658 Air Pollution Monitoring, Simulation and Control · from an enclosed multi-storey car park and...

Determination of sources of PAH in urban street

dusts by a neural networking technique

G. Gray,* P. Robotham,* R. Gill,* P. Corcoran*

"School of Environmental and Applied Sciences, University of

School of Engineering, University of Derby, Derby, UK

Abstract

Street dust samples collected from a network of urban, suburban and industriallocations within Derby were analysed for PAH concentrations by reverse phaseHPLC and fluorescence detection. Profiles of 12 PAH were determined foreach sample and standardised profiles generated for source categoriesincluding : engine oil, petrol vehicle exhaust and diesel vehicle exhaust. Thevehicle exhaust profiles were derived from weathered dust samples collectedfrom an enclosed multi-storey car park and a bus depot for petrol and dieselrespectively. Engine oil profiles were established, by analysing crankcase oilsfrom a range of vehicle types. Other source profiles were obtained from theliterature. Source apportionment was conducted on all the samples using aneural network. Vehicle exhaust emissions were found to be the dominantsources of PAH in street dusts throughout the city, including pedestrianisedareas of the city centre. Marked differences in the contribution of petrol anddiesel sources were observed over short distances, reflecting the comparativeusage of sites by heavy goods vehicles and public service vehicles.

Introduction

Polycyclic Aromatic Hydrocarbons (PAH) are thought to be formed asprecursors to soot formation during the combustion of carbonaceous fuels andcould therefore be present in the emissions of any combustion process. Therelative abundance of an individual PAH species being dependant upon theparticular conditions under which it was formed. If individual engines orfurnaces of a given type have broadly similar operating conditions, they shouldalso emit similar profiles of PAH. PAH based source profiles are potentiallysuitable for apportioning contributions between different types of combustionprocesses.

Transactions on Ecology and the Environment vol 8, © 1996 WIT Press, www.witpress.com, ISSN 1743-3541

658 Air Pollution Monitoring, Simulation and Control

PAH were initially used in hydrological studies, as markers for combustionsources in general and urban surface drainage e.g. Evans*. Source profileswere developed based on the abundance of selected PAH to determine therelative importance of potential transport related sources of PAH with respectto the contamination of soil and water systems e.g. Singh et alA PAH basedsource profiles are now being used increasingly for the apportionment ofatmospheric pollution e.g. Rogge et al/, Sharma & Patil* . One of the mostwidely used approaches to the apportionment problem is the use of chemicalmass balance models (CMB) e.g. Chow et al/Chemical mass balance models are based on the assumption of proportionalcontinuity within the profiles of all the selected sources. This is not to say thatthere is necessarily conservation of mass between the point of discharge andthe receptor site. Only that all the constituent species within the source profileare modified equally by any loss processes, such as deposition, dilution,reactions, etc. that act on the emissions. This is a reasonable assumption whenusing profiles based on concentrations of elemental species on airborneparticulates. However, for profiles based on hydrocarbons this assumptiononly approximates to being valid for a short period following their discharge.This applies equally to PAH, therefore either the receptor site must be locatedclose to all of the suspected sources or terms must be incorporated into themodel to account for the effect of loss processes on the source profiles as theyare perceived at the receptor site e.g. Pistikopoulos .

The central relationship within a CMB, as expressed below, is theassertion that the mass of any species at the receptor site is the linear sum ofthe contributions from all sources.

Where 'Q is the concentration of species i at the receptor site.

&jj is the concentration of species i in the emissions of source j.

Sj is the fraction of species i at the receptor site that was contributed bysource j.

If more than one species is used, then the single terms above arereplaced by matrices of values and the equation can be rewritten in matricalform.

C = AS (2)Where

C is the vector (n,l) of measured concentrations.

A is the matrix (n,p) of p source profiles, each containing n species.


Air Pollution Monitoring, Simulation and Control 659

S is the vector (p,l) of source contributions.

The contribution of material to the receptor site from each source is determined

by solving Equation 2 for S. Unfortunately PAH are normally only present inenvironmental media at extremely low concentrations and this is especiallytrue of atmospheric samples. Consequently individual species are often presentat concentrations below the limit of detection when analysed and the resultingprofiles are incomplete. If the CMB is solved under these conditions byregression or iterative techniques then the performance of the model can beseverely impaired and the only option may be to collect a larger sample.The aim of this work was to investigate the potential of solving such a CMBmodel using a neural network and this paper represents the initial stage in thedevelopment of a method that would be applicable to small scale studies inwhich only a limited number of samples were collected.

Methodology

Street dust samples were collected from pedestrian walkways within the cityof Derby (UK), at sites currently used to monitor kerbside pollutant levels. Themajority of these were adjacent to roads but some where located withinpedestrianised areas. In addition to this multiple reference samples werecollected from potential sources of PAH to the street dusts.

Comparative studies of street dusts from asphalt paved and concretepaved surfaces have found weathered asphalt to make only a very minorcontribution to the total PAH burden of street dusts e.g. Takada et al/. A meansource profile was not established for asphalt, for use in this study. A meancrankcase oil source profile was produced by determining the PAH content ofa range of six petrol fuelled vehicles and three diesel fuelled vehicles.

Particulate samples were collected from the internal, vertical surfacesof a multi-storey carpark (MSCP). The MSCP was set back from major roadsand the height restricted entrance limited access to just cars and motorcycles.This car exhaust source profile is composed of emissions from catalyticallyregulated and unregulated petrol vehicles of a wide range of ages and a small(unqualified) contribution from light diesel sources. The samples arecomposed of particles which were deposited over a period of four weeks,between the surface being swept clean and the sample being collected. Theoriginal emissions will have been modified to varying extents by several ofthe loss processes acting on the receptor site profiles, but the subdued lightlevels in the MSCP should have limited losses due to photolysis.

Initial attempts to obtain passenger service vehicle (PSV) exhaustsamples from the main bus depot in Derby were abandoned when it was sincethe exhaust profile of the buses warming their engines prior to leaving wasrecognised to be markedly different to the exhaust profile of the engines when



hot. In the absence of a suitable site for the collection of samples, values wereinstead selected from the literature. Khalili et al.® collected four, three hourlong, time averaged air samples from an indoor bus parking garage in Chicago(USA) using a Hi-Vol. sampler with filter and PUF collection media. The PAHcontent was determined by GC-MS and the average concentrations of 20 PAHspecies were reported and this was adapted to a profile of the same nine PAHspecies as the other sources. This is a profile of fresh total airborne PSVexhaust, but in the absence of a more appropriate alternative it was used as adeposited dust profile.

All samples were soxhlet extracted into 50 ml of Dichloromethane. Theextract was rotary evaporated to near dryness, before being taken up in 2 ml ofPentane. A fractionation column composed of 3 g 100 mesh silica ( 3% wateradded ) overlain by 2 g activity III alumina ( 6% water added ) was packed wetin 30% dichloromethane in pentane and the column was washed through withpentane prior to use. The sample was added to the column and 15 ml ofPentane followed by 40 ml of 30% Dichloromethane in Pentane solution runthrough the column. The first 10 ml of elutant was discarded. The remaining45 ml was rotary evaporated to near dryness and taken up in 5 ml ofAcetonitrile and spiked with Phenanthrene, Chrysene, Benzo[a]pyrene andCoronene.

Determination of the mass of PAH in the sample was achieved byCrompack GRAS HPLC using a Phenomenex 125 x 3.250 mm Envirosep-ppreverse phase C^ micro column in conjunction with Schimadzu RF5001Fluorescence detector fitted with a flow cell. Individual PAH were identifiedby a retention index value based on the position of the PAH in thechromatogram relative to the spike species listed above. A total of 12 PAHspecies were quantified in each sample. These were Phenanthrene (PA),Flouranthene (FL), Pyrene (PYR), Triphenylene (TRI), Benzoflourene (BF),Chrysene (CHR), Benzo[e]pyrene (BeP), Benzo[a]pyrene (BaP),Dibenz[a,h]anthracene (DBA), Benzo[ghi]perylene (BghiP) and Coronene(COR). All solvents were HPLC grade and were used without any pre-treatment.

Normalised profiles were prepared for each sample by expressing theabundance of each PAH species as a fraction of the total mass of the 12species for that sample. As the number of species within each profile wasreduced, the profiles were recalculated accordingly. It was with this in mindthat values were initially reported as molalities, so that equal weight is attachedto the abundance of each PAH species.

Neural Network Development

Only nine of the quantified PAH species were used in the modelling exercise.Triphenylene and Benzo[e]Pyrene were not detected in most of the referencesamples and were not used as a result of this. When present, Phenanthrene



accounted for a substantial proportion of the total PAH in the referencesamples. Therefore variations in the abundance of Phenanthrene exerted adisproportionally large influence over the normalised values of the otherspecies in the profile. As evaporation could be expected to result inconsiderable variation in the concentration of Phenanthrene in street dustsamples it was not included in the profile. The new smaller profiles wererecalculated to give species abundance as a proportion of the total the ninePAH.

The target output vector S? of source contributions was constructedfrom randomly generated combinations of the three sources; oil, car exhaust

and PSV exhaust. A corresponding input vector C of receptor profiles wascalculated as the product of the target output vector and the matrix of source

profiles A.

C = ASj (3)

A total of 300 training samples were generated. 270 samples were used totrain the model with 30 randomly selected cases used as unseen data to test theperformance of the trained network.

This network performed well when used on the simulated unseen data,but was less successful in coping with the variability of the real data and thenetwork outputs frequently became saturated. In order to determine the likelysuccess of using neural networks to solve this class of problem, two alterationswere made;

1. The profile was reduced in size to just five species.The final profiles contained just five PAH, these were Fluoranthene,

Pyrene, Benzanthracene, Benzo[a]pyrene and Benzo[ghi]perylene. The revisedsource profiles are shown in Table 1

PetrolDieselOil

FL0.4421680.202382D375655

PYR0.2872420.0354360.362517

Bk0.0302470.3189450.047645

B[a]P0.0121220.249776

0

BfeTOP0.228220.1434610.214183

Total1.000001.000001.00000

Table 1: Normalised Source Profiles

2. The training set was constructed so that it reflected the nature of the realdata more closely.

The mean value for each of the five species in the normalised profilesof the real data were calculated and the average deviation about the meandetermined. The training data was again constructed from randomly generatedvalues as described above, except this time the final values were adjusted by arandomly generated error term. This term was the product of a random



number, the standard error of determination for that species and a term for theaverage deviation about the mean of values for that species within the real datawhich was weighted to reflect the abundance of each source. The error termcould be either a positive or a negative quantity. Any negative values generatedthrough the use of the error term were set equal to zero. As a result the networkwas exposed to a higher proportion of noise within the training data and anincreased frequency of zero values.The option of restricting the magnitude of values for each species in thetraining data to that found in the real data was considered, but the option wasnot pursued as it was feared that it could severely impair the ability of thenetwork to generalise when presented with unusual cases.Again 300 training sets were prepared and 30 of these used as unseen data withwhich to test the performance of the model. The network itself wasrestructured to accommodate the re-sizing of the input and output vectors, butotherwise remained unchanged. The basic structure of the network issummarised in Table 2 and Figures 1 The network was trained over 100 000

Table 2: Structure of Neural Network Used For Analysis of Dust Samples

LayerInput NeuronesHidden NeuronesOutput Neurones

Number of Neurones

543

Activation FunctionNone

SigmoidLinear Scaled (0-1)

Input =Vector

InputLayer

HiddenLayer

OutputLayer

OutputVector

Figure 1: Architecture of Neural Network



training cycles and the weights adjusted by standard back propagation at theend of each exposure to the complete training set. The network was shown theunseen data in feed forward mode once every 5 000 cycles. During thetraining stage the learning rate of the network was set to 0.9, with a momentumof value 0.6.

Performance of Network with Unseen Training Data

The absolute error in the prediction of source contributions (the targetoutput vector) from the unseen training data was calculated for each source andexpressed as a fraction of the target value. The total predicted contributionsfrom all three sources accounted for 100% ± 0.01% of the input PAH.However the predicted contribution from individual sources did not necessarilymatch the predicted target value. Consequently any error in the prediction ofone source contribution was reflected in the value apportioned to the othersources. The network generated after 30 000 training cycles was selected toprocess the sample profiles. Further training beyond 30 000 cycles did notsubstantially improve the predictive ability of the network, but may haveimpaired it's ability to generalise when presented with unusual cases.Inspection of the performance of the network after 30 000 training cycles asexpressed in Figure 2 reveals that over half the petrol and diesel exhaustcontributions are predicted to a degree of accuracy, no worse than might havebeen achieved under CMB using linear least squares regression.

2.5

S»f%co

2

1.5

0.5

0.1 0.2 0.3 0.4 0.5

Percentile

0.6 0.7

. Ftetrol —« - Diesel. . Oil

Figure 2: Predictive Error Associated With Each Source Type


Table 3: Breakdown of Source Apportionment Results by Site

ON

Normalised Profiles for Street Dust Samples

PAHPAFLPYRTRIBFBACHRBePBaPDBABghiPCOR

City Centre Sites

Pedestrianised Areas

Site 180.1610.0590.1820.0180.0280.02301380.0670.0190.0200.0210.266

Site 190.1820.0360.0530

0.0050

0.0900.0370.0090.0110.0050.572

Mixed Traffic andPSV

Site 250.2090.0520.0560.0300

0.0170.1640.2200.0350.1020.0300.086

Site 360.2070.12100

0.0440.0380.2890.1120.0090.0050.0620.114

Mixed Suburban/Industrial Sites

Mixed Traffic

Site 20.0630.1600.1360.0180.0100.0120.0870.05800

0.0280.429

Site 120.1560.1110.0470

0.0230.0130.2140.1070.0080.0230.1010.196

Mixed Traffic and PSV

Site 50.0970.0280.0630.0110.0520.0250.1680.0810.0140

0.1210.341

Site 110.1350.0710.1380.0210.0080.0190.1320.1770.0290.0100.0690.193

Result of Apportionment by Network Based on 5 PAH ProfileSource

Car exhaustPSV exhaust

Oil

Site 1848%9%43%

Site 1963%7%30%

Site 2548%24%27%

Site 3660%33%7%

Site 268%2%17%

Site 1279%4%17%

Site 554%18%29%

Site 1155%10%36%

o

o

I:

"

o

Cun§



Discussion

The trained network was applied to predict the PAH contributions and theresults are given in Figure 2. It is evident that car exhaust is predicted as themajor source of PAH to all sites. Although a common range of contributionsare attributed to car exhaust at all sites within the city centre there are markeddifferences between the contribution from crankcase oil and heavy dieselvehicles exhaust, as represented by the PSV source. The pedestrianised areaswithin the city centre operate a system of limited access to delivery and servicevehicles and with only limited exhaust emissions in the immediate vicinity,fugitive oil could represent a significant source of PAH at these sites. The citycentre streets contain the greatest concentration of PSVs in the city, wherethey account for 15-30% of vehicles during the day. The PSV exhaustcontribution is correspondingly high. PSVs represent less than 5% of traffic atmost other sites in the city and this fact is also reflected in the apportionmentof the PAH at suburban sites.Overall the network has produced results that conform well to the situation onthe ground in so far as it is understood. However further refinement of both themodel and in particular the source profiles will be required in order to reducethe error associated with the predictions to a more acceptable level.

Conclusions

As an alternative technique for resolving the source contribution vector of achemical mass balance model, neural networks offer some potentialadvantages over traditional techniques. In particular the ability toaccommodate the non-linear relationship between PAH contribution andsource type and apportion sources using incomplete receptor sample profiles.This would have particular applications to the apportionment of trace organicswithin atmospheric samples.

Acknowledgement

The authors wish to acknowledge the assistance of John Anglsea in theconstruction and operation of the neural network.

References

1. Evans K., Gill R., Robotham P. The Source , Composition and Flux ofPolycyclic Aromatic Hydrocarbons in Sediments of the River Derwent,Derbyshire UK, Water Soil Air Pollut., 1990,51, 1-12.



2. Singh A., Gin M., Ni F., Christensen E. A Source-Receptor Method ForDetermining Non-Point Sources of PAHs to the Milwaukee Harbour Estuary,Wat. Sci. Tech., 1993,28, 8-9, 91-102.

3. Rogge W., Hildemann L., Mauzurek M., Cass G., Simonelt B. Sources offine Organic Aerosol. 3. Road Dust, Tire Debris and Organometallic BrakeLining Dust: Roads as Sources and Sinks, Environ. Set. TechnoL, 1993, 27, 9,1892-1904.,

4. Sharma V.K. & Patil R.S. Chemical Mass Balance Model for SourceApportionment of Aerosols in Bombay, Environ. Monit. and Ass., 1994, 29,75-88.

5. Chow J. C., Liu C.S., Cassmassi J., Watson J.G., Lu Z., Pritchett L.C. ANeighbourhood-Scale Study of PM^ Source Contributions in Rubidoux,California, Atmos. Environ., 1992,26A, 4, 693-706.

6. Pistikopoulos P., Masclet P. & Mouvier G. A Receptor model Adapted toReactive Species: Polycyclic Aromatic Hydrocarbons; Evaluation of SourceContributions in an Open Urban Site-I. Particle Compounds, Atmos. Environ.,1990, 24A,5, 1189-1197.

7. Takada H.,Onda T., OguraN. Determination of Poly cyclic AromaticHydrocarbons in Urban Street Dusts and Their Source Materials by CapillaryGas Chromatography, Environ. Sci. Technol, 1990, 24, 8, 1179-1186.

8. Khalili N.,Scheff P., Holsen T. PAH Source Fingerprints for Coke Ovens,Diesel and Gasoline Engines, Highway Tunnels and Wood CombustionEmissions, Atmos. Environ.,1995, 29,4, 533-542.


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