Dispersion modeling

& EnFuser

Research manager/FMI

Ari Karppinen+ the team

11/2016

AQMg : the aims

1. Development and evaluation

of air quality models : from

microscale to global scale

2. Integration of meteorological

models (including climate) and

dispersion models

3. Efficient use of all available

measurement information

4. Application of models, and

dissemination of information:

!

Integrated use of models and data

Monitoring Models Satellite

Goal: operational system taking into account all

sources of information

ECMWFHIRLAM

RCR

HIRLAM

MBE

AROME,

LAPS

Weather prediction

models

Dispersion models -

long-range, regional

Dispersion and effects

models – urban, local

PALM

FINFLO,FLUENT(CFD),

DNS-code development

SILAM LRT,

meso-scale,

radioactivity , pollen

Modelling system - FMI

OSPM (NERI), street

canyon

UDM-FMI, urban

CAR-FMI, roadside

HILATAR

LRT, meso-scale

Aerosol process models:

UHMA (U Helsinki, FMI)

MONO32 (U Helsinki, Stadia)

SALSA(UH,UKU,FMI)

SILAM-APMs

MPP-FMI, Meteorological

pre-processing model

ESCAPE, chemical

accidents

BUOYANT, fires

EXPAND (FMI, YTV)

population exposure

Jalkanen, J.-P., L. Johansson, and J. Kukkonen, 2016. A comprehensive inventory

of ship traffic exhaust emissions in the European sea areas in 2011. Atmos. Chem.

Phys., 16, 71–84, 2016. http://www.atmos-chem-phys.net/16/71/2016/acp-16-71-

2016.pdf.

Modelled geographical distribution of CO2 emissions

from global shipping in 2015.

A new detailed dataset of the emissions of shipping globally

AROME

NWP modelHIRLAM-MBE

NWP model

Physiography,

forest mapping

Aerobiological

observations

SILAM now

Satellite

observations

Phenological

observations SILAM

CTM model

EVALUATION:

NRT model-measurement

comparison

Aerobiological

observations

Meteorological

data: ECMWF

Online AQ

monitoring

Phenological

models

Fire Assimilation

System

HIRLAM-RCR

NWP model

AQ

products

CLRTAP/EMEP

emission data

http://silam.fmi.fi

Satellite

observations

Globalboundary cond.

+own simulations

WRF

TVM

SILAM v.5: modules and capabilities

• Modules

• 8 chemical and physical transformation modules (6 open for operational use),

• 6 source terms (all open),

• 2 aerosol dynamics (one open)

• 3D- and 4D- Var

• Domains: from global to beta-meso scale (~1km resolution)

• Meteo input:

• ECMWF

• HIRLAM, AROME, HIRHAM, ECHAM, and any other who can write GRIB-1 or GRIB-2

• WRF

SOx

Acid-basic

CB4

Pollen

General PM

Radioactive

Passive

self-decayLong-lived

multi-media

Transformations

Area

Point

Nuclear bomb

Source types

Map of

species

masses

Em

iss

ion

Tra

ns

form

ati

on

Dynamics

Advection

diffusion

Aerosol

dynamics

Bio-VOC

Pollen

Sea salt

Simple

Basic

Transformation

Dry

Wet

Deposition

Initialization,

3D-Var adjust.

Control

Type: forward, adjoint, 4D-Var

European AQ forecast ( SO2, NO, NO2, CO, O3, PM10, PM2.5)

see also : http://silam.fmi.fi/

CAMS: Copernicus Atmosphere Monitoring service

( https://atmosphere.copernicus.eu/ )

+ Clearly largest

forecasting ensemble up

to date, for main gaseous

and PM pollutants

+ A concerted effort with

a better overall reliability

and versalitility

- Can still be improved:

mass closure of PM, non-

antropogenic PM

- Structure and

treatments of models are

variable (e.g. data

assimilation, evaluation)

Example: Global forecasts of aerosol concentrations - sulphates

Global AQ forecast ( SO2, NO, NO2, CO, O3, PM10, PM2.5)

see also : http://silam.fmi.fi/

Forest fires,

volcanoes,

etc…

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The other “extreme”:

Nested Multi-Scale System in the PALM

Large-Eddy Simulation Model

Practical example:

Helsinki City

Urban planning

29/11/2016 14

The Domain: 4 * 2 & 2*1 km

Resolution: 1m ; 0.1 s

29/11/2016 15

29/11/2016 16

29/11/2016 17

The options:

29/11/2016 18

Nested LES-modelling proved to be

a useful tool for City planning

Fusion of meteorological and air

quality information

Idea: to combine ALL available

information (models, measurements,

land use, traffic, population data..)

to achieve the ”optimal” view of the

state of environment

Background(we already have everything we need..)

• Availability of open access (environmental) data has improved in the past few years rapidly (see e.g . https://www.europeandataportal.eu/en)

• real time air quality measurements

• air quality modelling results (nowcasting/forcasting)

• meteorological measurements & model data

• land use data

• Detailed 3D data on urban areas

• OpenStreet maps

• BUT: e.g. in India & China, the situation could bemuch better, especially with the openess of data

29.11.2016 21

Background(..to make something useful out of it )

• we want to give a detailed & accurate view of current AirQuality esepcially at urban (=populated) areas

• and..

• we also want to forecast airquality (1-2 days ahead)

• Taking into account the huge increase in availabilityof crucial/useful data , we should be able to thismuch better than we used to do with ”traditional” 1st/2nd or even 3rd generation modelling methods..

• but.. HOW to do it ???

29.11.2016 22

2323

Regional AQ Forecasts

Emission

Inventories

(if available)

Meteorological DataAQ measurements

GIS-

Datasets

FMI-ENFUSER

• Information fusion using open access

and globally available datasets

• Hourly average NO2, PM2.5, PM10 and

O3

• Adapts to recent measured AQ data

• Can be calibrated and used without local

emission inventories

Operational modelling system

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FMI-ENFUSER(runs every 1-6h)

Information

extraction(every 10 minutes)

Calibration

update(1-4 times per year)

Model output(figures, animations,

gridded data, etc)

Web Portal

For selected cities in Finland FMI-ENFUSER produces high resolution

(15x15 m) AQ information

- Includes AQ forecasts for the next 12-48 hours

- Model data accessible through a Web Portal

NO2, PM2.5, PM10, O3, AQI

Amazon Web Service, HerokuApp, Dropbox

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The EnFuser (= our solution)

FMI-ENFUSER = (The Finnish Meteorological Institute’s ENvironmental

information FUsion SERvice)

Developed in projects : EU/PESCaDO, CLEEN MMEA, TEKES-INKA*, TEKES-CITYZER*, CLIMOB*

• Combination of statistical

modeling(LUR) and dispersion

modeling

• Dispersion modeling without

emission data (~inverse modeling)

• On the other hand, statistical

modeling using physically

realistic weighting factors based

on dispersion modelling

• model calibrated based on

measurements

• E.g. 100 000+ observation

(Helsinki area)Real meteorology taken into account in the statistical

modeling

26

Land use data

• OpenStreetMap (OSM)

• Land use, street network and buildings

• 5 x 5m, open global data

• (<= similar work done in SP!)

• Building are important for many reasons:

• E.g. proxy for PM2.5 source

• Combining emission surveys and OSM data

we can create a detailed small scale wood

Combustion emission mapping for

Helsinki area

• Turbulence /winds / street canyons

• The OSM-makes it possible to identify

the street canyon environments

-> realistic meteorological scenarios

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Most recent

measurments

Regional scale

concentrations

(SILAM)

1800 x 1200 gridpoints

– all the points have

gone through the

adjustment cycle

• Meteorological data (HIRLAM/HARMONIE) fromFMI’s Open Data portal

• SILAM regional AQ forecasts

• Long range transport of pollutants and boundaryconditions for ENFUSER

• Globally available

• Important for forecasting

• STEAM shipping emissions

• Global emission inventory

• Important for coastal locations

Synergy with other FMI models

29.11.2016 28

29

Approach Benefits

Due to calibration, information on emission sources not

absolutely necessary

- Known emission inventories can still be included

Latest sensor measurements & modelled data can be included

in the pool of information- Individual sensor quality (and measurement height) taken into account

- Weather forecast + regional background forecast => AQ forecasting possible

Example emission inventory: shipping

emissions (Rotterdam, FMI-STEAM)

can be included as ’puff emissions’

minute by minute.

Industrial emissions can also be

included with the same methodology.

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Built to estimate

seasonal averages

based on Land-

Use-Regression

Built to estimate

hourly concentration

in HMA based on

Dispersion

modelling

Built to estimate

hourly concentration

in Finland, based on

Dispersion and LUR

modelling with

fusion

Performance evaluation

Root mean squared error for estimated seasonal NO2 concentrations

Online /real-time evaluaton

29.11.2016 31

Integrated solution offered together with a sensor network

• High resolution assessment of air quality in an area ratherthan a collection of point observations

Tool for administrative users

• Warning system and pre-emptive decision making

Indoor air quality management

• Optimization of air intake control using ENFUSER’s forecastsand 3D air quality data

Mobile applications with GPS tracking

• Assessment of personal pollutant exposure on a minute-by-minute basis

High resolution annual pollutant concentration maps for cities

• Assessment of annual average air quality right outside of yourhome

Planned practical applicatios

29.11.2016 32

A demonstration in Langfang, China, utilizing 5 installed PM2.5 sensors.

Continues with more sensors (3x) and regional AQ model data being included.

Also, EnFuser will be demonstrated(2016 ) in Delhi, India as a part of CLIMOB project.

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Demonstration in Asia

Figure: A closer look at the hourly PM2.5 concentration in Langfang,

given by FMI-ENFUSER visualization toolbox.

An example of estimated PM2.5 concentration time

series based on the sensor data in Langfang. The

selected example location is just outside of Silver City

Hotel, Langfang

• Johansson, L., Epitropou, V., Karatzas, K., Karppinen, K., Wanner, L., Vrochidis, S., Bassoukos, A., Kukkonen, J. and Kompatsiaris I. Fusion of meteorological and air quality data extracted from the web for personalized environmental information services. Environmental Modelling & Software, Elsevier, Volume 64, February 2015, Pages 143–155, 2014

Conclusions

29.11.2016 34

We are ready to…

utilize the data form

dense urban

measurement São

Paulo!

for Real time , high-

resolution AQ-mapping

+ forecasts

Huge potential

• The methodology would bridge the gap between modeling and the measurements especially in difficult environments like megacities (= like São Paulo)

• Basic requirements :

• dense measurement

network : good coverage

of all relevant environments

• Supporting information

• available:

• land use, traffic,

• population density

Thank You for Your

attention !