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Did policies to abate atmospheric emissions from traffic have a positive effect in London?

Anna Font and Gary Fuller16th November 2016IAQM AGM & Discussion Meeting

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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?

Source: DfT, 2014

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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

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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.

Which policies are working?

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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)

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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

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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

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Results: trends in 2005 - 2009

Overall trend ΔPM10: -3.9% year-1 *

trends in ΔPM10

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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

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Results: trends in 2010 - 2014

Overall trend ΔPM10: 1.1% year-1 Overall trend ΔPM2.5: -28.4% year-1 *

trends in ΔPM10 trends in ΔPM2.5

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Results: summary trendsPollutant 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] *

ΔPM2.5B −0.5 [−0.7, −0.4] * -14.7 [-1.1, -28.4] *

Brackets denote 95% confidence interval; * Statistically significant; A Only one site (Marylebone); B Only three sites (Marylebone, Brent and Tower Hamlets)

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Results: trends traffic counts2005 – 2009 2010 – 2014

Dieselization of the fleet +33% +25%Vehicle Cat. Trend

All vehiclesΔveh·day-1 year-1 −402.6 [−557.5, −247.8] −196.6 [−286.4, −106.8]%·year-1 -1.0 -0.5

Cars & taxisΔveh·day-1 year-1 −371.5 [−500.3, −242.7] −167.4 [−247.1, −87.7]%·year-1 -1.3 -0.6

BusesΔveh·day-1 year-1 36.9 [26.2, 47.6] −9.1 [−21.1, 2.8]%·year-1 3.2 -0.7

MotorcyclesΔveh·day-1 year-1 −3.0 [−8.9, 2.8] −5.4 [−11.3, 0.4]%·year-1 -0.3 -0.4

HGVsΔveh·day-1 year-1 −11.5 [−21.7, −1.2] 29.5 [14.2, 44.8]%·year-1 -0.63 1.7

LGVsΔveh·day-1 year-1 −2.3 [−24.2, 19.5] −19.2 [−47.3, 8.8]%·year-1 -0.1 -0.4

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Results: Comparing pollutant trendsTrends 2010 -2014

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Results: Comparing pollutant trends

→ → coarse fraction

Trends 2010 -2014

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Results: Comparing pollutant trendsTrends 2010 -2014

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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]*

• 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

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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)

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Results: looking for patternsn = 31 sites

0 1 2 3 4 5 13 14 15

Number of Clusters

Num

ber o

f Crit

eria

01

23

45

6

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Results: trends NOx vs trends trafficPeak car phenomena

++ buses & in some roads, ++ HGVs ++ NOX

TfL retrofit program

2010 - 2014

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Results: trends PM10 vs trends traffic

Role of traffic speed in non-exhaust emissions from the roads

Peak car phenomena

2010 - 2014

Results: trends PM2.5 vs traffic trends2010 - 2014

Reduction traffic +

Improvement exhaust emissions

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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 **

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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.

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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

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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.

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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

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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.

28www.environment-health.ac.uk

Thanks for your attentionThanks 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.ukgary.fuller@kcl.ac.uk