Concentrations and fluxes of seven volatile organic compounds (VOCs) were
measured between August and December 2012 at a rooftop site in central
London as part of the ClearfLo project (Clean Air for London). VOC
concentrations were quantified using a proton transfer reaction mass
spectrometer (PTR-MS) and fluxes were calculated using a virtual disjunct
eddy covariance technique. The median VOC fluxes, including
aromatics, oxygenated compounds and isoprene, ranged from 0.07 to 0.33 mg m
Currently over 50 % of the global population lives in urban areas, and with increasing migration to urban centres, air quality remains a high public health priority. In the European Union, including in the UK, volatile organic compound (VOC) emissions are subject to control under the European Commission Directive 2008/50/EC and emission reducing technologies have been implemented, yet urban air pollution continues to be a concern. VOCs from both anthropogenic and biogenic sources impact urban air quality and climate through their contribution to tropospheric ozone and aerosol particle formation. Some VOCs, including benzene and 1,3-butadiene are also carcinogens that can directly affect human health (Kim et al., 2001). Most VOCs in urban areas are assumed to come from fuel combustion or evaporative emissions (Kansal, 2009; Srivastava et al., 2005). However, in summer urban vegetation may act as an additional source of VOCs such as methanol, isoprene and monoterpenes, even in cities with a temperate climate and little green space such as London or Manchester (Langford et al., 2009, 2010b).
Emission inventories such as the London Atmospheric Emissions Inventory (LAEI,
In this study we present flux and concentration measurements of seven selected volatile organic compounds made over 5 months in central London using the virtual disjunct eddy covariance method. The aims of this study were to (i) quantify VOC fluxes above an urban canopy using proton transfer reaction mass spectrometry and virtual disjunct eddy covariance; (ii) investigate seasonal, diurnal and spatial differences in VOC fluxes and concentrations; (iii) examine possible major source contributions of speciated VOCs in central London; and (iv) compare measured fluxes with those estimated by both the LAEI and the NAEI.
These observations were made as part of the ClearfLo (Clean air for London) project, which provided integrated short-term and long-term measurements of meteorology, gas phase and particulate pollutants over London and surrounding areas during 2011 and 2012 (Bohnenstengel et al., 2015).
Micrometeorological flux measurements were made during the period 7 August–19 December 2012 from a flux tower located on the roof of
a building belonging to King's College, University of London
(51.511667
The sampling inlet and sonic anemometer were mounted on a triangular mast
(Aluma T45-H) at approx. 60.9 m (2.3 times mean building height,
Map of central London overlaid with the Ordnance Survey
grid including the measurement site (KCL) at King's College (green point)
with references to the geography of Greater London and Great Britain.
Outlines of the areas that contribute the maximum (
The weather in 2012 was somewhat cooler than the 1981 to 2010 long-term mean for London during summer and autumn, with several cold fronts bringing up to twice as much precipitation and associated winds as average, suppressing pollution levels. However, during the period of the Olympic and Paralympic games (27 July–12 August and 29 August–9 September 2012) the weather was hot and dry, causing sustained pollution peaks. Winter 2012/2013 was generally warmer and drier in London than the 1981–2010 mean (Met Office, 2013).
The CSAT3 sonic anemometer (Campbell Scientific Inc., Utah, USA) and inlet were faced toward
the predominant wind direction (SW) to minimise flow distortion. Data from
the sonic anemometer were logged at a frequency of 10 Hz and flux
measurements were calculated using 25 min averaging periods. The rotation
angle theta (
VOC concentrations were measured using a high-sensitivity proton transfer
reaction (quadrupole) mass spectrometer (PTR-MS) (Ionicon Analytik GmbH,
Innsbruck, Austria) with three Varian turbomolecular pumps
(see for example de Gouw and Warneke, 2007;
Hayward et al., 2002; Lindinger et al., 1998, for more detailed description of
the instrument). Air was drawn through an inlet co-located with the sonic
anemometer. Sample air was purged through a
Summary of instrument operating parameters and average meteorological conditions during the measurements in central London, August–December 2012.
The logging program was written in LabVIEW (National Instruments, Austin,
Texas, USA) and operated the PTR-MS in multiple ion detection (MID) and SCAN
modes for VOC concentrations of nine selected masses and a range of the
protonated mass spectrum
The PTR-MS cannot distinguish between different compounds with the same
integer mass; therefore, isobaric interference can occur. For example,
Single point calibrations were performed on-site once a month using a
certified multiple component VOC gas standard (Ionimed, part of Ionicon
Analytik GmbH, Austria), which was validated by cross-calibration with a
second independent VOC standard (Apel Riemer Environmental Inc., CO, USA).
Before and after the campaign, multistep calibrations were performed with
both standards. Standards were diluted with catalytically converted zero
air, since cylinder concentrations were approx. 1 ppm
Summary of 25 min VOC fluxes and mixing ratios above central London during August–December 2012.
Fluxes were calculated according to
Karl et al. (2002) and Langford
et al. (2009, 2010b) using
Flux losses due to the attenuation of high and low frequency eddies were
estimated for our measurement setup. High frequency flux attenuation was
estimated to be on average 11 % using the method of Horst (1997), and a
correction was applied. Attenuation from low frequency fluctuations for a 25 min flux period was investigated by reanalysing the sensible heat fluxes
for longer averaging periods of 60, 90, 120 and 150 min. The coordinate
rotation was applied to the joined files, which acted as a high pass filter
to the three wind vectors, confirming that fluctuations of eddies with a
longer time period than the averaging time did not contribute to the flux
measurement (Moncrieff et al., 2004). The fluxes were compared to the
25 min average fluxes, which had the coordinate rotation applied before
joining, again to ensure only turbulent fluctuations of
Many of the 25 min resolved flux measurements were close to the limit of
detection (LoD), based on 1 standard deviation using the method of
Spirig et al. (2005), with an average fail rate of 82 %.
Various techniques to statistically analyse or replace values below the LoD
have been developed (Clarke, 1998). However, they often result in
significant bias, either high or low depending on the value substituted,
because values tend to be below the LoD when fluxes are indeed small (Helsel
and Hirsch, 1992). In this study, our analysis focused on diurnally averaged
flux profiles and we decided not to filter out individual flux values on the
basis of being < LoD in order to avoid this bias. When averaging the
25 min flux data it is appropriate to also average the LoD which, as shown
by Langford et al. (2015), decreases with the square root of the number
of samples averaged (
The traffic densities used for the analysis were obtained from a nearby site at Marylebone Road (approx. 3 km to the NW) and consisted of hourly vehicle counts covering the period 7–22 August 2012. The major roads of the Strand and the Thames Embankment surrounding the measurement site support a comparable traffic volume with an annual average of 50 000–80 000 vehicles per day (Department for Transport, 2014) and diurnal patterns in traffic are likely to be similar across central London.
Photosynthetically active radiation (PAR) and CO
Although there are no operational footprint models for urban environments
that take the complex topography and spatial variability in building height
and surface heat fluxes into account, the analytical footprint model of
Kormann and Meixner (2001) has previously been applied in
non-homogeneous terrain (Helfter et
al., 2011; Neftel et al., 2008). The Kormann–Meixner (KM) model determines
the 2-D footprint density function explicitly from micrometeorological
parameters, which are provided by the eddy covariance measurements, i.e.
friction velocity (
The KM footprint calculation requires the Monin–Obukhov stability parameter
(
Average diurnal cycles of measured VOC fluxes and mixing ratios are shown in
Fig. 2 with descriptive statistics for all the data summarised in
Table 2. Largest median (interquartile range in parenthesis) fluxes per day
were from C
Part 1: average diurnal profiles in local time for selected VOC
fluxes (mg m
Oxygenated compounds commonly have relatively long atmospheric lifetimes and
widespread origin including anthropogenic and biogenic sources and
photochemistry, resulting in elevated concentrations and less pronounced
diurnal profiles (Atkinson, 2000). Most VOC fluxes and
concentrations were comparable to or lower than those previously observed in
London (Langford et al., 2010b) and
other UK cities (Langford et al.,
2009), although C
Diurnal profiles of aromatic fluxes and concentrations presented two clear rush hour peaks during the morning and evening (07:00–10:00 and 17:00–20:00 local time). Concentration peaks are thought to be linked to additional advection of traffic-related pollution from larger commuter roads outside of the city centre, as well as boundary layer effects and photochemistry. VOC concentration measurements at canopy height can be affected by boundary layer depth (Vilà-Guerau de Arellano et al., 2009). The rush hour emission peaks mostly coincide with the boundary layer expansion and collapse and therefore the effect of each factor cannot be separated. The morning concentration peak was slightly higher than the evening peak across traffic-related species even though fluxes tended to be larger during the evening rush hour. Morning emissions enter a shallow nocturnal boundary layer leading to relatively larger concentrations compared to higher afternoon emissions entering a developed boundary layer leading to relatively lower concentrations. This enhanced dilution effect is found more often during summer when the boundary layer mixing height is higher (Fig. 2). Therefore, the regression analyses below only refer to data from August (cf. Sect. 3.1.2 for comparisons with winter). Furthermore, increased photochemical degradation during the day removes VOCs, further contributing to the midday minimum in mixing ratios. The diurnal flux profiles of methanol, acetone, isoprene and to a smaller extent acetaldehyde showed one large peak just after midday (approx. 13:00 local time), which was only reflected in the concentration profiles of acetone and isoprene. Acetaldehyde concentrations presented a slight double peak similar to mixing ratios of aromatics. Methanol has a relatively long atmospheric lifetime and therefore high background concentrations, and hence mixing ratios showed no distinct diurnal profile.
Aromatic compound fluxes closely followed the diurnal profile of traffic
density with good correlations (
The aforementioned concentration dilution due to boundary layer expansion
resulted in negative correlations between boundary layer height and aromatic
mixing ratios in August (
Examples, using isoprene, of averaged VOC fluxes (left)
and mixing ratios (right) as a function of photosynthetically active
radiation (PAR) (
Diurnal profiles by month with confidence intervals and
bar charts showing hourly averages for the respective month and
representative compound (top) fluxes (mg m
VOC fluxes and concentrations plotted as a function of PAR showed strong daytime (defined as 06:00 to 18:00
local time) correlations for methanol, acetaldehyde and isoprene fluxes
(
Modelling studies have indicated that the contribution of secondary
atmospheric formation to VOC concentrations could be more significant,
especially in urban areas, during summer, i.e. with high PAR and
temperatures (de Gouw et al., 2005; Harley and Cass,
1994). Acetone fluxes reached a maximum when PAR and temperature were around
1000
Summary of site meteorology by month in central London during 2012.
Most compounds showed larger fluxes in August and September than in October, November and December with the exception of acetaldehyde, which also showed increased fluxes in December (Fig. 4 top). Increased acetaldehyde fluxes in December may have resulted from an additional source, such as domestic biomass burning (Andreae and Merlet, 2001; Lipari et al., 1984), although there are only few residential buildings in this area of London. Only toluene fluxes in September were significantly higher than in other months and benzene fluxes showed no significant seasonal differences. Seasonal variability in fluxes was likely due to increased emissions in summer, especially for compounds with biogenic and secondary atmospheric sources. Average monthly meteorological parameters are summarised in Table 3.
Mixing ratios of aromatics were generally lower in summer and highest in
December (Fig. 4 bottom). This is likely due to less dilution effects in
winter when the boundary layer is shallow or from advection of additional
sources such as heating, since there was no increase in fluxes. Generally,
in summer the boundary layer mixing height is higher and collapses later in
the evening which maintains the dilution effect for VOC concentrations. In
winter the average boundary layer mixing height is lower. It develops later
in the morning and collapses earlier in the afternoon, which could increase
not only
overall VOC mixing ratios but also individual maxima, e.g. during rush
hours. Comparing average diurnal profiles of compound mixing ratios with
boundary layer height during summer and winter shows that aromatic compound
concentrations were associated with negative correlations in summer (cf.
Sect. 3.1.1) which became positive during winter (
Increased summer mixing ratios of oxygenated compounds and isoprene
indicated a temperature dependent, possibly biogenic source contribution.
While biogenic emissions may be advected from outside of the city, the
concurrent increase in isoprene fluxes suggests the source to be largely
local to the flux footprint. The temperature-dependent fraction of observed
isoprene mixing ratios, which may include advected pollution, was estimated
using the isoprene temperature response function from Fig. 9 in Langford
et al. (2010b), which estimated a 30 and 20 % contribution in August
and September respectively. These values were significantly higher than for
isopentane, a non-biogenic compound available from the Automatic
Hydrocarbon Network, to which the same analysis was applied. The temperature-dependent component of isoprene in October, November and December showed no
significant difference to that of isopentane, suggesting the biogenic
component was reduced or absent at lower temperatures. High correlations of
An attempt was made to model the biogenic isoprene component during August
and September using the light and temperature algorithms of Guenther et al. (1995), hereafter termed G95. The foliar-emissions-based model calculates
VOC fluxes as follows:
Figure 5 shows that the modelled isoprene fluxes using the calculated
base emission rate compared well with the measured fluxes by wind direction.
Linear regressions from wind directions that have a strong anthropogenic
component are lower, e.g. W (
Selected scatter plots of representative correlations of
VOC
Correlations of VOC
Benzene to toluene (
The observed ratios compared well with those of other European cities, which
showed
Top: 24 h back trajectories from the NOAA HYSPLIT
trajectory model during selected days in August 2012 corresponding to
periods of low (left) and high (right) benzene/toluene concentration ratios.
Daily release in 3 h intervals (10 m height) for 24 h prior. Bottom:
scatter plots showing benzene-to-toluene concentration ratios during
9 August 2012 (left) and 12 August 2012 (right) with linear
regression with 95th confidence interval, regression equation and
coefficient (
Wind speed and direction can play a role for
The median monthly
Median (IQR) concentration ratios for benzene to C
Good correlations were found among averaged VOC fluxes plotted as a
function of averaged CO
The presence of a strong separate CO
Scatter plots showing averaged flux and concentration
regressions of isoprene and benzene as a function of CO
Median VOC
Polar annulus and polar plots were constructed for VOC fluxes and mixing
ratios respectively and representative compounds are shown (Fig. 9). Polar
plots use a generalized additive model to interpolate between wind
direction and wind speed averaged data points within the OpenAir package in
R (see Carslaw and Ropkins, 2012; Hastie and Tibshirani, 1990; Wood, 2006).
Polar annulus plots averaged by time of day instead of wind speed show
diurnal variability with wind direction. The majority of the time (83 %),
unstable and near neutral conditions prevailed (
Polar annulus and polar plots for isoprene (
Largest fluxes for all compounds were from the NW with either one daytime
peak (e.g. isoprene) or two distinct rush hour peaks (e.g. benzene) (Fig. 9, top). On average, fluxes were largest from the W > E
The average length of the maximum flux footprint contribution (
The River Thames to the S may have caused the low fluxes associated with S winds (i.e. squares 1, 2 and 3). Contributions of traffic-related compound fluxes were statistically significant from the W (i.e. squares 4, 5, and 7), followed by the N (square 8) and E (squares 6 and 9) likely from the nearby heavily trafficked roads (Kingsway, Charing Cross, Strand and Blackfriars areas respectively). Biogenic compound fluxes were highest from the W and E, which coincides with significant nearby green areas within the flux footprint.
Correlations of fluxes with grid square contributions in the footprint can
also give information on emission source strengths within the respective
grid square (Fig. 1). Generally positive correlations with fluxes across
most compounds were seen from the W (squares 4, 5 and 7), confirming that
high emission rates from sources within these grid squares were driving the
large fluxes. The strongest correlations of fluxes with contributions from
squares 4, 5 and 7 were seen during October and November (
Highest mixing ratios with wind direction were from E > N
The LAEI and NAEI produce
yearly emission estimates over the 1 km
Bar chart showing scaled comparisons of LAEI and NAEI
estimates against measured fluxes in t km
LAEI emission estimates included contributions from major (69 %) and minor roads (4 %) as well as evaporative emissions (27 %) (LAEI). No data were available on cold start emissions for benzene. The calculated standard errors provided some uncertainty approximation. Measured fluxes compared well with emission estimates, although the LAEI predicted slightly smaller benzene fluxes. Comparisons of fluxes with wind directions (Sect. 3.3) agreed well with the LAEI emission estimates for the respective grid squares with highest emissions from squares 4, 5, 7 and 8 (i.e. W and N directions). This comparison assumes that the benzene fluxes during the measurement period were representative of annual emissions with any significant seasonal variation in benzene emission rates captured in this 5-month period. Section 3.1.2 confirmed that there was little month-to-month variability in the benzene flux.
Using speciated VOC emission contributions (percent of total VOC emissions) for
2006 (Bush et al., 2006) and emission maps from 2012 for total non-methane
VOC emissions, speciated estimates could be compared with observations
(Fig. 10). The NAEI includes a wide range of emission sources divided into
11 SNAP (Selected Nomenclature for sources of Air Pollution) sectors
including industrial, commercial and residential processes, transport, waste
treatment, solvent use, point sources, agriculture and nature, although the
latter two were unavailable for the London urban area. NAEI estimates for
benzene exceed the LAEI due to the inclusion of a wider range of sources
beyond traffic-related emissions. Total C
Our measurements show that vehicle emissions are the dominant source of the fluxes and concentrations of VOCs in central London, although biogenic sources and secondary atmospheric formation may make a significant contribution, particularly in summer for some compounds. There were observable spatial variations in flux rates, which result from the varying spatial distribution of emission types and strengths of emission sources, such as vegetation and traffic. Temporal variations in relative source strengths can be seen in the diurnal and seasonal profiles, reflecting the diurnality and seasonality of some of the driving factors. The measured VOC fluxes mostly originated from an area within a 1 km radius around the measurement site but some instances of pollution advection were seen to affect concentrations at the site. However many of the spatio-temporal differences in the observed mixing ratios were attributable to changes in emission sources and strengths combined with effects of meteorological conditions. The diurnal and seasonal dynamics of the boundary layer mixing height are significant drivers of changes in observed VOC concentrations at the site.
The biogenic component of isoprene emissions was modelled using the G95 algorithm, and the calculated base emission rate closely matched previous published values for urban areas. Even in this central urban area with a temperate climate there is a detectable biogenic component to isoprene emissions. Because of the relative importance of isoprene in atmospheric chemistry, its inclusion in photochemical pollution models is essential.
Close agreement between the flux footprint contributions and the LAEI for benzene emissions, a compound which is thought to be accurately estimated in the inventory but associated with high measurement uncertainty, gives confidence in the PTR-MS measurements. Good agreement was also seen with methanol estimated from the NAEI, but other compounds were all greatly underestimated in the emissions inventory.
This study provides further evidence for the successful implementation of VOC flux measurements in heterogeneous urban landscapes when measurement sites fulfil basic eddy covariance criteria. Further VOC flux observations are essential for the validation of “bottom-up” emission inventories, especially as the latter are widely used for regulatory and compliance purposes.
E. Nemitz and B. Langford planned the measurement campaign; A. Valach made the measurements with the help of B. Langford and E. Nemitz; A. Valach processed the data and completed the analyses with the help of B. Langford. C. N. Hewitt designed the study, obtained funding and supervised the work. A. Valach prepared the manuscript with support from all the co-authors.
This work was funded by the UK Natural Environment Research Council (NERC)
through the ClearfLo project (Clean Air for London; NERC grant NE/H003169/1)
and the National Capability function of the Centre for Ecology &
Hydrology. Amy Valach thanks NERC for a PhD studentship. David Carslaw
(King's College London) and the NOAA Air Resources Laboratory (ARL) provided
the HYSPLIT back trajectories. Lisa Whalley (University of Leeds) provided
the OH data. Sue Grimmond (University of Reading), Simone Kotthaus
(University of Reading) and the urban meteorology research group at King's
College London provided site access, meteorology and CO