Ghude s 20150707_1700_upmc_jussieu_-_amphi_34

Air Pollution and Climate Change linkages and Health Impact Assessment

Evaluating air quality impact on mortality and crop yields in South Asia

Sachin D. Ghude Chinmay Jena,

Speaker: Dilip Chate ([email protected]) G.Beig Indian Institute of Tropical Meteorology ,Pune, India R. Kumar G. Pfister M.Barth National Center for Atmospheric Research (NCAR), Boulder, USA V. Ramanathan S Cripps Institute of Oceanography, UCSD, San Diego, USA

CFCC, 7-10th July, UPMC Jussieu - Amphi 34, UNESCO, Paris (France)

Outline

Premature mortality in India for population exposure to O3 and PM2.5 levels

Motivation

Life losses and economic damage

Conclusion

Future scope

Reductions in crop yields due to surface ozone levels: a case study for India

Motivation

Crop production losses and economic damage

Conclusion

Future scope


In a U.S. multicity study, Bell et al. (2004): a 10-ppb

increase in 24-hr average O3 concentration show a 0.52%

increase in all-cause mortality.

In 12 major cities in Canada, Burnett et al. (2004):

a 30.6-ppb change in 2-day moving average O3 concentration

with a 2.74% change in non-accidental mortality.

Lelieveld, et al. (2013): Globally, the premature

mortality rate for population exposure to O3 levels is ~0.8

million/year with highest rate for India

Background and Motivation for population exposure to O3

Cont……….


No study either with model simulations or with air

quality monitoring network data found to be focused on finer

spatial scale over India, where population over 1.25 billion of

different quality of life are living in the variety of environments

across the 29 states and 7 union territories.

Atmospheric NOx, SOx, CO, BC shows much better

correlations with ambient PM levels.

In India, respiratory disease for population exposure to

O3 concentrations found to be of magnitude similar to those by

PM (Lelieveld, et al. 2013) and not an artifact of confounding

by air pollution due to PM


Lelieveld, et al Atmos. Chem. Phys. 13, 7737–7766, 2013


SO2

HIGHLY WATER-SOLUBLE GASES

DO NOT REACH THE LUNGS

IRRITATING THE AIRWAY EPITHELIUM OF THE

UPPER RESPIRATORY TRACT

98% OF SO2 MAY BE ABSORBED IN THE

NASOPHARYNX DURING NASAL BREATHING

NO2

POORLY WATER-SOLUBLE GAS

DEPOSITS FAR MORE

PERIPHERALLY IN A RESPIRATORY TRACT

DOES NOT REACH THE ALVEOLI

O3

DOES NOT DISSOLVE IN WATER

REACHES THE LUNGS


When O3 is absorbed through the lining of the lung, it

creates free radicals in the lining of the lung that damages

lung function.

So the people most at risk from O3 exposure are those

with impaired breathing. O3 inhalation causes inflammation

in the lungs and the bronchia.

Once exposed to O3, respiratory system tries to prevent

it from entering lungs. This reflex reduces the amount of

oxygen inhale. Inhaling less oxygen makes hearts to work

harder.

For people already suffering from cardiovascular

diseases or respiratory diseases like asthma, high O3 episodes

can be debilitating and even fatal to them.


Air Quality under Public Scrutiny

2/3rd of the deaths and lost life-years

associated with air pollution on a global

scale occur in Asia

PM concentrations in most of the

megacities in South Asia frequently

exceed the air-quality limits

NAAQ Standards

WHO-AQG



Air quality concerns

Metro Cities/Urban Areas 90 non-attainment cities 32% population leaves in urban areas Dominant Sources: Vehicular Emissions, Small/Medium Scale Industries, Gensets, Biomass burning, etc. Pollutants: NOx, SPM/RSPM & CO

Critically Polluted Areas 26 critically polluted areas (3 times exceed NAAQS) Dominant Sources: Industries-Power Plants, Refineries, Chemical Plants Pollutants: NOx ,SPM/RSPM, SO2 VOCs, PAHs, etc.

Rural Areas Indoor air pollution: Use of Biomass, Coal, kerosene, etc. Outdoor air pollution: Unpaved roads, Biomass burning, Gen-sets etc. Pollutants: SPM/RSPM, CO, etc.


Reasons for high Air Pollution in India

(1) Poor quality of fuel

(2) Wrong citing of industries

(3) Old process technology

(4) Poor vehicle design

(5) Predominance of older vehicles

(6) Inadequate inspection and maintenance facilities

(7) Uncontrolled growth of vehicle population

(8) No pollution control system for small/medium scale industries

(9) Reduced performance of thermal power plant


Deposition of PM in the Respiratory system

> 5 μm

<< 0.1 μm

0.2-1.5 μm

Deposition of mono-disperse particles

diameter (1-26 µm) in ideal respiratory

system geometry solved using Renolds

averaged Navier-Stokes turbulent model for

inhalation flow rates of 30 to 90 l/min


24%

OC

Nitrate

Ammonium

EC

OP

Sulfate

5%

4%

Urban Aerosols Fractions

37%

11%

19%


WRF-Chem (Hourly ozone PM2.5)

Meteorology Emission Dist wise Population

>NAAQS

AP Gridded Premature mortalities

Gridded (CP) Population

Total mort

(sum CPL)

Economic damage

General outline of the different steps involved in the data analysis for estimate premature mortalities


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

Period : One Year ( 2005, Hourly simulations)

Resolution : 36 km x 36 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 : HTAP-V2

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

Biogenic : MEGAN (online)

WRF-Chem Simulation for Year 2005


Relative Risk (RR) for Different health endpoints

Economic Loss (Value of loss output)



Surface daytime averaged O3 and PM2.5 (2 4-h averaged) During winter and Summer season 2011


All cause premature mortalities due to O3 and PM2.5 exposure

625,047 excess cases (53% IGP Region)

355,046 excess cases (47% IGP Region)

1.2 billion


Indian State-wise premature mortalities due to O3 and PM2.5 exposure

State-wise lost life expectancy for population exposure to PM2.5 and economic loss for population exposure to O3 and PM2.5

lost life expectancy is 1.65±0.5 year Delhi: 4.2±1.4 years

Economic loss = 110.2 million USD

Conclusions:

Our study suggests that the widespread PM2.5 and Ozone

pollution under present emission levels has considerable

impact on human mortalities and life expectancy in India.

The present-day premature mortalities due to PM2.5 (625,000) and

Ozone (355,000) exposure caused approximately 110.2 million USD

economic loss, which is sufficient enough to provide medical care to

1.81 million people in India. April 2015, the Environmental Ministry of Government of India launched

a national Air Quality Index (AQI) as a major aggressive initiative for

improving air quality in urban areas, for air pollution-mitigation and to

meet clean- air standards for reducing the public health risk.

Results may have important policy implications considering the projected

future increase in PM2.5 and Ozone


Emissions (Megacity scale)

Global scale Emissions

Cross-boundary Transports

Recommendations

Air Quality/Weather Network

(monitored, forecasts )

Population Exposure

assessments Health Outcomes

Multi-pollutant relative risks for different UV exposure, temperature, RH

Interpretations

Future Scope on Atmospheric Pollution and Human Health in India

Data + Research

Environmental Health Index Module

Regional Emissions

Global Emissions

Weather Data

Air Quality data

SAFAR AQMS-AWS

IITM NETWORK

Current/Forecast 1. EHI 2. Age 3. Health

advisories

Conceptual Framework for Environmental Health Index

Environmental Health Index

Health Data

(ICD-10)

ICMR/Medical Org Age/gender/occupation

/address/diagnosis /date in &out/Mortality


Fluorescence imaging: soybean plant responses to

elevated levels of ozone (Kim, et al., 2001)

What do we know about ozone Impact on vegetation and ecosystem?

Damage leaf and reduce growth & yield

Reduce carbon uptake by metabolizing less CO2 (indirect global Warming)

Reduces carbon flow from atmosphere to roots and reduces nitrogen fixation in soil (nitrogen runoff)

Reduce canopy evapo-transpiration and soil water (increase sensible heat)

Adams et al., 1989, adapted by Chameides et al., 1999

• India is World’s third largest food producer

• Food grain production increased from 130 M tons in 1980 to 240 M tons in 2010 ($260B/year) behind China and USA

• Yield/hectare is low as per world standards (60th)

• Wheat yield is: 3 tons/hectare compared with 12 tons/hectare in industrialized countries

• POTENTIALS by 2030: • Global Average power house with $164B exports ($30B now) • Average output of $ 620 B/yr • Income of rural households increase six fold

Relevant statistics Maitra and Zainulbhai,2013; Reimagining India.McKinsey Co


Yield has gone up between 1970 – 2011, But…

GDP share of agriculture has fallen from 43% to 16%


Trend in wheat production in top wheat producing states in India…

Burney and Ramanathan, 2015, PANS

Avg warming+ 0.7 C Monsoon Rainfall: -7%

Climate Changes over India

Burney and Ramanathan, 2015, PANS

Another cause could be the Air Pollution…….

Future Change in O3


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 (Sept. 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.


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


Yield for various crops during 2005

Source: Special data dissemination standard-Directorate of economics and statistics (SDDS-DES),

Ministry of Agriculture, Government of India.

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


Simulated daytime (> 50 W/m2 global radiation ) mean surface ozone concentration

Kharif (Cotton, Rice & Soybeans)

Rabi (Wheat & Rice)


Model Evaluation with observations (Delhi)

(Pune)

Wheat production and loss (Rabi) during 2005

Production : 71 MT

Loss : 3.5 (± 0.8) MT

Rice production and loss (Kharif) during 2005

Production : 95.1 MT

Loss : 2.1 ( ± 0.8) MT

Soyabean production and loss (Kharif) during 2005

Production : 8.6 MT

Loss : 0.23 (± 0.16) MT

Cotton production and loss (Kharif) during 2005

Production : 3.3 MT

Loss : 0.17 (± 0.10) MT

Aggregated reduction for top ten wheat and rice producing sates in India

Wheat loss is greatest in Maharashtra (17%) Rice loss greatest in Punjab (8%) Punjab and Haryana (< 1%)


Estimated Economic Loss (year 2005) due to ozone damage

Commodities Production

(million tone)

Loss

(million tone)

Fraction loss

(%)

Economic

damage

(billion USD)

Soyabean 8.6 0.23 (±0.16) 2.7 (±1.9) 0.06 (±0.12)

Cotton 3.3 0.1 (±0.10) 5.3 (±3.1) 0.07 (±0.04)

Wheat 71 3.5 (±0.8) 5.0 (±1.2) 0.62 (±0.15)

Rice 95.1 2.1(±0.8) 2.1(±0.9) 0.54 (±23)

Total Economic Loss : 1.29 (± 0.47) billion USD2005


Conclusion:

Nationally aggregated relative yield loss of wheat, Rice, Cotton and Soybeans due to high O3 exposure totals 5.6 million tons amounting ~1.3 billion USD2005 Economic loss.

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


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


Thank you


Ghude s 20150707_1700_upmc_jussieu_-_amphi_34

Science