Comparison of receptor models for source apportionment of volatile organic compounds in Beijing, China

Identifying the sources of volatile organic compounds (VOCs) is key to reducing ground-level ozone and secondary organic aerosols (SOAs). Several receptor models have been developed to apportion sources, but an intercomparison of these models had not been performed for VOCs in China. In the present study, we compared VOC sources based on chemical mass balance (CMB), UNMIX, and positive matrix factorization (PMF) models. Gasoline-related sources, petrochemical production, and liquefied petroleum gas (LPG) were identified by all three models as the major contributors, with UNMIX and PMF producing quite similar results. The contributions of gasoline-related sources and LPG estimated by the CMB model were higher, and petrochemical emissions were lower than in the UNMIX and PMF results, possibly because the VOC profiles used in the CMB model were for fresh emissions and the profiles extracted from ambient measurements by the two-factor analysis models were "aged".     

Sensitivity of a molecular marker based positive matrix factorization model to the number of receptor observations

To investigate the impact of the number of observations on molecular marker-based positive matrix factorization (MM-PMF) source apportionment models, daily PM_2.5 samples were collected in East St. Louis, IL, from April 2002 through May 2003. The samples were analyzed for daily 24-h average concentrations of elemental and organic carbon, trace elements, and speciated particle-phase organic compounds. A total of 273 sets of observations were used in the model and consisted of all valid sets of observations from the year long data set minus one sixth of the measurements, which were collected every 6th day and were analyzed by different chemical analysis techniques. In addition to the base case of 273 samples, systematic subsets of the data set were analyzed by PMF. These subsets of data included 50% of the observations (135–138 days), 33% of the observations (90–92 days) and 20% of the observations (52–56 days). In addition, model runs were also examined that used 48-h, 72-h, 6-day, and weekly average concentrations as model inputs. All MM-PMF model runs were processed following the same procedures to explore the stability of the source attribution results. Consistent with previous MM-PMF results for East St. Louis, the main sources of organic aerosol were found to be mobile sources, secondary organic aerosols (SOAs), resuspended soil and biomass combustions, as well as an n-alkane dominated point source and other combustion sources. The MM-PMF model was reasonably stable when the number of observations in the input was reduced to ninety, or approximately 33% of observations present in the base case. In these cases, the key factors including resuspended soil, mobile and secondary factors, which accounted for more than 70% of the measured OC concentrations, were stable as defined by a relative standard deviation (RSD) of less than 30%. Similar results were obtained from the smaller data subsets, but resulted in larger uncertainties, with several of these factors yielding RSD of greater than 30%. The three factors with the largest OC contributions were more stable than the other minor factors, even when the number of observations was nominally 50 days. Secondary organic aerosol (SOA) was the most stable factor observed in the model runs. Since it is unclear if these results can be broadly applied to all MM-PMF models, additional studies of this nature are needed to assess the broader applicability of these conclusions. Until such studies are implemented, this paper provides a foundation to design future studies in sampling strategies for source apportionment using MM-PMF.     

Spatial gradients and source apportionment of volatile organic compounds near roadways

Concentrations of 55 volatile organic compounds (VOCs) (C₂–C₁₂) are reported near a highway in Raleigh, NC. Thirty-minute samples were collected at eight locations, ranging from approximately 10–100 m perpendicular from the roadway. The highest concentrations of VOCs were generally measured closest to the roadway, and concentrations decreased exponentially with increasing distance from the roadway. The highest mean concentration for individual VOCs were for ethylene (3.10 ppbv) (mean concentration at x = 13 m), propane (2.27 ppbv), ethane (1.91 ppbv), isopentane (1.54 ppbv), toluene (0.95 ppbv), and n-butane (0.89 ppbv). Concentrations at the nearest roadway location (x = 13 m) were generally between 2.0 and 1.5 times those from the farthest roadway location (x = 92 m). The data were apportioned into four source categories using the EPA Chemical Mass Balance Model (CMB8.2): motor vehicle exhaust, compressed natural gas, propane gas, and evaporative gasoline. The majority of the VOCs resulted from motor vehicle exhaust (67 ± 12%) (% of total VOC at x = 13 m ± S.D.). Compressed natural gas, propane gas, and evaporative gasoline accounted for approximately 15%, 7% and 1% of the total VOC emissions, respectively, at x = 13 m.     

Levels and source apportionment of volatile organic compounds in southwestern area of Mexico City

Thirteen volatile organic compounds (VOCs) were quantified at three sites in southwestern Mexico City from July 2000 to February 2001. High concentrations of different VOCs were found at a Gasoline refueling station (GS), a Condominium area (CA), and at the University Center for Atmospheric Sciences (CAS). The most abundant VOCs at CA and CAS were propane, n-butane, toluene, acetylene and pentane. In comparison, at GS the most abundant were toluene, pentane, propane, n-butane, and acetylene. Benzene, a known carcinogenic compound had average levels of 28, 35 and 250 ppbC at CAS, CA, and GS respectively. The main contributing sources of the measured VOCs at CA and CAS were the handling and management of LP (Liquid Propane) gas, vehicle exhaust, asphalt works, and use of solvents. At GS almost all of the VOCs came from vehicle exhaust and fuel evaporation, although components of LP gas were also present. Based on the overall results possible abatement strategies are discussed.     

Application of positive matrix factorization in characterization of PM(10) and PM(2.5) emission sources at urban roadside

The 24-h average coarse (PM₁₀) and fine (PM_2.5) fraction of airborne particulate matter (PM) samples were collected for winter, summer and monsoon seasons during November 2008-April 2009 at an busy roadside in Chennai city, India. Results showed that the 24-h average ambient PM₁₀ and PM_2.5 concentrations were significantly higher in winter and monsoon seasons than in summer season. The 24-h average PM₁₀ concentration of weekdays was significantly higher (12-30%) than weekends of winter and monsoon seasons. On weekends, the PM_2.5 concentration was found to slightly higher (4-15%) in monsoon and summer seasons. The chemical composition of PM₁₀ and PM_2.5 masses showed a high concentration in winter followed by monsoon and summer seasons.The U.S.EPA-PMF (positive matrix factorization) version 3 was applied to identify the source contribution of ambient PM₁₀ and PM_2.5 concentrations at the study area. Results indicated that marine aerosol (40.4% in PM₁₀ and 21.5% in PM_2.5) and secondary PM (22.9% in PM₁₀ and 42.1% in PM_2.5) were found to be the major source contributors at the study site followed by the motor vehicles (16% in PM₁₀ and 6% in PM_2.5), biomass burning (0.7% in PM₁₀ and 14% in PM_2.5), tire and brake wear (4.1% in PM₁₀ and 5.4% in PM_2.5), soil (3.4% in PM₁₀ and 4.3% in PM_2.5) and other sources (12.7% in PM₁₀ and 6.8% in PM_2.5).     

GIS-based source identification and apportionment of diffuse water pollution: perfluorinated compound pollution in the Tokyo Bay basin

To efficiently reduce perfluorinated compound (PFC) pollution, it is important to have an understanding of PFC sources and their contribution to the pollution. In this study, source identification of diffuse water pollution by PFCs was conducted using a GIS-based approach. Major components of the source identification were collection of the monitoring data and preparation of the corresponding geographic information that was extracted from a constructed GIS database. The spatially distributed pollution factors were then explored by multiple linear regression analysis, after which they were visually expressed using GIS. Among the 35 PFC homologues measured in a survey of the Tokyo Bay basin, 18 homologues were analyzed. Pollution by perfluorooctane sulfonate (PFOS) was explained well by the percentage of arterial traffic area in the basin, and the 84% variance of the measured PFOS concentration was explained by two geographic variables, arterial traffic area and population. Source apportionment between point and nonpoint sources was conducted based on the results of the analysis. The contribution of PFOS from nonpoint sources was comparable to that from point sources in several major rivers flowing into Tokyo Bay. Source identification and apportionment using the GIS-based approach was shown to be effective, especially for ubiquitous types of pollution, such as PFC pollution.     

Determination and analysis of PM10 source apportionment during episodes of air pollution in Central Eastern European urban areas: The case of wintertime 2006

Source apportionment of air pollution due to particulate matter with an aerodynamic diameter <10 μm (PM₁₀) was investigated in Central Eastern European urban areas. A combination of four methods was developed to distinguish long-range transport (LRT) and regional transport (RT) from local pollution (LP) sources as well as to discern the involvement of traffic or residential sources in LP. Sources of PM₁₀ events of pollution were determined in January 2006 in representative Polish cities using monitored air quality and meteorological data, backward air mass trajectories, correlation and principal component analysis (PCA). Daily patterns of PM₁₀ levels show that several peak episodes were registered in Poland; January 21–30th being the most polluted days. Air mass back-trajectory analysis shows that all cities were under the influence of LRT from North-eastern origins (Russia–Belarus–Ukraine), most were also under LRT from Southern origin (Slovakia, Czech Republic), and northern cities were under national RT influence. PCA analysis shows that ion-sums of secondary inorganic aerosols account for LRT pollution while arsenic and chromium represents markers of RT (industrial) and LP (residential) sources of PM₁₀, respectively. Determination of several ratios (REG/UB, REG/TRAF, TRAF/UB) calculated between PM₁₀ levels measured at regional background (REG); urban background (UB) and traffic (TRAF) monitoring sites shows that, with ratios REG/UB ≥ 0.57, PM₁₀ episodes in both Szczecin and Warsaw bore a marked RT origin. The lower REG/UB ≤ 0.35 in the Southern cities of Cracow and Zabrze indicates that LP was the main contributor to the observed episodes. Only PM₁₀ episodes in Southern-western Poland (Jelenia Góra) were clearly of LP origin as characterized, by the lowest REG/UB ratio (<0.2). The high TRAF/UB ratios obtained for all cities (close to 1) indicate that there was a great uniformity of PM levels on an urban scale owing to the meteorologically stagnant conditions. A high correlation between PM₁₀, NO₂ and CO confirms that traffic emission represented a common and an important LP source of urban pollution in most Polish cities during January 2006. On the other hand PM₁₀ which is also highly correlated with SO₂ in 4 cities out of 6, indicates that coal combustion through domestic heating or industrial activities was also an important LP source of PM₁₀. Finally, extremely unfavourable meteorological conditions caused by the influence of a Siberian high-pressure system were found to be associated with the occurrence of severe PM₁₀ episodes of pollution.     

夏季白洋淀颗粒的有机质碳氮同位素特征及其来源解析

为阐明白洋淀颗粒有机质碳氮同位素空间分布差异及其来源,于2022年9月测定了白洋淀夏季悬浮颗粒物样品中颗粒有机碳 (POC) 、颗粒有机氮 (PON) 、δ¹³C和δ¹⁵N,并运用MixSIAR模型对颗粒有机质来源进行分析。结果表明,白洋淀内POC和PON质量分数分别为3.55%~21.91%和0.44%~2.93%,全淀区POC和PON整体水平处于8.60%±5.52%和1.14%±0.72%,受入淀河流的影响,POC、PON整体空间分布存在较大差异;δ¹³C和δ¹⁵N的范围分别为−25.27‰~−32.95‰和3.86‰~7.32‰,呈由淀南向淀北逐渐偏正的趋势,表明由南向北外源贡献升高,自生源贡献降低。贝叶斯混合模型计算结果表明,悬浮颗粒有机质主要来源于浮游植物 (28.60%~37.40%) 、陆源植物 (22.40%~34.30%) 和水生植物 (30.20%~31.30%) ,内源自生贡献率高达59.90%。基于上述研究,提出在适当的时期通过收割淀内芦苇等挺水植物及适当的对沉水植物及藻类残体进行打捞的工程措施,可有效降低自生源对于有机质的贡献,进而切断有机质对于水环境中有机碳氮的贡献,确保水质的达标和稳固提升。本研究结果可为白洋淀的水质保护及修复提供理论参考。     

Contribution of motor vehicle emissions to organic carbon and fine particle mass in Pittsburgh, Pennsylvania: Effects of varying source profiles and seasonal trends in ambient marker concentrations

We present estimates of the vehicular contribution to ambient organic carbon (OC) and fine particle mass (PM) in Pittsburgh, PA using the chemical mass balance (CMB) model and a large dataset of ambient molecular marker concentrations. Source profiles for CMB analysis are selected using a method of comparing the ambient ratios of marker species with published profiles for gasoline and diesel vehicle emissions. The ambient wintertime data cluster on a hopanes/EC ratio–ratio plot, and therefore can be explained by a large number of different source profile combinations. In contrast, the widely varying summer ambient ratios can be explained by a more limited number of source profile combinations. We present results for a number of different CMB scenarios, all of which perform well on the different statistical tests used to establish the quality of a CMB solution. The results illustrate how CMB estimates depend critically on the marker-to-OC and marker-to-PM ratios of the source profiles. The vehicular contribution in the winter is bounded between 13% and 20% of the ambient OC (274±56–416±72 ng-C m⁻³). However, variability in the diesel profiles creates uncertainty in the gasoline–diesel split. On an OC basis, one set of scenarios suggests gasoline dominance, while a second set indicates a more even split. On a PM basis, all solutions indicate a diesel-dominated split. The summer CMB solutions do not present a consistent picture given the seasonal shift and wide variation in the ambient hopanes-to-EC ratios relative to the source profiles. If one set of source profiles is applied to the entire dataset, gasoline vehicles dominate vehicular OC in the winter but diesel dominates in the summer. The seasonal pattern in the ambient hopanes-to-EC ratios may be caused by photochemical decay of hopanes in the summer or by seasonal changes in vehicle emission profiles.     

Correlations between mutagenic activity of organic extracts of airborne particulate matter, NOx and sulphur dioxide in southern Germany: results of a two-year study

Goals, Scope and Background Among other substances, sulphur dioxide (SO2), nitric oxide (NO) and nitrogen dioxide (NO2) are parameters which are routinely measured to describe basic air quality. Organic extracts of airborne particulate matter contain mutagenic chemical compounds of different origins. The aim of the study was to find correlations between routine monitoring data and mutagenic activity of organic extracts of simultaneously drawn samples.Methods Specimens were collected over a period of two years at 8 sampling sites in south-west Germany. Simultaneously, concentrations of NO, NO2, and SO2 were measured on-line within the framework of the official air monitoring network of Baden-Württemberg, Germany. Dust samples were collected for biotesting using high volume air samplers equipped with glass fibre filters. After sampling was completed, filters were extracted and samples were prepared for biological testing. Mutagenic activity was tested by means of the plate incorporation assay (Ames test) using S. typhimurium TA98 and TA100 tester strains. During the first year of the study, all tests have been performed with and without metabolic activation. Additionally, a series of tests has been performed in parallel with TA98 and TA98NR.Results and Discussion Comparison of Ames test data obtained with and without metabolic activation indicates no statistically significant difference between both methods. Therefore, during the second year of the study, all tests have been performed without metabolic activation. Average yearly activities at the sampling sites were between 1 und 27 Revertants per m³ (Rev/m³). High activities were preferably found at congested sites (Karlsruhe, up to 95 Rev/m³). However, peak values of over 100 Rev/m³ were found in other places where pollution by traffic is significantly lower. The reason for these high level values is not evident. Tests performed using TA98NR tester strain indicate a significant share (average 31%) of compounds requiring activation by nitroreductase for mutagenic activity. Average mutagenic activity can be correlated to routine monitoring parameters. Comparison of averaged data for particular sampling sites indicates significant correlation between nitric oxide and mutagenic activity in TA98 (r²=0.90), while correlation between nitrogen dioxide (0.84) or sulphur dioxide (0.52) and mutagenic activity is weaker. For TA100, correlations are generally weaker than for TA98. Comparison of data for mutagenic activity and routine monitoring data of distant sites being sampled simultaneously shows parallel behaviour.Conclusions Results from this study show that mutagenic activity can be compared to seasonal and local variations of gaseous indicator air pollutants. Tester strain TA98 generally shows the best correlations. Although pollution by particle-bound mutagenic substances is significantly higher during the cold season than during summer on average, mutagenic activity of airborne dust is not a continuous effect. During winter, peak levels as well as low pollution periods can occur. Even during winter time mutagenic activity can reach very low levels typical for summertime. Comparison of results for distant sampling sites where samples have been collected simultaneously indicate that “classical” indicators of air pollution and bacterial mutagenicity of organic extracts from airborne particulate matter are influenced by connected effects. Seasonal trend of mutagenic activity, in particular, is similar to the concentrations of nitrogen oxide. NO is a strong indicator for vehicle exhaust gases. It is concluded that the average mutagenic activity at particular sites can be estimated using NO concentrations as an indicator.