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Temperature, Air Pollution, and Human Health Burden in Urban India.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Temperature, Air Pollution, and Human Health Burden in Urban India./
Author:	Nori-Sarma, Amrutasri.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	152 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
Contained By:	Dissertations Abstracts International81-10B.
Subject:	Environmental health. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22588350
ISBN:	9798607313258

Temperature, Air Pollution, and Human Health Burden in Urban India.
Nori-Sarma, Amrutasri.

Temperature, Air Pollution, and Human Health Burden in Urban India. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 152 p.

Source: Dissertations Abstracts International, Volume: 81-10, Section: B.

Thesis (Ph.D.)--Yale University, 2019.

This item must not be sold to any third party vendors.

Temperature and air pollution are linked to various adverse health outcomes in urban environments and will pose ever-increasing substantial health threats under climate change. The majority of the evidence regarding the health risks posed by extreme heat and air pollution is based on studies in North America and Europe. India is one of many developing countries where there is little epidemiologic evidence of the relationship between extreme heat and health, or air pollution exposure and health. The health impacts of heat waves and air pollution exposure in India are likely to differ from the impacts in other regions, due to different underlying adaptive capacity and baseline exposures in the population, as well as differences in health status and population characteristics as well as physical systems (e.g. emissions sources, urban characteristics, and local meteorology). This dissertation examines the relationship between temperature and human health, as well as air pollution exposures in urban India.The first project, in Chapter 2, summarizes the availability of existing registry data within the civil registration system in India, and outlines the opportunities and challenges for using these data in health studies despite the fact that these data are not collected for the purposes of epidemiologic studies. I illustrate many challenges for researchers, governments, and record keepers inherent to data collection in developing countries: creating and maintaining a centralized record-keeping system; standardizing the data collected; obtaining data from some local agencies; assuring data completeness and availability of back-ups to the datasets; as well as translating, cleaning, and comparing data within and across localities. I suggest that despite these challenges, local "small-data" resources may better serve the assessment of health outcomes than exposure-response functions which have been extrapolated from data collected in other areas of the world.In the second project, Chapter 3, I utilize the registry data collected in the first project to assess the relationship between heat waves and mortality in Northwestern India, using time-series methods, as well as assessing for potential effect modification by heat wave characteristics (intensity, duration, and timing in season of heat waves). Community-specific average daily maximum temperature over the entire record ranged from 32.5 - 34.2oC (90.5 - 93.6oF). I find that across communities, total mortality increased 18.1% during heat wave days compared with non-heat-wave days [95% confidence interval (CI): -5.3%, 47.3%], with the highest risk in Jaipur (29.9% [95% CI: 24.6%, 34.9%]). Evidence of effect modification by heat wave characteristics (intensity, duration, and timing in season) was limited. My findings indicate health risks associated with heat waves in communities with high baseline temperatures. In the third project, Chapter 4, I further utilized the same registry data collected in the first project in conjunction with Propensity Score Matching (PSM) to obtain the relative risk of mortality and number of attributable deaths (i.e. absolute risk which incorporates the number of heat wave days) under a variety of heat wave definitions (n=13) incorporating duration and intensity. I found that relative risk of heat waves (risk of mortality comparing heat wave days to matched non-heat wave days) varied by heat wave definition, and ranged from 1.28 [95% CI: 1.11, 1.46] in Churu (utilizing the 95th %ile of temperature for at least 2 consecutive days) to 1.03 [95% CI: 0.87, 1.23] in Idar and Himmatnagar (utilizing the 95th %ile of temperature for at least 4 consecutive days). I found a trend towards higher risk for heat waves later in season. Some heat wave definitions displayed similar attributable mortalities despite differences in the number of identified heat wave days. These findings provide opportunities to assess the "efficiency" (or number of days versus potential attributable health impacts) associated with alternative heat wave definitions. Findings on effect modification and trade-offs between number of days identified as "heat wave" versus health effects provide tools for policy makers to determine the most important criteria for defining thresholds to trigger heat wave alerts.Finally, in the fourth project, Chapter 5, I describe my efforts to monitor (using low-cost samplers) and model (using multiple spatial interpolation methods) nitrogen dioxide (NO2) in Mysore, a rapidly urbanizing city in Karnataka, India. I found that NO2 concentrations ranged from 0.3 to 51.9 ppb across the four seasons of the study period, with higher concentrations in the center of the city. In the LUR model (R2 = 0.535), proximity to major roads, point sources of pollution such as industrial sites and religious points of interest (PoI), land uses with high human activity, and high population density were associated with higher levels of NO2. Proximity to minor roads and coverage of land uses characterized by low human activity were inversely associated with air pollution. Cross-validation of the results confirmed the reliability of the model. Kriging estimates accounted for residual spatial correlation. The combination of passive NO2 sampling and LUR/kriging modeling methods allowed for characterization of NO2 patterns in Mysore. While previous work indicates traffic pollution as a major contributor to ambient air pollution levels in urbanizing centers in Asia, my results indicate the influence of other pollution factors (e.g., point sources), as well as highly localized characteristics of the urban environment (e.g., proximity to religious points of interest) in urban India. Despite a reputation as one of the cleanest cities in India (Tomar, 2018), areas of Mysore consistently experienced pollution in excess of WHO health-protective guidelines for NO2.The results from these projects indicate the likelihood of significant human health burden attributable to heat waves and air pollution exposure in urban India. Findings help lay foundation for future studies and could aid in developing policies and strategies towards protecting environment and human health.

ISBN: 9798607313258Subjects--Topical Terms:

543032
Environmental health.
Subjects--Index Terms:

Air pollution

Temperature, Air Pollution, and Human Health Burden in Urban India.
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500 $a Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
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520 $a Temperature and air pollution are linked to various adverse health outcomes in urban environments and will pose ever-increasing substantial health threats under climate change. The majority of the evidence regarding the health risks posed by extreme heat and air pollution is based on studies in North America and Europe. India is one of many developing countries where there is little epidemiologic evidence of the relationship between extreme heat and health, or air pollution exposure and health. The health impacts of heat waves and air pollution exposure in India are likely to differ from the impacts in other regions, due to different underlying adaptive capacity and baseline exposures in the population, as well as differences in health status and population characteristics as well as physical systems (e.g. emissions sources, urban characteristics, and local meteorology). This dissertation examines the relationship between temperature and human health, as well as air pollution exposures in urban India.The first project, in Chapter 2, summarizes the availability of existing registry data within the civil registration system in India, and outlines the opportunities and challenges for using these data in health studies despite the fact that these data are not collected for the purposes of epidemiologic studies. I illustrate many challenges for researchers, governments, and record keepers inherent to data collection in developing countries: creating and maintaining a centralized record-keeping system; standardizing the data collected; obtaining data from some local agencies; assuring data completeness and availability of back-ups to the datasets; as well as translating, cleaning, and comparing data within and across localities. I suggest that despite these challenges, local "small-data" resources may better serve the assessment of health outcomes than exposure-response functions which have been extrapolated from data collected in other areas of the world.In the second project, Chapter 3, I utilize the registry data collected in the first project to assess the relationship between heat waves and mortality in Northwestern India, using time-series methods, as well as assessing for potential effect modification by heat wave characteristics (intensity, duration, and timing in season of heat waves). Community-specific average daily maximum temperature over the entire record ranged from 32.5 - 34.2oC (90.5 - 93.6oF). I find that across communities, total mortality increased 18.1% during heat wave days compared with non-heat-wave days [95% confidence interval (CI): -5.3%, 47.3%], with the highest risk in Jaipur (29.9% [95% CI: 24.6%, 34.9%]). Evidence of effect modification by heat wave characteristics (intensity, duration, and timing in season) was limited. My findings indicate health risks associated with heat waves in communities with high baseline temperatures. In the third project, Chapter 4, I further utilized the same registry data collected in the first project in conjunction with Propensity Score Matching (PSM) to obtain the relative risk of mortality and number of attributable deaths (i.e. absolute risk which incorporates the number of heat wave days) under a variety of heat wave definitions (n=13) incorporating duration and intensity. I found that relative risk of heat waves (risk of mortality comparing heat wave days to matched non-heat wave days) varied by heat wave definition, and ranged from 1.28 [95% CI: 1.11, 1.46] in Churu (utilizing the 95th %ile of temperature for at least 2 consecutive days) to 1.03 [95% CI: 0.87, 1.23] in Idar and Himmatnagar (utilizing the 95th %ile of temperature for at least 4 consecutive days). I found a trend towards higher risk for heat waves later in season. Some heat wave definitions displayed similar attributable mortalities despite differences in the number of identified heat wave days. These findings provide opportunities to assess the "efficiency" (or number of days versus potential attributable health impacts) associated with alternative heat wave definitions. Findings on effect modification and trade-offs between number of days identified as "heat wave" versus health effects provide tools for policy makers to determine the most important criteria for defining thresholds to trigger heat wave alerts.Finally, in the fourth project, Chapter 5, I describe my efforts to monitor (using low-cost samplers) and model (using multiple spatial interpolation methods) nitrogen dioxide (NO2) in Mysore, a rapidly urbanizing city in Karnataka, India. I found that NO2 concentrations ranged from 0.3 to 51.9 ppb across the four seasons of the study period, with higher concentrations in the center of the city. In the LUR model (R2 = 0.535), proximity to major roads, point sources of pollution such as industrial sites and religious points of interest (PoI), land uses with high human activity, and high population density were associated with higher levels of NO2. Proximity to minor roads and coverage of land uses characterized by low human activity were inversely associated with air pollution. Cross-validation of the results confirmed the reliability of the model. Kriging estimates accounted for residual spatial correlation. The combination of passive NO2 sampling and LUR/kriging modeling methods allowed for characterization of NO2 patterns in Mysore. While previous work indicates traffic pollution as a major contributor to ambient air pollution levels in urbanizing centers in Asia, my results indicate the influence of other pollution factors (e.g., point sources), as well as highly localized characteristics of the urban environment (e.g., proximity to religious points of interest) in urban India. Despite a reputation as one of the cleanest cities in India (Tomar, 2018), areas of Mysore consistently experienced pollution in excess of WHO health-protective guidelines for NO2.The results from these projects indicate the likelihood of significant human health burden attributable to heat waves and air pollution exposure in urban India. Findings help lay foundation for future studies and could aid in developing policies and strategies towards protecting environment and human health.
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