This document discusses issues related to air quality health effect studies in India. It notes that Indian studies need to address specificities like: 1. Understanding integrated exposure over time spent in different microenvironments and excluding confounding factors like weather. 2. The relationship between mortality and annual average air quality is complex due to large seasonal variations in India. 3. Investigating claims that Indians have lower immunity requires rigorous studies to establish causation, not just conjecture. 4. Future Indian studies would benefit from being longitudinal, multi-disciplinary involving multiple institutions and locations.

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Issues for Air Quality Health Effect
Studies: Address Indian specificities
Mukesh Sharma
Professor
Environmental Engineering and Management Programme
Department of Civil Engineering
Indian Institute of Technology Kanpur

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Evidences
Respiratory system
Cardiovascular System
Chromosomes and DNA
Lung Cancer
Neurobehavioral systems
More challenging
Immune alteration
Climate change and health
What is new that we wan to know?
Uncertainties in knowledge? India Specific?

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Continued…
–59% –64% –35% +163% –7%
Coronary
Heart
Disease
Stroke Other CVD COPD All Other
Causes
3.0
2.5
2.0
1.5
1.0
0.5
0
1965-1998 1965-1998 1965-1998 1965-1998 1965-1998
Center for Disease Control and Prevention, US (1999)
Mortality

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Ray and Lahiri, CNCI Kolkota

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1
n
i
i
NOAEL
RfD
UF MF



  
  1
n
i
i
LOAEL
RfD
UF MF



  
 
UF = 10H x 10A x 10S
Long-term Effects
Having Threshold
(criteria pollutants)

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Slope factor, Cancer Potency factor or Unit Risk factor
Slope factor = risk/(mg/kg-day)
Unit Risk Factor = risk/(µg/m3)
No Threshold
(Carcinogens)

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COHb level is not to exceeded, 2%
The proposed standards are with factor of safety of 2:
· 20.0 mg/m3 (17 ppm) for 1 hour
· 6.0 mg/m3 (5 ppm) for 8 hours.
It was deliberated that Indian standards are already much stringent for CO.
However, low immunity of Indians require stringent standard – conjecture or reality?
Proposed Standard: 4000 µg/m3 (1-hr avg), 2000 µg/m3 (8-hr average)
– most stringent in the world

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Results
Day1 Day2 ..Day15 Mean Day1 Day2 ..Day15
P1 PEF
1
PEF2 ..PEF15 PEF
R
PEF
1
PEF
2
PEF1
5
P2 : : : : : : :
: : : : : : : :
P33 : : : : : : :
Mean PEF PEF
1
PEF
2
PEF1
5
while looking at PEF, one can look into the variation of PEFR of an individual
with respect to his/her mean PEFR performance
Suggested by Pope and Dockery (1992)

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Juhilal Colony (JC) Cohort
Participant No.
0 5 10 15 20 25
FEV1(L)
0
1
2
3
4
5
Mean Variation from Predicted Value -0.31 L
Participant No.
0 5 10 15 20 25
FVC(L) 0
1
2
3
4
5
6
Predicted FVC
Observed FVC
Mean Variation from Predicted Value -0.40 L
Variation in Observed FEV1, FVC from Reference
Juhilal Colony Cohort

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Mortality Model for PM2.5 (annual) Pope et al. (2002)
Relative Risk (RR) Cardiopulmonary mortality
RR = [(x+1)/(xo+1)]β
β = 0.15515 (0.0562 – 0.2541)
Lung-cancer mortality
RR = [(x+1)/(xo+1)]β
β = 0.2321 (0.08563 – 0.37873)
x = PM2.5 (annual) xo= Background PM2.5 ug/m3
Do these models work – do we know the β
Our annual air quality level simple to understand
(large seasonal variation)
PM 2.5 – Mortality

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Relative Risk of Cardiopulmonary mortality for North Zone
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8 Patna
Chandigarh
Bhilai
Raipur
Korba
Delhi
Ahmedabad
Ankaleshwar
Surat
Vadodara
Vapi
Rajkot
Jamnagar
Faridabad
YamunaNagar
Jamshedpur
Bhopal
Indore
Jabalpur
Nagda
Satna
Talcher
Rourkela
Rayagada
Ludhiana
Jalandhar
MandiGobindgarh
Kota
Alwar
Jodhpur
Udaipur
Jaipur
Anpara
Varanasi
Lucknow
Gajraula
Kanpur
Noida
Dehradun
Kolkata
Howrah
Haldia
City
RelativeRisk
Mean Relative Risk Aceptable Relative Risk
To bring mean RR down to 1.05 across all cites for Cardiopulmonary Mortality
Required PM2.5 (annual) = 40 ug/m3 … this also ensures RR for lung cancer at 1.08
Cardiopulmonary mortality is about 15 times higher than Lung cancer mortality

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Air Quality – Rains - Health
Why our Annual Standard is tricky?

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Issues for our studies…
•Integrated exposure – time, concentration, microenvironments
•Understand and exclude Confounding factors
•Weather vs Air Pollution
•Mortality and annual average
•Immunity and its alteration (kanpur tuberculosis); never get causal
understanding
•Sensitive population
•Quantified criterion
•Longitudinal log-term study
•Multi disciplinary multi institutional, multi location