Michael Hannigan - US grants

Pollution sensing and diagnosis have long been separated from the people most impacted by them. It was conducted by specialists with expensive and scarce equipment. As a result, testing was infrequent and decisions on mitigation were made by central planners with limited access to data. Worse yet, individuals with the ability to dramatically limit dangerous exposure via minor changes in behavior have been left blind to the relationship between their daily actions and exposure to pollution. Advances in computing, sensing, and wireless communication technologies have the potential to allow those most affected by pollution to participate in pollution sensing, rational cost assessment, and mitigation.

This project focuses on developing a distributed mobile system for socially-collaborative environmental monitoring, which greatly reduces the problem of environmental sensing data scarcity, supports richer environmental sensing data analysis, and enables better environment awareness and protection via social collaboration. This project will develop a system composed of inexpensive sensing and computation devices purchased by individuals for their own edification and protection. These embedded systems will communicate with each other and aggregate data, enabling multi-sensor localization of pollution
sources and quantification of the potential damage by each polluter. By measuring pollution and modeling its impact, it will be possible to associate pollution sources with the costs they impose. Furthermore distributed networking will allow individuals to actively participate in and socially collaborate on environmental monitoring and protection.

Nearly half the world?s population, mostly in the developing world, cooks over open flames on a daily basis. This releases greenhouse gas and exposes people to toxic emissions that contribute to respiratory disease. In Ghana and other countries in the "meningitis belt," emissions from cooking have been linked to meningitis. We hypothesize that widespread use of efficient, or "clean", cookstoves - which produce less smoke than open fires even while burning the same available materials - will reduce people's exposure to toxic emissions, improve health outcomes, and improve regional air quality. To test this hypothesis, we will introduce cookstoves into households in northern Ghana. In addition to determining whether they do, in fact, improve air quality and health outcomes, we will explore the social and economic factors that encourage or discourage cook stove use, and consider the impacts of climate change. Our methods combine 1) a randomized experiment intended to increase use of efficient cookstoves; 2) social surveys to measure effects on cooking behavior and self-reported health outcomes; 3) collection of physical data to measure the impacts on emissions and air quality; 4) integrated modeling of the relationships among cooking practices, air quality, climate variability, respiratory illness and bacterial meningitis; and 5) simulations of emissions and climate change scenarios and their potential impacts on regional air quality and health outcomes over time.

Our results will enhance understanding of the social and physical systems that shape air quality in the African Sahel and particularly in northern Ghana, contribute valuable observations in a part of the world where observations are sparse, and empower communities in Africa. The work will provide quantitative knowledge that can be used to develop novel strategies to control the spread of meningitis in the face of future threats. Our results will also shed light on the interactions among human behavior, air quality, climate variability, exposure, and health outcomes, and how future changes in these factors may combine in Africa and other areas. Affordable and effective instrumentation developed and applied as part of this project will be tested and available for use in areas around the globe. Further, we will develop novel university curricula and train African and American university students in data collection. Finally, our participatory epidemiology approach, in which community members help measure air quality and report disease, will empower local communities that do not have access to vaccines. Ultimately, we will work with communities to generate scenarios in which realistic changes in cooking practices interact with climatic processes to produce improvements in air quality that reduce the burden of respiratory illness and bacterial meningitis in the African Sahel.

1240584
Ryan

The current energy system in the United States relies on finite resources that are the major cause of climate change and a key source of global conflict. A sustainable energy system - one that uses renewable, low-carbon, affordable, and local energy sources - may be decades away. Natural gas is seen as the "bridge fuel" to a more sustainable energy system because natural gas combustion emits smaller amounts of greenhouse gases than coal combustion. However, conflicts have arisen between accelerated natural gas development and water and air resources protection. These conflicts are becoming acute in the Rocky Mountain region, which has always played an important role in the energy system of the United States. Most of the recent growth in natural gas production is the result of extracting gas from "unconventional" sources (coal-bed methane, shale gas, tight gas) with the techniques of horizontal drilling and hydraulic fracturing. Hydraulic fracturing requires large volumes of water that are chemically amended and injected to increase the permeability of the gas-bearing formations. The fracturing fluid left in ground and the fracturing fluid that returns to the surface (flowback), along with produced water, present risks to ground and surface waters. Natural gas extraction results in atmospheric emissions, particularly the release of greenhouse gases, oxides of nitrogen, and volatile organic compounds tied to the generation of ozone. These stresses on local water and air resources must be weighed against the benefits of natural gas production for the nation and the public must be provided with reliable information to make decisions about energy sources and resource protection. This Sustainability Research Network (SRN) addresses the conflict between natural gas extraction and water and air resources protection with the development of a social-ecological system framework with which to assess the conflict and to identify needs for scientific information. Scientific investigations will be conducted to assess and mitigate the problems. Outreach and education efforts will focus on citizen science, public involvement, and awareness of the science and policy issues.
The intellectual merit of this SRN proposal includes (1) examination of the effects of natural gas development on water and air resources by analyzing trade-offs between local, regional, and national costs and benefits in environmental, social, and economic domains (social-ecological systems); (2) review of industry practices for hydraulic fracturing, well drilling and casing, and gas collection infrastructure for best management practices recommendations (natural gas infrastructure); (3) investigation of the hydrologic processes that determine impacts of natural gas extraction on groundwater withdrawal and contaminant transport in drinking water aquifers and surface waters (water quantity); (4) characterization of the potential risks of fracturing fluid migrating to drinking water aquifers, the injection or discharge of flowback and produced water, and the mitigation of these risks by treatment of the flowback and produced waters (water quality); (5) improved spatial and temporal monitoring of air pollutants by a combination of high-resolution mobile sampling and the use of personal air monitors as an example of "citizen science" feeding data to air quality models that assess the local, regional, and national implications of natural gas development (air quality); and (6) quantitative and qualitative assessment of the health risks, both chemical and non-chemical, associated with water and air exposure.
The broader impact of this SRN includes improved public understanding of the effects of natural gas development on water and air resources and better decision-making regarding the local effects and regional and national benefits and costs of natural gas development. The broader impacts will be achieved through extensive education and outreach activities: (1) dissemination of best management practices in collaboration with all stakeholders, (2) diverse communication about scientists and scientific activity that will reach a broad portion of the public, (3) collaboration with Native American tribes and other under-represented groups disproportionately affected by natural gas development, (4) educational efforts aimed at providing appreciation for the science-policy interface at the university and K-12 levels, and (5) engagement of the public through citizen science, workshops, and scenario planning.

Ambient exposure to ground-level air pollution is linked to adverse health effects in many populated areas of the world. However, advances in relating air pollution exposure to sustainable communities are hindered by limited direct observations of exposure and the coarseness of regional and global air quality models used for decision making. As a result, existing models do not resolve the scales of variability in either pollutant concentrations or population distributions necessary to accurately assess exposure nor provide the type of probabilistic uncertainty bounds required for policy.

This project aims to assimilate comprehensive cyber information for use in air quality management. It advances the interdisciplinary field of cyber-environmental research through investigation into (1) cyber-scale data analysis to harness and distill valuable cyber information to support community-scale air pollution modeling; (2) micro-environment targeted sensing to augment community-scale studies with accurate, on-demand, and in situ sensing capabilities; and (3) scalable exposure modeling and analysis by solving a complex, spatiotemporally varying problem with high-dimensional data containing cyber, sensing, and model outputs.

Advances in cyber-environmental research have the potential to improve government policy making, regulations, and personal choices with regard to environmentally sustainable community development. Results of this project can apply across a wide spectrum of sectors including energy, transportation, and healthcare. This project broadens vertical research and education integration across information technologies and environmental science and engineering, to both graduate and undergraduate students. Through in-field trials, this project offers unique real-world education and research opportunities to attract students from underrepresented groups and industrial professionals.

Adoption of potentially welfare-improving technologies remains frustratingly low in many contexts. Improved cookstoves are a prime example: while cleaner-burning stove technologies have potential health, environmental, and social benefits, efforts to disseminate these technologies have fallen short and the practice of cooking with biomass over open fires remains dominant throughout much of the developing world. The central aim of this proposal is to study how economic incentives ("prices"), social learning ("peers"), and subjective beliefs ("perceptions") interact to influence technology adoption dynamics. We do so through a field experiment that offers new stoves at different price levels to groups of households with and without social ties to households that have already received stoves. Our results will inform and future efforts to disseminate welfare-enhancing technologies to larger population groups.

Our conceptual model of households' technology adoption and use decisions highlights multiple potential interactions among prices, peers, and perceptions. Key research questions that will be addressed through our experiments include how price affects perceived quality of a new technology, how these perceptions are modified by exposure to peers that have experience with the technology, and how perceptions change over time based on one's own experience and (objective and subjective) technology performance. We implement a novel identification strategy for identifying these effects, using the preexisting and exogenously controlled distribution of free stoves in combination with uncorrelated, cluster-randomized assignment to different stove subsidy levels. By explicitly measuring perceptions in conjunction with other outcome variables in the experiment (including both surveys and physical indicators of stove use and impacts on personal exposure to pollutants), the researchers will be able to test specific models about how prices and peers (prior adoption interact in belief formation) a key issue in the technology adoption literature.

All cyber-physical systems (CPS) depend on properly calibrated sensors to sense the surrounding environment. Unfortunately, the current state of the art is that calibration is often a manual and expensive operation; moreover, many types of sensors, especially economical ones, must be recalibrated often. This is typically costly, performed in a lab environment, requiring that sensors be removed from service. MetaSense will reduce the cost and management burden of calibrating sensors. The basic idea is that if two sensors are co-located, then they should report similar values; if they do not, the least-recently-calibrated sensor is suspect. Building on this idea, this project will provide an autonomous system and a set of algorithms that will automate the detection of calibration issues and preform recalibration of sensors in the field, removing the need to take sensors offline and send them to a laboratory for calibration. The outcome of this project will transform the way sensors are engineered and deployed, increasing the scale of sensor network deployment. This in turn will increase the availability of environmental data for research, medical, personal, and business use. MetaSense researchers will leverage this new data to provide early warning for factors that could negatively affect health. In addition, graduate student engagement in the research will help to maintain the STEM pipeline.

This project will leverage large networks of mobile sensors connected to the cloud. The cloud will enable using large data repositories and computational power to cross-reference data from different sensors and detect loss of calibration. The theory of calibration will go beyond classical models for computation and physics of CPS. The project will combine big data, machine learning, and analysis of the physics of sensors to calculate two factors that will be used in the calibration. First, MetaSense researchers will identify measurement transformations that, applied in software after the data collection, will generate calibrated results. Second, the researchers will compute the input for an on-board signal-conditioning circuit that will enable improving the sensitivity of the physical measurement. The project will contribute research results in multiple disciplines. In the field of software engineering, the project will contribute a new theory of service reconfiguration that will support new architecture and workflow languages. New technologies are needed because the recalibration will happen when the machine learning algorithms discover calibration errors, after the data has already been collected and processed. These technologies will support modifying not only the raw data in the database by applying new calibration corrections, but also the results of calculations that used the data. In the field of machine learning, the project will provide new algorithms for dealing with spatiotemporal maps of noisy sensor readings. In particular, the algorithms will work with Gaussian processes and the results of the research will provide more meaningful confidence intervals for these processes, substantially increasing the effectiveness of MetaSense models compared to the current state of the art. In the field of pervasive computing, the project will build on the existing techniques for context-aware sensing to increase the amount of information available to the machine learning algorithms for inferring calibration parameters. Adding information about the sensing context is paramount to achieve correct calibration results. For example, a sensor that measures air pollution inside a car on a highway will get very different readings if the car window is open or closed. Finally, the project will contribute innovations in sensor calibration hardware. Here, the project will contribute innovative signal-conditioning circuits that will interact with the cloud system and receive remote calibration parameters identified by the machine learning algorithms. This will be a substantial advance over current circuits based on simple feedback loops because it will have to account for the cloud and machine learning algorithms in the loop and will have to perform this more complex calibration with power and bandwidth constraints. Inclusion of graduate students in the research helps to maintain the STEM pipeline.

This study is focused on improving the understanding of the interactions between soluble iron (Fe) and organic material in fine particles in the atmosphere. This research is relevant to the larger question of how atmospheric iron affects the processing and aging of the organics in atmospheric particulate matter. The results will provide information useful to atmospheric chemists and to environmental health professionals.

The project will examine the importance of the Fe-assisted oxidation route of water-soluble organic carbon. It consists of three tasks: (1) field sampling, (2) characterization of the particulate matter, and (3) photochemical laboratory experiments. The field samples will be analyzed to assess the sources of the atmospheric Fe, since the source of the Fe may determine its chemical speciation and, thus, its solubility and availability to chemical reactions. Aqueous extracts of the field samples will be subjected to an artificial aging process and undergo analysis for oxidation state and the chemical speciation of total iron, and the production of reactive oxygen species will be quantified. By investigating the role of transition metals and taking into account aqueous chemistry, this project will offer a more complete picture of how organic compounds are "aged" in the atmosphere. The results will provide useful information on the role iron plays in the processing of organic compounds in atmospheric particulate matter and the potential impacts of these mixed particles on human health.

Wildfires and their environmental and social impacts are growing in the US and around the world. Prescribed (Rx) burning can be an effective management technique to reduce vegetation and reduce the risk of catastrophic wildfires. Despite its potential benefits, the annual extent of Rx burning has stayed the same or decreased across much of the country in recent years. This is due in part to the fact that Rx burning entails its own risks, including smoke exposure and the possibility of a planned fire escaping control. Making Rx burning decisions requires making informed tradeoffs between these risks and the risks associated with uncontrolled wildfires. To inform these decisions, this project assesses two key questions. First, how do decisions by those who implement Rx burning and by residents in nearby communities shape potential Rx burning impacts? To answer this question, the research team takes measurements near a large number of Rx burn sites over time, capturing information such as how much smoke residents near Rx burns are exposed to and whether experience with Rx burning influences support for this management approach. Second, how do perceived risk-risk tradeoffs influence support for expanded Rx burning to mitigate wildfire risk among wildland-urban interface (WUI) and non-WUI residents? To answer this question, the researchers survey residents in WUI and non-WUI areas to understand risk perceptions and policy support. Understanding these tradeoffs is critical to shaping risk management policy. <br/> <br/>Prescribed (Rx) burning entails carefully planned fires lit under controlled conditions to manage fuels that can spur catastrophic fires and WUI community disasters. The US National Cohesive Wildland Management Strategy identifies Rx burning as a cost-effective solution, but its use has remained limited, particularly in the American West. Managing wildfire and other climate-related hazards involves weighing risk-risk tradeoffs in order to develop effective, balanced strategies that protect communities and the environment. This interdisciplinary project uses a mixed-methods approach to advance knowledge on decision making under risk in the context of natural hazard management. The researchers develop a novel conceptual model that adapts the concept of risk-risk tradeoffs to the context of Rx burning decisions. This conceptual model guides the interdisciplinary research design to address two key research questions: 1) How do decisions by implementers and residents shape Rx burning impacts for WUI communities in proximity to Rx burn sites? To answer this question, the team generates and analyzes a first-of-its-kind dataset integrating measurements of environmental conditions, smoke emissions, built environment (housing) characteristics, air quality, exposure, health, and perceived experience measures near Rx burn sites over multiple years. 2) How do perceived risk-risk tradeoffs influence support for expanded Rx burning to mitigate wildfire risk among WUI and non-WUI residents? The team implements a large survey-based choice experiment to elicit risk-risk tradeoffs and assess Rx burning support, analyzing data to understand how different risk attributes and individual characteristics shape risk perceptions and policy support. The research team is engaging with stakeholders, including policy-makers and regulators, wildfire managers and Rx burn implementers, and members of the public, throughout the research process to ensure that the results are relevant and useful.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This RAPID project will investigate indoor air pollution associated with the recent Marshall Fire in Colorado, that started as a grass fire on December 30, 2021. Smoke from the fire infiltrated into undamaged homes surrounding the burnt areas and left ash and soot behind, noticeably impacting indoor air quality. Measurements of volatile organic compounds (VOCs) and particulate matter (PM) are being made inside and around multiple homes in the area beginning on January 8, 2022. This RAPID project will provide support for the continuation of these measurements to assess the sustained fire impacts on air quality and for the analysis and presentation of the results.

This project will address the following questions: (i) What is the composition of indoor air in smoke-impacted homes after the Marshall Fire? (ii) Which physical processes control the continued release of pollutants from smoke-impacted homes? (iii) What is the composition of the contaminated ash and soot left behind in homes? (iv) What are the continuing emissions of air pollutants in the burnt area? (v) What levels of PM were people exposed to during and after the Marshall fire?

This information is of interest to local communities and health departments. Five graduate students will be trained to assist with this project.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.