scott hamilton

Ricardo-AEA

© Ricardo-AEA Ltd

www.ricardo-aea.com

Dr Scott Hamilton, Ricardo-AEA

Experiences, approaches, and lessons

learned.

Dispersion modelling in Riyadh, Saudi Arabia

© Ricardo-AEA LtdRicardo-AEA in Confidence2

• Riyadh roads dispersion model, the story so far

• What we have promised to do

• What we have been doing

• What we’ve still to do!

Topics


• Our client is the ArRiyadh Development Authority- similar to the GLA

• Develop an integrated numerical air dispersion model for the city of

Riyadh, Saudi Arabia

• This should include road transport, industrial sources, natural

sources

• This should be based on USEPA codes, and should be without

ongoing cost to our client (other than GIS)

• The solution should run on a standard office computer

• It should be scientifically accessible to all ArRiyadh Development

Authority officers- we’ll be “handing them the keys” at the end

• The model should be flexible enough to test large scenarios (e.g.

forthcoming metro system)

• It should provide robust results

Our scope


Riyadh location


Riyadh model domain

Model domain is about 3000km2 (Similar scale to Greater London)


Riyadh air quality project

Prototype Google Earth contour outputs- GIS-AERMOD road model


Satellite air quality data for Riyadh (the MEGAPOLI project)

Whilst we wont use this data directly in the project, its useful as it shows the city is quite

isolated from other major regional emissions sources.

Source: Max Planck Institute


11,000km of roads, split into 21000 links

EMME Traffic model- peak hour


Detailed road geometries, from EMME traffic model

Accompanying model report also has

fleet split (very basic) and road

categorisation.


Some road traffic context

M25 AADT Location

1st highest flow 227585 Hillingdon

2nd 201673 Slough

3rd 187391 Surrey

4th 187391 Bucks

5th 187116 Surrey

Riyadh AADT

1st highest flow 299539

2nd 298504

3rd 288246

4th 287544

5th 286725

21st 230923


Traffic conditions

11


Some road traffic context

Year

(Arabic Calendar)

Vehicles per

person

Number of registered

vehiclesPopulation in Riyadh

14250.291,243,0484,261,077

14260.311,379,6094,378,794

14270.341,514,5984,499,141

14280.361,671,9184,622,421

14290.391,845,0624,748,876

14300.422,068,4774,878,723

14310.462,283,2455,000,000

14320.562,925,7035,250,000

14330.653,602,5965,500,000

UK had 34.5 million vehicles in 2012, about 0.55 per capita.


Some road traffic context- diurnal patterns

There’s no public transport system in Riyadh, and a very high proportion

of trips originate and terminate in the city. Prayer time and evening

leisure trips sustain traffic peaks through out the day.


Saudi AQ standards

Species Time weighted average (μgm3) Averaging time Allowed exceedances

Nitrogen dioxide 660 1hr 2 per 30 days

100 annual n/a

Sulphur dioxide 730 1hr 2 times per annum

365 24hr 1 time per annum

80 annual n/a

Benzene 5 annual n/a

PM10 340 24hr 24 per annum

80 annual

PM2.5 35 24hr 24 times per annum

15 annual

Ozone 235 1hr 2 times per 30 days

157 8hr 2 times per 7 days

Hydrogen Sulphide 150 24hr 10 per annum

40 annual


Saudi vehicle emission standards

Heavy-Duty Diesel Engines

Emissions from heavy-duty engines in Saudi Arabia were originally meant to be

regulated by Euro II standards. However, these regulations have not been fully

implemented, and issues regarding adequate enforcement exist.

Reference Legislation Date

Euro II N/A

Passenger Cars and Light Duty Vehicles

The current set of standards for new light-duty diesel vehicles are based on

Euro 2 regulations. Originally, Euro 3 standards were meant to be introduced

for new vehicle models as of January 2010. However, these regulations have

yet to be approved, meaning that implementation of Euro 3 is still uncertain.

Reference Legislation Date

Euro II 01/01/2004

Euro III 01/01/2010*


Technical challenges for the Riyadh integrated dispersion

model (roads focus)

• Riyadh is a very large city- we are

modelling more than 3000km2, Greater

London is 1573km2.

• Model run times are unmanageable using

conventional techniques

• We still need to achieve a high level of

detail but it should run efficiently on ADA IT

and GIS systems and be updateable to

support future modelling efforts

• It should use open source software (other

than ArcGIS)

• During the discovery phase of the

project we developed a methodology

that deals with all of these challenges

Riyadh model domain


Overall methodology for Riyadh dispersion model

Our modelling approach1. We are modelling the regional air pollution climate

for a baseline year(s) using WRF/CMAQ- this will

provide our chemical (and maybe meteorological)

boundary conditions for the city model

2. We will model industrial sources with existing ADA

data for a baseline year using AERMOD- we will

develop scripts to run AERMOD from ArcGIS- this

makes the model easily updateable. The city’s

airports will be included in this model.

3. We will model road sources for a baseline year (the

most complex source of all) using a model

developed specifically for the city- GIS-AERMOD

4. We will provide the integrated model in an ArcGIS

toolset coded in Python that ADA colleagues can

use to interrogate datasets, combine the sub model

outputs, update emissions and model new

scenarios


• We’re also building a monitoring network of 17

automatic stations, developing a data management

platform similar to UK-AIR and constructing an AQ

Index.

• I’ll leave those to the real experts

What we’re doing but I’m not discussing…..


CMAQ- early development for Riyadh

Emissions tools –we will initially use global

emissions data available from the Emissions of

Atmospheric Compounds and Compilation of

Ancillary Data (ECCAD) web site

http://eccad.sedoo.fr/eccad_extract_interface/JS

F/page_login.jsf . The ECCAD website has

emissions from several global and regional

projects available in a common data format. The

Arabian Peninsula is included in these data.

Boundary Atmospheric Composition

(Chemical boundary conditions) - these are

required to represent the global background

conditions. We propose to use the data available

on global atmospheric composition from the

Monitoring Atmospheric Composition and

Climate (MACC) catalogue. http://www.gmes-

atmosphere.eu/catalogue.

http://eccad.sedoo.fr/eccad_extract_interface/JSF/page_login.jsf

http://www.gmes-atmosphere.eu/catalogue


Riyadh emissions calculations

• All emissions calculated in the

GIS (ArcMap or QGIS)

• Best evidence currently is Euro

2/II and 3/III standards

• 11,000km of roads (21,000 links)

populated in <2min

• No need to do any

geoprocessing in Excel or

Access

EMEP Guide 2013

EF = (a + c * V + e * V²)/(1 + b * V + d * V²)

Petrol Car Euro 2


USEPA Hotspot Conformity Methodology- roads as area

sources

4m

2.3m

2.3m

Appendix J of the guidance


GIS-AERMOD

In the GIS we turn the

road emissions into

8m x 8m cells

The emissions are

now small area

sources with

emissions in gkms

We treat these area sources in

the same way as the USEPA

Hotspot Guidance series

recommendations- i.e.

AERMOD is appropriate, roads

as area sources, suggested

parameters a proxy for vehicle

induced turbulence


GIS-AERMOD- dispersion kernels

200m

8m resolution

440m

40m resolution

2.2km

200m resolution

11km

1000m resolution

55km

The fine resolution grids

characterise dispersion of

emissions from roads close to

the receptor

The coarse resolution grids

provide a background

concentration from roads

further away- of course in an

hour emissions can’t travel

this far this fast under a

Gaussian formulation but we

can live with that

Not to scale

Kernels are set up to closely align with the USEPA method

for modelling road traffic in their Hotspot Conformity

Analysis guidance- specifies release height for the area

source for example


GIS-AERMOD

The AERMOD kernel is passed

over the gridded emissions from

the previous step to give

concentrations in ug/m3.

In this way we can develop concentration fields

from the road traffic sector at very high resolution

and with manageable run times (Riyadh takes about

40min)

This diagram shows the model captures steep air

pollution gradients at near road distances.

The coarse grid deals with further away

~200m


Dispersion plots


GIS-AERMOD- development in Riyadh- demo runs

• Up to 137,000,000 individual predictions

• About 415,000,000,000 calculations in ArcGIS

• Run time in ArcGIS for the road model is about 10 to 40 minutes

currently for the entire Riyadh urban area (including much desert)

• Model resolution is good enough to tell us pollutant concentrations

at individual properties in the city so we can tie this data to health

stats later if need be.

• Every prediction cell contains a contribution from every major road

in the city.

• The model isn’t kernel monogamous, we can use any good

dispersion model.

Whole city (8m resolution) District of city Street level plot


Riyadh dispersion calculations- demo


“We should probably test GIS-AERMOD in the near field….”

The aim of this brief study was to test

whether the GIS-AERMOD model

developed for Riyadh can replicate

pollution gradients modelled in

ADMS-Roads. Quite a stress test for a

city level model. We also wanted to

know if it will be good enough for city

wide exposure estimates

I tested the model for 3 surface

roughness values, for a 800m x 800m

domain. Concentrations of NO2

calculated empirically from NOx.

Grid height 1.5m

GIS-AERMOD is set to a resolution of

8m, ADMS-Roads was set to provide

concentrations at same grid points

using idealised emission rate

(1g/km/s), and 3 months of met data

from Glasgow Airport.

Domain

8m resolution grid,

10000 points

Roads


0.1m surface roughness results

ADMS Roads GIS-AERMOD

Results are similar across the grid for both

models. The graph shows points within 16m

of the roadside along the east to west link,

disregarding concentrations on the road

surface.

RMSE= 4ugm3

Same symbology for both models


0.5m surface roughness results






surface.

RMSE= 4ugm3



1m surface roughness results






surface.

RMSE= 3ugm3



1m roughness concentration transects

Red line is a transect through the whole domain, graphs show concentrations along the line

Blue=

GISAERMOD

Red= ADMS

Roads


“We should probably do some wind modelling…..”

To support the initial stages of the work

we looked at wind conditions in the city

We modelled the city in WRF (3km),

and used the netCDF data to prepare

3D wind fields in CALMET (0.5km).

Thus the wind fields are finely adjusted

to account for local terrain and land

use effects that are difficult to capture

at high resolution in WRF.

Additionally we were interested in

establishing whether the 2D wind field

over the city is reasonably constant, or

whether local land use effects have a

visible impact on observed winds.

The city is quite flat, though there are

some hills to the west.

Domain

Roads

CALMET domain 2nd WRF domain ~1500km


Seasonal winds in Riyadh


3D wind field over Riyadh

• The plot below shows a 3D WRF model of the Riyadh airshed. The model was

output from a nested prognostic model (WRF) at 3km resolution, and passed

through a diagnostic met model (CALMET) to develop terrain adjusted 3D wind

fields


3D wind field over Riyadh

• The plot below shows an animated wind field over a 7 day time series.

Note the different wind directions the different vertical levels.


Ground level winds in Riyadh

• The plot below shows an example of a 2D WRF/CALMET derived wind

field over the city. The wind direction is reasonably constant across the

built up region of Riyadh- this is an important finding that supports the

use of GIS-AERMOD for the road traffic modelling.


Ground level winds in Riyadh

• The animation below shows the same 2D wind field, note the changes

in wind direction tend to affect the city as whole in the same hour.


Modelled/Measured Winds

• Good agreement between WRF/CALMET and observed winds at the

Riyadh Airbase site. There is a slight bias in the wind direction.

WRF/CALMET wind rose- 17th-24th November 2014

observed modelled


Modelled/Measured Winds

• Good agreement between WRF/CALMET and observed winds at the

King Khalid Airport met site

WRF/CALMET wind rose- 17th-24th November 2014

observed modelled


Modelled wind speeds at both met sites

• Very similar wind speed distributions at the two met measurement

sites. Average wind speed very similar, lending further weight to the

argument that we can use a single met site to represent Riyadh

Air base King Khalid Airport

Measured average= 3.5ms Measured average= 2.5ms


Meteorological conclusions

• The analysis suggests that wind conditions are indeed

reasonably consistent across the city at any given time, for both

wind speed and direction.

• This means that we can probably use a single modelled or

measured meteorological dataset to reflect conditions across the

city in AERMOD (industrial sources) and GIS-AERMOD (road

sources.

• The GIS-AERMOD methodology is however flexible enough to

assimilate more than one meteorological observation site. We

can extract an unlimited number of AERMOD ready met stations

from the 3D WRF wind fields.


GIS-AERMOD- UK test case (Southampton)- good agreement,

low error

GIS-AERMOD modelled Road NOx concentrations (µg.m-3)

Error in this model is about + 3 micrograms of NO2


London dispersion model- traffic NO2 2013

• LAEI 2010 traffic data

• Flow, composition and speed

• 2013 emission factors derived from COPERT

IV

• Non-road concentrations from Defra LAQM

maps

• NOx calculated in the GIS model in about 90

secs, dispersion fields with empirical NO2

conversion in 5 minutes.


London dispersion model- agreement at AURN sites

• NO2 annual mean concentrations

• Comparison with London AURN

measured values in 2013

• The model “as is” underpredicted

somewhat- reasons unclear (could

be emission factors, traffic activity

data, empirical NO2 function etc etc)

• In general, after accounting for

systematic underprediction, the

model does a good job (especially

as it only took a day to create!

• RMSE value of 4.4μgm3


Google Earth example with 3D building models


London dispersion model- traffic NO2 2013 contour lines

and Opendata basemaps

QGIS plots

OSM data


London dispersion model- traffic NO2 2013

Google Earth example


Conclusions

• Ricardo-AEA are about two years into a major air quality project including models,

measurements, data platforms and a AQI

• We’re quite far along with the roads model, less so with the industrial modelling.

CMAQ just getting started

• GIS-AERMOD does a good job of recreating the same pollution gradients in the

ADMS-Roads results for the cases we looked at

• Transects through the model domains for all cases show good agreement,

therefore the model is a good candidate for exposure assessment

• The run time of GIS-AERMOD for a city scale case (10min or so) is trivial but the

results are pretty good at this stage

• London case study using LAEI data took about half a day of effort from receipt of

raw traffic data to finished plots

• Model run time for London domain was <10 minutes for emissions and dispersion

• Riyadh runs are looking promising, but we wait for the automatic network with

baited breath!

© Ricardo-AEA Ltd

www.ricardo-aea.com

T:

E:

W:

Ricardo-AEA Ltd

The Gemini Building

Fermi Avenue

Harwell, Didcot,

OX11 0QR

Dr Scott Hamilton

+44 (0)1235 753716

[email protected]

www.ricardo-aea.com

scott hamilton

Environment

city of riyadh

riyadh roads dispersion

endthe model

road transport

road categorisation

major sources

detailed road geometries

projections of traffic