Using Ambient Air Quality Monitoring Data to Reveal Emission Trends from Road Traffic on the M4

Transcription

1 Using Ambient Air Quality Monitoring Data to Reveal Emission Trends from Road Traffic on the M4 Duncan Whyatt 1, Roger Timmis 2, Andy Malby 3 1 Lancaster Environment Centre, Lancaster University 2 Environment Agency for England and Wales 3 Scottish Environmental Protection Agency d.whyatt@lancaster.ac.uk

2 Presentation Outline Introduction The Conditional Approach Visualisation Conditional Selection Non-Target-Source Subtraction Trend Evaluation Case Study M4 at Hillingdon Morning Rush Hour All Hours Conclusions Acknowledgments References

3 Introduction The informatics potential of air-quality data is rarely fully exploited Data mining techniques have been around for many years If used in structured & systematic way they could Help distinguish between source & meteorological variations in the ambient record Track source impacts on a regular basis Gain early insights into effectiveness of policy interventions Could also be used in smarter forms of model validation

4 In the future we could Develop smarter monitoring networks which are more cost effective (more for less) Provide richer measures for new directives, e.g. exposure reduction (from hotspots to footprints) Presentation today is all about extracting more information from air quality monitoring data for decision-making purposes

5 Spatial & Temporal Scales

6 Essential Elements Compliance with EU standards Management interventions Strategic planning decisions Observed and/or modelled NWP e.g. annual average daily traffic flows

7 2. The Conditional Approach Structured methodology 4 stages Uses a subset of data conditionally selected to exclude impacts from non-relevant sources Adopts specific nomenclature, e.g. target-source and non-target-source From bespoke to consistent and objective analysis convergence over time?

8 3. Case Study: Source Performance (M4) Year Year NO 2 mg.m -3 Annual mean NO 2 (µgm -3 ) Annual mean limit value = 40 mg.m -3

9 Unconditioned Ambient Time-Series Have NO 2 concentrations changed over time? mgm -3 at 0.73 mgm -3 year (p > 0.050) mg.m 3 yr -1 Decadal time-series - all hours, all days, all wind speeds, all wind directions, all traffic conditions

10 Step 1: Visualisation Assess how pollutant varies by wind speed, wind direction, day-of-week and time-of-day (hourly data) Identify conditions under which target source contributions are dominant over non-target-source contributions

11 Step 2: Conditional Selection Focus on the spatial & temporal characteristics most likely to be representative of the target source enhance the signal-to-noise ratio Filtering vs. occupancy trade-off Filters applied to the Hillingdon series Wind Direction: o Wind Speed: 3-6 ms -1 Day-of-Week: Monday-Friday Time-of-Day: M4 (target source) prominent in filtered record but still some influence from non-target-sources

12 Step 3: Non-Target-Source Subtraction Remove non-target-source contributions from conditionally-selected records on a concurrent basis Requires data from an upwind monitor Harmondsworth - 1km SW of Hillingdon Teddington - 20km SW of Hillingdon Upwind Downwind Source Non-target-source contribution indicates how the background has changed over time Incremental record (target-source minus non-targetsource) can be tracked over time and source performance implied

13 Step 4: Trend Evaluation (Incremental Record) Assess rate and significance of change in targetsource impacts over time morning rush hour Raw Data (all hours) : mgm -3 at 0.73 mgm -3 year (p > 0.050) Conditioned Data (morning rush-hour) : mgm -3 at 3.26 mgm -3 year (p < 0.001) Target Source impact has increased over time, background has remained static

14 Understanding the Increase in NO 2 : Rush Hour Statistically significant increase in week-day morning rush-hour concentrations Could this simply be explained by an increase in the volume of traffic? Probably not And the background has remained static So have vehicles on the M4 become more polluting?

15 Understanding the Increase in NO 2 : All Hours Additional traffic count & composition data used to investigate increases on a per-vehicle basis 3 sub-periods with different characteristics : Free-flowing, high % HGVs : Congested, low % HGVs : Transitional Exploited to determine which vehicle classes are most problematic at which times of day

16 Understanding the Increase in NO 2 HGVs deliver the highest absolute impacts per vehicle LGVs deliver the highest relative increase in impacts per vehicle during congested conditions Increase in NO 2 more likely to be as a result of stop-start driving than additional traffic Results consistent with recent research findings diesel fleet penetration, calls for real-world emissions testing

17 4. Conclusions Conditional approaches can be used to extract sourceperformance signals from air-quality data Trends in source performance can be tracked over time Such techniques unlikely to be used in isolation generally in conjunction with other methods Our study was opportunistic but demonstrates the value of having well-placed foreground and background monitors essential monitoring is maintained (valueadded can be considerable)

18 Even clearer insights could be gained if monitoring networks were designed with conditional analysis in mind Our approach involves making judgements on a range of considerations More consistent and objective conditional techniques can be built up from additional case studies over time (convergence)

19 5. Acknowledgments The Environment Agency for funding the initial stages of this research (Science Project SC03004) NERC for funding the majority of this research through their Knowledge Exchange scheme (NE/G001138/1) Val Beale (Hillingdon Council) for access to Harmondsworth monitoring data

20 6. References Carslaw, D.C. and Ropkins, K., 2012, OpenAir an R Package for Air Quality Data Analysis. Environmental Modelling and Software 27-28, p52-61 Malby, A.R., Whyatt, J.D. and Timmis, R.J., 2013, Conditional Extraction of Air-Pollution Source Signals from Air-Quality Monitoring. Atmospheric Environment 74, p OpenAir -