Integrated Environmental Health Impact Assessment of Air Pollu9on - - PowerPoint PPT Presentation

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Integrated Environmental Health Impact Assessment of Air Pollu9on - - PowerPoint PPT Presentation

Mar 11, 2023 164 likes β€’583 views

Assessing Popula.on Exposure with Air Quality Modelling augus.n.cole;e@ineris.fr French Na.onal Ins.tute for Industrial Environment and Risks Integrated Environmental Health Impact Assessment of Air Pollu9on and Climate Change in Mediterranean

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Assessing Popula.on Exposure with Air Quality Modelling augus.n.cole;e@ineris.fr

French Na.onal Ins.tute for Industrial Environment and Risks

Integrated Environmental Health Impact Assessment

  • f Air Pollu9on and Climate Change in Mediterranean Areas

Interna.onal Centre for Theore.cal Physics, Trieste, Italy 23-27 April, 2018

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Accidental Risk = f(hazard , probability) Chronic Risk = f(toxicity , exposure)

Risk Assessment

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Integrated Assessment

Human Ac.vity Air Pollutant Emissions Atmospheric Concentra.ons Exposure to Air Pollu.on Health Impacts

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Exposure to ambient air pollu.on

Scales

Spa.al

  • Individual
  • Urban
  • Country
  • Con.nent
  • Global

Temporal

  • Day to day
  • Annual
  • Life.me

Tools

Observa.ons

  • Ci.zen monitors
  • Regulatory network
  • Satellites

Algorithm

  • Air quality models
  • Land use Regression
  • Data Assimila.on
  • Data fusion
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SLIDE 6

Air Quality Modelling

Chemistry-Transport (determinis9c) Geosta9s9cal regressions (sta9s9cs) Pros

  • More physical
  • Sensi.vity to changing condi.ons
  • Well fi;ed / calibrated

Cons

  • Complex
  • Prone to model biases
  • Lower sensi.vity to changes

Of noteworthy importance for air quality:

  • Non-linear chemistry, produc.on of secondary species (O3, PM)
  • Long range transport of air pollutants
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SLIDE 7

Chemistry-Transport Modelling

Global Chemistry Regional Meteorology Emissions of Trace species

IPSL/CEA

Regional Chemistry Transport

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Regional Chemistry Transport model: what’s inside ?

  • The physics & chemistry

β€‹πœ–π‘‘/πœ–π‘’ =𝒗𝛼𝑑+π‘„π‘ π‘π‘’π‘£π‘‘π‘’π‘—π‘π‘œβˆ’π‘€π‘π‘‘π‘‘

advec.on, diffusion chemistry, emission, deposi.on

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Regional Chemistry Transport model: what’s inside ?

  • Transport

– Advec.on (laminar flow) – Mixing / Turbulence

  • Planetary boundary layer
  • Large scale convec.on
  • Deposi.on

– Dry:

  • air/surface interac.on at the ground, role of

vegeta.on and subsequent impacts

– Wet:

  • scavenging of hydrophilic species (gas or aerosols)
  • In cloud (inc. fog), or in precipita.on (removal)
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Regional Chemistry Transport model: what’s inside ?

  • Chemistry

– Gas-phase

  • ~100-300 species / reac.ons

– Aerosols

  • Chemistry: ~5-50 species / reac.ons
  • Microphysics: Nuclea.on, Coagula.on,

Condensa.on

  • Organics, Inorganics (sulphate, nitrate,

ammonium), Naturals (ash, dust, sea salts)

– Heterogeneous chemistry

  • Photochemistry

– Solar irradiance (role of clouds)

OH O O O O TLBIPERO2 +NO OH O O O TLBIPERO OH O O O N + O
  • O
TLBIPERNO3 0.9 0.1 0.2 0.2 OH O O O TLOBIPEROH OH O O OH TLBIPER2OH 0.6 +RO2 +HO2 OH O O O OH TLBIPEROOH O O C5DICARB +NO3 O O O TLEPOXMUC O O O O O TLEMUCCO3 0.2 +NO3 +OH 0.3 O O O O C615CO2O2 hn +NO
  • r
+NO3 +HO2 O O O O H TLEMUCCO2H O O O O O H TLEMUCCO3H O O O O O2NO TLEMUCPAN + NO2 +OH +RO2 0.7 0.3 hΞ½ + OH or hΞ½ O H O O C615CO2OH O O O OH C615CO2OOH + H O 2 + OH or hΞ½ +NO or +NO3 +RO2 0.2 0.6 hn hn +OH
  • G. Lanzafame
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Regional Chemistry Transport model: what’s inside ?

  • Chemistry

– Al.tude dependance

  • Planetary Boundary Layer
  • Tropospheric
  • Stratospheric

– Surface dependance

  • Urban
  • Snow
  • Forests
  • Deserts
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Regional Chemistry Transport model: the engine

  • Many available tools:

– A few regional CTMs : CMAQ, CAMx, EMEP, CHIMERE, LOTOS, WRF- CHEM, Polair3D, MOCAGE, MATCH, SILAM, …

  • ~50,000 lines of numerical code (fortran, c++, python)
  • Runs on high performance computers (100-5000 CPUs)
  • A specificity of CTMs: large amount of i/o
  • Run.me

– Assessment:

  • Europe low-res (50km): 1yr simulated in 1 day / 100 CPUs

– Forecast:

  • Europe high-res (10km): 5 days simulated in 3 hrs / 300 CPUs
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Chemistry-Transport Modelling

Global Chemistry Regional Meteorology Emissions of Trace species

IPSL/CEA

Regional Chemistry Transport

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  • Meteorology

– Prognos.c:

  • u, v, t, q, P

– Diagnos.c:

  • u* Surface fric.on
  • PBL depth
  • Turbulent mixing
  • Precipita.on
  • Solar irradiance
  • Temporal scale

– Day to day forecast – Annual assessment – Decadal/Century (Climate)

  • Sources

– Opera.onal weather centres (NCEP, ECMWF) – In-house (e.g. open source WRF) – Climate projec.ons (IPCC)

Regional Chemistry Transport model: input data METEOROLOGY

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  • Global Chemistry

– Specific need for regional/local air quality model – Large scale inflow

  • Intercon.nental pollu.on plumes
  • Desert dusts
  • Stratospheric intrusions
  • Temporal scale

– Day to day (ex: plumes) – Monthly averages

  • Sources

– Opera.onal /Research Centres (NCAR, ECMWF) – Climate (ACCMIP, CCMI)

Regional Chemistry Transport model: input data GLOBAL CHEMISTRY

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  • Natural processes

– Desert dust:

  • Landuse maps + erosion

– Volcanoes

  • Con.nuous & sporadic

– Biogenic VOCs

  • Ecosystem models

– Pollens

  • Ecosystem models

– Wildfires

  • Sporadic loca.on & intensity

Regional Chemistry Transport model: input data: EMISSIONS

Wildfires (Turquety et al.) Desert Dusts (Menut et al.)

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  • Anthropogenic ac.vi.es (Β« pollu.on Β»)

– SOx, NOx, COV, primary PM, NH3, CO, CH4 – Industry, Residen.al, Traffic, Agriculture, Waste, Shipping, Aircrars

  • Spa.alisa.on

– Emission fluxes generaly provided as country totals – Spa.lized using proxies:

  • Popula.on
  • Traffic
  • Large point sources

Regional Chemistry Transport model: input data: EMISSIONS

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  • Sources

– Databases of officially reported fluxes

  • Ac.vity data
  • Emission factors

– Inversion (satellite + models)

  • Observa.onally constrained
  • Useful to benchmark reported fluxes
  • Not linked to ac.vity

– Long term projec.ons

  • Policy targets
  • Technology
  • Macro-economics

Regional Chemistry Transport model: input data: EMISSIONS

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Chemistry-Transport Modelling

Global Chemistry Regional Meteorology Emissions of Trace species

IPSL/CEA

Regional Chemistry Transport

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Nitrogen oxides (NOx) have a short life.me and are thus located close to the main emission sources

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Ozone (O3) is found over much larger areas because of its longer life.me

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Desert dust are present in the natural

  • atmosphere. The source is so massive that it

can also remain in the atmosphere over long distances

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Volcanic erup.on cons.tute a massive source of ash, or here sulphur dioxide (SO2).

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Anthropogenic fine par.culate ma;er (PM2.5) are today the main threat to human health

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Models and Observa.ons

  • Valida.on
  • Assimila.on
  • Fusion
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Model valida.on

  • Comparing observa.ons to models interpolated in

.me & space

  • Typology of observa.ons

– Surface: Regulatory AQ networks (Note: low cost sensors not yet mature enough for valida.on) – Profiles: balloon sounding, aircrars, lidar – 3D: satellite

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Model valida.on

  • Variety of sta.s.cal indicators

– e.g. fairmode.jrc.ec.europa.eu

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Model valida.on

Sca;erplots Soccer plots Taylor plots Target plots Log/log sca;erplots Score dashboard

Fairmode/JRC

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Model valida.on

Instantaneous comparison of AOD long term average to minimise cloud effect New perspec.ves: TropOMI

Sen.nel 5P launched 2017

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Data Assimila.on

  • Feeding the model online with observa.ons (in situ, satellite…)
  • Various approaches :

– Ensemble (Kalman Filter) – Varia.onnal (3D-Var, 4D-Var):

  • need for a deriva.on of the model

Bocquet et al., ACP 2015

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Data fusion

  • Correct the model offline

(postprocessing) with observa.ons

  • Op.mal interpola.on: Geosta.s.cs

(kriging) using a combina.on of

– Model – In situ – Satellite

Von Donkelaar, EST, 2016

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Sta.s.cal adapta.on

  • Weather Forecast
  • Emissions
  • Landuse
  • Boundary Condi9ons

AQ AQ Mod Model el Sta tati tisti tical FC FC Hy Hybrid rid FC FC Statistical forecast at stations Combining point statistical forecast to 2D deterministic model with geostastical krigging D+1 D+1 D+0 D+1 Deterministic forecast

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Model use cases

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Forecasts: Copernicus Atmospheric Monitoring Service

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Emergency Support

EyjawallajΓΆkull volcanic erup.on, Iceland, 2010

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Emergency Support

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Lubrizol, industrial mercaptan leak, 2013

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Assessment: long term exposure

European Environment Agency, 2017 AQ Report PM2.5 Ozone

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Wrap-Up

  • Determinis.c air quality models:

– Complex numerical tools – Prone to biases

  • require valida.on / data fusion / assimila.on
  • Why using air quality models to assess exposure?
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Integrated Assessment

Human Ac.vity Air Pollutant Emissions Atmospheric Concentra.ons Exposure to Air Pollu.on Health Impacts

Chemistry Transport Models: Β§ Sensi.vity to changes Β§ Secondary Pollutant Forma.on Β§ Long Range Transport

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INERIS Youtube Channel https://youtu.be/xuUsEOL0Lj8