ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-2085-201760 years of UK visibility measurements: impact of meteorology and
atmospheric pollutants on visibilitySinghAjitBlossWilliam J.https://orcid.org/0000-0002-3017-4461PopeFrancis D.f.pope@bham.ac.ukhttps://orcid.org/0000-0001-6583-8347School of Geography, Earth and Environmental Sciences, University of
Birmingham, Birmingham, B15 2TT, UKFrancis D. Pope (f.pope@bham.ac.uk)13February20171732085210116August201626August20166January201717January2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/2085/2017/acp-17-2085-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/2085/2017/acp-17-2085-2017.pdf
Reduced visibility is an indicator of poor air quality. Moreover, degradation
in visibility can be hazardous to human safety; for example, low visibility
can lead to road, rail, sea and air accidents. In this paper, we explore the
combined influence of atmospheric aerosol particle and gas characteristics,
and meteorology, on long-term visibility. We use visibility data from eight
meteorological stations, situated in the UK, which have been running since
the 1950s. The site locations include urban, rural and marine environments.
Most stations show a long-term trend of increasing visibility, which is
indicative of reductions in air pollution, especially in urban areas.
Additionally, the visibility at all sites shows a very clear dependence on
relative humidity, indicating the importance of aerosol hygroscopicity on the
ability of aerosol particles to scatter radiation. The dependence of
visibility on other meteorological parameters, such as wind speed and wind
direction, is also investigated. Most stations show long-term increases in
temperature which can be ascribed to climate change, land-use changes (e.g.
urban heat island effects) or a combination of both; the observed effect is
greatest in urban areas. The impact of this temperature change upon local
relative humidity is discussed.
To explain the long-term visibility trends and their dependence on
meteorological conditions, the measured data were fitted to a newly developed
light-extinction model to generate predictions of historic aerosol and gas
scattering and absorbing properties. In general, an excellent fit was
achieved between measured and modelled visibility for all eight sites. The
model incorporates parameterizations of aerosol hygroscopicity, particle
concentration, particle scattering, and particle and gas absorption. This new
model should be applicable and is easily transferrable to other data sets
worldwide. Hence, historical visibility data can be used to assess trends in
aerosol particle properties. This approach may help constrain global model
simulations which attempt to generate aerosol fields for time periods when
observational data are scarce or non-existent. Both the measured visibility
and the modelled aerosol properties reported in this paper highlight the
success of the UK's Clean Air Act, which was passed in 1956, in cleaning the
atmosphere of visibility-reducing pollutants.
Introduction
The meteorological definition of visibility is the “distance
at which the contrast of a given object with respect to its background is
just equal to the contrast threshold of an observer” (WMO, 1992, 2015). In
general, good visibility is a desirable feature of any geographical location,
and its importance should not be neglected (Doyle and Dorling, 2002). Poor
visibility (< 2.0 km; Founda et al., 2016) can affect the
transportation of goods and people, whether it is by rail, road, sea or air.
Low visibility can lead to accidents and thus is a concern for public safety.
Tourism is often dependent on good visibility for
appreciation of points of interest
(Singh and Dey, 2012). For example a study at Grand Canyon Park in the USA
has shown that visitor frequency in the park has reduced as visibility
decreased (Trijonis et al., 1990).
Typically in cloud-free sky, visibility can vary from ca. 5 to 100 km
dependent on atmospheric composition and conditions. Visibility is reduced by
the interaction of light with atmospheric gases and aerosol particles which
can absorb or scatter the light; consequently visibility is greatest within
non-polluted, pristine atmospheres, other factors (e.g. meteorology) being
equal. Many previous studies have investigated the link between atmospheric
composition and visibility (Jinhuan and Liquan, 2000; Schichtel et al., 2001;
Wu et al., 2005; Park et al., 2003, 2006; Yang et al., 2007; Tiwari et
al., 2011; Founda et al., 2016; Cao et al., 2012; Watson and Chow, 2006).
These investigations demonstrate that visibility is markedly influenced by
the size, chemical composition and concentration of airborne particles.
Reduced visibility is attributed mainly to high concentrations of aerosol
particles, and, in general, scattering effects are the dominant
visibility-reducing mechanism within the atmosphere. Within heavily polluted
atmospheres, visibility can decrease rapidly due to the presence of aerosol
particles (Husar et al., 1981). For example, during the 1952 London smog
events visibility declined to a few metres due to high air pollution (caused
by a rise in smoke and other pollutant concentrations in the atmosphere;
Wilkins, 1954) as discussed in detail by Brimblecombe (1987). More recently,
a study by Sati and Mohan (2014) also found sharp decreases in visibility due
to increased particulate matter (PM) and NO2 concentrations during a smog
event in November 2012 in Delhi, India.
Similarly, Zhang et al. (2006) described the PM influence upon visibility
reduction at Beijing, China. Festivals involving fireworks, which release
aerosol particles upon detonation, are a good example of spatially and
temporally localized pollution events which may lead to reduced visibility
(Singh et al., 2015; Seidel and Birnbaum, 2015; Kong et al., 2015).
In addition to aerosol and gas concentrations and composition, specific
meteorological conditions can also affect visibility (Sloane, 1983). There
exists a body of literature on urban visibility studies which attempt to
connect visibility with meteorological parameters (e.g. Hänel, 1972;
Clarke et al., 1978; Lee, 1983, 1990; Haywood and Boucher, 2000). Whilst
temperature (T), relative humidity (RH), wind speed (ws) and
wind direction (wd) do not affect clear-sky visibility directly,
they can influence the sources and sinks of the trace gases and aerosol
particles in the atmosphere. For example, high wind speeds can re-suspend
dust particles and generate sea spray aerosol particles. Windy conditions can
also lead to a cleaning effect by replacing polluted air with cleaner air.
Temperature can influence the production of secondary organic aerosol (SOA)
particles, for example, via the chemical formation and partitioning between
the gas and particle phase. RH not only affects the
sources and sinks of gases and aerosols; it also directly influences the size
and composition of aerosol particles. Nearly all atmospheric aerosol
particles are hygroscopic to some degree; hence, their size is dependent upon
the local RH. As RH increases, hygroscopic particles take up water, through
absorption and adsorption, and grow in size, volume and weight. The addition
of water also changes the overall particle composition. This typically lowers
the mean refractive index of the particle since the refractive index of water
is lower than other common aerosol components, such as minerals, organics,
sulfates and nitrates (Harrison et al., 2004). Under high-humidity
conditions, a high particle loading in the lower atmosphere can increase fog
formation and thus severely reduce visibility (Tiwari et al., 2011). It has
previously been shown that monthly variations in visibility are negatively
correlated with RH (Singh and Dey, 2012). Other studies have shown how the RH
effect on particle hygroscopic growth can influence visibility change (Liu
et al., 2012). Thus, both PM loading and meteorological factors, such as
relative humidity, are important for the assessment of the causes of
visibility reduction. Other factors may also be important such as vegetation
density, industrial development, urbanization and human population since
these factors affect surface type and can effect aerosol deposition (Diederen
et al., 1985).
In the last few years, worldwide interest in atmospheric visibility has
grown, but few studies examine UK visibility. Previously, a long-term trend
analysis of visibility was performed at eight UK weather stations between
1950 and 1997 by Doyle and Dorling (2002), where improved visibility was
identified at most of the stations, mainly after 1973 due to oil crises and
less consumption. Summer visibility trends for five different sites in London
and southern England for the period of 1962–1979 were analysed by Lee
(1983), and it was also found that a rise in visibility was observed at all
sites. Gomez and Smith (1984) quantified the seasonal visibility trends at
Oxford during 1926–1985 and observed a clear reduction in visibility from
1926 to 1944, a notable rise after World War II from 1944 to 1952 and
another reduction from 1952 to 1966 (mainly in the summer season); the
visibility improved again after 1966 in all seasons due to the reduction in
aerosol concentration (Gomez and Smith, 1987). It is also found that, since
the 1956 Clean Air Act, fog occurrence has declined at Oxford and nearby
rural areas due to a drop in smoke concentration, urban heat island effect and
other public activities (Gomez and Smith, 1984). Analyses by Lee (1985) in
central Scotland for the period of 1962–1982 have mentioned the effect
of the 1973 oil crisis on visibility and air quality, where a significant
increase in visibility was shown primarily in urban areas due to a major
reduction in sulfate aerosol concentration. A similar study on historical
visibility trends at 22 different UK meteorological stations (includes urban,
rural and marine areas) during 1962–1990 was performed by Lee (1994). A
clear rise in visibility was identified at most of the sites due to reduction
in coal and smoke emissions (Lee, 1994). Furthermore, a steady reduction in
fog frequency with improved visibility correlated with decreased smoke
pollution at Glasgow airport was noted (Harris and Smith, 1982). The
correlation between various air pollutants (such as NH4+ and
non-marine pollutants SO42- and NO3-) and visibility in
northwest England, UK, were also performed in the 1980s, where strong negative
correlations were found between visibility and these pollutants (Colbeck and
Harrison, 1984). At present, most UK urban cities are relatively polluted
(Defra, 2011) compared to rural locations, with pollutant sources dominated
by vehicular emissions (Colvile et al., 2001). The 1956 Clean Air Act led to
general improvements in UK air quality; however, there still exist many
negative effects of air quality on the UK population such as impaired human
health (Defra, 2011; Harrison et al., 2015).
The present study investigates visibility in the UK focusing on eight
specific sites. The same sites were previously investigated by Doyle and
Dorling (2002), who presented long-term UK visibility trends for 1950–1997
and the dependence of the measured visibility on meteorological conditions.
In this paper we build upon the work of Doyle and Dorling (2002) to analyse
UK visibility trends from 1950 to 2013. Furthermore, we extend the analysis
by investigating causes of the observed visibility trends; in particular we
investigate the role of air pollutant concentrations in UK visibility. The
outputs from this work help to explain historic visibility trends in the UK.
A new model is also presented which can aid in future visibility prediction
under different climate and pollution scenarios.
Data
Daily archived horizontal visibility data, defined as the visibility distance
along a horizontal line at the earth's surface, were obtained from the
British Atmospheric Data Centre (BADC), which is run by the UK's Natural
Environment Research Council (www.badc.nerc.ac.uk). The archive
contains visibility data, in addition to other relevant meteorological
parameters, archived at an hourly time resolution. In addition to visibility,
the following meteorological parameters were also utilized: RH; wind speed;
wind direction; air temperature; rainfall; and present weather (PR) code, which
provides further qualitative detail about the weather conditions. A
description of the present weather codes is provided in the table
(http://badc.nerc.ac.uk/data/ukmo-midas/WH_Table.html) at
www.badc.nerc.ac.uk. Unfortunately the use of present weather codes
largely ceased with the introduction of automated meteorological stations, and
insufficient PR codes were available after the year 1997. It is noted that
if the present weather codes had been available they would have been useful to
screen the data for rain or other precipitation events. Due to unavailability
of present weather codes during the required study period (1950–2012), data
filtration was done on the bases of RH limits instead of PR codes. Data were
removed when the relative humidity reading was > 99 %, which is highly
suggestive of rain or other precipitation events. Removal of data with RH
> 99 % removes between 0.91 and 3.44 % of the data, dependent on
site location. Since the ability of visibility observers is affected by light
levels, with greater difficulty encountered in night-time measurements (Lee,
1990), the daily data used for this study were all measured at 12 noon for all
sites.
Meteorological data were collected for the eight UK stations which possess
near-continuous time series data starting in the 1950s and continuing to
the present day. The eight stations are Aldergrove, Heathrow, Ringway,
Nottingham, Plymouth, Tiree, Leuchars and Waddington, and details of the
stations are given in Table 1 and Fig. 1.
The visibility data sets are based on ground-based measurement using a
variety of techniques. More details of visibility observations method are
found in the UK Met Office Surface Data Users Guide
(https://badc.nerc.ac.uk/data/ukmo-midas/ukmo_guide.html). Until the
late 1990s all visibility measurements were performed by a human observer.
Subsequently data collection was automated using visibility sensors
(visiometers). See Table S1 in the Supplement for details on measurement type used
and dates of service.
Study stations with area and length of data description
No.Station nameStation codeAreaPeriodLength of data(src id)(in years)1Aldergrove1450Urban (airport)1950–2012632Heathrow708Urban (airport)1950–2012633Ringway1135Urban (airport)1950–2004554Nottingham556Urban1957–2012565Plymouth1336Urban (near coastal area)1950–2012636Tiree18974Rural (airport, near coastal area)1957–2012567Leuchars235Rural (RAF, near coastal area)1957–2012568Waddington384Rural (RAF, airport)1950–201263
* RAF stands for Royal Air Force.
Geographical location of measurement stations used. Location point
colours describe location type: red – urban airport; blue – urban; purple
– rural/remote; and green – rural airport. Also presented are mean wind rose
statistics for the whole time period (approximately 60 years) for all eight
stations.
There are advantages and disadvantages with both human observation and
visiometers. Clearly from a manpower perspective, visiometers are preferred.
Human observation provides a true measure of visibility since the observer is
looking for objects located at a known distances away from their location;
however, the visibility measurements are imprecise by nature since results
can vary according to the contrast and illuminance thresholds (ability to
discern and sensitivity to light, respectively) of the observer's eyes (WMO,
2008). Since human observation requires objects to observe, the measurement is
quantized by the geographical spread of available objects; i.e. there is not a
continuum of measurement locations. Consequently, human observations provide
a lower limit to the actual visibility. Distances between objects to observe
can be large especially at the longer distances measured
(> 10 km), which leads to reductions in accuracy at high visibility. At high elevation
the visibility calculation can be different from that at the surface (Malm et
al., 1981). Visiometers automatically measure the extinction of light over a
small distance (typically ca. 1 m) and from the measured extinction
can estimate visibility. In particular automatic visibility measuring
instruments consist of a light transmitter and receiver; the light extinction
observed between the transmitter and receiver is then used to estimate the
visibility (Jebson, 2008). These automated estimates of visibility are more
objective and reproducible than human observation. However, since the
visiometer only measures air local to the device, it can be much more affected
by variations in local air quality. This is likely to be a more important
consideration at urban meteorological sites, where air composition is more
heterogeneous than at rural sites, due to the greater number of
pollutant sources in urban areas.
The change from human to automatic measurement occurred at different times
for the different sites (see Table S1). It is clear for most sites
investigated that the changeover from manually observed to automatically
measured data leads to step changes in the visibility reported; see Fig. 2
and further discussion in methodology section. This is unsurprising given the
discussion above. In particular, clear deviations away from the long-term
trend measured under human observation are observed at Aldergrove, Plymouth
and Tiree stations once automation was introduced (see Table S1). After
consultation with the UK Met Office it was noted that automated sensors can
be unreliable during high-visibility events when compared to human readings.
In particular automatic sensors perform sub-optimally at coastal sites unless
the sensor is cleaned regularly, due to accumulation of sea salt residue.
Unfortunately, the Tiree station was reported to fall into this category.
To assess the effects of the gaseous pollutant nitrogen dioxide (NO2) on
visibility, daily ground-based measured data of NO2 were obtained from the
Department of Environment Food and Rural Affairs (Defra)
(https://uk-air.defra.gov.uk/) for one observing station (Harlington),
closely co-located to the Heathrow meteorological station (ca. 2.1 km away).
NO2 data were only available for 9 years (2004–2012) of the visibility
study period.
MethodologyTrend analysis of visibility and other meteorological parameters
Sixty-year trend analyses have been performed on the visibility data set
described in Sect. 2. For long-term trend analysis each day's value was
averaged (simple mean) to determine trends over decadal, annual and seasonal
cycles. The seasonal periods were defined, as is typical, as winter
(December–February), spring (March–May), summer (June–August) and autumn
(September–November). Diurnal, day-of-the-week and monthly averaged trends
of visibility and RH were determined at each site using the 60-year data
set, where weekdays and weekend are categorized as Monday–Friday and
Saturday–Sunday, respectively.
To examine the hygroscopic growth effect of aerosol particles upon
visibility, the decadal data sets were disaggregated into RH bins. The
aerosol hygroscopic growth effect on visibility was examined by using decadal
mean visibility within specific relative humidity bins with the following
boundaries: 52.5–57.5, 57.5–62.5, 62.5–67.5, 67.5–72.5, 72.5–77.5,
77.5–82.5, 82.5–87.5, 87.5–92.5 and 92.5–97.5 %. We excluded data
with RH > 97.5 % due to the likely presence of fog and mist at RH greater
than this threshold.
To highlight the daily variation in RH, histograms of daily RH (at 12:00)
were generated using the following boundaries: 0–10, 10–20, 20–30, 30–40,
40–50, 50–60, 60–70, 70–80, 80–90 and 90–100 %.
To evaluate the dominant meteorology at each site, several meteorological
analyses were conducted. Wind rose plots using the complete data set time
series were generated to highlight the dominant wind speed and direction for
all sites. Decadal-seasonal bivariate polar plots of visibility using wind
direction and wind speed allow for spatial analysis of likely pollution
sources (Carslaw and Ropkins, 2012). Finally time series plots of the
following meteorological parameters were generated: RH, wind speed, wind
direction and air temperature. These calculations were performed using the
timePlot function in the openair package for the statistical program R, which
works on vector functions for wind direction averaging.
Estimation of aerosol and gas phase properties through analysis of
RH-dependent visibility
In this section the contribution of aerosol particles and gases to
visibility is estimated via mathematical modelling. In general horizontal
visibility (V) can be defined via Koschmieder Eq. (1), where horizontal
visibility shows an inverse relationship with the extinction coefficient
(βext). In Eq. (1), the constant (k) is equal to 3.912,
which assumes a contrast threshold of 2 % (Koschmieder, 1924). The
constant (k) is a measured by the threshold sensitivity of the observer's
eye (Schichtel et al., 2001; Chang et al., 2009), which can vary from 2 to
5 % (Appel et al., 1985).
V=k/βext
The extinction coefficient depends upon (βext) and is the sum of the
scattering (βsca) and absorption coefficients
(βabs) as shown in Eq. (2).
βext=βsca+βabs
In the atmosphere, aerosol particles and gas phase species can
contribute to both light scattering and absorption. However, the contribution of
gas phase scattering to the total extinction is negligible except in the most
pristine environments. Hence under UK conditions, the scattering component of
the extinction coefficient can be assumed to be completely dominated by the
presence of aerosol particles.
The ability of an individual particle to scatter radiation is dependent on
its size, shape, morphology and refractive index (Appel et al., 1985; Liu and
Daum, 2000). The particle scattering coefficient (βsca) can
be estimated by Mie theory as shown in Eq. (1) (Tang, 1996);
βscat=∫0∞πD22Qscatα,λ,nNfDdD,
where D represents particle diameter, the aerosol size distribution is
given by Nf(D) and α is the size parameter (α=πD/λ). N is particle number concentration, and Qscatα,λ,n is single-particle scattering cross section,
which depends upon size parameter (α), wavelength (λ) and
refractive index (n, which is composition-dependent). All these particle
characteristics can change as the particle undergoes water uptake or loss
which is dependent on the local RH. To parameterize the aerosol scattering
enhancement due to water uptake, an approach similar to Titos et al. (2014)
is taken. The scattering enhancement is parameterized using a single
hygroscopicity parameter (γ) using Eq. (4), where βscaRH and βscadry are the aerosol scattering coefficients under specified RH
conditions and completely dry conditions, respectively.
βscaRHβscadry=1-RH100-γ
Rearranging Eqs. (1), (2) and (4) allows for the relationship in Eq. (5) to
be derived, where βabs(RH) and
βabsdry are the combined aerosol and
gas absorption coefficients under specified RH conditions and completely dry
conditions, respectively.
Vis(RH)=3.9121-RH100-γ×3.912Visdry-βabs(dry)+βabs(RH)
To reduce the number of parameters within Eq. (5), it is assumed that βabs(RH)=βabs(dry). This
assumption always holds for gas absorption, and it is largely true for
aerosol particles as well, although it is noted that particle absorption can
increase due to lensing effects in mixed-phase aerosol, and this lensing
effect will be affected by aerosol water content (e.g. Lack and Cappa, 2010).
Equation (5) can be further simplified by assuming that all absorption due to
both gases and particles is negligible compared to the RH-dependent aerosol
scattering, leading to the two-parameter Eq. (6).
logVis(RH)=γlog1-RH100+logVis(dry)
Equations (5) and (6) can be used to obtain information about aerosol
scattering and gas and aerosol absorption, with associated assumptions,
through fitting of the measured visibility at a given RH. Equation (6) is
linear and so can be fitted using the linear least-squares fitting algorithm,
whereas Eq. (5) requires a non-linear least-squares fitting algorithm. The
statistical program R was used for all fittings (Version 0.99.489). The
“lm” algorithm was used for linear fitting, and the “nls” fitting
algorithm was used for the non-linear fitting. The nls algorithm was
always initially run with no lower or upper boundaries for the three fitting
parameters (Vis(dry), βabs and γ) specified. However,
when fits produced negative values for βabs, which are
physically impossible, a lower boundary for βabs was
specified to be zero.
Gas absorption
All gases scatter radiation via Rayleigh scattering, but the effect is
negligible in all but the most pristine visibility conditions (which are not
observed in this study). The only atmospheric gas present at levels that lead
to significant absorption of visible light is NO2 (Ferman et al., 1981;
Groblicki et al., 1981). The contribution of NO2 to visibility can be
quantified by its absorption coefficient (βNO2abs).
The effect of the NO2 absorption coefficient, at 550 nm wavelength, was
calculated using the relationship from Groblicki et al. (1981), shown in
Eq. (7), where [NO2] is the NO2 in parts per million (ppm).
βNO2abs=3.3×10-4NO2
Historical trend of annual and seasonal visibility derived from
daily (12:00) observations by station: (a) Aldergrove
(b) Heathrow, (c) Leuchars, (d) Nottingham,
(e) Plymouth, (f) Ringway, (g) Tiree,
(h) Waddington. Shading indicates changes in measurement
methodology, where white is human observation, while blue and red are
automated observation using different instruments. For further details see
Table S1.
Results and discussionHistorical trend of annual and seasonal visibility
The annual and seasonal mean visibility at 12:00 have been calculated for
all eight stations; see Fig. 2. The effect of changing the visibility
observation technique from human observation to automatic observation via
visiometers (which is highlighted by different shading in Fig. 2) is very
clear at some sites. In particular, two stations, Tiree and Aldergrove, do
not show realistic values after the changeover from human to automated
measurement, with the changeovers coinciding with large and sustained drops
in recorded visibility. The effect of human-to-automated-measurement changeovers at
Heathrow, Leuchars, Nottingham, Ringway and Waddington sites appears to be
minimal, with the pre-changeover long-term trends being continued after the
changeover. Furthermore the annual data from these sites exhibit similar
year-to-year variance before and after changeover. The long-term trend at the
Plymouth site is similar before and after changeover, but the year-to-year
variance is much reduced once measurement automation is installed. This
likely indicates strong localized sources (ship and traffic emissions from
nearby ports and roads) close to the visiometer at the Plymouth site.
Henceforth it is assumed that all stations except Aldergrove and Tiree are
performing adequately for both human and automated visibility measurement.
Therefore the time series, as shown in Fig. 2, are used in their entirety for
the analysis of these six stations. The time series data for the Aldergrove
and Tiree stations are used up until automation occurs.
A similar variation in visibility trends is observed for the period of
1950–1997 to that in Doyle and Dorling (2002). However, this study
reports overall lower visibility values than Doyle and Dorling
(2002). These differences are due to slightly different data filtering
methodologies. Doyle and Dorling (2002) filtered data for 12:00, relative
humidity > 90 % and PR codes of 00-05 in their statistical analysis
for the period of 1950–1997. However, due to uncertainty and unavailability
of PR code after 1997 we did not use these codes. Furthermore we performed
mean averaging for statistical analysis, where data are filtered for 12 noon
and relative humidity > 99 %. The details of uncertainty and
unavailability of PR codes and the used data filtration method are given in the Data
and Methodology sections.
Clear trends of increasing annual visibility are observed for four sites:
Ringway, Waddington, Nottingham and Heathrow, with the rate of visibility
increase being 0.339±0.016, 0.293±0.010, 0.235±0.023 and
0.201±0.018kmyear-1, respectively, where standard errors
were determined at the 95 % confidence interval. A, more gradual
increasing trend was observed at the Leuchars site (0.157±0.019kmyear-1). The Plymouth site shows a more variable trend
with increases from ca. 1950 to 1990 followed by decreases from ca. 1990 to
2006, which is then followed by more increases in the most recent
measurements. The long-term trend for Plymouth in the period 1950–2013 is near constant
(0.040±0.021kmyear-1). Both the Aldergrove and Tiree
sites, with the automated data omitted, show near-constant long-term
visibility with long-term rates of visibility change calculated to be
0.0562±0.021 and -0.0892 ± 0.014 kmyear-1,
respectively.
The seasonal trends for the eight sites are detailed in Table 2. Poorest
visibility was observed in the winter season compared to other seasons mostly
due to the seasonal rise in RH (discussed in Sect. 4.3). Another reason is
the greater concentration of particles in the environment due to lower
mixing-layer height in the winter season (Jayamurugan et al., 2013). Furthermore,
the long-term rate of visibility change in the winter season is significantly
higher than in the spring, summer and autumn seasons for all stations
apart from the Ringway station. At Ringway station the rate of change of
visibility is higher in spring (0.363±0.018kmyear-1) than
in winter (0.330±0.020kmyear-1). All stations
show positive rates of visibility change in winter season except for Tiree
(-0.186 ± 0.012 kmyear-1). It is also observed that
Aldergrove station shows a negative rate of visibility change in the summer
season (-0.417 ± 0.036 kmyear-1).
The improvement in median visibility at most of the sites can be seen in
Fig. S1 in the Supplement. Moreover, box plots of the decadal visibility are also
produced showing the median, interquartile range, outliers etc. (see
Fig. S2).
Rate of change of visibility (in kmyear-1) with their
standard error at the 95 % confidence interval.
Improved visibility at most of the sites is due to reduction in air pollution
and the likely changes in fuel use and consumption that took place after 1956
Clean Air Act. The Clean Air Act was introduced with the aims of reducing
smog, smoke and sulfur dioxide concentrations in the environment. In
particular, the policy focused on industrial emission sources and
reduction (Williams, 2004). Recently, Harrison et al. (2015) shown that
concentration of sulfur dioxide, coal smoke, nitrogen dioxide, suspended
matter (black smoke) and PM were significantly reduced in the UK over the last
5 decades as the result of switching to cleaner fuels after the 1956 Clean Air
Act.
Rainfall data have been used to investigate the impact on visibility for all
eight study stations. Daily rainfall data from 12:00 averaged over each year
are shown in Fig. S3. Figure S4 shows a comparison between annual average
visibility that has been filtered for when rainfall is present (hourly
rainfall > 0 mm) and non-filtered data. The percentage of data
removed by filtering for rain accounts for 8–13 % of the total data,
dependent upon the site location, with the Tiree and Aldergrove sites having
the highest percentage of rainfall. It is observed that filtering for
rainfall only results in very small visibility increases for some stations.
Overall the effect is negligible in most circumstances. Therefore the
non-filtered data are used in this study.
Evaluation of historical wind dataWind roses for the eight stations
A graphical representation of historical wind speed and direction at the
eight chosen stations is shown in Fig. 1 using the wind rose polar
co-ordinate representation. These graphs describe the most probable wind
speeds and directions over the whole time series (Carslaw and Ropkins, 2012).
As expected, the graphs show that the predominant wind directions in the UK
are from the southwest. However, there are clear variations between the
different stations. The range of wind speed varies from 0 to
35 ms-1 dependent upon location, with the more coastal sites
experiencing greater average wind speeds.
Analysis of influence of wind speed and wind direction on
visibility
Decadal-seasonal bivariate polar plots are presented for all eight stations
in Fig. S7; these diagrams provide information on the variation of visibility
with wind speed and direction and can suggest locations for visibility-degrading
sources. The detailed analyses of each site are given below:
Aldergrove: overall, lower values of visibility were observed when
the wind was from the south to east, while above-average values were
collected when the wind was from the north to west. Intermediate
visibility was generally observed when the wind came from the south to west
or north to east quadrants. Distinct differences are observed between the
different seasons. In particular, in the summer visibility with wind from the
north to west was higher than in other seasons in every decade.
It is clearly seen that visibility has improved the most when wind comes from
the south to east, which covers mainland urban areas such as
Belfast, the major regional city. It is noted that the seasonal and polar
trends are similar between the visiometer (1950s–1990s) and human-derived
(2000s–2010s) data sets even though the absolute magnitudes are different as
noted above.
Heathrow: low visibility was observed whenever wind speeds were
lower than 5 ms-1 in any direction, which implies a significant
local source of visibility-degrading pollutants. Since Heathrow is the site
of a major international airport, with commensurate road and other transport
infrastructure, this is not surprising. Overall, lower visibility is also
seen when the wind direction comes from the northeast to southeast,
which is consistent with visibility-reducing pollution arriving from the
greater London area. The highest visibilities are typically observed when the
wind direction is from the north to southwest, which is consistent with the less
densely populated surrounding areas. In particular during summer visibility
in the northwest wind direction was higher than other seasons in
every decade. It is identified that visibility has improved in all wind
directions, but most significantly in the easterly direction, which covers the
London urban centre. The change in visibility illustrates the dramatic
improvement of air quality in London since the introduction of the Clean Air
Act in the 1950s (Brimblecombe, 2006).
Leuchars: two distinct spatial groupings of visibility are clearly
observed. When the wind direction comes from the northeast to southwest
(clockwise), visibility is generally lower, and it is generally higher when
the wind direction is from the northeast to southwest (anticlockwise). The
lowest visibilities are from the southeast in all seasons. The
spatial pattern of low visibility suggests a maritime aerosol source as the
major source of visibility reduction, whilst high visibility was associated
with air which had passed over predominantly rural Scotland. Visibility
in the northwesterly wind direction was higher in the summer months, as
expected (see Figs. 2 and 3), than in other seasons in every decade.
Nottingham: like Heathrow, the poorest visibility conditions
occurred when wind speed was below 10 ms-1, suggesting local
sources of visibility-degrading pollutants. Visibility is often lowest when
the wind comes from the southeast, consistent with the relative placement of
Nottingham city centre to this direction (the meteorological station is
actually located in Watnall just about 5 miles from Nottingham city centre).
Visibility is generally highest when the wind comes from the west and
southwest directions, which is largely consistent with air masses passing
over less urban areas compared to the other wind directions. During the
summer months, visibility in the southwest was
higher than in other seasons in every decade. It is clear from Fig. 2 that
visibility has increased in all seasons, and the strongest improvement is
seen in air from the southeast as seen in Fig. S7.
Plymouth: in general, the lowest visibility was observed when the
wind comes from the southeast to southwest, which is consistent with
maritime air causing the lowest visibility, which suggests a maritime source
of aerosol causing visibility degradation. The highest visibilities are
observed when wind comes from the northwest to northeast, and in
particular the northeast; this is consistent with air masses passing over
relatively rural areas. Regardless of the direction of wind, the summer
months showed higher visibility than all other seasons. It is identified that
visibility has improved over time for all wind directions.
Ringway: overall visibility was poor at low wind speeds and when the
wind direction was from the northeast to southeast. Ringway is the location
of Manchester International Airport, so, like Heathrow, there is likely to be
a significant local source of visibility-degrading pollutants arising from
the airport and its associated infrastructure. The wind directions associated
with higher visibility are a lot more variable in time and space than
other locations. However, in general, high wind speeds from
either the northwest or southwest directions are often associated with
higher visibility. Since the 1960s visibility has improved for all wind
directions. In particular, visibility associated with air masses coming from
the direction of the greater Manchester area to the north has shown a marked
increase since the 1970s.
Mean monthly visibility and RH (left side) and average weekday
visibility normalized to Sunday mean values (right side) at all eight sites:
(a) Aldergrove, (b) Heathrow, (c) Leuchars,
(d) Nottingham, (e) Plymouth, (f) Ringway,
(g) Tiree, (h) Waddington.
Tiree: the island of Tiree has by far the highest visibility at low
wind speeds. Overall low visibility was observed when wind came from the west
to southeast, while highest visibility occurred with wind from the northeast.
The spatial variation of low visibility is consistent with a maritime source
of visibility-impairing aerosols. The higher the wind speed, typically the
lower the visibility, which is consistent with greater aerosol production from
greater wave activity (Venkataraman et al., 2002). The higher visibility from
the northeast is consistent with air masses passing over the larger rural
highlands of Scotland. Visibility was relatively stable for all wind
directions for all decades of the human observation data series, which is
consistent with this rural maritime site being largely unperturbed by
anthropogenic pollution.
Waddington: in general, lower visibility is observed when wind
speeds are lower than 10 ms-1, which is consistent with local
pollution sources. Low visibility is also observed when the wind direction is
from the east to southeast, which potentially indicates a maritime source.
Higher visibility is observed from the west at high wind speeds. Visibility
has improved for wind from all directions since the 1970s.
Overall it is clear that visibility has improved at most of the sites for
most local wind directions. The most marked improvements in visibility are
seen in directions in which air masses pass over major metropolitan areas such as
greater London and greater Manchester. Whilst most of the visibility changes
can be ascribed to the location of the meteorological stations with respect
to either urban or maritime sources, it is noted that for most sites the wind
direction with the lowest visibility overall is often from the east, i.e.
continental Europe, and hence synoptic-scale pollution events which affect
visibility. Poor air quality, in the UK, is often associated with
synoptic-scale events originating in continental Europe (Charron et al., 2007a, b,
2013; Lee et al., 2006; Crilley et al., 2015)
Correlation between RH and visibility: seasonal, day-of-the-week
and decadal effects
Figure 3 provides monthly values for visibility and RH, averaged over the
whole time series, for each station. This figure clearly illustrates that
visibility shows a strong seasonal cycle which is anti-correlated with RH at
all stations. The relationship at Tiree is less strong compared to the other
seven sites. The geographical location of Tiree, which is a maritime island,
is the likely reason for the RH trend being different to the other stations.
Tiree island has a very flat landscape, which does not provide shelter from
wind in any direction; this directly affects the local meteorology (Holliday,
2004). Overall, the monthly trends indicate that visibility is lowest in
winter and highest in summer, with spring and autumn being intermediate in
visibility values.
In addition to the seasonal cycle, there is a clear day-of-the-week effect on
visibility changes at most sites (Fig. 3), where visibility improves sharply
at the weekend, with Sunday showing the highest visibility. It is observed
that visibility improves on Sunday from 5 to 12.5 % (depending upon area)
as compared to other weekdays (Monday–Friday). Lower traffic and industrial
emissions at the weekend are the likely reasons for better visibility at the
weekend due to less pollutant emissions. The inherent assumption in this
analysis is that traffic is higher during weekdays than during the weekend.
It is noted that visibility tends to peak on Sunday (rather than both
Saturday and Sunday), and this may reflect the non-negligible timescale
required for pollutant removal by wind-driven dispersion; i.e. the build-up
of pollution during weekdays is not fully dispersed until Sunday. The same
argument explains why visibility is typically higher on Mondays than
the other weekdays later in the week.
Decadal visibility in a specific range of relative humidity (left
side) and number of days in percent during different relative humidity (right
side).
The long-term decadal (1950s–2010s) variation in visibility with RH is shown
in Fig. 4, for all eight stations, where the visibility is averaged within RH
bins. A qualitatively similar pattern has been observed for all stations:
visibility is observed to vary strongly with relative humidity, which clearly
indicates a significant particle hygroscopicity effect on visibility. It is
noted that very high RH can also be indicative of precipitation, which also
decreases visibility.
To further highlight the effect of RH on visibility, the mean monthly
visibility trend is compared to RH for the 60 years of data recorded at the
Waddington station; see Fig. S5. A scatter plot of visibility versus RH
reveals a clear near-linear relationship (linear fit R2=0.60) between
the variables. Removal of the long-term trend in the visibility data was
achieved by fitting the visibility to a quadratic function and subtracting
the quadratic function from the time series. A scatter plot of the long-term
detrended visibility data versus RH reveals a more linear relationship (R2=0.66) where every rise in RH of 10 % results in a reduction of
approximately 5 km of visibility.
Effect of long-term changes in meteorological parameters upon
visibility
The long-term trends in visibility are compared to the other recorded
meteorological parameters: RH, air temperature, wind speed and wind direction
(Fig. S6). It is observed that at most of the stations RH decreases as
average air temperature increases. The previous literature has observed that the UK
mean air temperature and sea surface temperature have increased by about 1
and 0.7 ∘C, respectively, between the early 1970s and mid-2000s
(Jenkins, 2007). However, overall UK mean RH decreased about 2.7 %
between 1961 and 2006 (Jenkins, 2007). This reduction in RH is also seen
more widely at the mid-latitudes (Willett et al., 2014). The temperature
change is likely due to climate change, land-use (urban heat island) effects
or a combination of both. Clearly, urban heat island effects can only affect
stations that are located in urban areas (Fig. S6). However, as Fig. S6
shows, visibility is strongly related to relative humidity and hence to the
air temperature of a given location, highlighting a possible indirect effect
of climate change and urban heat island effects on regional visibility. The
correlation statistics between visibility, relative humidity, air temperature
and wind speed are provided for all stations in Table S2.
Mathematical fitting of measured visibility
Equations (5) and (6) were fit to the decadal visibility data subset into
distinct RH bins, as detailed in Sect. 3.2. It is found that the fitted data
are able to match the observed visibility extremely well (R2> 0.98) for
all stations; for example see Fig. 5 for Heathrow station. The last decade,
starting in 2010, has the poorest fit, albeit still with an R2=0.95, but
only cosists of 3 years of data.
We have quantified, in Sect. 4.1, that the decadal observed visibility has
improved at most of the stations, which is a direct indicator of change in
the combination of aerosol concentration, aerosol composition, gas
concentration and RH. To better understand these changes in visibility, the
absorption coefficient (βabs), scattering coefficient (βsca), particle hygroscopicity parameter (γ) and dry
visibility (Vis(dry)) have all been calculated via the constructed model (Eq. 4)
described in Sect. 3.2.
The determined model output parameters (Vis(dry), γ,
βsca and βabs) are presented in Fig. 6,
where analysis has been carried out for all sites within each decade;
however, the following discussion only considers data that were measured
manually, due to the impacts of measurement methodology changes noted above.
A clear improvement in calculated dry visibility was observed for Plymouth,
Heathrow, Ringway, Nottingham and Waddington, while only minor changes were
observed at Aldergrove, Leuchars and Tiree (Fig. 6a and Table S3). Broadly,
the five sites in England are similar with all showing an upwards trend in
visibility, whereas the Scottish and Northern Irish sites have greater dry
visibilities but less of a discernible trend with time.
Comparisons of modelled and observed visibility in a specific range of
RH using Eq. (4) at Heathrow station. The observed visibility is presented
with standard error bars at the 95 % confidence interval.
Model output parameters (a) dry visibility and
(b) gamma, and (c) absorption coefficient and
(d) scattering coefficient at 75 % from the 1950s to
2010s. The green shaded region shows the start of the visiometer era at most
of the stations (see Table S1 to see the starting year of visiometer
measurement).
The derived value for γ has decreased slightly at Heathrow, Leuchars
and Ringway sites over those decades (Fig. 6b and Table S2), which indicates
a decrease in hygroscopicity over the time (and a concomitant improvement in
visibility). Tiree is the only station which showed increased hygroscopicity
parameter values, implying a rise in aerosol particle hygroscopicity, which
results in a drop in visibility. The other stations like Aldergrove, Ringway,
Plymouth and Waddington show very little change in hygroscopicity parameter
values.
Reductions in scattering coefficient are observed at all sites except
Aldergrove. The scattering coefficients calculated at RH = 75 % are
shown in Fig. 6d. Larger decreases in the scattering coefficient are observed
at the urban sites than at the rural sites. Reductions are also observed
in the absorption coefficient at most sites, but there is much more
variability than in the scattering coefficient. It is interesting to note
that the two most remote sites, both in Scotland, have increasing absorption
coefficients, which is potentially indicative of episodes of long-range
transport of absorbing aerosol to these pristine sites becoming more
frequent. As expected, both the absorption and scattering coefficients show
an inverse relationship with the observed visibility (Fig. 6a, c).
The change in the fitted values for dry visibility and the scattering coefficient
is not significantly affected by the change in visibility measurement from
human observation to visiometers. Contrastingly, the absorption coefficient
and gamma values are much more influenced by measurement technique. This
likely indicates that local sources have markedly different absorption and
hygroscopicity parameters compared to more regional sources, whereas their
local and regional scattering properties are relatively similar.
The modelled scattering coefficient, at 75 % RH, is always higher than
the absorption coefficient for all sites and times. However, at lower RH the
two values become more comparable; see Fig. S8, which examines the
contribution of the scattering coefficient to the total extinction
coefficient at Heathrow. The non-negligible contribution of the absorption
coefficient to the total extinction coefficient indicates that the model
shown in Eq. (5) is not appropriate for the data reported in this paper.
However, for other locations with lower concentrations of absorbing species,
gas or aerosol, the model may be valid, and the benefit of a linear fitting
algorithm, compared to a non-linear algorithm, could be exploited. It is
shown the contribution of aerosol scattering to total extinction has remained
relatively constant over time, which indicates that the reduction in
particulate matter has decreased both the absorbing and scattering fractions
in equal measure.
Seasonal decadal changes in aerosol parameters were calculated for the
Heathrow station (Fig. S9). In general, an improved dry visibility with
reduced βabs and γ values was observed for all seasons
over time. However, during winter months the greatest improvement in dry
visibility with a reduction in βabs was noted.
Trends in visibility for those data acquired at a single RH value of 70 %
(67.5–72.5 %) during the period of the 1950s to 1990s were investigated for
the Heathrow site to demonstrate the disaggregation of the RH effect on
visibility from the aerosol concentration effect upon visibility. At constant RH,
a clearly improved visibility was determined for the study period (Fig. S10).
The result implies that significant changes in aerosol composition/concentration
are driving the visibility trend. Hence improving air quality contributes
significantly to better visibility.
Gas contribution to visibility change over Heathrow airport.
YearNO2NO2Total extinctionAbsorption% contribution of NO2concentration(ppm)coefficient (km-1)coefficient in km-1in total(µgm-3)by all effects(βNO2abs)extinction(using E1)by NO2coefficient200438.30.02030.14750.00671±0.00234.5±1.5200538.50.02040.14250.00675±0.00234.7±1.6200636.90.01960.19780.00648±0.00223.3±1.1200736.90.01970.18550.00649±0.00293.5±1.4200834.70.01850.17590.00600±0.00263.4±1.4200936.30.01930.16810.00636±0.00233.8±1.2201034.40.01830.17550.00604±0.00233.4±1.3201133.60.01790.16140.00589±0.00253.6±1.5201234.60.01840.15500.00507±0.00243.5±1.51970*69.20.03680.23700.01215.12
* Estimated values given for 1970 (see main text for
details).
Effect of nitrogen dioxide gas upon visibility at Heathrow
The potential influence of NO2 levels upon visibility was analysed using
data from the Harlington station (proximate to the Heathrow site) for the
period 2004–2012. The annual mean concentration of NO2 varied from 33.6
to 38.5 µgm-3, peaking in 2005 (Table 3). The NO2
influence on observed visibility (in the RH bin centred at 75 %
(72.5–77.5 %)) was greatest in the year of 2005 (where it contributed
4.7±1.6 % in total extinction) and lowest for 2012 (3.3±1.5 % in total extinction), with the remaining visibility reduction
being caused by aerosol extinction. Overall, during 2004–2012 NO2
contributed approximately 4 % to the observed visibility change, while
the remaining 96 % contributed arose from aerosol particles and fog.
However it is worth considering the contribution of NO2 towards the total
extinction coefficient during the 1970s, when visibility was very low
(16.5 km) as compared to 2012 (25.2 km) and NO2 levels
being higher. Unfortunately NO2 data are not available before 2004 at the
nearby Heathrow site, but a recent study shows that NOx emission in the
UK has almost doubled in the time period 1970–2012 (Harrison et al., 2015).
Using the UK NOx record for 1970 from Harrison et al. (2015), we assumed
the annual mean NO2 concentration in 1970 is double what is measured in
the year 2012 (34.6 µgm-3) as emission estimates are
approximately related to concentration. This assumption does not take into
account the changing vehicle fleet with corresponding changing emissions of
NO and NO2 (Carslaw and Rhys-Tyler, 2013). Using these data the absorption
coefficient for NO2 was calculated. In particular, a higher absorption
coefficient (βNO2abs) in 1970
(0.0121 km-1) than in 2012 (0.00507 km-1) was
identified. However, the contribution of NO2 to the total extinction
coefficient remained at 5.2 % in 1970, only about 2 % higher than in
2012.
Conclusions
Long-term trends in visibility for eight meteorological stations
situated in the UK have been investigated. In general, visibility has
improved at most of the stations through time. The improvements are greatest
in urban areas and are attributed to reductions in aerosol particle loadings
and decreases in atmospheric RH. Visibility was found to be lowest during
winter and highest in the summer due to seasonal variations in RH and likely
changes in the mixing-layer height. The rate of change of visibility was
higher in winter for all stations with the exception of Ringway. A sharp
positive increment (5–12.5 %) in visibility was observed on Sundays, as
compared to other days of the week (Monday–Saturday), which is most likely
due to weekend reductions in traffic and other particulate matter emission
sources.
Bivariate polar plots of visibility, which account for both the influence of
both wind speed and wind direction, explained the influence of wind on likely
source areas of visibility-reducing aerosols. These bivariate polar plots
identified likely locations for visibility-reducing pollutant sources and
their variation over time. Overall, an improved visibility at most of the
stations in almost all directions was observed with notable improvements when
the air masses moved over metropolitan areas, for example, greater Manchester
and greater London areas. At most sites, low visibility was observed when the
winds came from the direction of continental Europe, which may indicate an
influence of regional pollution events leading to visibility reductions.
Significant changes in visibility were observed with changes in relative
humidity, which indicates a strong dependency of visibility on aerosol
hygroscopicity. The measured RH at all sites was typically in the range of
60–80 %, and variations of a few percent in this RH range can have
significant effects on visibility. Many sites showed long-term decreases in
RH, which correlated with increases in air temperature, and had the effect of
improving visibility. If the trend of increasing RH continues, the UK can
expect further improvements in visibility for the same pollutant loading.
Calculations indicate that the majority of visibility reduction is caused by
PM; however, a non-negligible contribution of light absorption is due to
NO2 gas. For the Heathrow station, over the time period 2004–2012, light
absorption by NO2 was calculated to contribute approximately 4 % to
the total visibility reduction, with the remainder caused by PM absorption
and scattering. The NO2 contribution was likely to have been significantly
higher in prior decades due to the higher NOx emissions and hence
atmospheric concentrations.
A light-extinction model was developed to explain the dependency of
visibility upon meteorology and aerosol characteristics. The agreement
between the modelled and measured visibility is excellent. The model suggests
that there have been significant changes in aerosol concentration over the
last 60 years. The model incorporates parameterizations of aerosol
hygroscopicity, particle concentration, particle scattering, and particle and
gas absorption. The developed model is easily transferrable and applicable to
other data sets worldwide.
Visibility can be used as a proxy for aspects of air quality, in particular
particulate matter and nitrogen dioxide. Visibility measurements can
extend back for hundreds of years, whilst air quality measurements typically
only go back decades, albeit with a few sparse data sets going back longer in
time. The approach demonstrated in this paper has potential for generating
historical air quality indications for locations with visibility records.
Data availability
Hourly visibility data along with meteorological parameters can be downloaded
at http://badc.nerc.ac.uk/. NO2 data used in this study are
available at https://uk-air.defra.gov.uk/.
The Supplement related to this article is available online at doi:10.5194/acp-17-2085-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
We thank the University of Birmingham for supporting Ajit Singh through the
Elite Scholarship Scheme. We are also thankful to the UK Met Office and Defra
for the provision of data used in this research. We also appreciate both
reviewers for their valuable comments and suggestions, which have improved
the quality of the manuscript.
Edited by: T. Takemura
Reviewed by: two anonymous referees
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