Chemical Transport Modeling

Chemical Transport Modeling

Peter J. Adams Center for Atmospheric Particle Studies (CAPS)

Civil and Environmental Engineering

Engineering and Public Policy

Carnegie Mellon University

University of Pittsburgh

4 April 2011

Slide credits: Cliff Davidson, Neil Donahue, Yunha Lee, Spyros

Pandis, Jeff Pierce, Rob Pinder, Win Trivitayanurak, Knut von

Salzen

Outline

• 1) Chemical transport models (CTMs) • Overview: formulation and solution

• 2) Processes:

• 3) Sample Applications

• 4) How models fail • How to be an intelligent user of models

• …and consumer of model output

Chemical Transport Models: CTMs

• Overview

• Complex, 3D computer simulations of air quality

• Goal is to predict pollutant concentrations,

ci(x,y,z,t):

• For various species i: typically ~100

• At many locations x,y,z: typically ~104-105

• As a function of time t: day, week, year+

• Given

• Emissions

• Meteorological conditions

• Model description of relevant chemical & physical

processes

Processes To Consider

• Emissions

• Atmospheric Transformations (“chemistry”)

• Chemical production / loss

• Physical transformations: phase changes, particle

growth, etc

• Transport

• Vertical dilution (stability, mixing height, etc)

• Horizontal transport

• Deposition

• Wet deposition

• Dry deposition

East – West (x)

Ver

tica

l Hei

ght

(z)

Grid Set-up:

A + B C

Advection

.(u )iciRReaction

Emission, Ei Deposition, Di

CTMs: Overview

• Divide atmosphere into 3D grid of locations

• Each grid box has a mass balance (“continuity” equation)

• Use rate of change (dci/dt) to step forward in time

Each Box: Atmospheric Processes

Emissions (E) Deposition (D)

Flow Out (Fout) Flow In (Fin) Production - Loss

(P – L)

Mass Balance

Equation:

Mi = Mass of a given chemical species in a given model box

(kg/s)

How CTMs Work

• Inside each grid cell is a (familiar) mass balance for each species i:

• Solution procedure: • Start at t=0

• Calculate burdens at t= t (short time later)

• Repeat for many t’s until time period of interest is done

• Typical time steps, t, are 10 min to 1 hour

• For global model, t = 1 hour so mass balance equation solved:

(8760 box-1 yr-1)(30,000 boxes)(240 species) = 6 x 1010 yr-1

iioutiiniiii LPFFDE

dt

dM,,

tLPFFDEMM iioutiiniiioifi ,,,,

Sample Output: Ozone

Sample Output: Carbon Monoxide

Emission Factors

• Emissions typically proportional to amount of “activity” that generates them:

E = (Emission Factor) x (Activity Level)

• All important details are buried in emission factor

• lots of work required to measure/estimate them

• Significant remaining challenges

• Volatility (and therefore evaporation) of primary organic particles

• Size distribution of primary particles, especially in ultrafine mode

• Widespread, highly variable “area” sources (e.g. ammonia, biogenic VOCs)

• Some emissions are “online” vs “offline”

• Offline: pre-calculated input data fed into model (e.g. vehicles)

• Online: emissions rate depends on meteorological conditions (e.g. isoprene, wind-driven sea spray and mineral dust)

• Emissions are nearly always a significant source of uncertainty in any CTM modeling study

Emissions: Broad Categories

Natural Anthropogenic

Point Volcano (SO2) Power plant smokestack

(SO2)

Line VOCs from cars on

highway

Area VOCs from

vegetation

NH3 from livestock on

farm

more uncertain

less uncertain

Meteorological Data

• Daily / seasonal variations in pollutant

concentrations largely a result of meteorological

variability

• Different CTMs (with same emissions,

chemistry, etc) can produce very different

predictions based on their meteorology

Meteorological Data

• “Offline” met data (assimilated meteorology)

• CTM model is (pure) CTM-only

• Met fields are imported from a separate meteorological model (even “historical” meteorology is still a model simulation)

• Input data files are pretty large

• Precludes any chemistry-climate interactions (e.g. aerosol forcing affecting meteorology)

• No computer time spent on meteorological simulation (…often small compared to CTM calculations anyway)

• Often met fields have been evaluated more extensively

• “Online” met data (predicted meteorology)

• CTM model is part of a larger “host” model that includes meteorology

• Host model may be GCM (global climate model) or regional meteorology model (e.g. MM5, WRF)

• No need for meteorological input data files

• Allows chemistry-climate feedbacks (e.g. aerosol cloud lifetime effect)

• If your chemistry feeds back on climate, need to evaluate perturbed met fields as well as your chemistry simulation

Meteorological Data

• Historical reanalysis data (most common offline met data) • Best attempt at met fields that match some specific time period in the past

• Useful for comparison against observations, especially short-term observations (e.g. field campaign data)

• Bear in mind that reanalysis data is part observations and part model

• Assimilation (aka “nudging”) • Process by which reanalysis data is generated

• Run climate or meteorological model for historical time period

• At each time step, available observations are imported to model by “nudging”

• Nudging = changing model values to be closer to observations

• Nudging is complex art, best to “outsource” this activity to meteorologists

• Not all reanalysis parameters are equally good • Geopotential fields (pressure distributions) are always nudged → resulting

large-scale wind patterns tend to be quite good

• Other variables (clouds, precipitation) are usually not nudged (problematic to do so)

• → still depend on meteorological model’s clouds and precipitation schemes (weak points in any met model)

• → reanalysis data often rains at the wrong time

• Pays to have meteorological expertise and well evaluated wind fields

Transport Algorithms

• Advection algorithms are conceptually simple

but numerically complex

x (location)

x i-th grid cell

Ci Wind speed, u

Advection Algorithms

x

x

i-th grid cell

Ci

x

x

Wind speed, u

Initial state

(t=0)

Later state,

exact (t= t)

Later state,

remapped

(t= t)

u t

Remapped to

conserve mass New Ci New Ci+1


x

Ci

x

x

Wind speed, u

Initial state

(t=0)

Later state,

exact (t=3 t)

Later state,

remapped

(t=3 t)

• Repeated remapping leads to “numerical diffusion”

• Consider 3 time steps at 3u t= x

Ci

Piecewise Parabolic Algorithm

x

i

Initial state

(t=0)

i-1 i+1 For grid box i

1. Look at adjacent boxes

2. Infer probable concentration gradient within box i

3. Fit with parabola

4. Advect parabola

x

i

x

i i-1 i+1

Ci Ci+1

Ci-1


• Goal of advection algorithm is to minimize

numerical diffusion

• Results from

• Too much remapping because…

• Ci (species conc.) assumed constant within a grid cell

• Common schemes allow ci to vary within a grid

cell

• Piecewise parabolic method

• Lin and Rood, 1996

• Moment-based methods

• Prather, 1986

• Numerical method usually not significant source

of error/uncertainty

Model For Deposition Velocity

A. Surface Layer

-turbulent eddy transport

B. Quasilaminar Sublayer

-Diffusion

-Inertial impaction

C. Interactions with the Surface

D. Gravitational Transport

-Settling velocity

Surface Layer

Quasi-laminar

Sublayer

Surface

Reference height

grcrbr(z)ar(z)tr

(z)d

v111

Dry Deposition: General Observations

• Ultrafine mode

• Diffusion gives higher vd than accumulation mode

• But coagulation tends to dominate losses under most

conditions

• Accumulation mode • Minimal deposition velocity

(timescale is ~months)

• Wet deposition matters more

• Coarse mode • Dry deposition is dominant

removal (timescale ~1 day)

• Strongly size-dependent → useful to track several size categories to account for differing vd

Depositio

n V

elo

city

Particle Diameter

Acc. Ultra

-fine Coarse

Wet Deposition

• Dominant removal for fine particulate matter

• Depends on meteorological conditions

• Predicting clouds and precipitation is a weak point in meteorological forecast and climate models • Different global models estimate

atmospheric lifetime of fine PM between ~4 and ~8 days

• Not uncommon for model to have rain at wrong time

Chemistry

• Typical ozone photochemical mechanism has >50

species

• VOCs are lumped into categories based on reactivity

• Coupled differential equations, especially through

radical species (e.g. HOx)

• Equations are “stiff” (wide range of timescales from very

short-lived to long-lived species)

• Distinction between “species” and “tracers”

• Species: everything that participates in chemistry (e.g. OH, O3,

VOCs, CO, etc)

• Tracer: sufficiently long-lived to be transported from cell to cell

(OH excluded)

• Numerics typically done with “GEAR” solvers

Aerosol Thermodynamic Modeling Approaches

• Bulk Equilibrium

CONS: Loses size information, timescales, mixing of particles

PROS: Fast, reliable; can be merged with size-resolved models given simple assumptions

• Size-resolved equilibrium (e.g. Pilinis and Seinfeld, 1987; Jacbson, 1999)

CONS: Timescales, infinite solutions, convergence problems

PROS: Average speed, size information

• Hybrid (Koo et al., 2003)

CONS: Some mixing of particles, more expensive than equilibrium

PROS: Relative fast, reliable, some size information

• Full dynamics (e.g. Pilinis et al., 2000)

CONS: Extremely stiff close to equilibrium, computationally intensive

PROS: Rigorous, size information

Particle Size Distributions

• For particulate matter, size governs • Penetration into lungs

• Deposition from atmosphere

• Visibility

• Climate impacts (e.g. on clouds)

Particle

diameter (Dp)

Number of

particles

Numerical Algorithms: Archetypes

Sectional (fixed or moving): n(Dpi)

Modal: (Ni, Dpgi, i) Moment-based: N, S, M

? (any distribution

consistent with number, surface area, mass)

• CTMs can treat particle size in different ways

Bulk: mass only

No representation of particle size; can be ok

for predicting PM2.5

Tips: Learning a Model

• Codes are long, complex (50,000+ lines)

• Reading code not very useful

• Documentation better

• User manual (if it exists)

• Papers (info spread across many as model develops

over time)

• People (model how to as an “oral culture”)

• Balance “reading about” with “learning by

doing”

• Reproduce past results

• “play” with model by changing emissions,

parameters

Modeling Tips: Budget Diagnostics

• One great benefit of CTMs: a world with perfect

information (no measurement limitations)

• In principle, should always be able to say why something

happened in the model

• In practice, storage constraints limit model predictions that are

output

• Minimum output (“diagnostics”) are c(x,y,z,t)

• Budget diagnostics are powerful, often overlooked

• Rates of sources and sinks (production and loss, Pi and Li)

• At steady state:

• Any change in model burden can be analyzed as

follows:

ii LPiL

M

iPM

ii PPM

(note it is changes in lifetime rather than changes in loss rates per se)

= lifetime

M = burden/mass

≈ source effect + sink effect

Model Skill

• How good are models?

• Depends…

• Do we understand physics / chemistry?

• Do we know the relevant emissions?

• So model skill / reliability will depend a lot on

what species you are doing, where, etc

• Following slides are example (Karydis et al.

2007)

• PMCAMx CTM

• July 2001

• Various components of PM2.5

Model Skill

Sulfate PM2.5

• Sulfate is best case scenario

• Chemistry (SO2→sulfate) is

understood

• Emissions are predominantly

from well monitored point

sources (power plants with

CEMS)

• Errors here largely result of

errors in model meteorology

• Bias: 5-10%; Error: 50-60%

Model Skill

Nitrate PM2.5

• Nitrate is tough

• Chemistry (NOx→nitrate) is

understood

• But hypersensitive to other

errors

• Emissions are mix of point

sources (power plants) with

automobiles

• Measurements are problematic

• Bias: 50-60%; Error: ~90%

• (better in winter when nitrate is

more abundant)

Model Skill

Organic PM2.5

• Organics also tough

• Chemistry is poorly understood

• Emissions are predominantly

area sources & wide variety

• Errors here largely result of

errors in model meteorology

• Bias: ~30%; Error: 50-60%

Model Evaluation: CN10

•Captures variation in CN10 over two orders of

magnitude

•Average error (LMNE) is a factor of 2

How Models Fail

• Poorly understood physics / chemistry

• Examples: new particle formation, secondary

organic PM

• A collective failure

• Science understood but system is complex

• Examples: clouds

• Mostly a problem of horizontal (or time) resolution of

model

• Bad input data

• Emissions (e.g. sea spray, mineral dust)

• Meteorology and deposition (e.g. everything)

How Models Fail: Deposition

• COSAM: intercomparison of global sulfur models

• Same emissions, but models differ by factor of 2 mostly due to meteorology and deposition

Barrie et al., 2001

Objectives of CTM Modeling

Most CTM modeling achieves one of 4 objectives:

1) Hypothesis testing

→does a newly discovered or hypothesized process make a difference in realistic atmospheric context?

2) Inferring unknown parameters (inverse modeling)

→what (uncertain) inputs are consistent with available observations (model outputs)?

3) Testing completeness of our knowledge

→can we predict observed behavior based on what we know?

4) Policy decision-making

→what will be effect of a specific emissions control?

Primary vs Secondary Particles

Sensitivity of

CCN to secondary avg. = 11% increase

Sensitivity of

CCN to primary avg. = 27% increase

• Sensitivity of cloud-active particles (CCN) to uncertainties in primary (emitted) and secondary (nucleated) particles

• Indicates need to prioritize measurements of ultrafine particles from combustion sources

Ternary

Binary

PE

PE/3

CTMs: Inverse Modeling

Arellano, A.F., P.S. Kasibhatla, L. Giglio, G.R. van der Werf, J.T. Randerson, and G.J. Collatz, Time-dependent

inversion estimates of global biomass-burning CO emissions using Measurement of Pollution in the Troposphere

(MOPITT) measurements, JGR, 111 (D9), 2006.

Chemical Transport

Model Find CO

emissions from

these regions

…consistent with

MOPITT CO

measurements

Careful: confounding or unknown factors

CTMs: Testing Our Knowledge

Selin et al., JGR, 2007

Observations

Model Base Case

Model Sensitivity Cases (Blue/Green)

Arctic:

Obs: Large springtime depletion

Model: ~constant

→missing halogen chemistry?

NH Midlatitudes:

Obs: Modest late summer depletion

Model: reproduces well

→OH/O3 photooxidation working ok

Organic PM: Testing Our Knowledge

“Urban” “Urban

Downwind”

“Rural

Remote”

•HOA ≈ fresh vehicle exhaust

•OOA: complex, but largely from atmospheric

oxidation of VOCs

•OOA fraction is high, even in urban areas

•Chemical production of organic material more

important than previously recognized

(Zhang et al.,

GRL 2007)

Organic PM: Model Improvements

• Limitations of earlier models • Treated primary organic particles (e.g. vehicle

exhaust) as non-volatile

• Limited chemistry

• New processes • All organics PM is semi-volatile (condense &

evaporate between gas & particle phases)

• Secondary organic PM from intermediate VOCs

• New observations: volatility, oxygenation,

isotopic


• Updates to global OA

• semi-volatile POA

• IVOCs and their

chemistry…

• Predicted OA (left) is

not better than

traditional model

• Met fields / deposition

always going to give

factor of ~2 or so

• Probable tuning in

previous model


• Shantanu’s updates give obvious improvement in OOA/OA ratios (and volatility distribution)

• You need an indicator that is specific to / sensitive to your process of interest (usually not aerosol mass)

Emissions Control Strategies: Ammonia and PM

Source: CMU Ammonia Inventory (2003)

Livestock, 54%

Fertilizer, 25%

Vehicles, 6%

Industrial, 2%

Domestics, 8%

Wild animals, 4%Wildfire, 1%

Misc., 1%

Nationally, agricultural

operations (green) are

the largest sources

Besides contribution to

PM2.5, ammonia is

problematic because:

•Odor

•Eutrophication

•Soil acidification

Emissions Control Policy

• EPA has pursued SO2/NOx cuts from vehicles

and power plants

• Ammonia controls have been unattractive

because

• Uncertainties in emissions overall

• Area sources are more difficult to quantify / enforce

• PM2.5 only sensitive to ammonia under some

conditions (cold, free ammonia)

• Control technologies more diverse and untested

SO4-NO3-NH3 (iPM2.5) Thermodynamics

SO42- (p) HNO3 (g)

NH3 (g) NH4+ (p) NH3 (free)

2 x SO42-

> Keqm(T,RH)?

NH4NO3 (p)

Sulfate-Nitrate “Substitution”

Sulfate aerosol ↓

Free ammonia ↑

Nitrate aerosol ↑

Net aerosol mass ~

Cold T

favors

nitrate

formation

Ammonia is an “enabler” for nitrate PM

formation

NH3 and Fine Inorganic Particulate Matter (iPM2.5)

Source: West et al. 1999 Marginal PM2.5: Nonlinear

Aerosol Mass Response to Sulfate Reductions in the

Eastern United States JAWMA 49

Sulfate-

nitrate

substitution

regime

National Inventory

Counties.shp0

< 4040 - 120120 - 250

250 - 500500 - 1500

kg NH3 km-2

PMCAMx Chemical Transport Model

• 36 km x 36 km

• 97 cells (E-W) x 90 cells (N-S)

• CBM-IV gas phase chemistry

• CMU aerosol modules

• 14 layers (summer) 16 layers (winter)

Simulations

• January / July: two weeks each

• Every combination of

• SO2 / NOx / NH3 reductions

• 10%, 20%, 30%, 40%, 50%

• 125 scenarios total

Effectiveness: iPM2.5 Sensitivity %

Reduction

in iP

M2.5

January July 50

40

10

30

20

50%

NH3

50%

NOx

50%

SO2

50%

NH3

50%

NOx

50%

SO2

∆iPM2.5 for 10% NH3 Emission Reduction (Jan)

CTMs: Decision-Making Tools

Pinder, R.W., P.J. Adams, and S.N. Pandis, Ammonia emission controls as a cost-

effective strategy for reducing atmospheric particulate matter in the eastern

United States, Environmental Science & Technology, 41 (2), 380-386,

2007.

• Inorganic PM2.5 sensitive to ammonia

emissions in wintertime

• Relatively insensitive to SO2 controls

during winter (sulfate-nitrate

substitution)

• Ammonia emissions reductions

appear to be cost-effective strategy

for PM2.5

Objectives of CTM Modeling

Most CTM modeling achieves one of 4 objectives:

1) Hypothesis testing

→does a newly discovered or hypothesized process make a difference in realistic atmospheric context?

2) Inferring unknown parameters (inverse modeling)

→what (uncertain) inputs are consistent with available observations (model outputs)?

3) Testing completeness of our knowledge

→can we predict observed behavior based on what we know?

4) Policy decision-making

→what will be effect of a specific emissions control?

“Meta-research”: real problems solved with experiments and measurements; models about setting priorities and testing completeness

Large uncertainties and

gaps in knowledge

Well understood system

Summary

• Primary strength of CTMs: Our major attempt at being comprehensive • Most ambient and experimental work is necessarily focused on a given

location or process, mostly in isolation

• CTMs attempt to treat all processes (in theory) and most (in practice) in some detail

• Interactions/competitions between processes can be examined

• Integrate across different atmospheric conditions/regimes (in time and space)

• Building an aerosol model requires tradeoffs • Probably best to have a clear application: e.g. CCN or PM2.5

• Difficult to build a model that is all things to all people and all applications

• “Benchmark” models (comprehensive, accurate) versus “engineering” models (fast, used for production runs)

• Important to know model strengths/weaknesses • Inorganic chemistry and US SO2 emissions well understood → PM sulfate

well predicted

• SOA chemistry poorly understood (a problem not unique to modelers) → models more useful for “what if” rather than prediction

Chemical Transport Modeling

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