Prof Sujit Sahu - University of Southamptonsks/teach/sks_met_office.pdf · Track record on...

Air pollution in England and Walesand their health effects

Prof Sujit Sahu

http://www.soton.ac.uk/∼sks/

Presentation to Met Office, June 2019

Sujit Sahu: http://www.soton.ac.uk/∼sks/ 1

Track record on modelling and forecasting air pollution

Have worked on air pollution modelling since 2003.

Worked with scientists in the USEPA and generated impacton forecasting air pollution.

Author of the REF2014 Impact Case Study: Using NovelStatistical Modelling Techniques to deliver More AccurateAir Pollution Forecasts.

0.0

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70.2 66.6

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The forecasts werefor the whole of theEastern US.

Still have thesoftware packageand methodologicaldetails.


Track record in working with the Met Office

1 EPSRC funded project (2010-2012, £341,363) MetSim: aHospital Simulation Support Tool Using MeteorologicalInformation to Improve the Planning and Management ofHealth Services.

Worked with Dr Christophe Sarran and Cardiff.Journal of Royal Stats Soc. paper: Sahu et al. (2014).

2 EPSRC funded project (2013-2016, £365,643) A rigorousstatistical framework for estimating the long-term healtheffects of air pollution.

Worked with Christophe Sarran, Paul Agnew and Lucy.

Output of the project: generation of 64GB air pollution datafor England and Wales. Available from my webpage.

Statistically rigorous evaluation of health effects of pollution.

World-wide interests with Webinar hosted by the RoyalStatistical Society in February 2018.


How good is the actual outdoor air quality?

Data from the Automatic Urban and Rural Network.

Map of 346 local andunitary authorities.

144 AURNmonitoring locationsare blue * and red 4.

Challenges: (i) Howdo we estimate airpollution at any newlocation?(ii) How can werelate air pollution tohealth outcomedata?


Very sparse air pollution data in the UK

Our study was for the years 2007-2011.

New sites added in after 2008 EU directive.

Website hosted by DEFRA (Department for EnvironmentFood and Rural Affairs) provides downloadable data.

Pollutant 2007 2008 2009 2010 2011 OverallNO2 31,975 32,024 32,378 32,535 33,914 162,826

O3 22,830 19,565 18,895 19,419 20,070 100,779PM10 18,399 17,542 15,638 14,499 15,881 81,959PM2.5 1,754 4,164 17,044 18,298 18,529 59,789

Table: Number of available observations out of the total number ofobservations in a year, which is 52560 (365× 144) for non-leap yearand 52704 (366× 144) for leap year.


How do the data look like?

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010

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040

050

0

NO

2

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050

100

150

O3

(b): O3

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Rural Urban RKS

020

4060

8010

0

PM

2.5

(d): PM2.5

Figure: Boxplots of the daily average concentrations for eachpollutant by three site types.


Our contribution

1 To model daily levels of four major pollutants namely, NO2,O3, PM10 and PM2.5, for the period 2007–2011.

2 To build a spatio-temporal model that allows us tointerpolate at any unobserved location.

3 To integrate output from a computer simulation modelAQUM (Air Quality Unified Model) (Savage et al. 2013).


Results for NO2 and O3 model validation.

NO2: Fitting N = 100,138, validation N=62,688, SD=36.98Model RMSPE MAPE Bias Coverage (%) R2

Simple Kriging 32.87 22.88 2.56 69.59 0.53Linear model 30.46 19.63 –5.09 94.43 0.60Best model 17.65 12.99 0.41 97.42 0.89

O3: Fitting N = 64,373, validation N=36,406, SD=22.15Simple Kriging 13.30 9.86 –2.95 78.25 0.80Linear model 16.0 12.42 8.47 93.86 0.69Best model 10.17 7.59 0.07 91.72 0.89

Table: Assessment of predictive performance for a range of modelsfor NO2 and O3. R2 denotes the sample correlation coefficientbetween the predictions and actual observations.


Results for PM10 and PM2.5 model validation.

PM10: Fitting N = 52,625, validation N=29,334, SD=11.95Model RMSPE MAPE Bias Coverage (%) R2

Simple Kriging 7.34 4.75 –0.75 64.96 0.77Linear model 9.98 6.74 –1.74 93.70 0.61Best model 5.48 3.56 –0.65 90.03 0.81

PM2.5: Fitting SS = 38,481, validation SS=21,308, SD=9.51Model RMSPE MAPE Bias Coverage (%) R2

Simple Kriging 4.63 2.96 –0.72 67.84 0.81Linear model 8.03 5.30 –1.87 92.73 0.60Best model 4.30 2.66 –0.97 82.38 0.85

Table: Assessment of predictive performance for a range of modelsfor PM10 and PM2.5. R2 denotes the sample correlation coefficientbetween the predictions and actual observations.


Summary of RMSEs for daily data for London only

Model RMSPE MAPE Bias R2 Cover (%)PM10: Fitting N = 11,828, validation N=1,393

Best model 3.81 2.85 0.87 0.85 89.37Pirani et al 2014 4.75 – – 0.63 –

Table: Model validation and model choice measures for PM10 using24 fitting sites and 5 validation sites within London only.


Aggregating to local and unitary authority (LUA) areas

Map of 346 LUAs in England andWales.

A 1-kilometer square grid(151248 gray dots) issuperimposed.

Average air pollution in any LUAis the block average of thepollutions in the gray dots fallingwithin that LUA.

Our best Bayesian model is used to interpolate (modelbased Kriging) the air pollution at the gray dots.Thus we produce air pollution estimate at any LUA at anytime point (daily, monthly, annual)!


Figure: Local authority-wise annual prediction plot for NO2 and theirstandard deviations (bottom panel) for 2011.


Figure: Local authority-wise annual prediction plot for O3 and theirstandard deviations (bottom panel) for 2011.


Figure: Local authority-wise annual prediction plot for PM10 and theirstandard deviations for 2011.


Figure: Local authority-wise annual prediction plot for PM2.5 and theirstandard deviations for 2011.


Estimating health effects

Let Ykt denote the number of hospitalisation in the k th localauthority in the t th month.k = 1, . . . ,323 local authorities in Englandt = 1, . . . ,60 months in five years, 2007-2011.

Ykt ∼ Poisson (EktRkt)

log(Rkt) = α+ β1v̂kt + β2jsakt + β3housekt + ψkt

Ekt is directly standaridised hospitalisation (age and sex)counts nationally.Rkt : Relative risk,v̂kt ; pollution estimate.jsakt : Average job seekers allowance.housekt : Average house price.ψkt : space-time random effect.


Results from the health outcome model

RR Lower 2.5% Upper 97.5% Pollutant SDNO2 1.028 1.021 1.033 16.07

PM10 1.026 1.011 1.039 4.90PM2.5 1.006 0.993 1.020 4.11

O3 0.997 0.994 0.999 7.30

Table: Estimated health effects from each pollutant for a range ofmodels. All results are presented as relative risks for a one standarddeviation increase in pollution.

An estimated 2.8% increased risk of hospitalisation due toone sd increase in exposure to NO2Implies 17,000 extra hospital admissions per year, as thereare around 613,000 admissions per year in England.This implies a potential annual spending of £4.76 millionassuming a week’s hospital stay on average for eachpatient.


Conclusions

1 We have proposed a number of spatio-temporal statisticalmodels which worked well for all four important pollutants,PM10, PM2.5, O3, NO2.

2 Our model fills up the sparsity of the observed air qualitydata by integrating output from the AQUM which areavailable over a fine grid.

3 Our models also improve similar other modelling attempts(e.g. Pirani et al.).

4 We are able to measure long term exposure since we havemodelled daily data for a 5 year period for whole of UK, forall four pollutants.

5 We are not aware of any similar study offering high qualityair pollution estimates along with their individual errorrates.


Pollution data available

1 Exposure estimates, and their uncertainties, from our bestmodel:

1 for all four pollutants2 at both daily and annual time scales3 for the five years 2007-20114 at the 151248 1-kilometer grid points5 and also for all the local authorities in England and Wales

2 are available online. Total size is about 64GB.3 From my website: http://www.southampton.ac.uk/∼sks/4 Thus we provide the most accurate empirically verified air

pollution estimates at 1-kilometer grid in E & W.5 Now working to aggregate upto other geographies:

electoral wards, lower layer super output areas (LSOA) etc.

6 Currently writing an ATI project to generate 77GB data for2012-2018.


Discussion

1 Statistical models have the added advantage of producingthe correct prediction uncertainty in air pollution estimates,which are required by the health outcome model.

2 MCMC sampling also enables us to estimate theuncertainties in the spatial (point to local authority level)and temporal (daily to monthly or annual) aggregates.

3 Statistical modelling is preferable since the models providea complete description of the data, not only the summariesand averages, but also the variability of the data.

4 All the assumptions in the modelling are also explicit whichenables their scrutiny and can suggest to re-modelling forfurther improvement.

5 By building a statistical model we are able to account for(rather than ‘adjust for’) the effects of all the variables (e.g.site types).


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