Second Workshop on Regional Climate Modeling and Extreme

Events over South America"Modeling Air Quality in São Paulo

with WRF-CHEMEmission Processing and Modeling

Rita YnoueLAPAt (Laboratório de Análise dos Processos Atmosféricos)

DCA – IAG – USP Nov 8th 2018

Departamento de Ciências Atmosféricas

LAPAt – Laboratório de Análises dos Processos Atmosféricos

Website: http://www.lapat.iag.usp.br/Maria de Fátima Andrade (maria.andrade@iag.usp.br)

Fabio L. T. Gonçalves (fabio.goncalves@iag.usp.br)

Adalgiza Fornaro (adalgiza.fornaro@iag.usp.br)

Rita Yuri Ynoue (rita.ynoue@iag.usp.br)

O grupo estuda os processos associados à poluição atmosférica, com destaque para estudos da região metropolitana de São Paulo. Os projetos

concentram-se nas seguintes áreas:➢ Caracterização física e química de aerossóis atmosféricos; Caracterização química de deposição úmida (águas de chuva, nevoeiro).

➢ Gases atmosféricos: hidrocarbonetos (C2-C11), etanol, carbonilas, amônia, metano, dióxido de carbono, etc

➢Modelagem de remoção de matéria particulada e gases: deposição úmida e seca.

➢Modelagem da dispersão e formação de poluentes atmosféricos (smog fotoquímico).

➢ Quantificação e classificação de bioaerossóis.

Micro-Orifice Uniform

Deposit Impactor (MOUDI)

Dichotomous Sequential Air Sampler,

Partisol®-Plus 2025-D (www.thermo.com)MiniVol

Composição elementar e iônica do material particulado

Sondagens de ozônio

EDX (Energy Dispersive X-ray Fluorescence)Cromatografia iônica

CG-FID (Cromatografia gasosa )

Hidrocarbonetos (C2-C11)

Personal cascate impactor (PCIS)Amostrador automático de

águas de chuva

Experimentos em túneis

Modelagem da qualidade

do ar:

Emissão, dispersão e

formação de poluentes

Emissão de NOx

Bruna Segalin, doutoranda

Sergio Ibarra, doutorando

Atmospheric emissions, transport, transformation, and deposition of trace gases

(Source: Aneja et al., 2003, 2006b)

Good emissions estimates are the start

of any environmental assessment

Emissions: natural and anthrop. sources

Calculating emissions

• Emissions = Emission Factor x activity data• Emission Factors: mass of polutant produced per

unit of activity– Vary by pollutant, equipment/source– Found in models, national tables, source tests

• Example: How much CO does a light duty vehicleemit in one day?

• Emission fator (MOBILE)=2g of CO/km• Activity level (Transportation Agency)=30 km/day• Emission = 60 g CO/day

Alicia Edwards & Clint Farr-ADEQ-Air non point & mobile source program

Brazilian Environmental ProtectionAgency

• IBAMA (Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis): Federal Environmental Protection Agency

• CETESB (Companhia Ambiental do Estado de São Paulo): Local EPA

CETESB, 2016

CETESB´s emissions inventory

CETESB, 2016

Emissions Processing toAir Quality Models

Air quality models needs good quality emission input, which should...

describe all (anthropogenic) sourcesbe consistent in time and spaceprovide the best (scientific) estimate for the emission

Top-down x Bottom-up approaches

https://vito.be/sites/vito.be/files/styles/image_full_width/public/media/land_s8_topic-air_emission_inventorying-2-eng.jpg?itok=DqGIlrN1

Emissions Terminology

• Inventory: estimate of pollutant emissions at a given spatial unit

• Model Grid: 3-d representation of the earths surface based on discrete and uniform spatial units, i.e. grid cells

• Speciation: conversion of inventory pollutant species to model pollutant species

• Gridding: conversion of inventory spatial units to model grid cells

• Temporalization: conversion of inventory temporal units to those requires by an air quality model

Zac Adelman and Craig MattocksCarolina Environmental Program

Emissions Processing

Area Mobile Point Biogenic

Emissions Processing Steps

AQM-ready Emissions:

Zac Adelman and Craig MattocksCarolina Environmental Program

Emissions Processing

• Purpose: convert emissions data to formats required by air quality model

• Primary functions– Import data into system

– Spatial allocation (gridding)

– Chemical allocation (speciation)

– Temporal allocation

– Merge

– Quality assurance

Zac Adelman and Craig MattocksCarolina Environmental Program

Emissions Processing Steps

• Data Import– Inventory categories

• Area

• Point

• Mobile

• Biogenic

• Gridded

– ASCII or gridded binary

– Country/state/county estimates

– Annual estimates

– Pollutants include bulk VOC and PM2.5

• Spatial Allocation– Inventory spatial units →model grid

– Requires spatial surrogates

Zac Adelman and Craig MattocksCarolina Environmental Program

Emissions Processing Steps

• Chemical Allocation– Tons →Moles

– Converts inventory pollutants to air quality model species

– Model-dependent speciation profiles

– NOx → NO + NO2

– VOC → PAR, OLE, etc.

– PM2.5→ NO3, SO4, etc.

• Temporal allocation– Inventory units → hourly

emissions

– Requires temporal profiles

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DiurnalZac Adelman and Craig MattocksCarolina Environmental Program

Available gridded emissions database

Air quality forecasting withWRF-CHEM in MASP

WRF-CHEM in MASP

• Academic and Operational simulations• GFS (FNL) 1º x 1º analysis and forecasts for

meteorological IC and BC• Operational run:

– GFS 00UTC– 48 hours– 1 grid, 9x9 km2 horizontal resolution– Meteorological outputs: intercomparison with other

models(http://www.master.iag.usp.br/ind.php?inic=00&pos=1&prod=previsao_glob)

Emissions

• CETESB emissions inventory for mobilesources (annual value for MASP): CO, NOx, VOCs, SOx, PM10

• Emission Factors: CETESB, Tunnel Experiments(LAPAt)

• VOC´s speciation: Tunnel Experiments (LAPAt)

• Vehicular activities: CETESB

• Spatial distribution: Night lights, IED, SMOKE, Street density

Vehicular emissions

• Daily pollutant emission (pol) by type of vehicle(veic):

• Emission (pol,veic) = activity (veic) * emissionfactor (pol,veic)*N(veic)

– Emission = [mass/time] (g/hour)– Activity = [length/time] (km/day)– Emission factor = [mass/length] (g/km)– N = number of type of vehicle

Vehicular Fleet – Denatran e CETESB, 2012

Estado Capital Cap/Estado

SP 23286890 6795228 29,2

RJ 5212996 2326286 44,6

MG 8295192 1519438 18,3

ES 1481976 178463 12,0

PR 5954243 1371431 23,0

SC 3940467 290566 7,4

48171764 12481412 25,9

VEICULOS MOVIDOS A GASOLINA (VEIC 1)

42%

VEICULOS MOVIDOS A ETANOL (VEIC 2)

3%

VEICULOS MOVIDOS A FLEX (VEIC 3)

36%

CAMINHOES (DIESEL -VEIC 4A)

3%

ONIBUS URBANO (DIESEL - VEIC 4B)

3%

ONIBUS RODOVIARIO (DIESEL - VEIC 4C)

0%

TAXIS (GAS - VEIC 5)0%

MOTOS MOVIDOS A GASOLINA (VEIC

6A)12%

MOTOS FLEX (VEIC 6B)1%

TIPOS

Activity (source: SPTRANS)

Activity (km/day)

VEICULOS MOVIDOS A GASOLINA (VEIC 1) 41,09

VEICULOS MOVIDOS A ETANOL (VEIC 2) 41,09

VEICULOS FLEX (VEIC 3) 41,09

CAMINHOES (DIESEL - VEIC 4A) 109,58

ONIBUS URBANO (DIESEL - VEIC 4B) 164,38

ONIBUS RODOVIARIO (DIESEL - VEIC 4C) 164,38

TAXIS (GAS - VEIC 5) 0

MOTOS MOVIDOS A GASOLINA (VEIC 6A) 136,98

MOTOS FLEX (VEIC 6B) 136,98

Emission factors (g/km)

GAS ETA FLEX CAMINHOESMOTOS

GASMOTOS

FLEX

exa_co 5,43 12 5,13 4,95 9,15 9,02

exa_nox 0,34 1,12 0,32 9,81 0,132 0,129

exa_so2 0,029 0,014 0,021 0,61 0,0097 0,0093

exa_c2h5oh 0,079 0,253 0,166 0,61 0,079 0,305

exa_hcho 0,0089 0,011 0,0098 0,61 0,0152 0,0155

exa_ald 0,014 0,03 0,022 0,61 0,0164 0,0188

exa_pm 0,15 0,15 0,15 0,44 0,05 0,05

exa_voc 0,425 1,3 0,434 2,05 1,08 1,08

vap_voc 0,23 0,25 0,24 0 0 0

liq_voc 2 1,5 1,75 0 1,2 1,2

Cetesb (2010)IAG

VOC splitting (mol/100g)

VAPORS LIQUID EXHAUST VAPORS LIQUID EXHAUST VAPORS LIQUID EXHAUST

1 e_eth 0.025000 0.000000 0.282625 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

2 e_hc3 0.240000 0.213150 0.435206 0.000000 0.000000 0.000000 0.000000 0.000000 0.048995

3 e_hc5 0.450000 0.157299 0.158620 0.000000 0.000000 0.977799 0.000000 0.000000 0.057741

4 e_hc8 0.000000 0.192629 0.076538 0.000000 0.000000 0.000000 0.000000 0.000000 0.296627

5 e_ol2 0.038240 0.000000 0.341600 0.000000 0.000000 0.948944 0.000000 0.000000 0.318889

6 e_olt 0.200000 0.082045 0.143212 0.000000 0.000000 0.076220 0.000000 0.000000 0.385318

7 e_oli 0.460000 0.179849 0.161406 0.000000 0.000000 0.076220 0.000000 0.000000 0.000000

8 e_iso 0.000000 0.001146 0.004554 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

9 e_tol 0.085000 0.058353 0.140506 0.000000 0.000000 0.015079 0.000000 0.000000 0.235115

10 e_xyl 0.000000 0.119330 0.157456 0.000000 0.000000 0.039490 0.000000 0.000000 0.008360

11 e_ket 0.000000 0.000000 0.000083 0.000066 0.000066 0.016767 0.000000 0.000000 0.000012

12 e_ch3oh 0.000000 0.000000 0.001841 0.002200 0.002200 0.005539 0.000000 0.000000 0.000003

13 e_c2h5oh * 0.350000 0.605079 0.000000 2.170.610 2.170.610 0.000000 0.000000 0.000000 0.000000

14 e_hcho * 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.322544

15 e_ald * 0.000000 0.059507 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.075100

ETANOLGASOLINA DIESEL

Air quality model domain

Satellite view of earth at night

Night lights for horizontal distribution

Imagem e processamento de dados pela

NOAA's National Geophysical Data Center.

MASP

Horizontal distribution of emission

MASP

SMOKE - Sparse Matrix OperatorKernel Emissions

3 x 3 km2. Albuquerque, 2010

The International Environmental Database System (IED): 1 x 1 km2

The International Sustainable Systems Research Center (ISSRC) 2012

Com relação às emissões veiculares, está sendo utilizado um esquema de inclusão de fontes móveis para S. Paulo e Rio de Janeiro baseado nas seguintes informações:• dados de contagem veicular e velocidade dos veículos da Companhia de Engenharia de Tráfego (CET), (Desempenho do Sistema Viário Principal, Volume e Velocidade, 2012);• Contagem de veículos realizada no estudo de Túnel – IAG• Informações de inventário de fontes móveis CETESB e Estatísticas da Associação Nacional dos Fabricantes de Veículos Automotores –ANFAVEA• Fator de emissão calculado em experimentos de túneis• Fator de emissão COPERT 4 (Nziachristos y Samaras, 2009)• Informação Maplink• Dados Rede de Ruas (www.openstreetmap.org). Sistema de informação geográfica aberta. Contém uma descrição do tipo de rua segundo sua importância e função.

•

Vehicular sources – spatial distribution

Road net fromwww.openstreetmap.org

9km x 9km Horizontal Grid

Density (total road length/cell) Hypothesis: no. vehicles proportional to density

Temporal distribution

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CO e COVs

NO, NO2, SO2 e MP

Atmospheric emissions, transport, transformation, and deposition of trace gases

(Source: Aneja et al., 2003, 2006b)

Good emissions estimates are the start

of any environmental assessment

Operational WRF-CHEM

• 9 x 9 km2, 100 x 100 grid points, 27 vert levels• Timestep: 45 s• http://www.lapat.iag.usp.br/aerossol/wrf9/index.php

VEIN MODELSergio Ibarra PhD Thesis

CO Emissions (g at peak hour)

http://jornal.usp.br/wp-content/uploads/gCO.html

PM Emissions (g at peak hour)

http://jornal.usp.br/wp-content/uploads/gPM.html

• Thank you!

• rita.ynoue@iag.usp.br