Short-term intra-urban variability of UFP number concentration and size distribution October 2013 campaign

Transcription

1 Distribution: Restricted Final report Short-term intra-urban variability of UFP number concentration and size distribution October 2013 campaign E. Frijns, J. Van Laer, P. Berghmans, B. Bergmans, F. Lenartz Study accomplished under the authority of VMM 2014/MRG/R/59 February 2014

2 All rights, amongst which the copyright, on the materials described in this document rest with the Flemish Institute for Technological Research NV ( VITO ), Boeretang 200, BE-2400 Mol, Register of Legal Entities VAT BE The information provided in this document is confidential information of VITO. This document may not be reproduced or brought into circulation without the prior written consent of VITO. Without prior permission in writing from VITO this document may not be used, in whole or in part, for the lodging of claims, for conducting proceedings, for publicity and/or for the benefit or acquisition in a more general sense.

3 Distribution List DISTRIBUTION LIST Christine Matheeussen, VMM Jeroen Staelens, VMM Edward Roekens, VMM I

4 Table of Contents TABLE OF CONTENTS Distribution List I Table of Contents II List of Figures III List of Tables V CHAPTER 1 Introduction Background Aim 2 CHAPTER 2 Material & methods Sampling Sites Intra-urban Materials UFP Meteo NO Black Carbon PM Methods Diffusion correction Instrument correction Statistical analysis 12 CHAPTER 3 Results and Discussion Meteorology Intra Urban Temporal variability number concentration Spatial variability number concentration Size distribution variability NO 2 versus UFP PM Black Carbon 32 CHAPTER 4 Conclusions and Recommendations Summary and Conclusions Recommendations 36 References 37 II

5 List of Figures LIST OF FIGURES Figure 1 Relative position of the sites in the city of Antwerp 4 Figure 2 Urban background site VMM Monitoring Station 4 Figure 3 Ring Road site next to the Antwerp-East junction 5 Figure 4 Sampling at the Ring Road site 5 Figure 5 Suburban site next to the Scheldt 6 Figure 6 Suburban sampling site 7 Figure 7 Wind rose at the urban background location (hourly averaged data) 14 Figure 8 Daily average wind direction 15 Figure 9 Time profile temperature and relative humidity 15 Figure 10 Hourly average UFP concentration during week days at the Monitoring Station, Suburban and Ring Road Site 16 Figure 11 Hourly average UFP concentration during weekend at the Monitoring Station, Suburban and Ring Road Site 17 Figure 12 Linear regression plot between ultrafine particle number concentrations at the Monitoring Station site and at the Suburban site 18 Figure 13 Linear regression plot between ultrafine particle number concentrations at the Monitoring Station site and at the Ring road site 19 Figure 14 Coefficients of Divergence calculated for the Monitoring Station Suburban pair. The 1 st quartile, median and 3 rd quartile are presented by the box. The whisker represents the 5% and 95% values 20 Figure 15 Coefficients of Divergence calculated for the Monitoring Station Ring Road pair. The 1 st quartile, median and 3 rd quartile are presented by the box. The whisker represents the 5% and 95% values 21 Figure 16 Size distribution during week days at the three locations 22 Figure 17 Size distribution during the weekend at the three locations 23 Figure 18 Particle number size distributions during week days, during (a) night time (20:00-06:00), (b) morning commute (07:00-10:00), (c) midday (11:00-14:00) and (d) evening commute (15:00-19:00) at the three locations 24 Figure 19 Particle number size distributions during weekend, during (a) night time (20:00-06:00), (b) morning commute (07:00-10:00), (c) midday (11:00-14:00) and (d) evening commute (15:00-19:00) at the three locations 25 Figure 20 Correlation coefficients between the Monitoring Stations and Urban, Ring Road, Suburban or Public park sites for six different size ranges 26 Figure 21 The coefficient of Divergence between the Monitoring Stations and Urban, Ring Road, Suburban or Public park sites for six different size ranges 27 Figure 22 Weekly average NO 2 concentrations at the three locations 28 Figure 23 One week average UFP concentration per size range (SMPS) versus one week average NO 2 concentration at all three sites 29 Figure 24 Daily average PM10 concentration (µg/m 3 ) at the Monitoring Station and Suburban location 30 Figure 25 Linear regression plot of the PM10 concentration at the Monitoring Station location (filter method) against the PM10 concentration at the Suburban location (optical online). 31 Figure 26 Linear regression plot of the 30 minute average PM10 concentration against the 30 minute average UFP concentration (SMPS) at the Monitoring Station and at the Suburban location. 31 Figure 27 Hourly average BC concentration during week days at the Monitoring Station (670 nm), Suburban (880 nm) and Ring (880 nm) 32 III

6 List of Figures Figure 28 Hourly average BC concentration during weekend at the Monitoring Station (670 nm), Suburban (880 nm) and Ring (880 nm) 33 Figure 29 Linear regression plot of the BC concentration at the Monitoring Station (670 mn) against the BC concentration at the Suburban (880 nm) or the Ring (880 nm) location. 34 Figure 30 Linear regression plot of the BC concentration against the UFP concentration (SMPS) at the Monitoring Station (670 mn), the Suburban (880 nm) or the Ring (880 nm) location 35 IV

7 List of Tables LIST OF TABLES Table 1 Overview housings and monitors per measuring site 8 Table 2: Scan time and size range per instrument type 9 Table 3 Instrumental correction factors for particle counters 11 Table 4 Instrumental correction factors for size distribution monitors 12 Table 5 Instrumental correction factors for black carbon monitors 12 Table 6 Correlation coefficient (r) and coefficient of determination (R 2 ) for the UFP NO 2 relation for different particle size ranges 29 V

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9 CHAPTER 1 Introduction CHAPTER 1 INTRODUCTION 1.1. BACKGROUND Joaquin (Joint Air Quality Initiative) is a new EU cooperation project supported by the INTERREG IVB North West Europe program ( The aim of the project is to support healthoriented air quality policies in Europe. To achieve this, the project will provide policy makers with the necessary evidence on the current local and/or regional situation (e.g. measurements of emerging health relevant parameters), provide them with best-practice measures that can be taken and motivate them to adapt and strengthen their current air quality policies The project is divided into 3 work packages, each of which is specifically tailored to target the aim identified above. WP1 will focus on the development and implementation of novel air quality monitoring infrastructure for pollutants associated with the most dangerous aspects of air pollution on human health. Action 1 of work package 1 (WP1A1) is entitled Setup and operation of a next generation Watchdog Network for health pertinent pollution parameters. In four cities in NWE Europe, cutting edge instrumentation will be used to measure airborne concentrations of black carbon and the number concentration and size distribution of ultrafine particles (UFP). The monitors will be located at sites where statutory air quality parameters are measured such as nitrogen oxide (NOx) concentrations and the mass concentration of particulate matter (PM). To achieve this goal, a new monitoring infrastructure will be constructed (part of a NWE air quality Observatory and complementary to the existing EU network), comprising four new monitoring stations in Antwerp, Amsterdam, Leicester and Brighton. Real-time measurements of UFP number and size distribution and of black carbon concentrations will be made. As a first step, the instrumental approaches used for UFP monitoring were assessed. From literature review and a laboratory test, an evaluation was made of current commercially available UFP devices in order to choose appropriate instrumentation and methodology to measure number concentration and size distribution under routine measuring network conditions. Based on the evaluation three different monitors were selected and purchased: 3 x Grimm SMPS 5420 with L-DMA (GGD, VMM, ECN) 3 x TSI UFP-Monitor 3031 (UoL, UoB, ECN) 5 x TSI EPC 3783 (GGD, VMM, ECN, UoL, UoB) Before positioning these instruments in their new monitoring stations across Europe the instruments were compared at a temporary test site in Antwerp. The results of this comparison are described in the report: UFP instrument comparison at an urban background location in Antwerp (VITO, 2013/MRG/R/172). After the comparison all instruments were moved to different locations in Antwerp for a measurement campaign (February 2013) which is described in the report: Short-term intra-urban variability of UFP number concentration and size distribution (VITO, 2013/MRG/R/173). The study 1

10 CHAPTER 1 Introduction evaluated ultrafine particles in the vicinity of a busy road, particularly as they were transported downwind. The study also analysed the spatial temporal variability of UFP number size distributions and particle number concentrations at five locations in Antwerp and explored the homogeneity of the UFP exposure within the sampling area. It was found that: All locations showed morning and evening rush hours in their day profiles; Differences in concentrations and size distributions were caused by the proximity to local sources, the source strength and traffic patterns; The size fractions to nm probably had a more regional character due to the fact that all scattered sites showed a strong correlation; VMM Borgerhout is classified as a community-representative monitoring site for UFP; Spatial variability was lowest between the Public Park and Monitoring Station (most homogenous) and highest between the Urban and Ring Road location (most heterogeneous); Highest spatial divergence was observed for the smallest particle size range (20-30 nm). The spatial temporal variability study was repeated in October 2013 but with limited locations. The present report describes the results of the spatial temporal variability study at three of the four locations (Urban background/monitoring Station, Ring Road and Suburban) that were monitored in February The integration of the results from the fourth location (Public Park location) and the comparison of the October 2013 campaign with the February 2013 campaign will be performed by VMM and is not described in the present report AIM The aim of the present study is to repeat the intra-urban variability study from February 2013 with a limited number of locations and analyse the spatial temporal variability of UFP number size distributions and particle number concentrations and explore the homogeneity of the UFP exposure within the sampling area. 2

11 CHAPTER 2 Material & methods CHAPTER 2 MATERIAL & METHODS 2.1. SAMPLING SITES INTRA-URBAN The intra-urban study was conducted at five sites in the Antwerp region in February Five different types of air quality locations were selected as they were needed to find out if the existing monitoring station is representative for UFP concentrations in the Antwerp region. The sites could be classified as: 1. Urban background site (VMM Monitoring Station); 2. Urban site; 3. Ring Road site; 4. Suburban site; 5. Public Park site. The relative position of the sites are shown in Figure 1. Only four sites were selected during the October 2013 campaign: the Urban background site, the Ring road site, the Suburban site and the Public park site. VITO was responsible for the measurements at the Ring road and Suburban sites. VMM was responsible for the urban background and Public Park locations. This section describes the different site characteristics. 3

12 CHAPTER 2 Material & methods Figure 1 Relative position of the sites in the city of Antwerp Urban background (VMM Monitoring Station) The urban background site is situated adjacent to the Plantin & Moretuslei, a 2 x 2 lane main street (Figure 2). The Plantin & Moretuslei runs generally east and west through the city. A bus stop is situated in the immediate vicinity of the sampling site. Figure 2 Urban background site VMM Monitoring Station 4

13 CHAPTER 2 Material & methods Ring Road The Ring Road site is situated next to the highway R1 which consists of 11 lanes (6 lanes southerly, 5 lanes northerly) in four different carriageways (Figure 3). The site is located close to the junction Antwerp-East where the E313 connects with the R1. The instruments were located next to the traffic lanes in the Brussels direction. A pumping station was situated further away from the Ring Road, behind the monitoring infrastructure. Figure 3 Ring Road site next to the Antwerp-East junction Figure 4 Sampling at the Ring Road site 5

14 CHAPTER 2 Material & methods The Ring Road site is situated approximately 1200 meters from the Monitoring station in a straight line. The instruments were situated approximately 10 meters (shoulder plus verge) from the right lane (Figure 4). A container was situated approximately 40 meters from the right lane (Figure 4 right). This container was used by Donaldson for filter experiments. No interference was expected because no particle emitting instruments were applied. Suburban The suburban site is the only site that is situated on the other bank of the river Scheldt. The river lays about 120 meters in easterly direction from the monitoring location. The closest road, the Thonetlaan, is about 40 meters from the site in westerly direction (Figure 5). This road has two wide lanes with a large verge in between (total of 30 meters). The site itself is characterized by grass, shrubs and small trees. A few meters from the location is a community centre for elderly (Figure 6). Figure 5 Suburban site next to the Scheldt 6

15 CHAPTER 2 Material & methods Figure 6 Suburban sampling site The suburban site is situated approximately 3300 meters from the Monitoring station in a straight line MATERIALS UFP The UFP instruments were positioned in two trailers (Ring Road, Suburban) and in a stationary air quality monitoring station (Urban background). The measuring cabins were all equipped with an air conditioning system. At two locations (Ring Road and Urban background) particle number concentration was measured with an Environmental Condensation Particle Counter (EPC 3783, TSI Inc.). At the Suburban location particle number concentration was measured with a Condensation Particle Counter (CPC 3786, TSI Inc.). The size distribution in the size range from 10 nm to 1 µm was measured with a scanning mobility particle sizer (SMPS consisting of CPC 5420, L-DMA, 85 Kr (185 MBq), Grimm) at the Urban background location and with two IfT customized scanning mobility particle sizers (SMPS consisting of TSI CPC 3772, Long DMA, 63 Ni (100 MBq)) at the Ring Road and Suburban location. The EPC at the urban background location was connected to an environmental sampling system (TSI ) at a sampling rate of 3 lpm. The SMPS at the urban background location was connected to a Grimm sampling system with TSP sampling pipe and nafion dryer and sampled with 1.5 lpm. The instruments at the Suburban and Ring Road location were connected to a sampling system with a PM 10 head, nafion dryer and long sampling line with a high flow rate (2.4 m³/h). 7

16 CHAPTER 2 Material & methods Location Housing Monitor Urban background Monitoring Station Grimm-SMPS EPC 3783 Ring Road Trailer IfT cust. SMPS EPC 3783 Suburban Trailer IfT cust. SMPS CPC 3786 Table 1 Overview housings and monitors per measuring site An overview of the instrumental setup at each site is given in Table 1. A more detailed description of the monitoring and sampling systems can be found in the report: UFP instrument comparison at an urban background location in Antwerp (VITO, 2013/MRG/R/172) METEO Meteorological conditions (temperature, relative humidity, wind speed and wind direction) were monitored at the Antwerp Luchtbal location (42M802) and assumed to be representative for all sites NO 2 One week average NO 2 concentrations were measured (IVL passive samplers) at the Suburban and Rind Road location to determine a possible relation between two traffic related pollutants NO 2 and UFP. At the Monitoring Station NO 2 concentration (half hour average) was measured online with a Thermo Scientific Chemiluminescence NO-NO 2 -NOx Analyzer model 42i BLACK CARBON Half hour average black carbon concentrations were measured at the three locations using different instruments. At the Monitoring Station black carbon was monitored with a Thermo Scientific Model 5012 Multi- Angle Absorption Photometer (MAAP). The model 5012 MAAP measures atmospheric BC loading using a radiative transfer scheme to particle loaded glass fibre filters. The black carbon content of the aerosols are continuously determined by simultaneously measuring the optical absorption and scattering of light by the particles collected on the filter tape. The combination of these two techniques provides a measurement of the black carbon content. At the Ring Road location black carbon was monitored with a Magee Scientific AE22 Dual wavelength aethalometer (370, 880 nm). The AE22 measures light absorption of carbon particles at two wavelengths: 880 nm (IR), quantitative for the mass of Black or Elemental Carbon; and 370 8

17 CHAPTER 2 Material & methods nm (UV), indicating the presence of aromatic organic compounds such as are found in wood smoke, biomass-burning smoke, and tobacco smoke. At the Suburban location black carbon was monitored with a Magee Scientific AE33 7-wavelength aethalometer (370, 470, 520, 590, 660, 880, 950 nm). The AE33 measures light absorption by suspended aerosol particles at seven wavelengths, from 370 nm (UV) to 950 nm (IR). The BC MAAP measurements were compared with the light absorption of carbon particles at the 880 nm wavelength from the AE22 and AE PM10 At the Monitoring Station an ESM FH 62 I-R monitor (also known as FAG monitor) was used for general PM10 monitoring. This instrument uses the technique of β-attenuation (the attenuation of β-rays by a filter is directly related to the amount of mass on the filter). To avoid condensation of water on the filter the inlet is heated. This process not only leads to the loss of water, but also of certain semi-volatile compounds such as ammonium nitrate. Therefore VMM applies an experimentally determined calibration factor (1.25) to the ESM measurements so that these PM10 data can be considered to be equivalent with the gravimetric reference method. At the Suburban and Ring Road location Grimm Particle Dust Monitors (Model 1.109) were used to monitor PM10 concentrations. The Grimm PDM optical particle counter measures particle number concentration in 31 size channels from µm METHODS The measurements were conducted between the 8 th of October and the 4 th of November An instrument comparison was made from the 30 th of September till the 8 th of October at the Monitoring Station location to determine instrumental correction factors. The size distribution monitors measured at slightly different time intervals in slightly different size ranges which are presented in Table 2. Instrument Scan time (min) Size range Grimm SMPS nm µm IfT custom. SMPS nm Table 2: Scan time and size range per instrument type The EPC and CPC logged total number concentration at a 1 minute average, counting particles in the size range from 7 nm till 1 µm for the EPC and from 2.5 nm till 1 µm for the CPC. The weather station logged continuously at a 30 minute interval. The online Black Carbon and NO 2 concentration were measured at a 30 minute interval. The NO 2 passive samplers were exposed on a weekly basis. At the Monitoring Station PM10 was measured at a 30 minute interval and at the Suburban and Ring location with a 5 minute interval. The standard operating protocol required weekly visits to check equipment performance and perform maintenance. Instrument operation was checked remotely every day. 9

18 CHAPTER 2 Material & methods All data were reviewed and screened for irregularities. Data collected during instrumental errors was removed DIFFUSION CORRECTION There are five main mechanisms which may lead to particle losses on to the surface of a sampling tube; these are sedimentation (gravitational), thermophoresis, electrostatic, inertial impaction and diffusion (Friedlander, 2000, Hinds, 1999). Of all potential losses, those due to diffusion and inertial impaction are most important for ambient particle measurements (Hinds, 1999). The second of these is only important under turbulent flow conditions and for particles larger than 100 nm (Lee and Gieseke, 1994). Gormley and Kennedy (1949) first derived the equation for diffusional losses in a fully developed laminar flow through a tube of circular cross section. Hinds (1999) developed a simplified expression (accuracy of 1%) for calculating the penetration efficiency P which was used in this study. The penetration efficiency P is the fraction of entering particles (N in ) that exit (N out ) through a tube. The formula used (Hinds, 1999) is presented below: P = 1-5.5µ 2/ µ for µ<0.009 P = exp(-11.5 µ ) exp(-70.1 µ ) for µ>0.009 µ = DL/Q D = diffusion coefficient of the particles L = length of the tube Q = volume flow rate through the tube The IfT customized SMPS and EPC connected to the high flow sampling system were corrected using the following formula from Gormley and Kennedy (1949): P = 1-5.5µ 2/ µ for µ<0.007 P = exp(-11.5 µ ) exp(-70.1 µ ) exp(-179 µ ) for µ>0.007 The difference between the outcomes of the two formulas is limited. A description for each instrument type correction, is given below. Grimm SMPS The Grimm SMPS software already takes internal diffusion losses into account. Only diffusion losses were calculated for the sampling system and sampling lines. IfT customized SMPS For each size bin of the IfT customized SMPS three penetration factors were calculated: one for determining the internal diffusion losses for the CPC (D 50 ), one for determining the internal 10

19 CHAPTER 2 Material & methods diffusion losses in the SMPS and one for determining the losses in the sampling system including sampling lines. The combination of all three was used for determining the losses for the SMPS system. EPC To calculate diffusion losses a size distribution is necessary. Because size distribution monitors (IfT SMPS or Grimm SMPS) were present at each site also diffusion losses for the EPC s were calculated. Raw size distribution data and calculated EPC penetration factors were used to calculate diffusion corrected size distribution data. The diffusion corrected total number concentration determined with the size distribution monitors was divided by the raw total number concentration determined with the size distribution monitors and this factor was used to correct the 10 minute averaged EPC data INSTRUMENT CORRECTION After diffusion loss correction the data was also corrected for instrumental differences. The correction factors are described below and were determined during the one week comparison at the Monitoring Station location in Borgerhout from the 30 th of September till the 8 th of October. Because of instrument malfunction the correction factors from the CPCs, BC monitors and PM10 counters were determined differently. The EPC and CPC instrumental correction factors are shown in Table 3. The correction factor for the Ring Road EPC could be determined using about 10 hours data (7/10/ :20-22:00 and 8/10/2013 0:10-6:40) during the Monitoring Station comparison. The Suburban CPC was not working during the Monitoring Station comparison. The correction factor for the Suburban CPC was determined by comparing the Ring road EPC with the Suburban CPC at VITO (outdoor with Environmental Sampling System in weather proof housing and indoor) after ending the measurement campaign. The correction factor corrects also for the difference in measurement range between the EPC (7 nm 1 µm) and the CPC (2.5 nm 1 µm). We expect that the determination of the Suburban CPC correction factor at VITO (rural) instead at Antwerp (urban) will not result in a significantly different correction factor because a representative number concentration range was measured at VITO. Locations Correction factor Monitoring Station Suburban Ring Road Table 3 Instrumental correction factors for particle counters 11

20 CHAPTER 2 Material & methods The instrumental correction factors for the size distribution monitors can be found in Table 4. Locations/ Ranges SMPS Monitoring Station Ring Road Suburban < > totaal Table 4 Instrumental correction factors for size distribution monitors The instrumental Black Carbon correction factors can be found in Table 5. The factors were derived from a comparison at the Monitoring Station location in a different time frame. The monitors were compared from the 4 th of April till the 24 th of May. Locations Correction factor Monitoring Station 670 nm Suburban 880 nm Ring Road 880 nm Table 5 Instrumental correction factors for black carbon monitors The gravimetric 24 hour PM10 data from the Monitoring station (Leckel) was used to recalculate the PM10 counts (Grimm PDM 1.109) to PM10 in µg/m 3 and to additionally correct (factor 1.02) the PM10 concentration from the ß attenuation monitor (regression analysis with intercept at zero) STATISTICAL ANALYSIS The correlation coefficient (r) is a standard method used to evaluate the (linear) relationship between paired data points. The coefficient can vary from 0 (no correlation, independent data points) to ± 1 indicating perfect positive or negative correlation. The correlation coefficient helps to determine what fraction of the number concentrations at any particular site can be explained by the concentrations simultaneously measured at the other sites. One limitation of this method, however, is that perfect correlation can be observed between two sites where the concentrations vary by a consistent factor. In other words high correlations between paired sites would only imply uniform temporal variation (Lianou et al. 2007). Therefore, calculating r alone would not necessarily provide sufficient information to characterize the variability between two sites. Another useful method to characterize the spatial variability between site pairs is the coefficients of divergence (COD) method (Moore et al 2009). The COD is defined as: 12

21 CHAPTER 2 Material & methods ( ) ( ) where x ij is the ith concentration measured at site j for a given sampling period, j and k are two different sites, and n is the number of observations (Krudysz et al. 2009). By inspection, the COD for a given site pair will vary from 0 where concentrations are identical at both sites to 1 where concentrations are highly different. The COD therefore specifically addresses the limitation to the correlation coefficient described above. A low COD value indicates a high level of homogeneity in concentrations between site pairs, and a high COD, the opposite. CODs larger than 0.2 can be considered heterogeneous (Wilson et al. 2005). For the intra-urban variability study COD s were calculated to determine the variability between the VMM monitoring station and the other sites. 13

22 CHAPTER 3 Results and Discussion CHAPTER 3 RESULTS AND DISCUSSION 3.1. METEOROLOGY The wind rose is presented graphically in Figure 7 showing the wind conditions, direction and speed, during the measurements campaign (8 October 4 November 2013) at Antwerp Luchtbal (30 m height). Figure 7 Wind rose at the urban background location (hourly averaged data) The wind rose shows prevailing South West wind directions (28 and 25 %). The average wind speed was 4.9 m/s and could be described as a moderate breeze. Daily average wind directions are shown in Figure 8. The dominant wind direction during the first half week of the Monitoring Station comparison (1 October 8 October) was East. During The second half it turned to South, North-West and South again. During the measurement campaign the dominant wind direction was South West except for the North-Easterly winds on the 11 th of October. The temperature time profile (Figure 9) shows daily recurring patterns: higher daytime and lower night time temperatures. The temperature varied approximately between 4 C and 23 C. Relative humidity varied roughly between 57 and 100%. The average temperature and relative humidity were 12.7 C and 87% RH. 14

23 CHAPTER 3 Results and Discussion Figure 8 Daily average wind direction Figure 9 Time profile temperature and relative humidity 15

24 CHAPTER 3 Results and Discussion 3.2. INTRA URBAN TEMPORAL VARIABILITY NUMBER CONCENTRATION Two Environmental Particle Counters (EPC at Monitoring Station and Ring location) and one Condensation Particle Counter (CPC at Suburban location) were available for the intra urban variability study. Hourly average particle number concentrations were calculated using the 10 minute average data from 8 th October 16:00 till 31 st October 17:50 (23 days). Regression plots and COD analysis were made using only data lines when data was present at all three locations (roughly between 11/10/2013 8:20 and 23/10/2013 2:20 resulting in 9.5 days with data). Diurnal variability during week day The hourly average diurnal variability in particle number concentrations (EPC and CPC) during weekdays is shown in Figure 10. The error bars represent standard deviations. Figure 10 Hourly average UFP concentration during week days at the Monitoring Station, Suburban and Ring Road Site In Figure 10 the hourly average UFP concentrations at the Monitoring Station site are compared with the Suburban and Ring Road site. UFP concentrations at the Monitoring Station and Ring Road site followed the same trend. Morning (4:00-10:00) and evening rush hours (15:00-21:00) occurred at the same time. Slight increases in UFP number concentration were also measured at the Suburban site during rush hours, but not pronounced. 16

25 CHAPTER 3 Results and Discussion Highest UFP concentrations of almost 70,000 particles cm -3 (highest diurnal hourly average peak) and highest temporal variability (largest standard deviation) were measured at the Ring Road site. Lowest diurnal hourly average UFP concentrations of about 30,000 particles cm -3 were measured during night time (1:00-2:00) at the Ring road location. Total average UFP concentration was about 51,000 particles cm -3. Lowest UFP concentrations and less temporal variability were measured at the Suburban site. Total average UFP concentration was about 9,000 particles cm -3. The total average UFP concentration at the Monitoring Station location was about 17,000 particles cm -3. Diurnal variability during Weekend The hourly average diurnal variability in particle number concentrations (EPC and CPC) during weekends is shown in Figure 11. The error bars represent standard deviations. As shown in Figure 11 highest UFP concentrations were measured during the evening rush hours at all three locations (55,000 particles cm -3 Ring; 16,000 particles cm -3 Monitoring Station; 8,000 particles cm -3 Suburban). The morning rush hour starts later and is less pronounced when comparing week days (Figure 10) with weekend (Figure 11). During the weekend higher UFP concentrations were measured at the Ring road site than at the Monitoring Station and Suburban site. This relation was also found for week days. Total average UFP concentrations during the weekend for the Monitoring Station, Ring road en Suburban location were respectively: 42,000 particles cm -3 Ring; 11,000 particles cm -3 Monitoring Station; 6,000 particles cm -3. Average UFP concentrations were lower during the weekend than during week days. Figure 11 Hourly average UFP concentration during weekend at the Monitoring Station, Suburban and Ring Road Site 17

26 CHAPTER 3 Results and Discussion Correlation coefficient (r) In Figure 12 and Figure 13 linear regression plots are made of respectively the total 10 minute average UFP particle number concentration at the Suburban and Ring Road location against the total 10 minute average UFP particle number concentration at the Monitoring Station. Coefficients of determination (R 2 ) and regression coefficients (rc) were calculated. A strong correlation coefficient was calculated comparing the datasets from the Monitoring Station with the Suburban location (R 2 = / r = ) when the intercept was forced to go through zero (standard error 3,490 particles cm -3 ). Total average particle number concentrations at the Suburban site were less than half of the concentrations at the Monitoring Station site (rc = solid line). Not forcing the intercept to go through zero resulted in a lower standard error of 3,048 particles cm -3, a regression coefficient of 0.29 (dashed line), an intercept of 3,478 particles cm -3, a R 2 = and a r = resulting in a moderately strong correlation. This fit didn t seem to be logical because the trend line is bending away resulting in an intercept. The intercept could not be explained because the Suburban location does not have higher concentrations than the urban background location (Monitoring Station) at any time. It can be discussed if the deviating data points at the highest concentrations are outliers or not. Removing the highest concentrations (<40,000 #/cm 3 ) from the dataset still resulted in a lower regression coefficient (rc =0.3450) than forcing the trend line to go through zero (rc = ). The comparison with a forced intercept is considered the most representative to determine differences in temporal variability between the Suburban and Monitoring Station location. Figure 12 Linear regression plot between ultrafine particle number concentrations at the Monitoring Station site and at the Suburban site 18

27 CHAPTER 3 Results and Discussion A larger temporal variation was found for the comparison between the Monitoring Station and the Ring Road site (Figure 13). A strong correlation was found between the Ring Road and Monitoring Station data pair (R 2 = / r = ) when the intercept was forced to go through zero (standard error 20,987 particles cm -3 ). UFP concentrations were about 2.6 times higher at the Ring Road compared to the Monitoring Station site (solid line). Not forcing the intercept to go through zero resulted in a lower standard error of 16,139 particles cm -3, a regression coefficient of 1.32 (dashed line), an intercept of 27,409 particles cm -3, a R 2 = and a r = resulting in a moderately strong correlation. In this comparison the intercept can be considered as the local source contribution originating from the traffic at the Ring road. Figure 13 Linear regression plot between ultrafine particle number concentrations at the Monitoring Station site and at the Ring road site SPATIAL VARIABILITY NUMBER CONCENTRATION As described in paragraph the correlation coefficient has a limitation that perfect correlation can be observed between two sites although the concentrations can vary by a consistent factor. Therefore the coefficient of divergence (COD) was calculated for the four data pairs. Figure 14 shows the overall hourly CODs calculated across the Monitoring Station and Suburban data pair. The median COD varied between 0.25 and CODs larger than 0.2 can be considered heterogeneous. This suggests that overall the total particle number concentrations were moderately heterogeneous for these sites. Lowest CODs were found during night time, early morning and mid-day. Highest CODs and highest spreading were found during morning rush hours. It seems the data pair was more comparable when emission levels were lower. Less traffic is present during night time resulting in less UFP emission. 19

28 CHAPTER 3 Results and Discussion 1,0 Box Plot of multiple variables Monitoring Station - Suburban 0,8 0,6 COD 0,4 0,2 0,0 0:00-1:00 1:00-2:00 2:00-3:00 3:00-4:00 4:00-5:00 5:00-6:00 6:00-7:00 7:00-8:00 8:00-9:00 9:00-10:00 10:00-11:00 11:00-12:00 12:00-13:00 13:00-14:00 14:00-15:00 15:00-16:00 16:00-17:00 17:00-18:00 18:00-19:00 19:00-20:00 20:00-21:00 21:00-22:00 22:00-23:00 23:00-0:00 Median 25%-75% 5%-95% Figure 14 Coefficients of Divergence calculated for the Monitoring Station Suburban pair. The 1 st quartile, median and 3 rd quartile are presented by the box. The whisker represents the 5% and 95% values The CODs for the Monitoring Station-Ring Road pair is presented in Figure 15. Median CODs vary between 0.40 and 0.59 and were higher than the median CODs from the Suburban-Monitoring Station pair. More variability in the UFP number concentration next to the Ring Road is a possible explanation. It is interesting that the highest median CODs can be found between 23:00 and 6:00. A possible explanation could be the earlier start of morning rush hour during week days and the higher UFP concentration from 21:00 till 3:00 during the weekend at the Ring Road location. 20

29 CHAPTER 3 Results and Discussion 1,0 Box Plot of multiple variables Monitoring Station-Ring Road 0,8 0,6 COD 0,4 0,2 0,0 0:00-1:00 1:00-2:00 2:00-3:00 3:00-4:00 4:00-5:00 5:00-6:00 6:00-7:00 7:00-8:00 8:00-9:00 9:00-10:00 10:00-11:00 11:00-12:00 12:00-13:00 13:00-14:00 14:00-15:00 15:00-16:00 16:00-17:00 17:00-18:00 18:00-19:00 19:00-20:00 20:00-21:00 21:00-22:00 22:00-23:00 23:00-0:00 Median 25%-75% 5%-95% Figure 15 Coefficients of Divergence calculated for the Monitoring Station Ring Road pair. The 1 st quartile, median and 3 rd quartile are presented by the box. The whisker represents the 5% and 95% values SIZE DISTRIBUTION VARIABILITY In the February 2013 campaign UFP monitors were used to measure the UFP size distribution at two of five sites (Suburban and Public Park). A UFP Monitor measures particle number concentration in six size ranges (20-30, 30-50, 50-70, , and >200 nm). To be able to compare the size distribution data from the February with the October 2013 campaign the SMPS data (Monitoring Station, Suburban, Ring Road) were recalculated to the six size ranges of the UFP Monitor. Average size distributions, correlation coefficients and coefficients of determination were calculated using the 10 minute data from 8 th October 16:00 till 31 st October 17:50 (19.4 days). 21

30 CHAPTER 3 Results and Discussion Figure 16 Size distribution during week days at the three locations Figure 16 shows the size distribution at all three locations during week days. All three profiles showed the same trend. From 50 nm to 200 nm ultrafine particle number concentrations decreased. Highest particle number concentrations were measured in the size range from nm. Less fresh particles and more aged particles were present. This is remarkable for the Ring Road and Monitoring Station location because more fresh particles (20-30 nm) were expected due to the proximity of traffic. The absence of a bimodal distribution can be attributed to the absence or insignificant impact of industrial sources. Weekend size distributions are presented in Figure 17. The difference between week days and weekend size distributions trends was limited. 22

31 CHAPTER 3 Results and Discussion Figure 17 Size distribution during the weekend at the three locations The week day and weekend size distributions were also subdivided in four daily periods: night time (20:00-06:00), morning commute (07:00-10:00), midday (11:00-14:00) and evening commute (15:00-19:00) to see if differences between those periods were present. The week day size distributions are presented in Figure 18 and the weekend in Figure 19. No large differences were found between the total size distribution in Figure 16 and the periodic size distributions in Figure 18 a-d. This was also the case for the weekend size distributions present in Figure 17 and Figure 19 a-d. 23

32 CHAPTER 3 Results and Discussion (a) (c) (b) (d) Figure 18 Particle number size distributions during week days, during (a) night time (20:00-06:00), (b) morning commute (07:00-10:00), (c) midday (11:00-14:00) and (d) evening commute (15:00-19:00) at the three locations 24

33 CHAPTER 3 Results and Discussion (a) (c) (c) (d) Figure 19 Particle number size distributions during weekend, during (a) night time (20:00-06:00), (b) morning commute (07:00-10:00), (c) midday (11:00-14:00) and (d) evening commute (15:00-19:00) at the three locations 25

34 CHAPTER 3 Results and Discussion Figure 20 Correlation coefficients between the Monitoring Stations and Urban, Ring Road, Suburban or Public park sites for six different size ranges To find out if particular size ranges at the Monitoring Station site were better correlated with certain size ranges at the Ring Road or Suburban sites correlation coefficients were calculated for each size range and diagrammatized in Figure 20. Moderate correlations (r = ) were found for the Ring Road-Monitoring Station pair for the size ranges nm to nm. The Ring Road site was locally more influenced by the high traffic intensities explaining this moderate correlation. The fraction >200 nm was moderately strong correlated. The nm fraction showed the weakest correlation because this fraction is more susceptible to temporal variability caused by volumes of vehicular emissions and particle transformation rates. This high temporal divergence was also found in the Los Angeles basin (Krudysz et al, 2009). The Suburban Monitoring Station pair showed strong correlations for the to >200 nm fractions. These size fractions probably have a more regional character. The nm and nm fractions are more locally influenced which is shown by the moderately strong correlation in Figure 20. The coefficients of divergence (COD) were also calculated for the two data pairs and six size ranges to determine the extent of spatial variability. Large spatial variability was found for particles in all size ranges (CODs >0.2) except the >200 nm size range. Mild heterogeneity was found for the Monitoring Station Suburban dataset and moderate heterogeneity was found comparing the Ring Road with the Monitoring dataset. 26

35 CHAPTER 3 Results and Discussion Figure 21 The coefficient of Divergence between the Monitoring Stations and Urban, Ring Road, Suburban or Public park sites for six different size ranges NO 2 VERSUS UFP One week average NO 2 concentrations were measured with passive samplers at the Suburban and Rind Road location. At the Monitoring Station NO 2 concentrations (half hour average) were measured online. The online data was weekly averaged using the corresponding time frame. The online data from the Monitoring Station location was not continuous. No data was available for the first week from the 9 th till the 14 th of October. During the second week only 2.5 days of data was available and in the last week (week 4) only 1.8 days. Comparison between the NO 2 concentrations at the Monitoring Station with the Suburban and Ring Road site in the third week (Figure 22) revealed highest NO 2 concentrations at the Ring Road, followed by the Monitoring Station and Suburban location which was to be expected. Highest traffic intensities corresponded with highest NO 2 concentrations. NO 2 concentrations at the Ring Road location were 2.7 to 4.4 times higher than at the Suburban location. 27

36 CHAPTER 3 Results and Discussion Figure 22 Weekly average NO 2 concentrations at the three locations To determine a possible relation between two traffic related pollutants NO 2 and UFP a linear regression plot is presented in Figure 23. Particle number concentrations in the six different size ranges measured with the SMPS were weekly averaged and compared with the weekly NO 2 concentrations. The datasets from all three locations were used simultaneously. The coefficients of determination and correlation coefficients are presented in Table 6. A strong positive correlation was found between the UFP total concentration and NO 2 data pairs (R 2 = , r = 0.972). For the individual size ranges strongest positive correlation was found for the particle size range nm (r = 0.960). For particle size ranges from nm strong correlations were found as well (r = ). The fraction > 200 nm showed a moderately strong correlation (r = 0.758). Traffic related exhaust pollutant NO 2 unveiled a close link to the measured ultrafine particle number concentrations, especially for the smallest size fractions. NO 2 can be used to some extent as an indicator for particle number concentration in close proximity to road traffic conditions. Previous studies also confirmed particle number concentration correlating well with traffic byproducts such as NO 2 (Johansson et al, 2007, Kwasny et al 2010) 28

37 CHAPTER 3 Results and Discussion Figure 23 One week average UFP concentration per size range (SMPS) versus one week average NO 2 concentration at all three sites Size range r R 2 (nm) > Total Table 6 Correlation coefficient (r) and coefficient of determination (R 2 ) for the UFP NO 2 relation for different particle size ranges 29

38 CHAPTER 3 Results and Discussion PM10 PM10 concentrations were measured at the Monitoring Station location using filter sampling and online ß-attenuation measurements. At the Ring Road and Suburban location PM10 was measured online using an optical measurement technique. The PM10 Ring Road location dataset was discontinuous and limited, especially comparing 24 hour average values. The Ring Road dataset was not taken into account when comparing the different locations. The daily average PM10 concentrations at the Monitoring Station location (filter and online) and the Suburban location (online) from 9 till 30 October (22 days) are presented in Figure 24. The PM10 concentrations for both locations (Monitoring Station and Suburban) and for both instruments at the Monitoring Station location followed the same trend. In general the ß attenuation monitor gave higher PM10 concentrations than the filter sampling method. The Suburban PM10 concentrations were higher than the Monitoring Station PM10 concentrations. These differences cannot be explained by a single reason. In the first place different measurement techniques and instruments will result in different measurement values. In the second place the Suburban data set (counts) was recalculated (µg/m 3 ) using one dust density that was determined on a limited data set at a different location (Monitoring Station). During comparison the trailer was not optimally positioned for PM monitoring because of varying wind directions round the surrounding buildings. The uncertainty of the dataset caused by the density recalculation is estimated by at least 10 to 20%. Finally the Suburban location could have been also influenced by nearby harbour activities (e.g. ship emissions, industrial activities, loading and unloading sand). Figure 24 Daily average PM10 concentration (µg/m 3 ) at the Monitoring Station and Suburban location 30

39 CHAPTER 3 Results and Discussion Figure 25 Linear regression plot of the PM10 concentration at the Monitoring Station location (filter method) against the PM10 concentration at the Suburban location (optical online). Figure 26 Linear regression plot of the 30 minute average PM10 concentration against the 30 minute average UFP concentration (SMPS) at the Monitoring Station and at the Suburban location. 31

40 CHAPTER 3 Results and Discussion The linear regression plot of the daily average PM10 concentration at the Monitoring Station against the Suburban location is shown in Figure 25. A strong correlation (r = ) was found corresponding with the similar trend at the two locations that was seen in Figure 24. Thirty minute average PM10 concentrations at both locations were also compared with 30 minute average total UFP number concentrations measured with the SMPS. The regression analysis is presented in Figure 26 showing a moderate correlation for the Monitoring Station location (r = ) and a mild correlation for the Suburban location (r = ). These findings are in agreement with the results from previous studies (Kwasny et al 2010, Boogaard et al 2010, Dos Santos-Juusela et al 2013, Morawska et al 2008). They all showed UFP poorly correlated with PM10. UFP is strongly correlated with NOx indicating the importance of vehicle exhaust emissions. PM10 concentrations are not only affected by vehicle exhaust but also by e.g. factors that control road dust generation. This explains the slightly better correlation at the Monitoring Station location because more traffic is present not only affecting particle number concentration (vehicle exhaust emission) but also affecting resuspension of heavier particles BLACK CARBON Hourly average black carbon concentrations were calculated using the 30 minute average data from 21/10/ :30 to 31/10/ :00 (10.3 days). Data from the same period was used for the regression plots. The hourly average diurnal variability in black carbon concentrations during weekdays (8 days) is shown in Figure 27. The error bars represent standard deviations. Figure 27 Hourly average BC concentration during week days at the Monitoring Station (670 nm), Suburban (880 nm) and Ring (880 nm) 32

41 CHAPTER 3 Results and Discussion On weekdays BC peaked in the mornings between 4:00 and 10:00 and in the evening between 15:00 and 21:00 at all three locations. BC concentrations were highest at the Ring Road location corresponding with highest traffic intensities. Lowest BC concentrations were found at the Suburban location. At the Monitoring Station location BC decreased significantly between the morning and evening rush hour which was not the case for the Ring Road location. This is probably the result of large numbers of vehicles passing the Ring Road continuously during the whole day. The single peak between 11:00 and 12:00 at the Suburban location resulted from high BC concentrations during one of eight days. A possible interference of ship emissions cannot be ruled out for this occasion. Figure 28 Hourly average BC concentration during weekend at the Monitoring Station (670 nm), Suburban (880 nm) and Ring (880 nm) On weekend days (1 weekend) diurnal variations of BC concentrations were less pronounced and the highest peak concentration was found during evening rush hours after returning from weekend activities like family visits or trips (Figure 28). 33