A DYNAMIC SPACE-TIME MODEL FOR PARTICULATE MATTER AIR POLLUTIONNICHOLAS HAMM

Context AiREAS initiative Data Objectives Methods Results Conclusions

OVERVIEW

2

AiREAS civic initiative City-level air-quality monitoring and modelling Low cost sensors. High resolution of measurements in space and time. New possibilities for insights into air pollution at daily/sub-

daily time scales links to projects on traffic management and environmental epidemiology.

Large data volume. Data quality is a concern.

CONTEXT

3

Civic-initiative: local government, industry, SME's, universities, citizen groups.

Aim for a healthy city. Based in the City of Eindhoven,

The Netherlands (90 km2; 220,000 people)

Low-cost “Airboxes” housing sensors that measure different air pollutants

PM1, PM2.5, PM10 + NO2, O3, ultrafine particules

AIREAS CIVIC INIATIVE

www.aireas.com

4

32 “Airboxes” throughout the city Modified Shinyei PD42 optical

sensor Particle counts, calibrated to

PM10, PM2.5, PM1 Hourly observations

Missing data Noisy data Interpolation mapping

DATABACKGROUND

5

DATADISTRIBUTION OF AIRBOXES

6

We need a model to satisfy the following objectives1. Fill in gaps in the time-series2. Filter outliers3. Interpolate to un-sampled locations 4. Use minimal additional data.

Trial using a subset of the data: 2 weeks of observations 1-14 October 2014 32 locations, 336 observations (14 days 24 hours) Focus on PM10

OBJECTIVES

7

DATA TIME SERIES

8

Treat time as discrete and space as continuous

Measurement equation

: response (PM10) : covariates vary in space : coefficients vary in time only : space-time varying intercept ∼ 0, : uncorrelated error points in time (336) and measurement locations (32)

METHODS SPACE-TIME DYNAMIC MODEL (GELFAND ET AL 2005; FINLEY ET AL. 2012)

9

Time-varying regression coefficients

∼ 0, Σ

Space-time-varying intercept

∼ 0, ⋅; ,

; , ; exp /

METHODS TRANSITION EQUATIONS

10

Inference is performed in a Bayesian framework using MCMC Gibbs sampling with Metropolis step for Non-informative priors Normal distributions for the ’s Inverse Gamma distributions for and Uniform distribution for ’s Inverse Wishart distribution for Σ

20,000 iterations, check for convergence Implement using spBayes & coda packages in R

METHODS IMPLEMENTATION

11

Exploratory analysis found no significant relationship with meteorological data. Spatially varying covariates not currently available. Simplify the model to

Three experiments1. Isolated missing values. Remove 500 values at random and

predict them.2. Extended periods of missing values. Three sensors removed on

day 8. One sensor removed for three days.3. Predict at unsampled locations. Remove a sensor for the entire

period.

METHODSEXPERIMENTS

12

RESULTSEXPERIMENT 1 – ISOLATED MISSING VALUES

Time series of Σ 6.13 5.24, 7.19

13

RESULTSEXPERIMENT 1

Time series of Σ 6.13 5.24, 7.19 Time varying spatial

parameters Clear spatial structure

0.6

Temporal variability dominates Σ

14

RESULTSEXPERIMENT 1 – ISOLATED MISSING VALUES

Successfully fills in missing values, RMSE = 1.4 g m-3

15

Median Median

Median CI on prediction

Observed values

Median Median

Median CI on prediction

Observed values RESULTSEXPERIMENT 2 – EXTENDED PERIOD OF MISSING VALUES

Successfully fills in missing values, RMSE = 1.8 g m-3

16

Median Median

Median CI on prediction

Observed values

Extended period – 3 days at Airbox 19

RESULTSEXPERIMENT 2 – EXTENDED PERIOD OF MISSING VALUES

17

Reliability of results highly variable. Consider Airbox 19 – red dot

RESULTSEXPERIMENT 3 – PREDICTION AT UN-SAMPLED LOCATIONS

10

27

32

37

4

24

26

18

RESULTSEXPERIMENT 3 – PREDICTION AT UN-SAMPLED LOCATIONS

10

27

32

267

4

24

26

19

Applied a method based on space-time dynamic models to fine space-time resolution air quality data.

Temporal signal dominates, but spatial signal is still strong. Missing values were predicted accurately. Method is promising for data

cleaning. Prediction at un-sampled locations is inaccurate. Spatial correlation alone does not support accurate predictions Need to consider spatial and spatial-temporal covariates to support

prediction Method shows promise for outlier detection.

CONCLUSIONS

20