use and geographical factor such as elevation played a vital role in predicting
morbidity from asthma and COPD.
In areas where the levels of pollution were relatively low, Chang et al. (2019)
examined associations between asthma morbidity and local ambient air pollution. A
time series analysis of asthma hospital admissions and fine particulate matter
pollution (PM 2.5 ) in central Mississippi during 2003–2011 were conducted. It
showed an estimated 7.2 times increase in daily all-age asthma emergency room
admissions per 10 μg/m
3 increase in the 3-day average of PM 2.5 levels.
In India, particulate matter, that is, PM 10 , is regularly monitored at Centre
Pollution Control Board operated sites across the country. These data were utilized
to estimate relative risk (RR) of all-cause mortality cases in Chennai and Delhi
(Health Effects Institute Research Report 2011). The contributory factors being
many owed the large uncertainty in estimating premature mortality due to ambient
PM 2.5 exposures.
Firstly, there are very few PM 2.5 monitoring sites in some of the major cities and
many of them do not have consistent quality-controlled data for a long enough
period. Moreover, these sites are located in urban areas that leaves most of India
unmonitored. Further highly variable aerosol fine mode fraction across space and
time (Dey and Di Girolamo 2010) suggests the difficulty in inferring PM 2.5 from
particulate matter measurements especially PM 10 . Secondly, very limited cohort
study exists to specifically quantify cause-specific RR from ambient PM 2.5 exposure
in India. Extrapolation of relative risk (RR) in a study conducted recently in India for
household air pollution exposure to that of ambient PM 2.5 exposure (Balakrishnan
et al. 2013; Smith et al. 2014) does not provide the right solution either due to the
insufficient data of ambient PM 2.5 exposures through India.
Thirdly, recent estimates of premature mortalities using integrated exposure risk
function (IER) considered uniform baseline mortality across India and raw estimate
of satellite based PM 2.5 in the study on global burden of diseases (GBD 2013;
Murray et al. 2015). In order to address the issues aforementioned, researchers
have made use of satellite based PM 2.5 estimates to calculate the relative risk of
the premature mortality caused due to chronic obstructive pulmonary disease
(COPD), ishchemic heart disease (IHD) and lung cancer throughout India
(Sourangsu and Sagnik 2016).
The broad-area coverage of satellites may allow atmospheric remote sensing
thereby offering a unique opportunity to monitor air quality at all levels including
continental, national and regional scales. Satellite estimates could be an alternative
indicator of air quality in pollution-based epidemiological studies (Zhiyong and
Ranga Rao 2009).
18 Geospatial Modelling of Air Pollution and its Impact on Health of Urban. . .
381
morbidity from asthma and COPD.
In areas where the levels of pollution were relatively low, Chang et al. (2019)
examined associations between asthma morbidity and local ambient air pollution. A
time series analysis of asthma hospital admissions and fine particulate matter
pollution (PM 2.5 ) in central Mississippi during 2003–2011 were conducted. It
showed an estimated 7.2 times increase in daily all-age asthma emergency room
admissions per 10 μg/m
3 increase in the 3-day average of PM 2.5 levels.
In India, particulate matter, that is, PM 10 , is regularly monitored at Centre
Pollution Control Board operated sites across the country. These data were utilized
to estimate relative risk (RR) of all-cause mortality cases in Chennai and Delhi
(Health Effects Institute Research Report 2011). The contributory factors being
many owed the large uncertainty in estimating premature mortality due to ambient
PM 2.5 exposures.
Firstly, there are very few PM 2.5 monitoring sites in some of the major cities and
many of them do not have consistent quality-controlled data for a long enough
period. Moreover, these sites are located in urban areas that leaves most of India
unmonitored. Further highly variable aerosol fine mode fraction across space and
time (Dey and Di Girolamo 2010) suggests the difficulty in inferring PM 2.5 from
particulate matter measurements especially PM 10 . Secondly, very limited cohort
study exists to specifically quantify cause-specific RR from ambient PM 2.5 exposure
in India. Extrapolation of relative risk (RR) in a study conducted recently in India for
household air pollution exposure to that of ambient PM 2.5 exposure (Balakrishnan
et al. 2013; Smith et al. 2014) does not provide the right solution either due to the
insufficient data of ambient PM 2.5 exposures through India.
Thirdly, recent estimates of premature mortalities using integrated exposure risk
function (IER) considered uniform baseline mortality across India and raw estimate
of satellite based PM 2.5 in the study on global burden of diseases (GBD 2013;
Murray et al. 2015). In order to address the issues aforementioned, researchers
have made use of satellite based PM 2.5 estimates to calculate the relative risk of
the premature mortality caused due to chronic obstructive pulmonary disease
(COPD), ishchemic heart disease (IHD) and lung cancer throughout India
(Sourangsu and Sagnik 2016).
The broad-area coverage of satellites may allow atmospheric remote sensing
thereby offering a unique opportunity to monitor air quality at all levels including
continental, national and regional scales. Satellite estimates could be an alternative
indicator of air quality in pollution-based epidemiological studies (Zhiyong and
Ranga Rao 2009).
18 Geospatial Modelling of Air Pollution and its Impact on Health of Urban. . .
381