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Particle number concentration near road traffic in Amsterdam (the Netherlands): Comparison of standard and real-world emission factors
Accepted Manuscript Particle number concentration near road traffic in Amsterdam (the Netherlands): comparison of standard and real-world emission factors M.P. Keuken, M. Moerman, M. Voogt, P. Zandveld, H. Verhagen, U. Stelwagen, D. Jonge de PII:
S1352-2310(16)30180-7
DOI:
10.1016/j.atmosenv.2016.03.009
Reference:
AEA 14491
To appear in:
Atmospheric Environment
Received Date: 14 December 2015 Revised Date:
2 March 2016
Accepted Date: 4 March 2016
Please cite this article as: Keuken, M.P., Moerman, M., Voogt, M., Zandveld, P., Verhagen, H., Stelwagen, U., Jonge de, D., Particle number concentration near road traffic in Amsterdam (the Netherlands): comparison of standard and real-world emission factors, Atmospheric Environment (2016), doi: 10.1016/j.atmosenv.2016.03.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Particle number concentration near road traffic in Amsterdam (the Netherlands): comparison of standard and real-world emission factors Keuken M.P.a,b, Moerman M.a, Voogt M.a, Zandveld P.a, Verhagen, H.a, Stelwagen, U.a*, Jonge de D.c a
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: TNO, Netherlands Organization for Applied Research, Utrecht, the Netherlands
: Centre for Atmospheric and Instrumentation Research, University of Hertfordshire, Hatfield, United Kingdom c
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: GGD-Amsterdam, Public Health Authority, Amsterdam, the Netherlands * Corresponding author: [email protected]
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ABSTRACT
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In this study, NOx and particle number concentration (PNC) at an urban background and a
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traffic location were measured in the city of Amsterdam (the Netherlands). Modelled and
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measured contributions to NOx and PNC at the traffic location were used to derive real-
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world PN emission factors for average urban road traffic. The results for NOx were
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applied to validate our approach. The real-world PN emission factors (#.km-1) were
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2.9E+14 (urban road) and 3E+14 (motorway). These values were at least a factor eight
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higher than dynamometer-based PN emission factors from COPERT 4 and HBEFA
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databases. The real-world PN emission factors were used to model the contribution to PNC
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near road traffic in 2014. This was two to three times higher than the PNC urban
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background along urban roads over 20,000 vehicles per day and near motorways. The
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discrepancy between dynamometer-based and real-world emission factors demonstrates the
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need for more PNC observations to assess actual PN emissions from road traffic.
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Keywords: particle number; road traffic; emission factors
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1.
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INTRODUCTION
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It is well known that exposure to emissions from road traffic is associated with increased
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health risks (e.g. HEI, 2009). In urban areas are traffic emissions a major source of
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ultrafine particles, which are particles with a diameter smaller than 100 nm (Kumar et al.,
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2011). The particle number concentration (PNC) is considered to be representative for the
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concentration of ultrafine particles (Chow and Watson, 2007). Traffic-based ultrafine
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(e.g. lubrication oil, combustion products and sulphur compounds). The latter particles are
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formed when exhaust emissions cool in ambient air. Recently, the WHO (2013) concluded
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that “there is considerable evidence that ultrafine particles can contribute to the health
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effects of particulate matter”. Data on causal links between exposure to ultrafine particles
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and adverse health effects were however too scarce to recommend a specific air quality
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guideline. Recent studies (Klompmaker et al., 2015; Puustinen et al., 2007) indicate a
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relatively large temporal and spatial variability of PNC in urban areas and consequently, a
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relatively large variability in exposure to PNC of the urban population. Considering the
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variability in PNC, dispersion modelling (Holmes and Morawska, 2006; Vardoulakis et al.,
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2007) is preferable to land-use regression (LUR) modelling (Klompmaker et al., 2015) to
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estimate PNC near road traffic in urban areas. It is recognised that accurate emission
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factors for particle number (PN) from road traffic is a main source of uncertainty in
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dispersion modelling of PN (Holmes and Morawska, 2006).
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Traffic-related emission factors are generally obtained by the following methods (Franco et
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al., 2013):
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Standard emission factors: These are derived from measurements in exhaust emissions during test cycles simulating different driving conditions (urban,
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motorway) at a dynamometer test facility. In Europe, exhaust emissions are
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regulated according to “Euro classes”, which specify emission limits for
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regulated compounds for the year that a type of vehicle enters the European
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road traffic: Euro 0 (pre-1992), Euro 1 (1992), Euro 2 (1996), Euro 3 (2000),
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Euro 4 (2005), Euro 5a/b (2009/2011) and Euro 6 (2014). Dynamometer-based
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emission factors for PN have been published for Euro 1 to 3 vehicles (COPERT
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4, 2014) and Euro 1 to 6 vehicles (HBEFA, 2015). This is related to total and non-volatile PN. In the latter case, exhaust emissions are heated to 3500C, volatile particles are removed, and followed by PN measurements of particles larger than 23 nm. Following this procedure, there is an emission limit of
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6E+11 non-volatile PN per kilometre for light duty vehicles (EC, 2012): from
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2011 for diesel (Euro 5b and 6) and from 2014 for petrol (Euro 6). The rationale
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to
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emission data as compared to total PN emission data (Glechaskiel et al., 2012).
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The latter strongly depends on dilution conditions, after-treatment devices,
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types of fuel and lubricant, among others. Under real-world conditions,
regulate non-volatile PN (and not total PN) is the higher reproducible
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formed in ambient air (Kumar et al., 2011). Consequently, large differences
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may be expected between standard and real-world PN emission factors and thus
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the need to develop real-world emission factors. It is noted that dynamometer-
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based PN emission factors for pre-Euro 5b and 6 vehicles were derived from
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dynamometer measurements of total PN without a particle size threshold of 23
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nm and without removal of volatile particles;
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Real-world emission factors: Measurements are carried out at road-side locations (up and down wind of motorways; in road tunnels; at urban
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background and street canyon locations) or following individual vehicles by
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portable emission measurement systems (PEMS). These measurements are
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combined with data on traffic and meteorology as input for inverse modelling to
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obtain emission factors (Kumar et al., 2011). It is assumed that secondary
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particle formation is mainly important during the first few seconds after exhaust
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emissions and therefore, dispersion of PN in urban areas is treated as a
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conservative pollutant (Ketzel and Berkowicz, 2004). It is noted that real-world
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emission factors are representative of average road traffic, while standard
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emission factors are linked to particular vehicle types.
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Real-world emission factors may deviate from standard emission factors as real-world
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driving conditions may differ from test cycles, real-world road traffic includes vehicles
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with different levels of maintenance and related emissions, or exhaust emissions may be
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deliberately reduced during a test cycle (EPA, 2015).
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In the Netherlands, real-world NOx emission factors are derived by combining
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dynamometer testing with on-road PEMS measurements. The annually updated emission
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factors of NOx for road traffic in the Netherlands were applied in this study (www.rivm.nl).
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In contrast for PN emissions, no dynamometer testing or on-road measurements were
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conducted in the Netherlands. Dynamometer-based emission factors for PN from COPERT
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and HBEFA were therefore used in this study. However, due to the lack of standardized
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testing conditions for pre-Euro 5/6 vehicles and the limited number of tested vehicles, the
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available emission factors for PN are considered “first estimates”.
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The aim of this study was to derive real-world emission factors for PN for average urban
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traffic and, subsequently, to model the contribution to PNC near roads and motorways in
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the city of Amsterdam (the Netherlands) for 2014. The approach in the study was to
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measure PNC and NOx at the urban background and a traffic location in a typical street
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ACCEPTED MANUSCRIPT canyon. A street canyon model was applied to simulate the contribution from traffic
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emissions to PNC and NOx at the traffic location. The NOx data was used to validate the
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approach in this study, while the modelled and measured contributions to PNC were used
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to evaluate the PN emission factors from COPERT and HBEFA. Since both emission
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inventories from COPERT and HBEFA are widely used in Europe, the outcome of the
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study is relevant to improve the spatially resolved assessment of PNC in urban areas across
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Europe.
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METHODOLOGY
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2.1 Measurements
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The measurements of NOx and PNCs were carried out in the period April to June 2015 at
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three sites in the Netherlands presented in Fig. 1. The Amsterdamse Bos site (52o 19’17 N;
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4o 50’52 E) was a temporary urban background location in a park between Schiphol
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Airport and the city of Amsterdam. The Vondelpark site (52o 21’35 N; 4o 51’57 E) is an
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urban background location in the National Air Quality Monitoring Network (NAQMN)
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and is located in a park near the centre of Amsterdam. The Jan van Galenstraat site (52o
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22’29 N; 4o 51’38 E) is located in a street canyon and a traffic location in the NAQMN.
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The width of the street is approximately 20m and the buildings on both sides of the street
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are approximately 12.5m high. The measurements were conducted on the roadside at 6m
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from the road axis and a sampling height of 2.5m.
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Figure 1: The three monitoring locations in Amsterdam: the urban background location in the Amsterdamse Bos (1), the urban background location in the Vondelpark (2) and the traffic location in the Jan van Galenstraat (3).
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At location 1, only PNCs were measured, while at locations 2 and 3 both NOx and PNCs.
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NOx concentrations were measured with a chemiluminescence automatic monitor Model
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200E (Teledyne API, USA) with a lower detection limit of 1 µg.m-3 NO2 and a response
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time of 20 seconds. PNCs were measured with two different Condensation Particle
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Counters – CPCs: a butanol-based CPC-3775 (TSI, USA) with a 50% cut-off size of 4 nm
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and a water-based CPC-3783 (TSI, USA) with a 50% cut-off size of 7 nm. The response
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time of both CPCs is a few seconds. Details on the measurement campaign are presented in
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Table 1.
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Table 1: Details of the measurements and the number of hourly observations (n).
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Location
Period
Components/Instrument
Observations
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1 (test site)
01/05–29/05/2015
PNC (non-dried)
n = 637
CPC-3775
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2 (urban)
continuous
NOx/PNC (dried) 200E/CPC-3783
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3 (traffic)
22/04- 18/06/2015
Schiphol (airport)
15/04– 08/05/2015
traffic counting
continuous
meteorology
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NOx/PNC (non-dried) 200E/CPC-3775
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n = 1,377
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n = 1,377
n = 585
n = 1,377
2.2 Quality assurance (QA)
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The quality assurance of NOx and PNC measurements concerned the following aspects:
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NOx: NOx concentrations were measured by the monitoring network of the Public Health Organization in Amsterdam (www.ggd-amsterdam.nl) in accordance to QA
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procedures of the NAQMN. This involved a certified procedure for NOx
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concentrations in ambient air and automatic daily calibration of the NOx monitor
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with a certified gas mixture of 800 ppb NO in nitrogen.
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PNC: PN sampling was conducted using approximately 1.5m stainless steel tubing with 1cm i.d. and a flow of 5 l.min-1 controlled by critical orifices. This ensured
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laminar flow conditions to limit particle losses in the sampling tubes. The sampling
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flow at all locations was checked at the start and the end of the study period. The
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PNC measurements with a CPC-3783 at location 2 were part of the European-
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funded project entitled Joaquin (www.joaquin.eu), which involved the drying of the sampling flow with Nafion membrane tubing. The quality assurance of the PNC measurements with CPC-3775 at locations 1 and 3 followed the requirements of the European “Aerosols, Clouds and Trace gases Research Infrastructure” project
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(www.actris.net). This involves inter-comparison and calibration of the monitoring
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equipment six times per year. At locations 1 and 3, the sampling flow was not
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dried.
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ACCEPTED MANUSCRIPT 2.3 Meteorological and traffic data
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The meteorological parameters of wind speed and wind direction were retrieved from the
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“Schiphol” site of the National Meteorological Monitoring Network (www.knmi.nl) at
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about 6 kilometres from the traffic location. The measurements taken at Schiphol Airport
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are considered to be representative of the wind speed and wind direction at rooftop level of
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buildings at the traffic location. Traffic data at the traffic location in this study were
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collected from counting by the Municipality of Amsterdam. Traffic data for other roads
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with more than 10.000 vehicles per day in Amsterdam were available from traffic models
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at a web-based tool (www.nsl-monitoring.nl). The average traffic intensity during the study
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period, combined for both directions, was 16,300 (weekly average), 16,600 (working days)
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and 15,600 (weekend) vehicles per day. The diurnal variation in traffic intensity and wind
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speed at the traffic location during the study period is presented in Fig. 2.
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Figure 2: Diurnal variation of the traffic intensity on working days and at weekends (left yaxis) and the wind speed at roof top level (right y-axis) in the study period at the traffic location.
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Traffic composition at the traffic location was aggregated into three classes: light-duty
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vehicles – LDV (passenger cars and vans), middle-duty vehicles – MDV (trucks with a
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gross weight between 3.5 and 14 tons) and heavy-duty vehicles – HDV (trucks with a gross
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weight of over 14 tons and buses). This resulted in the following average traffic
ACCEPTED MANUSCRIPT composition: 94% LDV, 2% MDV and 4% HDV (working days) and 97% LDV, 1% MDV
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and 2% HDV (weekend). In section 3.2, the hourly traffic intensity and composition were
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combined with emission factors of NOx and PN for the relevant vehicle categories and EU
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classification in 2014. The resulting hourly emission rates for NOx and PN were used to
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model the hourly traffic contributions to NOx and PNC at the traffic location, as elaborated
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in section 2.5.
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2.4 Data preparation
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Data were collected in different time formats: meteorological data in UTC (“Coordinated
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Universal Time”), the air quality data in local “winter time” and the traffic data in local
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“summer time”. Before data analysis, all hourly average data were synchronised to local
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“summer time”.
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The consequences of PNC measurements under different sampling conditions (dried at
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location 2 versus non-dried at locations 1 and 3) and different CPCs (CPC-3783 at location
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2 and CPC-3775 at locations 1 and 3) were investigated. Firstly, simultaneous PNC
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measurements were conducted for a five-hour period at location 2 using dried sampling
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and the CPC-3783 monitor next to non-dried sampling and the CPC-3775 monitor. Linear
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regression of the results at location 2 provided the following relation: dried/CPC-3783 =
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0.5 * non-dried/CPC-3775 with a Pearson regression coefficient (R) of 0.4. Secondly,
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simultaneous PNC measurements were performed in the period 1-29 May 2015 at the two
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urban background locations: location 1 using non-dried sampling and the CPC-3775 and at
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location 2 using dried sampling and the CPC-3783. In total, 176 hours were selected when
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the wind direction was not coming from Schiphol Airport. The reason for this is that
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Schiphol Airport is a major source of ultrafine particles with different contributions to the
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PNCs at locations 1 and 2 (Keuken et al., 2015). Linear regression of the results at
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locations 1 and 2 provided the following relation: dried/CPC-3783 = 0.5 * non-dried/CPC-
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3775 with a Pearson regression coefficient (R) of 0.6. From both these two tests, it was
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concluded that drying the sampling flow and measurements with the CPC-3783 monitor
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resulted in consistently lower PNCs compared to non-dried sampling and the CPC-3775.
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This may be attributed to particles losses during drying (Puustinen et al., 2007) and the
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larger cut-off size of 7 nm for the CPC-3783 compared to 4 nm for the CPC-3775. In order
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to compare the PNC measurements at the urban background (location 2) with the traffic
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location (location 3), the results from location 2 were multiplied by a factor 2.
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Hours for which observation data were missing due to equipment failure occurred for 0%
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(NOx) and 3% (PNC). Hours with a wind speed less than 2 m.s-1 or a wind direction “990”
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(“variable wind direction”) were removed from the dataset because atmospheric transport
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of air pollutants under these conditions is limited. This represented 14% of the hours
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during the study period. In order to prevent the impact of the aforementioned PN emissions
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from the airport Schiphol on the monitoring locations, hours with directions coming from
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Schiphol were removed from the dataset. This involved 15% of the hours. The next step in
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the data preparation was to calculate the hourly average “increments” in NOx and PNC at
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the traffic location: the difference in hourly average NOx and PNC at the traffic and the
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urban background locations. In order to derive emission factors for average urban road
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traffic, increments with values lower than the 10th percentile (4.5 µg.m-3 NOx and – 5,000
233
#.cm3 PNC) and higher than the 95th percentile (105 µg.m-3 NOx and 55,000 #.cm3 PNC)
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were removed from the data set. Low values represent hours with likely no traffic volume
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at the traffic location and high values hours with likely stagnating traffic. From 1,377 hours
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with observations, a total number of 685 hours with joint NOx and PNC increments
237
remained for data analysis. It is noted that a negative value for the 10th percentile in the
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PNC increments indicates that the aforementioned correction factor for the urban
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background is likely too high. This will be further discussed in section 4.
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2.5 Modelling and statistical analysis
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The atmospheric dilution of traffic emissions in street canyons depends on the width of a
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street, the height of the buildings, the presence of trees and the roof-level wind speed and
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direction (Vardoulakis et al., 2007). In this study, the street canyon model “CAR” (Eerens
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et al., 1993) was used to estimate the contribution of road traffic emissions to air quality at
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the traffic location. Under the hourly average version of the CAR model, the street
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orientation against the hourly average wind direction at roof-top level was considered
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(Weinhold et al., 2008). The hourly modelled and measured contributions to NOx and PNC
249
at the traffic location were used to derive a real-world emission factor for PN from average
250
urban road traffic.
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252
contribution to PNC near road traffic in the city of Amsterdam in 2014. Modelling was
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conducted with the regulatory models in the Netherlands: SRM1 for urban roads and
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SRM2 for motorways. SRM1 is derived from the CAR model and SRM2 from a line
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source model based on a Gaussian plume model (Holmes and Morawska, 2006).
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The statistical evaluation of the increments observed and modelled at the traffic location
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included: a.) comparison of the means, b.) Pearson correlation coefficient (R) describing
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the linear relationship between the observations and modelling results, c.) the slope of the
259
linear relation and d.) the percentage of the model predictions which are within a range of a
260
factor 0.5 to 2 of the observed concentrations (FAC2). (Vardoulakis et al., 2007).
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3.
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3.1 NOx and PNCs measured
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The average concentrations of NOx and PNC on working days and at the weekend at the
264
urban background and the traffic locations are presented in Table 2.
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Table 2: Average NOx and PNC at the urban background and traffic locations, and the
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increment between both locations in the study period.
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Location
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NOx (µg.m-3)
urban background
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traffic location
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increment (traffic-urban)
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PNC (#.cm-3)
working days/weekend
22/17
18,500/17,200
66/45
31,600/28,800
44/28
13,100/11,600
The increment (“the difference between air quality at the traffic location and the urban
274
background”) at the traffic location for NOx and PNC reflects the contribution from road
275
traffic emissions. Fig. 3 presents the diurnal variations in the increment for NOx (A) and
276
PNC (B).
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Figure 3: Diurnal variation in the increments for NOx (A) and PNC (B) on working days
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and at the weekend at the traffic location in the study period.
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ACCEPTED MANUSCRIPT In Fig. 3A, the relatively high increments for NOx on working days between 7 a.m. and 10
284
a.m. may be explained by the relatively high traffic intensity (and thus high emissions) and
285
low wind speed (and thus low atmospheric dilution) during these hours (see: Fig.2). The
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diurnal pattern for NOx during the weekend lacks the traffic peak in the morning. The
287
average increments were 44 (working days) and 28 (weekend) µg NOx per m3. The higher
288
average increment of 36% on working days compared to the weekend is explained mainly
289
by the change in traffic composition rather than increased traffic intensity. Traffic on
290
working days includes twice as many MDV and HDV, for which the NOx emission factor
291
is twenty times higher than for LDV, although the actual volume of traffic increases by just
292
6% compared to the weekend (see: section 2.3).
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In addition, two relevant issues should be noted when comparing the diurnal variation in
294
the increments for NOx in Fig. 3A and for PNC in Fig. 3B. Firstly, the diurnal variation for
295
PNC follows the traffic volume more closely, as presented in Fig. 2. Secondly, the average
296
increments for PNC during working days and the weekend were, respectively, 13,100 and
297
11,600 particles per cm3. Unlike NOx, this increase of 11% for PNC is explained mainly by
298
the increase in the traffic volume of 6% (see: section 2.3) and less by the change in traffic
299
composition on working days. The latter is less relevant for PNC, since the PN emission
300
factors vary less between LDV, MDV and HDV than the emission factors for NOx, as will
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be explained in more detail in the next section.
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3.2 Standard PN emission factors for urban road traffic
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Standard emission factors for total PN were published by COPERT 4 (2014) and HBEFA
305
(2015). These data are less extensive than those for regulated air pollutants in Europe and
306
were considered as “first estimates” (see: Section 1). In Table 3, standard PN emission
307
factors for road traffic under urban driving conditions are summarised.
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Table 3: Standard emission factors of PN (#.km-1) for private cars (PC), vans, middle-duty vehicles (MDV) and heavy-duty vehicles (HDV).
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Emission factors of PN from COPERT 4 (2014)/HBEFA (2015)
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x 1013 (#.km-1)
diesel
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vans petrol
MDV
HDV
buses
(3.5-14 ton)
(14-32 ton)
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Euro 0-2
33/ 5.7
0.6/ <0.1
65/ 7.5
50/ 6.6
110/ 12
-
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Euro 3
16/ 3.7
<0.1/ <0.1
33/ 3.1
50/ 10
110/ 19
-
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Euro 4
8/ 2.3
<0.1/ <0.1
16/ <0.1
0.4/ 4.3
0.9/ 8.4
-
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Euro 5-6
0.8/ <0.1
<0.1/ <0.1
1.6/ <0.1
2.6/ 6.2
1.7/ -
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*: Figures in bold were missing from the COPERT data and replaced by estimates.
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HBEFA published a complete set of emission factors. For COPERT the missing data in
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Table 3 were complemented with estimates, which were not based on experimental data
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but a number of assumptions and adjustments, as follows. It was assumed that emission
331
factors for vans were a factor two higher than emission factors for diesel PC in the same
332
Euro class. The rationale was that almost all vans in the Netherlands are diesel-powered
333
and their average weight is considerable higher than an average PC (www.cbs.nl). This
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probable overestimated the emissions from vans as indicated by the data from HBEFA in
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Table 3. The remaining missing COPERT data for PC inferred for Euro 4 from Euro 3 by
336
dividing by a factor of two, for Euro 5 from Euro 4, by dividing by a factor of ten and for
337
Euro 6 to be similar as Euro 5. The rationale for lower PN emissions from Euro 4
338
compared to Euro 3 was the introduction of particulate traps in Euro 4 and the decrease in
339
the sulphur content in fuel in the same year when Euro 4 was introduced (Kumar et al.,
340
2011). In diesel from 350 to 50 ppm sulphur and in petrol from 150 to 50 ppm sulphur,
341
while from 2009 onwards the maximum sulphur content in both fuels is 10 ppm. The
342
rationale for a factor ten lower Euro 5 and 6 emission factors compared to Euro 4 was the
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requirement for Euro 5 and 6 to comply with the European emission limit of 6E+11 solid
344
particles larger than 23 nm per km (EC, 2012). The estimated PN emission factors for Euro
345
5 and 6 in the COPERT data set for PCs and vans in Table 3 were higher than the limit
346
value since, the PN emission factors in Table 3 concern total PN emissions without a size
347
threshold. For buses, only Euro 5 and 6 were reported in Table 3, because the majority of
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ACCEPTED MANUSCRIPT pre-Euro 5 buses have been replaced in the Netherlands. The data in Table 3 illustrated that
349
the COPERT emission factors were a factor ten higher than HBEFA emission factors. In
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order to apply a conservative estimate of the PN emissions from urban road traffic in this
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study, the COPERT emission factors in Table 3 were combined with the average traffic
352
composition of urban road traffic in the Netherlands in 2014.
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Fig. 4 shows the average traffic composition for urban road traffic in the Netherlands in
354
2014 according to vehicle type, fuel and Euro class (www.cbs.nl). Since the city of
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Amsterdam is one of the four largest urban areas in the Netherlands, it was assumed that
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the presented data in Fig. 4 were representative for Amsterdam.
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Figure 4: Distribution of various types of vehicles (PC, vans, MDV, HDV and buses), fuel
360
type (diesel and petrol) and Euro classification (Euro 0 to 6) as percentage of the total
361
number of vehicles in urban areas in the Netherlands in 2014.
362
The data in Fig. 4 illustrate that 50% of the vehicles in urban areas in the Netherlands in
363
2014 consisted of Euro 4 to 6 passenger cars (PC). Furthermore, about 35% of PC and
364
almost all vans, MDV, HDV and buses run on diesel fuel, while the remaining 65% of PC
365
were mainly petrol-driven, with only about 2% running on other fuels (LNG and LPG) or
366
electricity. The traffic data were combined with the COPERT emission factors in Table 3
367
to derive average PN emissions from urban road traffic in 2014 and presented in Fig. 5.
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Figure 5: PN emissions (#.km-1) from urban road traffic in the Netherlands in 2014 for
371
different vehicle types (PC, vans, MDV, HDV and buses), Euro classes (Euro 0 to 6) and
372
COPERT emission factors.
373
Comparison of Fig. 4 and Fig. 5 illustrates that PN emission from vans dominate total PN
374
emissions from road traffic due to their relative high emission factors (see: Table 3). In the
375
next section, the validity of the dynamometer-based emission factors from COPERT and
376
HBEFA was investigated.
377
3.3 Real-world emission factor of PNs for urban road traffic
378
Of the 1,377 hours of observations, a total of 685 hours remained with valid joint
379
increments of NOx and PNC at the traffic location (see: section 2.4). For these hours, the
380
contribution of road traffic emissions to NOx and PNC at the traffic location was modelled
381
with the CAR model as follows (see: section 2.5). Figures for average hourly traffic
382
volume and composition (LDV, MDV and HDV) were obtained from traffic counting at
383
the traffic location (see: section 2.3). Average emission factors of NOx for urban road
384
traffic, taking into account the Euro class distribution in 2014 were available from the
385
annual update in the Netherlands: 0.4 (LDV), 7 (MDV) and 9 (HDV) g NOx per kilometre
386
(www.rivm.nl). These emission factors are assumed to be representative for road traffic at
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the traffic location. Using the hourly traffic composition and related emission factors, the
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hourly contribution to NOx at the traffic location in the study period was modelled. The
389
modelled contribution and measured increment at the same hours in the study period are
390
presented as a time series and a scatter plot in Fig. 6.
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Figure 6: The measured and modelled contribution to NOx at the traffic location as a time
397
series (A) and a scatter plot during working days and the weekend (B).
398 399
The time series in Fig. 6A illustrates that the measured and modelled contributions to NOx
400
correspond reasonably closely with a Pearson R: 0.6. The scatter plot in Fig. 6B shows that
401
on working days (blue) and at the weekend (green), the relationship between the measured
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403
during working and weekend days for LDV, MDV and HDV (see: section 2.3), this
404
indicates that the applied emission factors applied were accurate. The statistical data are
405
presented in Table 4. The statistics for NOx are in line with other studies of hourly
406
modelled and observed contribution to NOx in street canyons (Vardoulakis et al., 2007).
407
A similar approach as for NOx was followed for PN. The PN emission factors (#.km-1):
408
1.8E+13 (PC), 1.7E+14 (vans), 9.2E+13 (MDV), 6.8E+13 (HDV) and 1.7E+13 (buses)
409
were derived from the COPERT data (see: Table 3) taking into account the Euro
410
classification of the average urban car traffic in the Netherlands in 2014 (see: Fig. 4). From
411
the emission factors for PC (85% of the car fleet) and vans (11% of the car fleet), a PN
412
emission factor of 3.5E+13 was derived for LDV. Using the hourly traffic composition and
413
related emission factors, the hourly contribution to PNC at the traffic location in the study
414
period was modelled and presented with the measured contribution as a time series and a
415
scatter plot in Fig. 7.
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Figure 7: The measured and modelled contribution to PNCs at the traffic location as a
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time series (A) and a scatter plot with standard and real-world PN emission factors (B).
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425
dynamometer-based COPERT emission factors) and measured contributions to PNC are
426
not closely aligned. This is mainly attributable to inaccurate PN emission factors, since
427
other relevant parameters such as wind speed, wind direction, traffic volume, traffic
428
composition and street canyon model were similar to the NOx analysis. Subsequently, real-
429
world PN emission factors were derived from the ratio between the average measured
430
(14,000) and modelled (1,600) contribution to PNC at the traffic location. This resulted in
431
a ratio of eight. Multiplying standard PN emission factors by the value eight provided real-
432
world emission factors for LDV, MDV and HDV in 2014. Subsequently, the hourly
433
modelled contributions to PNC at the traffic location were recalculated and presented in
434
Fig. 7 as “real-world EF”. The time series in Fig. 7A illustrate the improved alignment of
435
the “real-world EF” modelled PNC contribution with the measured PNC compared to the
436
“standard EF” modelled PNC. The correlation between measured and modelled PNC
437
contribution remains however rather poor with a Pearson R: 0.2. In the scatter plot Fig. 7B,
438
the data for working days and the weekend are not shown separately, unlike NOx in Fig.
439
6B due to the small differences in PN emissions between working days and the weekend.
440
The statistical data from comparing the measured and real-world modelled PN are
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presented in Table 4.
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Table 4: Statistical parameters for comparison of the hourly measured and modelled
444
contributions to NOx and PNC at the traffic location in the study period (N = 686). NOx (µg.m-3)
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PNC (#.cm-3)
modelled
measured
modelled
measured
39.1
39.3
14,000
14,000
average
448
Pearson correlation (R)
0.6
0.2
449
slope
0.8
0.7
450
FAC2
64%
44%
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The mean fractional error (FAC2) values for NOx and PNC indicate that respectively 64%
453
and 44% of the modelled contributions with real-world emission factors are within a range
454
of a factor 0.5 to 2 of the measured contributions. Considering uncertainties in the Euro
455
classification of road traffic at the traffic location, the representativeness of real-world
456
emission factors for actual driving conditions and the variability in wind parameters, the
ACCEPTED MANUSCRIPT 457
correlation between the hourly modelled and measured contribution at the traffic location
458
is reasonable for NOx but rather poor for PNC. This underlines the need to develop more
459
accurate real-world emission factors for PNC.
460
3.4 Annual average PNC in 2014
462
The established real-world PN emission factors were used to model the annual average
463
contribution to PNC along urban roads and within 25m of motorways in the city of
464
Amsterdam in 2014. The average traffic composition in Amsterdam in 2014 was 96%
465
LDV, 1% MDV, 2% HDV and 1% buses (on urban roads) and 94% LDV, 3% MDV and
466
3% HDV (on motorways). The calculated average PN emission factors for urban and
467
motorway traffic were respectively 2.9E+14 and 3E+14. Meteorological data and traffic
468
data for roads with over 10,000 vehicles per day, were available on a web-based tool in the
469
Netherlands (www.nsl-monitoring.nl). The results are presented in Fig. 8.
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Figure 8: The modelled contribution to PNCs near road traffic in the city of Amsterdam in
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2014.
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Fig. 8 illustrates that PNC were particularly elevated near motorways with over 100,000
475
vehicles per day and along street canyons with over 20,000 vehicles per day.
476 477
4.
DISCUSSION AND CONCLUSION
478
One important uncertainty in our study was correction of the urban background of PNC
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with a factor two to 18,100 #.cm-3 in the study period. In two earlier studies, the
480
background in Amsterdam was 18,000 in 2003 (Puustinen et al., 2007) and 9,600 in 2013
ACCEPTED MANUSCRIPT (Klompmaker et al., 2015). The low background in 2013 may be attributed to sampling
482
period between 09:00 and 16:00 to avoid rush-hour traffic. Given lower PN emission
483
factors for the 2014 car fleet compared to 2003 and differences in instruments and
484
measurement strategies, it was concluded that a correction of a factor two in our study
485
resulted in a too high urban background and consequently, a too low increment of PNC at
486
the traffic location. This may explain the relative high percentage of 26% of outliers in the
487
increment of PNC compared to 2% for NOx (see: section 2.4). Therefore, the outcome of
488
our study likely underestimated the established real-world emission factor of PN.
489
A second uncertainty concerns the hourly modelled contributions from traffic emissions to
490
NOx at the traffic location. The National Institute for Environment and Public Health
491
(RIVM) has validated our modelling approach by comparing the measured and modelled
492
contributions to NO2 at 400 locations in the Netherlands in 2010 and 2011 (www.rivm.nl).
493
The difference in annual average NO2 was less than 1 µg.m-3 in a range of 30 to 50 µg NO2
494
per m3. Our study shows that the hourly average measured (39.1) and modelled (39.3)
495
contribution to NOx (µg.m-3) at the traffic location also corresponds closely. From the good
496
agreement between modelled and measured contribution to NOx, it was concluded that the
497
street canyon model and traffic data are adequate to estimate the hourly contribution of
498
traffic emissions to air quality at the traffic location. The factor eight difference between
499
the modelled and measured contributions to PNC at the traffic location can therefore
500
mainly be attributed to inaccurate standard PN emission factors. This confirms our
501
assumption in that current dynamometer-based PN emission factors from COPERT and
502
HBEFA which are even a factor ten lower than from COPERT, as illustrated in Table 3,
503
may be qualified as “first estimates”. The main reasons for the differences between
504
dynamometer-based and real-world emission factors are attributed to different cut-off size
505
diameter of PNC instruments, non-volatile versus volatile PNC measurements and non-
506
representative dynamometer test conditions compared to real-world driving conditions.
507
The real-world PN emission factors along urban roads was 2.9E+14 #.km-1 and near
508
motorways 3E+14 #.km-1 in Amsterdam in 2014. This corresponds with the range of real-
509
world PN emission factors of 1.3 to 4E+14 #.km-1 (urban) and 2 to 5E+14 (motorway) in
510
Europe in the period 2005 to 2010, as presented in the review by Kumar et al. (2011).
511
The ratio in emission factors for HDV/LDV in our study was a factor 2, while other studies
512
(e.g. Nickel et al., 2013; Wang et al., 2010) found ratios ranging from 4 to 22. The wide
513
range in ratios was attributed by Nickel et al. (2013) to different lower size cut-offs of the
514
applied PNC instruments: the lower the size cut-off, the larger the ratio. Another
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explanation is that a lower percentage in diesel LDV contributes to a higher ratio in
516
emission factors for HDV/LDV. This is illustrated by the low ratios of 2 and 4 in our study
517
in 2014 and in Germany in 2007 (Nickel et al., 2013) with respectively 35% and 40%
518
diesel LDV. Contrary a ratio of 22 was found in a study in 2008 in Denmark with less than
519
20% diesel LDV (Wang et al., 2010).
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5.
ACKNOWLEDGEMENTS
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This study received financial support from the Netherlands Ministry of Infrastructure and
523
the Environment. We are grateful to the Royal Netherlands Meteorological Institute, the
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National Institute for Public Health and Environment and the Department of Traffic and
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Spatial Planning of the Municipality of Amsterdam for providing meteorological and road
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traffic data.
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6.
REFERENCES
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Bismarck-Osten von, C., Birmili, W., Ketzel, M., Massling, A., Petäjä, T. Weber, S.
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Chow, J.C., Watson, J.G., 2007. Survey of measurement and composition of ultrafine particles. Aerosol and Air Quality Research 7, 121-173.
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Eerens, H.C., Sliggers, C.J., Hout van den, K. D., 1993. The CAR model: the Dutch
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method to determine city street air quality. Atmospheric Environment Vol. 27B, 4,
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HBEFA, 2015. Handbook of Emission Factors for Road Transport, 3.2 ed., 2015 INFRAS, Bern, Switzerland.
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HEI, 2009. Traffic-Related Air Pollution: A critical review of the literature on emissions,
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Ketzel, M., Berkowicz, R., 2004. Modelling the fate of ultrafine particles from exhaust
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Keuken, M.P., Moerman, M., Zandveld, P., Henzing, J.S., Hoek, G., 2015. Total and size-
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Klompmaker, J.O., Montagne, D.R., Meliefste, K., Hoek, G., Brunekreef, B., 2015. Spatial
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Kumar, P., Ketzel, M., Vardoulakis, S., Pirjola, L., Britter, R., 2011. Dynamics and
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Nickel, C., Kaminski, H., Hellack, B., Quass, U., John, A., Klemm., O., Kuhlbusch, T.A.J.,
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Meddings, C., Thomas, S., Harrison, R., Ayres, J.G., Zee van der, S., Hoek, G., 2007.
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Weinhold, O., Visser, G., Klok, L., Jonkers, S., Meijer, E., 2008. Experiments with an
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hourly street canyon dispersion model. Proceedings of the 12th conference on
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Real world PN emission factor for traffic eigth times standard emission factor Real world PN emission factors: 2.9E+14 (urban) and 3E+14 (motorway) #.km-1 PNC near urban roads three times higher than urban background
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S1352-2310(16)30180-7
DOI:
10.1016/j.atmosenv.2016.03.009
Reference:
AEA 14491
To appear in:
Atmospheric Environment
Received Date: 14 December 2015 Revised Date:
2 March 2016
Accepted Date: 4 March 2016
Please cite this article as: Keuken, M.P., Moerman, M., Voogt, M., Zandveld, P., Verhagen, H., Stelwagen, U., Jonge de, D., Particle number concentration near road traffic in Amsterdam (the Netherlands): comparison of standard and real-world emission factors, Atmospheric Environment (2016), doi: 10.1016/j.atmosenv.2016.03.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
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Particle number concentration near road traffic in Amsterdam (the Netherlands): comparison of standard and real-world emission factors Keuken M.P.a,b, Moerman M.a, Voogt M.a, Zandveld P.a, Verhagen, H.a, Stelwagen, U.a*, Jonge de D.c a
5 b
6 7
: TNO, Netherlands Organization for Applied Research, Utrecht, the Netherlands
: Centre for Atmospheric and Instrumentation Research, University of Hertfordshire, Hatfield, United Kingdom c
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: GGD-Amsterdam, Public Health Authority, Amsterdam, the Netherlands * Corresponding author: [email protected]
9
ABSTRACT
11
In this study, NOx and particle number concentration (PNC) at an urban background and a
12
traffic location were measured in the city of Amsterdam (the Netherlands). Modelled and
13
measured contributions to NOx and PNC at the traffic location were used to derive real-
14
world PN emission factors for average urban road traffic. The results for NOx were
15
applied to validate our approach. The real-world PN emission factors (#.km-1) were
16
2.9E+14 (urban road) and 3E+14 (motorway). These values were at least a factor eight
17
higher than dynamometer-based PN emission factors from COPERT 4 and HBEFA
18
databases. The real-world PN emission factors were used to model the contribution to PNC
19
near road traffic in 2014. This was two to three times higher than the PNC urban
20
background along urban roads over 20,000 vehicles per day and near motorways. The
21
discrepancy between dynamometer-based and real-world emission factors demonstrates the
22
need for more PNC observations to assess actual PN emissions from road traffic.
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Keywords: particle number; road traffic; emission factors
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1.
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INTRODUCTION
28
It is well known that exposure to emissions from road traffic is associated with increased
29
health risks (e.g. HEI, 2009). In urban areas are traffic emissions a major source of
30
ultrafine particles, which are particles with a diameter smaller than 100 nm (Kumar et al.,
31
2011). The particle number concentration (PNC) is considered to be representative for the
32
concentration of ultrafine particles (Chow and Watson, 2007). Traffic-based ultrafine
ACCEPTED MANUSCRIPT particles mainly consist of solid carbon and metals mixed with volatile secondary particles
34
(e.g. lubrication oil, combustion products and sulphur compounds). The latter particles are
35
formed when exhaust emissions cool in ambient air. Recently, the WHO (2013) concluded
36
that “there is considerable evidence that ultrafine particles can contribute to the health
37
effects of particulate matter”. Data on causal links between exposure to ultrafine particles
38
and adverse health effects were however too scarce to recommend a specific air quality
39
guideline. Recent studies (Klompmaker et al., 2015; Puustinen et al., 2007) indicate a
40
relatively large temporal and spatial variability of PNC in urban areas and consequently, a
41
relatively large variability in exposure to PNC of the urban population. Considering the
42
variability in PNC, dispersion modelling (Holmes and Morawska, 2006; Vardoulakis et al.,
43
2007) is preferable to land-use regression (LUR) modelling (Klompmaker et al., 2015) to
44
estimate PNC near road traffic in urban areas. It is recognised that accurate emission
45
factors for particle number (PN) from road traffic is a main source of uncertainty in
46
dispersion modelling of PN (Holmes and Morawska, 2006).
47
Traffic-related emission factors are generally obtained by the following methods (Franco et
48
al., 2013):
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Standard emission factors: These are derived from measurements in exhaust emissions during test cycles simulating different driving conditions (urban,
51
motorway) at a dynamometer test facility. In Europe, exhaust emissions are
52
regulated according to “Euro classes”, which specify emission limits for
53
regulated compounds for the year that a type of vehicle enters the European
54
road traffic: Euro 0 (pre-1992), Euro 1 (1992), Euro 2 (1996), Euro 3 (2000),
55
Euro 4 (2005), Euro 5a/b (2009/2011) and Euro 6 (2014). Dynamometer-based
56
emission factors for PN have been published for Euro 1 to 3 vehicles (COPERT
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4, 2014) and Euro 1 to 6 vehicles (HBEFA, 2015). This is related to total and non-volatile PN. In the latter case, exhaust emissions are heated to 3500C, volatile particles are removed, and followed by PN measurements of particles larger than 23 nm. Following this procedure, there is an emission limit of
61
6E+11 non-volatile PN per kilometre for light duty vehicles (EC, 2012): from
62
2011 for diesel (Euro 5b and 6) and from 2014 for petrol (Euro 6). The rationale
63
to
64
emission data as compared to total PN emission data (Glechaskiel et al., 2012).
65
The latter strongly depends on dilution conditions, after-treatment devices,
66
types of fuel and lubricant, among others. Under real-world conditions,
regulate non-volatile PN (and not total PN) is the higher reproducible
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formed in ambient air (Kumar et al., 2011). Consequently, large differences
69
may be expected between standard and real-world PN emission factors and thus
70
the need to develop real-world emission factors. It is noted that dynamometer-
71
based PN emission factors for pre-Euro 5b and 6 vehicles were derived from
72
dynamometer measurements of total PN without a particle size threshold of 23
73
nm and without removal of volatile particles;
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Real-world emission factors: Measurements are carried out at road-side locations (up and down wind of motorways; in road tunnels; at urban
76
background and street canyon locations) or following individual vehicles by
77
portable emission measurement systems (PEMS). These measurements are
78
combined with data on traffic and meteorology as input for inverse modelling to
79
obtain emission factors (Kumar et al., 2011). It is assumed that secondary
80
particle formation is mainly important during the first few seconds after exhaust
81
emissions and therefore, dispersion of PN in urban areas is treated as a
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conservative pollutant (Ketzel and Berkowicz, 2004). It is noted that real-world
83
emission factors are representative of average road traffic, while standard
84
emission factors are linked to particular vehicle types.
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Real-world emission factors may deviate from standard emission factors as real-world
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driving conditions may differ from test cycles, real-world road traffic includes vehicles
87
with different levels of maintenance and related emissions, or exhaust emissions may be
88
deliberately reduced during a test cycle (EPA, 2015).
89
In the Netherlands, real-world NOx emission factors are derived by combining
90
dynamometer testing with on-road PEMS measurements. The annually updated emission
91
factors of NOx for road traffic in the Netherlands were applied in this study (www.rivm.nl).
92
In contrast for PN emissions, no dynamometer testing or on-road measurements were
93
conducted in the Netherlands. Dynamometer-based emission factors for PN from COPERT
94
and HBEFA were therefore used in this study. However, due to the lack of standardized
95
testing conditions for pre-Euro 5/6 vehicles and the limited number of tested vehicles, the
96
available emission factors for PN are considered “first estimates”.
97
The aim of this study was to derive real-world emission factors for PN for average urban
98
traffic and, subsequently, to model the contribution to PNC near roads and motorways in
99
the city of Amsterdam (the Netherlands) for 2014. The approach in the study was to
100
measure PNC and NOx at the urban background and a traffic location in a typical street
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102
emissions to PNC and NOx at the traffic location. The NOx data was used to validate the
103
approach in this study, while the modelled and measured contributions to PNC were used
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to evaluate the PN emission factors from COPERT and HBEFA. Since both emission
105
inventories from COPERT and HBEFA are widely used in Europe, the outcome of the
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study is relevant to improve the spatially resolved assessment of PNC in urban areas across
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Europe.
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2.
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METHODOLOGY
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2.1 Measurements
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The measurements of NOx and PNCs were carried out in the period April to June 2015 at
112
three sites in the Netherlands presented in Fig. 1. The Amsterdamse Bos site (52o 19’17 N;
113
4o 50’52 E) was a temporary urban background location in a park between Schiphol
114
Airport and the city of Amsterdam. The Vondelpark site (52o 21’35 N; 4o 51’57 E) is an
115
urban background location in the National Air Quality Monitoring Network (NAQMN)
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and is located in a park near the centre of Amsterdam. The Jan van Galenstraat site (52o
117
22’29 N; 4o 51’38 E) is located in a street canyon and a traffic location in the NAQMN.
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The width of the street is approximately 20m and the buildings on both sides of the street
119
are approximately 12.5m high. The measurements were conducted on the roadside at 6m
120
from the road axis and a sampling height of 2.5m.
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Figure 1: The three monitoring locations in Amsterdam: the urban background location in the Amsterdamse Bos (1), the urban background location in the Vondelpark (2) and the traffic location in the Jan van Galenstraat (3).
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At location 1, only PNCs were measured, while at locations 2 and 3 both NOx and PNCs.
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NOx concentrations were measured with a chemiluminescence automatic monitor Model
127
200E (Teledyne API, USA) with a lower detection limit of 1 µg.m-3 NO2 and a response
128
time of 20 seconds. PNCs were measured with two different Condensation Particle
129
Counters – CPCs: a butanol-based CPC-3775 (TSI, USA) with a 50% cut-off size of 4 nm
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and a water-based CPC-3783 (TSI, USA) with a 50% cut-off size of 7 nm. The response
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time of both CPCs is a few seconds. Details on the measurement campaign are presented in
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Table 1.
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Table 1: Details of the measurements and the number of hourly observations (n).
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Location
Period
Components/Instrument
Observations
138
1 (test site)
01/05–29/05/2015
PNC (non-dried)
n = 637
CPC-3775
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2 (urban)
continuous
NOx/PNC (dried) 200E/CPC-3783
141 142
3 (traffic)
22/04- 18/06/2015
Schiphol (airport)
15/04– 08/05/2015
traffic counting
continuous
meteorology
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NOx/PNC (non-dried) 200E/CPC-3775
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n = 1,377
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n = 1,377
n = 585
n = 1,377
2.2 Quality assurance (QA)
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The quality assurance of NOx and PNC measurements concerned the following aspects:
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NOx: NOx concentrations were measured by the monitoring network of the Public Health Organization in Amsterdam (www.ggd-amsterdam.nl) in accordance to QA
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procedures of the NAQMN. This involved a certified procedure for NOx
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concentrations in ambient air and automatic daily calibration of the NOx monitor
153
with a certified gas mixture of 800 ppb NO in nitrogen.
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PNC: PN sampling was conducted using approximately 1.5m stainless steel tubing with 1cm i.d. and a flow of 5 l.min-1 controlled by critical orifices. This ensured
156
laminar flow conditions to limit particle losses in the sampling tubes. The sampling
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flow at all locations was checked at the start and the end of the study period. The
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PNC measurements with a CPC-3783 at location 2 were part of the European-
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funded project entitled Joaquin (www.joaquin.eu), which involved the drying of the sampling flow with Nafion membrane tubing. The quality assurance of the PNC measurements with CPC-3775 at locations 1 and 3 followed the requirements of the European “Aerosols, Clouds and Trace gases Research Infrastructure” project
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(www.actris.net). This involves inter-comparison and calibration of the monitoring
164
equipment six times per year. At locations 1 and 3, the sampling flow was not
165
dried.
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The meteorological parameters of wind speed and wind direction were retrieved from the
170
“Schiphol” site of the National Meteorological Monitoring Network (www.knmi.nl) at
171
about 6 kilometres from the traffic location. The measurements taken at Schiphol Airport
172
are considered to be representative of the wind speed and wind direction at rooftop level of
173
buildings at the traffic location. Traffic data at the traffic location in this study were
174
collected from counting by the Municipality of Amsterdam. Traffic data for other roads
175
with more than 10.000 vehicles per day in Amsterdam were available from traffic models
176
at a web-based tool (www.nsl-monitoring.nl). The average traffic intensity during the study
177
period, combined for both directions, was 16,300 (weekly average), 16,600 (working days)
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and 15,600 (weekend) vehicles per day. The diurnal variation in traffic intensity and wind
179
speed at the traffic location during the study period is presented in Fig. 2.
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Figure 2: Diurnal variation of the traffic intensity on working days and at weekends (left yaxis) and the wind speed at roof top level (right y-axis) in the study period at the traffic location.
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Traffic composition at the traffic location was aggregated into three classes: light-duty
185
vehicles – LDV (passenger cars and vans), middle-duty vehicles – MDV (trucks with a
186
gross weight between 3.5 and 14 tons) and heavy-duty vehicles – HDV (trucks with a gross
187
weight of over 14 tons and buses). This resulted in the following average traffic
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and 2% HDV (weekend). In section 3.2, the hourly traffic intensity and composition were
190
combined with emission factors of NOx and PN for the relevant vehicle categories and EU
191
classification in 2014. The resulting hourly emission rates for NOx and PN were used to
192
model the hourly traffic contributions to NOx and PNC at the traffic location, as elaborated
193
in section 2.5.
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2.4 Data preparation
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Data were collected in different time formats: meteorological data in UTC (“Coordinated
197
Universal Time”), the air quality data in local “winter time” and the traffic data in local
198
“summer time”. Before data analysis, all hourly average data were synchronised to local
199
“summer time”.
200
The consequences of PNC measurements under different sampling conditions (dried at
201
location 2 versus non-dried at locations 1 and 3) and different CPCs (CPC-3783 at location
202
2 and CPC-3775 at locations 1 and 3) were investigated. Firstly, simultaneous PNC
203
measurements were conducted for a five-hour period at location 2 using dried sampling
204
and the CPC-3783 monitor next to non-dried sampling and the CPC-3775 monitor. Linear
205
regression of the results at location 2 provided the following relation: dried/CPC-3783 =
206
0.5 * non-dried/CPC-3775 with a Pearson regression coefficient (R) of 0.4. Secondly,
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simultaneous PNC measurements were performed in the period 1-29 May 2015 at the two
208
urban background locations: location 1 using non-dried sampling and the CPC-3775 and at
209
location 2 using dried sampling and the CPC-3783. In total, 176 hours were selected when
210
the wind direction was not coming from Schiphol Airport. The reason for this is that
211
Schiphol Airport is a major source of ultrafine particles with different contributions to the
212
PNCs at locations 1 and 2 (Keuken et al., 2015). Linear regression of the results at
213
locations 1 and 2 provided the following relation: dried/CPC-3783 = 0.5 * non-dried/CPC-
214
3775 with a Pearson regression coefficient (R) of 0.6. From both these two tests, it was
215
concluded that drying the sampling flow and measurements with the CPC-3783 monitor
216
resulted in consistently lower PNCs compared to non-dried sampling and the CPC-3775.
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This may be attributed to particles losses during drying (Puustinen et al., 2007) and the
218
larger cut-off size of 7 nm for the CPC-3783 compared to 4 nm for the CPC-3775. In order
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to compare the PNC measurements at the urban background (location 2) with the traffic
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location (location 3), the results from location 2 were multiplied by a factor 2.
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Hours for which observation data were missing due to equipment failure occurred for 0%
223
(NOx) and 3% (PNC). Hours with a wind speed less than 2 m.s-1 or a wind direction “990”
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(“variable wind direction”) were removed from the dataset because atmospheric transport
225
of air pollutants under these conditions is limited. This represented 14% of the hours
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during the study period. In order to prevent the impact of the aforementioned PN emissions
227
from the airport Schiphol on the monitoring locations, hours with directions coming from
228
Schiphol were removed from the dataset. This involved 15% of the hours. The next step in
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the data preparation was to calculate the hourly average “increments” in NOx and PNC at
230
the traffic location: the difference in hourly average NOx and PNC at the traffic and the
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urban background locations. In order to derive emission factors for average urban road
232
traffic, increments with values lower than the 10th percentile (4.5 µg.m-3 NOx and – 5,000
233
#.cm3 PNC) and higher than the 95th percentile (105 µg.m-3 NOx and 55,000 #.cm3 PNC)
234
were removed from the data set. Low values represent hours with likely no traffic volume
235
at the traffic location and high values hours with likely stagnating traffic. From 1,377 hours
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with observations, a total number of 685 hours with joint NOx and PNC increments
237
remained for data analysis. It is noted that a negative value for the 10th percentile in the
238
PNC increments indicates that the aforementioned correction factor for the urban
239
background is likely too high. This will be further discussed in section 4.
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2.5 Modelling and statistical analysis
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The atmospheric dilution of traffic emissions in street canyons depends on the width of a
243
street, the height of the buildings, the presence of trees and the roof-level wind speed and
244
direction (Vardoulakis et al., 2007). In this study, the street canyon model “CAR” (Eerens
245
et al., 1993) was used to estimate the contribution of road traffic emissions to air quality at
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the traffic location. Under the hourly average version of the CAR model, the street
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orientation against the hourly average wind direction at roof-top level was considered
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(Weinhold et al., 2008). The hourly modelled and measured contributions to NOx and PNC
249
at the traffic location were used to derive a real-world emission factor for PN from average
250
urban road traffic.
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252
contribution to PNC near road traffic in the city of Amsterdam in 2014. Modelling was
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conducted with the regulatory models in the Netherlands: SRM1 for urban roads and
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SRM2 for motorways. SRM1 is derived from the CAR model and SRM2 from a line
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source model based on a Gaussian plume model (Holmes and Morawska, 2006).
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The statistical evaluation of the increments observed and modelled at the traffic location
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included: a.) comparison of the means, b.) Pearson correlation coefficient (R) describing
258
the linear relationship between the observations and modelling results, c.) the slope of the
259
linear relation and d.) the percentage of the model predictions which are within a range of a
260
factor 0.5 to 2 of the observed concentrations (FAC2). (Vardoulakis et al., 2007).
261
3.
262
3.1 NOx and PNCs measured
263
The average concentrations of NOx and PNC on working days and at the weekend at the
264
urban background and the traffic locations are presented in Table 2.
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Table 2: Average NOx and PNC at the urban background and traffic locations, and the
266
increment between both locations in the study period.
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Location
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NOx (µg.m-3)
urban background
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traffic location
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increment (traffic-urban)
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PNC (#.cm-3)
working days/weekend
22/17
18,500/17,200
66/45
31,600/28,800
44/28
13,100/11,600
The increment (“the difference between air quality at the traffic location and the urban
274
background”) at the traffic location for NOx and PNC reflects the contribution from road
275
traffic emissions. Fig. 3 presents the diurnal variations in the increment for NOx (A) and
276
PNC (B).
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Figure 3: Diurnal variation in the increments for NOx (A) and PNC (B) on working days
281
and at the weekend at the traffic location in the study period.
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284
a.m. may be explained by the relatively high traffic intensity (and thus high emissions) and
285
low wind speed (and thus low atmospheric dilution) during these hours (see: Fig.2). The
286
diurnal pattern for NOx during the weekend lacks the traffic peak in the morning. The
287
average increments were 44 (working days) and 28 (weekend) µg NOx per m3. The higher
288
average increment of 36% on working days compared to the weekend is explained mainly
289
by the change in traffic composition rather than increased traffic intensity. Traffic on
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working days includes twice as many MDV and HDV, for which the NOx emission factor
291
is twenty times higher than for LDV, although the actual volume of traffic increases by just
292
6% compared to the weekend (see: section 2.3).
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In addition, two relevant issues should be noted when comparing the diurnal variation in
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the increments for NOx in Fig. 3A and for PNC in Fig. 3B. Firstly, the diurnal variation for
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PNC follows the traffic volume more closely, as presented in Fig. 2. Secondly, the average
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increments for PNC during working days and the weekend were, respectively, 13,100 and
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11,600 particles per cm3. Unlike NOx, this increase of 11% for PNC is explained mainly by
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the increase in the traffic volume of 6% (see: section 2.3) and less by the change in traffic
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composition on working days. The latter is less relevant for PNC, since the PN emission
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factors vary less between LDV, MDV and HDV than the emission factors for NOx, as will
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be explained in more detail in the next section.
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3.2 Standard PN emission factors for urban road traffic
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Standard emission factors for total PN were published by COPERT 4 (2014) and HBEFA
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(2015). These data are less extensive than those for regulated air pollutants in Europe and
306
were considered as “first estimates” (see: Section 1). In Table 3, standard PN emission
307
factors for road traffic under urban driving conditions are summarised.
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Table 3: Standard emission factors of PN (#.km-1) for private cars (PC), vans, middle-duty vehicles (MDV) and heavy-duty vehicles (HDV).
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Emission factors of PN from COPERT 4 (2014)/HBEFA (2015)
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x 1013 (#.km-1)
diesel
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vans petrol
MDV
HDV
buses
(3.5-14 ton)
(14-32 ton)
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Euro 0-2
33/ 5.7
0.6/ <0.1
65/ 7.5
50/ 6.6
110/ 12
-
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Euro 3
16/ 3.7
<0.1/ <0.1
33/ 3.1
50/ 10
110/ 19
-
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Euro 4
8/ 2.3
<0.1/ <0.1
16/ <0.1
0.4/ 4.3
0.9/ 8.4
-
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Euro 5-6
0.8/ <0.1
<0.1/ <0.1
1.6/ <0.1
2.6/ 6.2
1.7/ -
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*: Figures in bold were missing from the COPERT data and replaced by estimates.
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HBEFA published a complete set of emission factors. For COPERT the missing data in
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Table 3 were complemented with estimates, which were not based on experimental data
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but a number of assumptions and adjustments, as follows. It was assumed that emission
331
factors for vans were a factor two higher than emission factors for diesel PC in the same
332
Euro class. The rationale was that almost all vans in the Netherlands are diesel-powered
333
and their average weight is considerable higher than an average PC (www.cbs.nl). This
334
probable overestimated the emissions from vans as indicated by the data from HBEFA in
335
Table 3. The remaining missing COPERT data for PC inferred for Euro 4 from Euro 3 by
336
dividing by a factor of two, for Euro 5 from Euro 4, by dividing by a factor of ten and for
337
Euro 6 to be similar as Euro 5. The rationale for lower PN emissions from Euro 4
338
compared to Euro 3 was the introduction of particulate traps in Euro 4 and the decrease in
339
the sulphur content in fuel in the same year when Euro 4 was introduced (Kumar et al.,
340
2011). In diesel from 350 to 50 ppm sulphur and in petrol from 150 to 50 ppm sulphur,
341
while from 2009 onwards the maximum sulphur content in both fuels is 10 ppm. The
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rationale for a factor ten lower Euro 5 and 6 emission factors compared to Euro 4 was the
343
requirement for Euro 5 and 6 to comply with the European emission limit of 6E+11 solid
344
particles larger than 23 nm per km (EC, 2012). The estimated PN emission factors for Euro
345
5 and 6 in the COPERT data set for PCs and vans in Table 3 were higher than the limit
346
value since, the PN emission factors in Table 3 concern total PN emissions without a size
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threshold. For buses, only Euro 5 and 6 were reported in Table 3, because the majority of
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the COPERT emission factors were a factor ten higher than HBEFA emission factors. In
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order to apply a conservative estimate of the PN emissions from urban road traffic in this
351
study, the COPERT emission factors in Table 3 were combined with the average traffic
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composition of urban road traffic in the Netherlands in 2014.
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Fig. 4 shows the average traffic composition for urban road traffic in the Netherlands in
354
2014 according to vehicle type, fuel and Euro class (www.cbs.nl). Since the city of
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Amsterdam is one of the four largest urban areas in the Netherlands, it was assumed that
356
the presented data in Fig. 4 were representative for Amsterdam.
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Figure 4: Distribution of various types of vehicles (PC, vans, MDV, HDV and buses), fuel
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type (diesel and petrol) and Euro classification (Euro 0 to 6) as percentage of the total
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number of vehicles in urban areas in the Netherlands in 2014.
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The data in Fig. 4 illustrate that 50% of the vehicles in urban areas in the Netherlands in
363
2014 consisted of Euro 4 to 6 passenger cars (PC). Furthermore, about 35% of PC and
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almost all vans, MDV, HDV and buses run on diesel fuel, while the remaining 65% of PC
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were mainly petrol-driven, with only about 2% running on other fuels (LNG and LPG) or
366
electricity. The traffic data were combined with the COPERT emission factors in Table 3
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to derive average PN emissions from urban road traffic in 2014 and presented in Fig. 5.
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Figure 5: PN emissions (#.km-1) from urban road traffic in the Netherlands in 2014 for
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different vehicle types (PC, vans, MDV, HDV and buses), Euro classes (Euro 0 to 6) and
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COPERT emission factors.
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Comparison of Fig. 4 and Fig. 5 illustrates that PN emission from vans dominate total PN
374
emissions from road traffic due to their relative high emission factors (see: Table 3). In the
375
next section, the validity of the dynamometer-based emission factors from COPERT and
376
HBEFA was investigated.
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3.3 Real-world emission factor of PNs for urban road traffic
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Of the 1,377 hours of observations, a total of 685 hours remained with valid joint
379
increments of NOx and PNC at the traffic location (see: section 2.4). For these hours, the
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contribution of road traffic emissions to NOx and PNC at the traffic location was modelled
381
with the CAR model as follows (see: section 2.5). Figures for average hourly traffic
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volume and composition (LDV, MDV and HDV) were obtained from traffic counting at
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the traffic location (see: section 2.3). Average emission factors of NOx for urban road
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traffic, taking into account the Euro class distribution in 2014 were available from the
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annual update in the Netherlands: 0.4 (LDV), 7 (MDV) and 9 (HDV) g NOx per kilometre
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(www.rivm.nl). These emission factors are assumed to be representative for road traffic at
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the traffic location. Using the hourly traffic composition and related emission factors, the
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hourly contribution to NOx at the traffic location in the study period was modelled. The
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modelled contribution and measured increment at the same hours in the study period are
390
presented as a time series and a scatter plot in Fig. 6.
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Figure 6: The measured and modelled contribution to NOx at the traffic location as a time
397
series (A) and a scatter plot during working days and the weekend (B).
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The time series in Fig. 6A illustrates that the measured and modelled contributions to NOx
400
correspond reasonably closely with a Pearson R: 0.6. The scatter plot in Fig. 6B shows that
401
on working days (blue) and at the weekend (green), the relationship between the measured
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403
during working and weekend days for LDV, MDV and HDV (see: section 2.3), this
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indicates that the applied emission factors applied were accurate. The statistical data are
405
presented in Table 4. The statistics for NOx are in line with other studies of hourly
406
modelled and observed contribution to NOx in street canyons (Vardoulakis et al., 2007).
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A similar approach as for NOx was followed for PN. The PN emission factors (#.km-1):
408
1.8E+13 (PC), 1.7E+14 (vans), 9.2E+13 (MDV), 6.8E+13 (HDV) and 1.7E+13 (buses)
409
were derived from the COPERT data (see: Table 3) taking into account the Euro
410
classification of the average urban car traffic in the Netherlands in 2014 (see: Fig. 4). From
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the emission factors for PC (85% of the car fleet) and vans (11% of the car fleet), a PN
412
emission factor of 3.5E+13 was derived for LDV. Using the hourly traffic composition and
413
related emission factors, the hourly contribution to PNC at the traffic location in the study
414
period was modelled and presented with the measured contribution as a time series and a
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scatter plot in Fig. 7.
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Figure 7: The measured and modelled contribution to PNCs at the traffic location as a
422
time series (A) and a scatter plot with standard and real-world PN emission factors (B).
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425
dynamometer-based COPERT emission factors) and measured contributions to PNC are
426
not closely aligned. This is mainly attributable to inaccurate PN emission factors, since
427
other relevant parameters such as wind speed, wind direction, traffic volume, traffic
428
composition and street canyon model were similar to the NOx analysis. Subsequently, real-
429
world PN emission factors were derived from the ratio between the average measured
430
(14,000) and modelled (1,600) contribution to PNC at the traffic location. This resulted in
431
a ratio of eight. Multiplying standard PN emission factors by the value eight provided real-
432
world emission factors for LDV, MDV and HDV in 2014. Subsequently, the hourly
433
modelled contributions to PNC at the traffic location were recalculated and presented in
434
Fig. 7 as “real-world EF”. The time series in Fig. 7A illustrate the improved alignment of
435
the “real-world EF” modelled PNC contribution with the measured PNC compared to the
436
“standard EF” modelled PNC. The correlation between measured and modelled PNC
437
contribution remains however rather poor with a Pearson R: 0.2. In the scatter plot Fig. 7B,
438
the data for working days and the weekend are not shown separately, unlike NOx in Fig.
439
6B due to the small differences in PN emissions between working days and the weekend.
440
The statistical data from comparing the measured and real-world modelled PN are
441
presented in Table 4.
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Table 4: Statistical parameters for comparison of the hourly measured and modelled
444
contributions to NOx and PNC at the traffic location in the study period (N = 686). NOx (µg.m-3)
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PNC (#.cm-3)
modelled
measured
modelled
measured
39.1
39.3
14,000
14,000
average
448
Pearson correlation (R)
0.6
0.2
449
slope
0.8
0.7
450
FAC2
64%
44%
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The mean fractional error (FAC2) values for NOx and PNC indicate that respectively 64%
453
and 44% of the modelled contributions with real-world emission factors are within a range
454
of a factor 0.5 to 2 of the measured contributions. Considering uncertainties in the Euro
455
classification of road traffic at the traffic location, the representativeness of real-world
456
emission factors for actual driving conditions and the variability in wind parameters, the
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correlation between the hourly modelled and measured contribution at the traffic location
458
is reasonable for NOx but rather poor for PNC. This underlines the need to develop more
459
accurate real-world emission factors for PNC.
460
3.4 Annual average PNC in 2014
462
The established real-world PN emission factors were used to model the annual average
463
contribution to PNC along urban roads and within 25m of motorways in the city of
464
Amsterdam in 2014. The average traffic composition in Amsterdam in 2014 was 96%
465
LDV, 1% MDV, 2% HDV and 1% buses (on urban roads) and 94% LDV, 3% MDV and
466
3% HDV (on motorways). The calculated average PN emission factors for urban and
467
motorway traffic were respectively 2.9E+14 and 3E+14. Meteorological data and traffic
468
data for roads with over 10,000 vehicles per day, were available on a web-based tool in the
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Netherlands (www.nsl-monitoring.nl). The results are presented in Fig. 8.
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Figure 8: The modelled contribution to PNCs near road traffic in the city of Amsterdam in
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2014.
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Fig. 8 illustrates that PNC were particularly elevated near motorways with over 100,000
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vehicles per day and along street canyons with over 20,000 vehicles per day.
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4.
DISCUSSION AND CONCLUSION
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One important uncertainty in our study was correction of the urban background of PNC
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with a factor two to 18,100 #.cm-3 in the study period. In two earlier studies, the
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background in Amsterdam was 18,000 in 2003 (Puustinen et al., 2007) and 9,600 in 2013
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period between 09:00 and 16:00 to avoid rush-hour traffic. Given lower PN emission
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factors for the 2014 car fleet compared to 2003 and differences in instruments and
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measurement strategies, it was concluded that a correction of a factor two in our study
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resulted in a too high urban background and consequently, a too low increment of PNC at
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the traffic location. This may explain the relative high percentage of 26% of outliers in the
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increment of PNC compared to 2% for NOx (see: section 2.4). Therefore, the outcome of
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our study likely underestimated the established real-world emission factor of PN.
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A second uncertainty concerns the hourly modelled contributions from traffic emissions to
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NOx at the traffic location. The National Institute for Environment and Public Health
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(RIVM) has validated our modelling approach by comparing the measured and modelled
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contributions to NO2 at 400 locations in the Netherlands in 2010 and 2011 (www.rivm.nl).
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The difference in annual average NO2 was less than 1 µg.m-3 in a range of 30 to 50 µg NO2
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per m3. Our study shows that the hourly average measured (39.1) and modelled (39.3)
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contribution to NOx (µg.m-3) at the traffic location also corresponds closely. From the good
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agreement between modelled and measured contribution to NOx, it was concluded that the
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street canyon model and traffic data are adequate to estimate the hourly contribution of
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traffic emissions to air quality at the traffic location. The factor eight difference between
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the modelled and measured contributions to PNC at the traffic location can therefore
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mainly be attributed to inaccurate standard PN emission factors. This confirms our
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assumption in that current dynamometer-based PN emission factors from COPERT and
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HBEFA which are even a factor ten lower than from COPERT, as illustrated in Table 3,
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may be qualified as “first estimates”. The main reasons for the differences between
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dynamometer-based and real-world emission factors are attributed to different cut-off size
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diameter of PNC instruments, non-volatile versus volatile PNC measurements and non-
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representative dynamometer test conditions compared to real-world driving conditions.
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The real-world PN emission factors along urban roads was 2.9E+14 #.km-1 and near
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motorways 3E+14 #.km-1 in Amsterdam in 2014. This corresponds with the range of real-
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world PN emission factors of 1.3 to 4E+14 #.km-1 (urban) and 2 to 5E+14 (motorway) in
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Europe in the period 2005 to 2010, as presented in the review by Kumar et al. (2011).
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The ratio in emission factors for HDV/LDV in our study was a factor 2, while other studies
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(e.g. Nickel et al., 2013; Wang et al., 2010) found ratios ranging from 4 to 22. The wide
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range in ratios was attributed by Nickel et al. (2013) to different lower size cut-offs of the
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applied PNC instruments: the lower the size cut-off, the larger the ratio. Another
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explanation is that a lower percentage in diesel LDV contributes to a higher ratio in
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emission factors for HDV/LDV. This is illustrated by the low ratios of 2 and 4 in our study
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in 2014 and in Germany in 2007 (Nickel et al., 2013) with respectively 35% and 40%
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diesel LDV. Contrary a ratio of 22 was found in a study in 2008 in Denmark with less than
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20% diesel LDV (Wang et al., 2010).
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5.
ACKNOWLEDGEMENTS
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This study received financial support from the Netherlands Ministry of Infrastructure and
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the Environment. We are grateful to the Royal Netherlands Meteorological Institute, the
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National Institute for Public Health and Environment and the Department of Traffic and
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Spatial Planning of the Municipality of Amsterdam for providing meteorological and road
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traffic data.
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Real world PN emission factor for traffic eigth times standard emission factor Real world PN emission factors: 2.9E+14 (urban) and 3E+14 (motorway) #.km-1 PNC near urban roads three times higher than urban background
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