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Levels of daily particulates (PM 2.5 ) were monitored at two sites in Karachi, Pakistan. One site (Korangi) is an industrial and residential neighborhood, while the other (Tibet Center) is a commercial and residential area near a major highway. Monitoring was done daily for a period of six weeks during spring, summer, fall and winter. Particulate levels were extraordinarily high, with the great majority of days falling into the “unhealthy for sensitive groups” or “very unhealthy” categories. The mean PM 2.5 levels in Karachi exceeded the WHO's 24 h air quality guideline almost every day and often by a factor of greater than 5-fold. Daily emergency room (ER) visits and hospital admissions for cardiovascular diseases were obtained by review of medical records at three major tertiary and specialized hospitals. ER and hospitalizations were reported relative to days in which the concentration of PM 2.5 was less than 50 μg/m ³ , and by 50 μg/m ³ increments up to 300 μg/m ³ . There were statistically significant elevations in rates of hospital admissions at each of the PM 2.5 categories at the Korangi site, and at concentrations >150 μg/m ³ at the Tibet Center site. ER visits were significantly elevated only at PM 2.5 concentrations of between 151 and 200 μg/m ³ at both sites. These results show that the extremely elevated concentrations of PM 2.5 in Karachi, Pakistan are, as expected, associated with significantly elevated rates of hospital admission, and to a lesser extent, ER visits for cardiovascular disease.
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Review article
Haider A. Khwaja,
Zafar Fatmi,
Daniel Malashock,
Zafar Aminov,
Azhar Siddique,
and David O. Carpenter
Khwaja HA, Fatmi Z, Malashock D, Aminov Z, Siddique A, Carpenter DO. Eect of air pollution on
daily morbidity in Karachi, Pakistan, Journal of Local and Global Health Science, 2012:3 http://
Wadsworth Center, New York
State Department of Health,
Albany, NY
Department of Environmental
Health Sciences, School of Public
Health, University at Albany,
Albany, New York, USA;
Department of Community Health
Sciences, The Aga Khan University,
Karachi, Pakistan;
Chemistry Department,
University of Karachi, Karachi,
Institute for the Health and the
Environment, University at Albany,
Albany, New York, USA
Author for Correspondence
Haider A. Khwaja, PhD
Wadsworth Center, NYS Department
of Health
Empire State Plaza, PO Box 509
Albany, NY 12201-0509
Eect of air pollution on daily
morbidity in Karachi, Pakistan
Levels of daily particulates (PM2.5) were monitored at two sites in Karachi, Pakistan. One
site (Korangi) is an industrial and residential neighborhood, while the other (Tibet Center) is a
commercial and residential area near a major highway. Monitoring was done daily for a period
of six weeks during spring, summer, fall and winter. Particulate levels were extraordinarily
high, with the great majority of days falling into the “unhealthy for sensitive groups” or “very
unhealthy” categories. The mean PM2.5 levels in Karachi exceeded the WHO’s 24 hour air
quality guideline almost every day and often by a factor of greater than 5-fold. Daily emergency
room (ER) visits and hospital admissions for cardiovascular diseases were obtained by review
of medical records at three major tertiary and specialized hospitals. ER and hospitalizations
were reported relative to days in which the concentration of PM2.5 was less than 50 µg/m3,
and by 50 µg/m3 increments up to 300 µg/m3. There were statistically significant elevations
in rates of hospital admissions at each of the PM2.5 categories at the Korangi site, and at
concentrations >150 µg/m3 at the Tibet Center site. ER visits were significantly elevated only at
PM2.5 concentrations of between 151 and 200 µg/m3 at both sites. These results show that the
extremely elevated concentrations of PM2.5 in Karachi, Pakistan are, as expected, associated
with significantly elevated rates of hospital admission, and to a lesser extent, ER visits for
cardiovascular disease.
Keywords: Particulates; cardiovascular disease; respiratory disease; air monitoring; mega-city;
developing country
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Ambient air pollution is a significant factor shaping public health. Recent epidemiological
studies in developed countries have highlighted an association between urban concentrations
of air pollution and health effects [1-26]. Adverse health effects of air pollution primarily include
pulmonary [27-32] and cardiovascular diseases [33-45]. Particulate matter with an aerodynamic
diameter less than 10 mm (PM10) [46-49], especially less than 2.5 mm (PM2.5) [6, 50-53], and total
suspended particulate matter (TSP) [22, 54] have been identified as major pollution factors
affecting health. The focus of research on health effects of air pollution has shifted to smaller
particles. Because of the smaller diameter, PM2.5 can be deposited deeper in the lung alveoli
where they cannot be removed by the ciliary action, a process that removes larger particulates,
and consequently have negative effects on the lung [55]. Due to the large surface area of PM2.5,
toxins (e.g., organic compounds and heavy metals) can be absorbed onto the surface. Organs
such as the lung and heart, cells, and DNA can be damaged by the toxins. The PM2.5 particles are,
therefore, regarded as being more toxic than PM10 and TSP.
Most studies investigating an association between levels of air pollution and rates of
human disease have been conducted in developed countries where concentrations of air
pollution, climatic conditions, and many other factors are significantly different from those
in most developing countries. Air pollution is a critical problem in Asian cities with possibly
serious health impact. The World Health Organization (WHO) estimates that urban air pollution
contributes each year to about 800,000 deaths and 4.6 million healthy life-years lost worldwide,
but the burden is not equally distributed: approximately 65% of the deaths and lost life-years
occur in the developing countries of Asia [56]. This disparity demonstrates an urgent need for
conducting and evaluating environmental studies on the health effects of air pollution in major
cities of Asia.
The concurrent increase in the population, industrialization, energy use, and the number
of automobiles on the roads every year is giving rise to a threatingly high rate of increase in air
pollutants in the urban areas of Pakistan (population = 173 million in 2010). A study of air pollution
levels in major urban centers found that two major cities in Pakistan have one of the highest
TSP loading recorded so far in any mega city of the world (Figure 1) [57]. Karachi, located in the
southeastern part of Pakistan on the Arabian Sea, has a population of 18 million and is one of
the most heavily polluted mega cities in the world with serious human health risks [57-59]. The
city is congested with a large number of motor vehicles (> 1.3 million). It has a large industrial base
in and around the city. Three major industrial areas, Landhi, Korangi, and Sindh Industrial Trading
Estates (SITES), are shown on the map (Figure 2). Air pollution health studies that incorporate
emergency room visits and daily admissions to hospitals are advantageous, as they make it
possible to examine the relationship between acute health outcomes and daily variations in air
pollution [19,28,34-36,39,40,60,61]. However, not a single study of the health effects of air pollution
has been performed in this developing mega city.
To respond to the call for an in-depth investigation of the health effects of ambient air
pollution in one of the largest cities of the developing world [62], we conducted a time-series
study of the effects of short-term exposure to outdoor air pollution on daily morbidity for
cardiovascular diseases. This research will provide the scientific community and policy makers
as well as the public information about the health effects of poor ambient air quality and how it
compares to other developing and developed countries of the world. The specific aim is to carry
out a long-term environmental project to investigate the association between concentrations
of PM2.5 and hospital admissions and emergency room (ER) visits for cardiovascular diseases
among residents of various communities in Karachi.
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Figure 1. Comparison of TSP Concentrations at Karachi and Islamabad with those
reported for the various megacities of the world. Reproduced with permission
from Parekh et al (2001).
Figure 2. Map of Karachi, Pakistan.
Study location. This study covered a period of 12 months (August 2008 to August 2009) and
was carried out in the city of Karachi. The study population consisted of all people who resided in
various communities in Karachi and who visited ERs and/or were admitted to two major tertiary
care hospitals serving the city [National Institute of Cardiovascular Diseases (NICVD) and Aga
Khan University Hospital (AKUH with primary diagnoses of cardiovascular diseases.
The megacity Karachi (Figure 2) is the most urbanized, industrialized, and affluent city in
Pakistan. It is located in the southeastern part of Pakistan on the Arabian Sea (Latitude 240 51'
N; Longitude 670 02' E). Most of the land consists largely of flat or rolling plains, with hills on
the north and west and an undulating plain and long coastal area in the south-east. The hills
in Karachi are the off-shoots of the Kirthar Range extending from north to south for about 300
km. The highest point of these hills is about 528 m in the extreme north of the city. The city is
comprised of 18 urban/sub-urban towns (Baldia, Bin Qasim, Gadap, Gulberg, Gulshan, Jamshed,
Kemari, Korangi, Landhi, Liaquatabad, Lyari, Malir, New Karachi, North Nazimabad, Orangi, Saddar,
Shah Faisal, and Sindh Industrial Trading Estate) and 6 cantonments, with a population of 18
Khwaja et al, Journal of Local and Global Health Science 2012:3
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million, representing about 10% of Pakistans total. About 40% of the city’s population lives in low
income, non-zoned settlements and squatter colonies or “Kachi Abadis”.
Karachi’s climate is subtropical, almost desert like with scanty rainfall (mean annual
rainfall 256 mm), the bulk of which occurs during the July – August monsoon season. Pollutants
are pushed towards the shore during the northeast monsoon season and inland during the
southwest monsoons. The city is quite humid in the summer (85% humidity in August - the
wettest month) and is relatively dry in the winter (58% humidity in December - the driest month).
The average monthly temperature varies between 130C and 340C. During the summer (April
– August), the temperatures are high, ranging from 30 – 440C. Karachi has a large industrial
base in and around the city including oil-fired power plants, cement factories, steel mills, scrap
metal recycling plants, shipping, railroad yards, foundries, jute and textiles, oil refineries, heavy
petrochemical industries, automobile assembly plants, pharmaceuticals, printing and publishing
plants, food processing plants, paper mills, chemical, glass and ceramics, battery, tanneries, brick
kilns, and several light industries. There is also solid waste incineration and open burning of
municipal wastes.
Air pollution monitoring. Air pollution monitoring for PM2.5 was conducted at two fixed
stations located at Korangi (industrial/residential) and Tibet Center (commercial/residential),
located on M.A. Jinnah road in Saddar town (Figure 2) [63]. The M. A. Jinnah road is located in the
midst of a large central business district and is the main ring road in Karachi that is the busiest
road throughout the day and late evening hours. Approximately 300,000 vehicles pass M. A.
Jinnah road daily. The Korangi industrial area is the second largest industrial area of Karachi.
Approximately 2000 industries of various types are located in this area, which include refineries,
textile, chemical, and tanneries (> 100 units). The criteria for site selection included: i) location
of these stations should not be in the direct vicinity of vehicular traffic or industrial sources; ii)
locations should not be influenced by local pollution sources and should avoid buildings, trees, or
housing large emitters such as coal-, waste-, or oil-burning boilers, furnaces, flues, wood burning,
and incinerators; iii) unrestricted airflow around the sampler; and iv) sampler should be placed at
4-7 m above the ground level so that the air was relatively well mixed and less likely to be strongly
affected by sources in the immediate vicinity. Thus the PM2.5 monitoring results reflect the
general background urban air pollution level rather than local sources such as vehicular traffic or
industrial emissions.
The sampler (Figure 3) consisted of a housing, power supply, UPS as a backup, gooseneck,
5.72 cm inner diameter rubber stopper, 47 mm filter holder, data logger, mass flow meter, a pump
with a flow controller, elapsed time indicator, and a Teflon coated aluminum cyclone separator
(Model URG-2000-30EH) URG Corporation, Chapel Hill, NC, USA) with a cut size of 2.5 mm operated
at a flow rate of 16.7 L/min. PM2.5 samples for mass determination were collected on pre-weighed
47 mm 2.0 mm polytetrafluoroethylene (PTFE) membrane filters (Whatman Inc. Florham Park, NJ,
USA) with a polypropylene support ring (sequentially numbered). Sampling duration was 24 h (7:00
a.m to 7:00 a.m) at each site for 6 weeks in each quarter (i.e., January – March; April – June; July –
September; and October – December). The resulting total sampled air volume was ~ 24 m3. At the
end of each 24 h period, filters were carefully removed from the sampling device and were placed
inside the individual polyethylene petri-dishes. Graduate students under the expert supervision
of faculty members operated the monitoring sites.
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Figure 3. Schematic of the PM2.5 sampler (left) and image of sampler used in the study (right).
The laboratory for weighting PM2.5 was maintained as a “clean room” conditions to minimize
contamination, air flow interferences, and electrostatic charges. The filters, both before and after
sampling, were visually inspected for pinhole, loose material, discoloration, and non-uniformity.
They were conditioned for a minimum of 24 h in a controlled temperature/relative humidity
(RH) room to minimize the effects of water artifact (if any). The filter weights were obtained on a
microbalance (ATI CAHN, Model C-44). PM2.5 mass concentration was calculated in mg/m3 as the
difference in filter weight before and after sampling divided by the total sampled air volume.
Hospital data acquisition. Daily records of patients who visited ERs and/or were admitted
for cardiovascular diseases in NICVD and AKUH were collected. A protocol was developed for the
collection of patient’s information by trained team physicians. The data provided information
on age, gender, date of ER visit and/or admission and discharge, and the principal diagnosis
as well as demographic information. Data from AKUH was retrieved from a well-established
hospital information management system (HIMS). Our analysis focused on finding the association
between PM2.5 and daily cardiovascular hospitalizations and ER visits. It was not able to address
events that happened after admission. We reviewed the computerized records for the three
hospitals for the study period to identify all cases that were seen in ERs and/or admitted to the
hospitals for cardiovascular diseases. The causes of ER visits and/or hospital admissions were
coded according to our Classification of Cardiovascular Diseases. Prior approvals from Ethical
Review Committees of the participating hospitals and the Institutional Review Boards of the New
York State Department of Health and the University at Albany were obtained.
Meteorologic/weather variables. To allow adjustment for the effect of weather on ER visits
and hospital admissions, meteorological data (temperature - mean, maximum, and minimum
in 0C, relative humidity - mean, maximum, and minimum expressed as percentages, barometric
pressure, wind speed, and wind direction) were obtained as electronic files on a daily basis during
the sampling period from: All the computerized data of
PM2.5 and meteorological conditions were reviewed.
Quality control. An extensive Quality Assurance/Quality Control Program was maintained
throughout this investigation to ensure the integrity of data collected in the field. The QA/QC
plan included: detailed description of field and laboratory methods used, equipment calibration
procedures, procedures for field blanks, and storage conditions. One field filter blank check was
analyzed per batch of 10 samples. The microbalance was regularly checked with NIST-traceable
standard calibrated weights.
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The PTFE filters used for PM2.5 mass are comparatively small, each weighing ~ 150 milligrams.
Because of the size and weight of particles that are collected on these filters, net weights were
measured in micrograms. With the operational constraints of the microbalances, the introduction
of water vapor on the filters, and the measurement of small particles and filter loadings, the
weighing laboratory was designed to perform under strict operational criteria. Weights were
considered valid if duplicate weights were within 10 mg of each other.
Throughout this study, careful consideration was given to all possibilities for the
introduction of errors, either systematic or random, during each of the discrete steps that
comprise the sequence of operations, ranging from sample and hospital data collection to
data analysis. The computerized data for all the hospitals, PM2.5 results, and meteorological
conditions were reviewed thoroughly by at least two independent team members.
Statistical analysis. We created separated data records for each PM2.5 monitoring site
and hospital, including the number of ER visits per day and admissions per day, the daily
concentrations of PM2.5 , and the daily mean temperature and relative humidity. Summary
statistics were calculated for each variable including mean, standard deviation, minimum,
maximum, and percentiles. We conducted analyses on hospital data, pollutant, and
meteorological variables, stratified by location, hospital, ER visits and/or hospital admissions,
age, and gender. A generalized linear model (GLM) using negative binomial regression [28,64]
was used to conduct a time series analysis of daily counts of hospital data, daily concentrations
of PM2.5 (µg/m3), and covariates in order to estimate the influence of air pollution on ER
visits and hospital admissions due to cardiovascular diseases. In addition, data were modeled
with generalized linear Poisson model with scaled variance estimates to account for Poisson
overdispersion [61]; however, results yielded large ratio values between deviances to degree of
freedom, possibly indicating the presence of model misspecification or over dispersion. Negative
binomial regression yielded results with acceptable ratios, and consequently was chosen for final
models. Deviance and Pearson residuals were used to assess goodness of fit.
Models were adjusted for the effects of temporal trends and meteorological variables by
including the following: dummy variables for day of the week (DOW), holidays, season, cubic
splines of day (study day) with knots at the first sampling of each month, and cubic splines of
average temperature and relative humidity. Lag time between pollutant measurements and ER
visits and/or hospital admissions were evaluated using 0-, 1-, 2-, and 3-day lags for cardiovascular
diseases. Data were analyzed using the SAS statistical package (SAS Institute Inc., Cary, NC, USA;
Version 9.2).
Results and Discussion
No prior studies exist in Pakistan examining the association between levels of air pollution
with daily morbidity due to cardiovascular diseases. These preliminary results will provide
scientific community, public, and policy makers with estimates of current risks of cardiovascular
ER visits and hospitalization as a result of poor air quality. A total of 24,124 (68.6%) ER visits
and 11,023 (31.4%) hospital admissions (HAs) due to cardiovascular diseases among adults and
children living in various communities in Karachi occurred in the study period. Table 1 gives
the summary statistics for daily counts of ER visits and HAs at both sites, broken down into age
groups and by gender. Variation among the hospitals is demonstrated by the fact that the most
ER visits and hospitalizations were determined at the NICVD, which is the largest cardiac center
in the country and receives only patients with cardiovascular diseases. Overall, there were more
male hospitalizations than female. HAs were more common among males, which accounted
for approximately 58% of the total number of patients from both AKUH and NICVD (63.2% and
57.2%, respectively). Female ER visits were somewhat higher than for males However, variations
among hospitals were observed, with females comprising 39.4% and 56.6% at AKUH and NICVD,
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respectively. Men and women between the ages of 40 and 60 comprised the greatest percentage
of all HAs and ER visits.
Our hospital data indicated that the leading causes of cardiovascular disease are ischemic
heart disease (IHD, 37%), hypertension (HTN, 25%), and myocardial infarction (MI, 12%).
Cardiomyopathy (CMP, 3%) and mitral stenosis (MS, 3%) were the principal causes of ER visits and
hospital admissions in the “All Other” category. Figure 4 shows the breakdown of ER visits and
hospital admissions due to top three cardiovascular diseases by age. A persistent elevation in ER
visits and hospital admissions with age is evident. Adults between 41 and 60+ years of age were
the group seeking health care most frequently. Ischemic heart disease followed by HTN and MI
were the most common causes of admissions in this age group. ER visits due to IHD followed by
HTN and MI were more frequent in the 51-60 age groups. The percentage distribution observed in
the 41-50 or 60+ years of age and older group was slightly decreased relative to that observed for
the 51-60 years old. The lack of increased ER visits and hospital admissions in children between
0-12 years of age is not surprising, since young children are usually kept home, are less exposed
to outdoor air pollution and are less likely than older adults to suffer from cardiovascular
disease. This observation is consistent with earlier studies in developed countries by Schwartz
and Dockery [65], showing that the increased risk of deaths was greatest in the elderly, and was
greatest for cardiovascular disease. A more detailed examination of particulate matter-related
risk by deciles of age [66] showed the risk beginning to increase at approximately 40 years of age
and reaching its maximum for those 75 years of age and older.
Figure 4. Percentage of ER visits and hospital admissions according to age categories and by
leading CVDs observed during the study.
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Figure 5 shows the mean daily temperature and humidity during the study period.
Figure 5. Plots of 24-hour mean temperature and relative humidity in Karachi,
August 2008 - August 2009.
Temperature and humidity varied between 150C and 350C (mean ~ 300C except 20.40C in
the cold season) and between 24% and 88%, respectively, reflecting the subtropical climate
in Karachi. The winter season in Karachi is dominated by cold, dry air (mean humidity = 57%)
and ground-based inversion occur frequently and increases the concentration of pollutants.
Consequently, people are likely to go outdoors and are expected to have higher risks of ambient
air pollution exposure. In contrast, summer season is governed by high temperature and humidity
(mean temperature = 300C ; mean humidity = 74%), people generally use air conditioning, thus
expected to be less exposed to outdoor air pollution.
Descriptive statistics of PM2.5 concentrations and meteorological variables are presented
in Table 2. The one-year monitoring data revealed that the mass concentration of Karachi PM2.5
at the Korangi site showed a seasonal variation with higher values in winter (112 µg/m3) and
autumn (106 µg/m3), lower values in spring (94.8 µg/m3), and the lowest in summer (87.5 µg/
m3), whereas at the Tibet Center site the ranking was winter > summer > spring > autumn. Low
boundary layer heights combined with increased emissions from heating sources and biomass
burning may lead to high PM2.5 concentrations in the winter at both the sites. However, better
dispersion of pollutants caused by the increased boundary layer heights and precipitation in
summer may likely lower the PM2.5 concentrations. The same findings were also observed in the
urban area of Beijing [67], with the highest PM2.5 concentrations observed in the winter and the
lowest concentrations found in the summer.
Air quality index (AQI) is a good indicator of daily air quality and its implications for short-
term health. The higher the AQI value, the greater the level of air pollution and health risk. The AQI
was calculated [68] for PM2.5 at Korangi and Tibet Center (Figure 6), with the highest pollutant-
specific value reported as a “level of health concern” (moderate: 16 - 35 µg/m3, unhealthy for
sensitive groups: 36 - 65 µg/m3, unhealthy: 66 - 150 µg/m3, very unhealthy: 151 - 250 µg/m3, and
hazardous: 251 - 300 µg/m3). Based on the air quality index, it is evident that there were 19% days
of unhealthy air quality for sensitive groups, 67% days of unhealthy air quality, and 12% days of
very unhealthy air quality during the entire study period at Korangi site. A study by Luginaah
et al., [15] found associations between ambient air pollution and daily hospital admission of
respiratory diseases especially among females in the Windsor, Canada “area of concern”. The
mean PM10 concentration was 50.6 ± 35.5 µg/m3 and based on the air quality index, there were
165 days of poor air quality, 583 days of moderate air quality, and 1,352 days of good air quality
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during the entire study period (>2000 days). On the basis of the multi-pollutant index (MPI), Gurjar
et al., [69] report Dhaka, Beijing, Cairo, and Karachi as the most polluted, whereas Osaka-Kobe,
Tokyo, Sao Paulo, Los Angeles, New York, and Buenos Aires as the least polluted megacities.
Figure 6. Air Quality Index (AQI) at Korangi and Tiber Center in Karachi, Pakistan,
August 2008 – August 2009.
PM2.5 concentrations in Karachi are among the highest ever documented in the world. The
World Health Organization’s (WHO) 24 h air quality guideline for PM2.5 is 20 µg/m3 [70]. The mean
concentrations of PM2.5 in Karachi were in “exceedance” by a factor of at least five, implying that
airborne fine particles in Karachi have significant adverse impacts on health.
The time-series analysis showed evidence of positive associations of ambient fine particle
air pollution, meteorological factors, and seasonal parameters with ER visits and hospital
admissions due to cardiovascular diseases in Karachi. Table 3 presents the relative risk estimates
of hospital data per 50 µg/m3 in pollution. As a result of there being fewer data observed for
PM2.5 concentrations in the range 251 – 300 µg/m3, we have combined this category with PM2.5
(201 – 251 µg/m3). Note that although fine particulate air pollution was measured at two different
sites, Korangi and Tibet Center, increases in same day PM2.5 concentrations were associated with
an increase in ER visits and HAs at both sites. Statistically strongest relationships were observed
for HAs (RR = 1.613, 95% CI = 1.274 – 2.043 for Korangi; RR = 2.036, 95% CI = 1.424 – 2.911 for Tibet
Center) for PM2.5 concentrations (151 – 200 µg/m3). Unexpectedly the relations were found to
be less strong at the very highest PM2.5 concentrations –possibly residents avoid going outside
on days of extreme pollution. This analysis provides to the growing evidence linking ambient
particulate matter with daily morbidity in developing countries.
There have been a number of epidemiologic studies in developed countries that have
reported significant associations between short-term exposures to ambient particulate air
pollution and cardiovascular mortality. Studies reporting particulate matter associations with
cardiovascular hospitalizations have been more recent. Our findings complement substantial
evidence on positive associations between day-to-day variation in PM2.5 concentrations and
hospitalizations for cardiovascular diseases in developed countries. Dominici et al., [35] reported
a positive association of PM2.5 with risk for hospital admission for cardiovascular and respiratory
diseases in 204 US urban counties for 1999-2002. The largest association was for heart failure,
which had a 1.28% (95% CI, 0.78% - 1.78%) increase in risk per 10 µg/m3 increase in same-day
PM2.5 . Ito et al., [71] analyzed Medicare admission data for Detroit, Michigan for 1992-1994, along
with size fractionated particle concentration data from a nearby monitoring station in Windsor,
Ontario and showed positive associations of PM2.5 for hospitalization for ischemic heart disease,
heart failure, pneumonia, and chronic obstructive pulmonary disease (COPD). A study by Haley
et al., [36] that analyzed cardiovascular disease hospitalizations in New York State between
2001 and 2005 found the strongest association with heart failure. The increased risk (95% CI) of
hospitalizations per 10 µg/m3 increase in PM2.5 was found to be 0.46% and 1.51% for ischemic
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heart disease and heart failure, respectively. Mean concentrations of PM2.5 reported in this
study for various locations in New York State and New York City ranged from 11.1 to 15.5 µg/m3
. Relative risk estimates (95% CI) for all cardiovascular diseases from our study indicate that an
increase in PM2.5 concentrations of 10 µg/m3 at Korangi and Tibet Center is associated with an
increase in 1.6% HAs and 1.3% and 1.6% ER visits, respectively. The mean annual concentration of
PM2.5 at Korangi and Tibet Center is 100.1 and 76.6 µg/m3 , respectively. At these levels, the risk
associated with ambient particulate concentration far exceeds that which has been observed in
developed countries.
Our study has strengths and limitations. The strengths include the site of study, which is
includes the largest public hospital in the country for heart diseases serving the population of
the urban metropolitan city of Karachi. The study design and the quality of hospital data are also
major strengths. Moreover, our results are consistent with those in developed countries relating
CVD hospitalizations to exposure to particulate air pollution. Limitations of this study are the
same as in other studies of this kind. First, we assume that air pollutant concentrations measured
at fixed sites serve as a proxy for the personal exposure during the study period. Factors such
as outdoor and indoor emission sources, time spent outdoors, time spent indoors, life style, and
smoking may influence the validity of this assumption. However, personal exposure assessment
is not realistic. Second, the potential miscoding and diagnosis of CVD events may introduce a bias
in daily time-series analyses. We used only primary diagnosis, an approach that should diminish
misclassification of outcomes. Third, we monitored only HAs and ER visits and did not have
information about outpatient or physician office visits, ER visits and hospitalizations at other
hospitals. In addition, our study does not include patients with CVD who did not seek treatment,
or died at home or en-route to the hospital. We also cannot distinguish multiple hospitalizations
for one individual from those for several individuals. For these reasons our study is likely to
under-represent the true risk of ambient air pollution to the residents of Karachi. In spite of
these limitations our results provide strong evidence that significant morbidity is associated with
PM2.5 air pollution.
This study is one of the first to investigate the relationship between particulate air pollution
and cardiovascular diseases in a mega city in a developing country where particulate levels are
extraordinarily high. The PM2.5 levels averaging about 5 - 7 fold higher than the WHO guideline
on a “good” day, and with frequent peaks at levels as high as 279 µg/m3. Particulates were
measured at two sites, one more industrial than the other, but in general the patterns over time
were similar. Our studies show, as has been demonstrated elsewhere in developed countries,
that higher levels of PM2.5 are associated with a striking elevation in rates of ER visits and
hospitalizations for cardiovascular diseases (ischemic heart disease, hypertension, myocardial
infarction). Because of the striking levels of air pollution that we have documented, it is
imperative that further investigation of health outcomes in mega cities of developing countries
be performed.
This work was supported by Pakistan-US Science and Technology Cooperative Program
(administered by National Academy of Sciences) under the grant # PGA-7251-07-010. Authors
would like to thank Wadsworth Center, NYSDOH, University at Albany, Higher Education
Commission, Pakistan, Aga Khan University Hospital, and NICVD, Karachi and physicians who
provided and collected the health data used in this study. We owe a particular debt of gratitude
to Ms. Kelly Robbins, Drs. Vincent Dutkiewicz, Amber Sinclair, Aneeta Khoso, Sumayya Saied, Ms.
Naseem Parvez Ali, and Mr. Kamran Khan who assisted us in all aspects of this work.
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... Several studies on air pollution and its effects have primarily focused on developed countries (Khwaja et al., 2012), resulting in adequate environmental regulations. In contrast, developing countries in Southeast Asia remain adversely affected by air pollution. ...
... Karachi is the largest city in Pakistan, with an annual growth rate of 6 %. As the most affluent Pakistani city, Karachi is home to major industries as described in Khwaja et al. (2012) and Lurie et al. (2019), representing 30 % of country's manufacturing sectors (Barletta et al., 2002;Anjum et al., 2021). Currently, there are 4.14 million registered vehicles plying the roads of Karachi. ...
... Karachi is the only megacity which does not have the mass transit system. Open burning of municipal garbage and waste incineration are also a serious environmental problem in Karachi (Khwaja et al., 2012). In this and other major cities of Pakistan, comprehensive assessments of mass concentrations, chemical compositions, sources, and health risks of PM 2.5 are scarce, which limits the policy makers ability to implement appropriate pollution control measures. ...
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Like many urban centers in developing countries, the effect of air pollution in Karachi is understudied. The goal of this study was to determine the chemical characterization, temporal and seasonal variability, sources, and health impacts of fine particulate matter (PM2.5) in Karachi, Pakistan. Daily samples of PM2.5 were collected using a low-volume air sampler at two different sites Makro and Karachi University over the four seasons between October 2009 and August 2010. Samples were analyzed for black carbon (BC), trace metals, and water-soluble ions. Results showed that the annual average concentrations of PM2.5 at Makro and Karachi University were 114 ± 115 and 71.7 ± 56.4 μg m-3, respectively, about 22.8 and 14.3-fold higher than the World Health Organization annual guideline of 5 μg m-3. BC concentrations were 3.39 ± 1.97 and 2.70 ± 2.06 μg m-3, respectively. The concentrations of PM2.5, BC, trace metals, and ions at the two sites showed clear seasonal trends, with higher concentrations in winter and lower concentrations in summer. The trace metals and ionic species with the highest concentrations were Pb, S, Zn, Ca, Si, Cl, Fe, and SO42-. The air quality index in the fall and winter at both sites was about 68 %, which is "unhealthy" for the general population. Positive Matrix Factorization revealed the overall contribution to PM2.5 at the Makro site came from three major sources - industrial emissions (13.3 %), vehicular emissions (59.1 %), and oil combustion (23.3 %). The estimates of expected number of deaths due to short-term exposure to PM2.5 were high in the fall and winter at both sites, with an annual mean estimate of 3592 at the Makro site. Attention should be paid to the reduction of inorganic pollutants from industrial facilities, vehicular traffic, and fossil fuel combustion, due to their extremely high contribution to PM2.5 mass and health risks.
... The standard air of a region can be contaminated and not be retreated (Manisalidis et al., 2020). South, East Asian countries including Pakistan, India, Bangladesh and China with a burgeoning population (45% of the world population) are predominantly affected by hazardous air pollution mainly caused by an abundance of population, rapid economic growth, industrialization and urbanization (Pakistan, 2006) with an associated increase of energy demand (Khwaja et al., 2012;Vadrevu et al., 2017). The IQAir report from thirty most pollutant cities, twenty-seven belong to South Asia, whereas Lahore and Karachi are top ten in this list (Lin et al., 2022;IQAir, 2020). ...
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Air quality in Karachi, Pakistan appears to be deteriorating in the world due to rapid increase in population, economic growth and subsequent increase in urbanization and energy demand. This study Re-is about the cumulative effects of anthropogenic activities on air chemistry of the study area atmosphere with ground base concentration measurements of gaseous air pollutants (SO2, NO2, CO, CO2 and O3), particulates (PM10 and TSP), Methane, Lead and Noise with temperature and seasonal influences on Karachi city. The primary goal of this study was to define spatial and temporal distribution of air pollutants with ArcGIS, seasonal behavior of airborne contaminants, convert the five major pollutants termed as criteria pollutants into Air Quality Index (AQI) and their temperature association for future prospects. The maximum average values of four seasons concentrations of air pollutants were found SO2=64.5 ug/m3 (GR), NO2=55.5 ug/m3 (FB), CO= 8.00 mg/m3 (CC), CO2=645 ug/m3 (NZ), O3=56.7 ug/m3 (ST), PM10=225 μg/m3 (CC), TSP=402 ug/m3 (CC), Methane=1.65 m/gm3 (CC), Lead=5.1 ug/m3 (ST), and Noise=85 dB (GR). The minimum four seasons average concentration values with monitoring location as {SO2=48.2 ug/m3 (FB), NO2=44.6 ug/m3 (NZ), CO=4.1 mg/m3 (BC), CO2=601 ug/m3 (JH), O3=42.4 ug/m3 (GR), PM10=150 ug/m3 (BC), TSP=226 ug/m3 (JH), Methane=0.68 mg/m3 (BC), Lead=32 ug/m3 (GZ), and Noise=81 dB (BC). The spatial-temporal analysis of air quality revealed that the pollutants in the summer are higher in industrial and high-density traffic junctions. In this study, temperature and air quality are significantly associated, while rainfall and relatively high humidity days are negatively correlated. High temperature months have highest air pollution values, whereas the washout impact of precipitation and relative humidity have the lowest levels. The analysis of air quality index parameters demonstrated a high coherence among NO2, CO and O3 with variation in
... If per capita CO 2 emissions are increased by one metric ton, life expectancy will be reduced by 2.934 years on average. Similar results are obtained by Ilyas et al. (2010), Khwaja et al. (2012), and Asghar et al. (2020) for Pakistan. The results also reveal that renewable energy consumption has a positive and insignificant impact on health status. ...
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Since the start of this century, much attention has been given to economic growth and environmental changes and their effects on human beings. The present study has developed a comprehensive model to discuss the nexus between economic growth, environmental degradation, and public health. Furthermore, renewable energy consumption and public health spending are used as moderators to make the model more inclusive. The time series data from 1972 to 2020 has been used, and a regression path modeling tool SPPS-PROCESS Model 29, has been applied to carry out the results. The results showed a positive and significant effect of economic growth on environmental degradation, while renewable energy consumption reduces environmental degradation. Furthermore, environmental degradation is negatively affecting the health status in Pakistan. The results of the total effects showed that economic growth positively contributes to public health with a low coefficient. The indirect conditional impact of economic growth on human health through the mediating role of environmental degradation becomes positive from negative in the long run due to renewable energy and public spending on health. Based on the result, some policies are suggested in the last section of this study.
... Karachi is Pakistan's most populous and industrialized city and one of the megacities in the world with a 2.4% annual population growth rate (Irfan et al., 2014). It has a population of over 20 million residents and is in a period of huge economic development (Khwaja et al., 2012). Karachi is an industrial hub that is home to 20,000 small and large industrial units in various areas including steel mills, cement factories, oil-fired power plants, oil refineries, chemical industries, brick kilns, scrap metal recycling, and foundries (Lurie et al., 2019). ...
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Karachi, Pakistan, is a priority site for air pollution research due to high emissions of air pollutants from vehicular traffic, industrial activities, and biomass burning, as well as rapid growth in population. The objectives of this study were to investigate the levels of gaseous pollutants (NO, NO2, O3, HNO3, and SO2) in Karachi, to determine temporal and seasonal variations, to compare Karachi’s air quality with other urban centers, to identify relationships with meteorological conditions, to identify source characterization, and to perform a backward-in-time trajectory analysis and a health impact assessment. Daily samples of gaseous pollutants were collected for six consecutive weeks in each of the four seasons for a year. Daily maximum concentrations of NO (90 parts per billion by volume (ppbv)), NO2 (28.1 ppbv), O3 (57.8 ppbv), and SO2 (331 ppbv) were recorded in fall, while HNO3 (9129 parts per trillion by volume (pptv)) was recorded in spring. Seasonal average concentrations were high in winter for NO (9.47 ± 7.82 ppbv), NO2 (4.84 ± 3.35 ppbv), and O3 (8.92 ± 7.65 ppbv), while HNO3 (629 ± 1316 pptv) and SO2 (20.2 ± 39.4 ppbv) were high in spring and fall, respectively. The observed SO2 seasonal average concentration in fall (20.2 ± 39.4) was 5 times higher than that in summer (3.97 ± 2.77) with the fall 24-h average (120 ppbv) exceeding the WHO daily guideline (7.64 ppbv) by a factor of about 15.7. A health impact assessment estimated an increase of 1200 and 569 deaths due to short-term exposure to SO2 in fall and spring, respectively. Chronic daily intake estimated risk per 1000 was 0.99, 0.47, 0.45, and 0.26 for SO2 in fall, NO in winter, O3 in winter, and NO2 in spring, respectively. This study confirms the effect of poor urban air quality on public health and demonstrated the influence of photochemical reactions as well as unfavorable meteorological conditions on the formation of secondary pollutants.
... Karachi has been subjected to substantial growth of heavy industries and immense utilization of coal. This has resulted in the levels of fine particulate matter averaging 5-7 fold higher than the WHO guidelines [16]. Moreover, Karachi is an overcrowded city of more than 20 million people [3,17] and as of 2013, had an annual traffic circulation of about 6.3 billion commuters in the city's public transport system [3]. ...
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Black carbon (BC) exposure and inhalation dose of a commuter using four traffic modes (car, bus, auto-rickshaw, and motorbike) were monitored in Karachi, Pakistan. The real-time exposure concentrations in office-peak and off-peak hours were recorded during the winter season using microAeth® AE51 BC monitors. Exposure concentrations were higher in peak hours and were reduced to half in the off-peak time. The inclination levels of the inhaled dose were similar, and this trend was observed with all four modes of commute. The motorbike was found to be the most exposed mode of transportation, followed by auto-rickshaws, cars, and buses, respectively. However, the order was reversed when accounting for inhaled doses, e.g., the inhalation dose for auto rickshaws was highest, followed by the bus, motorbike, and car, respectively. Spatiotemporal analysis reveals that driving roads with lower traffic intensity and fewer intersections resulted in lower exposures. Therefore, traffic intensity, road topology, the timing of the trip, and the degree of urbanization were found to be the major influences for in-vehicle BC exposure.
... (Yadav et al.,2018). The coincidence of the re activities with the unfavorable meteorological conditions such as a stable boundary layer with near-surface temperature inversion results in the cold and polluted air trapped close to the surface, promoting the hazy conditions (Ghosh et al., 2019;Khwaja et al., 2012). ...
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Stubble-burning in northern India is an important source of atmospheric particulate matter (PM) and trace gases, which significantly impact local and regional climate, in addition to causing severe health risks. Scientific research on assessing the impact of these burnings on the air quality over Delhi is still relatively sparse. The present study analyzes the satellite-retrieved stubble-burning activities in the year 2021, using the MODIS active fire count data for Punjab and Haryana, and assesses the contribution of CO and PM 2.5 from such biomass-burning activities to the pollution load in Delhi. The analysis suggests that the satellite-retrieved fire counts in Punjab and Haryana were the highest among the last five years (2016–2021). Further, we note that the stubble-burning fires in the year 2021 are delayed by ~ 1 week compared to that in the year 2016. To quantify the contribution of the fires to the air pollution in Delhi, we use tagged tracers for CO and PM 2.5 emissions from fire emissions in the regional air quality forecasting system. The modeling framework suggests a maximum daily mean contribution of the stubble-burning fires to the air pollution in Delhi in the months of October-November 2021 to be around 30–35%. The quantification of this contribution is critical from the crop-residue and air-quality management perspective for policymakers in the source and the receptors regions, respectively.
... Numerous epidemiological studies have demonstrated that exposure to PM has adverse health effects on the cardiovascular and respiratory systems, and can lead to premature death [13][14][15][16][17]. In one study of ten European cities, road traffic emissions (which consist of both PM and gases) were associated with 14% of the cases of incident childhood asthma and 15% of cases of childhood asthma aggravation [7]. ...
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Air pollution impacts human health and the environment, especially in urban cities with substantial industrial activities and vehicular traffic emissions. Despite increasingly strict regulations put in place by regulatory agencies, air pollution is still a significant environmental problem in cities across the world. The objective of this study was to evaluate the environmental pollution from stationary and mobile sources using real-time monitoring and sampling techniques to characterize size-segregated particulate matter (PM), black carbon (BC), and ozone (O3) at the Port of Albany, NY. Air pollution monitoring was carried out for 3 consecutive weeks under a 24-hour cycle in 2018 at the New York State Department of Environmental Conservation (NYSDEC) site within the Port. Sampling was done with an AEROCET 531, optical particle sizer (OPS), ozone monitor, and MicroAeth AE51. Higher mass and number concentrations of size-segregated particles were observed during the daytime. PM2.5 and PM10 concentrations ranged from 1 to 271 micrograms per cubic meter (µg/m3) and 1 to 344 µg/m3, respectively. While these values do not exceed the level of the USEPA 24-hour standards, frequent sharp peaks were observed at higher concentrations. Size-segregated PM at sizes 0.3 µm and 0.374 µm recorded maximum concentrations of 10,1631 particle number per cubic centimeter (#/cm3) and 43,432 #/cm3, respectively. Wide variations were observed in the particle number concentrations for 0.3 µm, 0.374 µm, and 0.465 µm sizes, which ranged from 1,521 to 10,1631 #/cm3; 656 to 43,432 #/cm3; and 311 to 29,271 #/cm3, respectively. BC concentration increased during morning and evening rush hours with the maximum concentration of 11,971 ng/m3 recorded at 8:00 AM. This suggests that mobile sources are the primary contributor to anthropogenic sources of BC within the Port. Episodic elevations in the concentrations of size-segregated PM and BC confirmed the contribution of industrial and vehicular activities around the Port of Albany. This study underscores the importance of measuring particles on a size-segregated basis in order to more fully understand the contributions of the multiple sources present within and surrounding a port environment.
... Severe haze is seen over the southern parts of the Indian Continent during the winter season [13] because stubble fires have become rampant in Northern India (air mass movement carrying pollutants), especially in Punjab, Haryana, and some parts of western Uttar Pradesh. The low temperature in winter, especially from October to December, results in inversion conditions which act as a favourable condition for pollutants concentrating in the lower troposphere [14], which leads to experiencing poor air quality in New Delhi and NCR (National Capital Region) that are listed among the top ranking most-polluted city areas in the world since 1990. ...
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The problems of atmospheric pollutants are causing significant concern across the globe and in India. The aggravated level of atmospheric pollutants in the surrounding environment poses serious threats to normal living conditions by deteriorating air quality and causing adverse health impacts. Pollutant concentration increases during harvesting seasons of Kharif/Rabi due to stubble burning and is aggravated by other points or mobile sources. The present study is intended to monitor the spatio-temporal variation of the major atmospheric pollutants using Sentinel-5P TROPOMI data through cloud computing. Land Use/Land Cover (LULC-categorization or classification of human activities and natural coverage on the landscape) was utilised to extract the agricultural area in the study site. It involves the cloud computing of MOD64A1 (MODIS Burned monthly gridded data) and Sentinel-5P TROPOMI (S5P Tropomi) data for major atmospheric pollutants, such as CH4, NO2, SOX, CO, aerosol, and HCHO. The burned area output provided information regarding the stubble burning period, which has seen post-harvesting agricultural residue burning after Kharif crop harvesting (i.e., rice from April to June) and Rabi crop harvesting (i.e., wheat from September to November). The long duration of stubble burning is due to variation in farmers’ harvesting and burning stubble/biomass remains in the field for successive crops. This period was used as criteria for considering the cloud computing of the Sentinel-5P TROPOMI data for atmospheric pollutants concentration in the study site. The results showed a significant increase in CH4, SO2, SOX, CO, and aerosol concentration during the AMJ months (stubble burning of Rabi crops) and OND months (stubble burning of Kharif crops) of each year. The results are validated with the ground control station data for PM2.5/PM10. and patterns of precipitation and temperature-gridded datasets. The trajectory frequency for air mass movement using the HYSPLIT model showed that the highest frequency and concentration were observed during OND months, followed by the AMJ months of each year (2018, 2019, 2020, and 2021). This study supports the role and robustness of Earth observation Sentinel-5P TROPOMI to monitor and evaluate air quality and pollutants distribution.
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Introduction Environmental contamination has become a severe problem as a result of urbanization and industrialization, particularly because of the release of heavy metals which have drawn a lot of attention due to their harmful effects, persistence, and bioaccumulation in the environment. Methods The present study, which was conducted from July to October 2019, provides the spatial distribution, contamination levels, and health risks assessment for four heavy metals, Pb, Zn, Cr and Ni, in the urban area within Baghdad city, depending on the type of activity and the nature of land use. Results The results show that the average value of the contamination levels for the four heavy metals were within the reference values of soil in Baghdad city. The pollution assessment indices geo-accumulation index (Igeo) and integrated pollution index (IPI), carcinogenic and noncarcinogenic health index (HI), and hazard quotient (HQ) were studied. The results of Igeo and IPI in Sheikh Omar (industrial area), Baghdad Al-Jadida (commercial area), and Sadr City (high population density) were relatively greater than in the other areas. The IPI value ranged from 1.51 in Sheikh Omar to 0.75 in Taji (agricultural area). Conclusions The values of the health risk assessment indices (HI and HQ) indicate that the levels of the four heavy metals in the studied sites were within the safe limits (
As of today, increased air pollution has disrupted the air quality levels, deeming the air unsafe to breathe. Traditional systems are hefty, costly, sparsely distributed, and do not provide ubiquitous coverage. The interpolation used to supplement low spatial coverage induces uncertainty especially for pollutants whose concentrations vary significantly over small distances. This chapter proposes a solution that uses satellite images and machine/deep learning models to timely forecast air quality. For this study, Lahore is chosen as a study area. Sentinel 5-Precursor is used to gather data for Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), and Carbon Monoxide (CO) for years 2018-2021. The data is processed for several AI models, where convolutional neural networks (CNN) performed the best with mean squared error (MSE) 0.0003 for the pollutants. The air quality index (AQI) is calculated and is shown on web portal for data visualization. The trend of air quality during COVID-19 lockdowns is studied as well, which showed reduced levels of NO2 in regions where proper lockdown is observed.
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The APHEA 2 project investigated short-term health effects of particles in eight European cities. In each city associations between particles with an aerodynamic diameter of less than 10 m (PM 10) and black smoke and daily counts of emergency hospital admissions for asthma (0–14 and 15–64 yr), chronic obstructive pulmonary disease (COPD), and all-respiratory disease (65 yr) controlling for environmental factors and temporal patterns were investigated. Summary PM 10 effect estimates (percentage change in mean number of daily admissions per 10 g/m 3 increase) were asthma (0–14 yr) 1.2% (95% CI: 0.2, 2.3), asthma (15–64 yr) 1.1% (0.3, 1.8), and COPD plus asthma and all-respiratory (65 yr) 1.0% (0.4, 1.5) and 0.9% (0.6, 1.3). The combined estimates for Black Smoke tended to be smaller and less precisely estimated than for PM 10. Variability in the sizes of the PM 10 effect estimates between cities was also investigated. In the 65 groups PM 10 estimates were positively associated with annual mean concentrations of ozone in the cities. For asthma admissions (0–14 yr) a number of city-specific factors, including smoking prevalence, explained some of their variability. This study confirms that particle concentrations in European cities are positively associated with increased numbers of admissions for respiratory diseases and that some of the variation in PM 10 effect estimates between cities can be explained by city characteristics.
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Efforts to understand and mitigate the health effects of particulate matter (PM) air pollution have a rich and interesting history. This review focuses on six substantial lines of research that have been pursued since 1997 that have helped elucidate our understanding about the effects of PM on human health. There has been substantial progress in the evaluation of PM health effects at different timescales of exposure and in the exploration of the shape of the concentration-response function. There has also been emerging evidence of PM-related cardiovascular health effects and growing knowledge regarding interconnected general pathophysiological pathways that link PM exposure with cardiopulmonary morbidity and mortality. Despite important gaps in scientific knowledge and continued reasons for some skepticism, a comprehensive evaluation of the research findings provides persuasive evidence that exposure to fine particulate air pollution has adverse effects on cardiopulmonary health. Although much of this research has been motivated by environmental public health policy, these results have important scientific, medical, and public health implications that are broader than debates over legally mandated air quality standards.
The relation between air pollution and mortality in London was examined for the winters of 1958–1972. The data exhibited a high degree of autocorrelation, requiring analyses using autoregressive models. There was a highly significant relation between mortality and either particulate matter or sulfur dioxide (after controlling for temperature and humidity), both overall and in each individual year. Graphic analysis revealed a nonlinear relation with no threshold, and a steeper exposure-response curve at lower air pollution levels. in models with both pollutants, particulate matter remained a significant predictor with about a 10% reduction in its estimated coefficients, while sulfur dioxide was insignificant, with a large drop in its estimated coefficient The authors conclude that particulates are strongly associated with mortality rates in London, and the relation is likely causal.
Although some consensus has emerged among the scientific and regulatory communities that the urban ambient atmospheric mix of combustion related pollutants is a determinant of population health, the relative toxicity of the chemical and physical components of this complex mixture remains unclear. Daily mortality rates and concurrent data on sizefractionated particulate mass and gaseous pollutants were obtained in eight of Canada's largest cities from 1986 to 1996 inclusive in order to examine the relative toxicity of the components of the mixture of ambient air pollutants to which Canadians are exposed. Positive and statistically significant associations were observed between daily variations in both gas- and particulate-phase pollution and daily fluctuations in mortality rates. The association between air pollution and mortality could not be explained by temporalvariation in either mortality rates or weather factors. Fine particulate mass (less than 2.5 μm in average aerometric diameter) was a stronger predictor of mortality than coarse mass (between 2.5 and 10 μm). Size-fractionated particulate mass explained 28% of the total health effect of the mixture, with the remaining effects accounted for by the gases. Forty-seven elemental concentrations were obtained for the fine and coarse fraction using nondestructive x-ray fluorescence techniques. Sulfate concentrations were obtained by ion chromatography. Sulfate ion, iron, nickel, and zinc from the fine fraction were most strongly associated with mortality. The total effect of these four components was greater than that for fine mass alone, suggesting that the characteristics of the complex chemical mixture in the fine fraction maybe a better predictor of mortality than mass alone. However,the variation in the effects of the constituents of the fine fraction between cities was greater than the variation in the mass effect, implying that there are additional toxic components of fine particulate matter not examined in this study whose concentrations and effects vary between locations. One of these components, carbon, represents half the mass of fine particulate matter. We recommend that measurements of elemental and organiccarbon be undertaken in Canadian urban environments to examine their potential effects on human health.
Lahore, Pakistan is an emerging megacity that is heavily polluted with high levels of particle air pollution. In this study, respirable particulate matter (PM2.5 and PM10) were collected every sixth day in Lahore from 12 January 2007 to 19 January 2008. Ambient aerosol was characterized using well-established chemical methods for mass, organic carbon (OC), elemental carbon (EC), ionic species (sulfate, nitrate, chloride, ammonium, sodium, calcium, and potassium), and organic species. The annual average concentration (±one standard deviation) of PM2.5 was 194 ± 94 μg m−3 and PM10 was 336 ± 135 μg m−3. Coarse aerosol (PM10−2.5) was dominated by crustal sources like dust (74 ± 16%, annual average ± one standard deviation), whereas fine particles were dominated by carbonaceous aerosol (organic matter and elemental carbon, 61 ± 17%). Organic tracer species were used to identify sources of PM2.5 OC and chemical mass balance (CMB) modeling was used to estimate relative source contributions. On an annual basis, non-catalyzed motor vehicles accounted for more than half of primary OC (53 ± 19%). Lesser sources included biomass burning (10 ± 5%) and the combined source of diesel engines and residual fuel oil combustion (6 ± 2%). Secondary organic aerosol (SOA) was an important contributor to ambient OC, particularly during the winter when secondary processing of aerosol species during fog episodes was expected. Coal combustion alone contributed a small percentage of organic aerosol (1.9 ± 0.3%), but showed strong linear correlation with unidentified sources of OC that contributed more significantly (27 ± 16%). Brick kilns, where coal and other low quality fuels are burned together, are suggested as the most probable origins of unapportioned OC. The chemical profiling of emissions from brick kilns and other sources unique to Lahore would contribute to a better understanding of OC sources in this megacity.
This second edition of Hilbe's Negative Binomial Regression is a substantial enhancement to the popular first edition. The only text devoted entirely to the negative binomial model and its many variations, nearly every model discussed in the literature is addressed. The theoretical and distributional background of each model is discussed, together with examples of their construction, application, interpretation and evaluation. Complete Stata and R codes are provided throughout the text, with additional code (plus SAS), derivations and data provided on the book's website. Written for the practising researcher, the text begins with an examination of risk and rate ratios, and of the estimating algorithms used to model count data. The book then gives an in-depth analysis of Poisson regression and an evaluation of the meaning and nature of overdispersion, followed by a comprehensive analysis of the negative binomial distribution and of its parameterizations into various models for evaluating count data.