OR/17/049 Atmospheric composition

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Ward, R S1, Smedley, P L1, Allen, G2, Baptie, B J1, Daraktchieva, Z3, Horleston, A7, Jones, D G1, Jordan, C J1, Lewis, A4, Lowry, D5, Purvis, R M4, and Rivett, M O6. 2014. Environmental baseline monitoring project: phase II - final report. British Geological Survey Internal Report, OR/17/049.

(1) British Geological Survey, (2) University of Manchester, (3) Public Health England, (4) University of York (National Centres for Atmospheric Science), (5) Royal Holloway University of London, (6) University of Birmingham, (7) University of Bristol

Introduction[edit]

An atmospheric baseline is a set of measured data at a specified fixed location that are statistically representative of the local atmospheric composition, and which reflects the role of existing local, regional and global pollution sources as 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 by 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 both baseline sites between 1 February 2016 and 31 January 2017 to provide a consistent comparison and interpretation.

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 greenhouse 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. what wider area 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 therefore to establish the utility of the baseline for this future purpose.

The Universities of Manchester and York have been carrying out measurements for air quality and greenhouse gases for a period of 12 months to define the atmospheric baseline for two locations, Kirby Misperton (hereafter referred to as ‘KM’) , in the Vale Of Pickering, North Yorkshire and Little Plumpton (hereafter referred to as ‘LP’) in Lancashire. In the remainder of this report, the analysis of greenhouse gases baseline data is provided by authors from the University of Manchester. Analysis of air quality data is provided by authors at the University of York.

In our Phase 1 report (reference) we discussed the technical specifications of the instrumentation at two atmospheric composition monitoring sites 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 greenhouse gas emissions that may pre-exist any future exploration for shale gas in each location. For further information on the instrumentation, siting, and greenhouse gas baseline rationale, please consult our Phase 1 report. The discussion in this report will assume that the reader is familiar with those details.

The baseline dataset[edit]

Site selection[edit]

The position of the measurement sites (see Figure 24) was selected so as to be downwind of future potential exploratory shale gas extraction infrastructure (to optimise potential future operational monitoring) in order to obtain a representative local baseline ahead of any exploratory activity. Sites consist 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 24    Top left: photograph of the Little Plumpton measurement site, top right: map showing location of the measurement site and proposed Cuadrilla site to the north of the A583 at Little Plumpton. Bottom left: photograph of the measurement site within the Kirby Misperton Third Energy Site, bottom right: map showing location of the measurement site. © University of Manchester, 2017.

Monitoring site details[edit]

On beginning the project in September 2015, new instrumentation was procured and site locations were selected for installation. By late January 2016, both sites were fully operational and collecting the full suite of data detailed in Table 1 below. In the remainder of this discussion, the Little Plumpton site will be referred to as LP and the Kirby Misperton site as KM. The University of Manchester (Dr Grant Allen, WP lead) takes responsibility for the maintenance of the LP site and the University of York for the KM site. Greenhouse gas data analysis here is the responsibility of Dr Allen, Dr Iq Mead and Mr Joseph Pitt (University of Manchester) and air quality data analysis is provided by Prof Ally Lewis and Dr Ruth Purvis (University of York).

Table 1     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 Kirby Misperton Frequency
Meteorological Data (T, q, p, 3D wind vector) Nov 2014 Jan 2015 1 minute
NO, NO2, NOx Dec 2015 Jan 2016 1 minute
O3 Dec 2015 Jan 2016 1 minute
PM1, PM2.5, PM4, PM10 Nov 2015 Jan 2016 1 minute
NMHCS Jan 2016 Jan 2016 weekly
CH4 Nov 2014 Jan 2016 1 minute
CO2 Nov 2014 Jan 2016 1 minute

The KM site is situated on a Third Energy site (KM8) near to the village of Kirby Misperton, North Yorkhsire where planning permission has been granted to carry out hydraulic fracturing exploration. 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 for the same activity. Both sites have been established with the land-owner's permission and a full risk assessment carried out prior to installation of the monitoring station.

Instrumentation[edit]

All instrumentation at the KM site and all air quality instruments at LP were purchased using funding from the Department of Energy and Climate Change (DECC, now known as 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. 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[edit]

In addition to fixed-receptor-site monitoring, four 2-day measurement campaigns were undertaken using the RHUL mobile greenhouse gas laboratory following the procedures and protocols outlined in Zazzeri et al. (2015). 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 for each site in Section. The dates and locations of the mobile surveys were:

  • Fylde — 9–10 March 2016, 27–28 July 2016,
  • Vale of Pickering — 26–27 October 2016, 10–11 January 2017

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 lab 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 d13C 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 ppm, depending on meteorological conditions. These classifications will be used as the basis of the interpretation given in Section 3.

Calibration and quality assurance[edit]

Both sites employ quality assurance (QA) and quality control (QC) 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 was 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. A full list of instrument technical specifications and precision is available in our Phase 1 report.

The calibration and maintenance procedures for each instrument are detailed in Table 2 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 2     Detailed descriptions of the QA/QC for data collected at both KM and LP measurement sites.
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[edit]

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[edit]

The wind speed and wind direction statistics observed at the LP site over the full measurement period are shown in Figure 25 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 25). The colour scale in Figure 25 then illustrates the corresponding proportion of winds in each direction for a range of surface wind speeds (see colour legend in Figure 24).

Figure 25    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 25), 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 southeast were also frequent, accounting for 22% of the period, while northerly and easterly quadrant 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 ms-1). 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.

Kirby Misperton wind climatology[edit]

The wind speed and wind direction statistics observed at the KM site over the full measurement period are shown in Figure 26, again as a conventional wind rose.

Figure 26    Wind rose for the KM 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 (see colour legend) defines the wind speed (redder colours indicating strong wind speeds >6 ms-1 and yellow and pale yellow colours indicate light or stagnant winds, respectively). © University of Manchester, 2017.

The dominant wind directions are from the western and southern quadrants (collectively accounting for ~45% of the time), with the most frequent winds from a south-westerly direction (>30% of the period). This is also the direction from which the strongest winds are observed (orange and red colours in Figure 26), typically coinciding with the passage of mid-latitude cyclones over the UK mainland. Within this westerly quadrant, the dominant wind speed is between 2–4 ms-1, with occasionally strong winds peaking up to ~15 ms-1 in storm conditions (<0.5% of the time). It should be noted that the frequency of strong winds in the range 6–12 ms-1 (~3% of the time) is much reduced compared with the LP site.

Northerly, easterly and southerly quadrant wind directions were much less frequent, representing 8–20% in each quadrant over the course of the 12 month baseline. Wind speeds from these quadrants were also dominated by light breeze conditions in the range 2–4 ms-1 with no identifiable instances of winds greater than 12 ms-1. This is broadly due to the same factors that define the LP wind climatology: 1) that wind speeds from those 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. An important difference between the LP and KM site is seen in the strength of westerly and south-westerly winds, which appear to significantly moderate by virtue of the position of KM far inland from the western coast of the UK mainland. The mean deviation of the wind direction to a more dominant south-westerly direction at KM, compared with the dominant westerly direction seen at LP over the course of the baseline, can be expected to be linked to the track of midlatitude cyclones, which typically follow a direction toward the northeast as they pass over the UK mainland, especially between late autumn and early spring when storm activity (and hence wind speed) is climatologically most intense.

These subtle differences in wind speed and direction between the two sites suggest that pollution sources contributing to airmasses arriving at the KM site from different wind directions will differ greatly, especially for westerly and south-westerly directions. Winds from the west and south will typically represent 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 the cities of the midlands and North West England. Such 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).

A further difference at the KM site relates to (albeit infrequent) easterly and south-easterly wind directions, which, unlike LP, represent airmasses that have more recently passed over Europe. To summarise, the position and wind climatology observed at LP preclude the more obvious definition of a ‘background’ wind direction (as is the case for westerlies at LP), that can be assumed to represent a Northern Hemispheric average compositional state. However, the position of the KM site directly to the north east of the Third Energy site makes the dominant south-westerly wind direction highly favourable for any future operational comparative assessment with the caveat that the more variable nature of polluted airmasses from this wind direction (due to other regional UK sources upwind) may lead to more variable concentration statistics when diagnosing any future incremental changes due to on-site activity. However, as in the case of LP, 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 local inputs, which are often seen as shorter-lived but more intense 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[edit]

This Section reports and discusses the greenhouse gas baseline for both the Little Plumpton and Kirby Misperton sites. The analysis of an air quality baseline will be presented separately in Soil gas.

In our Phase 1 report we discussed the technical specifications of the instrumentation at two atmospheric composition monitoring sites 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 greenhouse gas emissions that may be pre-existing any future exploration for shale gas in each location. For further information on the instrumentation, siting, and greenhouse gas baseline rationale, please consult our Phase 1 report and the information earlier in this Section of our Phase 2 report. The discussion in this report will assume that the reader is familiar with those details.

In this section we shall present the statistical analysis of the greenhouse gas baseline dataset and mobile vehicle surveys of nearby greenhouse gas sources at each site in turn; and 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.

Little Plumpton[edit]

Fixed measurement site climatology[edit]

Figure 27 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 28 and Figure 29 go on to illustrate how the measured concentrations relate to their coincidently-measured wind direction for each greenhouse gas, while Figure 30 and Figure 31 show the same information but also displays how the relationship between measured concentration and wind direction varies 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 27). These periods coincide with times of westerly winds seen in Figure 28 and Figure 29, and as the orange and red colours in the times series of Figure 30 and Figure 31; 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 28 and Figure 29, when comparing with Figure 30 and Figure 31 (where green and yellow colours indicated easterly and south-easterly wind directions). These features are consistent with an interpretation that suggests 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 28 and Figure 29, compared with Figure 30 and Figure 31 (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 32 and discussed further below).
Figure 27    Time series of carbon dioxide (red) and methane (grey) in units of ppm measured at LP between 1 Feb 2016 and 31 Jan 2017. N.b ‘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 28    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 29    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 30    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 31    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 32 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 32, 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 29 to Figure 32).
  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 28 and Figure 29). Note that the darkest red colours in this cluster correspond to >40 total days of measurement each within the baseline period.
Figure 32    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 spatial scale. When temporally averaged (as data in Figure 32 have been), 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 line modes seen in Figure 32 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 32 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 33 and Figure 34 illustrate a polar bivariate representation of the relationship between both wind speed and direction and greenhouse gas concentration. The colour scale in Figure 33 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 33 correspond to light winds (0–2 m/s) from the south east indicating a well-constrained local source for both gases. Figure 34 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 southeast. 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 33 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 Stated, 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 33    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 34    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 further differentiate the role of local, regional and more distant (long range inter-continental) pollution sources, 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 reanalysis 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 35 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 35 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 35    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 36 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 days) 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.

Figure 36    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.

To further investigate 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 has 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 37, 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 37 left), we see the dominant westerly airmass (Cluster 1 – green), a less frequent background airmass from the northwest (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 37 right—shown in purple).

Figure 37    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, 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 readily observed as distinct surface footprints over different upwind areas. The westerly and northerly zones (clusters 1 and 4 in Figure 37 left) define Atlantic maritime origins (seen as the blue and purple trajectories in Figure 38, 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 38) 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 39, which illustrates the divergent nature of each cluster in terms of their long-range airmass histories. Figure 39 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 28 to Figure 31 earlier.

Figure 38    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 39    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 40 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 40 (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 40    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 41, which is essentially the same as Figure 40 but rescaled to better illustrate the variability of methane concentration for these (less enhanced) airmass clusters. Several salient features emerge: 1) that both clusters display a diurnal maximum at midday (the opposite to 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 41    Same as Figure 40, but rescaled to better illustrate temporal variability for less-enhanced clusters 1 and 4 for methane concentration patterns. © University of Manchester, 2017.
Figure 42    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.

Repeating this analysis for CO2 (seen in Figure 42 and Figure 43), 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 37. 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 43. While clusters 1 and 4 are seen to have very similar seasonal trends, there are some marked differences, especially in the diurnal variability (Figure 43 bottom left) and when comparing weekday with weekend (Figure 43 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 43    Same as Figure 42, but rescaled to better 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 38 further, we shall discuss results from the mobile vehicle surveys in the following Section.

Little Plumpton mobile vehicle surveys of methane emission sources[edit]

Maps of the area sampled by the mobile vehicle campaign in March 2016 can be seen in Figure 46 and Figure 47, colour-coded for sampled CH4 concentration. The same, but for the July 2016 survey, can be seen in Figure 48 and Figure 49. The March campaign is summarised in the Figure 44 Keeling plot with individual source plumes identified by both surveys in Figure 45a–f. A summary of the findings is given in Table 3.

Figure 44    Summary Keeling plots of 1/CH4 ppm vs measured carbon 13 for all major methane sources located during the March 2016 campaign, highlighting the difference in line slope. © Royal Holloway University London, 2017.
Figure 45    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 London, 2017.

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 3), 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 48 and Figure 49).

Table 3    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 46    Survey route for March 9 2016, starting at Charnock Richard services. The largest methane plume observed was emanating from the Jamieson landfill at Fleetwood. Contains Ordnance Data © Crown Copyright and database rights 2017. © Royal Holloway University London, 2017.
Figure 47    Survey route for 10 March 2016, ending in Preston. The largest methane plumes observed were from gas leaks and cow barns (see inset). © Royal Holloway University London, 2017.
Figure 48    Survey route for 27 July 2016, starting in Preston. The largest methane plumes observed were from landfill and cows. © Royal Holloway University London, 2017.

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 frequently 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 49), 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 49    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. © Royal Holloway University London, 2017.

Little Plumpton – summary[edit]

The summary features of the greenhouse gas baseline at Little Plumpton 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 27). These periods coincide with times of westerly winds seen in Figure 28 and Figure 29, and as the orange and red colours in the times series of Figure 30 and Figure 31; 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 28 and Figure 29, when comparing with Figure 30 and Figure 31 (where green and yellow colours indicated easterly and south-easterly wind directions). These features are consistent with an interpretation that suggests 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 28 and Figure 29, compared with Figure 30 and Figure 31 (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 32).

The climatological annualised GHG statistics for the LP site are shown in Table 4. 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 3.8.1.2). 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 as discussed earlier.

Table 4    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

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 do not represent any cause for local alarm in this author’s opinion.

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.

Kirby Misperton[edit]

Fixed-site greenhouse gas baseline[edit]

A time series of the data collected at the KM8 site over the baseline period is shown in Figure 50. A general correlation between variability in CO2 and CH4 can be seen, consistent with that seen for the LP site. Figure 51 and Figure 52 illustrate how the measured GHG concentrations relate to wind direction and wind speed. Unlike the LP site, Figure 50 illustrates that all wind directions occasionally display enhanced greenhouse gas concentrations relative to the background. Significant elevations relative to the background are most often seen for south easterly and easterly winds.

When interpreted together, Figure 51 to Figure 54 distil several important and internally-consistent summary features (some quite similar to those discussed for LP), 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 400–420 parts per million (ppm) and 1.8–2 ppm, respectively (as seen in Figure 51 and Figure 52). These periods coincide with times of westerly winds seen in Figure 51 and Figure 52, and as the orange and dark orange colours in the times series of Figure 53 and Figure 54; and represent a typical seasonally-variant Northern Hemispheric average concentration for these greenhouse gases.
  • There are prolonged periods (several consecutive days) of marginally enhanced CO2 and CH4 (between 410–450 ppm and 1.9–2.5 ppm, respectively. These periods coincide most often with moderate (0–4 m/s) south-easterly winds (see Figure 3), when comparing with Figure 53 Figure 54 (where green and yellow colours indicated easterly and south-easterly wind directions). These features are consistent with an interpretation that suggests that these episodes represent regional pollution inputs from continental Europe and the cities of Southern England, including London.
  • There are short-lived (less than a few hours) but large enhancements (often referred to as ‘spikes’) in the time series data (greater than 2.5 ppm CH4 and 450 ppm CO2). These coincide most often with very light (0–2 m/s) easterly and south-easterly and northerly wind directions seen in Figure 51 and Figure 52, compared with Figure 53 and Figure 54 (where easterly winds are seen in green colours). These features in the data, often superimposed on the more regional increment describe above, are expected to represent local (<10 km upwind) sources such as nearby agricultural activities, roads, and landfill. It is notable that such transient enhancements at KM typically extend to lower maximal concentrations compared with the much larger enhancements seen at LP due to the increased presence of nearby agriculture and major roads at the LP site.
  • 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 55 and discussed further below).

Interpreting this further, it can be seen that westerly wind directions typically (but not exclusively) bring relatively unpolluted (background concentration) air to the KM site. And, like LP, 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 50    Time series of carbon dioxide (red) and methane (grey) in units of ppm measured at KM between 1 Feb 2016 and 30 Jan 2017. N.b – ‘d’ refers to the water-vapour-corrected (or dry) measurement by the UGGA instrument. © University of Manchester, 2017.
  • Figure 51    Concentrations (as per colour scale) in air as a function of wind direction for carbon dioxide (units of ppm), as measured at KM in the baseline period. Radial extent contours define 2% frequency intervals. © University of Manchester, 2017.
  • Figure 52    Concentrations (as per colour scale) in air as a function of wind direction for methane (units of ppm), as measured at KM in the baseline period. Radial extent contours define 2% frequency intervals. © University of Manchester, 2017.
Figure 53    Methane concentration time series, colour-coded for wind direction as per legend as measured at KM in the baseline period. © University of Manchester, 2017.
Figure 54    Carbon Dioxide concentration time series, colour-coded for wind direction as per legend as measured at KM in the baseline period. © University of Manchester, 2017.

Figure 55 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). We see that there is one very prominent correlation between the two greenhouse gases, and a number of (very infrequent) features where enhancements of CH4 are seen at times when no change is CO2 is observed, as follows:

  1. A dominant mixing line (traced by red and yellow colours) with a relationship of [CO2]=215.2[CH4]+386.5 ppm — representing co-emission (or bulk mixing) of nearby CO2 and CH4 sources upwind to the east and south east (based on understanding of how such concentrations relate to wind direction in Figure 51 to Figure 54). The gradient at KM is almost twice that seen at LP suggesting that CO2 sources dominate the relative mix of these 2 gases in airmasses received at KM (compared to LP).
  2. A number of clear (but very infrequent) CH4 excursions (seen as the blue horizontal lines in Figure 55) to relatively high ambient concentrations of up to 13 ppm (>6 times background), where very little change in CO2 concentration is observed. However, these features represent only 635 minutes of sampling (~6.5 hours) and are noted to occur mostly in the spring 2016 months in light south-southeasterly wind conditions (see Figure 53). These features are consistent with a methane-only (highly localised) source, associated with wind directions from ~200 degrees (southsouthwesterly — see Figure 28). Given that the existing Third Energy well-head is positioned ~100 m upwind from the measurement site in this direction, we suggest that these enhancements may well represent fugitive emissions of CH4 from the existing gas extraction site.
  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 51 and Figure 52). Note that this dominant background cluster corresponds to >210 total days of measurement within the baseline period.

The dominant mixing line seen in Figure 55 corresponds to frequent easterly and south-easterly wind directions. Considering the location of KM, these wind directions represent air that has passed over continental Europe and the cities of southern England, respectively (including London). As discussed earlier for LP, 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 KM site.

To interpret more local sources of pollution (within ~10 km), we focus 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, especially given the observation of potential fugitive emissions at the existing Third Energy site concerning CH4 discussed in point 2 above.

Figure 55    Coincident CO2 and CH4 concentrations measured at KM. 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.

Figure 56 and Figure 57 illustrate a polar bivariate representation of the relationship between both wind speed and direction and greenhouse gas concentration. The colour scale in Figure 56 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 56 correspond to light winds (0–2 m/s) from the south west indicating a well-constrained local source for both gases (suggested to be the existing well-head at the Third Energy site). Figure 57 shows how the absolute measured concentration relates to wind direction and wind speed, which again shows the dominant southerly, south-easterly and south-westerly origin of more elevated CH4 and CO2 concentrations. The fact that the red area does not extend to higher wind speeds in the southwest 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 source(s) dominate the strongest enhancements. The role of longer range (regional, national and continental) sources (mainly to the southeast) is therefore to add a smaller increment to the much larger local emission source(s) to the southwest that dominate periods of enhancement in southerly wind conditions. The lighter blue areas seen in Figure 55 and Figure 56 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 the fetch to the west, which would include northern UK cities and the Pennines as well as potential longer range emissions from Ireland and in intercontinental transport from the United Stated, 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. In other words, the westerly airmass at KM can be characterised as being broadly similar to the clean westerly airmass seen at LP but with the addition of UK sources over land between the two sites such as the cities of Manchester, Leeds and Sheffield (as well as expected smaller contributions from biogenic emissions over the Pennines such as peat).

Figure 56    Polar bivariate representation of methane (left) and carbon dioxide (right) as a function of wind direction at KM. 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 57    Polar bivariate representation of methane (left) and carbon dioxide (right) as a function of wind direction and wind speed measured at KM. 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 again examine the airmass history. This can be interpreted using Hysplit Lagrangian back trajectories over the previous 5 days with endpoints at the location of the KM8 site at 6-hourly intervals across the measurement period (~1200 trajectories in total between 1 Feb 2016 and 31 Jan 2017).

Figure 58 shows the airmass history of air sampled at KM8 throughout the baseline period. This statistical representation of the history of air should be interpreted as a surface ‘footprint’, illustrating a surface area over which air measured at KM8 has been influenced by potential surface sources. Figure 59 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 orange colours in Figure 58 indicate that air received at KM8 is most characterised by air that has previously passed over North West England and Wales, while the green colours show that the entire UK mainland contributes to the annualised footprint, with wider-scale contact with Atlantic and Arctic Ocean and continental Europe (blue colours in Figure 58).

Figure 59 shows the same trajectories but sub-sampled by meteorological season. This figure illustrates that for all but the winter season, airmasses arriving at KM8 in the baseline period display a variety of upwind histories from all directions. In the winter season, we see that airmasses most often originate (within the past 5 days) from latitudes to the south of KM8. This pattern is consistent with the analysis and conclusions drawn about the local meteorology discussed earlier. The key difference at KM8, compared with LP, is that there is greater contact with the UK mainland (and therefore regional GHG emission sources) to the west of the site in the dominant westerly wind regime.

Figure 58    5-day airmass history surface Langrangian trajectory footprint statistics for the period 1 Feb 2016 to 31 Jan 2017, as seen from KM8 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 59    5-day airmass history Lagrangian trajectory surface footprint statistics for the period 1 Feb 2016 to 31 Jan 2017 by meteorological season (e.g. DJF refers to December, January and February), with trajectory endpoints at KM8 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.

To investigate the nature of the four broad airmass types received at KM8 further, as identified and discussed earlier, we now examine the temporal patterns and airmass history for each airmass classification. To achieve this, the polar bivariate data seen in Figure 57 has been used to categorize the baseline dataset into four principal clusters by the method of K- means clustering, following the same method described in the LP analysis. The resulting clusters can be seen in Figure 58, which illustrate (as defined zones) the dominant relationships between concentration, wind direction, and wind speed. 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 60), we see the dominant westerly airmass (Cluster 1 – green), a less frequent background airmass from the northwest (Cluster 3, orange), a regionally (enhanced GHG concentration) airmass from the northwest (Cluster 4 – yellow), and a moderate-to-highly enhanced airmass (Cluster 2 – blue). For carbon dioxide, we see similar clustering, but with a more dominant southerly zone cluster (Cluster 1 – in Figure 60 right — shown in green).

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 61, 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 readily observed as distinct surface footprints over different upwind areas.

The westerly and northerly zones (clusters 3 and 4, respectively in Figure 60 left) define Atlantic maritime origins with upwind passage over the UK mainland (seen as the blue and pink trajectories, respectively, in Figure 61, consistent with our earlier conclusion that air received at KM from these locations broadly represents a Northern Hemispheric average composition with some sources over the UK mainland upwind. The regionally enhanced (elevated concentration) cluster (1) 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 61) show a continental and southern England 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 and European regional increment defined by the dominant mixing line in Figure 55). The mean trajectory path for each cluster is shown in Figure 62, which illustrates the divergent nature of each cluster in terms of their long-range airmass histories. Figure 62 also shows the percentage of time over the baseline period associated to each cluster, further reinforcing the conclusions discussed using the more simple wind rose analysis described earlier, which shows that westerly origins dominate (36.6%), followed by easterly airmasses (24.9%).

Figure 60    Derived 4-mode K-means clusters of dominating wind-concentration relationships for: left (methane); and right: carbon dioxide as sampled at KM. Radial direction indicates wind direction, while radial length defines wind speed. © University of Manchester, 2017.
Figure 61    5-day back trajectories ending at KM corresponding to the time of each data point associated with the 4 principal clusters identified in Figure 35 (left) for methane, with orange corresponding to Cluster 1, green to Cluster 2, blue to Cluster 3, and pink to Cluster 4. © University of Manchester, 2017.

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 63 shows the temporal statistics 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).

Figure 62    Mean path of 5-day Lagrangian back trajectories seen in Figure 36, ending at KM8 for each of the four principal airmass clusters defined in Figure 35. 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 KM8 are classified within each principal cluster defined in Figure 35 (left). © University of Manchester, 2017.

When illustrated in this way, we can clearly observe very different diurnal behaviour for cluster 2 (the easterly and south-easterly airmass, most elevated in terms of CH4 — corresponding to the green trajectories in Figure 61 and Figure 62), relative to clusters 1, 3, and 4. In particular, we see a consistent and repeatable diurnal minimum at around 2 pm on every day of the week across the whole year. This diurnal minimum on Cluster 2 is best observed in Figure 63 (bottom left), which shows the average over all days of the year. We also see a marked increase in winter months for Cluster 2 (Figure 63 middle panel). A similar diurnal and seasonal pattern was seen for LP and linked to local (<10 km) sources. Such a pattern is consistent with the diurnal 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 due to solar heating), further indicating a dominant role for local sources, which might be expected to accumulate overnight before being diluted and detrained in daylight hours. Clusters 1, 3 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 KM8 baseline receptor site.

Figure 63    Temporal statistics of the methane climatology at KM 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.
Figure 64    Same as previous figure, but rescaled to better illustrate temporal variability for less-enhanced clusters 1 and 4 for methane concentration patterns. © University of Manchester, 2017.

An additional and curious feature is seen for Cluster 1 at the weekend, where there is a repeatable enhancement in CH4 concentrations between 10 am and 11 am on both Saturdays and Sundays. This is best seen in Figure 64 (top panel), which focuses further on Cluster 1 and 4. The cause of this enhancement is unknown but is a feature repeatedly observed over the course of the baseline period during south-westerly wind conditions. The strength of this local emission source may be variable as the 95% confidence intervals are wide around this feature; however this is more likely the result of variable meteorology (variance in wind direction within the constraints of the Cluster 1 airmasses). This feature is linked to the springtime transient (strong) CH4 elevations discussed earlier and therefore suggests that emissions from the Third Energy site, or other local sources upwind to the southeast, may be responsible. However, such a feature must be linked to a specific activity and a specific source of CH4 (and not CO2), occurring for only a short time in the mornings of each weekend day. This weekend feature dominates the overall annual-average diurnal statistics for Cluster 1 (seen in Figure 39 bottom left). Without further knowledge of the hour-by-hour weekend activities in the KM area, it is not possible to link this feature to a specific source directly. However, we note this important feature here in the baseline and the need for further investigation as a case study into any operational phase.

Repeating this analysis for CO2 (seen in Figure 65 and Figure 66), we see similar diurnal patterns due to boundary layer ventilation (Figure 65 top panel) for Cluster 1. However, unlike methane, a clear seasonal minimum is observed in August for all clusters. This feature (consistent with that seen at LP) 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). Clusters 2, 3 and 4 represent more background (less elevated CO2 conditions) from westerly and northerly origins predominantly. Clusters 1 and 4 are shown in more detail in Figure 66. While clusters 1 and 4 are seen to have very similar seasonal trends, there are some marked differences, especially in the diurnal variability (Figure 66 bottom left). The lack of a diurnal signal in Cluster 4 (less elevated westerly origins) is consistent with an absence of significant local sources for this cluster, while the clear diurnal signal for Cluster 1 suggests that local sources dominate enhancements relative to the background.

To investigate the nature of local methane emission sources (biogenic and anthropogenic) in cluster 1 in Figure 65 further, we shall discuss results from the mobile vehicle surveys in the following Section.

Figure 65    Temporal statistics of the CO2 climatology at KM 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.
Figure 66    Same as previous figure, but rescaled to better illustrate temporal variability for less-enhanced clusters 1 and 4 for methane concentration patterns. © University of Manchester, 2017.
Figure 67    KM area map and sampling route on 26 October 2016, colour-coded for instantaneously-measured CH4 concentration. Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS user community. © Royal Holloway University London, 2017.

Mobile vehicle surveys[edit]

Maps of the area sampled by the mobile vehicle campaigns in October 2016 can be seen in Figure 67 and Figure 68, colour-coded for sampled CH4 concentration. The same, but for the January 2017 survey, can be seen in Figure 69 and Figure 70, with a map of principal CH4 emission sources identified in the mobile campaign (Figure 71). Keeling plots for individual source-type plumes identified by the survey are plotted in Figure 72 and Figure 74 and a summary of the findings given in Table 5.

Figure 68    KM area map and sampling route on 27 October 2016, colour-coded for instantaneously-measured CH4 concentration. Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS user community. © Royal Holloway University London, 2017.

The main persistent plume sampled in the KM8 area was the gas outtake facility at Pickering, with CH4 concentrations of up to 20 ppm (10 x atmospheric background) when directly adjacent to the site with a perpendicular wind direction, suggesting that local fugitive emission may contribute significantly to the KM local baseline. This is consistent with the analysis of the fixed site measurements discussed in the previous section. This plume had a consistent carbon isotopic signature of -42 to -41‰ across both mobile campaigns in the KM area (Figure 71). The most persistent cow barn signal was from Blandsby Lane Farm, with concentrations up to 3 x background and a source signature of -64‰. Narrow plumes were measured downwind of 2 other cow barns in the vale. The closest landfill to the proposed well pad is the small Caulklands landfill near Thornton-le-Dale. A plume up to 30% above background during both campaigns had a consistent signature of -57‰ measured during westerly winds.

Three more landfills further afield were measured only on the second campaign (January 2017) due to more suitable wind directions for plume intersection, two of which had methane excess of less than 30% in the measured plumes. The plume from Seamer Carr near Scarborough could not be intersected close to site but suggests a signature of -57‰ also. The largest landfill in the region is near Rufforth, York where up to 60% above background was measured. The signature was slightly depleted at -60‰. The small Todd Waste Management Centre pit south of Knapton formed a narrow but persistent plume up to the ridge crest of 30% above background, but with a more depleted signature (Figure 72).

Figure 69    KM area map and sampling route on 10 January 2017, colour-coded for instantaneously-measured CH4 concentration. © Royal Holloway University London, 2017.

One currently unidentified source, with a plume up to 40% above background, was intersected during both campaigns between the railway line and West Heslerton, the position depending on the changing strength and direction of the WSW to WNWly winds. While the Third Energy processing plant is only 2 km upwind from this location, the wind directions at the time of plume intersection suggest that the source is further south; either at Mill Grange or East Knapton Farm, but these were not accessible directly downwind. No methane plume was measured directly downwind of pigs dispersed across the field near Mill Grange. The consistent isotopic signature of this plume at -62 to -61‰ for both campaigns (Figure 72) suggests that it is a biogenic rather than a natural gas (thermogenic) source.

During the January campaign (Figure 69 and Figure 70), a very narrow spike was recorded along the road east of Kirby Misperton with concentrations up to 13 ppm, but this was one to two measurements wide and moving around in the wind so could not be sampled for isotopic analysis. This suggests a fugitive emission point source in the roadside ditch.

Figure 70    KM area map and sampling route on 11 January 2017, colour-coded for instantaneously-measured CH4 concentration. © Royal Holloway University London, 2017.
Figure 71    Area map of principal CH4 emission sources in the KM area as identified by the RHUL mobile surveys. Includes mapping data licensed from Ordnance Survey. © Crown Copyright and/or database right 2017. Licence number 100021290 EUL.
Figure 72    Keeling plots of 1/CH4 ppm vs measured carbon 13 for each major methane source identified in the Pickering region in October 2016 and January 2017: a) Pickering gas outtake plant, b) Blandsby Lane cow barn c) Caulklands Landfill, d) Unidentified biogenic source. Sources were observed in both campaigns with October shown in black and January in red. © Royal Holloway University London, 2017.
Figure 73    Keeling plots of 1/CH4 ppm vs measured carbon 13 for all waste sources in the Pickering region sampled during the January 2017 campaign. © Royal Holloway University London, 2017.
Table 5    Summary of bag sampling in the Vale of Pickering 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 6.9 -42
Recycling 2.6 -53
Active landfill 0.8 -60 to -57
Unidentified plume 0.6 -61
Waste Management Centre 0.7 -63
Cow barns 1.8 -64

Kirby Misperton – summary[edit]

The summary features of the greenhouse gas baseline at Kirby Misperton 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 400–420 parts per million (ppm) and 1.8–2 ppm, respectively (as seen in Figure 51 and Figure 52). These periods coincide with times of westerly winds seen in Figure 51 and Figure 52, and as the orange and dark orange colours in the times series of Figure 53 and Figure 54; and represent a typical seasonally-variant Northern Hemispheric average concentration for these greenhouse gases.
  • There are prolonged periods (several consecutive days) of marginally enhanced CO2 and CH4 (between 410–450 ppm and 1.9–2.5 ppm, respectively. These periods coincide most often with moderate (0–4 m/s) south-easterly winds (see Figure 26), when comparing with Figure 53 and Figure 54 (where green and yellow colours indicated easterly and south-easterly wind directions). These features are consistent with an interpretation that suggests that these episodes represent regional pollution inputs from continental Europe and the cities of Southern England, including London.
  • There are short-lived (less than a few hours) but large enhancements (often referred to as ‘spikes’) in the time series data (greater than 2.5 ppm CH4 and 450 ppm CO2). These coincide most often with very light (0–2 m/s) easterly and south-easterly and northerly wind directions seen in Figure 51 and Figure 52, compared with Figure 53 and Figure 54 (where easterly winds are seen in green colours). These features in the data, often superimposed on the more regional increment describe above, are expected to represent local (<10 km upwind) sources such as nearby agricultural activities, roads, and landfill. It is notable that such transient enhancements at KM8 typically extend to lower maximal concentrations compared with the much larger enhancements seen at LP due to the increased presence of nearby agriculture and major roads at the LP site.

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 55).

The climatological annualised GHG statistics for KM8 are given in Table 6 below. The mean concentrations of CO2 and CH4 are very slightly elevated (1.0% in the case of CO2, and 11.4% 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 KM8 on land and exposed to sources of emission both locally and regionally. We note that this mean background is lower than the mean composition of the LP site in terms of GHGs. The one-standard-deviation variability around the mean is smaller than LP (5.8% for CO2 and 9.3% for CH4). The higher CH4 variability (compared with CO2) is suggested to be linked to the nature of local sources (such as thermogenic fugitive emission suggested in Fixed-site greenhouse gas baseline and in the mobile surveys discussed in Mobile vehicle surveys. The interquartile and interdecile ranges for both gases are constrained to 3.4% for CO2 and 6.8% for CH4 relative to the mean, while the extremes (99th percentiles), extend to 22.6% and 34.0% of the mean for CO2 and CH4, respectively. This extreme variability at KM8 is far smaller than the equivalent statistics for LP (Little Plumpton – summary). This demonstrates that for the vast majority of the period (95%), concentrations do not vary by more than ~20% relative the mean for both these greenhouse gases at most). However, shorter period, extreme events (accounting for 0.1% of the baseline period), can see concentrations of up to ten times CH4 relative to the mean climatological concentration. Such periods are identified with episodic local emissions, lasting for a few hours at most as discussed earlier, and linked to late morning periods on weekend days in south-westerly wind conditions (especially in spring months in the baseline). These episodic features are worthy of further case study attention during any operational phase and may represent a local fugitive emission source, such as the existing Third Energy site and well-head.

Table 6    Summary climatological statistics evaluated over the baseline period for GHG concentrations measured at the fixed baseline site at KM.
CO2 (ppm) CH4 (ppb)
Mean 416.84 2060.95
Std Dev 24.12 191.26
Q0.1 377.53 1891.22
Q1 387.80 1909.52
Q10 394.99 1932.56
Q25 403.49 1953.00
Q50 410.11 1997.65
Q75 423.65 2094.49
Q90 444.64 2271.55
Q99 510.28 2761.41
Q99.9 580.91 3452.08

In all cases, it must be stressed that the levels of greenhouse gas concentrations seen at the KM8 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 do not represent any cause for local alarm in this author’s opinion.

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 Kirby Misperton. This is because the location of the baseline site directly to the northeast of the field where Third Energy holds an exploratory licence, means that any significant fugitive emission should be readily observable against the signal (and statistics) seen for this wind direction in the baseline dataset. However, the existing Third Energy site and our observation of episodic emissions associated with a wind direction linked to the existing well-head do complicate any future comparison. As such, we recommend that the weekday statistics only should be used for such a comparison. 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.

Comparison of both sites[edit]

Comparing data from both measurement sites offers insight into the potential transferability of baseline datasets. In this section, we briefly compare the measurements at each site in the baseline period.

Figure 74 illustrates CO2 data collected for the baseline period of simultaneous measurement at both the KM (grey) and LP (red) sites. It can be seen that there are many periods where CO2 is simultaneously enhanced at both locations. However, there are notable times when this is not the case, or when one site appears to lag the other by up to a day or two. Such lag patterns reflect the advection of airmasses across the UK and also indicate that both sites often sample similarly polluted airmasses in terms of CO2. However, the picture is much more complicated for CH4 (Figure 75). While some peaks in CH4 are observed at similar times at both sites, the magnitude of the enhancement compared with the ~2 ppm background is markedly different, with LP invariably seen to be much enhanced compared with KM. Many such periods coincide with light easterly winds. It is interesting to note that LP is directly upwind of KM in this wind regime and that the enhancements seen at LP could be expected to represent sources of methane in the fetch between the two sites.

To summarize, the differences between the two sites, especially in terms of CH4, illustrate the need for local baseline (and directly analogous operational) monitoring. A baseline at one location is clearly not applicable as a set of useful comparable (or contextual) statistics at any other location. The method of airmass clustering is powerful in differentiating the role of local and long-range sources, and the airmass history and meteorological analysis here clearly shows that local (<10 km) sources dominate the contribution to statistically elevated concentration observations. In the case of LP, an absence of significant upwind GHG sources to the west, makes future observations from this wind direction especially useful for characterising future fugitive emission linked to shale gas in that area. However, existing signals in the baseline at KM may complicate this, requiring us to isolate specific periods (and airmass histories) in the baseline to provide the correct baseline comparison statistics (e.g. weekdays and not weekend days in the case of KM).

Figure 74    Carbon Dioxide time series for the KM site (grey) and LP site (red) in the baseline period.
Figure 75    Methane time series for the KM site (grey) and LP site (red) for the baseline period. © University of Manchester, 2017.

Air quality baseline[edit]

This section reports the Air Quality (AQ) baseline for both the Kirby Misperton and Little Plumpton sites.

The statistical analysis of the AQ baseline dataset for both sites 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 at both locations 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 at both locations and this uses a range of metrics including annual, 1 hour and 8 hour means.

The baseline dataset[edit]

The dataset used in this report was data collected using surface monitors located at Kirby Misperton and Little Plumpton and covers the observation period of 1 February 2016 until 31 January 2017. The dataset includes local meteorology (2 m above ground), nitrogen oxides (NO and NO, collectively NOx), particulate matter in a number of aerodynamic size ranges (PM), ozone (O3) and speciated non methane hydrocarbons (NMHCs). The data is archived and publically accessible at the NERC Centre for Environmental Data Analysis (CEDA) at 1 minute intervals, except NMHCs which are reported as weekly values. Data is available via the following link http://browse.ceda.ac.uk/browse/badc/env-baseline. The environment baseline is examined on a site by site basis, but the climatologies of pollution are then further compared to each other and then to other similar regional UK monitoring sites operated by Defra and other agencies.

Results and discussion[edit]

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 http://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.
a. Conversion 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 (PM2.5) 25 μg/m3 1 year Limit value none n/a
Nitrogen dioxide (NO2) 200 μg/m3 1 hour Limit value 18 104.7 ppb
40 μg/m3 1 year Limit value none 20.9 ppb
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 1.88ppb
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 KM and LP[edit]

Table 8 shows a summary of the annual means of various air pollutants at both KM and 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 exceed 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 KM and LP locations for various air pollutants and comparison against annual mean limit values.
Pollutant Annual Mean at KM Annual mean at LP Annual mean Limit value
Ozone 20.4 ± 10.4 ppb 19.6 ± 10.1 ppb 60.1 ppb
PM2.5 12.3 ± 9.7 μg/m3 9.3 ± 7.8 μg/m3 25 μg/m3
PM10 9.0 ± 10.8 μg/m3 7.9 ± 8.9 μg/m3 40 μg/m
NO 1.3 ± 3.3 ppb 2.5 ± 6.4 ppb No limit value
NO2 4.6 ± 6.9 ppb 6.1 ± 6.6 ppb 20.9 ppb
NOx 5.9 ± 9.1 ppb 8.9 ± 12.1 ppb No limit value
Benzene 0.1 ± 0.01 ppb 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 data value of 75% was used when calculating all exceedances.

Table 9    Summary of statistics for KM and 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 KM Number of 8-hours exceedances LP 8-hour limit
Ozone 2 (25 allowed per year, averaged over 3 years) 1 (25 allowed per year, averaged over 3 years) 60.1 ppb
Number of 24-hours exceedances KM Number of 24-hours exceedances LP 24 hour limit
PM10 2 (35 allowed per year) 0 50 μg/m3
Number of 1-hours exceedances KM Number of 1-hours exceedances LP
NO2 0 0 200 μg/m3

The O3 exceedances at KM were in May and July, the LP one in July, all 3 of these were when temperatures in the UK were high resulting in photochemical production of O3.

The 24 hour mean at KM for PM10 was exceeded on the 12–13th March, 2016. Comparison with national records reveal that this was a pollution episode in the UK attributed to low wind speeds and an influx of air containing high particles from Northern Europe. While the PM10 at LP does not exceed the daily limit it does show the highest measurements for the month of March in the same period.

Spatially resolved air pollution climatologies[edit]

The annual mean values for air pollution allow for comparison against national targets. NOx, O3, PM and meteorological data has all been collected at 1 minute time resolution and this is advantageous for data analysis as a more detailed climatology of air pollution can be constructed the local scale.

The hourly averaged time-series for parameters are shown in Figure 76 but these tend to only show synoptic / seasonal scale variability.

Higher O3 is seen in the spring/early summer at both sites, and this is typical for the UK. This behaviour is also seen for the High Muffles AURN site. There are also a few periods of high O3 in the summer months which are associated with high temperatures and anticylonic weather conditions.

From the time series it can be observed that there are times when the sites are affected by higher levels of pollution in the form of NO, NO2 and particles, visible in the spikes in Figure 76. The majority of the high NOx spikes seen in the data are due to local influence and a lot are due to vehicle movements on or near the site. These NOx spikes are also correlated with lower ozone, which is related to atmospheric chemistry; in the immediate vicinity of high NO emission, where O3 is lost in the reaction 1:

NO + O3 → NO2 + O2            Reaction 1

The NO2 and NOx measurements at LP have a large data gap in the summer period due to an instrument failure.

Figure 76    Annual time series at the KM site for (a) O3 (b) NO, NO2 and NOx (c)PM1, PM2.5, PM4, PM10 and PMTOTAL. © University of York, NCAS, 2017.

A more detailed analysis can be performed with minute-averaged data including detailed diurnal and source apportionment which are shown for each site individually.

Kirby misperton detailed analysis[edit]

Metrics

To enable a full baseline climatology of air pollution to be established it is important to examine the influence of wind direction. Table 10 reports the annual means for pollutant measured under the Air Quality Directive whereas Table 17 reports those metrics by individual wind sector. The full list of 5th, 95th percentile, mean and median for all wind sectors is in Appendix 1 - Kirby Misperton air sector metrics. As is common in the UK Easterly and South easterly air mass are often the most polluted since these bring air often the SE of England and from continental Europe. The lowest concentrations of air pollution are typically observed during periods of westerly airflow.

Table 10    Annual means for each wind sector for KM site.
N NE E SE S SW W NW
O3 (ppb) 22.4 21.4 22.1 19.3 18.5 20.0 24.1 22.7
NO (ppb) 1.1 1.1 1.0 1.5 1.5 1.6 0.9 0.9
NO2 (ppb) 3.3 3.6 4.0 5.4 6.2 5.8 2.8 3.2
NOx (ppb) 4.4 4.7 5.1 6.8 7.7 7.4 3.7 4.2
PM2.5 (μm/m3) 7.4 9.2 12.8 13.3 12.3 7.6 6.1 3.9
PM10 (μm/m3) 10.4 12.6 16.9 17.0 15.6 10.9 8.7 6.4

Percentiles for all the AQ parameters are displayed in the wind roses in Seismicity and discussed more fully later in the report.

Diurnal variation of air pollution at KM (Figure 77)

The O3 diurnal 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. However the NOx and PM display different diurnals cycles. The fact that these are different in shape is the first indication that the PM and NOx may have different sources at times. 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 relative distribution of NO to NO2 is balanced towards NO2 indicating that very close-by combustion sources are not dominating the local NOx.

The working week (Mon–Fri) is clear in the O3 and NOx measurements with NOx being highest during the week and decreasing in at the weekend, whereas O3 is highest on the weekend due to reduced titration from NO. Diurnal cycles for the in situ air quality parameters are shown in Figure 77. These could be from local traffic or as the site is already a conventional gas well it may be due to vehicles actually on site. These emissions all reduce on the weekend giving the O3 time to recover and increase.

Figure 77    Diurnal variations at KM8 for (a) O3 (b) NOx and (c) PM. © University of York, NCAS, 2017.

Hebdominal Cycles (Figure 78)

Figure 78    Hebdomadal cycles at KM8 for (a) O3, (b) NOx and (c) PM. © University of York, NCAS, 2017.

Annual cycles at KM (Figure 79)

Figure 79    Annual cycles at KM for (a) O3, (b) NOx and (c) PM. © University of York, NCAS, 2017.

These show typical cycles in the context of UK air quality and have already been partially discussed in Section 3.1.3. Annual cycles for the in situ air quality parameters are shown in Figure 79 usually shows a peak in Spring but February 2016 showed high O3 levels, this is also shown in Little Plumpton and High Muffles so is a regional and not a local effect. The lack of a peak in the PM and O3 levels in the summer months, indicate that there were rather few high pollution events in the summer of 2016. High concentrations of O3 and PM can arise during pollution episodes, which are short periods of high levels of pollutions and are usually associated with low wind speed weather periods, and often air flow from continental Europe.

Source apportionment for KM8

Figure 80 shows percentiles 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 of those by wind direction (radial axis) and concentration. The grey line is the mean for the data set. The plots are separated into season with Spring (March, April, May), Summer (June, July, August), Autumn (September, October, November) and winter (December, January, February).

Figure 81 shows polar plots for the same pollutants, with concentrations (colour scale), wind direction (radial scale) and wind speed.

For many situations concentrations would be expected to decrease with increasing wind speed due to increased dilution but there are some instances where this process can lead to increases, for example due to plume grounding or the transport of air over long distances. Combining the two types of data analysis may give some indication of source regions of pollutants, and this is done below.

Figure 80    Percentile rose to show the 5th, and 95th percentiles for (a) O3, (b) PM2.5,(c) PM10, (d) NO, (e) NO2, (f) NOX. © University of York, NCAS, 2017.

O3 concentrations are highest concentrations in the spring and winter, in both these months the highest concentrations arise 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. This is seen more clearly at the Little Plumpton site which has fewer local sources of pollution and is discussed more fully in Little Plumpton (LP) detailed analysis.

Figure 81    Polar plots for KM (a) O3, (b) PM2.5,(c) PM10, (d) NO, (e) NO2, (f) NOX. © University of York, NCAS, 2017.

By breaking the analysis down into season it can be seen that the peaks in particulate concentration are in spring and winter but from two different source regions. The peak in the spring is when the wind is from the easterly direction and concentration highest at the higher wind speeds, NOx does not show the same structure in the measurements so it can be assumed that this is not due to road traffic. As particle suspension can increase with increasing wind speed, this could be due to sea spray from the east coast or particles from spoil heaps or similar. It may also reflect an agricultural source to the east of the site, and this would coincide when ammonia emissions are generally at their highest from muck spreading. The PM peak in the winter is from the southerly direction, there are higher NOx concentrations in this period so these may be due to road traffic influences.

NO and NO2 measurements show different measurement profiles. NO is highest in the winter whereas NO2 highest in the autumn. The peak in NO at the lowest wind speeds is in part a reflection of the increased influence of location site emissions and local traffic. There shows a general trend for higher NOx concentrations from the south in the summer and winter, this will be due to extra traffic on the A64, the major route to the east coast and exceptional busy at this time of year.

Little Plumpton (LP) detailed analysis[edit]

Metrics for LP

Table 11 reports those metrics by individual wind sector. The full list of 5th, 95th percentile, mean and median for all wind sectors is in Appendix A. As is common in the UK Easterly and South-easterly air mass are often the most polluted since these bring air often the SE of England and from continental Europe. The lowest concentrations of air pollution are typically observed during periods of westerly airflow. The LP site also has the influence of the major road that is to the south of the site and its influence can be clearly seen in the NOx and PM measurements from those wind sectors.

Table 11    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 at LP (Figure 82)
The O3 diurnal 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 the NOx and PM display similar diurnals cycles, this is different to the KM site. The fact that these are similar in shape is an indication that the PM and NOx have similar sources. The diurnal at LP 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 will be the effect of the late afternoon/evening rush hour.

Figure 82    Diurnal variations a LP for (a) O3 (b) NOx and (c) PM. © Univ York, NCAS, 2017 (FIGURE NOT AVAILABLE)
Figure 83    Hebdomadal cycles for at LP for (a) O3, (b) NOx and (c) PM. © Univ York, NCAS, 2017 (FIGURE NOT AVAILABLE)

Hebdominal variation of air pollution at LP (Figure 83)
The working week (Mon–Fri) is clear in the O3 and NOx measurements with NOx being highest during the week and decreasing in at the weekend, whereas O3 is highest on the weekend due to reduced titration from NO. There is a slight anomaly mid-week when the NOx appears to reduce. It is currently unclear as to why this difference should occur as there are no immediate reasons why traffic volumes on a Wednesday in the area would be lower. For example, there is no nearby college or university, a type of source that is known to have atypical activity levels on Wednesdays.

Annual variation of air pollution at LP (Figure 84)
These show typical cycles in the context of UK air quality and have already been partially discussed in section 6.2.2 and 6.2.3.4 for KM. Annual cycles for the in situ air quality parameters are shown in Figure 84. As in previous plots for LP, the NOx and PM show similar cycles, again the influence of Preston New Road is seen in the results.

Figure 84    Annual cycles at LP for (a) O3, (b) NOx and (c) PM. © Univ York, NCAS, 2017 (FIGURE NOT AVAILABLE)

Source apportionment for LP
Figure 85 shows percentiles 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 of those by wind direction (radial axis) and concentration. The grey line is the mean for the data set. The plots are separated into season with Spring (March, April, May), Summer (June, July, August), Autumn (September, October, November) and winter (December, January, February).

Figure 86 shows polar plots for the same pollutants, with concentrations (colour scale), wind direction (radial scale) and wind speed.

Figure 85    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. © Univ York, NCAS, 2017.
Figure 86    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. © Univ York, NCAS, 2017.

As mentioned previously there were instrument problems in Summer 2016 so the NO, NO2 and NOx measurements are not continuous and limited.

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 at KM and LP[edit]

Non methane hydrocarbon (NMHC) have been taken on a weekly at both sites. A summary of NMHC for KP and LP is shown in Table 12 and Table 13 respectively. 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 12    Summary of NMHC measurements at KM, N =59. All NMHC have an uncertainty of <10%.
Hydrocarbon Annual mean (ppb) Minimum Value (ppb) Maximum Value (ppb)
Ethane 2.17 0.43 5.99
Ethene 0.66 0.08 2.05
Propane 1.06 0.04 5.61
Propene 0.14 0.02 0.63
Isobutane 0.21 LOD 1.1
Nbutane 0.44 0.02 1.64
Isopentane 0.17 0.02 0.91
Npentane 0.14 0.02 1.16
Benzene 0.14 0.03 0.33
Toluene 0.12 LOD 0.47
Table 13    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 which currently legislated is benzene and the annual mean at both sites is well below the limit value for the UK at both sites. Box and whisker plots to show the distribution of 4 selected NMHCs are shown in Figure 87. Measurements from the two monitoring sites, KM and LP, are compared to measurements to borehole samples (BH), these samples were taken at various borehole locations around the Vale of Pickering in conjunction with water sampling.

Figure 87     Plots to show the distribution of (a) benzene, (b) ethane, (c) propane and (d) ethene. KM = Kirby Misperton, LP = Little Plumpton, BH = Borehole samples. © Univ York, NCAS, 2017.

The box plots show similar distributions of NMHCs at KM and LP but with benzene and toluene being slightly higher at LP due to traffic influence.

General conclusions[edit]

The atmospheric composition work package has shown the importance of a baseline before any future activities in a region. From an air quality perspective, it is essential that this baseline covers 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. This also highlights the need for continuous measurements where possible to enable a full analysis of sources in the region.