A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are poised to reshape transportation and mobility by replacing humans as the driver and service provider. While the primary stated motivation for vehicle automation is to improve safety and convenience of road mobility, this transformation also provides a valuable opportunity to improve vehicle energy efficiency and reduce emissions in the transportation sector. Progress in vehicle efficiency and functionality, however, does not necessarily translate to net positive environmental outcomes. Here, we examine the interactions between CAV technology and the environment at four levels of increasing complexity: vehicle, transportation system, urban system, and society. We find that environmental impacts come from CAV-facilitated transformations at all four levels, rather than from CAV technology directly. We anticipate net positive environmental impacts at the vehicle, transportation system, and urban system levels, but expect greater vehicle utilization and shifts in travel patterns at the society level to offset some of these benefits. Focusing on the vehicle-level improvements associated with CAV technology is likely to yield excessively optimistic estimates of environmental benefits. Future research and policy efforts should strive to clarify the extent and possible synergetic effects from a systems level to envisage and address concerns regarding the short- and long-term sustainable adoption of CAV technology.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149443/1/EEICAV_Taiebat et al (2018)_Environmental Science & Technology.pdfDescription of EEICAV_Taiebat et al (2018)_Environmental Science & Technology.pdf : Main articl

Mauritius since the last glacial:environmental and climatic reconstruction of the last 38 000 years from Kanaka Crater

A 10 m long peat core from the Kanaka Crater (20° 25′ S, 57° 31′ E), located at 560 m elevation in Mauritius, was analyzed for microfossils. Eight radiocarbon ages show the pollen record reflects environmental and climatic change of the last ca. 38 cal ka BP. The record shows that the island was continuously covered by forest with Erica heath (Philippia) in the uplands. Cyperaceous reedswamp with Pandanus trees was abundant in the coastal lowlands as well as locally in the waterlogged crater. The record shows changes in climatic humidity (wet from 38.0 to 22.7 cal ka BP, drier from 22.7 to 10.6 cal ka BP, and wetter again from 10.6 cal ka BP to recent) as the main response to climate change. A high turnover in montane forest species is evidenced at 22.7 cal ka BP and at the start of the Holocene. The limited altitudinal ranges in the mountains of Mauritius (maximum altitude 828 m), and changing humidity being more important than changing temperature, suggests that in response to climate change a reassortment in taxonomic composition of montane forests might be equally important as displacement of forest types to new altitudinal intervals. We found weak impact of the latitudinal migration of the Intertropical Convergence Zone and data suggest that the Indian Ocean Dipole is a more important driver for climatic change in the southwest Indian Ocean

Wastewater Surveillance for SARS-CoV-2 on College Campuses: Initial Efforts, Lessons Learned, and Research Needs

Wastewater surveillance for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging approach to help identify the risk of a coronavirus disease (COVID-19) outbreak. This tool can contribute to public health surveillance at both community (wastewater treatment system) and institutional (e.g., colleges, prisons, and nursing homes) scales. This paper explores the successes, challenges, and lessons learned from initial wastewater surveillance efforts at colleges and university systems to inform future research, development and implementation. We present the experiences of 25 college and university systems in the United States that monitored campus wastewater for SARS-CoV-2 during the fall 2020 academic period. We describe the broad range of approaches, findings, resources, and impacts from these initial efforts. These institutions range in size, social and political geographies, and include both public and private institutions. Our analysis suggests that wastewater monitoring at colleges requires consideration of local information needs, sewage infrastructure, resources for sampling and analysis, college and community dynamics, approaches to interpretation and communication of results, and follow-up actions. Most colleges reported that a learning process of experimentation, evaluation, and adaptation was key to progress. This process requires ongoing collaboration among diverse stakeholders including decision-makers, researchers, faculty, facilities staff, students, and community members

How many bird and mammal extinctions has recent conservation action prevented?

Aichi Target 12 of the Convention on Biological Diversity (CBD) aims to ‘prevent extinctions of known threatened species’. To measure its success, we used a Delphi expert elicitation method to estimate the number of bird and mammal species whose extinctions were prevented by conservation action in 1993 - 2020 (the lifetime of the CBD) and 2010 - 2020 (the timing of Aichi Target 12). We found that conservation prevented 21–32 bird and 7–16 mammal extinctions since 1993, and 9–18 bird and 2–7 mammal extinctions since 2010. Many remain highly threatened, and may still become extinct in the near future. Nonetheless, given that ten bird and five mammal species did go extinct (or are strongly suspected to) since 1993, extinction rates would have been 2.9–4.2 times greater without conservation action. While policy commitments have fostered significant conservation achievements, future biodiversity action needs to be scaled up to avert additional extinctions

How many bird and mammal extinctions has recent conservation action prevented?

Aichi Target 12 of the Convention on Biological Diversity (CBD) contains the aim to ‘prevent extinctions of known threatened species’. To measure the degree to which this was achieved, we used expert elicitation to estimate the number of bird and mammal species whose extinctions were prevented by conservation action in 1993–2020 (the lifetime of the CBD) and 2010–2020 (the timing of Aichi Target 12). We found that conservation action prevented 21–32 bird and 7–16 mammal extinctions since 1993, and 9–18 bird and two to seven mammal extinctions since 2010. Many remain highly threatened and may still become extinct. Considering that 10 bird and five mammal species did go extinct (or are strongly suspected to) since 1993, extinction rates would have been 2.9–4.2 times greater without conservation action. While policy commitments have fostered significant conservation achievements, future biodiversity action needs to be scaled up to avert additional extinctions.https://wileyonlinelibrary.com/journal/conlMammal Research Institut

Monitoring Viruses in Wastewater to Support Public Health: Development and Demonstration of Improved Approaches for Two Applications

Viruses in wastewater present public-health challenges as well as public-health opportunities. I consider both herein. I begin with a systematic literature review of nearly 300 studies, published from 2000 to 2018, that document applications of flow cytometry (FCM) to ensure microbial water quality and hence facilitate safe and effective water treatment, distribution, and reuse. I find that while there is a large body of evidence supporting widespread adoption of FCM as a routine method for microbial water-quality assessment, key knowledge gaps impede the technique from realizing its full potential. One of these gaps is robust protocols for FCM-based analysis of waterborne viruses. In this dissertation, I hypothesize that a fractional factorial experimental design is a better alternative to the “pipeline” strategy commonly followed for FCM protocol optimization. I then demonstrate my approach, using a fractional factorial experimental design to optimize staining of the bacteriophage T4 prior to FCM analysis. My results yield a specific protocol for reliably identifying and quantifying T4 bacteriophage through FCM.I also explain why manual gating of FCM data using a series of two-dimensional plots—the typical approach to FCM data analysis—is problematic, especially with respect to applications of FCM to facilitate advanced water treatment and reuse. I suggest that algorithmic clustering approaches could expedite and improve FCM data analysis, and could even help position FCM as a technique for real-time microbial water-quality monitoring. I test this theory by generating FCM data from two solutions: (i) a mixed-target solution containing a variety of environmentally relevant viral surrogates, and (ii) an environmental-spike solution comprising T4 bacteriophage in a wastewater matrix. I first analyze these data through manual gating, and then compare results to results obtained through algorithmic clustering: specifically, by coupling the OPTICS ordering algorithm with either manual or automated identification of clusters from the OPTICS-ordered data. I demonstrate that OPTICS-assisted clustering can in some cases work as well or better than manual gating of FCM data—and is certainly far faster and less labor-intensive. OPTICS-assisted clustering can also point to features in FCM data that are difficult to detect through manual gating alone. However, I also find that more needs to be done to position OPTICS as a reliable tool for automated, objective analysis of FCM data from environmental samples, especially data generated from challenging biological targets like viruses in challenging matrices like wastewater.I explore wastewater-borne viruses as a public-health opportunity through the lens of the COVID-19 pandemic. Wastewater-based epidemiology (WBE) quickly became recognized as a useful complement to clinical testing following the pandemic’s onset. However, little is known about sub-community relationships between wastewater and clinical data. I present a novel framework for probabilistically aligning wastewater and clinical data with high spatial granularity. I use this framework to uncover clear sub-regional (i.e., sub-city) and building/neighborhood-scale correlations between wastewater and clinical data collected through the Healthy Davis Together (HDT) pandemic-response initiative in Davis, CA. In addition, I hypothesize that multiple imputation (using an expectation maximization-Markov chain Monte Carlo (MCMC) approach) of non-detects in wastewater qPCR data is less likely to bias results than more commonly used non-detect handling methods (e.g., censoring or single imputation). I use the HDT data to test this hypothesis. I find that while results obtained using different non-detect handling methods are similar, they are not the same—indicating the importance of specifying non-detect handling method in WBE studies. I also find that the EM-MCMC method yields somewhat better agreement between clinical and wastewater data than do the other non-detect handling methods examined. Refinements to the algorithm, tuning parameters, and variable groupings used in this dissertation could further recommend the EM-MCMC method for wastewater-data analysis in the future.I conclude the dissertation with a discussion of lessons learned from my experience helping launch, grow, and manage the HDT WBE program. Conducting WBE requires significant investments of time, money, labor, and expertise. Given that much information gleaned from wastewater is not directly actionable, and/or duplicates information from other sources, it is prudent to consider whether these investments are worth it. I present seven recommendations for end users seeking to incorporate WBE into COVID-19 response: (1) avoid redundancy between clinical testing and WBE; (2) emphasize statistical thinking, data analysis, and data management; (3) define action thresholds; (4) monitor fewer sites more frequently; (5) build on existing infrastructure and programs for wastewater collection and analysis; (6) be prepared to adapt as the pandemic evolves; and (7) keep an eye on the future, including by proactively searching for emerging variants of concern

Individual-Based Modeling of Collective Dynamics

Collective dynamics play an important role in facilitating group movement, decision-making, and other large-scale behaviors in a wide variety of biological systems. In recent years, technological advances have made it possible to probe deeper into the microscopic factors underlying these macroscopic phenomena using computer-assisted mathematical modeling and data analysis. In this thesis, I describe and validate a mathematical individual-based model of collective motion developed by Couzin et al. (2005). I demonstrate how diffusion mapping, a relatively new data-mining technique, can be used to systematically analyze simulation data generated by the Couzin model to identify microscopic influences that can cause a coherent group to break apart. I find that group breakups occur when the orientation of the group deviates from its coherent direction by approximately 90°, and that changes in the orientation of only a few members of the group may play a disproportionate role in initiating an irreversible change in the orientation of the group as a whole. I suggest that an understanding of the breakup mechanism could be used to inform improved methods of controlling harmful locust swarms, illustrating this potential application with two case studies: the 1986-1989 outbreak of desert locusts (Schistocerca gregaria) in the Sahel region of northern Africa, and the 2010-2011 outbreak of Australian plague locusts (Chortoicetes terminifera) in southeastern Australia

Flow virometry for water-quality assessment: Protocol optimization for a model virus and automation of data analysis

Flow virometry (FVM) can support advanced water treatment and reuse by delivering near real-time information about viral water quality. But maximizing the potential of FVM in water treatment and reuse applications requires protocols to facilitate data validation and interlaboratory comparison—as well as approaches to protocol design to extend the suite of viruses that FVM can feasibly and efficiently monitor. In the npj Clean Water article "Flow virometry for water-quality assessment: Protocol optimization for a model virus and automation of data analysis," we address these needs by first optimizing a sample-preparation protocol for a model virus (T4 bacteriophage) using a fractional factorial experimental design. We then compare manual and algorithmic methods of analyzing complex FCM data collected by applying the optimized protocol to (i) a clean solution spiked with a variety of biological and non-biological viral surrogates [mixed-target experiment], and (ii) tertiary treated wastewater effluent spiked with T4 bacteriophage and two sizes of fluorescent polystyrene beads [environmental spike experiment]. This repository contains the FCM data used to develop the optimized protocol and to test the two analytical methods.For our analysis, we used the following tools: FlowJoTM 10 software (Becton Dixon & Company) to manually open and analyze the .fcs files, and to convert these files to .csv files where needed; Rstudio (version 2021.9.1.372) for downstream analysis of results and data obtained using FlowJo; MATLAB® software (version R2021a; MathWorks) to perform additional downstream analysis; and Excel (version 16.68; Microsoft) to manually inspect the .csv files. A list of free and open-source alternative programs that can be used to analyze the .fcs files contained in this repository can be found at https://floreada.io/flow-cytometry-software. A variety of free and open-source alternative programs (e.g., Google Sheets) exist to analyze the .csv files contained in this repository. For the optimization experiments, subsequent direct analysis of these data (manual gating and calculation of the number, mean fluorescence intensity, and coefficient of variation of all gated particles) was performed using FlowJoTM 10 software (Becton Dixon & Company). The FrF2 package in Rstudio (version 2021.9.1.372) was then used to quantify the main and two-way interaction effects of each factor tested in the optimization. Documentation for this package is available at https://www.rdocumentation.org/packages/FrF2/versions/2.1/topics/FrF2-package. For the mixed-target and environmental spike experiments, subsequent direct analysis of these data through manual gating was performed using the same FlowJo software. The FlowJo software was then used to export the gated data to .csv files, FlowJoTM 10 software. A log transformation was applied to these data, after which features were standardized by centering and rescaling to standard deviation 1. Rstudio (version 2021.9.1.372) was used to apply the OPTICS implementation available in the dbscan package (Hahsler et al. 2019). MATLAB® software (version R2021a; MathWorks) was then used to inspect reachability plots of the OPTICS-ordered data for manual extraction. Finally, we applied the opticskxi package available in R (Charlton 2019) for automated extraction, with parameters described in the companion article to this dataset.Funding provided by: Bureau of ReclamationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100006450Award Number: Award R18AC00106All data were collected by analyzing a 10-mL volume of the sample in question using the 488 nm (blue) solid-state laser, the lowest possible instrument flowrate (5 mL/min), and a FITC = 800 threshold on a NovoCyte 2070V Flow Cytometer coupled with a NovoSampler Pro autosampler (Agilent). Green fluorescence (FITC) intensity was collected at 530 ± 30 nm; forward and side scatter (FSC and SSC) intensities were collected as well. For the optimization experiments, 10 mL of an unstained control was run after each sample. The instrument was flushed in between each sample and control by running 150 mL of 1x NovoClean solution (Agilent) followed by 150 mL of MQ water through the SIP at the highest instrument flow rate (120 mL/min). Instrument performance was ensured by performing the instrument's built-in quality control (QC) test at least monthly. The FCM data were exported directly to .fcs (the standard format for flow cytometry/virometry data) files. All of the raw .fcs files used for the optimization experiments, mixed-target experiments, and environmental spike experiments are provided in this repository. For the mixed-target and environmental spike experiments, these .fcs files were then manually gated and exported to .csv files for use in downstream, algorithmically assisted analysis. Each of these .csv files is provided in this repository as well