Air pollution is linked to various adverse health effects with the majority of evidence based in North America and Europe. Nepal is one of the many countries where little such research has been conducted, despite rising air pollution levels. The impacts of air pollution in developing countries is likely to differ from those in other regions due to higher air pollution levels and differences in environment, health status, and population characteristics. This dissertation examined air pollution exposure mainly from traffic and health impacts in Kathmandu Valley, the major urban area in Nepal.
The first chapter describes a systematic review on air pollution's human health impacts for Nepal to summarize the state of scientific evidence and identify research gaps. I identified 89 studies, of which 23 linked air pollution to health. Direct exposure measurements were for short time periods; most studies used indirect exposure methods (e.g., questionnaire). Most health studies had small sample sizes with almost all focusing on respiratory health. In this chapter, I provide recommendations for future research with larger studies and more health outcomes. Altogether the review demonstrated higher levels of pollution in Nepal than in other parts of the world where air pollution and health associations are well documented, which suggests large health impacts.
The second chapter describes my investigation of the impacts of particulate matter with aerodynamic diameter ≤10μm (PM10) and hospital admissions (respiratory, cardiovascular, other, total) as well as effect modification by individual and community characteristics for Kathmandu Valley. Daily PM 10 averaged 120μg/m3 with daily maximum reaching 403μg/m 3. A 10 µg/m3 increase in PM10 was associated with increased risk of hospitalization of 1.00% (95% confidence interval, 0.62-1.38%), 1.70% (0.18-3.25%), 2.29% (0.18-4.43%), and 0.75% (0.181.33%) for total, respiratory, cardiovascular, and other admissions, respectively. I did not find strong evidence of effect modification by age, sex, or socioeconomic status. Findings provide direct scientific evidence on substantial health impact in the Valley and this is the first large study of ambient air pollution in Nepal.
In the third chapter, I summarize the design, implementation, and summary results from field measurements performed for landuse regression (LUR) modeling in the urban areas of Kathmandu Valley, Nepal. Over the study area, 135 sites were allocated using stratified random sampling based on building density and road density and purposeful sampling. In 2014, four sampling campaigns were performed, one per each season, for two weeks each where nitrogen dioxide (NO2) was measured using duplicate Palmes tubes at 135 sites and at 28 sites in Kathmandu Village Development Committees (VDCs). Ogawa badges were used to measure NO2 and nitrogen oxides (NOX). High completion rate by campaign was observed for both Palmes tubes (90.4-95.6%) and Ogawa badges (78.6-100%). Good reliability of measurement was noted based on overall duplicate Palmes tubes and collocated Palmes tubes and Ogawa badges. Annual average NO2 by sites ranged from 1.62 to 349ppb for the study area. NO2 concentration was generally higher in Kathmandu and Lalitpur in comparison to the three other urban VDCs. No significant difference in NO2 by building and road density strata was observed. Highest average NO2 was 29.9 ppb observed during pre-moonsoon, and lowest of 15.2ppb during monsoon. Negative association between NO2 levels and logarithm of distance from major road was observed although compared to past research in other regions a strong correlation was not observed.
In the fourth chapter, I describe development of a LUR model for urban areas of Kathmandu Valley based on NO2 data collected using Palmes tubes at 135 locations in 2014. Various geographical variables (e.g, landuse, building footprint) were used as predictor variables in the model. Predictor variables were computed for buffer sizes 25-400m around each monitoring site. The final model accounted for 51% of the variance in NO2 levels. Length of major road, built area, and industrial area were all positively correlated with NO2 concentration while normalized difference vegetation index (NDVI) was observed to be negatively correlated with NO2 concentration in the model. Cross validation of the results confirmed the reliability of the model. Findings demonstrate NO2 annual average concentration was higher in Kathmandu and Lalitpur than in Kirtipur, Thimi, and Bhaktapur with variability present within each VDC. A LUR model allows understanding of intraurban variation of traffic pollution for better exposure estimation for future epidemiological studies.
The results from these projects indicate high air pollution in Kathmandu Valley, Nepal and likelihood of significant human health burden. Findings help lay foundation for future studies and could aid in developing policies and strategies towards protecting environment and human health.
|Advisor:||Bell, Michelle L.|
|School Location:||United States -- Connecticut|
|Source:||DAI-B 78/01(E), Dissertation Abstracts International|
|Subjects:||Public health, Epidemiology, Environmental science|
|Keywords:||Air Pollution, Case Crossover, Kathmandu, Land Regression, Nepal, Traffic|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be