The study, carried out by Liza Hovhannisyan of the A. I. Alikhanyan National Laboratory in Yerevan, Armenia, uses the CORSIKA Monte Carlo code, a well-established tool for simulating cosmic-ray air showers that includes the effects of atmospheric electric fields on particle transport. The simulations model a vertically oriented, uniform electric-field layer 2000 meters thick above the detector level, subdivided into twenty 100-meter slices so that the vertical growth of the electron population can be tracked in detail. Four high-altitude observational sites with distinct atmospheric densities were chosen, all of which have recorded thunderstorm-related particle enhancements: Aragats in Armenia at roughly 3000 meters above sea level, Nor Amberd at about 2000 meters, Lomnický Štít in the High Tatras at approximately 2600 meters, and the LHAASO site in China at around 4400 meters. Across the four configurations, the simulated field regions span altitudes of roughly 4000 to 6400 meters, where air densities range from 0.66 to 0.89 kilograms per cubic meter.

The electric fields applied in each configuration were tuned to the local runaway threshold, which scales with the normalized air density. Because thinner air at high altitude lowers the threshold field, the same absolute field strength means something quite different at LHAASO than it does at Nor Amberd. The simulations therefore used field ranges of 180 to 210 kilovolts per meter at LHAASO, 200 to 230 at Aragats, 210 to 240 at Lomnický Štít, and 230 to 260 at Nor Amberd, varied in steps of 10 kilovolts per meter. Each simulation injected a single seed electron with an initial energy spectrum drawn from the EXPACS model, following a power law between 1 and 300 megaelectronvolts, and between 500 and 1000 events were run for each field configuration to ensure statistical stability. Electrons and gamma rays were tracked until their energies fell below 0.05 megaelectronvolts, with particle numbers recorded at every layer boundary.

Within the supercritical region where the applied field exceeds the local runaway threshold, the number of runaway electrons grows approximately exponentially with distance, and the characteristic e-folding length of that growth is the central parameter of avalanche theory. A widely used empirical relation, introduced by J. R. Dwyer in 2003, links this avalanche length to the electric-field excess above the threshold through a single proportionality coefficient, with Dwyer reporting a value of about 7.2 times ten to the third kilovolts. When Hovhannisyan fitted this standard relation separately to the effective avalanche lengths extracted from the vertical electron profiles at each station, the results told a revealing story. The station-specific coefficients ranged from 8.6 to 11.2 times ten to the third kilovolts, all larger than the original value, and the spread between stations persisted even after fit-derived uncertainties were taken into account. A single global fit across all sixteen simulation points yielded a best-fit coefficient of about 9.58 times ten to the third kilovolts, but with a coefficient of determination of only about 0.90, leaving noticeable inter-station scatter.

The remedy was to let the coefficient itself depend on air density. By introducing a generalized relation with an additional density-dependent term, in which the avalanche length is multiplied by the normalized air density raised to a fitted exponent, the joint fit across all four stations produced a coefficient of about 6.39 times ten to the third kilovolts and an exponent of minus 0.923, with an uncertainty of plus or minus 0.083. That exponent, statistically indistinguishable from minus one, indicates an approximately inverse dependence on atmospheric density beyond what is already captured by the density-scaled runaway threshold. The improvement in descriptive power was striking: the coefficient of determination rose from roughly 0.90 to roughly 0.99, and plots comparing simulated and predicted avalanche lengths showed much better agreement, particularly at the larger avalanche lengths where the standard formulation deviated most from a one-to-one relation.

The study then turned to a second, complementary stage of avalanche evolution: what happens to the particles after they leave the accelerating field. Additional simulations for the Aragats configuration placed the detector level 25, 50, 100, and 200 meters below the lower boundary of the electric field, keeping all other parameters fixed. The results revealed sharply divergent transport behavior between the two particle species. The electron spectrum was progressively attenuated with increasing field-free distance, with a pronounced suppression of the high-energy component, and the electron population decreased rapidly and monotonically. Gamma rays, by contrast, persisted over larger distances, attenuated more gradually through stochastic interactions such as Compton scattering, photoelectric absorption, and pair production. At a separation of 100 meters, the electron component was already strongly depleted while a substantial gamma-ray component remained, making that distance a natural calibration point.

This asymmetry matters for a phenomenological quantity called the free path distance, introduced in earlier work to characterize how far energetic particles can propagate beyond the electric-field region and still reach ground-based detectors. Because the maximum electron energy at the field boundary cannot be measured directly, the formulation estimates it from the maximum gamma-ray energy at the detector through an empirical electron-gamma energy coefficient. For the 100-meter calibration separation, the simulated gamma-ray spectra changed little over the propagation distance, with characteristic maximum energies of about 29 to 31 megaelectronvolts at both levels, validating the use of the detector value as a proxy. Electron spectra, however, shifted markedly toward lower energies: at Aragats, the characteristic maximum dropped from roughly 38 megaelectronvolts at the field exit to around 16 megaelectronvolts after 100 meters of propagation through air.

To quantify the electron-gamma coefficient, the study employed a bootstrap-based statistical procedure with 2000 Poisson resampling realizations of the energy histograms, applying a criterion that defines the characteristic maximum energy as the highest energy with at least three consecutive bins containing more than five counts, which suppresses sensitivity to isolated outliers. The station-specific median coefficients clustered tightly, from about 1.31 at Aragats to 1.46 at Lomnický Štít, and pooling all stations yielded a median of about 1.38, in close agreement with an independent global least-squares optimization that gave 1.375. The adopted value of 1.37 replaces the earlier empirical coefficient of 1.2, which the bootstrap analysis showed systematically underestimates the free path distance at all four stations. The second parameter, the altitude-dependent electron energy-loss rate, was obtained from the NIST ESTAR stopping-power database scaled by local air density, yielding values between roughly 0.17 and 0.23 megaelectronvolts per meter across the sites. Applying the recalibrated formulation reproduced free path distances of 85 to 105 meters, consistent with the 100-meter calibration scale, and an independent test at a 50-meter separation reconstructed a median of 69.9 meters, correctly identifying the short-distance regime.

The practical payoff of this work lies in the interpretation of observations. Mountain observatories such as Aragats, Lomnický Štít, and LHAASO routinely detect particle bursts during thunderstorms, and inferring the structure and strength of the electric fields inside thunderclouds from those detections depends on having reliable models of both avalanche growth and post-field transport. By showing that a single density-independent coefficient leaves systematic residuals across sites spanning a substantial range of atmospheric densities, and by demonstrating that a simple power-law correction removes nearly all of that scatter, the study provides a unified scaling that can be applied across different altitudes and storm conditions. The recalibrated free path distance formulation likewise gives experimentalists a firmer bridge between the gamma-ray spectra they measure and the electron populations that produced them at the edge of the accelerating field.

The author notes that the results are derived for idealized, uniform electric-field configurations and should not be generalized to arbitrary thunderstorm field structures without further validation, a caveat that reflects the persistent difficulty of measuring the spatial and temporal structure of fields inside storm clouds. Even so, the near-perfect fit achieved by the density-dependent formulation, together with the stability of the recalibrated transport coefficients across four very different atmospheric environments, marks a meaningful step toward a common physical framework for the growing family of high-energy atmospheric phenomena. All simulation inputs, analysis scripts, and processed outputs are publicly archived, allowing other groups to reproduce the workflow and extend the calibration to new observational sites and more realistic field geometries.

Subject of Research: Density-dependent scaling and transport of relativistic runaway electron avalanches in thunderstorm electric fields

Article Title: Relativistic runaway electron avalanches: unified density-dependent scaling and transport

Article References: Hovhannisyan, L. (2026). Relativistic runaway electron avalanches: unified density-dependent scaling and transport. Geoscientific Model Development, 19(19), 9289-9300. https://doi.org/10.5194/gmd-19-9289-2026

Image Credits: AI Generated

DOI: 10.5194/gmd-19-9289-2026

Keywords: relativistic runaway electron avalanches, thunderstorm electric fields, cosmic rays, gamma-ray glows, terrestrial gamma-ray flashes, CORSIKA simulations, air density, avalanche length, free path distance, thunderstorm ground enhancements, Monte Carlo modeling, high-altitude observatories

Chemical Insights’ footprint at the meeting is unusually broad for a single research organization. According to the institute, its scientists are hosting one symposium, delivering five presentations, leading one workshop and presenting six poster sessions. The topics span some of the most pressing exposure questions of the moment: the chemical emissions produced when electric vehicles catch fire, the smoke that wildfires carry into communities, the volatile organic compounds released by resins used in dental 3D printing, and the hidden chemical ingredients in everyday consumer products ranging from cosmetics to mobility aids. Together, the presentations illustrate how modern exposure science connects laboratory measurements of emissions to real-world estimates of the doses people receive.

One of the most technically ambitious threads in the Chemical Insights program concerns electric vehicle fires. Lithium-ion battery failures, known as thermal runaway events, can release dense clouds of gases and fine particles containing metals and other hazardous species. Three separate contributions address this emerging risk from complementary angles. A poster presentation documents exposure to particulate matter and metals during controlled burns of electric vehicles, while another characterizes gas and particle-phase emissions from full-scale electric vehicle fires and discusses the implications for emerging exposure risks. A third presentation describes the physicochemical and toxicological characterization of particle emissions collected from those fires, linking the physical and chemical properties of the particles to their biological activity. A related poster examines the emissions profile of thermal runaway in mobility-aid batteries and evaluates how fire suppression systems influence those emissions.

The significance of this work lies in the gap it addresses. As electric vehicles, e-bikes, scooters and other battery-powered devices proliferate, first responders, occupants of nearby buildings and communities downwind of fire incidents face exposure scenarios that traditional fire-safety research never fully characterized. Battery fires can burn hotter and release different chemical species than conventional hydrocarbon fires, including metal-containing particles that may penetrate deep into the respiratory tract. By conducting controlled burns under laboratory conditions and collecting emissions for detailed analysis, researchers can begin to quantify what is released, in what quantities, and with what toxicological signatures — the raw material needed for exposure and risk assessments that protect firefighters, investigators and the public.

Wildfire smoke, another major theme at the meeting, receives dedicated attention in a scientific session on estimating and monitoring wildfire smoke exposures. Wildfire events have grown in frequency and intensity across many regions, pushing smoke plumes hundreds or thousands of kilometers from the fire line and exposing populations far removed from the flames. Estimating what people actually inhale during these events is a formidable measurement challenge: smoke composition changes as it ages in the atmosphere, monitoring networks are unevenly distributed, and individuals move through microenvironments with very different concentrations. The session reflects a growing effort across the exposure science community to combine ground sensors, satellite data, models and personal monitoring to produce exposure estimates that are accurate enough to support public health decisions such as school closures, clean-air shelter recommendations and targeted advisories for vulnerable groups.

Indoor and occupational exposures receive their share of the spotlight as well. One presentation examines the toxicity of volatile organic compound emissions from a dental 3D printing resin using a human alveolar tissue model. Dental laboratories and clinics increasingly rely on resin-based additive manufacturing, which can emit volatile organic compounds during printing and post-processing. Testing those emissions against human lung tissue models in the laboratory offers a way to assess potential respiratory toxicity without relying solely on animal studies or chemical-by-chemical literature reviews. It is a concrete example of a broader methodological trend in toxicology: the use of advanced in vitro systems derived from human cells to evaluate the hazards of complex chemical mixtures rather than single compounds in isolation.

Consumer products present a different kind of exposure puzzle, and two Chemical Insights posters tackle it directly. One, titled “The Ingredients You Can’t Read: Expanding Access to Consumer Product Chemical Data,” addresses a persistent transparency problem — many products contain chemical ingredients that are not disclosed on labels, leaving consumers, researchers and regulators without the information needed to assess exposure. A companion poster describes the integration of chemical profiling with Monte Carlo modeling for multi-route exposure risk assessment of cosmetic spray products. Monte Carlo methods allow researchers to propagate uncertainty across the many variables that shape real exposures, including how often a product is used, how much is applied, and how much of each chemical is inhaled, absorbed through the skin or ingested. Combining analytical chemical profiling of the products themselves with probabilistic modeling produces risk estimates that reflect the full range of real-world use patterns rather than a single worst-case scenario.

Several contributions push the methodological frontier of the field. A presentation on a systematic framework for exposure reconstruction from biomarker data addresses one of exposure science’s hardest inverse problems: working backward from chemicals measured in blood, urine or other biological samples to infer the magnitude, timing and source of the original exposure. Biomarkers integrate exposure across all routes and sources, but interpreting them requires careful modeling of how chemicals are absorbed, distributed, metabolized and eliminated. Another presentation leverages non-targeted analysis of human biomonitoring data, an analytical strategy that screens samples for a vast array of chemical features without deciding in advance which compounds to look for, allowing previously unrecognized exposures to surface. Chemical Insights is also participating in a scientific session on artificial intelligence and machine learning in exposure science, a rapidly growing area where algorithms are being applied to everything from sensor networks to large biomonitoring datasets.

The workshop on advancing exposure forensics for environmental health protection highlights another emerging discipline. Exposure forensics applies the investigative tools of exposure science to determine the source, pathway and timing of chemical exposures after the fact — whether in litigation, in outbreak investigations, or in community health studies where residents suspect an environmental cause of illness. Building capacity in this area requires shared methods for chemical fingerprinting, source attribution and reconstruction of past exposures, along with standards for how such evidence is communicated to regulators, courts and affected communities. A presentation titled “Beyond the Logo: Advancing the Exposome Through Collaborative Exposure Science at UL Research Institutes” situates the institute’s portfolio within the exposome concept, the ambitious framework that seeks to capture the totality of environmental exposures a person experiences across a lifetime, from chemicals and pollutants to psychosocial stressors.

Finally, a poster on the CIExpo Portfolio describes an effort to integrate exposure data from source to intake — that is, to trace chemicals from the moment they are emitted by a product, material or process through the environmental pathways they travel to the point where they enter a human body. That source-to-intake chain is the conceptual backbone of exposure science, and integrating data across it remains one of the field’s central technical challenges, requiring alignment of emission measurements, environmental fate modeling, microenvironmental monitoring and human activity patterns. Taken together, the Chemical Insights program at the ISES Annual Meeting offers a snapshot of where exposure science is heading: toward complex real-world scenarios such as battery fires and wildfire smoke, toward human-relevant toxicological models and non-targeted chemistry, and toward computational tools — from Monte Carlo simulation to machine learning — capable of turning fragmented measurements into actionable knowledge for protecting public health.

Subject of Research: Chemical exposure science research on emissions from electric vehicle fires, wildfire smoke, 3D printing and consumer products

Article Title: Chemical Insights’ research to make an impact at The International Society of Exposure Science Annual Meeting

Article References: Chemical Insights’ research to make an impact at The International Society of Exposure Science Annual Meeting. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: exposure science, ISES Annual Meeting, electric vehicle fires, thermal runaway, wildfire smoke, volatile organic compounds, 3D printing resin, biomonitoring, Monte Carlo modeling, consumer product chemicals, particulate matter, UL Research Institutes