New Insight into the Measurements of Particle-Bound Metals in the Urban and Remote Atmospheres of the Sarajevo Canton and Modeled Impacts of Particulate Air Pollution in Bosnia and Herzegovina
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Abstract
The
Sarajevo Canton Winter
Field
Campaign 2018 (SAFICA) was a project that
took place in winter 2017–2018 with an aim to characterize
the chemical composition of aerosol in the Sarajevo Canton, Bosnia
and Herzegovina (BiH), which has one of the worst air qualities in
Europe. This paper presents the first characterization of the metals
in PM
10 (particulate matter aerodynamic diameters ≤10
μm) from continuous filter samples collected during an extended
two-months winter period at the urban background Sarajevo and remote
Ivan Sedlo sites. We report the results of 18 metals detected by inductively
coupled plasma mass spectrometry (ICP-MS) and electrothermal atomic
absorption spectrometry (ETAAS). The average mass concentrations of
metals were higher at the Sarajevo site than at Ivan Sedlo and ranged
from 0.050 ng/m
3 (Co) to 188 ng/m
3 (Fe) and
from 0.021 ng/m
3 (Co) to 61.8 ng/m
3 (Fe), respectively.
The BenMAP-CE model was used for estimating the annual BiH health
(50% decrease in PM
2.5 would save 4760+ lives) and economic
benefits (costs of $2.29B) of improving the air quality. Additionally,
the integrated energy and health assessment with the ExternE model
provided an initial estimate of the additional health cost of BiH’s
energy system.
Abstract
Air pollution in a global winter hotspot
of the Sarajevo
Canton, Bosnia and Herzegovina, comes from diverse sources and burdens
society’s human health and economy.
Recent studies have reported associations between particulate air pollution and daily mortality rates. Population-based, cross-sectional studies of metropolitan areas in the United States have also found associations between particulate air pollution and annual mortality rates, but these studies have been criticized, in part because they did not directly control for cigarette smoking and other health risks. In this prospective cohort study, we estimated the effects of air pollution on mortality, while controlling for individual risk factors. Survival analysis, including Cox proportional-hazards regression modeling, was conducted with data from a 14-to-16-year mortality follow-up of 8111 adults in six U.S. cities. Mortality rates were most strongly associated with cigarette smoking. After adjusting for smoking and other risk factors, we observed statistically significant and robust associations between air pollution and mortality. The adjusted mortality-rate ratio for the most polluted of the cities as compared with the least polluted was 1.26 (95 percent confidence interval, 1.08 to 1.47). Air pollution was positively associated with death from lung cancer and cardiopulmonary disease but not with death from other causes considered together. Mortality was most strongly associated with air pollution with fine particulates, including sulfates. Although the effects of other, unmeasured risk factors cannot be excluded with certainty, these results suggest that fine-particulate air pollution, or a more complex pollution mixture associated with fine particulate matter, contributes to excess mortality in certain U.S. cities.
We conducted an extended follow-up and spatial analysis of the American Cancer Society (ACS) Cancer Prevention Study II (CPS-II) cohort in order to further examine associations between long-term exposure to particulate air pollution and mortality in large U.S. cities. The current study sought to clarify outstanding scientific issues that arose from our earlier HEI-sponsored Reanalysis of the original ACS study data (the Particle Epidemiology Reanalysis Project). Specifically, we examined (1) how ecologic covariates at the community and neighborhood levels might confound and modify the air pollution-mortality association; (2) how spatial autocorrelation and multiple levels of data (e.g., individual and neighborhood) can be taken into account within the random effects Cox model; (3) how using land-use regression to refine measurements of air pollution exposure to the within-city (or intra-urban) scale might affect the size and significance of health effects in the Los Angeles and New York City regions; and (4) what exposure time windows may be most critical to the air pollution-mortality association. The 18 years of follow-up (extended from 7 years in the original study [Pope et al. 1995]) included vital status data for the CPS-II cohort (approximately 1.2 million participants) with multiple cause-of-death codes through December 31, 2000 and more recent exposure data from air pollution monitoring sites for the metropolitan areas. In the Nationwide Analysis, the influence of ecologic covariate data (such as education attainment, housing characteristics, and level of income; data obtained from the 1980 U.S. Census; see Ecologic Covariates sidebar on page 14) on the air pollution-mortality association were examined at the Zip Code area (ZCA) scale, the metropolitan statistical area (MSA) scale, and by the difference between each ZCA value and the MSA value (DIFF). In contrast to previous analyses that did not directly include ecologic covariates at the ZCA scale, risk estimates increased when ecologic covariates were included at all scales. The ecologic covariates exerted their greatest effect on mortality from ischemic heart disease (IHD), which was also the health outcome most strongly related with exposure to PM2.5 (particles 2.5 microm or smaller in aerodynamic diameter), sulfate (SO4(2-)), and sulfur dioxide (SO2), and the only outcome significantly associated with exposure to nitrogen dioxide (NO2). When ecologic covariates were simultaneously included at both the MSA and DIFF levels, the hazard ratio (HR) for mortality from IHD associated with PM2.5 exposure (average concentration for 1999-2000) increased by 7.5% and that associated with SO4(2-) exposure (average concentration for 1990) increased by 12.8%. The two covariates found to exert the greatest confounding influence on the PM2.5-mortality association were the percentage of the population with a grade 12 education and the median household income. Also in the Nationwide Analysis, complex spatial patterns in the CPS-II data were explored with an extended random effects Cox model (see Glossary of Statistical Terms at end of report) that is capable of clustering up to two geographic levels of data. Using this model tended to increase the HR estimate for exposure to air pollution and also to inflate the uncertainty in the estimates. Including ecologic covariates decreased the variance of the results at both the MSA and ZCA scales; the largest decrease was in residual variation based on models in which the MSA and DIFF levels of data were included together, which suggests that partitioning the ecologic covariates into between-MSA and within-MSA values more completely captures the sources of variation in the relationship between air pollution, ecologic covariates, and mortality. Intra-Urban Analyses were conducted for the New York City and Los Angeles regions. The results of the Los Angeles spatial analysis, where we found high exposure contrasts within the Los Angeles region, showed that air pollution-mortality risks were nearly 3 times greater than those reported from earlier analyses. This suggests that chronic health effects associated with intra-urban gradients in exposure to PM2.5 may be even larger between ZCAs within an MSA than the associations between MSAs that have been previously reported. However, in the New York City spatial analysis, where we found very little exposure contrast between ZCAs within the New York region, mortality from all causes, cardiopulmonary disease (CPD), and lung cancer was not elevated. A positive association was seen for PM2.5 exposure and IHD, which provides evidence of a specific association with a cause of death that has high biologic plausibility. These results were robust when analyses controlled (1) the 44 individual-level covariates (from the ACS enrollment questionnaire in 1982; see 44 Individual-Level Covariates sidebar on page 22) and (2) spatial clustering using the random effects Cox model. Effects were mildly lower when unemployment at the ZCA scale was included. To examine whether there is a critical exposure time window that is primarily responsible for the increased mortality associated with ambient air pollution, we constructed individual time-dependent exposure profiles for particulate and gaseous air pollutants (PM2.5 and SO2) for a subset of the ACS CPS-II participants for whom residence histories were available. The relevance of the three exposure time windows we considered was gauged using the magnitude of the relative risk (HR) of mortality as well as the Akaike information criterion (AIC), which measures the goodness of fit of the model to the data. For PM2.5, no one exposure time window stood out as demonstrating the greatest HR; nor was there any clear pattern of a trend in HR going from recent to more distant windows or vice versa. Differences in AIC values among the three exposure time windows were also small. The HRs for mortality associated with exposure to SO2 were highest in the most recent time window (1 to 5 years), although none of these HRs were significantly elevated. Identifying critical exposure time windows remains a challenge that warrants further work with other relevant data sets. This study provides additional support toward developing cost-effective air quality management policies and strategies. The epidemiologic results reported here are consistent with those from other population-based studies, which collectively have strongly supported the hypothesis that long-term exposure to PM2.5 increases mortality in the general population. Future research using the extended Cox-Poisson random effects methods, advanced geostatistical modeling techniques, and newer exposure assessment techniques will provide additional insight.
[a
]Department
of Chemistry, Faculty of Science, University
of Sarajevo , 71000, Sarajevo, Bosnia and Herzegovina
[b
]Environmental
Hygiene Unit, Institute for Medical Research
and Occupational Health , 10000, Zagreb, Croatia
[c
]Department
of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina 27599-7400, United States
[d
]Department
of Biotechnology, University of Rijeka , 51000, Rijeka, Croatia
[e
]Clean Air
Institute , Washington, DC 20005, United States
[f
]Laboratory
of Atmospheric Chemistry, Paul Scherrer
Institute , 5232, Villigen, PSI, Switzerland
[g
]Center
for
Atmospheric Research, University of Nova
Gorica , SI-5270, Ajdovščina, Slovenia
[h
]Multiphase
Chemistry Department, Max Planck Institute
for Chemistry , Hahn-Meitner-Weg
1, 55128 Mainz, Germany
Permits the broadest form of re-use including for commercial purposes, provided that
author attribution and integrity are maintained (
https://creativecommons.org/licenses/by/4.0/).
History
Date
received
: 15
October
2021
Date
accepted
: 17
February
2022
Date
revision received
: 15
February
2022
Funding
Funded by: H2020 Marie Sklodowska-Curie Actions, doi 10.13039/100010665;
Award ID: 101028909
Funded by: SEE Change Net Foundation, doi NA;
Award ID: NA
Funded by: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, doi 10.13039/501100001711;
Award ID: IZSEZ0_189495
Funded by: European Cooperation in Science and Technology, doi 10.13039/501100000921;
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