Hazard Assessment Research Articles

Hazard assessment of fenozan, a released non-intentionally added substance from polyester-based can coating

Since the safety of new-generation polyester-based internal coatings regarding the migration of non-intentionally added substances (NIAS) is poorly documented, studies are needed to identify NIAS originating from these food-contact materials (FCM). The aim of this study was to identify volatile and semi-volatile NIAS from polyester-based coatings in order to assess their hazard and ensure consumers’ safety with regard to exposure from canned food. Extraction and migration tests were carried out on a single polyester-coated tin plate (5 batches) using two solvents: acetonitrile and ethanol 95%, then FCM’s extracts and migrates were analysed by GC-MS. An antioxidant degradation (hydrolysis) product, 3-(3,5-di-tert-butyl-4-hydroxyphenyl)propionic acid or fenozan (CAS RN: 20170-32-5), was identified and confirmed by reference standard in all migrates. To assess fenozan’s toxicity, several in vitro bioassays, such as the Ames test (to assess point mutation), the micronucleus assay (to detect chromosomal aberrations), and the iodide uptake assay (to study one mode of action for thyroid disruption) were conducted. Fenozan was negative in the Ames test on three strains of S. typhimurium (TA98, TA100, and TA1535) and on one strain of E.coli (WP2), with and without metabolic activation system (S9 mix) using direct incorporation and pre-incubation methods. The in vitro micronucleus assay conducted on HepG2 cells also exhibited a negative response following a 4-hour treatment with the S9 mix, and a 48-hour treatment without the S9 mix. A weak inhibitory effect was obtained when testing fenozan in the iodide uptake assay using rat thyroid FRTL-5 cells. Significant inhibition started from 800 µM of fenozan, with a maximal inhibition of almost 47% at 1000 µM. The findings indicate that fenozan exhibits an anti-thyroid activity in vitro.

Distribution-agnostic landslide hazard modelling via Graph Transformers

In statistical applications, choosing a suitable data distribution or likelihood that matches the nature of the response variable is required. To spatially predict the planimetric area of a landslide population, the most tested likelihood corresponds to the Log-Gaussian case. This causes a limitation that hinders the ability to accurately model both very small and very large landslides, with the latter potentially leading to a dangerous underestimation of the hazard. Here, we test a distribution-agnostic solution via a Graph Transformer Neural Network (GTNN) and implement a loss function capable of forcing the model to capture both the bulk and the right tail of the landslide area distribution. An additional problem with this type of data-driven hazard assessment is that one often excludes slopes with landslide areas equal to zero from the regression procedure, as this may bias the prediction towards small values. Due to the nature of GTNNs, we present a solution where all the landslide area information is passed to the model, as one would expect for architectures built for image analysis. The results are promising, with the landslide area distribution generated by the Wenchuan earthquake being suitably estimated, including both zeros, the bulk and the extremely large cases. We consider this a step forward in the landslide hazard modelling literature, with implications for what the scientific community could achieve in light of a future space–time and/or risk assessment extension of the current protocol.

The Identification and Quantification of Hidden Hazards in Small Scale Reservoir Engineering Based on Deep Learning: Intelligent Perception for Safety of Small Reservoir Projects in Jiangxi Province

This study aims to enhance the detection and assessment of safety hazards in small-scale reservoir engineering using advanced image processing and deep learning techniques. Given the critical importance of small reservoirs in flood management, water supply, and ecological balance, the effective monitoring of their structural integrity is crucial. This paper developed a fully convolutional semantic segmentation method for hidden danger images of small reservoirs using an encoding–decoding structure, utilizing a deep learning framework of convolutional neural networks (CNNs) to process and analyze high-resolution images captured by unmanned aerial vehicles (UAVs). The method incorporated data augmentation and adaptive learning techniques to improve model accuracy under diverse environmental conditions. Finally, the quantification data of hidden dangers (length, width, area, etc.) were obtained by converting the image pixels to the actual size. Results demonstrate significant improvements in detecting structural deficiencies, such as cracks and seepage areas, with increased precision and recall rates compared to conventional methods, and the HHSN-25 network (Hidden Hazard Segmentation Network with 25 layers) proposed in this paper outperforms other methods. The main evaluation indicator, mIoU of HHSN-25, is higher than other methods, reaching 87.00%, and the Unet is 85.50%, and the Unet++ is 85.55%. The proposed model achieves reliable real-time performance, allowing for early warning and effective management of potential risks. This study contributes to the development of more efficient monitoring systems for small-scale reservoirs, enhancing their safety and operational sustainability.

Four years of landslide observation with anthropogenic loading as an additional trigger - Analysis of seasonal and annual variability of physical parameters

Determining the structure and evolution of landslides is crucial for geophysical hazard assessment. In this study, we employed an approach integrating the methodologies of multi-channel analysis of surface waves (MASW) and electrical conductivity to image temporal and spatial changes within a landslide in southern Poland. The area, located in the Outer Carpathians, experiences significant climate fluctuations, compounded by anthropogenic activities such as recreational skiing requiring artificial snow.Our combined seismic and electrical methods techniques reveal the landslide's susceptibility to environmental factors on both annual and seasonal scales. Additional analysis, including data clustering and remote sensing, identifies three distinct landslide zones with varying vulnerability to natural and anthropogenic influences.While focusing on a specific area, our approach has global applicability to similar mass movements. This research addresses a gap in understanding time-dependent geophysical observations of moisture-driven landslides, providing valuable insights for hazard identification and mitigation strategies.

Seismicity and fractal analysis in Aswan region, southern Egypt

Seismic activity in Aswan is influenced by the complex interactions of tectonic plates, the accumulation of stress, and the presence of geological fault systems. It revealed that epicenters are well distributed along four fault segments in a conjugate pattern, indicating a prominent E-W compressional stress. This research aims to explore the characteristics of seismicity and seismotectonics, with a focus on assessing their implications for risk reduction and disaster management in this densely populated region. A data review from the Egyptian National Seismological Network (ENSN) identified 464 earthquakes occurred between 2000 and 2021, with local magnitudes ranging from 0.3 to 4.4, and depths up to 25 km. The calculated Gutenberg-Richter b-value is approximately 0.87 ± 0.05, indicating a gradual stress accumulation. The current analysis shows a more consistent level of moderate seismic activity, unlike previous studies in Aswan region that reported a wide range of b-values from 0.554 to 1.07. This suggests that while earlier research captured a wider range of seismic behaviors, recent data indicates a stabilization in earthquake frequency and intensity. Additionally, the fractal dimension (Dc) calculated at 1.57 ± 0.04 shows an intermediate level of complexity and reflecting a clustering pattern of earthquakes. The variations in the b-value with different magnitudes and depths signify the involvement of active smaller faults, responsible for earthquakes up to magnitude 2.2, which then transition to fractured zones inducing earthquakes up to magnitude 2.5. This transition is followed by a decline in seismic activity, indicating regions that are potentially more likely to experience larger earthquakes. Moreover, stress disparities at various depths contribute to smaller earthquakes within the 5–10 km depth range. Return period analysis suggests that the earthquakes of magnitude 3.7 or higher are expected to occur approximately once every decade in Aswan. These findings are of utmost importance for earthquake risk reduction, hazard assessment, and the sustainable development of Aswan area.

Are Hazard Assessment Methods in the Assessment of Chemical Alternatives Suitable for REACH?

The assessment of chemical alternatives for hazardous substances is an important prerequisite for avoiding regrettable substitution, and several methods have been developed in the past to perform such a hazard assessment for chemical alternatives. We investigate here whether GreenScreen, Cradle to Cradle, multiple-criteria decision analysis (MCDA), the Pollution Prevention Options Analysis System, the U.S. EPA Safer Choice Standard and Criteria, and the GHS column model 2020 from IFA use similar criteria for the evaluation of substances as Article 57 of the European chemicals regulation, REACH, and how suitable these methods are for assessing per- and polyfluoroalkyl substances. MCDA and GreenScreen were analyzed in detail using two different data sets. The results of the assessments show that none of the investigated hazard assessment methods use the same criteria as described in Article 57 of REACH. It was also not possible to parametrize multi-attribute value theory (MAVT), a commonly used MCDA method, to align with Article 57 of REACH by using the relatively simple objective hierarchy that has been proposed in previous publications. There is therefore an urgent need for a modified/new method that can be used in the future to assess organic substances that are used within the European Economic Area.

Size-frequency distribution of submarine mass movements on the Palomares continental slope (W Mediterranean)

In this work, over 3620 km2 from the Palomares continental slope, which is located in the W. Mediterranean Sea, was analysed to quantify the impact of recent mass movements on this margin. A total of 936 landslides were identified, mapped and characterised by defining several morphometric variables that outline the accumulated impact of landslides equivalent to 918 km2 and 10.34 km3 of eroded sediment on the continental slope. The smallest event area was 0.0014 km2, whereas the largest event area was 32.48 km2. Smaller scars with a higher headwall gradient tend to dominate when the environment is steeper, and major mass movements are located on open slopes and structural highs. However, the slight or null correlations between variables indicate that a wide range of sizes may occur on any slope gradient and at any depth.The Palomares continental slope is intensively affected by mass movements. Compared with other passive margins (e.g., the U.S. Atlantic continental margin), landslides mobilised a limited amount of sediment, although it is comparable to other Mediterranean areas where small- to moderate-sized events are characteristic.The cumulative size distribution can be defined by a power-law function that describes events larger than 0.7 km2 with an exponent of α = 1.269. These results are consistent with those of other published inventories, including onshore cases. This result allows us to assume that the scale-invariant properties of the events are mapped. Scale-invariant properties can be explained by different models; self-organised criticality (SOC) is probably the most assumed by the scientific community, although alternative models may be nominated. Each model has important implications in terms of the landslide distribution and long-term landslide history of any slope. Alternative scenarios, such as submarine slopes, with more precise landslide inventories may contribute to new hazard assessment models that consider scaling exponents derived from size–frequency distributions.

A Comparative Study for Evaluating the Groundwater Inflow and Drainage Effect of Jinzhai Pumped Storage Power Station, China

Various hydrogeological problems like groundwater inflow, water table drawdown, and water pressure redistribution may be encountered in the construction of hydraulic projects. How to accurately predict the occurrence of groundwater inflow and assess the drainage effect during construction are still challenging problems for engineering designers. Taking the Jinzhai pumped storage power station (JPSPS) of China as an example, this paper aims to use different methods to calculate the water inflow rates of an underground powerhouse and evaluate the drainage effect caused by tunnel inflow during construction. The methods consist of the analytical formulas, the site groundwater rating (SGR) method, and the Signorini type variational inequality formulation. The results show that the analytical methods considering stable water table may overestimate the water inflow rates of caverns in drained conditions, whereas the SGR method with available hydro-geological parameters obtains a qualitative hazard assessment in the preliminary phase. The numerical solutions provide more precise and reliable values of groundwater inflow considering complex geological structures and seepage control measures. Moreover, the drainage effects, including a seepage-free surface, pore water pressure redistribution, and hydraulic gradient, have been accurately evaluated using various numerical synthetic cases. Specifically, the faults intersecting on underground caverns and drainage structures significantly change the groundwater flow regime around caverns. This comparative study can not only exactly identify the capabilities of the methods for cavern inflow in drained conditions, but also can comprehensively evaluate the drainage effect during cavern construction.

Recent and active faulting along the exposed front of the Northern Apennines (Italy): New insights from field and geochronological constraints

Establishing genetic links between active shallow faulting and deep seismogenic sources is challenging, especially in areas where seismogenic faults lack clear and readily interpretable geological evidence at the surface. The architecture of the Pedeapenninic margin of the Northern Apennines (Italy) reflects a regional-scale and complex NE-verging blind thrust system, which is dissected by transpressive/transtensive faults resulting from active NE-SW orogenic compression. The local geological framework is defined by allochthonous ocean-derived units resting atop Pliocene-to-present successions exposed along the Northern Apennines margin and to the NE of it, while the innermost chain sector mostly contains Adria-related units. We present results from field structural-geological investigations to identify and characterize potential active faults along the margin. There, the Pliocene-to-present units are faulted and folded, indicating that tectonic activity is still on-going, thus contributing to the local seismic hazard. Top-to-the NE and SW normal faults are common in the area and deform the Pliocene-to-present succession together with NE-SW strike-slip and transpressional/transtensional faults. Based on field evidence, we define four potentially active thrust segments affecting Middle Pleistocene to Holocene deposits exposed along the margin. Calcite U-Th dating on samples from faults extend the most recent datable tectonic activity back to the Middle Pleistocene. Paleostress analysis from inversion of fault-slip data from the most recent identified striated fault planes constrains a NE-SW shortening direction parallel to the Apennines regional migration direction. A distinct but coaxial extensional stress regime, recorded by structures measured within Plio-Pleistocene formations, was also identified. Our results offer a sound starting point for future investigations aimed at improving our understanding of active and seismogenic faulting in the area, so as to create robust Probabilistic Seismic and Fault Displacement Hazard Assessment (PSHA and PFDHA) models that can implement refined seismic hazard maps benefitting from structural-geological deterministic inputs in addition to the classic seismological constraints.

Quantitative Assessments of the Liquefaction Hazard of Soils considering Possible Strong Earthquakes in Seismically Active Regions of Russia

Introduction The seismogenic liquefaction of the soil poses a great hazard to society and the environment. Therefore, it is actively studied in many countries. In Russian engineering and seismological practice, this area is not sufficiently developed. The deterministic approach still prevails in Russian research on this topic. More modern probabilistic estimates are very rare. Methods This paper describes examples of both deterministic and probabilistic assessments of the seismogenic liquefaction hazard performed in certain areas of the Russian territory with different seismogeological conditions. Deterministic estimates were made using the Iwasaki-Seed-Finn methods and their modifications. Probability distribution functions of a random variable, the “seismic potential of liquefaction” (SPL), were developed for probabilistic estimates. These functions are regional in nature and take into account two types of uncertainties. The first is the uncertainty in achieving “critical” values by the SPL value in the event of potentially dangerous earthquake sources for a given location. The second is the uncertainty in the very occurrence of these sources in a given place for a given period of time. The “critical” SPL values are determined by the strength properties of the site soils. All estimates are based on multivariate calculations using various models of strong ground motions and seismicity. In all cases, the probability of liquefaction of water-saturated sandy and sandy-loam deposits was estimated which were found near the seabed and at depths of up to 80 m in the waters of Pogibi cape (the coast of Sakhalin island), in the districts of Sochi and Novorossiysk, as well as in land conditions (Stavropol, Krasnodar). Results The results of the research made it possible to correctly (at the quantitative level) take into account this component of the seismic hazard of the studied territories. Conclusion The variants of practical use of the obtained data are offered. An assessment of the possibilities and limitations of the developed methodology is made, and ways to improve it are outlined.

Risk Assessment of Overturning of Freestanding Non-Structural Building Contents in Buckling-Restrained Braced Frames

The increasing demand in structural engineering now extends beyond collapse prevention to encompass business continuity planning (BCP). In response, energy dissipation devices have garnered significant attention for building response control. Among these, buckling-restrained braces (BRBs) are particularly favored due to their stable hysteretic behavior and well-established design provisions. However, BCP also necessitates the prevention of furniture overturning—an area that remains quantitatively underexplored in the context of buckling-restrained braced frames (BRBFs). Addressing this gap, this research designs BRBFs using various design criteria and performs incremental dynamic analysis (IDA) with artificially generated seismic waves. The results are compared with previously developed fragility curves for furniture overturning under different BRB design conditions. The findings demonstrate that the fragility of furniture overturning can be mitigated by a natural frequency shift, which alters the threshold of critical peak floor acceleration. These results, combined with hazard curves obtained from various locations across Japan, quantify the mean annual frequency of furniture overturning. The study reveals that increased floor acceleration in stiffer BRBFs can lead to a 3.8-fold higher risk of furniture overturning compared to frames without BRBs. This heightened risk also arises from the greater hazards at shorter natural periods due to stricter response reduction demands. The probabilistic risk analysis, which integrates fragility and hazard assessments, provides deeper insights into the evaluation of BCP.

Assessment of natural radioactivity and radiological hazards in rocks from Thiruvananthapuram, Kerala

Assessment of natural radioactivity and radiological hazards in rocks from Thiruvananthapuram, Kerala

Machine learning-assisted risk evaluation of heavy metals in the Hainan gold mining region, China.

This study employed machine learning (ML) to thoroughly investigate the impact of informal mining activities on the distribution and pollution status of heavy metals in soils near private gold mines in Hainan Province, southern China, a region known for its ecological sensitivity and economic importance. By systematically collecting surface soil samples and samples at depths of 0.5-1m from 175 drilling sites, a comprehensive quantitative analysis was conducted on major heavy metal elements, including lead (Pb), copper (Cu), cadmium (Cd), nickel (Ni), mercury (Hg), chromium (Cr), arsenic (As), and zinc (Zn). Combined with evaluation methods such as the Pollution Load Index (PLI), Normalized Pollution Index (NIPI), and Ecological Risk Index (ERI), the study revealed a high level of soil pollution at informal mining sites. The findings indicated that the average concentrations of Pb, Cd, Hg, As, and Zn in surface soils significantly exceeded the background values for soils in China, with a pronounced positive correlation observed between these heavy metal elements in both surface and deep soil profiles (r > 0.5). Furthermore, leveraging the heavy metal content in surface soils and the constructed environmental indicators, the predictive accuracy for metal content in deep soils was found to range from R2 = 0.27 to 0.68, suggesting that informal mining activities have led to substantial variations in metal content across different soil profiles. Through the application of a random forest model for predictive analysis of the PLI, NIPI, and ERI, high prediction accuracy was achieved (R2 = 0.78, 0.86, and 0.60, respectively). The study demonstrates that informal mining activities not only elevate the risk of soil pollution but also alter the distribution patterns of heavy metals. Also, this study provides a crucial foundation for the scientific assessment of soil quality and potential environmental hazards, while also affirming the efficacy of ML techniques in forecasting soil quality parameters.

Ontology-Based Landslide Knowledge Representation and Hazard Assessment Research

The occurrence of landslides is characterized by suddenness and unpredictability, with many complex relationships among various influencing factors. This study, by introducing the ontology of knowledge engineering, summarizes the characteristics of landslide knowledge and constructs a multidisciplinary, multi-level, and multi-perspective ontology of the landslide domain. Through in-depth analysis of existing landslide research systems and methods, the study defines the basic concepts, attributes, and their complex relationships and rules related to landslides. This research expresses landslide knowledge using the Web Ontology Language (OWL), enabling effective storage and retrieval of landslide knowledge, thus promoting the systematization and standardization of knowledge in the landslide domain. Furthermore, leveraging the high knowledge density feature of the constructed landslide domain ontology, this study extends traditional landslide evaluation and prediction methods by introducing more evaluation indicators based on the constructed landslide ontology. The comprehensive evaluation of landslide risk is conducted using the contribution-weighting method, providing new insights and tools for landslide risk management and decision-making.

A landslide runout model for sediment transport, landscape evolution, and hazard assessment applications

Abstract. We developed a new rule-based, cellular-automaton algorithm for predicting the hazard extent, sediment transport, and topographic change associated with the runout of a landslide. This algorithm, which we call MassWastingRunout (MWR), is coded in Python and implemented as a component for the package Landlab. MWR combines the functionality of simple runout algorithms used in landscape evolution and watershed sediment yield models with the predictive detail typical of runout models used for landslide inundation hazard mapping. An initial digital elevation model (DEM), a regolith depth map, and the location polygon of the landslide source area are the only inputs required to run MWR to model the entire runout process. Runout relies on the principle of mass conservation and a set of topographic rules and empirical formulas that govern erosion and deposition. For the purpose of facilitating rapid calibration to a site, MWR includes a calibration utility that uses an adaptive Bayesian Markov chain Monte Carlo algorithm to automatically calibrate the model to match observed runout extent, deposition, and erosion. Additionally, the calibration utility produces empirical probability density functions of each calibration parameter that can be used to inform probabilistic implementation of MWR. Here we use a series of synthetic terrains to demonstrate basic model response to topographic convergence and slope, test calibrated model performance relative to several observed landslides, and briefly demonstrate how MWR can be used to develop a probabilistic runout hazard map. A calibrated runout model may allow for region-specific and more insightful predictions of landslide impact on landscape morphology and watershed-scale sediment dynamics and should be further investigated in future modeling studies.

Uncertainty-Aware Seismic Signal Discrimination using Bayesian Convolutional Neural Networks

Seismic signal classification plays a crucial role in mitigating the impact of seismic events on human lives and infrastructure. Traditional methods in seismic hazard assessment often overlook the inherent uncertainties associated with the prediction of this complex geological phenomenon. This work introduces a probabilistic framework that leverages Bayesian principles to model and quantify uncertainty in seismic signal classification by applying a Bayesian Convolutional Neural Network (BCNN). The BCNN was trained on a dataset that comprises waveforms detected in the Southern California region and achieved an accuracy of 99.1%. Monte Carlo Sampling subsequently creates a 95% prediction interval for probabilities that considers epistemic and aleatoric uncertainties. The ability to visualize both aleatoric and epistemic uncertainties provides decision-makers with information to determine the reliability of seismic signal classifications. Further, the use of Bayesian CNN for seismic signal classification provides a more robust foundation for decision-making and risk assessment in earthquake-prone regions.

Onboard multivariate hazard assessment for UIKKU chemical tanker by Gaidai reliability method

The current study outlines a novel multimodal structural reliability methodology, suitable for naval dynamic systems, that are either numerically simulated, or directly physically measured. Cross-correlations between the system's principal/key dimensions/components, along with the high dimensionality of complex naval dynamic systems, are not easily addressed by existing reliability methods, that are mostly univariate or bivariate. The objective of this study had been to apply a novel reliability/hazard assessment methodology to UIKKU chemical tanker onboard dynamic measurements to demonstrate the efficiency of the proposed multimodal structural reliability methodology. Current investigation utilized onboard measured ice loadings, exerted on the UIKKU tanker hull and propulsion devices during its Arctic voyage. Onboard dynamic data recordings then were interpreted to gain knowledge about dynamic load levels. The hull of UIKKU had been instrumented so that ice-induced loads/stresses on vessel shell plating had been monitored at various key locations, both in longitudinal as well as in vertical directions, along the vessel’s hull. Longitudinal bending stresses at several locations on the vessel hull and vessel vertical accelerations had been measured to analyze global vessel behaviour when colliding e.g., moderately heavy ice ridges. In addition to UIKKU onboard measurements onboard, Russian partners had instrumented icebreaker vessel Capitan Dranitsyn to real-time measure strains/stresses within ice belt grillage, located at the vessel’s bow. Icebreaker vessel Capitan Dranitsyn acted as escort icebreaker during the whole Arctic voyage. Obtained results may be utilized to harmonize IACS Polar ship rules. In the current study, the onboard measuring system along with measured results, during the whole voyage have been briefly represented.

A review of approaches to assessment of potential radon hazard of land plots

According to Rosstat statistics, in recent years there has been a tendency in the Russian Federation to increase the intensity of construction, while both the number of buildings put into operation and their total area are increasing. The assessment of the potential radon hazard of land plots for construction provides the possibility of timely inclusion of the necessary protective (preventive) measures against radon in the design of buildings. This is a legal requirement in cases where the density of radon flux from the ground surface within the building area exceeds the established hygienic standards (action levels). The paper presents a review of Russian and foreign approaches to assessing the potential radon hazard of land plots conducted within the framework of engineering and environmental surveys. The current regulatory requirements to radiation safety indicators of land plots for construction of residential, public and industrial buildings and facilities in the Russian Federation are analyzed. The main drawbacks of the algorithm for determining the potential radon hazard of land plots, used in the current guidelines MU 2.6.1.2398-08 approved at the federal level more than 15 years ago, are described. Critical remarks (the unsuitability of the value of density of radon flux from the ground surface for designing radon protective and mitigation (remedial) measures, lack of consideration of seasonal variations of radon flux density, etc.) accumulated over the years as a result of practical application of these guidelines are presented. Benefits and drawbacks of foreign approaches to assessing the potential radon hazard of the territory based on the results of measurements of radon concentration in soil gas, as well as the very possibility of a transition in Russian regulations from the density of radon flux from the ground surface to the radon concentration in soil gas, are analyzed. Some rational proposals of various Russian research teams on improving the algorithm for determining the potential radon hazard of land plots are considered.

EVs meet health: A complex-systems policy integration approach for electric vehicle adoption and its impact on respiratory and cardiovascular disease

The transition from gasoline-combustion to zero-emission vehicles represents one of the most ambitious public and private enterprises around the globe to mitigate air pollution and its subsequent effects on climate change. The present study uses the rapid transition in the state of California to analyze the connections between the increasing utilization of electric vehicles (EVs) and two critical health outcomes: Asthma and acute myocardial infarction (AMI) emergency department (ED) visits. We fit simultaneous quantile and interquartile-range regression models to health and sociodemographic data from the California Office of Health and Hazard Assessment and electric vehicles registration data, 2011–2017. Results show that changes in EV utilization, from 2011 to 2013 to 2015–2017, have larger and more favorable effects as asthma and AMI deteriorate across zip codes. Looking at zip codes located at the 75th and 25th percentiles of the change in the asthma- and AMI-related ED visits distribution, a 5-percentage points change in the EV-to-gasoline combustion utilization ratio is associated with a 25-percentage points (p<.01) and 12.5-percentage points (p<.01) decline in asthma- and AMI-related ED visits, respectively. Our findings also suggest that, should the U.S. meet the goals of Executive Order N-79-20 by 2035, we could observe about 37,000 fewer asthma-related ED visits (95% CI: 16,154–59,280) per year, with an overall possible annual savings of about $65.5 million dollars (95% CI: $29.1M – $106.7M), and a possible decline of about 19,292 AMI-related hospitalizations, with possible annual savings of about $425 million dollars (95% CI: $365M – $509M). Our findings are optimistic in that spillover effects from increasing EV utilization can reduce healthcare costs while improving population health. Research should point toward the development of public-private coalitional strategies to effectively bridge usually disparate sectors of the economy and policy-making like energy and health.

3D structural modelling of the Kopili fault zone in North-East India: Seismotectonic analysis utilising focal mechanism solutions of small-to-moderate earthquakes

This work presents new findings from recent seismic data which have never been exploited so far for the weak-to-moderately energetic (ML 2.3–6.4) earthquakes and to depict a 3D scheme for the Kopili Fault (KF) Zone. For the first time, we figure out different fault kinematics in the upper crust such as reverse and strike-slip mechanisms located along the KF zone. Moment Tensor (MT) solutions are successfully obtained through full-waveform inversions for 60 seismic events. The stress inversions are estimated based on the MT results as well as previous findings from 16 small to moderate earthquakes (MW 3.6–6.3). The MT solutions demonstrate that strike-slip kinematics is the most prevalent while a few thrust/normal earthquake events are observed in the area. The seismic cross-sections display that these earthquakes took place at a depth of 40 km and are mostly generated on the eastern side of the fault. According to the 3D spatial and temporal distribution of relocated events, the KF zone is not a completely vertical fault but rather one with a slight eastward dip. Our stress inversion results also indicate that this fault is equivalent to a fault plane with a dip angle of around 80˚, which is compatible with the previously estimated dip angle (75˚). We interpret that the research region is under a transpressional stress regime, which is likely to be the reason for the current activity of the KF zone. We postulate that our findings can be useful to enhance seismic hazard assessment in the densely populated region, which is also covered by vital infrastructure.