Incentives to diffuse advanced abatement technology under environmental liability law

Polluting firms with advanced abatement technology at their disposal have incentives or disincentives to share this technology with other polluting firms. The ‘direction’ and extent of those incentives depends on the liability rule applicable and the way technical change impacts marginal abatement costs. We establish that incentives for diffusion are socially optimal under strict liability and socially suboptimal under negligence if technical change lowers marginal abatement costs for all levels of abatement. Negligence may, however, induce better diffusion incentives than strict liability if technical change decreases (increases) marginal abatement costs for low (high) levels of abatement.     

Stochastic landslide vulnerability modeling in space and time in a part of the northern Himalayas, India

Little is known about the quantitative vulnerability analysis to landslides as not many attempts have been made to assess it comprehensively. This study assesses the spatio-temporal vulnerability of elements at risk to landslides in a stochastic framework. The study includes buildings, persons inside buildings, and traffic as elements at risk to landslides. Building vulnerability is the expected damage and depends on the position of a building with respect to the landslide hazard at a given time. Population and vehicle vulnerability are the expected death toll in a building and vehicle damage in space and time respectively. The study was carried out in a road corridor in the Indian Himalayas that is highly susceptible to landslides. Results showed that 26% of the buildings fall in the high and very high vulnerability categories. Population vulnerability inside buildings showed a value >0.75 during 0800 to 1000 hours and 1600 to 1800 hours in more buildings that other times of the day. It was also observed in the study region that the vulnerability of vehicle is above 0.6 in half of the road stretches during 0800 hours to 1000 hours and 1600 to 1800 hours due to high traffic density on the road section. From this study, we conclude that the vulnerability of an element at risk to landslide is a space and time event, and can be quantified using stochastic modeling. Therefore, the stochastic vulnerability modeling forms the basis for a quantitative landslide risk analysis and assessment.     

Bodenkontaminationen in Rüstungsaltlasten aus dem Ersten Weltkrieg

The results of the chemical analysis of soil samples from WWI munition plants showed that the most important compounds are dinitrobenzenes, trinitrotoluene and metabolites, and dinitronaphthalenes. The presence of 1,2-dinitrobenzene and 1,3-dinitrobenzene in soil samples shows that, during the production of dinitrobenzenes, in WWI, the raw dinitrobenzene was not washed with sulphite. The results show that the parameter list must be different from the parameter list for the analysis of soil samples from munition plants of WW II.     

Artificial intelligence in obstetric ultrasound: A scoping review

The objective is to summarize the current use of artificial intelligence (AI) in obstetric ultrasound. PubMed, Cochrane Library, and ClinicalTrials.gov databases were searched using the following keywords “neural networks”, OR “artificial intelligence”, OR “machine learning”, OR “deep learning”, AND “obstetrics”, OR “obstetrical”, OR “fetus”, OR “foetus”, OR “fetal”, OR “foetal”, OR “pregnancy”, or “pregnant”, AND “ultrasound” from inception through May 2022. The search was limited to the English language. Studies were eligible for inclusion if they described the use of AI in obstetric ultrasound. Obstetric ultrasound was defined as the process of obtaining ultrasound images of a fetus, amniotic fluid, or placenta. AI was defined as the use of neural networks, machine learning, or deep learning methods. The authors’ search identified a total of 127 papers that fulfilled our inclusion criteria. The current uses of AI in obstetric ultrasound include first trimester pregnancy ultrasound, assessment of placenta, fetal biometry, fetal echocardiography, fetal neurosonography, assessment of fetal anatomy, and other uses including assessment of fetal lung maturity and screening for risk of adverse pregnancy outcomes. AI holds the potential to improve the ultrasound efficiency, pregnancy outcomes in low resource settings, detection of congenital malformations and prediction of adverse pregnancy outcomes.     

Precopulatory sexual interaction in an arctiid moth (Utetheisa ornatrix): Role of a pheromone derived from dietary alkaloids

Summary Males of Utetheisa ornatrix have a pair of brushlike glandular structures, the coremata, which they evert from the abdomen during close-range precopulatory interaction with the female. Males experimentally deprived of coremata are less acceptable to females. The principal chemical associated with the coremata, identified as a pyrrolizine (hydroxydanaidal), has a proven pheromonal role: males raised under conditions where they fail to produce hydroxydanaidal are also less likely to succeed in courtship, and the compound itself, as its (-)-isomer, is capable of inducing the principal receptive response (wing raising) of the female. Evidence is presented indicating that Utetheisa derive hydroxydanidal from defensive pyrrolizidine alkaloids that they sequester from their larval foodplants (Crotalaria spp.). It is proposed that in addition to signalling male presence to the female, hydroxydanaidal may provide the means whereby the female assesses the alkaloid content of the male and therefore his degree of chemical protectedness. The argument is made that such pheromonal assessment of defensive capacity may occur also in other insects, including danaid butterflies, many of which share with Utetheisa a dependence on pyrrolizidine alkaloids for sex-pheromone production.     

Effects of interpolation and data resolution on methane emission estimates from rice paddies

Rice paddies are an important source of the greenhouse gas methane (CH₄). Global methane emission estimates are highly uncertain and do not account for effects of interpolation or data resolution errors. This paper determines such scaling effects for the influence of soil properties on calculated CH₄ emissions for the island of Java, Indonesia. The effects of different interpolation techniques, variograms and neighbor optimization were tested for soil properties by cross-validation. Interpolated organic carbon values were not significantly different from the original soil samples, in contrast to interpolated soil iron contents. Interpolation of soil properties coupled to a process-based model on CH₄emissions led to a significant change in distribution of calculated CH₄ emissions, i.e., the variance decreased. Effects of data resolution were examined by interpolating soil properties to derive data at different data resolutions and then calculating CH₄ emissions by applying the process-based model at these resolutions. The soil properties did not differ significantly for different data resolutions, in contrast to calculated CH₄ emissions. These scaling effects were caused by the combination of interpolation and a non-linear model. Real scaling effects may even be larger because small-scale variability was not accounted for. Scaling effects, including those caused by small-scale variability, have to be considered to achieve unbiased and less uncertain global CH₄emissions estimates from rice paddies.