OR/18/020 Atmospheric composition

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R S Ward1, G Allen2, B J Baptie1, L Bateson1, R A Bell1, A S Butcher1, Z Daraktchieva3, R Dunmore4, R E Fisher5, A Horleston6, C H Howarth3, D G Jones1, C J Jordan1, M Kendall6, A Lewis4, D Lowry5, C A Miller3, C J Milne1, A Novellino1, J Pitt2, R M Purvis4, P L Smedley1 and J M Wasikiewicz3. 2018. Preliminary assessment of the environmental baseline in the Fylde, Lancashire. British Geological Survey Internal Report, OR/18/020.

Introduction

An atmospheric baseline is a set of measured data at a specified fixed location that is statistically representative of the local atmospheric composition, and which reflects the role of existing local, regional and global pollution sources. The dataset should include inputs to air sampled over a period of time that is sufficient to capture typical ranges in meteorological conditions. An atmospheric baseline provides a set of statistical values against which the incremental impacts of new emissions, new pollution sources, or policy interventions, can be assessed at a later date using analogous comparative data. The baseline in air pollution conditions may be expected to vary by wind direction, time of day, and season, and meaningful statistics are established through long-term continuous observations.

The analysis in this report uses greenhouse gas concentration, principal air quality trace gas and particulate matter concentrations, and meteorological data, collected at the monitoring site at Preston New Road, Little Plumpton close to the shale-gas development site. These data represent the first full year of data. Monitoring is continuing and these further data will be reported in due course as part of a full baseline assessment.

The method of baseline interpretation used here allows us to explore the statistical climatology of the atmospheric environment at each site and to explore the mix of pollutant source-types that influences the local area by comparing meteorology (especially wind direction and wind speed) and trace gas concentrations (and correlations) as a function of time, such as time of day, day of week, seasonal and annual. We do this by discussing the mean state and variability of the measured data within relevant subsets of time over which we may expect the dataset to behave consistently and comparatively, e.g. days of the week versus weekends, winter versus summer, day versus night and differing wind directions, wind speeds, and surface pressure conditions. By comparing the differences between such regimes, we can attempt to unpick the causes of observed systematic differences and variability, and use data such as back-trajectory analysis to facilitate potential sources of gas emission upwind or nearby.

The analysis here often refers to what we describe as the airmass history. In atmospheric science, this term refers to the character of a volume of air in terms of any impacts on the air’s composition as air moves over and through its upwind environment. Airmass composition (e.g. trace gas concentrations) is continually perturbed as air moves through Earth’s atmosphere, experiencing chemical and dynamical changes associated with inputs (e.g. pollution sources), chemical modulation (due to atmospheric chemistry), physical modulation (due to dry and wet deposition) and diffusion/dispersion processes as airmasses mix as a function of the prevailing meteorology. The sum of all of these processes results in the measurements that we might see at a fixed location. Put simply, these impacts — in the context of air pollution — can be additive, representing a mix of pollution added to the airmass as it advects over various sources upwind, subtractive due to chemical and physical removal, and dispersive as airmasses mix with each other.

Detailed airmass characterisation in atmospheric science research requires the use of cutting edge chemical transport models and highly detailed and comprehensive (global) measurement datasets and remains the subject of much academic research well beyond the scope of this report and this project. Therefore, in this project, which is concerned with impacts on the local environment, we limit ourselves to the interpretation of local and regional pollutant sources and a relatively recent airmass history to interpret how these factors impact the measurement sites in a statistical framework to obtain a representative and meaningful baseline climatology.

A further objective was to advise on the spatial transferability of the climatology, (i.e. the wider area that the baseline can be extended to represent), and the temporal lifetime of the baseline (i.e. how far into the future the statistics can be reasonably assumed to be valid). This is because different locations will typically have very different existing local pollution sources and future development plans, such that baselines have finite extrapolation potential. As the greenhouse gas baseline is intended to provide a contextual source of information from which to compare any future measured increment in local pollution attributable to shale-gas activity, it will be important to establish the utility of the baseline for this future purpose.

In an earlier report (Smedley et al., 2015[1]), we discussed the technical specifications of the instrumentation at the atmospheric composition monitoring site built for the purpose of environmental baselining. We also described the rationale for site location, sampling frequency (1-minute resolution) and sampling duration (12 months) in the context of providing meaningful statistical comparative datasets and interpretation of local (defined here as <10 km from site) and far-field (>10 km from site) generalised sources of gas emissions that may predate any future exploration for shale gas in each location. For further information on the instrumentation, siting, and atmospheric composition baseline rationale, consult the project website: www.bgs.ac.uk/lancashire.

Site selection

The position of the atmospheric composition measurement site (see Figure 21) was selected so as to be downwind of exploratory shale gas extraction infrastructure (to optimise potential future operational monitoring) in order to obtain a representative local baseline ahead of any exploratory activity. The site consists of a mains-powered outdoor weatherproof enclosure containing all scientific instrumentation and a meteorological station to record local thermodynamics (winds and meteorological variables) to aid qualitative source apportionment based on airmass history.

Figure 21    Left: photograph of the Little Plumpton (LP) measurement site; right: map showing location of the measurement site and proposed Cuadrilla site to the north of the A583 at Little Plumpton. © University of Manchester, 2017.

Monitoring site details

On beginning the project in September 2015, new instrumentation was procured and site locations were selected for installation. By late January 2016, the monitoring site was fully operational and collecting the full suite of data detailed in Table 3 below.

Table 3    Measurements at both sites, dates when measurements became active, and measurement frequency (as streamed via the cloud). Note that NMHC refers to non-methane hydrocarbons and PM refers to particulate matter.
Species Little Plumpton Frequency
Meteorological Data (T, q, p, 3D wind vector) Nov 2014 1 minute
NO, NO2, NOx Dec 2015 1 minute
O3 Dec 2015 1 minute
PM1, PM2.5, PM4, PM10 Nov 2015 1 minute
NMHCs Jan 2016 weekly
CH4 Nov 2014 1 minute
CO2 Nov 2014 1 minute

The LP site is situated on privately-owned farmland near to the village of Little Plumpton, Lancashire, where planning permission has been granted to Cuadrilla to carry out drilling and hydraulic fracturing for shale gas exploration. The site has been established with the land-owner's permission and a full risk assessment carried out prior to installation of the monitoring station.

Instrumentation

Air-quality instruments at LP were purchased using grant funding from the Department of Business, Energy and Industrial Strategy (BEIS) and administered through the British Geological Survey, including the Whole Air Sampling (WAS) system used here to derive concentrations of hydrocarbons in free air. Greenhouse gas measurement instrumentation has been provided by the University of Manchester.

Air inlets positioned on 2–3 m high pylons draw air into the instruments to record instantaneous concentrations of trace gases and particulate matter in the air moving over the measurement sites with the prevailing wind. Data were recorded locally and also transmitted wirelessly to a data storage facility, from where the science team can monitor performance and nominal operation.

Mobile baseline methane monitoring

In addition to fixed-receptor-site monitoring, two 2-day measurement campaigns were undertaken using the Royal Holloway University of London (RHUL) mobile greenhouse gas laboratory following the procedures and protocols outlined in Zazzeri et al. (2015)[2]. These surveys were designed to characterise the types of existing greenhouse gas sources in the wider local area around each monitoring site. The results from these mobile surveys will be presented in the section Little Plumpton mobile vehicle surveys of methane emission sources. The dates and locations of the mobile surveys were 9–10 March 2016 and 27–28 July 2016.

Where repeatable plumes of methane were identified, with significant elevation of methane concentrations recorded for at least 20 seconds on forward and reverse driving profiles, the plumes were sampled for isotopic analysis by pumping air into Flexfoil bags. On average, 25 bags were filled during each 2-day campaign for subsequent analysis in the laboratory at RHUL.

Some source emissions might be expected to be continuous and measured on consecutive days and repeat campaigns, while others such as gas leaks may be repaired or pressure dependent, and emissions associated with animals may vary as they move around from barn to field. Plume receptor points sampled by the mobile laboratory were along accessible roads and tracks, so transecting a plume can be conceived to be entirely wind-dependent.

Each methane source has a typical carbon isotopic signature. These are conventionally assigned to a per mil (‰) scale for global carbon sources, which for methane gives δ13C ranging from -75 ‰ for biological sources to -15 ‰ for combustion sources. Well-mixed background air contains methane with an isotopic signature between -48 and -47 ‰. Background CH4 is typically between 1.9 and 2.0 parts per million (ppm), depending on meteorological conditions. These classifications will be used as the basis for interpretation of results.

Calibration and quality assurance

Quality assurance (QA) and quality control (QC) procedures have been employed for air quality and greenhouse gas concentration data covering all aspects of network operation, including equipment evaluation, site operation, site maintenance and calibration, data review and ratification. All instrumental calibrations are traceable through an unbroken chain to international standards to ensure high accuracy and known uncertainty in the gathered dataset. Metadata concerning the precision and guidance on use of the data is prepared for each measurement reported and will be available to view publicly on the Centre for Environmental Data Analysis (CEDA) after final QC approval. Data were checked online initially before being uploaded to the CEDA repository will be quality checked. Site visits occur at 3-weekly intervals to check the instruments physically, and to perform checks on analyser accuracy, precision and response times as well as calibration.

The calibration and maintenance procedures for each instrument are detailed in Table 4 below. Measurements of CO2 and CH4 were made using an Ultra-portable Greenhouse Gas Analyser (UGGA; Los Gatos Research Inc., USA). This instrument was calibrated on site using two standards traceable to the WMO greenhouse gas scales: X2007 and X2004A for CO2 and CH4 respectively. One standard was chosen to contain roughly ambient concentrations (403.69 ppm CO2 and 1901.00 ppb CH4), while the other was enhanced in both gases (603.02 ppm CO2 and 5051.07 ppb CH4). The concentration of both gases within the standards has been determined by EMPA, Switzerland, relative to the corresponding WMO scales. The instrument uncertainty can be quantified by the 1σ values given for the calibration parameters above. These values include uncertainties associated with instrument drift and the uncertainties associated with the calibration cylinder certification. Assuming these uncertainties are uncorrelated and normally distributed, CH4 measurements of 1900 ppb and 5000 ppb would have 95 percent confidence intervals equal to 10.49 ppb and 22.40 ppb respectively. Similarly, CO2 measurements of 400 ppm and 600 ppm would have 95 percent confidence intervals equal to 2.92 ppm and 3.83 ppm respectively.

Table 4    Detailed descriptions of the QA/QC for data collected at LP measurement site.
Parameter Calibration and maintenance procedure
NO and NO2 Traceable calibration cylinders from the National Physical Laboratory. Monthly checks of analyser accuracy, precision convertor efficiency.
Ozone Six monthly calibrations in the field by a calibration unit links to a primary UV photometric standard that is itself calibrated against a certified national source annually at the National Physical Laboratory.
Particulate matter Six monthly calibration in the field by a monodust (CalDust), monthly maintenance checks.
CO2 and CH4 Calibration of greenhouse gas concentration data is performed by routine reference to certified gas standards, traceable to the World Meteorological Organisation scale.
NMHCS Calibration of NMHCs is performed by reference to an NPL ozone precursor mix. This calibration scale has been adopted by the GAW-VOC network and hence the measurements of NMHCs made by this instrument are directly comparable to those made by all of the WMO-GAW global observatories.
Calibrations are performed each month or more frequently if field deployment allows. A long-term data set of the response of the instrument is held and regularly updated to ensure that the instrument responses do not change and to highlight any issues with stability of components within the gas standards used.

Meteorological baseline

The principal meteorological variable of interest to baseline characterisation and pollution source interpretation is the local wind speed and direction, as an indicator of the local airmass history (i.e. what source of pollution the sampled airmass may have passed over upwind). The instantaneous wind speed and direction can point us to relatively nearby sources of pollution (within ~10 km) where repeated and consistently elevated concentrations of trace gases are observed to correlate with wind direction and wind speed. When discussing more long-range sources of pollution (such as may be added over cities many 10s or 100s of km upwind), the timescales of interest to airmass history typically extend to no more than around 5 days. Beyond this time, the uncertainty in the path of air upwind (and the chemical changes in such air) increases rapidly and interpretation becomes meaningless. Therefore, we limit our analysis to these timescales of advection only.

We now describe the climatology of wind observed at the baseline sites and discuss what this means in the context of pollutant gas concentrations and sources that have been observed at the measurement stations.

Little Plumpton wind climatology

The wind speed and wind direction statistics observed at the LP site over the full measurement period are shown in Figure 22 as a conventional wind rose. This type of illustration simply shows the frequency (in percent of total time) of instances when wind blows from various directions (seen as the vector and radius in Figure 22). The colour scale in Figure 22 then illustrates the corresponding proportion of winds in each direction for a range of surface wind speeds (see colour legend in Figure 21).

Figure 22    Wind rose for the LP site, showing wind speed and direction statistics for the period 1 Feb 2016–30 Jan 2017. The radius defines the percentage of total time in each of 12 wind direction cones (30 degree span), while the colour scale defines the wind speed (redder colours indicating strong wind speeds >6 ms-1 and yellower and pale colours indicate light or stagnant winds, respectively). © University of Manchester, 2017.

As expected at the LP site (as for any exposed site in the UK) the dominant wind direction is from the western quadrant (~35% of the time), consistent with Blackpool’s location on the west coast of the UK mainland and exposed to the Atlantic mid-latitude storm track. This is also the direction from which the strongest winds are observed (red and dark red colours in Figure 22), typically coinciding with the passage of mid-latitude cyclones over the UK mainland. Within this westerly quadrant, the dominant wind speed is between 6–12 m/s (dark red colours), with extremely strong winds peaking up to 20 m/s in very rare storm conditions (<0.5% of the time).

This has important implications for the local baseline. The position of the LP site near to the Blackpool shoreline means that winds bringing air from the Atlantic may typically be expected to carry relatively well-mixed and background airmasses to the LP measurement site. In this context, a background can be conceived to be an airmass relatively unaffected by local or regional pollution sources, broadly representative of the average composition of Northern Hemispheric air at the time. These airmasses often represent the Northern Hemispheric seasonal average concentrations of greenhouse gases especially, as these gases are relatively inert on the time and spatial scales of advection across the Atlantic in mid-latitude cyclones. As these airmasses dominate the statistical climatology at the LP site, the baseline for this wind direction provides a very useful background from which to assess future local changes in pollution sources in the immediate upwind vicinity. The position of the LP site just 300 m directly to the east of the Cuadrilla site makes the dominant westerly wind direction highly favourable for any future operational comparative assessment.

Winds from the south-east were also frequent, accounting for 22% of the period, while northerly and easterly wind directions were less frequent, representing <20% in each quadrant over the course of the 12-month baseline. Wind speeds for these quadrants (all other than westerlies) were also typically much lighter (dominated by light breeze winds in the range 2–4 m/s). This is due to a number of factors: 1) that winds from these directions are moderated by passage over the mainland UK land surface, and 2) that winds from these directions usually represent flow in less frequent high-pressure regimes to the north and east or from low-pressure systems to the south and west. Light winds from these directions will typically carry airmasses that have spent a significant time in dynamic contact with the surface of the UK mainland and may also represent air that has passed over Western Europe. These airmasses may be expected to typically contain pollution added to the surface air as they pass over a range of anthropogenic (manmade) and biogenic (natural) sources of greenhouse gases and other pollution upwind of the measurement site; such as cities, landfill, industry, transport, agriculture etc. This air may be a mix of both local (<10 km distant), regional (UK mainland) and more distant (Western Europe) pollution sources, making it difficult to deconvolve the relative inputs of each. However, the frequency and duration of transient enhancements seen in trace gas concentration data offers important clues on the proximity (and type) of pollution source, as regionally impacted airmasses will typically display broad (longer timescale) and more invariant enhancements relative to background westerly airmasses, while local inputs are often seen as sharper and shorter-lived enhancements. This will be discussed further in the following sections, making use of additional airmass history tools such as back-trajectory analysis.

Greenhouse gas baseline

In this section, we present the statistical analysis of the greenhouse gas baseline dataset for LP and mobile vehicle surveys of nearby greenhouse gas sources in turn. We interpret this in the context of sources of emission and background using meteorological (and other) data to aid analysis. We conclude each sub-section by discussing the authors’ recommendations on the appropriate use of the baseline dataset for each site and how this concerns future monitoring for future comparative assessments.

Fixed measurement site climatology

Figure 23 illustrates the measured ambient CO2 and CH4 ambient concentrations at LP as a function of time across the full baseline period sampled at the fixed measurement site. Figure 24 and Figure 25 go on to illustrate how the measured concentrations relate to their coincidently-measured wind direction for each greenhouse gas, while Figure 26 and Figure 27 show the same information but also display the relationship between measured concentration and wind direction as a function of time. When interpreted together, these figures distil several important and internally-consistent summary features, which can be seen in the baseline dataset when comparing salient concentration features with wind direction:

  • There are clear periods of what can be defined as a ‘background’ (accounting for 50% of the period) — where CO2 and CH4 concentrations appear relatively flat at around 400 parts per million (ppm) and 2 ppm, respectively (as seen in Figure 23). These periods coincide with times of westerly winds seen in Figure 24 and Figure 25, and as the orange and red colours in the times series of Figure 26 and Figure 27; and represent a typical seasonally-variant Northern Hemispheric average concentration.
  • There are prolonged periods (several consecutive days) of marginally enhanced CO2 and CH4 (between 400–450 ppm and 2–4 ppm, respectively). These periods coincide most often with moderate south-easterly winds as seen in Figure 24 and Figure 25, when comparing with Figure 26 and Figure 27 (where green and yellow colours indicated easterly and south-easterly wind directions). These features are consistent with an interpretation that these episodes represent regional pollution inputs from cities to the south and east such as Manchester, and the cities of Central and Southern England.
  • There are short-lived (less than a few hours) but large enhancements (often referred to as ‘spikes’) in the time-series data (greater than 4 ppm CH4 and 500 ppm CO2). These coincide most often with light easterly and south-easterly and northerly wind directions seen in Figure 24 and Figure 25, compared with Figure 26 and Figure 27 (where easterly winds are seen in green colours). These features in the data, often superimposed on the regional increment describe above, are expected to represent local (<10 km upwind) sources such as nearby agricultural activities, roads, and landfill.
  • That, for most of the time (>90% of the period), CO2 and CH4 display common patterns, in that both gases are often seen at their respective background concentrations, or are mutually enhanced with a scalable linear relationship (as shown in Figure 28 and discussed further below).
Figure 23    Time series of carbon dioxide (red) and methane (grey) in units of ppm measured at LP between 1 Feb 2016 and 31 Jan 2017. Note: ‘d’ refers to the water-vapour-corrected (or dry) measurement by the UGGA instrument. © University of Manchester, 2017.

Interpreting this further, it can be seen that westerly wind directions invariably bring relatively unpolluted air to the LP site. Other wind directions deliver more complex airmasses likely comprising a wide mix of pollutant sources upwind, both local and regional, requiring additional interpretation (see below).

Figure 24    Concentrations (as per colour scale) in air as a function of wind direction for methane (units of ppm), as measured at LP in the baseline period. © University of Manchester, 2017.
Figure 25    Concentrations (as per colour scale) in air as a function of wind direction for carbon dioxide (units of ppm), as measured at LP in the baseline period. © University of Manchester, 2017.
Figure 26    Methane concentration time series, colour-coded for wind direction as per legend as measured at LP in the baseline period. © University of Manchester, 2017.
Figure 27    Carbon dioxide concentration time series, colour-coded for wind direction as per legend (in degrees) as measured at LP in the baseline period. © University of Manchester, 2017.

Figure 28 illustrates the correlation between simultaneously-measured CO2 and CH4 concentration in air, colour-scaled for sampling density (each count representing a one-minute data interval). Warmer colours indicate more frequent sampling. Clear correlations between the concentrations of the two greenhouse gases seen in plots of this type delineate so-called mixing lines. Such correlations (or mixing lines) often correspond to specific airmass types where co-emission from specific sources, or common airmass chemistry, may be active.

In Figure 28, we see that there are two broad correlations and one dominant feature, seen, as follows:

  1. A dominant mixing line (traced by red and yellow colours) with a relationship of [CO2]=132.1[CH4]+386.5 ppm — representing co-emission (or bulk mixing) of nearby CO2 and CH4 sources upwind to the east and north east (based on understanding of how such concentrations relate to wind direction in Figure 25 to Figure 28).
  2. A weaker but clear mixing line with a relationship of [CO2]=7.5[CH4]+386.5 ppm — representing co-emission (or bulk mixing) of CO2 and CH4 regional UK and longer-range sources upwind to the east and south east.
  3. A dominant red cluster centred at ~400 ppm CO2 and 2 ppm CH4 — this represents the dominant and frequent background signal seen in westerly Atlantic airmasses (Figure 24 and Figure 25). Note that the darkest red colours in this cluster correspond to >40 total days of measurement each within the baseline period.
Figure 28    Coincident CO2 and CH4 concentrations measured at LP. Colours indicate the frequency density of sampling (number of coincident measurements). One count refers to a one-minute period of data. © University of Manchester, 2017.

Mixing lines such as these are a powerful differentiator of source types, especially at the regional and national scale. When temporally averaged (as in Figure 28), they characterise airmasses that have passed over a large fetch of similar pollution source types and where the airmass has had time to mix internally. The two dominant mixing modes seen in Figure 28 are seen to correspond to the less frequent easterly, southerly, and south-easterly wind directions. Considering the location of LP, these wind directions represent air that has passed over the Pennines and the cities of Manchester, Leeds and Sheffield in the case of easterlies, and the cities of Birmingham and London in the case of south easterlies. While cities and infrastructure are a principal source of UK pollution (including greenhouse gases), biogenic sources of greenhouse gases, such as the biosphere, landfill and agriculture would also be expected to feature in the fetch of such airmasses when upwind of the LP site. The summative mix of these longer-range pollution types upwind for easterly and south-easterly wind directions gives rise to the dominant mixing line observed as the red and yellow trace in Figure 28 and described in summary point 2 above.

To interpret more local sources of pollution (within ~10 km), we must focus in detail on the more transient features in the high temporal resolution dataset. To do this on an event-by-event basis for a year of data would be meaningless (and impractical) in the context of the baseline analysis here, though event-led (case study) analysis may well be advisable during any operational monitoring. However, it is possible to interpret the relative role of proximal pollutant sources to the overall baseline by considering short-lived but significant excursions from the average baseline and comparing these with wind speed and direction.

Figure 29 and Figure 30 illustrate a polar bivariate representation of the relationship between both wind speed and direction and greenhouse gas concentration. The colour scale in Figure 29 highlights the wind speed and wind direction conditions that dominate the overall concentration average seen at the measurement site (as a weighted mean of concentration x frequency of occurrence). The red areas seen in both panels (CO2 and CH4) in Figure 29 correspond to light winds (0–2 m/s) from the south-east indicating a well-constrained local source for both gases. Figure 30 shows how the absolute measured concentration relates to wind direction and wind speed, which again shows the dominant south-easterly origin of elevated CH4 concentrations, but also demonstrates a subtly different origin of the greatest enhancements in CO2, which appear from both the south and south-east. Given the site’s location, these local CH4 sources to the south-east are likely to be the nearby dairy farm (on which the site is located) and the nearby A583 main road, while the southerly dominance in CO2 is likely mostly associated with passing traffic on the A583 main road. The fact that the red area does not extend to higher wind speeds in the south-east is consistent with an interpretation that longer-range sources of pollution may not contribute significantly to periods where the greatest enhancements in concentrations are sampled at the site, i.e. that local sources dominate the strongest enhancements. The role of longer-range (regional, national and continental) sources is therefore to add a smaller increment to the much larger local emission sources that dominate periods of enhancement in south-easterly wind conditions. The lighter blue areas seen in Figure 29 to the west indicate a long-range and diffuse source of the greenhouse gases, which is consistent with longer-range transport of moderately enhanced airmasses, from Ireland and in intercontinental transport from the United States, although this source’s relative contribution to the baseline is very much weaker than those upwind sources when airmasses are received from the south-east.

Figure 29    Polar bivariate representation of methane (left) and carbon dioxide (right) as a function of wind direction. The colour scale represents the fraction of total measurement time weighted for concentration enhancement relative to the global mean (as scaled for colour in units of ppm) and wind speed (defined by the radial component — each contour representing 5 m/s). See text for further details. © University of Manchester, 2017.
Figure 30    Polar bivariate representation of methane (left) and carbon dioxide (right) as a function of wind direction and wind speed. The colour scale represents the absolute measured concentration (as scaled for colour in units of ppm) and wind speed (defined by the radial length component — each contour representing 5 m/s). See text for further details. © University of Manchester, 2017.

To differentiate the role of local, regional and more distant (long-range inter-continental) pollution sources further, we now examine the airmass history, which can be interpreted using Lagrangian back trajectories. Back trajectories are a useful indicator of the path that air has taken in the atmosphere up to and over the previous 5 days. Beyond this time, the accuracy of hindcasted trajectories degrades rapidly due to numerical and meteorological uncertainty associated with Lagrangian transport models and the accuracy of reanalysed meteorological data. Put simply, back trajectories attempt to trace back the path of neutrally buoyant single particles in the atmosphere as they are carried on the wind (this is known as Lagrangian advection). Back-trajectory models use wind fields from meteorological reanalyses (hindcasted winds calculated by forecast models that use assimilated measured data).

In this analysis, we have used the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) and hourly United States National Centre for Environmental Prediction Global Forecast System reanalysis meteorological data at a spatial resolution of 0.5° x 0.5°. We have then calculated 5-day back trajectories with endpoints at the location of the LP site at 6-hourly intervals across the measurement period (~1200 trajectories in total between 1 Feb 2016 and 31 Jan 2017).

Figure 31 shows the airmass history of air sampled at LP throughout the baseline period. This statistical representation of the history of air can be interpreted as a surface ‘footprint’, illustrating a surface area over which air measured at LP has been influenced by potential surface sources. Figure 31 shows the frequency (as a fraction of total time, in this case as a percentage of the 12-month baseline period) that air has passed near to the surface in a latitude-longitude grid with a 1-degree spacing. The red colours indicate that air received at LP is most characterised by air that has previously passed over Ireland and the Atlantic Ocean. It also shows less frequent contact with the near-surface to the north.

Figure 31    5-day airmass history surface footprint statistics for the period 1 Feb 2016 to 31 Jan 2017, as seen from the LP site at a spatial resolution of 1 x 1 degree. Frequency refers to the fraction of the total trajectories passing over each lat/long grid cell. © University of Manchester, 2017.

Figure 32 shows the same trajectories but sub-sampled by meteorological season. This figure illustrates that for all but the winter season, airmasses arriving at LP in the baseline period display a variety of upwind histories from all directions (dominated by the Atlantic region to the west). In the winter season, we see that airmasses originate (within the past 5 days) almost exclusively from latitudes to the south of the LP site. This is not to say that airmasses in winter have no longer-range history in more northern latitudes, simply that contact with the near-surface over the preceding 5 days before sampling at LP is dominated by latitudes south of the LP site, i.e. that longer-range (>5 day) airmass histories may well be seen further to the north before being advected over the UK mainland in westerly (or other) flow regimes. This pattern is consistent with the analysis and conclusions drawn about the local meteorology discussed earlier and suggests that land-based long-range sources of pollution from the east (over the UK and mainland Europe) are experienced relatively infrequently compared to maritime air received from the west and the Atlantic at LP.

To investigate further the nature of the 4 broad airmass types arriving at LP identified earlier, we now examine the temporal patterns and airmass history for each airmass classification. To achieve this, the polar bivariate data seen in Figure 11 have been used to categorize the baseline dataset into four principal clusters by the method of K-means clustering. The resulting clusters can be seen in Figure 33, which illustrate (as defined zones) the dominant relationships between concentration, wind direction, and wind speed, which we have discussed earlier. Each zone can be thought of as representing an internally consistent subsample of the data based on the correlation between gas concentration, wind speed and wind direction.

For methane (Figure 33, left), we see the dominant westerly airmass (Cluster 1 — green), a less frequent background airmass from the north-west (Cluster 4, yellow), a regionally (enhanced GHG concentration) airmass (Cluster 2 — blue) and a highly enhanced airmass (Cluster 3 — orange). For carbon dioxide, we see similar features for clusters 1, 2, and 3, but with the more dominant southerly zone cluster (Cluster 2 in Figure 33 right — shown in purple).

Figure 32    5-day airmass history surface footprint statistics for the period 1 Feb 2016 to 31 Jan 2017 by meteorological season (e.g. DJF refers to Dec, Jan and Feb), with trajectory endpoints at the LP site at a spatial resolution of 1 x 1 degree. Frequency refers to the fraction of the total trajectories passing over each lat/long grid cell. © University of Manchester, 2017.
Figure 33    Derived 4-mode K-means clusters of dominating wind-concentration relationships for: left (methane); and right: carbon dioxide as sampled at LP. Radial direction indicates wind direction, while radial length defines wind speed. © University of Manchester, 2017.

We have used this clustering approach to sub-sample the dataset to investigate airmass histories corresponding to each zone (or cluster) by calculating back trajectories for meteorological conditions at the time of measurement of each data point inherent to each cluster. This is illustrated in Figure 34, which shows the trajectory climatology for each cluster corresponding to methane. When illustrated in this way, the difference between the 4 airmass classification origins can be observed readily as distinct surface footprints over different upwind areas. The westerly and northerly zones (clusters 1 and 4 in Figure 33, left) define Atlantic maritime origins (seen as the blue and purple trajectories in Figure 34, consistent with our earlier conclusion that air received at LP from these locations broadly represents a Northern Hemispheric average composition. The regionally enhanced (elevated concentration) cluster is seen in the green trajectories, which pass over the UK mainland to the west and continental Europe. The highly elevated trajectories (seen as orange in Figure 34) show an Atlantic and English Channel footprint, which further reinforces our conclusion that the observed elevations associated with this wind direction are associated with more localised (<10 km upwind) emission sources (added to a smaller UK mainland increment), as the longer-range airmass history would otherwise deliver cleaner airmasses than from Clusters 1 and 4. The mean trajectory path for each cluster is shown in Figure 35, which illustrates the divergent nature of each cluster in terms of their long-range airmass histories. Figure 35 also shows the percentage of time over the baseline period associated to each cluster, further reflecting the conclusions discussed using the simpler wind rose analysis described for Figure 24 to Figure 27 earlier.

Figure 34    5-day back trajectories ending at LP corresponding to the time of each data point associated with the 4 principal clusters identified in Figure 14 left for methane. © University of Manchester, 2017.
Figure 35    Mean path of 5-day back trajectories seen in Figure 15, ending at LP for each of the 4 principal airmass clusters. The percentage associated with each mean trajectory path defines the fraction of time (as fraction on 12 months in the baseline period) that airmasses arriving at LP are classified within each principal cluster defined in Figure 14.

We can now examine the temporal patterns associated with measured concentrations within each of these principal clusters. The diurnal, weekly, and seasonal variability observed for each cluster can give additional clues as to the nature of sources and their proximity to the receptor site. Figure 36 shows this for methane. The top panel shows the mean diurnal pattern and statistical variability (at the 95% confidence level of sampled variability around the calculated mean) in methane concentration as a function of time of day (and day of week) for each cluster (represented as an average over the entire baseline period). When illustrated in this way, we can clearly observe very different diurnal behaviour for cluster 3 (the most elevated airmass) especially, relative to clusters 1, 2, and 4. In particular, we see a consistent and repeatable diurnal minimum at around midday on every day of the week across the whole year. This diurnal minimum on Cluster 3 is best observed in Figure 36 (bottom left), which shows the average over all days of the year. We also see a marked increase in late winter months for Cluster 3. This pattern is consistent with the ventilation of the local boundary layer, as the height of the planetary boundary layer is lifted by convection in daylight hours (enhanced in summer months relative to winter), further indicating a dominant role for local sources, which might be expected to accumulate overnight before being diluted and detrained in daytime. Clusters 1, 2 and 4 do not display such a minimum, suggestive of longer-range origins where the timescales of diurnal boundary-layer ventilation (24 hours) are shorter than the timescales of advection (many days) between regional and distant sources and the baseline receptor site.

Figure 36    Temporal statistics of methane climatology at LP by time and day of week (top panel), time of day over all days (bottom left), month of year (bottom middle), and day of week (bottom right). © University of Manchester, 2017.

We now examine Clusters 1 and 4 for methane in more detail. These clusters represent the background airmasses from the west and north and therefore might be expected to display a more seasonal pattern associated with biospheric respirational activity across the Northern Hemisphere. This is illustrated in Figure 37. Same as Figure 36, but rescaled to illustrate temporal variability for less-enhanced clusters 1 and 4 for methane concentration patterns. © University of Manchester, 2017, which is essentially the same as Figure 36 but rescaled to illustrate the variability of methane concentration better for these (less enhanced) airmass clusters. Several salient features emerge: 1) that both clusters display a diurnal maximum at midday (the opposite of that seen in cluster 3); 2) that there is evidence for a mid-week maximum; and 3) that there is a summer minimum. Together, these features suggest that these clusters represent the northern hemispheric methane summertime minimum (as methane is oxidised by photochemistry), and that there may be a role for mid-week enhancements associated with long-range anthropogenic emissions (perhaps associated with intercontinental transport in westerlies from the United States). It should be noted that these much longer-range enhancements are very small (just 20 ppb peak-to-peak) relative to those in Cluster 3, which are up to 2 orders of magnitude (100 times) higher for more local sources.

Figure 37    Same as Figure 36, but rescaled to illustrate temporal variability for less-enhanced clusters 1 and 4 for methane concentration patterns. © University of Manchester, 2017.
Figure 38    Temporal statistics of carbon dioxide climatology by time and day of week (top panel), time of day over all days (bottom left), month of year (bottom middle), and day of week (bottom right), averaged for the whole baseline period. © University of Manchester 2017.

We see similar diurnal patterns due to boundary layer ventilation (Figure 19, top panel) for all clusters except cluster 4, which represents the relatively clean northerly airmass type seen in Figure 33. The largest diurnal variability is seen for cluster 4, which represents the southerly airmass, linked earlier to local CO2 emission sources and the nearby main road. However, unlike methane, a clear seasonal minimum is observed in August in all clusters. This feature is typical and expected to be due to the summer minimum in northern hemispheric CO2 concentration due to biospheric respiration (uptake), which peaks in the summer months. This is seen for all clusters simply because the relative change in the seasonal background CO2 concentration is significant when compared with the signal due to even very nearby CO2 emission sources, unlike CH4 (by virtue of the very small absolute mean global concentration of CH4 around 2 ppm, which means that small mass fluxes of CH4 can contribute a much greater relative signal on this much lower background). In the case of CO2, clusters 1 and 4 represent more background (less elevated conditions) from westerly and northerly origins, respectively. These are shown in more detail in Figure 39. While clusters 1 and 4 are seen to have very similar seasonal trends, there are some marked differences, especially in the diurnal variability (Figure 39, bottom left) and when comparing weekday with weekend (Figure 39, bottom right). Cluster 4 (northerly and north-westerly origin) does not display a clear diurnal signal and also appears to peak on Saturdays and Sundays relative to weekdays. The lack of a diurnal signal is consistent with an absence of local sources for this cluster. However, the weekend peak is suggestive of something quite different. Often, weekend signals may indicate a change in human social behaviour (e.g. increased traffic flow to recreational destinations). This could indicate weekend traffic movements to the town of Blackpool, which is to the north west of the baseline site. However, we might expect this to manifest in a daytime (or rush hour) maximum, which is not observed. We may speculate that the night-time weekend economy of the Blackpool area may explain the lack of such a diurnal trend on weekend days. However, this may be at the risk of over-interpreting the data available. Moreover, clusters 1 and 4 for CO2, like clusters 1 and 2 for methane, represent only small enhancements compared with their more elevated clusters and therefore such a signal is small compared with the role of more local emission sources.

Figure 39    Same as Figure 38, but rescaled to illustrate temporal variability for less-enhanced clusters 1 and 4 for carbon dioxide concentration patterns. © University of Manchester, 2017.

To investigate the nature of local methane emission sources (biogenic and anthropogenic) in cluster 3 in Figure 34, the results from the mobile vehicle surveys are discussed in the following Section.

Little Plumpton mobile vehicle surveys of methane emission sources

Maps of the area sampled by the mobile vehicle campaign in March 2016 can be seen in Figure 42 and Figure 43, colour-coded for sampled CH4 concentration. The same, but for the July 2016 survey, can be seen in Figure 44 and Figure 45. The March campaign is summarised in the Figure 40 Keeling plot with individual source plumes identified by both surveys in Figure 41 a–f. A summary of the findings is given in Table 5.

The main persistent methane plume in the Fylde region is the landfill at Fleetwood, which was detected on all measurement days despite different wind directions and detected up to 3 km from source across the River Wyre estuary. Concentrations up to 2.5 times the atmospheric background level were recorded peripheral to site. This has a distinctive isotopic signature of -57 ‰ (Table 5), which is typical of all active landfills measured to date by the RHUL group. The Clifton restored landfill gave a signature of -55 ‰, within the range of -56 to -53 ‰ measured for other pre-gas extraction landfill cells. Composting at -52 ‰ and natural gas leaks at -41 ‰ were detected during the March Fylde campaign but not during the July campaign (Figure 44 and Figure 45).

Ruminant emissions (dairy cows) were measured during both campaigns. During March, these were mostly emissions from barns, which formed sharp but narrow (<50 m wide) plumes with excess more than 50% above background. During July the cows were dispersed across fields, resulting in broad (>100 m wide) plumes with excess less than 20% above background. When the cows are in barns, the isotopic signature represents a mixture of emissions from breath and slurry (-60 ‰), whereas the breath source is much more predominant when the cows are in the fields and the waste liquids are partly absorbed by the ground (-64 ‰). Two cow barns emitting methane are close to the proposed well pad, at Plumpton Hall and Moss House. In July, outside of milking time, these cows were dispersed throughout neighbouring fields. The strong inversion on the morning of July 28 resulted in background methane at 2.2 ppm (Figure 45), which dispersed only after 11:00 am. Background samples were collected throughout this period, and a resulting Keeling plot for these suggests that the mixed source emissions entering Fylde from the south have a signal of -61 ‰ indicating dominance of ruminant and landfill emissions.

Figure 40    Summary Keeling plots of 1/CH4 ppm vs measured δ13C for all major methane sources located during the March 2016 campaign, highlighting the difference in line slope. © Royal Holloway University of London, 2017.
Figure 41    Keeling plots of 1/CH4 ppm vs measured carbon-13 for each major methane source identified in the Fylde region in March and July 2016: a) Cows in fields, b) Cows in barns, c) Active landfills, d) Restored landfill, e) Composting and sewage, f) Gas leaks. Sources observed in both campaigns show March in Black and July in Red (where observed). © Royal Holloway University of London, 2017.
Table 5    Summary of bag sampling in the Fylde region. Source methane excess and carbon isotopic signatures identified from Keeling plot analysis.
Source (bag samples) Max. excess over background (ppm) δ13C signature (‰)
Gas Leaks 1.3 -41
Composting and sewage 3.3 -52
Restored landfill 1.7 -55
Active landfill 2.4 -58
Cow barns 2.4 -59
Cows in fields 0.4 (2.9 within 2 m of a cow) -64 (-70)
Figure 42    Survey route for March 9 2016, starting at Charnock Richard services. The largest methane plume observed was emanating from the Jamieson landfill at Fleetwood. © Crown Copyright and/or database right 2018. Licence number 100021290 EUL. © Royal Holloway University of London, 2017.
Figure 43    Survey route for 10 March 2016, ending in Preston. The largest methane plumes observed were from gas leaks and cow barns (see inset). © Crown Copyright and/or database right 2018. Licence number 100021290 EUL. © Royal Holloway University of London, 2017.
Figure 44    Survey route for 27 July 2016, starting in Preston. The largest methane plumes observed were from landfill and cows. © Crown Copyright and/or database right 2018. Licence number 100021290 EUL. © Royal Holloway University of London, 2017.
Figure 45    Survey route for 28 July 2016, starting in Kirkham. The largest methane plumes observed were from landfill and cows. A well-developed inversion resulted in a high methane background until late morning as shown by the green colours along the route track. © Crown Copyright and/or database right 2018. Licence number 100021290 EUL. © Royal Holloway University of London, 2017.

Summary

The summary features of the greenhouse gas baseline at LP can be defined broadly as follows.

  • There are clear periods of what can be defined as a background (accounting for 50% of the period) — where CO2 and CH4 concentrations appear relatively flat at around 400 parts per million (ppm) and 2 ppm, respectively (Figure 23). These periods coincide with times of westerly winds seen in Figure 24 and Figure 25, and as the orange and red colours in the times series of Figure 26 and Figure 27. They represent a typical seasonally-variant Northern Hemispheric average concentration.
  • There are prolonged periods (several consecutive days) of marginally enhanced CO2 and CH4 (between 400–450 ppm and 2–4 ppm, respectively. These periods coincide most often with moderate south-easterly winds as seen in Figure 24 and Figure 25, when comparing with Figure 26 and Figure 27 (where green and yellow colours indicated easterly and south-easterly wind directions). These features are consistent with an interpretation that these episodes represent regional pollution inputs from cities to the south and east such as Manchester, and the cities of Central and Southern England.
  • There are short-lived (less than a few hours) but large enhancements (often referred to as ‘spikes’) in the time-series data (greater than 4 ppm CH4 and 500 ppm CO2). These coincide most often with light easterly and south-easterly and northerly wind directions seen in Figure 24 and Figure 25, compared with Figure 26 and Figure 27 (where easterly winds are seen in green colours). These features in the data, often superimposed on the regional increment described above, are expected to represent local (<10 km upwind) sources such as nearby agricultural activities, roads, and landfill.
  • For most of the time (>90% of the period), CO2 and CH4 display common patterns, in that both gases are often seen at their respective background concentrations, or are mutually enhanced with a scalable linear relationship (as shown in Figure 28).

The climatological annualised GHG statistics for the LP site are shown in Table 6. The mean concentrations of CO2 and CH4 are slightly elevated (4.5% in the case of CO2, and 18.4% for CH4) compared with the Northern Hemispheric tropospheric average for 2016 (~400 ppm and ~1850 ppb, respectively). This is expected due to the position of LP on land and exposed to sources of emission both locally and regionally. The one-standard-deviation variability around the mean is large (4.8% for CO2 and 29.5% for CH4), reflecting the variable airmasses that impact the site. The higher CH4 variability is suggested to be linked to the nature of local sources (such as agriculture and landfill identified in the mobile surveys discussed in Section Little Plumpton mobile vehicle surveys of methane emission sources. The interquartile and interdecile ranges for both gases are constrained to 6.5% for CO2 and 17% for CH4 relative to the mean, while the extremes (99th percentiles), extend to 16% and 215% of the mean for CO2 and CH4, respectively. This demonstrates that for the vast majority of the period (80%), concentrations do not vary by more than ~20% at most). However, shorter-period, extreme events (accounting for 1% of the baseline period), can see concentrations of CH4 double that of the mean climatological concentration. Such periods are identified with episodic local emissions, lasting for a few hours at most.

In all cases, it must be stressed that the levels of greenhouse gas concentrations seen at this site do not represent any known hazard to human health and are well within the typical range seen for any land-based measurement site. Even the largest transient enhancements seen in the collected dataset are in what would be considered to be a normal modern range and the conclusions drawn in this report on the existing sources of local pollution in our opinion do not represent any cause for local concern.

The statistics defined in the baseline period can be used in the following ways when comparing to analogous datasets collected in the future or during periods of new localised activity:

  • The background (hemispheric average concentrations) seen in airmasses associated with westerly and south-westerly origins lend themselves optimally to assessment of any incremental signal due to hydraulic fracturing in Little Plumpton. This is because the location of the baseline site directly to the east of the field where Cuadrilla holds an exploratory licence, which means that any significant fugitive emission should be readily observable against the otherwise very flat and clean signal seen for this wind direction in the baseline dataset. This will allow future work to positively identify (but not quantify mass flux for) the source of emissions on site as a function of time, linking such emissions (should they exist) to site activity and phases of production.
  • The observed statistics concerning pre-existing sources of nearby and regional pollution allow any shale-gas-linked emission (in future, should analogous data be collected for comparison) to be compared numerically with concentration statistics in the baseline for other (more elevated pre-existing) wind directions and emission source origins. This allows for a contextual comparison — where any localised elevations due to shale gas can be quantified statistically, as a fraction of the contribution to atmospheric composition due to non-local emission sources.

To summarise, the purpose of this analysis was to establish the baseline climatology for the area to allow future comparative interpretation. In the context of greenhouse gases, this concerns the future quantification of greenhouse gas mass flux to atmosphere (fugitive emissions) from shale-gas operations.

Table 6    Summary climatological statistics evaluated over the baseline period for GHG concentrations measured at the baseline site at LP.
CO2 (ppm) CH4 (ppb)
Mean 417.91 2191.04
Std Dev 20.17 646.10
Q0.1 387.21 1864.75
Q1 390.56 1893.93
Q10 397.75 1923.52
Q25 405.67 1942.68
Q50 412.04 2004.45
Q75 426.63 2202.35
Q90 444.25 2566.38
Q99 485.64 4730.81
Q99.9 542.37 9546.12

Air quality baseline

This section reports the Air Quality (AQ) baseline for the LP site. The statistical analysis of the AQ baseline dataset will be presented and interpreted in context of sources of emissions using meteorological data to aid analysis. The analysis provides information on the annual climatology of air pollution along with representative insight into shorter-term variability in air pollution. The baseline analysis is framed specifically with reference to the attainment of EC Directive air quality standards and this uses a range of metrics including annual, 1 hour and 8 hour means.

The baseline dataset

The dataset used in this report was from data collected for the first full year of monitoring (to 31 January 2017). The dataset includes local meteorology (2 m above ground), nitrogen oxides (NO and NO2, collectively NOx), particulate matter in a number of aerodynamic size ranges (PM), ozone (O3) and speciated non methane hydrocarbons (NMHCs). The data are archived and publicly-accessible at the NERC Centre for Environmental Data Analysis (CEDA) at 1-minute intervals, except NMHCs, which are reported as weekly values. Data are available via the following link: www.browse.ceda.ac.uk/browse/badc/env-baseline

The environmental baseline is examined and then compared with other similar regional UK monitoring sites operated by Defra and other agencies.

Results and discussion

Managing and improving air quality in the UK is driven by European (EU) legislation on ambient air-quality standards and also commitments to limit transboundary emissions, through the National Emissions Ceiling Directive and the Gothenburg Protocol. The 2008 Ambient Air Quality Directive (2008/50/EC) sets legally binding limits for outdoor air pollutants that impact on human health, and includes NO2, O3, benzene, 1,3 butadiene, PM10 and PM2.5. All these species have been measured as part of the baseline project.

Within the UK, ambient air quality is controlled with the aspiration that all locations meet either the prescribed Limit Values or Target Values depending on the species. EU Limit Values are legally binding concentrations that must not be exceeded. There are prescribed averaging times associated with each pollutant and for some, a number of exceedances are allowed in each year. Target values are meant to be attained where possible by taking all necessary measures not entailing disproportionate costs, often reflecting natural impacts on those pollutants that can lie outside of regulatory controls.

All EU directives are listed on https://ec.europa.eu/environment/air/quality/standards.htm.

The national air-quality objectives for data parameters measured as part of the AQ baseline are shown in Table 7.

Table 7    Air Quality EU directives for parameters measured at the baseline sites.
aConversion based on EC conversion (temperature 20°C and pressure 1013 mb).
Pollutant Concentration Averaging period Legal nature Permitted exceedances Approx conversion to ppba
Fine particles 25 μg/m3 1 year Limit value none n/a
Nitrogen dioxide 200 μg/m3 1 hour Limit value 18 104.7 ppb
(NO2) 40 μg/m3 1 year Limit value none 20.9 ppb
PM10 50 μg/m3 24 hours Limit value 35 n/a
40 μg/m3 1 year Limit value none n/a
Benzene 5 μg/m3 1 year Limit value none 1.88 ppb
Ozone 120 μg/m3 Maximum daily 8 hour mean Target value 25 days averaged over 3 years 60.1 ppb

Summary statistics of annual means of air pollutants at LP

Table 8 shows a summary of the annual means of various air pollutants at LP and a restatement of the annual directive limit value. An important immediate conclusion that can be drawn by the baseline study over the first year is that in terms of annual mean values, none of the monitored air pollutants exceeds annual mean Limit Values. For planning guidance, air-quality issues must be taken into account when ambient air pollution concentrations approach 75% of the Limit Values. No air pollutants at either site reach this threshold.

Table 8    Summary of annual statistics for LP for various air pollutants and comparison against annual mean limit values.
Pollutant Annual mean at LP Annual mean Limit value
Ozone 19.6 ± 10.1 ppb 60.1 ppb
PM2.5 9.3 ± 7.8 μg/m3 25 μg/m3
PM10 7.9 ± 8.9 μg/m3 40 μg/m
NO 2.5 ± 6.4 ppb No limit value
NO2 6.1 ± 6.6 ppb 20.9 ppb
NOx 8.9 ± 12.1 ppb No limit value
Benzene 0.2 ± 0.01 ppb 1.88 ppb

Thresholds with short-term mean values exist for some pollutants. These are listed in Table 9, along with the amount of times these values were exceeded. A threshold value of 75% was used when calculating all exceedances.

Table 9    Summary of statistics for LP short-term mean values for various air pollutants and comparison against short-term mean limit values, where these apply.
Pollutant Number of 8-hours exceedances LP 8-hour limit
Ozone 1 (25 allowed per year, averaged over 3 years) 60.1 ppb
Number of 24-hours exceedances LP 24 hour limit
PM10 0 50 μg/m3
Number of 1-hours exceedances LP
NO2 0 200 μg/m3

The O3 exceedance in July 2106 was when temperatures in the UK were high, resulting in photochemical production of O3.

Little Plumpton detailed analysis

Metrics
Table 10 reports metrics by individual wind sector. As is common in the UK, easterly and south-easterly air masses are often the most polluted since these bring air from south-east England and continental Europe. The lowest concentrations of air pollutants are typically observed during periods of westerly airflow. The LP site also has the influence of the major road to the south and its influence can be clearly seen in the NOx and PM measurements from those wind sectors.

Table 10    LP metrics by wind sector.
N NE E SE S SW W NW
O3 (ppb) 22.4 20.2 18.3 13.2 13.8 19.8 23.8 24.7
NO (ppb) 1.2 1.6 2.6 5.3 4.6 2.2 0.7 0.8
NO2 (ppb) 3.6 4.9 4.0 12.1 10.4 5.4 2.3 2.4
NOx (ppb) 4.9 6.5 5.1 18.1 15.7 8.0 3.1 3.1
PM2.5 (μm/m3) 4.8 5.5 10.5 13.8 10.6 5.8 5.4 4.8
PM10 (μm/m3) 7.9 10.0 17.9 18.0 13.8 9.6 9.7 8.6

Diurnal variation of air pollution (Figure 46)
The O3 diurnal concentration is lowest at night and peaks just after midday, as expected in the general context of UK oxidative air chemistry; this is a combination of boundary-layer height and photochemical production during the day and surface loss at night.

Both NOx and PM display similar diurnal cycles. The fact that these are similar in shape is an indication that they have similar sources. The diurnal variation is heavily influenced by road traffic. The NOx diurnal shows NO and NO2 increasing in the morning, which is probably due to the boundary-layer height and local traffic sources. The mid-afternoon peak relates to the effect of the late afternoon/evening rush hour.

Figure 46    Diurnal variations at LP for (a) O3 (b) NOx and (c) PM. © University of York, NCAS, 2017.

Hebdominal variation of air pollution (Figure 47)
The working week (Mon–Fri) is clear in the O3 and NOx measurements, with NOx being highest during the week and decreasing at the weekend. By contrast, O3 is highest at the weekend due to reduced titration from NO. There is a slight anomaly mid-week when the NOx appears to reduce. The reason for this difference is currently unclear.

Figure 47    Hebdomadal cycles for at LP for (a) O3, (b) NOx and (c) PM. © University of York, NCAS, 2017.

Annual variation of air pollution (Figure 48)

These show typical cycles in the context of UK air quality. Annual cycles for the in-situ air quality parameters are shown in Figure 48. As in previous plots, the NOx and PM show similar cycles; the influence of Preston New Road is seen in the results.

Figure 48    Annual cycles at LP for (a) O3, (b) NOx and (c) PM. © University of York, NCAS, 2017.

Source apportionment
Figure 49 shows percentile roses for the in-situ air quality parameters split by season. A percentile rose places the data into 5 bands (the colour-scale) and then plots each by wind direction (radial axis) and concentration. The grey line is the mean for the data set. The plots are separated into season: Spring (March, April, May), Summer (June, July, August), Autumn (September, October, November) and winter (December, January, February).

Figure 49    Percentile rose to show the 5th, and 95th percentiles for (a) O3, (b) PM2.5,(c) PM10, (d) NO, (e) NO2, (f) NOX at LP, limited data is available for Summer 2016 due to instrument failure. © University of York, NCAS, 2017.

Figure 50 shows polar plots for the same pollutants, with concentrations (colour scale), wind direction (radial scale) and wind speed.

Figure 50    Polar plots for LP (a) O3, (b) PM2.5,(c) PM10, (d) NO, (e) NO2, (f) NOX, limited data is available for Summer 2016 due to instrument failure. © University of York, NCAS, 2017.

We experienced instrument problems during Summer 2016 and so the NO, NO2 and NOx measurements are not continuous.

Ozone (O3) concentrations are highest concentrations in the spring, arising when the wind speed is at its highest and from the west. This is likely due to peak of the Northern Hemispheric and North Atlantic O3 and the impact of efficient long-range transport of this air to each site. Elevated O3 is indicative of an aged air mass as it is not a primary emission but produced through chemical reactions in the air mass. It is observed easily at the LP site due to its position on the west and clean background air observed from the west. The influence of the Atlantic Air is also shown in the PM measurements, which are all enhanced in the higher-wind-speed westerly air masses, particularly in the coarser fraction arising from maritime aerosols.

Local influence is also seen with the less frequent winds from the south and the east bringing a mix of locally and regionally polluted air masses to site. The major trunk road running alongside the site has been mentioned previously and is the source of local NOx and PM.

Non methane hydrocarbons

Non methane hydrocarbon (NMHC) samples have been taken weekly at the site. A summary of NMHC for LP is shown in Table 11. NMHCs are able to give an indication of air-mass origin, in areas of oil and gas production, higher lighter alkanes such as ethane and propane may be due to fugitive emissions.

Table 11    Summary of NMHC measurements at KM, N = 34. All NMHC have an uncertainty of <10%.
Hydrocarbon Annual mean (ppb) Minimum Value (ppb) Maximum Value (ppb)
Ethane 2.75 0.77 12.59
Ethene 0.69 0.15 2.93
Propane 1.17 0.16 5.99
Propene 0.17 0.03 0.85
Isobutane 0.36 0.03 2.03
Nbutane 0.94 LOD 5.94
Isopentane 0.28 LOD 1.13
Npentane 0.21 LOD 0.95
Benzene 0.19 0.02 0.41
Toluene 0.32 LOD 3.65

The only NMHC currently regulated is benzene and the annual mean benzene concentration is well below the Limit Value for the UK at both sites.

Summary

The atmospheric composition work package has shown the importance of establishing a baseline before any future activities in a region. From an air-quality perspective, it is essential that this baseline cover at least a whole year. This is highlighted not only in the O3 measurements which have a photochemical dependence, but also the PM measurements which show strong seasonal differences. The dataset also highlights the need for continuous measurements where possible to enable a full analysis of sources in the region.

References

  1. SMEDLEY, P L, WARD, R S, ALLEN, G, BAPTIE, B, DARAKTCHIEVA, Z, JONES, D G, JORDAN, C J, PURVIS, R M, and CIGNA, F. 2015. Site selection strategy for environmental monitoring in connection with shale-gas exploration, Vale of Pickering, Yorkshire and Fylde, Lancashire. British Geological Survey, (Keyworth, UK).
  2. ZAZZERI, G, LOWRY, D, FISHER, R E, FRANCE, J L, LANOISELLÉ, M, and NISBET, E G. 2015. Plume mapping and isotopic characterisation of anthropogenic methane sources. Atmospheric Environment, Vol. 110, 151–162.