PM2.5 source apportionment: reconciling receptor models for U.S. nonurban and urban long-term networks

Evaluating the influence of constant source profile presumption on PMF analysis of PM2.5 by comparing long- and short-term hourly observation-based modeling

Hourly PM_2.5 speciation data have been widely used as an input of positive matrix factorization (PMF) model to apportion PM_2.5 components to specific source-related factors. However, the influence of constant source profile presumption during the observation period is less investigated. In the current work, hourly concentrations of PM_2.5 water-soluble inorganic ions, bulk organic and elemental carbon, and elements were obtained at an urban site in Nanjing, China from 2017 to 2020. PMF analysis based on observation data during specific pollution (firework combustion, sandstorm, and winter haze) and emission-reduction (COVID-19 pandemic) periods was compared with that using the whole 4-year data set (PMF_whole). Due to the lack of data variability, event-based PMF solutions did not separate secondary sulfate and nitrate. But they showed better performance in simulating average concentrations and temporal variations of input species, particularly for primary source markers, than the PMF_whole solution. After removing event data, PMF modeling was conducted for individual months (PMF_month) and the 4-year period (PMF_4-year), respectively. PMF_month solutions reflected varied source profiles and contributions and reproduced monthly variations of input species better than the PMF_4-year solution, but failed to capture seasonal patterns of secondary salts. Additionally, four winter pollution days were selected for hour-by-hour PMF simulations, and three sample sizes (500, 1000, and 2000) were tested using a moving window method. The results showed that using short-term observation data performed better in reflecting immediate changes in primary sources, which will benefit future air quality control when primary PM emissions begin to increase.

Apportionment of Vehicle Fleet Emissions by Linear Regression, Positive Matrix Factorization, and Emission Modeling

Real-world emission factors for different vehicle types and their contributions to roadside air pollution are needed for air-quality management. Tunnel measurements have been used to estimate emission factors for several vehicle types using linear regression or receptor-based source apportionment. However, the accuracy and uncertainties of these methods have not been sufficiently discussed. This study applies four methods to derive emission factors for different vehicle types from tunnel measurements in Hong Kong, China: (1) simple linear regressions (SLR); (2) multiple linear regressions (MLR); (3) positive matrix factorization (PMF); and (4) EMission FACtors for Hong Kong (EMFAC-HK). Separable vehicle types include those fueled by liquefied petroleum gas (LPG), gasoline, and diesel. PMF was the most useful, as it simultaneously seeks source profiles and source contributions. Diesel-, gasoline-, and LPG-fueled vehicle emissions accounted for 52%, 10%, and 5% of PM2.5 mass, respectively, while ammonium sulfate (~20%), ammonium nitrate (6%), and road dust (7%) were also large contributors. MLR exhibited the highest relative uncertainties, typically over twice those determined by SLR. EMFAC-HK has the lowest relative uncertainties due to its assumption of a single average emission factor for each pollutant and each vehicle category under specific conditions. The relative uncertainties of SLR and PMF are comparable.

Source apportionment of primary and secondary PM2.5: Associations with pediatric respiratory disease emergency department visits in the U.S. State of Georgia

We developed a hybrid chemical transport model and receptor model (CTM-RM) to conduct source apportionment of both primary and secondary PM_2.5 (particulate matter ≤2.5 μm in diameter) at 36 km resolution throughout the U.S. State of Georgia for the years 2005 and 2007. This novel source apportionment model enabled us to estimate and compare associations of short-term changes in 12 PM_2.5 source concentrations (agriculture, biogenic, coal, dust, fuel oil, metals, natural gas, non-road mobile diesel, non-road mobile gasoline, on-road mobile diesel, on-road mobile gasoline, and all other sources) with emergency department (ED) visits for pediatric respiratory diseases. ED visits for asthma (N = 49,651), pneumonia (N = 25,558), and acute upper respiratory infections (acute URI, N = 235,343) among patients aged ≤18 years were obtained from patient claims records. Using a case-crossover study, we estimated odds ratios per interquartile range (IQR) increase for 3-day moving average PM_2.5 source concentrations using conditional logistic regression, matching on day-of-week, month, and year, and adjusting for average temperature, humidity, and holidays. We fit both single-source and multi-source models. We observed positive associations between several PM_2.5 sources and ED visits for asthma, pneumonia, and acute URI. For example, for asthma, per IQR increase in the source contribution in the single-source model, odds ratios were 1.022 (95% CI: 1.013, 1.031) for dust; 1.050 (95% CI: 1.036, 1.063) for metals, and 1.091 (95% CI: 1.064, 1.119) for natural gas. These sources comprised 5.7%, 2.2%, and 6.3% of total PM_2.5 mass, respectively. PM_2.5 from metals and natural gas were positively associated with all three respiratory outcomes. In addition, non-road mobile diesel was positively associated with pneumonia and acute URI.

PM2.5 Source Apportionment Using a Hybrid Environmental Receptor Model

A hybrid environmental receptor model (HERM) that unifies the theory of effective-variance chemical mass balance (EV-CMB) and positive matrix factorization (PMF) models was developed to support the weight-of-evidence approach of air pollution source apportionment. The HERM software is capable of (1) conducting EV-CMB analysis for multiple samples in a single model run; (2) calculating EV-CMB and PMF source contributions, as well as middle grounds between the two (i.e., hybrid mode), using partial source information available for the study region; (3) reporting source contribution uncertainties and sample-/species-specific fitting performance measures; and (4) interfacing with MS Excel for convenient data inputs/outputs and analysis. Initial testing with simulated and real-world PM_2.5 (fine particulate air pollutants with aerodynamic diameter <2.5 μm) data sets show that HERM reproduces EV-CMB results from existing software but with more tolerance to collinearity and better uncertainty estimates. It also shows that partial source information helps reduce rotational ambiguity in PMF, thus producing more accurate partitioning between highly correlated sources. Moreover, source profiles generated from the hybrid mode can be more representative of the study region than those acquired from other locales or calculated by PMF with no source information. Strategies to use HERM for source apportionment are recommended.

PM2.5 pollution from household solid fuel burning practices in Central India: 2. Application of receptor models for source apportionment

USEPA's UNMIX, positive matrix factorization (PMF) and effective variance-chemical mass balance (EV-CMB) receptor models were applied to chemically speciated profiles of 125 indoor PM_2.5 measurements, sampled longitudinally during 2012-2013 in low-income group households of Central India which uses solid fuels for cooking practices. Three step source apportionment studies were carried out to generate more confident source characterization. Firstly, UNMIX6.0 extracted initial number of source factors, which were used to execute PMF5.0 to extract source-factor profiles in second step. Finally, factor analog locally derived source profiles were supplemented to EV-CMB8.2 with indoor receptor PM_2.5 chemical profile to evaluate source contribution estimates (SCEs). The results of combined use of three receptor models clearly describe that UNMIX and PMF are useful tool to extract types of source categories within small receptor dataset and EV-CMB can pick those locally derived source profiles for source apportionment which are analog to PMF-extracted source categories. The source apportionment results have also shown three fold higher relative contribution of solid fuel burning emissions to indoor PM_2.5 compared to those measurements reported for normal households with LPG stoves. The previously reported influential source marker species were found to be comparatively similar to those extracted from PMF fingerprint plots. The comparison between PMF and CMB SCEs results were also found to be qualitatively similar. The performance fit measures of all three receptor models were cross-verified and validated and support each other to gain confidence in source apportionment results.

Personal exposure to fine particulate air pollution while commuting: An examination of six transport modes on an urban arterial roadway

Traffic-related air pollution in urban areas contributes significantly to commuters’ daily PM_2.5 exposures, but varies widely depending on mode of commuting. To date, studies show conflicting results for PM_2.5 exposures based on mode of commuting, and few studies compare multiple modes of transportation simultaneously along a common route, making inter-modal comparisons difficult. In this study, we examined breathing zone PM_2.5 exposures for six different modes of commuting (bicycle, walking, driving with windows open and closed, bus, and light-rail train) simultaneously on a single 2.7 km (1.68 mile) arterial urban route in Salt Lake City, Utah (USA) during peak “rush hour” times. Using previously published minute ventilation rates, we estimated the inhaled dose and exposure rate for each mode of commuting. Mean PM_2.5 concentrations ranged from 5.20 μg/m³ for driving with windows closed to 15.21 μg/m³ for driving with windows open. The estimated inhaled doses over the 2.7 km route were 6.83 μg for walking, 2.78 μg for cycling, 1.28 μg for light-rail train, 1.24 μg for driving with windows open, 1.23 μg for bus, and 0.32 μg for driving with windows closed. Similarly, the exposure rates were highest for cycling (18.0 μg/hr) and walking (16.8 μg/hr), and lowest for driving with windows closed (3.7 μg/hr). Our findings support previous studies showing that active commuters receive a greater PM_2.5 dose and have higher rates of exposure than commuters using automobiles or public transportation. Our findings also support previous studies showing that driving with windows closed is protective against traffic-related PM_2.5 exposure.

A Two-Stage Method to Estimate the Contribution of Road Traffic to PM₂.₅ Concentrations in Beijing, China

BACKGROUND

Fine particulate matters with aerodynamic diameters smaller than 2.5 micrometers (PM2.5) have been a critical environmental problem in China due to the rapid road vehicle growth in recent years. To date, most methods available to estimate traffic contributions to ambient PM2.5 concentration are often hampered by the need for collecting data on traffic volume, vehicle type and emission profile.

OBJECTIVE

To develop a simplified and indirect method to estimate the contribution of traffic to PM2.5 concentration in Beijing, China.

METHODS

Hourly PM2.5 concentration data, daily meteorological data and geographic information were collected at 35 air quality monitoring (AQM) stations in Beijing between 2013 and 2014. Based on the PM2.5 concentrations of different AQM station types, a two-stage method comprising a dispersion model and generalized additive mixed model (GAMM) was developed to estimate separately the traffic and non-traffic contributions to daily PM2.5 concentration. The geographical trend of PM2.5 concentrations was investigated using generalized linear mixed model. The temporal trend of PM2.5 and non-linear relationship between PM2.5 and meteorological conditions were assessed using GAMM.

RESULTS

The medians of daily PM2.5 concentrations during 2013-2014 at 35 AQM stations in Beijing ranged from 40 to 92 μg/m³. There was a significant increasing trend of PM2.5 concentration from north to south. The contributions of road traffic to daily PM2.5 concentrations ranged from 17.2% to 37.3% with an average 30%. The greatest contribution was found at AQM stations near busy roads. On average, the contribution of road traffic at urban stations was 14% higher than that at rural stations.

CONCLUSIONS

Traffic emissions account for a substantial share of daily total PM2.5 concentrations in Beijing. Our two-stage method is a useful and convenient tool in ecological and epidemiological studies to estimate the traffic contribution to PM2.5 concentrations when there is limited information on vehicle number and types and emission profile.

PM2.5 source apportionment with organic markers in the Southeastern Aerosol Research and Characterization (SEARCH) study

Positive matrix factorization (PMF) and effective variance (EV) solutions to the chemical mass balance (CMB) were applied to PM(2.5) (particulate matter with an aerodynamic diameter <2.5 μm) mass and chemically speciated measurements for samples taken from 2008 to 2010 at the Atlanta, Georgia, and Birmingham, Alabama, sites. Commonly measured PM(2.5) mass, elemental, ionic, and thermal carbon fraction concentrations were supplemented with detailed nonpolar organic speciation by thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS). Source contribution estimates were calculated for motor vehicle exhaust, biomass burning, cooking, coal-fired power plants, road dust, vegetative detritus, and secondary sulfates and nitrates for Atlanta. Similar sources were found for Birmingham, with the addition of an industrial source and the separation of biomass burning into open burning and residential wood combustion. EV-CMB results based on conventional species were qualitatively similar to those estimated by PMF-CMB. Secondary ammonium sulfate was the largest contributor, accounting for 27-38% of PM(2.5), followed by biomass burning (21-24%) and motor vehicle exhaust (9-24%) at both sites, with 4-6% of PM(2.5) attributed to coal-fired power plants by EV-CMB. Including organic compounds in the EV-CMB reduced the motor vehicle exhaust and biomass burning contributions at both sites, with a 13-23% deficit for PM(2.5) mass. The PMF-CMB solution showed mixing of sources within the derived factors, both with and without the addition of speciated organics, as is often the case with complex source mixtures such as those at these urban-scale sites. The nonpolar TD-GC/MS compounds can be obtained from existing filter samples and are a useful complement to the elements, ions, and carbon fractions. However, they should be supplemented with other methods, such as TD-GC/MS on derivitized samples, to obtain a wider range of polar compounds such as sterols, sugars, and organic acids. The PMF and EV solutions to the CMB equations are complementary to, rather than replacements for, each other, as comparisons of their results reveal uncertainties that are not otherwise evident.

IMPLICATIONS

Organic markers can be measured on currently acquired PM(2.5) filter samples by thermal methods. These markers can complement element, ion, and carbon fraction measurements from long-term speciation networks. Applying the positive matrix factorization and effective variance solutions for the chemical mass balance equations provides useful information on the accuracy of the source contribution estimates. Nonpolar compounds need to be complemented with polar compounds to better apportion cooking and secondary organic aerosol contributors.

Impact of gas/particle partitioning of semivolatile organic compounds on source apportionment with positive matrix factorization

To quantify and minimize the influence of gas/particle (G/P) partitioning on receptor-based source apportionment using particle-phase semivolatile organic compound (SVOC) data, positive matrix factorization (PMF) coupled with a bootstrap technique was applied to three data sets mainly composed of "measured-total" (measured particle- + gas-phase), "particle-only" (measured particle-phase) and "predicted-total" (measured particle-phase + predicted gas-phase) SVOCs to apportion carbonaceous aerosols. Particle- (PM2.5) and gas-phase SVOCs were collected using quartz fiber filters followed by PUF/XAD-4/PUF adsorbents and measured using gas chromatography-mass spectrometry (GC-MS). Concentrations of gas-phase SVOCs were also predicted from their particle-phase concentrations using absorptive partitioning theory. Five factors were resolved for each data set, and the factor profiles were generally consistent across the three PMF solutions. Using a previous source apportionment study at the same receptor site, those five factors were linked to summertime biogenic emissions (odd n-alkane factor), unburned fossil fuels (light SVOC factor), road dust and/or cooking (n-alkane factor), motor vehicle emissions (PAH factor), and lubricating oil combustion (sterane factor). The "measured-total" solution was least influenced by G/P partitioning and used as reference. Two out of the five factors (odd n-alkane and PAH factors) exhibited consistent contributions for "particle-only" vs "measured-total" and "predicted-total" vs "measured-total" solutions. Factor contributions of light SVOC and n-alkane factors were more consistent for "predicted-total" vs "measured-total" than "particle-only" vs "measured-total" solutions. The remaining factor (sterane factor) underestimated the contribution by around 50% from both "particle-only" and "predicted-total" solutions. The results of this study confirm that when measured gas-phase SVOCs are not available, "predicted-total" SVOCs should be used to decrease the influence of G/P partitioning on receptor-based source apportionment.

The PM2.5 chemical composition in an industrial zone included in a large urban settlement: main sources and local background

Chemical analyses, receptor modeling and meteorological data were combined to determine the composition and sources of PM2.5 sampled daily in a large area in Italy characterized by a high number of heterogeneous industrial emissions and contiguous to a major urban center. The PM2.5 local background in the area, i.e. the common basic composition and concentrations of PM2.5, was determined. Factor analysis-multiple linear regression analysis (FA-MLRA) was used to identify and quantify the main PM sources. Groups of samples with similar source contributions were then sorted using cluster analysis. The potential source location and the influence of long range transport were investigated by using the conditional probability function (CPF) and the potential source contribution function (PSCF) respectively. On an annual basis, five sources of PM were found relevant. Industrial emissions accounted for 3% of PM mass, whereas the main contribution to PM was related to a combination of ammonium nitrate, combustion (54%) and road traffic (36%), mainly related to urban emissions. The PM2.5 background was estimated to account for 20 μg m(-3). It comprises contributions of 55% ammonium nitrate and combustion, 46% road traffic, 6% fossil fuel combustion and 3% industrial emissions. Source contributions are influenced by both local atmospheric circulation and regional transport.

Gas/particle partitioning of 2-methyltetrols and levoglucosan at an urban site in Denver

In this study, a medium volume sampler incorporating quartz fiber filters (QFFs) and a polyurethane foam (PUF)/XAD/PUF sandwich (PXP) was used to collect 2-methyltetrols (isoprene tracer) and levoglucosan (biomass burning tracer) in gaseous and particle (PM2.5) phases. The measured gas/particle (G/P) partitioning coefficients (Kp,OMm) of 2-methyltetrols and levoglucosan were calculated and compared to their predicted G/P partitioning coefficients (Kp,OMt) based on an absorptive partitioning theory. The breakthrough experiments showed that gas-phase 2-methyltetrols and levoglucosan could be collected using the PXP or PUF adsorbent alone, with low breakthrough; however, the recoveries of levoglucosan in PXP samples were lower than 70% (average of 51.9–63.3%). The concentration ratios of 2-methyltetrols and levoglucosan in the gas phase to those in the particle phase were often close to or higher than unity in summer, indicating that these polar species are semi-volatile and their G/P partitioning should be considered when applying particle-phase data for source apportionment. The Kp,OMm values of 2-methyltetrols had small variability in summer Denver, which was ascribed to large variations in concentrations of particulate organic matter (5.14 ± 3.29 μg m–3) and small changes in ambient temperature (21.8 ± 4.05 °C). The regression between log Kp,OMm and log Kp,OMt suggested that the absorptive G/P partitioning theory could reasonably predict the measured G/P partitioning of levoglucosan in ambient samples.

Identification of the sources and geographic origins of black carbon using factor analysis at paired rural and urban sites

Black carbon particles, composed of forms of elemental carbon (EC), contribute significantly to regional and global warming. The origins of EC were examined in southeastern Canada as part of a source apportionment study using positive matrix factorization (PMF), performed on long-term PM2.5 chemical speciation data collected at two paired rural and urban sites. Comparisons of the urban and rural sites revealed a previously unrecognized EC-rich factor that accounted for 41-56% of the total EC in this region. This factor was characterized by the more thermally stable EC fractions that exhibit strong light absorption characteristics. While these EC fractions are often attributed to local diesel emissions, this interpretation was rejected for several reasons. The EC-rich factor was present in similar temporal patterns at both the high-traffic urban and low-traffic rural sites across this 600 km region. The geographic origins of the EC-rich factor were found to be Ohio and Western Pennsylvania regions with heavy industry and multiple coal-based electrical generating stations. The direct radiative forcing due to this EC-rich factor was roughly estimated to be +0.2 W m(-2), which represented a substantial portion of the aerosol induced warming in the region. Thus, this region was impacted by an important unidentified source of EC associated with long-range transport.

Positive matrix factorization of a 32-month series of daily PM2.5 speciation data with incorporation of temperature stratification

This study presents source apportionment results for PM2.5 from applying positive matrix factorization (PMF) to a 32-month series of daily PM2.5 compositional data from Denver, CO, including concentrations of sulfate, nitrate, bulk elemental carbon (EC) and organic carbon (OC), and 51 organic molecular markers (OMMs). An optimum 8-factor solution was determined primarily based on the interpretability of the PMF results and rate of matching factors from bootstrapped PMF solutions with those from the base case solution. These eight factors were identified as inorganic ion, n-alkane, EC/sterane, light n-alkane/polycyclic aromatic hydrocarbon (PAH), medium alkane/alkanoic acid, PAH, winter/methoxyphenol and summer/odd n-alkane. The inorganic ion factor dominated the reconstructed PM2.5 mass (sulfate + nitrate + EC + OC) in cold periods (daily average temperature < 10 °C; 43.7% of reconstructed PM2.5 mass) whereas the summer/odd n-alkane factor dominated in hot periods (> 20 °C; 53.1%). The two factors had comparable relative contributions of 26.5% and 27.1% in warm periods with temperatures between 10 °C and 20 °C. Each of the seven factors resolved in a previous study (Dutton et al., 2010b) using a 1-year data set from the same location matches one factor from the current work based on comparing factor profiles. Six out of the seven matched pairs of factors are linked to similar source classes as suggested by the strong correlations between factor contributions (r = 0.89 - 0.98). Temperature-stratified source apportionment was conducted for three subsets of the data in the current study, corresponding to the cold, warm and hot periods mentioned above. The cold period (7-factor) solution exhibited a similar distribution of reconstructed PM2.5 mass as the full data set solution. The factor contributions of the warm period (7-factor) solution were well correlated with those from the full data set solution (r = 0.76 - 0.99). However, the reconstructed PM2.5 mass was distributed more to inorganic ion, n-alkane and medium alkane/alkanoic acid factors in the warm period solution than in the full data set solution. For the hot period (6-factor) solution, PM2.5 mass distribution was quite different from that of the full data set solution, as illustrated by regression slopes as low as 0.2 and as high as 4.8 of each matched pair of factors across the two solutions.

Fine particulate matter and visibility in the Lake Tahoe Basin: chemical characterization, trends, and source apportionment

Speciated PM2.5 (particulate matter with an aerodynamic diameter<or=2.5 microm) data has been collected for about 20 yr (1990-present) at a rural location in the Lake Tahoe Basin (Bliss State Park) and about 15 yr (1989-2004) at an urban site in South Lake Tahoe. The Bliss State Park site is representative of the Desolation Wilderness, a Class I air quality area with visibility protection under the Clean Air Act. Carbonaceous aerosol dominated reconstructedfine mass at both sites, with 58% at Bliss State Park (BLIS) and 68% at South Lake Tahoe (SOLA). Fine mass at SOLA is 2.5 times that at BLIS, mainly due to enhanced organic and elemental carbon (OC and EC). SOLA experiences a winter peak in PM25 mainly due to OC and EC from residential wood combustion, whereas BLIS experiences a summer peak in PM2.5 mainly due to OC and ECfrom wildfires. Carbonaceous aerosol dominates visibility impairment, causing about 1/2 the reconstructed aerosol light extinction at BLIS and 70% at SOLA. Trend analysis (1990-2009) showed statistically significant decreases in aerosol extinction at BLIS on 20% best and 60% middle visibility days and statistically insignificant upward trends on 20% worst days. SOLA (1990-2003) showed statistically significant decreases in aerosol extinction for all day categories, driven by decreasing OC and EC. From the regional haze rule baseline period of 2000-2004 until 2005-2009, BLIS saw 20% best days improving and 20% worst days getting worse due to increased wildfire effects. Receptor modeling was performed using positive matrix factorization (PMF) and chemical mass balance (CMB). It confirmed that (1) biomass burning dominanted PM25 sources at both sites with increasing importance over time; (2) low combustion efficiency burning accounts for most of the biomass burning contribution; (3) road dust and traffic contributions were much higher at SOLA than at BLIS; and (4) industrial combustion and salting were minor sources.