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Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers’ performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California’s Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles. [ABSTRACT FROM AUTHOR]

The Low Carbon Fuel Standards (LCFS) represents a new policy approach designed to reduce carbon dioxide emissions by applying standards to all stages of motor fuel production. We use the synthetic control and difference-in-differences econometric methods, and Lasso machine learning to analyze the effect of the LCFS on emissions in California’s transportation sector. The three different techniques provide robust evidence that the LCFS reduced carbon dioxide emissions in California’s transportation sector by around 10%. Furthermore, our calculations show that improved air quality, due to the application of the LCFS, may have benefited California in the magnitude of hundreds of millions of dollars through an increase in worker’s productivity. [ABSTRACT FROM AUTHOR]

Autonomous Vehicle technology is quickly expanding its market and has found in Silicon Valley, California, a strong foothold for preliminary testing on public roads. In an effort to promote safety and transparency to consumers, the California Department of Motor Vehicles has mandated that reports of accidents involving autonomous vehicles be drafted and made available to the public. The present work shows an in-depth analysis of the accident reports filed by different manufacturers that are testing autonomous vehicles in California (testing data from September 2014 to March 2017). The data provides important information on autonomous vehicles accidents’ dynamics, related to the most frequent types of collisions and impacts, accident frequencies, and other contributing factors. The study also explores important implications related to future testing and validation of semi-autonomous vehicles, tracing the investigation back to current literature as well as to the current regulatory panorama. [ABSTRACT FROM AUTHOR]

Background: Climate change produces extremes in both temperature and precipitation causing increased drought severity and increased reliance on groundwater resources. Agricultural practices, which rely on groundwater, are sensitive to but also sources of contaminants, including nitrate. How agricultural contamination drives groundwater geochemistry through microbial metabolism is poorly understood. Methods: On an active cow dairy in the Central Valley of California, we sampled groundwater from three wells at depths of 4.3 m (two wells) and 100 m (one well) below ground surface (bgs) as well as an effluent surface water lagoon that fertilizes surrounding corn fields. We analyzed the samples for concentrations of solutes, heavy metals, and USDA pathogenic bacteria of the Escherichia coli and Enterococcus groups as part of a long term groundwater monitoring study. Whole metagenome shotgun sequencing and assembly revealed taxonomic composition and metabolic potential of the community. Results: Elevated nitrate and dissolved organic carbon occurred at 4.3m but not at 100m bgs. Metagenomics confirmed chemical observations and revealed several Planctomycete genomes, including a new Brocadiaceae lineage and a likely Planctomycetes OM190, as well novel diversity and high abundance of nano-prokaryotes from the Candidate Phyla Radiation (CPR), the Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanoarchaeota, Nanohaloarchaea (DPANN) and the Thaumarchaeota, Aigarchaeota, Crenarchaeota, Korarchaeota (TACK) superphyla. Pathway analysis suggests community interactions based on complimentary primary metabolic pathways and abundant secondary metabolite operons encoding antimicrobials and quorum sensing systems. Conclusions: The metagenomes show strong resemblance to activated sludge communities from a nitrogen removal reactor at a wastewater treatment plant, suggesting that natural bioremediation occurs through microbial metabolism. Elevated nitrate and rich secondary metabolite biosynthetic capacity suggest incomplete remediation and the potential for novel pharmacologically active compounds. [ABSTRACT FROM AUTHOR]

Increased fire frequency has been shown to promote alien plant invasions in the western United States, resulting in persistent vegetation type change. Short interval fires are widely considered to be detrimental to reestablishment of shrub species in southern California chaparral, facilitating the invasion of exotic annuals and producing “type conversion”. However, supporting evidence for type conversion has largely been at local, site scales and over short post-fire time scales. Type conversion has not been shown to be persistent or widespread in chaparral, and past range improvement studies present evidence that chaparral type conversion may be difficult and a relatively rare phenomenon across the landscape. With the aid of remote sensing data covering coastal southern California and a historical wildfire dataset, the effects of short interval fires (<8 years) on chaparral recovery were evaluated by comparing areas that burned twice to adjacent areas burned only once. Twelve pairs of once- and twice-burned areas were compared using normalized burn ratio (NBR) distributions. Correlations between measures of recovery and explanatory factors (fire history, climate and elevation) were analyzed by linear regression. Reduced vegetation cover was found in some lower elevation areas that were burned twice in short interval fires, where non-sprouting species are more common. However, extensive type conversion of chaparral to grassland was not evident in this study. Most variables, with the exception of elevation, were moderately or poorly correlated with differences in vegetation recovery. [ABSTRACT FROM AUTHOR]