Environmental monitoring using a drone-enabled wireless sensor network

Water quality monitoring traditionally occurs via resource intensive field surveys, such as when a researcher manually collects data in a stream. Limiting factors such as time, money, and accessibility often result in less oversight of impaired water bodies, significantly threatening ecosystemic health and related ecosystem services. According to the United States Environmental Protection Agency, 84% of rivers and streams within the United States remain unassessed, resulting in significant lapses in available data. Such lapses prohibit efficient and effective monitoring, restoration, and conservation efforts throughout the United States. The objective of this project was to employ an unmanned aerial vehicle to remotely collect data regarding water quality from a wireless sensor network. The site under analysis was Boones Run, a tributary of the South Fork of the Shenandoah River near Elkton, Virginia. This project served as a proof-of-concept that communication with a wireless sensor node has the capability to be deployed to collect data in remote areas efficiently and effectively. This system would be useful in areas where accessibility is difficult, and transmission of data for processing is not readily available due to the lack of network connectivity. Initial analysis of environmental data gathered by hand indicated that surrounding land use had a significant impact on Boones Run water quality. This conclusion was reached given the trends seen in dissolved oxygen, water temperature, pH, and conductivity data from upstream to downstream over time. The completion of this project also lead to the successful data flow amongst all parts in the wireless sensor network. Three sensors soldered to a breadboard and connected to an Arduino Uno were able to gather data and send it to a Raspberry Pi 0. The Raspberry Pi 0 acted as a temporary storage device for the data before it was sent wirelessly to a Raspberry Pi 3 acting as an access point. The Raspberry Pi 3 device was mounted to an unmanned aerial vehicle so it could be flown over the node to decrease data collection time as well as adding the ability to collect data from places that are otherwise difficult for humans to access

Supreme Court Legitimacy Under Threat? The Role of Cues in How the Public Responds to Supreme Court Decisions

Understanding how the public views the Court and its rulings is crucial to assessing its institutional stability. However, as scholars note, “People are broadly supportive of the court and believe in its ‘legitimacy’—that is, that Supreme Court rulings should be respected and followed. But we don’t know that much about whether people actually agree with the case outcomes themselves.” In this article, we highlight empirical research investigating the factors that affect public agreement with Court decisions, highlighting recent developments from our work. At the onset, it is to note that the public generally hears about the Court’s decisions from media sources, not from the Court itself. Legal rulings are jam-packed with jurisprudential jargon and technical language that can be difficult for a lay audience to understand, which is why the general public is especially likely to rely on heuristics— cognitive shortcuts—as cues to help them decipher the ruling and assess whether they agree or disagree. So, what do we know about the heuristic cues that affect how the public receives Supreme Court rulings? And how is the Court faring after the controversial 2022 ruling in Dobbs and other recent politically volatile case decisions?

A Study of Ozone at Railroad Valley, NV and Trinidad Head, CA

A STUDY OF OZONE AT RAILROAD VALLEY, NV and TRINIDAD HEAD, CA Ozone (Oᴣ) is a form of oxygen that protects the planet Earth from deadly ultraviolet rays emitted by the sun; without this triatomic molecule high in the atmosphere, life processes on the planet would be impossible. Ozone is an air pollutant and toxic in the lowest part of the atmosphere, and inhaling it could cause permanent damage to animals’ respiratory system. Long term exposure to high concentration of ozone has been linked with the development of asthma in children. Because of its complicated role in our atmosphere, scientists are studying its depletion and recovery in the stratosphere, and the minimization of ozone formation in the atmospheric boundary layer (the lowest part of the atmosphere). Here at NASA Ames Research Center (ARC), the Atmospheric Branch of Earth Science Division is conducting a study to examine and compare ozone concentrations in the atmospheric boundary layer (0 to ~2 km above the surface of the Earth) to those of the free troposphere (~2 km to ~10 km, where regional transport occurs), and to validate the accuracy of the ozone instrument used in the experiment. Using a 2BTechnology, Inc., Dual Beam Ozone Monitor installed inside the wing pod of an Alpha jet aircraft based at Moffett Field vertical profiles of ozone concentrations have been collected at Trinidad Head, California, and Railroad Valley (RRV), Nevada. The airborne data at Trinidad Head are also compared to standard measurements collected by the National Atmospheric and Oceanic Administration (NOAA) using a balloonborne DMT Electrochemical Concentration Cell Ozonesonde. My area of research is to support the calibration of the ozone instrument, to aggregate ozone measurements, and to analyze data collected from the three subject locations

Testing and Improving a UAV-Based System Designed for Wetland Methane Source Measurements

Wetlands are the single highest emitting methane source category, but the magnitude of wetland fluxes remains difficult to fully characterize due to their large spatial extent and heterogeneity. Fluxes can vary with land surface conditions, vegetation type, and seasonal changes in environmental conditions. Unmanned aerial vehicles (UAVs) are an emerging platform to better characterize spatial variability in these natural ecosystems. While presenting some advantages over traditional techniques like towers and flux chambers, in that they are mobile vertically and horizontally, their use is still challenging, requiring continued improvement in sensor technology and field measurement approaches. In this work, we employ a small, fast response laser spectrometer on a Matrice 600 hexacopter. The system was previously deployed successfully for 40 flights conducted in a four-day period in 2018 near Fairbanks, Alaska. These flights revealed several potential areas for improvement, including: vertical positioning accuracy, the need for sensor health indicators, and approaches to deal with low wind speeds. An additional set of flights was conducted this year near Antioch in California. Flights were conducted several meters above ground up to 15-25 m in a curtain pattern. These curtains were flown both upwind and downwind of a tower site, allowing us to calculate a mass balance methane flux estimate that can be compared to eddy covariance fluxes from the tower. Testing will better characterize the extent to which altitude drifts in-flight and how GPS values compare with measurements from the onboard LIDAR, as well as the agreement between two-dimensional wind speed and direction on the ground versus measured onboard the UAV. Hardware improvements to the sensor and GPS are being considered to help reduce these sources of uncertainty. Results of this testing and how system performance relates to needs for quantifying wetland fluxes, will be presented

Non-exercise equations to estimate fitness in white European and South Asian men

Cardiorespiratory fitness is a strong, independent predictor of health, whether it is measured in an exercise test or estimated in an equation. The purpose of this study was to develop and validate equations to estimate fitness in middle-aged white European and South Asian men.Multiple linear regression models (n=168, including 83 white European and 85 South Asian men) were created using variables that are thought to be important in predicting fitness (VO2 max, mL⋅kg⋅min): age (years); BMI (kg·m); resting heart rate (beats⋅min); smoking status (0=never smoked, 1=ex or current smoker); physical activity expressed as quintiles (0=quintile 1, 1=quintile 2, 2=quintile 3, 3=quintile 4, 4=quintile 5), categories of moderate- to vigorous-intensity physical activity (0=<75 min⋅wk, 1=75-150 min⋅wk, 2=>150-225 min⋅wk, 3=>225-300 min⋅wk, 4=>300 min⋅wk), or minutes of moderate- to vigorous-intensity physical activity (min⋅wk); and, ethnicity (0=South Asian, 1=white). The leave-one-out-cross-validation procedure was used to assess the generalizability and the bootstrap and jackknife resampling techniques were used to estimate the variance and bias of the models.Around 70% of the variance in fitness was explained in models with an ethnicity variable, such as: VO2 max = 77.409 - (age*0.374) - (BMI*0.906) - (ex or current smoker*1.976) + (physical activity quintile coefficient) - (resting heart rate*0.066) + (white ethnicity*8.032), where physical activity quintile 1 is 1, 2 is 1.127, 3 is 1.869, 4 is 3.793, and 5 is 3.029. Only around 50% of the variance was explained in models without an ethnicity variable. All models with an ethnicity variable were generalizable and had low variance and bias.These data demonstrate the importance of incorporating ethnicity in non-exercise equations to estimate cardiorespiratory fitness in multi-ethnic populations

Four Years of Airborne Measurements of Wildfire Emissions in California, with a Focus on the Evolution of Emissions During the Soberanes Megafire

Biomass burning is an important source of trace gases and particles which can influence air quality on local, regional, and global scales. With wildfire events increasing due to changes in land use, increasing population, and climate change, characterizing wildfire emissions and their evolution is vital. In this work we report in situ airborne measurements of carbon dioxide (CO2), methane (CH4), water vapor (H2O), ozone (O3), and formaldehyde (HCHO) from nine wildfire events in California between 2013 and 2016, which were sampled as part of the Alpha Jet Atmospheric eXperiment (AJAX) based at NASA Ames Research Center. One of those fires, the Soberanes Megafire, began on 22 July 2016 and burned for three months. During that time, five flights were executed to sample emissions near and downwind of the Soberanes wildfire. In situ data are used to determine enhancement ratios (ERs), or excess mixing ratio relative to CO2, as well as assess O3 production from the fire. Changes in the emissions as a function of fire evolution are explored. Air quality impacts downwind of the fire are addressed using ground-based monitoring site data, satellite smoke products, and the Community Multiscale Air Quality (CMAQ) photochemical grid model

Mitogen-activated kinase kinase kinase 1 inhibits hedgehog signaling and medulloblastoma growth through GLI1 phosphorylation

The aberrant activation of hedgehog (HH) signaling is a leading cause of the development of medulloblastoma, a pediatric tumor of the cerebellum. The FDA‑approved HH inhibitor, Vismodegib, which targets the transmembrane transducer SMO, has shown limited efficacy in patients with medulloblastoma, due to compensatory mechanisms that maintain an active HH‑GLI signaling status. Thus, the identification of novel actionable mechanisms, directly affecting the activity of the HH‑regulated GLI transcription factors is an important goal for these malignancies. In this study, using gene expression and reporter assays, combined with biochemical and cellular analyses, we demonstrate that mitogen‑activated kinase kinase kinase 1 (MEKK1), the most upstream kinase of the mitogen‑activated protein kinase (MAPK) phosphorylation modules, suppresses HH signaling by associating and phosphorylating GLI1, the most potent HH‑regulated transcription factor. Phosphorylation occurred at multiple residues in the C‑terminal region of GLI1 and was followed by an increased association with the cytoplasmic proteins 14‑3‑3. Of note, the enforced expression of MEKK1 or the exposure of medulloblastoma cells to the MEKK1 activator, Nocodazole, resulted in a marked inhibitory effect on GLI1 activity and tumor cell proliferation and viability. Taken together, the results of this study shed light on a novel regulatory mechanism of HH signaling, with potentially relevant implications in cancer therapy

Analyzing Carbon Dioxide and Methane Emissions in California Using Airborne Measurements and Model Simulations

Greenhouse gas (GHG) concentrations have increased over the past decades and are linked to global temperature increases and climate change. These changes in climate have been suggested to have varying effects, and uncertain consequences, on agriculture, water supply, weather, sea-level rise, the economy, and energy. To counteract the trend of increasing atmospheric concentrations of GHGs, the state of California has passed the California Global Warming Act of 2006 (AB-32). This requires that by the year 2020, GHG (e.g., carbon dioxide (CO2) and methane (CH4)) emissions will be reduced to 1990 levels. To quantify GHG fluxes, emission inventories are routinely compiled for the State of California (e.g., CH4 emissions from the California Greenhouse Gas Emissions Measurement (CALGEM) Project). The major sources of CO2 and CH4 in the state of California are: transportation, electricity production, oil and gas extraction, cement plants, agriculture, landfills/waste, livestock, and wetlands. However, uncertainties remain in these emission inventories because many factors contributing to these processes are poorly quantified. To alleviate these uncertainties, a synergistic approach of applying air-borne measurements and chemical transport modeling (CTM) efforts to provide a method of quantifying local and regional GHG emissions will be performed during this study. Additionally, in order to further understand the temporal and spatial distributions of GHG fluxes in California and the impact these species have on regional climate, CTM simulations of daily variations and seasonality of total column CO2 and CH4 will be analyzed. To assess the magnitude and spatial variation of GHG emissions and to identify local hot spots, airborne measurements of CH4 and CO2 were made by the Alpha Jet Atmospheric eXperiment (AJAX) over the San Francisco Bay Area (SFBA) and San Joaquin Valley (SJV) in January and February 2013 during the Discover-AQ-CA study. High mixing ratios of GHGs were observed in-flight with a high degree of spatial variability. To provide an additional method to quantify GHG emissions, and analyze AJAX measurement data, the GEOS-Chem CTM is used to simulate SFBA/SJV GHG measurements. A nested-grid version of GEOS-Chem will be applied and utilizes varying emission inventories and model parameterizations to simulate GHG fluxes/emissions. The model considers CO2 fluxes from fossil fuel use, biomass/biofuel burning, terrestrial and oceanic biosphere exchanges, shipping and aviation, and production from the oxidation of carbon monoxide, CH4, and non-methane volatile organic carbons. The major sources of CH4 simulated in GEOS-Chem are domesticated animals, rice fields, natural gas leakage, natural gas venting/flaring (oil production), coal mining, wetlands, and biomass burning. Preliminary results from the comparison between available observations (e.g., AJAX and CALGEM CH4 emission maps) and GEOS-Chem results will be presented, along with a discussion of CO2 and CH4 source apportionment and the use of the GEOS-Chem-adjoint to perform inverse GHG modeling