Impacts of household sources on air pollution at village and regional scales in India

Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM_(2.5) and O₃) using state-of-the-science emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM_(2.5) speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O₃ and PM_(2.5) levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions. Observed and predicted ozone levels over New Delhi during September 2015, December 2015, and September 2016 routinely exceeded the 8 h Indian standard of 100 µg m⁻³, and, on occasion, exceeded 180 µg m⁻³. PM_(2.5) levels are predicted over the SOMAARTH headquarters (September 2015 and September 2016), Bajada Pahari (a village in the surveillance site; September 2015, December 2015, and September 2016), and New Delhi (September 2015, December 2015, and September 2016). The predicted fractional impact of residential emissions on anthropogenic PM_(2.5) levels varies from about 0.27 in SOMAARTH HQ and Bajada Pahari to about 0.10 in New Delhi. The predicted secondary organic portion of PM_(2.5) produced by household emissions ranges from 16 % to 80 %. Predicted levels of secondary organic PM_(2.5) during the periods studied at the four locations averaged about 30 µg m⁻³, representing approximately 30 % and 20 % of total PM_(2.5) levels in the rural and urban stations, respectively

Learning from supply shocks in the energy market : evidence from local and global impacts of the shale revolution

Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, May, 2020Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020Cataloged from the official PDF of thesis.Includes bibliographical references (pages 97-100).In this thesis, we carry out three studies of the local and global impacts of supply shocks in energy markets, and also analyze certain properties of these markets. First, the relationship between US power plants and local air pollution is assessed from 2003 to 2016, by exploiting the information provided by the large deviations that occurred during that period due to the shale revolution. Next, fossil fuel trade is analyzed from a networks perspective, quantifying its properties. Finally, a general equilibrium model of fossil fuel trade is constructed to simulate the impact of a supply shock to a given country and in order to understand the impact of the shale revolution.by Bora Ozaltun.S.M. in Technology and PolicyS.M.S.M.inTechnologyandPolicy Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and SocietyS.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc

Reduction potentials for particulate emissions from household energy in India

Household access to clean energy is a priority for public health and the environment in low- and middle-income countries. However, past illustrative studies have explored benefits of replacing all polluting energy sources, a transition that is only theoretically possible. Factors that limit achievement of the entire theoretical reduction potential should be explored to inform programmatic decision making. We propose a hierarchy of reduction potentials for emissions from household energy, representing different implementation barriers. Following similar work in renewable energy, we propose four categories of reduction potentials beyond the theoretical maximum: distributional, technical, economic, and market. We apply this framework to household energy emissions using a high-resolution spatiotemporal emission inventory of India, a country chosen for its data availability and level of interest in mitigation. We explore distributional potential using distance from urban areas, technical potential by attributing emissions to energy services, and economic potential with a village- level proxy for likelihood of program success. For distributional potential (spatial accessibility), we find that applying reduction programs within 5 km of urban centers would achieve 36%–78% of the theoretical potential across seven regions in India; extension to 10 km yields reductions of 63%–90%. Technical and economic reduction potentials differ most greatly from theoretical potential in regions that contribute the most to national emissions. Even if some of the relationships underlying emission causes are not completely known, reflecting the factors that affect transitions can inform practitioners and programs seeking to scale and deliver clean energy solutions. We assert that including these important influences should be a goal of emission inventory development, beyond the simple quantification of baseline emissions