, which is supported by the results from other methods. An extended Gelencsér (EG) method incorporating 14C data, as well as the CMB and AMS/ACSM-PMF methods, generated a consistent source apportionment for fossil fuel related primary organic carbon. Coal combustion, traffic and biomass burning POC were comparable for CMB and AMS/ACSM-PMF. There are uncertainties in the EG method when estimating biomass burning and cooking OC. The POC from cooking estimated by different methods was poorly correlated, suggesting a large uncertainty when differentiating this source type. The PM2.5 source apportionment results varied between different methods. Through a comparison and correlation analysis of CMB, PMF and AMS/ACSM-PMF, the CMB method appears to give the most complete and representative source apportionment of Beijing aerosols. Based upon the CMB results, fine aerosols in Beijing were mainly secondary inorganic ion formation, secondary organic aerosol formation, primary coal combustion and from biomass burning emissions.

Spatial PM2.5 mobile source impacts using a calibrated indicator method

ABSTRACTMotor vehicles are major sources of fine particulate matter (PM2.5), and the PM2.5 from mobile vehicles is associated with adverse health effects. Traditional methods for estimating source impacts that employ receptor models are limited by the availability of observational data. To better estimate temporally and spatially resolved mobile source impacts on PM2.5, we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NOx) measurements as an indicator of mobile source impacts. We extended the original integrated mobile source indicator (IMSI) method in three aspects. First, we generated spatially resolved indicators using 24-hr average concentrations of EC, CO, and NOx estimated at 4 km resolution by applying a method developed to fuse chemical transport model (Community Multiscale Air Quality Model [CMAQ]) simulations and observations. Second, we used spatially resolved emissions instead of county-level emissions in the IMSI formulation. Third, we spatially calibrated the unitless indicators to annually-averaged mobile source impacts estimated by the receptor model Chemical Mass Balance (CMB). Daily total mobile source impacts on PM2.5, as well as separate gasoline and diesel vehicle impacts, were estimated at 12 km resolution from 2002 to 2008 and 4 km resolution from 2008 to 2010 for Georgia. The total mobile and separate vehicle source impacts compared well with daily CMB results, with high temporal correlation (e.g., R ranges from 0.59 to 0.88 for total mobile sources with 4 km resolution at nine locations). The total mobile source impacts had higher correlation and lower error than the separate gasoline and diesel sources when compared with observation-based CMB estimates. Overall, the enhanced approach provides spatially resolved mobile source impacts that are similar to observation-based estimates and can be used to improve assessment of health effects.Implications: An approach is developed based on an integrated mobile source indicator method to estimate spatiotemporal PM2.5 mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM2.5 source apportionment modeling results to generate daily mobile source impacts in the state of Georgia. The estimated source impacts can be used in investigations of traffic pollution and health.

PMF5.0 vs. CMB8.2: An inter-comparison study based on the new European SPECIEUROPE database

Receptor Models are tools widely adopted in source apportionment studies. We describe here an experiment in which we integrated two different approaches, i.e. Positive Matrix Factorization (PMF) and Chemical Mass Balance (CMB) to apportion a set of PM10 (i.e. Particulate Matter with aerodynamic diameter lower than 10μm) concentration values. The study was performed in the city of Genoa (Italy): a sampling campaign was carried out collecting daily PM10 samples for about two months in an urban background site. PM10 was collected on Quartz fiber filters by a low-volume sampler. A quite complete speciation of PM samples was obtained via Energy Dispersive-X Ray Fluorescence (ED-XRF, for elements), Ionic Chromatography (IC, for major ions and levoglucosan), thermo-optical Analysis (TOT, for organic and elemental carbon). The chemical analyses provided the input database for source apportionment by both PMF and CMB. Source profiles were directly calculated from the input data by PMF while in the CMB runs they were first calculated by averaging the profiles of similar sources collected in the European database SPECIEUROPE. Differences between the two receptor models emerged in particular with PM10 sources linked to very local processes. For this reason, PMF source profiles were adopted in refined CMB runs thus testing a new hybrid approach. Finally, PMF and the “tuned” CMB showed a better agreement even if some discrepancies could not completely been resolved. In this work, we compared the results coming from the last available PMF and CMB versions applied on a set of PM10 samples. Input profiles used in CMB analysis were obtained by averaging the profiles of the new European SPECIEUROPE database. The main differences between PMF and CMB results were linked to very local processes: we obtained the best solution by integrating the two different approaches with the implementation of some output PMF profiles to CMB runs.

Contribution of polycyclic aromatic hydrocarbon (PAH) sources to the urban environment: A comparison of receptor models

The aim of this study was to evaluate the contribution of the main emission sources of PAHs associated with PM2.5, in an urban area of the Rio Grande do Sul state. Source apportionment was conducted using both the US EPA Positive Matrix Factorization (PMF) model and the Chemical Mass Balance (CMB) model. The two models were compared to analyze the source contributions similarities and differences, their advantages and disadvantages. PM2.5 samples were collected continuously over 24h using a stacked filter unit at 3 sampling sites of the urban area of the Rio Grande do Sul state every 15days between 2006 and 2008. Both models identified the main emission sources of PAHs in PM2.5: vehicle fleet (diesel and gasoline), coal combustion, wood burning, and dust. Results indicated similar source contribution amongst the sampling sites, as expected because of the proximity amongst the sampling sites, which are under the influence of the same pollutants emitting sources. Moreover, differences were observed in obtained sources contributions for the same data set of each sampling site. The PMF model attributed a slightly greater amount of PAHs to the gasoline and diesel sources, while diesel contributed more in the CMB results. The results were comparable with previous works of the region and in accordance with the characteristics of the study area. Comparison between these receptor models, which contain different physical constraints, is important for understanding better PAH emissions sources in order to reduce air pollution.

Receptor modelling of fine particles in southern England using CMB including comparison with AMS-PMF factors

Abstract. PM2.5 was collected during a winter campaign at two southern England sites, urban background North Kensington (NK) and rural Harwell (HAR), in January–February 2012. Multiple organic and inorganic source tracers were analysed and used in a Chemical Mass Balance (CMB) model, which apportioned seven separate primary sources, that explained on average 53% (NK) and 56% (HAR) of the organic carbon (OC), including traffic, woodsmoke, food cooking, coal combustion, vegetative detritus, natural gas and dust/soil. With the addition of source tracers for secondary biogenic aerosol at the NK site, 79% of organic carbon was accounted for. Secondary biogenic sources were represented by oxidation products of α-pinene and isoprene, but only the former made a substantial contribution to OC. Particle source contribution estimates for PM2.5 mass were obtained by the conversion of the OC estimates and combining with inorganic components ammonium nitrate, ammonium sulfate and sea salt. Good mass closure was achieved with 81% (92% with the addition of the secondary biogenic source) and 83% of the PM2.5 mass explained at NK and HAR respectively, with the remainder being secondary organic matter. While the most important sources of OC are vehicle exhaust (21 and 16%) and woodsmoke (15 and 28%) at NK and HAR respectively, food cooking emissions are also significant, particularly at the urban NK site (11% of OC), in addition to the secondary biogenic source, only measured at NK, which represented about 26%. In comparison, the major source components for PM2.5 at NK and HAR are inorganic ammonium salts (51 and 56%), vehicle exhaust emissions (8 and 6%), secondary biogenic (10% measured at NK only), woodsmoke (4 and 7%) and sea salt (7 and 8%), whereas food cooking (4 and 1%) showed relatively smaller contributions to PM2.5. Results from the CMB model were compared with source contribution estimates derived from the AMS-PMF method. The overall mass of organic matter accounted for is rather similar for the two methods. However, appreciably different concentrations were calculated for the individual primary organic matter contributions, although for most source categories the CMB and AMS-PMF results were highly correlated (r2 = 0.69–0.91). In comparison with the CMB model, the AMS appears to overestimate the biomass burning/coal and food cooking sources by a factor of around 1.5 to 2 while estimates of the traffic source are rather similar for each model. The largest divergence is in the primary/secondary organic matter split, with the AMS estimating an appreciably smaller secondary component. Possible reasons for these discrepancies are discussed, but despite these substantial divergences, the strong correlation of the two methods gives some confidence in their application.

Application of an Ensemble-Trained Source Apportionment Approach at a Site Impacted by Multiple Point Sources

Four receptor models and a chemical transport model were used to quantify PM2.5 source impacts at the St. Louis Supersite (STL-SS) between June 2001 and May 2003. The receptor models used two semi-independent data sets, with the first including ions and trace elements and the second including 1-in-6 day particle-bound organics. Since each source apportionment (SA) technique has limitations, this work compares results from the five different SA approaches to better understand the biases and limitations of each. The source impacts calculated by these models were then integrated into a constrained, ensemble-trained SA approach. The ensemble method offers several improvements over the five individual SA techniques at the STL-SS. Primarily, the ensemble method calculates source impacts on days when individual models either do not converge to a solution or do not have adequate input data to develop source impact estimates. When compared with a chemical mass balance approach using measurement-based source profiles, the ensemble method improves fit statistics, reducing chi-squared values and improving PM2.5 mass reconstruction. Compared to other receptor models, the ensemble method also calculates zero or negative impacts from major emissions sources (e.g., secondary organic carbon (SOC) and diesel vehicles) for fewer days. One limitation of this analysis was that a composite metals profile was used in the ensemble analysis. Although STL-SS is impacted by multiple metals processing point sources, several of the initial SA methods could not resolve individual metals processing impacts. The results of this analysis also reveal some of the subjectivities associated with applying specific SA models at the STL-SS. For instance, Positive Matrix Factorization results are very sensitive to both the fitting species and number of factors selected by the user. Conversely, Chemical Mass Balance results are sensitive to the source profiles used to represent local metals processing emissions. Additionally, the different SA approaches predict different impacts for the same source on a given day, with correlation coefficients ranging from 0.034 to 0.65 for gasoline vehicles, -0.54-0.48 for diesel vehicles, -0.29-0.81 for dust, -0.34-0.89 for biomass burning, 0.38-0.49 for metals processing, and -0.25-0.51 for SOC. These issues emphasize the value of using several different SA techniques at a given receptor site, either by comparing source impacts predicted by different models or by using an ensemble-based technique.

Receptor models application to multi-year ambient PM 10 measurements in an industrialized ceramic area: Comparison of source apportionment results

Ambient PM 10 data collected in one of the largest industrialized ceramic areas of Europe were used to study similarities and differences in the source apportionment results from three widespread receptor models: chemical mass balance (CMB), positive matrix factorization (PMF) and principal component analysis (PCA). Particulate emissions were collected from a variety of sources including soil dust and different mixed raw materials used for the manufacture of ceramic tiles in the area. The chemical profiles of these emission sources are presented in this work. The analysis of the PMF scaled residuals was used as a diagnostic tool for adjusting species uncertainties and to assess the PMF model fit by comparison with the robust CMB results. The Q robust value, the degree of correlation between the predicted and measured species concentrations, the sample-by-sample correlation of the PMF source contributions compared with the CMB improved after the new error structure was used within the PMF model. The robustness of the CMB analysis used for the comparison with the PMF analysis was inspected by means of the CMB performances parameters as well as by comparing the results with a previous CMB analysis performed on the same database but with different speciated source profiles. Moreover, the results showed that PMF and PCA models were not able to distinguish between the two most important sources of crustal material in the selected area (one natural and one anthropogenic). With the CMB model a contribution from both sources was calculated without observing collinearity between the profiles. However, high correlation was found by adding the two crustal contributions from CMB and comparing the results with the single crustal factor from PCA and PMF. Low correlation was observed between the contribution values of the vehicular source for each model pairs. The lack of a local vehicular experimental profile for the CMB analysis and the non-specific chemical speciation performed for the ambient organic matter explained the low observed correlation.

Modeling air quality during the California Regional PM 10/PM 2.5 Air Quality Study (CPRAQS) using the UCD/CIT Source Oriented Air Quality Model – Part II. Regional source apportionment of primary airborne particulate matter

A comprehensive air quality modeling project was carried out to simulate regional source contributions to primary airborne particle concentrations in California's central Valley. A 3-week stagnation episode lasting from December 15, 2000 to January 7, 2001, was chosen for study using the air quality and meteorological data collected during the California Regional PM 10/PM 2.5 Air Quality Study (CRPAQS). The UCD/CIT source oriented air quality model was applied to this episode using both the source-oriented external mixture configuration and an internal mixture with artificial tracers so that source contribution information could be retrieved in less time. The majority of the predicted and measured primary airborne particulate matter mass was composed of elemental carbon (EC) and organic carbon (OC). Previous work has shown that base case EC and OC predictions made by the UCD/CIT model are in good agreement with observations. Model results from the current study show that the highest EC and OC concentrations occur in urban areas and along transportation corridors where primary emissions are largest. Lower concentrations of primary EC and OC are predicted at rural locations in the San Joaquin Valley (SJV). Source contributions predicted by the UCD/CIT air quality model were compared to receptor-oriented source apportionment results produced by the Chemical Mass Balance (CMB) model at Fresno and Angiola. The relative contributions from major sources predicted by the UCD/CIT model agree with the CMB model results, building confidence in the accuracy of the UCD/CIT model predictions at locations where the CMB results are not available. Wood smoke was identified as the major regional source of primary OC in airborne particles in the winter SJV episode, accounting for approximately 50% of the total PM 2.5. Diesel engines were also found to be a significant contributor to primary PM 2.5 OC and the largest contributor to the predicted PM 2.5 EC averaged over a typical day. EC contributions from wood smoke increased at night and sometimes reached as high as 40% of the total PM 2.5 EC. The contribution of fugitive dust to primary PM 2.5 mass was also predicted to be significant, especially in rural areas, but this result is likely biased high by the lack of an appropriate diurnal profile for dust emissions. The results of the current study suggest that reductions in wood burning and diesel engine activity would reduce the regional concentration of primary PM 2.5 during severe winter stagnation events in the SJV.

Source contributions to carbonaceous aerosols in the Tennessee Valley Region

Sources of carbonaceous aerosols collected from three sites of Chattanooga, TN (CH), Muscle Shoals, AL (MS), and Look Rock, TN (LR) in the Tennessee Valley Region (TVR) were apportioned using both organic tracer-based chemical mass balance (CMB) modeling and radiocarbon ( 14C) measurement and the results were compared. Eight sources were resolved by CMB, among which wood combustion (averaging 0.92 μg m −3) was the largest contributor to primary organic carbon (OC) concentrations, followed by gasoline exhaust (0.35 μg m −3), and diesel exhaust (0.18 μg m −3). The identified primary sources accounted for 43%, 71%, and 14% of measured OC at CH, MS, and LR, respectively. Contributions from the eight primary sources resolved by CMB could explain 107±10% of ambient elemental carbon (EC) concentrations, with diesel exhaust (66±32%) and wood combustion (37±33%) as the most important contributors. The fossil fractions in total carbon determined by 14C measurements were in reasonably good agreement with that in primary (OC+EC) carbon apportioned by CMB in the MS winter samples. The comparison between the 14C and CMB results revealed that contemporary sources dominated other OC in the TVR, especially in summertime (84% contemporary).

Fine particulate source apportionment using data from the USEPA speciation trends network in Chicago, Illinois: Comparison of two source apportionment models

Data from two of the United States Environmental Protection Agency's speciation trends network fine particulate matter sites within Chicago, Illinois were analyzed using the chemical mass balance (CMB) and positive matrix factorization (PMF) models to determine source contributions to the ambient fine particulate concentrations. The results from the two models were compared to determine the similarities and differences in the source contributions. This included examining the differences in the magnitude of the individual source contributions as well as the correlation between the contribution values from the two methods. The results showed that both models predicted sulfates, nitrates and motor vehicles as the three highest fine particle contributors for the two sites accounting for approximately 80% of the total. The PMF model attributed a slightly greater amount of fine particulate to the road salt, steel and soil sources while vegetative burning contributed more in the CMB results. Correlations between the contribution results from the two models were high for sulfates, nitrates and road salt with very good correlations existing for motor vehicles and petroleum refineries. The predicted PMF profiles agreed well with measured source profiles for the major species associated with each source.

The 2000/2001 Missoula Valley PM chemical mass balance study, including the 2000 wildfire season?seasonal source apportionment

Abstract One in twelve day sampling for ambient PM 2.5 was conducted at two sites in Missoula, Montana, from March 2000 to March 2001. In conjunction with measured PM 2.5 mass and chemical species collected from both an urban site two blocks from a major intersection and a rural site at the opposite end of Missoula Valley, fingerprints of significant sources of PM 2.5 specific to Missoula Valley served as inputs into a chemical mass balance (CMB) receptor model (version 8.0) to apportion the sources of the fine fraction. Model results revealed that the largest source throughout the year at both the urban and rural background sites was wood combustion, contributing an average of 41% to the fine fraction. The identified wood combustion included residential wood combustion during the winter, slash and prescribed burns from the surrounding areas during the spring and fall, and smoke from wildfires during the summer of 2000. The second largest source of PM 2.5 in Missoula Valley throughout the year was diesel exhaust (19%), followed by secondary ammonium nitrate (17%), a kraft recovery boiler (14%) and hog fuel boilers (6%) from local wood products industry, and street sand (5%). During the forest fire season of summer 2000, CMB results of samples collected during smoky conditions on 13 August 2000 and 25 August 2000 revealed that 81% of the ambient PM 2.5 came from wood combustion (forest fires).

Receptor Model Evaluation of the Southeast Michigan Ozone Study Ambient NMOC Measurements

Large-scale studies like the Southeast Michigan Ozone Study (SEMOS) have focused attention on quantifying and spedating inventories for volatile organic compounds (VOCs). One approach for evaluating the accuracy of a VOC emission inventory is the development of a chemical mass balance (CMB) receptor model for ambient non-methane organic compound (NMOC) measurements. CMB evaluations of ambient hydrocarbon data provide a sample-specific allocation of emissions to individual source categories. This study summarizes the results of an application of the CMB model to the NMOC data from the SEMOS study. Comparison of CMB results with emission inventory values for the Detroit area show that vehicle emissions are well represented by the inventory, as are architectural coatings and coke ovens. Estimated emissions from petroleum refineries and graphic arts industries are much lower in the inventory than determined from the receptor allocation. Under-reporting of fugitive VOC emissions from petroleum refineries is an ongoing problem. Emissions from graphic arts industries are underestimated in the inventory partly because of the broad characterization of the emission factor (i.e., mass emitted/capita), which may be less useful when specific locations and days are under consideration. This study also demonstrates the effectiveness of the CMB approach when used prospectively to track the implementation of emission control strategies. While vehicle emission concentrations were unchanged from 1988 to 1993, measurement-based CMB results suggest a decrease in evaporative emissions during this time period resulting from Reid vapor pressure (RVP) reductions (from 11.0 psi in 1988 to 8.6 psi in 1993) and fleet turnover. Changes in emissions from coke plants and petroleum refineries were also seen in the CMB allocations for these sources.

Anthropogenic contributions to the carbonaceous content of aerosols over the Pacific Ocean

During a joint U.S./U.S.S.R. research cruise from 3 May to 27 July 1987 both total and fine (< 2 μm) particulate material was sampled. This cruise started in the Hawaiian Islands and then proceeded to the Kamchatka peninsula, south to Wellington, New Zealand, south of Australia into the Indian Ocean, to Singapore and then returned to the Hawaiian Islands by sailing just north of the Equator. Particulate samples, collected on quartz fiber and Teflon filters, were analysed for organic and elemental carbon by transmission thermo-optical carbon analysis and for trace elements by energy dispersive X-ray fluorescence. In the Northern Hemisphere the total particulate, organic carbon and elemental carbon concentration ranges were, respectively, 10–25, 0.5–2.5 and 0–0.3 μg m −3. In the Southern Hemisphere they were, respectively, 5–10, <0.6 and <0.02 μg m −3. In the Northern Hemisphere the fine particulate concentration range was 2–15 μg m −3 and the fine fraction varied from 20 to 80% of total aerosol loading. In the Southern Hemisphere the fine particulate loading was 1.2–1.7 μg m −3 and was usually less than 20% of the total particulate mass. Chemical mass balance (CMB) modeling was used to determine possible anthropogenic particulate contributions to the ocean aerosol. Readily available source profiles were used for CMB modeling. Sea-salt aerosol was represented by either the conventional EPA marine source profile or by the average of ambient ocean aerosols sampled in very clean mid-ocean regions. Usually 60–90% of ambient particulate mass was “explained” by the CMB model. Sources such as soil, catalytic auto emissions and wood-burning emissions were found to be possible contributors to the ocean aerosol, especially in the Northern Hemisphere. Anthropogenic contributions were estimated to contribute from 10 to 30% of oceanic aerosol mass. Emissions from a hugh forest fire that burned in northern China during the spring of 1987 were possibly detected, but the CMB model cannot distinguish this source from emissions from heating and cooking with wood. Since anthropogenic emissions are mainly combustion emissions which usually contain a large carbon component, carbon data is essential to CMB modeling. The relatively good CMB results obtained in this study suggest that it might be useful to develop source profiles for major emission sources in those countries which contribute most directly to oceanic aerosols. Perhaps characteristic national or regional source profiles could be developed.