IAQM AGM 2016 - Dr Anna Font, KCL
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Dr Anna Font, King’s College London ‘Did Policies to abate atmospheric emissions from traffic have a positive effect in London’ IAQM AGM 2016, 16th November 2016
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IAQM AGM 2016 - Dr Anna Font, KCL
- 1. Did policies to abate atmospheric emissions from traffic have a positive effect in London? Anna Font and Gary Fuller 16th November 2016 IAQM AGM & Discussion Meeting 1
- 2. Which policies are working? • A large number of policy initiatives are being taken in the EU, in the UK and in London to improve air quality – EURO classes, LEZ, TfL bus retrofit program, etc. • Change in the vehicle fleet (dieselization, alternative fuelled vehicles...) • Difficult to evaluate which policy is working best / at all? 2 Source: DfT, 2014
- 3. Which policies are working? • If we had just one policy to be tested we could set up a experiment with an intervention and a control • But we have policies everywhere and different ones in different places. • So we look for the places where air pollution is improving fastest to find the best policy packages 3
- 4. Standard approach • Evaluate trends of single sites or aggregate metric across an area/city. • Masks heterogeneity. Our approach • Look at trends at individual sites. • Calculate overall trends using meta-analysis technique. 4 Which policies are working?
- 5. Methods • Trends in roadside increments (Δ) • > 75% data capture • Trends calculated between 2005-2009 and 2010-14 – 2005-2009: ΔNOX, ΔNO2 (N=47), ΔPM10 (N=45), ΔPN (N=1) – 2010-2014: ΔNOX, ΔNO2 (N=42), ΔPM10 (N=36), ΔPN (N=1), ΔPM2.5 (N=12), ΔCBLK (N=3) • Trends calculated using the Theil-Sen estimator adjusted for seasonality: trend and 95% confidence interval • Overall trend calculated by meta-analysis (linear random-effects model) – more weight (w) is given to sites with less variance (v) and more precision (w = 1/v) 5
- 6. Methods • Traffic data available from DfT as Annual Average Daily Flow (AADF) (#vehicles day-1) • Vehicle categories: cars & taxis, motorcycle, buses & coaches, light good vehicles (LGVs) and heavy goods vehicles (HGVs) • Trends were computed as the slope from the least-square linear model 6
- 7. 7 trends in ΔNOX trends in ΔNO2 Overall trend ΔNOX: 1.0% year-1 * Overall trend ΔNO2: 10.6% year-1 * Results: trends in 2005 - 2009
- 8. Results: trends in 2005 - 2009 8 Overall trend ΔPM10: -3.9% year-1 * trends in ΔPM10
- 9. 9 trends in ΔNOX trends in ΔNO2 Overall trend ΔNOX: -1.0% year-1 Overall trend ΔNO2: -4.8% year-1 * Results: trends in 2010 - 2014
- 10. Results: trends in 2010 - 2014 10 Overall trend ΔPM10: 1.1% year-1 Overall trend ΔPM2.5: -28.4% year-1 * trends in ΔPM10 trends in ΔPM2.5
- 11. Results: summary trends Pollutant Overall absolute trend ( µg m-3 year-1) or (# m-3 year-1) Overall percentage trend ( % year-1) 2005-2009 ΔNOx 0.9 [0.1, 1.7]* 1.0 [0.1, 2.0] * ΔNO2 1.6 [1.3, 2.0] * 10.6 [8.1 13.0] * ΔPM10 -0.2 [-0.3, 0.0]* -3.9 [-0.7, -7.1] * ΔPNA -1.3 [-1.7, -1.1]·104* -18.8 [-21.4, -17.0] * 2010-2014 ΔNOx -1.11 [-2.3, 0.04] -1.0 [0.04, -1.9] ΔNO2 -1.7 [-2.3, -1.0] * -4.8 [-3.0, -6.7] * ΔPM10 0.1 [-0.1, 0.3] 1.1 [-2.1, 4.3] ΔPNA -2.2 [-30.5, -11.4]·103* -9.7 [-11.3, -5.7] * ΔPM2.5 -0.7 [-1.0, -0.4] * -28.3 [-14.7, -42.0] * ΔCBLKB -0.6 [-1.0, -0.2] * -11.3 [-3.4, -19.2] * ΔPM2.5 B −0.5 [−0.7, −0.4] * -14.7 [-1.1, -28.4] * 11Brackets denote 95% confidence interval; * Statistically significant; A Only one site (Marylebone); B Only three sites (Marylebone, Brent and Tower Hamlets)
- 12. Results: trends traffic counts 2005 – 2009 2010 – 2014 Dieselization of the fleet +33% +25% Vehicle Cat. Trend All vehicles Δveh·day-1 year-1 −402.6 [−557.5, −247.8] −196.6 [−286.4, −106.8] %·year-1 -1.0 -0.5 Cars & taxis Δveh·day-1 year-1 −371.5 [−500.3, −242.7] −167.4 [−247.1, −87.7] %·year-1 -1.3 -0.6 Buses Δveh·day-1 year-1 36.9 [26.2, 47.6] −9.1 [−21.1, 2.8] %·year-1 3.2 -0.7 Motorcycles Δveh·day-1 year-1 −3.0 [−8.9, 2.8] −5.4 [−11.3, 0.4] %·year-1 -0.3 -0.4 HGVs Δveh·day-1 year-1 −11.5 [−21.7, −1.2] 29.5 [14.2, 44.8] %·year-1 -0.63 1.7 LGVs Δveh·day-1 year-1 −2.3 [−24.2, 19.5] −19.2 [−47.3, 8.8] %·year-1 -0.1 -0.4 12
- 13. Results: Comparing pollutant trends 13 Trends 2010 -2014
- 14. Results: Comparing pollutant trends 14 → → coarse fraction Trends 2010 -2014
- 15. Results: Comparing pollutant trends 15 Trends 2010 -2014
- 16. Results: trends @ Marylebone Rd Pollutant Trends (% year-1) Vehicle type Trends (% year-1) ΔPM10 -9.1 [-11.9, -5.3]* All vehicles 0.8 [-3.7, 5.3] ΔPM2.5 -13.1 [-15.8, -8.2]* Cars & taxis -1.0 [-6.6, 4.6] ΔCBLK -11.2 [-12.9, -8.8] * Buses & coaches 14.8 [10.6, 19.1]* ΔPN -9.7 [-11.3, -5.7]* HGVs 9.6 [7.1, 12.1]* 16 • All tracers decreased at similar rates • Significant increase in heavy vehicles in the road such as buses and lorries improvement in emissions standards * Statistically significant at p<0.05 Trends 2010 -2014
- 17. Results: looking for patterns • K-means cluster analysis used to group roads with the most similar trends for the time period 2010-2014 • Variables: trends in ΔNOX, ΔNO2 and ΔPM10 • Before clustering , each variable is normalized (mean = 0; variance = 1) • Exclude Wandsworth – Putney High St and Lambeth – Brixton Road (outliers) 17
- 18. Results: looking for patterns 18 n = 31 sites 0 1 2 3 4 5 13 14 15 Number of Clusters NumberofCriteria 0123456
- 19. Results: trends NOx vs trends traffic 19 Peak car phenomena ++ buses & in some roads, ++ HGVs ++ NOX TfL retrofit program 2010 - 2014
- 20. Results: trends PM10 vs trends traffic 20 Role of traffic speed in non- exhaust emissions from the roads Peak car phenomena 2010 - 2014
- 21. Results: trends PM2.5 vs traffic trends 2010 - 2014 Reduction traffic + Improvement exhaust emissions
- 22. Conclusions • In the period 2005-09 ΔNOX and ΔNO2 increased at a significant rate (1% and 11% year -1, respectively). • That reflected the growing evidence of real world emissions from diesel vehicles richer in NOX and primary NO2 • Tendency reversed in 2010-2014 with roads in London experienced a significant downward trend in ΔNOX and ΔNO2 (-1% and -5% year -1). • ** But not all places improved ** 22
- 23. Conclusions • SCR retrofits on Euro 3 buses effective. • ** SCR not working in HGVs in low-speed routes? ** • Changes in ΔNOX have some linkage to changes in buses and HGV flows. Are policies strong enough? • Current trends show ~10 to >20 years to LV compliance. • Hopefully Euro 6 / VI will help. 23
- 24. Conclusions • In 2005-2009 an overall decrease in PM10 concentration was observed on the majority of roads across London. • One of the possible explanations is the efficiency of diesel particle filters; another, is the general decrease in HGVs in this period reducing non-exhaust traffic emissions from resuspension, brake and tyre-wear 24
- 25. Conclusions • Although city-wide ΔPM2.5 decreased in 2010- 2014, ΔPM10 remained constant with indications of a slightly positive trend increased coarse fraction. • The increase in the coarse fraction was seen mainly in roads in outer London with increased HGVs. Changes in traffic at these locations counteracted the benefits of emissions control. 25
- 26. Conclusions • We have no policies to control resuspension, brake and tyre-wear apart from vehicle number and speed. • ΔPMcoarse increasing on faster flowing roads Traffic flow speed needed additionally to for changes in non-exhaust emissions • One road measured particle number and observed a decrease in its increment in both periods at a similar rate than ΔPM10 Fibeig et al. (2014): reduced particle mass emission from diesel vehicles is associated with a reduction in PN Jones et al. (2012) found 60% decrease in PN with ultra low S diesel in 2007 26
- 27. Conclusions • Where collocated, ΔPM2.5 decreased along with ΔCBLK confirming that the decrease was largely explained by a decrease in exhaust emissions. • These emissions changes were due to a combination of decreased traffic flows and also an improvement in emission standards. 27
- 28. www.environment- health.ac.uk Thanks for your attention Thanks to Transport for London and Greater London Authority for part funding The full study is published in Environmental Pollution (http://dx.doi.org/10.1016/j.envpol.2016.07. 026). anna.font_font@kcl.ac.uk gary.fuller@kcl.ac.uk 28
Editor's Notes
- A suite of policies have been implemented to improve air quality and reduce population exposure. The Euro emission standards were introduced in the early 1990s to reduce exhaust emissions from new vehicles and tighter standards have been introduced in the last two decades. They regulate particle mass and since Euro 5, a limit value for particle number was additionally introduced. At the London scale, the London Emissions Zone was introduced in 2008 and ban the entrance of the most polluted diesel heavy duty vehicles All and other local policies take place at the same time and it is difficult to evaluate which works best If we had just one policy to be tested we could set up a experiment with an intervention and a control - But we have policies everywhere and different ones in different places; And also, traffic composition and traffic conditions change from road to road. Therefore we look at the location which improved fastest to try to find which policy packages work best
- Evaluating the success of policies in improving air quality can be done though the study of trends in atmospheric concentrations in time. Most of the approaches calculate trends in concentrations using one monitoring site representative of a given exposure scenario (e.g. roadside site); or by averaging the concentrations from a group of similar sites. In large urban settings, such as London, trends in air quality monitoring sites have been classified by their distance to the city centre, for instance, inner and outer London. Although these methods are valid and useful, these approaches can mask a wide heterogeneity in the impact of policies across an urban area and especially in roadside locations where emissions have a large spatial and temporal variability. In this study we benefitted from the large number of monitoring sites across London and we looked at the trends in air pollutants at individual sites and calculated the overall trend by using meta-analysis technique traditionally used in epidiemological studies
- Trends in PM10 decreased at a significant rate in 2005-2009 (-4% /year) but remained stable in 2010-2014. Roadside PM2.5 concentrations were only available for the second period and showed a fast and significant decrease: -28% /year). Trends in CBLK were also calculated for the three AQMS measuring in 2010 - 2014: -11.3% / year. PM2.5 for the three CBLK sites were -15% / year. Trends in particle number were calculated for the only measuring site and negative during the two periods of time was observed; but it was much faster in the first one (-19%/ year) compared to the second (-10% / year).
- An overall significant decrease in total vehicles and cars and taxis was observed in 2005-2009 and 2010-2014 on those roads with a mean rate of -1.0 and -0.5%/ year for total vehicles; and -1.3 and -0.6 %/year for cars for each time period, respectively. HGVs, LGVs and motorcycles decreased in 2005-2009 but only the first was statistically significant. Buses and coaches observed a fast increase in 2005 - 2009 at 3.2% / year. Conversely, buses and coaches decreased in 2010-2014, along with motorcycles and LGVs although the later were not statistically significant. Conversely, an overall significant increase in HGVs was observed at a rate of 1.7% / year in 2010-2014.
- The rate of decrease in ΔNO2 and ΔNOX was similar on most roads in 2010-2014 (Fig 1A) with sites aligned on the 1:1 line. This indicates that abatement technologies tackled traffic NO2 emissions and had little effect on ΔNO. Around 1/3 of locations, including some busy central London roads, showed increased ΔNOX and ΔNO2 (top right Fig 1A). The increase in ΔNO2 was slower than the change ΔNOX indicating that both ΔNO and ΔNO2 increased. But there were another group of monitoring sites that NO2 concentrations decrease but total NOx increased. Therefore, NO concentrations increased in this period of time
- The comparison between the trends in PM2.5 and in PM10 indicated that the majority of sites in inner London experienced a downward trend in both PM fractions at similar rates. With the exception of Greenwich A206 Burrage Grove (GN0), sites in outer London experienced an increase in PM10 while PM2.5 decreased (sites in the right bottom quadrant); implying an increase in coarse PM fraction whilst the levels in fine fraction went down.
- The decrease in CBLK was consistent with the decrease PM2.5 with trends aligned along the 1:1 line.
- Different respond for trends in PM25 respect to trends in vehicles; some roads with more traffic, others with less but the majority had a decrease trend in PM2.5