https://research.birmingham.ac.uk/portal/files/22609805/Review_Particle_Number_Size_Distributions_FINAL_V5_POSTPROOF.pdf

Review: Particle number size distributions from seven major sources and implications for source apportionment studies

. In this study, the positive matrix factorization (PMF) receptor model (version 5.0) was used to identify and quantify major sources contributing to particulate matter (PM) number concentrations, using PM number size distributions in the range of 13 nm to 10 µm combined with several auxiliary variables, including black carbon (BC), elemental and organic carbon (EC/OC), PM mass concentrations, gaseous pollutants, meteorological, and traffic counts data, collected for about 9 months between August 2014 and 2015 in central Los Angeles, CA. Several parameters, including particle number and volume size distribution profiles, profiles of auxiliary variables, contributions of different factors in different seasons to the total number concentrations, diurnal variations of each of the resolved factors in the cold and warm phases, weekday/weekend analysis for each of the resolved factors, and correlation between auxiliary variables and the relative contribution of each of the resolved factors, were used to identify PM sources. A six-factor solution was identified as the optimum for the aforementioned input data. The resolved factors comprised nucleation, traffic 1, traffic 2 (with a larger mode diameter than traffic 1 factor), urban background aerosol, secondary aerosol, and soil/road dust. Traffic sources (1 and 2) were the major contributor to PM number concentrations, collectively making up to above 60 % (60.8–68.4 %) of the total number concentrations during the study period. Their contribution was also significantly higher in the cold phase compared to the warm phase. Nucleation was another major factor significantly contributing to the total number concentrations (an overall contribution of 17 %, ranging from 11.7 to 24 %), with a larger contribution during the warm phase than in the cold phase. The other identified factors were urban background aerosol, secondary aerosol, and soil/road dust, with relative contributions of approximately 12 % (7.4–17.1), 2.1 % (1.5–2.5 %), and 1.1 % (0.2–6.3 %), respectively, overall accounting for about 15 % (15.2–19.8 %) of PM number concentrations. As expected, PM number concentrations were dominated by factors with smaller mode diameters, such as traffic and nucleation. On the other hand, PM volume and mass concentrations in the study area were mostly affected by sources with larger mode diameters, including secondary aerosols and soil/road dust. Results from the present study can be used as input parameters in future epidemiological studies to link PM sources to adverse health effects as well as by policymakers to set targeted and more protective emission standards for PM.

/pdf/source-apportionment-of-ambient-particle-number-3hpiquobp2.pdf

Source apportionment of ambient particle number concentrations in central Los Angeles using positive matrix factorization (PMF)

The understanding of rapidly evolving concentrations of particulate matter (PMC) at signalised traffic intersections (TIs) is limited, but it is important for accurate exposure assessment. We performed “mobile” and “fixed-site” monitoring of size-resolved PMCs in the 0.25–34 μm range at TIs. On-road mobile measurements were made inside a car under five different ventilation settings on a 6 km long round route, passing through 10 different TIs. Fixed-site measurements were conducted at two types (3- and 4-way) of TIs. The aims were to assess the effects of different ventilation settings on in-vehicle PMCs and their comparison during delay conditions at the TIs with those experienced by pedestrians while crossing these TIs. We also estimated the zone of influence (ZoI) for PM10, PM2.5 and PM1 under different driving conditions and fitted the probability distribution functions to fixed-site data to understand the concentration and exposure dynamics of coarse and fine particles around the studied (3- and 4-way) TIs. The fine particles (PM2.5) showed a strong positive exponential correlation with the air exchange rates under different ventilation settings compared with coarse particles (PM2.5–10) showing an opposite trend. This suggested that the ventilation system of the car was relatively more efficient in removing coarse particles from the incoming outside air. On-road median PM10, PM2.5 and PM1 during delays at the TIs were ∼40%, 16% and 17% higher, respectively, compared with free-flow conditions on the rest of the route. About 7% of the average commuting time spent during delay conditions over all the runs at the TIs corresponded to 10, 7 and 8% of the total respiratory deposition dose (RDD) for PM10, PM2.5 and PM1, respectively. The maximum length of the ZoI for PM2.5 and PM1 was highest at the 4-way TI and the maximum length of the ZoI for PM10 was highest at the 3-way TI. The on-road average RDD rate of PM10 inside the cabin when windows were fully open was up to ∼7-times that for pedestrians at the TIs.

/pdf/concentration-dynamics-of-coarse-and-fine-particulate-matter-4b1afjizqm.pdf

Concentration dynamics of coarse and fine particulate matter at and around signalised traffic intersections

Airborne particulate size distribution data acquired in Pittsburgh from July 2001 to June 2002 were analyzed as a bilinear receptor model solved by positive matrix factorization (PMF). The data were obtained from two scanning mobility particle spectrometers and an aerodynamic particle sampler with a temporal resolution of 15 min. Each sample contained 165 size bins from 0.003 to 2.5 μm. Particle growth periods in nucleation events were identified, and the data in these intervals were excluded from this study so that the size distribution profiles associated with the factors could be regarded as sufficiently constant to satisfy the assumptions of the receptor model. The values for each set of five consecutive size bins were averaged to produce 33 new size intervals. Analyses were made on monthly data sets to ensure that the changes in the size distributions from the source to the receptor site could be regarded as constant. The factors from PMF could be assigned to particle sources by examination of the number size distributions associated with the factors, the time frequency properties of the contribution of each source (Fourier analysis of source contribution values), and the correlations of the contribution values with simultaneous gas phase measurements (O 3 , NO, NO 2 , SO 2 , CO) and particle composition data (sulfate, nitrate, organic carbon/ elemental carbon). Seasonal trends and weekday/weekend effects were investigated. Conditional probability function analyses were performed for each source to ascertain the likely directions in which the sources were located. Five factors were separated. Two factors, local traffic and nucleation, are clear sources, but each of the other factors appears to be a mixture of several sources that cannot be further separated.

Mining airborne particulate size distribution data by positive matrix factorization : Particulate matter supersites

https://research.birmingham.ac.uk/portal/files/27993268/Source_Apportionment_of_Wide_Range_Particles_ACCEPTED_MANUSCRIPT.pdf

Source apportionment of wide range particle size spectra and black carbon collected at the airport of Venice (Italy).

Environmental context Identifying the sources responsible for air pollution is crucial for reducing the effect of the pollutants on human health. The sources of the pollutants were found here by applying two mathematical models to data consisting of particle size distribution and chemical composition data. The identified sources could be used as the basis for controlling or reducing emissions of air pollution into the atmosphere. Abstract Particulate matter is common in our environment and has been linked to human health problems particularly in the ultrafine size range. In this investigation, the sources of particles measured at two sites in Brisbane, Australia, were identified by analysing particle number size distribution data, chemical species concentrations and meteorological data with two source apportionment models. The source apportionment results obtained by positive matrix factorisation (PMF) and principal component analysis–absolute principal component scores (PCA–APCS) were compared with information from the gaseous chemical composition analysis. Although PCA–APCS resolved more sources, the results of the PMF analysis appear to be more reliable. Six common sources were identified by both methods and these include: traffic 1, traffic 2, local traffic, biomass burning and two unassigned factors. Thus motor vehicle related activities had the greatest effect on the data with the average contribution from nearly all sources to the measured concentrations being higher during peak traffic hours and weekdays. Further analyses incorporated the meteorological measurements into the PMF results to determine the direction of the sources relative to the measurement sites, and this indicated that traffic on the nearby road and intersection was responsible for most of the factors. The described methodology that utilised a combination of three types of data related to particulate matter to determine the sources and combination of two receptor models could assist future development of particle emission control and reduction strategies.

Sources of ultrafine particles and chemical species along a traffic corridor: comparison of the results from two receptor models

Particulate matter is common in our environment and has been linked to human health problems particularly in the ultrafine size range. A range of chemical species have been associated with particulate matter and of special concern are the hazardous chemicals that can accentuate health problems. If the sources of such particles can be identified then strategies can be developed for the reduction of air pollution and consequently, the improvement of the quality of life. In this investigation, particle number size distribution data and the concentrations of chemical species were obtained at two sites in Brisbane, Australia. Source apportionment was used to determine the sources (or factors) responsible for the particle size distribution data. The apportionment was performed by Positive Matrix Factorisation (PMF) and Principal Component Analysis/Absolute Principal Component Scores (PCA/APCS), and the results were compared with information from the gaseous chemical composition analysis. Although PCA/APCS resolved more sources, the results of the PMF analysis appear to be more reliable. Six common sources identified by both methods include: traffic 1, traffic 2, local traffic, biomass burning, and two unassigned factors. Thus motor vehicle related activities had the most impact on the data with the average contribution from nearly all sources to the measured concentrations higher during peak traffic hours and weekdays. Further analyses incorporated the meteorological measurements into the PMF results to determine the direction of the sources relative to the measurement sites, and this indicated that traffic on the nearby road and intersection was responsible for most of the factors. The described methodology which utilised a combination of three types of data related to particulate matter to determine the sources could assist future development of particle emission control and reduction strategies.

Megan Wust

Papers

Sources of ultrafine particles and chemical species along a traffic corridor: comparison of the results from two receptor models

Sources of ultrafine particles and chemical species along a traffic corridor : comparison of the results from two receptor models