Seamless Prediction of Air Pollution in Africa

Seamless Prediction of Air Pollution in Africa, Nairobi,Kenya, 7-12 Obtober2019

A. S. Zakey&

The RegCM Egyptian Team-work

The Egyptian Meteorological Authority

A grid cell view of atmospheric chemistry

Advection/Diffusion

Advection/Diffusion

Advection/Diffusion

EmissionsWet & Dry Deposition

Cloud ProcessesChemistry

• What are the key processes for atmospheric chemistry?• Physical processes: advection, diffusion, deposition, clouds & precip• Chemical processes: emissions, gas-phase reactions, gas-to-particle conversion, aqueous chemistry, heterogeneous chemistry

RegCM-CHEM Gas-phase chemistry

- Chemical mechanism (list of chemical reactions and species to include)

- Solver (numerical method to solve the chemical differential equations)

• Mechanism: CBMZ (Zaveri and Peters, 1999)- 53 species, 132 reactions- Developed for long-term

simulations• Solver: Radical Balance

Method (RBM), Sillman

RegCM-CHEM

Solver

Gas Phase Chemical

Mechanism

• Incorporation of gas-phase chemistry requires:

Lateral Boundary Conditions (LBC)

• Limited area runs require information provided to the lateral boundaries

• Meteorological: ERA-Interim global data, updated 6-hourly

• Chemical: MOZART monthly climatology (Emmons et al., 2010) LBC variables

• Dynamics:MM5 Hydrostatic (Grell et al 1994)Non-hydrostatic (MM5 or WRF, in progress)

• Radiation:CCM3.6.6 (Kiehl 1996)RRTM

• Large-Scale Clouds & Precipitation:SUBEX (Pal et al 2000)

• Cumulus Convection:Grell (1993) + FC80 ClosureAnthes-Kuo (1977)MIT/Emanuel (1991)Betts-Miller (1993)STRACO (in progress)

• Boundary Layer:Holtslag (1990)

• Nesting:Numerous GCM/Reanalysis InterfacesOne-way nesting

• Biogenic Emiss:MEGANE (Twffic, 2010)

• Tracers/Aerosols:Qian et al (2001) – sulfur chem.Solmon et al (2005) – BC/OC chem.Zakey(2006,2008) – dust/ sea saltShalaby (2010) – gas-phase chem. (vectoizedvers. By Ashraf Zakey)

• Land Surface:BATS1e (Dickinson et al., 1993)SUB-BATS (Giorgi et al., 2003)CLM (Dai et al., 2003, Dai & Bi, in progress)IBIS (Foley; Winter in progress)

• Ocean Fluxes:BATS1e (Dickinson et al., 1993)Zeng et al (1998)Air-Sea Coupling (MITogcm, OASIS coupler, in progress)

• Computations:User-FriendlyMultiple PlatformsParallel Code

The Main Products

Gases: such as CO, O3, NOx, SO2

Aerosols: such as SO4, BC, OC, ANH4, ANO3, Sea Salt and Dust

Optical properties of Aerosols: such as Aerosol optical Depth, asymmetry parameter, and Radiative forcing.

Radiation: such as short and long wave net radiation.

Indices related to air quality and health: Air Quality Health Index and Ventilation index.

Common Meteorological parameters: Air temperatures, precipitation, Relative humidity, wind, and mean sea level pressure, in addition to other many different variables.

Parameter

Meteorological field: Initial and Boundary conditions (ICBC)

ERA-entrim (with 1.5 degree resolution)

Dynamical Core MM4 Hydrostatic (Giorgi et al., 1993a,b)

Radiation scheme CCM3 (Kiehl, 1996)

Large-Scale Clouds & Precipitation scheme

SUBEX (Pal et al., 2000)

Cumulus convection scheme Emanuel (1991) over Ocean & Land

Boundary Layer scheme Modified Holtslag (Holtslag, 1990)

Land Surface model BATS (Dickinson et al., 1993)

Interactive Aerosols Organic and black carbon, SO4 (Solmon et al. 2006)Dust (Zakey et al. 2006)Sea salt (Zakey et al. 2008)(with feedback interactions)

GAS phase chemistry CBMZ (Shalaby et al. 2011)

Configuration of RegCM4-Africa

Parameter

Center latitudeCenter longitudeNumber of points in x-axis & y-axisProjectionHorizontal resolutionVertical resolution

00.00 17.00 E192 & 167ROTMER (Rotated Mercator)50 km x 50 km 18 sigma-layers

Area Configuration

Chemistry:Condensed CBM-Z gas-phase chemistry (Zaveriand Peters, 1999).

Solver:Radical balance method (RBM) by (Sillman et al., 1991) and (Barth et al., 2002)

Photolysis rates:Tropospheric Ultraviolet-Visible Model (Madronich and Flocke, 1999) with cloud cover correction by (Chang et al., 1987)

Dry deposition: • “big leaf” multiple resistance model with

aerodynamic, quasi-laminar layer, andsurface resistance for 31 gaseous species.

• uptake resistance for vegetation, soil, water,snow and ice (20 land-use types).

• stomata and non-stomata resistances

Atmospheric Dust Cycle

[www.geo.cornell.edu ]

Emission

Transport

Deposition

Aerosol dust modeling in ( Zakey et al., 2006 ACP)

Soil texture (12 types, USDA)

Soil erodible dry aggregates

distribution (Shao et al. ,2002)

Land surface properties (BATS)

( roughness, soil humidity, cover

fractions)

RegCM atmospheric variables

( surface wind, air temperature, air

density)

DUST emission scheme

Zakey et al., 2006

Suspension Sand-blasting (Alfaro et

al., 1997, 2001)

Saltation (Marticorena et al. 1995)

Dust flux distribution

(3 log-normal emission modes)

Transport bins (up to 12), usually 4

Size dependent settling and surface déposition

Roughness and humidity correction

AOP / radiation

Input parameters

Regional climate modelling approach 0.01 – 20 µm

Surface properties

(roughness, humidity, vegetation)

10 µm – 10000 µm

Sand-blasting

Saltation

wind

Soil granulometry

Transport and removal

Zakey et al., 2006

Vis5D

RegCM-CHEM

ICBC

ICBCyyyymmddhh……

DEHM,MOZART

CHEM ECMWF, ERA40,NNRP1, NNRP2,EH5OM, FVGCM,

HadAMH, REGCM,……

CLIM

GlobalTerrestrial

Data

Terrain

ATM.yyyymmddhhRAD.yyyymmddhhSRF.yyyymmddhhSPC.yyyymmddhh

netCDF output

IPCC RCP, GFED, POET, RETRO,

EDGAR,……

EMISSIONS

Global 1x1SST Data

SST

GrADS SIGMAtoP

NCL

Global Climate ModelEH5OM run (2000)res.: 1.875o x 1.875o

IPCC-RCP4.5 Emissions(2010)

res.: 0.5o x 0.5o

MOZART CHEM ICBCs

Input parameters:

O3, H2O2, NO, NO2, N2O5, HNO3, SO2, SO4, CH4, HCHO, CO, C2H6, CH3CHO, PAN, nC4H10, oXylene, Isoprene, NH3, NH4NO3, ANIT, NH4HSO4, NH4

2SO4, CH3OH, CH3OOH, C2H5OH, C2H4, C3H6, C3H8, CH3COCH3, BIGENE, BIGALK, TOLUE, DMS

Input parameters:

SO2, CH4, CO, NH3, NOx, OC, BC, acids, alcohols, benzene, butanes,

chlorinated, esters, ethane, ethene, ethers, ethyne,

formaldehyde, hexanes_and_higher_alkan

es, ketones, other_alkanals,

other_alkenes_and_alkynes, other_aromatics, pentanes, propane, propene, toluene,

trimethyl_benzenes, xylene

Input parameters:

surface pressure,

geopotential,

relative humidity,

air temperature,

wind velocity

RegCM-CHEM Emissions Preproc tool:

ACCENT

VOCs On-lineMEGAN

Grid ModelDistribution

Grid Concentrations ofChemical Species

Splitting and speciation

Data

IPCC RCP

Summer (JJA) emissions in the Mediterranean, Middle East and northern Africaaveraged over 1998−2006 for (A) anthropogenic NOx, (B) anthropogenic alkanes(including butanes, ethane and propane), (C) anthropogenic sulfate, (D)anthropogenic black carbon, (E) anthropogenic organic carbon, and (F) isopreneemissions. Panels (A) to (E) are based on the MACCity inventory and panel (F) onthe online MEGAN-CLM emissions model

Emissions: Biogenic

• MEGAN - Isoprene Emission Factor Map ()•Baseline isoprene emission fluxes (g m-2

hr-1) • Modified for temperature, light, soil moisture, LAI, etc… (Ahmed T’s talk Weds)

3000 6000 9000 12000

A grid cell view of atmospheric chemistry in

Advection/Diffusion

Advection/Diffusion

Advection/Diffusion

EmissionsWet & Dry Deposition

Cloud ProcessesChemistry

• Representing this with the continuity equation

¶c

¶t= -v×Ñc+Fh +Fv +S - Rdry - Rwet +chem

• Large set of differential equations• Varying lifetimes of chemical compounds makes this a “stiff” system

Cloud-Aerosol Interaction in RegCM4: Indirect effects of aerosols over Africa

These three autoconversion schemes differ in their dependence on the total aerosolmixing ratio (xtot), which relates to the effective radius (re), and on the liquid watercontent (wL),

1. The first parameterization depends on Beheng (1994) (or hereinafter the "BH" scheme),which based on Lohmann and Feichter (1997)

2. The second parameterization depends on Tripoli and Cotton (1980) (hereinafter,referred here as "TC" scheme).

3. The third parameterization of autoconversion parameterization (R6 scheme) of Liu andDaum (2004), which accounts for the dispersion effect of the cloud droplets (Liu andDaum, 2004; Liu et al., 2004; Liu et al., 2007)

Parameterization of autoconversion

Autoconversion rates (P) in (106 kg kg-1 s-1) as a function of liquid watermixing ratio ( in (g kg-1) for different autoconversion schemes of KS, BH,TC, and R6.

¹ All means (SO42- + hydrophobic and hydrophilic OC and BC + 4 size bins of dust)² The direct effects include all simulated aerosols³ The 1st and 2nd indirect effects include only SO42- + hydrophilic OC and BC

Changes in temperature (K) due to the experiments of a) direct, b) Indir1, c) all_BH, d) all_TC, and e) all_R6 relative to the experiment of ctrl, during JJA 20065

Changes in precipitation (mm month-1) due to the experiments of a) direct, b) Indir1, c) all_BH, d) all_TC, and e) all_R6 relative to the experiment of ctrl, during JJA

2006

Taylor diagram for a) temperature and b) precipitation, over the West Africa (red points), and Central Africa (blue points) during JJA 2006.

Changes in total cloud cover (%) due to the experiments of a) direct - ctrl, b) Indir1 – ctrl, c) all_BH – ctrl_BH, d) all_TC – ctrl_TC, and e) all_R6 – ctrl_R6, during JJA

2006.

Changes in Aerosol optical depth (AOD) induced by a) direct, b) Indir1, c) all_BH, d) all_TC, and e) all_R6 relative to the AOD from Terra/MODIS at 550 nm, during JJA

2006

Simulated net radiative forcing (RF) at the surface (SRF) (W m-2) induced by a) direct, b) Indir1, c) all_BH, d) all_TC, and e) all_R6

relative to their control runs, during JJA 2006.

Simulated net radiative forcing (RF) at the top of atmosphere (TOA) (W m-2) induced by a) direct, b) Indir1, c) all_BH, d) all_TC, and e) all_R6 relative to their

control runs, during JJA 2006.

-40-35-30-25-20-15-10-505

direct Indir1 all_BH all_TC all_R6

Ra

d.

Fo

rcin

g (

Wm

-2)

a) Surface and TOA net RF over West Africaduring JJA 2006

NRF_SRF NRF_TOA-35-30-25-20-15-10

-505

direct Indir1 all_BH all_TC all_R6

Ra

d.

Fo

rcin

g (

Wm

-2)

b) Surface and TOA net RF over Central Africaduring JJA 2006

NRF_SRF NRF_TOA

direct Indir1 all_BH all_TC all_R6

ΔCLWP(g/m2) -04.37 -23.65 05.36 278.89 835.46 ΔTCLD (%) 00.53 -01.51 09.64 21.16 19.97 ΔLCLD (%) 03.15 00.18 06.81 -20.21 -58.51 ΔPALB (%) 04.02 05.27 12.14 41.84 49.62 ΔT (K) -00.49 -00.48 -01.03 -01.21 -00.92 ΔLHF(W/m2) -00.29 -00.54 -00.39 -03.67 -05.61 ΔP(mm/month) -0.07(-0.04%) -17.59(-10.02%) -1.95(-01.11%) -103.24(-58.83%) -149.61(-85.25)

Central Africa

direct Indir1 all_BH all_TC all_R6

ΔCLWP(g/m2) 06.02 -11.66 39.58 04.36 -16.68 ΔTCLD (%) 01.47 -0.43 05.12 12.95 10.98 ΔLCLD (%) 06.17 01.47 05.22 -13.76 -41.73 ΔPALB (%) 02.37 03.84 04.50 25.68 33.64 ΔT (K) -0.53 -00.48 -00.74 -01.20 -00.34 ΔLHF(W/m2) 00.11 -00.09 00.18 -01.43 -03.03 ΔP(mm/month) 09.09(5.74%) -06.04(-3.82%) 08.26(05.22%) -47.31(-29.00%) -90.38(-57.13%)

Seasonal means over West Africa and Central Africa for changing in cloud liquid waterpath (ΔCLWP), total cloud cover ΔTCLD, low cloud cover ΔLCLD, planetary albedo(ΔPALB), temperature (ΔT), total precipitation (ΔP), and latent heat flux (ΔLHF) due todirect, Indir1, all_BH, all_TC, and all_R6, relative to the control run (ctrl), averagingover the land only, during JJA 2006. The relative change in total cloud cover, planetaryalbedo, and precipitation is given in percent.

CODJFMAM

JJA SON

O3DJF

MAM

JJASON

AODDJF MAM

JJA SON

DJF MAM

JJA SON

BC

Dust Conc

DJF MAM

JJA SON

DJF MAMNH3

JJA SON

Aerosol: Dust (RegCM-dust)PM2.5

JJA

DJF

PM10

DJF MAMNOx

JJA SON

R

U/R

Rs/URs/U

R

Rs/U

R

R

R

R

R

Rs/U

Rs/U

I

map of GC region, showing the locationof all the EEAA air quality stations. Notethat in El-Abbasseya the instruments ofthe EMA and EEAA networks are installedin the same station. Only the stationswith colored labels are the ones used inthis study. The colored regions indicatethe geographic location of the four GCsectors (north, south, east, and west)considered in the data analysis. Thealphabetic symbols refer to the type ofthe station: R: Residential; Rs: Road side;I: Industrial; U: Urban. The colors of thesymbols refer to the componentsmeasured by the station: red: SO2; blue:NO2; green: CO; orange: O3; violet: PM10

Site No. Station Name Measured pollutantsCoordinates

Station TypebLocation

Lon. Lat.

1 El-Abbasseyaa O3 31°17' 30°04' U/RW

2 El-Kolaly SO2, NO2, PM10 31°14'' 30°03' Rs/UW

3 Nasr City SO2, NO2, PM10 31°19' 30°03' R E

4 El-Maadi SO2 31°16' 29°57' R S

5 El-Tebeen SO2, NO2, PM10 31°17' 30°10' I S

6 Abo-Zabaal SO2 31°24' 30°14' Rs/U E

7 Helioplis PM10, CO 31°21' 30°5' Rs/UE

8 Helwan SO2, PM10 31°20' 29°50' Rs/U S

9 Nasser Institute SO2, NO2, PM10 31°15' 30°6' Rs/U N

10 New Cairo NO2, PM10 31°21' 30°2' R E

11 El-Maasra SO2, NO2, PM10 31°17' 29°55' R S

12 6-October SO2, PM10 31°14' 30°03' Rs/U W

13 El-Mohandeseen SO2, PM10 31°12'' 30°5' R W

14 El-Salam SO2, PM10 31°16' 30°7' R S

15 Giza Square SO2, NO2, CO, PM10 31°12' 30°01' RW

16 Fum El-Khalij CO 31°13' 30°1' R S

17 Shobra El-Kheima PM10 31°14' 30°07' RN

Measuring Stations with Their Coordinates, Monitoring Type, and Measured Pollutants Available for the Period of Study (Mostafa et al. 2018)

MAM

Seasonal variation of lowest model level of nitrogen dioxide, surface ozone, carbon monoxide,sulfur dioxide, and PM10 over Egypt

1 3 5 7 9 11 13 15 17 19 21 23

Hours

1 3 5 7 9 11 13 15 17 19 21 23

Hours

(a) Site14 (N) (b) Site03 (E)

(c) Site13 (W) (d) Site04 (S)

Mod

el bi

as fo

r SO

2(µ

gm/m

3 )

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

1 3 5 7 9 11 13 15 17 19 21 23

Hours

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

1 3 5 7 9 11 13 15 17 19 21 23

Hours

RegCM-CHEM4 Model bias (µg/m3) for 3-year seasonally averaged daily concentrations of SO2 in (a) Site 14 (N), (b)Site 3 (E), (c) Site 13 (W), and (d) Site 4 (S)

(a) Site09 (N) (b) Site03 (E)

(c) Site13 (W) (d) Site11 (S)

Mod

el bi

as fo

r PM

10 (µ

gm/m

3 )

-300.0

-250.0

-200.0

-150.0

-100.0

-50.0

0.01 3 5 7 9 11 13 15 17 19 21 23

Hours

1 3 5 7 9 11 13 15 17 19 21 23

Hours

-300.0

-250.0

-200.0

-150.0

-100.0

-50.0

0.01 3 5 7 9 11 13 15 17 19 21 23

Hours

1 3 5 7 9 11 13 15 17 19 21 23

Hours

RegCM-CHEM4 Model bias (µg/m3) for 3-year seasonally averaged daily concentrations ofPM10 in (a) Site 9 (N), (b) Site 3 (E), (c) Site 13 (W), and (d) Site 11 (S)

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hours

-20.0

-15.0

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

1 3 5 7 9 11 13 15 17 19 21 23

Hours

1 3 5 7 9 11 13 15 17 19 21 23

Hours

(a) Site07 (E)

(b) Site15 (W)

(c) Site16 (S)

Mod

el bi

as fo

r CO

(µgm

/m3 )

RegCM-CHEM4 Model bias (µg/m3) for 3-year seasonally averaged daily concentrations of COin (a) Site 7 (E), (b) Site 15 (W), and (c) Site 16 (S)

-60.0-50.0-40.0-30.0-20.0-10.0

0.010.020.030.040.0

1 3 5 7 9 11 13 15 17 19 21 23

Hours

1 3 5 7 9 11 13 15 17 19 21 23

Hours

1 3 5 7 9 11 13 15 17 19 21 23

Hours-60.0-50.0-40.0-30.0-20.0-10.0

0.010.020.030.040.0

1 3 5 7 9 11 13 15 17 19 21 23

Hours

(a) Site09 (N) (b) Site03 (E)

(c) Site15 (W) (d) Site11 (S)

Mod

el bi

as fo

r NO

2(µ

gm/m

3 )

RegCM-CHEM4 Model bias (µg/m3) for 3-year seasonally averaged daily concentrations of NO2 in (a) Site 9, (b) Site 11, (c) Site 15, and (d) Site 3

(e) PM10 Model Bias

-255.0

-205.0

-155.0

-105.0

-55.0

-5.01 2 3 4 5 6 7 8 9 10 11 12

Site09 (N) Site10 (E) Site13 (W) Site11 (S)

(a) SO2 Model Bias

(d) O3 Model Bias

(b) NO2 Model Bias

(c) CO Model Bias

Mo

del

bia

s (µ

gm

/m3)

-30.0

-20.0

-10.0

0.0

10.0

20.0

30.0

1 2 3 4 5 6 7 8 9 10 11 12

Site09 (N) Site03 (E) Site02 (W) Site04 (S)

-70.0

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

1 2 3 4 5 6 7 8 9 10 11 12

Site09 (N) Site10 (E) Site15 (W) Site05 (S)

-20.0-18.0-16.0-14.0-12.0-10.0

-8.0-6.0-4.0-2.00.0

1 2 3 4 5 6 7 8 9 10 11 12

Site07 (E) Site15 (W) Site16 (S) 0.0

10.0

20.0

30.0

40.0

50.0

60.0

1 2 3 4 5 6 7 8 9 10 11 12

Site01 (W)

RegCM-CHEM4 Model bias (µg/m3) for 3-year monthly averaged concentrations of SO2, NO2, CO,O3, and PM10 at the available stations

SummaryThe densely populated Greater Cairo (GC) region suffers from severe air

quality issues caused by high levels of anthropogenic activities, such asmotorized traffic, industries, and agricultural biomass burning events,along with natural sources of particulate matter, such as wind erosion ofarid surfaces. Surface measured concentrations of particulate matter(PM10), sulfur dioxide (SO2), and ozone (O3) and its precursor’s gases(nitrogen dioxide, NO2; carbon monoxide, CO) were obtained for the GCregion. The PM10 concentrations were found to exceed remarkably theEgyptian Guidelines (150 µg/m3). These high levels of PM10 wererecorded throughout 68% of the period of measurement in someindustrial areas (El-Kolaly). The measured data of pollutants were used forboth the evaluation of environmental pollution levels and the validation ofthe online integrated regional climate chemistry model “RegCM-CHEM4”.Calculation of the bias between the model results and the measured datawas used to evaluate the model performance in order to assess its abilityin reproducing the chemical species over the area. The model was foundto reproduce the seasonal cycle of the pollutants successfully, but with alarge underestimation of the PM10 values. Validation of the RegCM-CHEM4 indicated that the emissions inventories of mobile sources andanthropogenic activities need to be improved especially with respect tolocal and regional activities in order to enhance air quality simulationsover GC region.

1. At low cloud liquid water mixing ratio with large size of cloud droplets (re = 10 µm)or low aerosol concentrations, the autoconversion rate will be accelerated by BHscheme more than TC, R6, and KS, respectively.

2. At low in-cloud liquid water ( ≤ 0.1 g kg-1) with smaller cloud droplets (re = 7.5µm) orhigh aerosol concentrations, the BH scheme will act faster than the other schemes,and as a consequence, the scheme of BH resulted in more precipitation than theothers.

3. The inclusion of the direct and 1st indirect effects increased the cooling at thesurface and TOA, which suppressed the precipitations over the west and centralAfrica.

4. With respect to precipitation, the simulations of all_R6 and all_TC had the minimumbias compared to the other simulations over the two domains.

5. With respect to temperature, all simulations have similar skills (RMS, correlation andnormalized standard deviation), with the simulations of all_BH and ctrl resulted inthe minimum bias (< 75%) over west and central domains, respectively, but all_R6and all_TC had the lowest skills.

Summary and Conclusion

References

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2. DF Zhang, XJ Gao, A Zakey, F Giorgi (2016), Effects of climate changes on dustaerosol over East Asia from RegCM3, Advances in Climate Change Research 7 (3),145-153

3. M. Santese, M.R. Perrone, A.S. Zakey , F.De Tomasi and F. Giorgi “Modeling ofSaharn dust outbreaks over the Mediterranean by RegCM3: Case studies”, Atmos.Chem. Phys., 10, 133-156.

4. Fabien Solmon, Marc Mallet, Nellie Elguindi, Filippo Giorgi, Ashraf Zakey, andAbdourahamane Konare, “Dust aerosol impact on regional preciptaion overwestern Africa, mechanisms and sensitivity to absortion properties”, GeophysicalResearch, Letter Vol. 35, L24705, doi:10.1029/2008GL035900.

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