Anthropogenic activity has caused increased concentrations of greenhouse gases (GHGs) in the atmosphere which has resulted in global warming gaining momentum. GHG emission estimates up to date are insufficient and in order to mitigate GHG emissions of anthropogenic origin fluxes of GHGs need to be mapped. The application of low-cost sensors (LCSs) as an environmental monitoring tool has been seen as a pathway with the ability to provide GHG estimates that can help us to identify sources and sinks. However, questions have been highlighted regarding the accuracy of LCSs in targeting GHGs in comparison to alternative environmental monitoring approaches. A network of LCSs with the ability to target carbon dioxide (CO2) and methane (CH4), among other environmental parameters of interest, were deployed at a wastewater treatment plant (WWTP) in Linköping, Sweden. A cross-checking procedure where LCS data were compared with data from high-precision measuring instruments UltraPortable Greenhouse Gas Analyser (UGGA) and air samples analysed with gas chromatography (GC), enabled evaluation of LCSs as an environmental monitoring tool. Despite uncertainties regarding individual sensors´ behaviour, results from this study show that LCS systems have the capability to bring valuable estimates of GHGs to the environmental monitoring field, although not yet at the precise level of reference equipment. The low cost of the LCS systems studied in this work open a range of possibilities because of their ability to provide large amounts of data on both a spatial and temporal scale that are not attainable for high-precision reference instruments. From the long-term measurements it can be concluded that the CO2 concentrations at the dewatered sludge deposit, and especially inside the screw conveyor building, are higher than global average levels of CO2. / Mänsklig aktivitet har orsakat ökade koncentrationer av växthusgaser i atmosfären vilket i sin tur har resulterat i att den globala uppvärmningen har accelererat. Estimeringar av växthusgasutsläpp har hittills varit bristfälliga och för att kunna lindra antropogena växthusgasutsläpp kartläggning av växthusgasers utbyten efterfrågas. Applicering av lågkostnads-sensorer som en ett verktyg inom miljöövervakning har setts som en möjlig väg i att tillhandahålla estimeringar av växthusgaser som kan hjälpa oss att identifiera källor och sänkor. Dock har frågor om hur precisa lågkostnads-sensorer är i jämförelse med alternativa miljöövervakningsmetoder lyfts. Ett nätverk av låg kostnads-sensorer som kan känna av koldioxid (CO2) and metan (CH4) och en rad andra miljömässiga parametrar av intresse, placerades ut på ett reningsverk i Linköping, Sverige. En procedur där sensordata ställdes mot data insamlad från de högprecisa instrumenten Ultra-Portable Greenhouse Gas Analyser och luftprover analyserande med gas kromatografi öppnade upp för möjligheten att utvärdera sensorernas prestanda. Trots osäkerheter i data gällande individuella sensorers beteende, så visar resultat från den här studien att låg kostnads-sensorsystem har kapaciteten att bidra med berikande estimeringar av växthusgaser till miljöövervakning området, dock ännu inte med samma precishet som för referensinstrument. Den låga kostnaden för lågkostnads-sensorsystemen som studeras i det här arbetet öppnar upp för en rad möjligheter tack vare att de kan tillhandahålla stora mängder data på både en spatial och temporal skala som inte är inom räckhåll för högprecisa referensinstrument. Från långtidsmätningarna kan det konstateras att koncentrationerna av CO2 vid slamavvattningslagret, och särskilt inne i skruvpressbyggnaden, är högre än det globala genomsnittsvärden för CO2 nivåer.

Environmental monitoring involves the quantification of microscopic cells and particles such as algae, plant cells, pollen, or fungal spores. Traditional methods using conventional microscopy require expert knowledge, are time-intensive and not wellsuited for automated high throughput. Multispectral imaging flow cytometry (MIFC) allows measurement of up to 5000 particles per second from a fluid suspension and can simultaneously capture up to 12 images of every single particle for brightfield and different spectral ranges, with up to 60x magnification. The high throughput of MIFC has high potential for increasing the amount and accuracy of environmental monitoring, such as for plant-pollinator interactions, fossil samples, air, water or food quality that currently rely on manual microscopic methods. Automated recognition of particles and cells is also possible, when MIFC is combined with deep-learning computational techniques. Furthermore, various fluorescence dyes can be used to stain specific parts of the cell to highlight physiological and chemical features including: vitality of pollen or algae, allergen content of individual pollen, surface chemical composition (carbohydrate coating) of cells, DNA- or enzyme-activity staining. Here, we outline the great potential for MIFC in environmental research for a variety of research fields and focal organisms. In addition, we provide best practice recommendations.

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Understanding the dynamic interplay between fire severity, topography, and tree mortality, is crucial for predicting future forest dynamics and enhancing resilience against climate change-induced wildfire regimes. This thesis develops a multi-sensor approach for automated estimation of tree mortality, then applies it to examine trends in tree mortality over a six-year period across a fire affected study site in the Trinity River basin in Northern California. The Random Forest model uses publicly available USGS 3D Elevation Program Lidar (3DEP) and NAIP imagery as inputs and is likely to be easily adaptable to other landscapes. The model had a Receiver Operating Characteristic Area Under the Curve (ROC AUC) score in training of 0.998. In multiple rounds of validation, using geographically distinct sets of holdout data, had mean accuracy of 0.998. The trained model was then used to assess tree mortality across a patchwork of different levels of burn severity at a site in Northern California. When applied to the study site significant variations were found in tree mortality across different fire severity treatments and landforms. This model shows potential for incorporation into predictive tree mortality models based on landform and climate.

I used results from camera traps set for tigers (Panthera tigris) during 2001-2011 in the Hukaung Valley Wildlife Sanctuary of northern Myanmar to assess overall biodiversity of large mammal and bird species, and to identify differences in photo rates inside and outside of the most protected core area of the Sanctuary. A total of 403 camera stations were deployed during October-July in the dry seasons of 2001-2011, 260 inside the Core area and 143 Outside. From 10,750 trap-nights I obtained 2,077 independent photos of wildlife species and 699 of domestic animals and humans, including 35 species of wild mammals (19 carnivores, 4 primates, 1 elephant, 6 even-toed ungulates, 1 pangolin, and 4 rodents) and 16 species of wild birds. Of these, 1 is considered critically endangered, 7 are endangered, 11 are vulnerable, and 5 are nearly threatened. Some species that probably occur in the Sanctuary (e.g., arboreal or semi-aquatic mammals) were not photographed, likely because of camera placement. In total, 48 wild species were photographed in the Core area vs. only 33 at locations Outside of the core area. Generally, few photos of any domestic animal species were obtained inside 9 the Core area, and no photos insurgents were obtained there, but many more photos of poachers and villagers, but also park rangers, were obtained there. Increased patrol efforts may have helped maintain species presence in the Core area, but differences in photo rates between areas were likely mostly influenced by differences in elevation, slope, density of streams, trails, and roads, and perhaps vegetative cover type. Tiger abundance is most influenced naturally by prey availability and anthropogenically by poaching. In the Hukaung Valley Wildlife Sanctuary in northern Myanmar, a major conservation area established to protect tigers, tiger presence has declined. This study was conducted to assess habitat occupancy and distribution of principal tiger prey species in the Core part of the Sanctuary by surveying for sign on 1650.9 km partitioned into 554 sampling units during November 2007 and May 2008. Using standard occupancy model in the program PRESENCE (6.2), habitat occupancy and detection probabilities were predicted and the best candidate model for each species was selected using the Akaike information criterion (AIC). By incorporating 7 environmental and 4 social covariates, the predicted habitat occupancy rates were 0.76 (SE=0.196, naïve estimate = 0.5162) for gaur (Bos gaurus), 0.91 (SE=0.03, naïve estimate = 0.7762) for sambar (Rusa unicolor), 0.57 (SE = 0.003, naïve estimate = 0.3195) for wild pigs (Sus scrofa) and 0.89 (SE = 0.001, naïve estimate = 0.7996) for muntjac (Muntiacus muntjak). Overall, shorter Euclidean distances to ranger stations and trails, decreased stream density, and broadleaved evergreen/semi-deciduous forest and relatively rare rain-fed cropland habitat occurrence positively influenced prey habitat occupancy; conversely, shorter Euclidean distances to villages, roads, and streams, higher elevations, and occurrence of mixed broadleaved and needle-leaved forest habitat 10 negatively influenced occupancy. In addition, Euclidean distance to ranger stations, trails, and roads positively affections species detections, whereas shorter Euclidean distance to villages and streams, high elevations, and high precipitation negatively affected detections. Results indicate that all four prey species were relatively well-distributed through the Sanctuary Core area. However, comparisons with tiger and prey indices of abundance elsewhere suggest that prey density is low and would not likely support many tigers.

In this dissertation, the design, implementation, and educational applications of a real-time water and weather monitoring system, developed to enhance water sustainability education and research, are discussed. This unique system, called LabVIEW Enabled Watershed Assessment System (LEWAS), is a real- world extension of various data acquisition modules that were successfully implemented using LabVIEW into a freshman engineering course (Engineering Exploration, ENGE 1024) at Virginia Tech. The outdoor site location measures water quality and quantity data including flow rate, pH, dissolved oxygen, conductivity, and temperature -- as indicators of stream health - for an on-campus impaired stream in real-time. In addition, weather parameters (temperature, barometric pressure, relative humidity and precipitation) are measured at the LEWAS outdoor site. The measured parameters can be accessed by remote users in a real-time through a web-based interface for education and research. LEWAS is solar powered and uses the campus wireless network through a high-gain antenna to transmit data to remote clients in real-time. Its power budget consisting of consumption (14 W), electrical storage, and generation (80 W, peak) is balanced to enable 24/7 operation regardless of weather conditions. An embedded computer with low power consumption and modules for communicating and storing data are installed in the field and it is programmed to process measured environmental parameters to be delivered to remote users. This computer is programmed both using a field programmable gate array (FPGA, for low power consumption and robust operation) and traditional microprocessor programming (for more flexibility). The environmental sensors of the system are routinely calibrated using established procedures. A LEWAS Development Platform was established to develop and test the system and to train and mentor several undergraduate and graduate students who helped in its implementation. A number of design and implementation challenges were overcome including extending campus Internet access to a location not included on the network and integrating hardware and software from three different sensor manufacturers into a unified software platform accessible over the Internet. To study the educational applications of LEWAS, an observational study was conducted as the system was gradually introduced to students in ENGE 1024 between 2009 and 2011. Positive student attitudes on the role of LEWAS to enhance their environmental awareness informed an experimental design implemented to study the motivational outcomes associated with the system. Accordingly, appropriate educational interventions and a hands-on activity on the importance of environmental monitoring were developed for both control and experiment groups, with only the latter given access to LEWAS to retrieve the environmental parameters for the activity. An instrument was developed on the theoretical foundation of the expectancy value theory of motivation and was administered to control and experimental groups in ENGE 1024. Altogether, 150 students participated in the study. Exploratory Factor Analysis (EFA) was applied which resulted in factors that group questions together based on interest, importance, real-time access, and cost (feasibility of monitoring). After conducting parametric and nonparametric statistical analyses, it was determined that there exists a statistically significant difference between control and experimental groups in interest, real-time, and cost factors. This finding implies that providing real-time access to environmental parameters can increase student interest and their perception of feasibility of environmental monitoring -- both major components of motivation to learn about the environment. Future extensions and applications of the system at Virginia Tech and beyond are discussed. / Ph. D.

The increasing level and dispersion of toxic chemicals in the environment has stimulated a need for accurate methods capable of detecting and quantifying the activity of toxicants. The primary objective of this study was to determine, through in vitro tests, the potential of three purified enzymes: α-chymotrypsin, acid phosphatase, and carbonic anhydrase for use in the early assessment of toxicants at environmentally significant levels. Activities of α-chymotrypsin and acid phosphatase were measured spectrophotometrically, while carbonic anhydrase activities were determined through monitoring a pH change. The chemical agents investigated included several heavy metals, common herbicides and pesticides, and various environmentally significant anions. In addition, several techniques were explored to amplify enzyme response to chemical agents. The results of the study demonstrated that α-chymotrypsin did not significantly respond to cadmium, nickel, or 2,4-D, and consequently, does not appear to be useful in indicating potential toxicity problems associated with these agents. The acid phosphatase enzyme system appears to be useful in signaling the presence of low levels of certain anions (fluoride at 0.45 mg/ℓ and nitrate at 7.3 mg/ℓ), but does not appear to have potential for detecting toxic activity due to cadmium, nickel, or 2,4-D. Enzyme inhibition induced by fluoride at 37°C was not altered by changing the assay temperature to 50°C. The results of the experiments with carbonic anhydrase show that the enzyme does not appear to be affected by cadmium or nickel ions. However, enzyme activity was inhibited by fluoride (4.5 mg/ℓ), sulfide (0.5 mg/ℓ), and nitrate (73 mg/ℓ). Enzyme inhibition was also induced by 10 mg/ℓ of atrazine, malathion, or carbaryl, and 150 to 500 mg/ℓ of 2,4-D. Inhibitory effects induced by sulfanilamide appeared to be slightly enhanced by the addition of Cd²⁺, Ni²⁺, or Zn²⁺ cations. These findings, although preliminary, suggest that carbonic anhydrase demonstrates potential for signaling the presence of anions, and appears to be useful in indicating potential toxicity problems due to pesticides and herbicides. / Master of Science

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 92

<p dir="ltr">Blow flies are a family of carrion insects that are among the first to arrive in the decomposition process. Blow flies are known to ingest carrion, feces, water, and occasionally nectar to meet nutritional requirements. These behaviors make blow flies a unique organism potentially containing genetic material from a variety of sources within one environment. Their global distribution and ease of capture makes them a strong candidate for resource monitoring and identification. While previous studies have evaluated the suitability of blow flies for vertebrate biodiversity estimates, no work has been done looking at their ability to ingest and store genetic material from plants and microbes present in water. It is also not known how long these DNA signals persist in the gut. Through DNA analysis of the blow fly gut, researchers can identify vertebrates that have recently died in an environment, what plant species are present, and what water source the insect utilized. Through lab colony (Phormia regina) feeding experiments, it was determined that at 25 ˚C and 50 % relative humidity, vertebrate and plant DNA persist in the gut for over 120 hours post-ingestion. Wild sample analysis of flies collected from Yellowstone National Park was performed to identify plant species ingested by P. regina in the wild. Following Sanger sequencing, top hits on BLASTn included Brassicales, Juglans cathyensis, and uncultured Candida. This is the first application of environmental DNA analysis techniques to insects for the purpose of plant identification. This work also attempts to characterize microbial profiles of the gut of P. regina for the purpose of water resource identification. Over a two-month collection period, samples were collected from different water resources across Indianapolis. Flies were exposed to these samples in a controlled feeding experiment, followed by sampling at 0- and 72-hours post-exposure. Gut samples were sequenced using Illumina and Operational Taxonomic Unit clustering grouped reads by sequence similarity for identification. Bacteria classes identified included Gammaproteobacteria, Bacteroidia, Flavobacteria, Alphaproteobacteria, Bacilli, Clostridia, Actinobacteria, Betaproteobacteria, and Fusobacteria. Many bacteria classes were common across water samples, although the abundance of each class changed between samples and across time. These unique microbial profiles can be used to identify water resources for potential contamination and chemical dumping. Further work is necessary to generate microbial profiles from the original water sources themselves and for generation of alpha and beta diversities. Overall, this work spans multiple fields. Species identification is important for biodiversity monitoring and environmental surveys. Utilizing blow fly derived DNA allows for detection of living and deceased vertebrates in an environment, plant life, and water quality within one sample. This work also has implications in forensic science, specifically wildlife forensics and chemical detection of clandestine laboratories and chemical weapon compounds.</p>

Supporting environmental monitoring with remote sensing data considerably increases cost effectiveness and reliability of traditional, manual solutions. Additionally, by means of automation, it bears the potential to prevent contaminations or disasters through the availability of timely and spatially dense data. To this end, we investigate the possibility to monitor gas and oil pipelines of a storage cavern by using optical, multi spectral data from the Copernicus Sentinel 2 satellites. Due to a lack of known disasters/contaminations, we resort to a monitoring approach based on statistical outliers. First results demonstrate the general capability of our approach to detect contaminations and generate warnings.