For an overview of the Incubator Program click here.
Atmospheric particulate matter source identification using excitation emission fluorescence spectroscopy
Project Lead: Jay Rutherford, UW Department of Chemical Engineering PhD Candidate eScience Liaison: Bernease Herman
Air pollution is estimated to cause 5.5 million premature deaths and result in 140 million disability adjusted life years annually. 87% of the world’s population lives with air pollution levels above the World Health Organization Guidelines. These facts make it the world’s largest environmental health risk. Air pollution consists of gases, liquids and solids. Tiny droplets of liquid and microscopic solids suspended in the atmosphere are referred to as aerosols or particulate matter (PM). PM comes from natural sources including sea spray, forest fires, and dust from soil as well as anthropogenic sources like combustion engines, road dust, industry, residential heating and agricultural burning. There is extensive research showing PM2.5 (particulate matter smaller than 2.5 microns in diameter) causes a variety of health problems that lead to premature death and reduced quality of life. Some studies show certain sources of PM2.5 pollution, traffic for example, are worse for health than others, however, there is not sufficient evidence from source specific studies to show this conclusively. Recently there has been a proliferation of low-cost instruments to measure PM2.5, but there is no accompanying low-cost method to determine the sources of PM that is needed to enable the study of source specific health effects.
To enable low-cost source apportionment, we are developing a method to analyze PM samples using fluorescence excitation-emission matrix spectroscopy (EEM). PM samples contain fluorescent compounds such as polycyclic aromatic hydrocarbons generated during combustion that can be extracted into a solvent for analysis by EEM spectroscopy. We have collected PM in the laboratory and analyzed extracts using EEM spectroscopy. Using these data, we trained a convolutional neural network (CNN) to distinguish the sources of air pollution present in the laboratory samples. We are working to optimize the CNN by understanding how the network uses the spectra to I identify the various laboratory sources. Following this optimization we will apply the method to environmental samples to evaluate its effectiveness comparted to other methods of source apportionment. We aim to develop this new technique to allow for source apportionment on large numbers of environmental samples so source specific health effects may be studied.
Beneficial competition under rationing: evidence from food delivery service
Project Lead: Kwong-Yu Wong, UW Department of Economics eScience Liaison: Jose Hernandez
Rationing is usually necessitated whenever some external constraints causing quantity of goods provided in lack of what is required (e.g. essential supplies in wartime, surgery needed, meals during peak hours etc.). In Economics literature, rationing is commonly regarded as welfare reducing because it easily induces wasteful competition such as wasting time by standing in line. However, whether rationing only induces wasteful competition is still an open question.
This project studies the beneficial competition under rationing in food delivery industry and helps quantify the welfare improvement resulting from the competition. In food delivery industry, rationing happens daily in peak hours. Such rationing induces customers to compete in calling in earlier for food delivery and hence restaurants (and delivery company) receive information earlier to have a larger flexibility in meeting the demand on-time. This will be one clear example of beneficial competition induced by rationing.
To quantify the overall welfare impact of such beneficial competition, we need to build a counterfactual where the beneficial competition is removed, in order to compare with the reality with such competition. One major technical challenge is to predict which delivery person will a delivery order be assigned to when the call-in time needs to be altered. Since customers compete in call-in time, artificially altering the call-in time removes the competition effect in counterfactual.
Once call-in time of an order is adjusted in counterfactual, how this order will then be assigned is unknown as this does not happen in reality. While we can naively guess the assignment by simply assigning the order to the closest delivery person, the assignment should in fact be much more complicated in considering factors such as their orders on-hand and the corresponding finishing time. This project aims at predicting the assignment with the help of statistical machine learning tool. With the assignment properly predicted, we can then simulate how the delivery will be completed in the counterfactual scenario and hence measure the outcomes (e.g. delay time in all delivery orders) to compare with the reality. The beneficial competition effect is then quantified by the difference in two scenarios.
A network analysis of tree competition: Which tree species make the best neighbors?
Project Lead: Stuart Ian Graham, UW Biology Department eScience Liaison: Ariel Rokem
Schematic diagram of one quadrant of a forest plot. Locations and sizes of trees are indicated by the circles and their diameters respectively. This diagram shows how this dataset enables a quantitative description of each tree’s competitive neighborhood. Image credit: Janneke Hille Ris Lambers
A quantitative understanding of how co-occurring tree species influence one another’s growth is required to predict how forest ecosystems will respond to climate change. Although competition with neighboring trees undoubtedly limits tree growth, the species identity of neighbors may have an important role in moderating this interaction.
For example, the seedlings of most conifer tree species have higher growth rates when close to an adult tree of the same species, whereas the opposite is true for many tropical tree species. However, it is currently unclear how these feedbacks influence the growth of adult trees. To fully comprehend the role of these feedbacks in structuring forest communities, we need to build an understanding of how trees respond to the species identity of their neighbors over their entire life cycle.
This project aims to create a statistical model to describe how the growth of adult trees is influenced by the size, species identity, and proximity of neighboring trees. It will use 40 years of growth data collected from 15 forest plots at Mount Rainier National Park, WA.
Within these 100 x 100 m plots, the location of each tree is mapped, such that the neighbors of any tree can be identified. In total, the dataset includes over 8000 trees from 10 species. The code used for this analysis will be released in a user-friendly format such that it can be used by the many forest management agencies that maintain similar datasets to the one used in this project.
Predicting human-mediated vectors for invasive species from mobile technology
Project Lead: Julian Olden, UW School of Aquatic and Fishery Sciences eScience Liaison: Spencer Wood
Invasive species pose a significant threat to ecosystem health and economies of nations across the globe. Freshwater recreational fishing is the largest and growing vector for invader introductions: specifically, because angler activities entangle invasive organisms on fishing gear, boat hulls, and outboard engines, or release non-native species after using them as live bait.Understanding angler movement and behavior can provide critical insight into the most effective implementation of prevention strategies (e.g. watercraft inspection stations, educational signage), thus reducing the introduction, spread and impact of invaders.
To date, angler behavior is inferred from sparsely conducted in-person interviews, creels, dairies and mail-in surveys, which tend to produce retrospective data that is limited in time and space and often reveals intensions or attitudes rather than actual behaviors. Moreover, these traditional approaches disproportionately target older anglers and thus fail to engage younger generations whose participation in fishing is rapidly increasing.
Mobile technologies offer a novel opportunity to efficiently collect information on angler behavior at fine spatial and temporal resolutions over broad spatial and temporal scales. Yet, social media and smartphone fishing applications remain an underutilized tool. Anglers are highly active on social media who often geotagged photographs of fish, and fishing applications provide waterbody-specific location of anglers. This can reveal angler behavior that continuous in both space and time, and can provide inexpensive and high-resolution regarding the potential dispersal pathways of aquatic invasive species.
The primary objective of this incubator is to leverage data from social media and mobile fishing applications to quantify angler activity and movement across the continental United States and assess species invasion risks associated with recreational fishing. Results from this incubator will directly inform interagency management interventions at both local and landscape scales by quantifying angler movement networks and determining how they change through time. Heavily-used locations of fish activity (i.e., highly-connected network nodes) are prime targets for collaborative regulatory approaches that focus on the implementation of prevention strategies, such as watercraft inspection and cleaning stations to remove invasive species, and educational signage to discourage anglers from releasing live bait into waterbodies.
Affective state analysis of ultrasonic vocalizations in animal models of mTBI/PTSD and neuropathic pain
Project Lead: Abigail G. Schindler, acting assistant professor, Psychiatry and Behavioral Services eScience Liaison: Valentina Staneva
Click to enlarge. Image credit: Abigail Schindler
Chronic health conditions (e.g. mental health, pain) are increasing in the US and contribute substantially to decreased quality of life, loss of productivity, and increased financial burden. Indeed, the CDC estimates that over 90% of annual health care expenditures are for people with one or multiple chronic health conditions. Translational research efforts using rodent models can provide much needed insight into underlying mechanisms of chronic health conditions and are needed in order to facilitate the search for therapeutic approaches that can reduce and/or prevent adverse/maladaptive outcomes.
Critically, accurate quantification of affective state (e.g. positive, negative, pain, fear) has historically been a challenge in rodent models, with current available methods suffering from high subjectivity, lack of throughput, and invasive methods, leading to lack of reproducibility across research labs and/or inability to translate to humans. One promising area of research in rodent affective state is ultrasonic vocalizations (USVs). USVs are a form of rodent communication thought to represent an unbiased metric of affective state (there are thought to be potentially different “call signatures” for pleasure, pain, fear, etc.), but are historically difficult to analyze and interpret.
Currently, there is no open-source software available for USV detection and/or analysis (although Matlab based options exist, e.g. DeepSqueak), limiting the applicability of USV research. With a focus on these USVs and open-source products, the current project seeks to develop a Python-based, high-throughput approach for 1) isolating USV calls and 2) assessing affective state. We have USV recordings from a variety of mouse groups (e.g. control, TBI, fear-induction, neuropathic pain) and our goal is to establish specific USV call repertoires/signatures related to specific affective states/experimental conditions/behavioral tasks/therapeutic treatments.
Interactions of tropical precipitation with atmospheric circulation and energy transport
Project Lead: Lauren Kuntz, Department of Oceanography eScience Liaison: Rob Fatland, with Purshottam Shivraj
The canonical view of precipitation driven atmospheric energy flux fails to explain regional structure. (Top) The simplified theory of Hadley Circulation suggests energy convergence along the surface of the equator with poleward divergence in the upper layers. (Bottom) The symmetry in this theory breaks down when exploring the regional patterns of precipitation, which vary zonally and meridionally.*
However, our canonical view of how precipitation impacts broad scale atmospheric circulation and energy transport relies on simplified models of the zonal mean; it fails to explain the vertical and meridional variability in precipitation events, as well as their impact on energy fluxes and circulation patterns.
Developing physical theories that capture this variability is immediately relevant to our understanding of regional climate patterns and projecting future changes in response to greenhouse gas forcing.
To address this, we plan to use over 15 years of satellite precipitation data to constrain the different modes of precipitation and their impact vertical energy convergence and divergence.
Using clustering methods, we will determine the dominant modes of precipitation in terms of their energy footprint, allowing us to explore the statistical patterns of these modes spatially and temporally.
With a better sense of where precipitation is driving energy converging and diverging regionally and vertically, we can explore how that fits into the broad scale circulation patterns and atmospheric energy budgets.
We will directly compare climatological means of latent heating from precipitation modes to observations of atmospheric energy flux, developing insight as to how the two are related.
Through this lens, we will also look at the variability of precipitation energy modes across timescales, with the goal of exposing links with circulation and energy transport variability in the atmosphere.
*Image annotation: Adler et al, 2003; image credit: Lauren Kuntz
Project Lead: John Osborne, Joint Institute for the Study of the Atmosphere and Ocean eScience Liaison: Bryna Hazelton
Clockwise from top left: Picture of a coastal Washington buoy in Hood Canal. Current locations of the CO2 buoys. Plot showing relationship between CO2 and dissolved oxygen. Time series of dissolved oxygen showing data errors that need to be caught.
The ocean plays a major role in controlling Earth’s climate by absorbing one quarter to one third of anthropogenic carbon dioxide released into the atmosphere through fossil fuel burning and land-use changes.
Between 2004 and 2018, NOAA and UW scientists have established 40 sites with time series of surface ocean pCO2 (partial pressure of CO2), of which 17 also include autonomous pH measurements.
These time series characterize a wide range of surface ocean conditions in different open-ocean (17 sites), coastal (13 sites), and coral reef (10 sites) regimes.
Our objective is to develop quality control procedures and methodologies using recovered data and existing quality controlled pCO2 data.
In particular we need quality control procedures developed for O2, Chl, and NTU. The quality control methodologies should be applicable to our real-time data streams.