Emission mitigation measures to ozone-induced wheat and rice damage in India

Dilip Chate (chate@tropmet.res.in) , Sachin D. Ghude & G. Beig et al.

Indian Institute of Tropical Meteorology (IITM), Pune, India

International Conference on Atmospheric Chemistry and Agricultural Meteorology

Pune, India 2 to 4th November 2015

Indian rice and wheat yields have grown over the past half

century to play significant roles in the world food economy:

India’s more than 1.25 billion people depend primarily on food

produced within the country, and other Asian and African

nations rely on imports of Indian rice.

In 2006, India imported over 6 million tons of wheat (∼$1.3

billion) and exported over 4.4 million tons of milled rice (∼6.6

million tons of paddy equivalent, ∼$1.5 billion).

During the 2007–2008 world food price crisis, with wheat

harvests failing elsewhere in the world, India restricted rice

exports out of concern for domestic food security.

Global food security is strongly linked with India’s rice and

wheat production.

Ch

ina

Ind

ia

Ind

on

esia

Ban

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Vie

tnam

Myan

mar

Th

ilan

d

Ph

ilip

pin

es

Bra

zil

US

A

0

20

40

60

80

100

120

140

160

180

200

Area (MHa)

Production (MT)

Productivity (T/Ha)

Country wise Area, Production and Productivity of Paddy Rice

1960 1970 1980 1990 2000 20100

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

Area (MHa)

Production (MT)

Productivity (T/Ha)

Area, Production and Productivity of Paddy Rice in India over Years

WB

Punjab

UP

AP

Orisa TN

Assam

Chhatisgarh

Karnataka

Haryana

Bihar

MH

Jharkhand

Gujrat

MP

Kerla

Others0

2

4

6

8

10

12

14

16

18

20

22

Area (MHa)

Production (MT)

Productivity (T/Ha)

State wise Area, Production and Productivity of Paddy Rice in India

WB

Punjab

UP

AP

Orisa

TN

Assam

Chhatisgarh

Karnataka

Haryana

Bihar

Others

MH

Jharkhand

Gujrat

MP

Kerla

0 2 4 6 8 10 12 14

Area (%)

Rice (2009-10)

UP

Orisa

Chhatisgarh

AP

Bihar

Punjab

Assam

TN

Others

Karnataka

Kerla

MH

MP

Harayana

Jharkhand

Gujrat

0 2 4 6 8 10 12

Production (%)

Rice (2009-10)

Punjab

TN

AP

Haryana

WB

Karnataka

UP

Gujrat

Assam

Orisa

Jharkhand

MH

Bihar

Chhatisgarh

MP

0 500 1000 1500 2000 2500 3000 3500 4000

Yield (Kg/ha)

Rice (2009-10)

Milled Rice

UP

MP

Pu

nja

b

Hary

an

a

Rajs

than

Bih

ar

MH

Gu

jrat

Utt

ara

Kh

an

d

HP

WB

J &

K

Karn

ata

ka

Oth

ers

Jh

ark

han

d

Assam

0

5

10

15

20

25

30

35

Area (%)

Production (%)

Wheat

UP

MP

Pu

nja

b

Hary

an

a

Rajs

than

Bih

ar

MH

Gu

jrat

Utt

ara

Kh

an

d

HP

WB

J &

K

Karn

ata

ka

Jh

ark

han

d

Assam

0

1000

2000

3000

4000

5000

Yield (Kg/Ha)

Wheat

Total Allocation of Food-grains = 54.926 (Millions Tons)

THE NATIONAL FOOD SECURITY ACT, 2013 (10th September 2013)

APAssamBihar

ChahatisDelhi

GujHaryana

HPJ&K

JharKhaKarnaKerla

MPMH

OdishaPunjRaj

TamilUP

UttaraWB

Others

0 2 4 6 8 10

Millions Tons

State-wise allocation of foodgrains

CMIP5 model based time series of temperature and precipitation anomalies

(historical and projections) from 1861-2099 relative to the 1961-1990 baseline for

the RCP scenario (Chaturvedi et al. 2012).

Under RCP2.6 the ensemble mean temperature increases by approximately 2ºC

over the period 1880s to 2070-2099 and 4.8 ºC in RCP8.5.

For RCP4.5 and RCP6.0 (moderate scenarios), the projected increase in

temperature ranges from 2.9ºC to 3.3ºC.

All-India ensemble mean annual precipitation rise of 7%, 9.4%, 9.4% and 18.7%

for RCP2.6, RCP4.5, RCP6.0 and RCP8.5 respectively by 2099 compared to the

1961-1990 baseline.

Water stress in India

Problem of Food Security

With only 2.3% share in world’s total land area, India has to ensure Food security of its ~1.25 billion population.

National Food Security Bill (10th September, 2013)

Ensure availability of sufficient food grains for domestics demand and access to adequate quantity of subsidies food for 820 million people

Under the provision of bill, about 61.2 Mt of cereals (27.6 Mt of wheat and 33.6 Mt of

rice) is expected to distribute annually in which ~820 million poor populations are able to purchase 60 kg of rice/wheat per person annually at subsidized rates (~@USD 3) prescribed by the Government of India.

Agriculture is broadest economic sector, plays a significant role in socio-economic fabric.

(a) Elevated ozone concentrations enhance opening of leaf pores, more

so in ‘drought’ (reduced water) than well watered plant, and (b) ozone

reduces root biomass in the grass species

(Hayes et al., 2012). (Dactylis glomerata)

Ozone-induced leaf damage was reported between 2007 and 2015

Red dots - ozone injury recorded in 2014/15 using the smart-phone App;

Orange dots – ozone injury reported at ICP Vegetation bio-monitoring sites;

Blue dots – ozone injury reported in the literature

WRF-Chem (Hourly ozone)

Meteorology Emission Dist wise

Crop production

AOT40

RYL (a*AOT40)

Grided Crop production loss

(CPL)

Dist wise sowing dates

Grided (CP) Crop

production

Soybeans Cotton Wheat Rice (a=0.0113) (a=0.0151) (a=0.163) (a=0.0445)

Mills et al. 2007, corrected AOT40 for offset

Total Loss

(sum CPL)

Economic damage

CPL=RYL/(1-RYL) x CP

Dingenen et al., 2009

General outline of the different steps involved in the data analysis to estimate crop production loss

Domain : South Asia (0 - 45° N, 55 -110 ° E)

Period : One Year ( 2005, Hourly simulations)

Resolution : 55 km x 55 km

Meteorology : NCAR NCEP/FNL

Gas Ph. Chem : MOZART

Aero Ph. Chem : GOCART

Boundary Cond. : MOZART-4 (updated every 6-h)

Photolysis :Madronich F-TUV A. Emissions : INTEX-B (For NOx: Intex-B, EDGAR v2.2, MACCity, REAS, Top Down)

Fire Emission : NCAR Fire Inventory (FINN) (plume rise)

Biogenic : MEGAN (online)

WRF-Chem Simulation for Year 2005

Comparison between observed and simulated NOx over India for different emission estimate and respective surface ozone distribution (for Jan-2005)

We used integrated approach

In order to assess the response of anthropogenic NOx and

VOCs mitigation action on Ozone-induced crop damage in

India we did additional two simulations for surface Ozone:

1. With no anthropogenic NOx emissions

2. With no anthropogenic VOCs emissions (reduction

scenario).

Allowed natural emissions (biogenic emissions of NOx

and VOCs and emissions from the biomass burning).

Assessed the yield reductions for 1 & 2 Cases

Compared with initial simulations (baseline scenario).

Concentration: response functions (Mills et al., 2007, were scaled such that relative yield is equal to 1 at zero exposure) We consider 90 days period over 15th June- 15th September as a kharif growing season for soybean, cotton and rice. December – February as rabi growing season for wheat For rice we allow exposure both during kharif and rabi season depending upon seasonal rice production fields and fraction of total annual rice production within each season

AOT 40 (Accumulation exposure over threshold of 40 ppb). n

AOT 40 = ([O3] – 40)i for [O3] > 40 ppb (radiation > 50 W m-2) i=1

Exposure metrics (AOT40) and exposure response functions

International Conference on Atmospheric Chemistry and Agricultural Meteorology

Pune, India 2 to 4th November 2015

Sachin Ghude, Bieg, Chate, D. M. Current Science, 2015

Current Science, 2015

(a)

(b)

Current Science, 2015

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Rice Wheat Rice+Wheat

Loss

es in

Milli

on to

ns

Total

VOCs

NOx

(a)

(b)

(c)

Wheat

Rice

Current Science, 2015

Conclusion: As far as we are aware, we have used the most suitable available spatial

crop distribution and production data for India and latest emission

inventories.

Using a regional chemistry transport model and AOT40 exposure indices

(CR relationship), we have estimates risk to crop damage caused by

ground level Ozone pollution for top 10 wheat and rice producing states

in India under present day emission scenario.

Three model runs were analyzed, baseline simulations with present

anthropogenic NOx and VOCs emission, without anthropogenic NOx

emissions and without anthropogenic VOCs emissions.

Later two simulations compared to assess the response of NOx and

VOCs mitigation action on Ozone-induced wheat and rice damage.

Our assessment indicates significant production losses for wheat about

2.2 million tones (3.3%) and for rice about 2.05 million tones (2.5%) due

to Ozone exposure. Continue-----

National aggregated yield loss of wheat and rice of 5.6 is roughly about 12% of the cereals require every year ( 61.2 Mt) under the provision of food security bill, or sufficient to feed approximately 94 million poor people(~32%) living below poverty line in India

Impact of anthropogenic NOx mitigation action shows relatively 93% (98% for Rice

and 90% for wheat) decrease in Ozone induced crop yield losses compared to

baseline scenario.

On the other hand, impact of anthropogenic VOCs emissions mitigation action

results in small changes of about 24% (97% for Rice and 89% for wheat) decrease

in Ozone-induced crop yield losses with respect to baseline scenario.

This result provides firsthand important information to policy makers to propose or

implement emission control of Ozone-precursors to benefit more security on

national food production.

More vulnerable rice producing states to relatively high Ozone exposure are Punjab

(0.85 million tones), Andhra Pradesh (0.33 million tones), Uttar Pradesh (0.25

million tones) and West Bengal (0.23 million tones).

Similarly, more wheat producing states vulnerable to high Ozone exposure are Uttar

Pradesh (0.61 million tones), Madhya Pradesh (0.49 million tones), Rajasthan (0.23

million tones) and Maharashtra (0.22 million tones).

Asian and African nations rely on imports of Indian rice with respect to wheat yield

Future scope:

1.Develop South Asia Specific dose response curve

2.Look at the impact of aerosols and winter time fog on crop yield

Decreases ground reaching radiation (reduce photosynthesis) Fog reduces radiation Fog acidity (unknown) 3.Climate impact

Increase in temperature Decrease in rainfall Increase in CO2

SA specific Concentration Response (CR) Function

This Study: -0.007 × AOT40 Mills (2007): -0.004 × AOT40

RICE

Wheat

This Study: -0.019 × AOT40 Mills (2007): -0.016 × AOT40

11/16/2015 Lecture 01 - Introduction 35

Observational ‘Platforms’

• Tower

• Aircraft

• Balloon

• Kite

• Ship

• Satellite

Kinds of atmospheric boundary layers

Marine Continental

Little diurnal

variability

Strong diurnal

variability

1-2 km (3 max,

maybe)

Up to 5 km over

deserts

Low Bowen ratio High Bowen ratio

Wave state

important

Surface shape

fixed, but

important

Deposition of Ozone

Ozone deposition (Flux)

3

3

3''

)15.273()( O

O

O XwTR

MF

xmax: 1418.7 m

xR (R=80%): 3104.2 m X* max: 20.8

X* R (R=80%): 45.5 Real-scale Footprint

Non-dimensional Master-Footprint

-200 ≤ zm/L ≤ 1

u* ≥ 0.2 ms-1

zm > 1 m

zm < h Dynamically homogeneous terrain

σw = 0.087 (ms-1); µ*(ms-1)= 0.97; Zm=10m;

h=1200m; Z0(m)=0.1

R(0-90%)=80%

2

31

2

2

2

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][

][][ 2

k

Ok

NOk

NOjPO

NO

NO + PO2 →NO2 + PO

(PO2 =RO2 +HO2)

(k2[NO][PO2])

Ф deviates positively from unity when some chemical process other than the reaction between NO and O3 converts NO to NO2.

peroxy radical (PO2) can be calculated

Chate, et al, Atmospheric Environment, 96, 353-358, 2014

NO + O3 →NO2 + O2 (1)

NO2 + hν → NO + O(3P) (for λ<420nm) (2)

O(3P) + O2 + M → O3 + M (3)

][

][][

1

2

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NOk

NOjO

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Deviations from the O3-NO-NO2-PSS in Metropolitans of India

If PSS is assumed, the O3 concentration Leighton

ratio, Ф can be calculated by,

Dynamic equilibrium during daytime (PSS)

Atmospheric Chemical Processes in the Troposphere

Chate, et al, Atmospheric Environment, 96, 353-358, 2014

OHHSOHOHHSO

OOHHSOOHOHHSO

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5.0

HNOON

HNONO

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OHHO

HNOHNOHNO aq 333 )(

OHNHNHNH aq 433 )(

SO2 dissolve in raindrops by

dissociation reactions

Ionic HSO3- is oxidized by

dissolved by oxidants (O3 or H2O2)

During daytime, NO2 is oxidized

During nighttime, HNO3

Acidity of raindrop by HNO3

Acidic raindrops are neutralized by

alkaline ions (NH4+, Ca2+, Na+, K+ etc.)

200 400 600 800 1000 1200

3.5

4.0

4.5

5.0

5.5

6.0

6.5

0.2 mm

1 mm

5 mm

pH

Fall distance (m)

Ca2+

without Ca+

0.1 0.2 0.3 0.4 0.5 0.6 0.7

3.5

4.0

4.5

5.0

5.5

6.0

Rainfall rate = 5 mm h-1

100 m

500 m

800 m

pH

Drop size (cm)

Ca2+

without Ca+

dz

)D(dm p )D(RiV)]S[H]S([DU

k6

dz

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gl

0

)(),()(2

3ppp dDDmDDEDV

D

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g

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DUN

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Dk

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1

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1

Elevated acidity in a lake or river is directly

harmful to fish as it corrodes the organic gill material

and attacks the calcium carbonates skeleton.

Acidity dissolves toxics metals such as

aluminum from the sediments.

Acid rain is harmful to terrestrial vegetations

because it leaches nutrients (potassium) and allows

them to exit the ecosystem by runoff.

Mountainous areas having granite bedrocks and

thin soils with little neutralizing capacity are sensitive

to acid rain.

Thank you