Predictive Analysis Using Data Mining And Gis To Study The Impact Of Air And Water Pollutants As One Of The Factors Affecting Human Health A Case Study

Author : Maninder Pal Singh Thind
Publisher :
ISBN 13 :
Total Pages : 159 pages
Book Rating : 4.:/5 (124 download)

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Download or read book Quantifying Air Quality, Human Health, and Climate Impacts from Energy Systems written by Maninder Pal Singh Thind and published by . This book was released on 2020 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: Atmospheric emissions from the energy sector contribute to air pollution and climate change. Harmful gases in ambient air degrade air quality; exposure to those gases can lead to health impacts locally and regionally. Greenhouse gases perturb the energy balance of the atmosphere, leading to higher temperatures (global warming) and thus impacting climate at a global scale. Air pollution is linked to exposure disparities among demographic groups (race, income). This dissertation explores air quality, health and climate impacts, and environmental injustice from emissions originating from energy systems. The overarching goals of this research work are to (i) quantify and compare metrics for greenhouse and noxious pollutants to evaluate environmental consequences from interventions, (ii) develop metrics and tools to quantify air quality and human health impacts from point and line sources, (iii) explore distributions of health impacts from air pollution by race, income, and geography, and (iv) demonstrate the use a reduced-complexity air quality model to quantify impacts from multiple energy systems. In this research, I focus on the fine particulate matter (PM2.5) and carbon dioxide (CO2) emissions. PM2.5 is the air pollutant that produces the largest monetized human health impacts in the United States (U.S.) and worldwide. PM2.5 can be directly emitted from combustion or other activities (primary PM2.5) or formed from precursors such as volatile organic compounds (VOCs), sulfur dioxide (SO2), oxides of nitrogen (NOx), and ammonia (NH3) (secondary PM2.5). Concentrations of PM2.5 species in the atmosphere are controlled by emissions, transport, chemistry, and deposition processes. The health impacts are a function of concentrations and the exposed population. Previous research has demonstrated the importance of fine spatial resolution for identifying and quantifying exposure disparities (environmental justice). I used a novel spatial air quality model called "Intervention Model for Air Pollution (InMAP)," combined with epidemiological research concerning air pollution and human health, to estimate health impacts of PM2.5 at a fine resolution. To understand climate impacts, I focus on carbon dioxide (CO2) which is a major greenhouse gas (81% of the total greenhouse gas emissions) emitted from complete combustion of carbon-containing fuels. This dissertation consists of three original studies focused on two energy sectors in the United States (U.S.): electricity generation and freight transportation. The methods employed in this work are based on two approaches: data-driven regression analysis and mechanistic air quality modeling using InMAP. Chapter 2 presents the data-driven empirical approach. Using linear regression between hourly changes in generation and emissions data, I investigate differences between average emission factors (AEFs) and average marginal emission factors (AMEFs) for CO2, SO2, and NOx at different spatial and temporal scales for a Midwest U.S. power market called the Midcontinent Independent System Operator (MISO). AEFs and AMEFs are two commonly used metrics for estimating emission benefits from energy-efficiency strategies. This is the first study that estimates AEFs and AMEFs for a U.S. Regional Transmission Organization (RTO). I find, for example, that marginal emission factors are generally higher during late night and early morning compared to afternoons. In general, AEFs tend to be larger than AMEFs (typical difference: ~20%), and thus may overestimate emission impacts from interventions in the power sector, relative to using AMEFs. Chapters 3 and 4 present a mechanistic modeling approach for investigating air quality and human health impacts from PM2.5 emissions. Chapter 3 presents a study that estimates exposure to and health impacts of PM2.5 from electricity generation in the U.S., for each of the seven Regional Transmission Organizations (RTOs), for each US state, by income, and by race. This research is the first national-scale investigation of environmental justice aspects of total PM2.5 from electricity generation. I find that average exposures are the highest for blacks, followed by non-Latino whites. Exposures for remaining groups (e.g., Asians, Native Americans, Latinos) are somewhat lower. Levels of disparity differ by state and RTO. Exposures are higher for lower-income than for higher-income, but disparities are larger by race than by income. Geographically, I observe large differences between where electricity is generated and where people experience the resulting PM2.5 health consequences; some states are net exporters of health impacts, other are net importers. Chapter 4 presents a study that investigates environmental health and climate impacts from inter-state road, rail, water, and air freight transportation in the U.S. This is the first detailed study to compare health, environmental justice, and climate impacts of four freight modes, studying each route separately. Average impacts per unit mass shipped are as follows. For all three impacts studied (PM2.5 health effects, racial-ethnic disparities in PM2.5 exposure, CO2 emissions), impacts are greatest for aircraft. Among non-aircraft modes: PM2.5 health effects are largest for rail, intermediate for barge, and lowest for truck; PM2.5 exposure disparities are largest for rail and are lower for truck and barge; climate impacts are largest for truck, intermediate for barge, and lowest for rail. Inter-state freight movement in the U.S. disproportionately impacts white non-Latinos relative to other racial-ethnic groups. This dissertation presents work to investigate air quality, health and climate impacts, and environmental justice-related issues from electricity generation and freight transportation. This work can be extended to other specific sectors of the economy and can be useful to scientists, planners, and policymakers to estimate environmental benefits of energy conservation programs and create policies that address environmental injustice. The metrics developed in this work can be applied by researchers to new electricity and transportation scenarios to understand their impacts and benefits.