Arson Investigation Research Articles

Discrimination of slight thermal damage to hair for arson investigation

To determine whether a suspect was in close contact with the fire source at a fire site through slight thermal damage to hair, a cone calorimeter was employed to simulate fire scene conditions as a standard radiant source. The research focused on analyzing the thermal behavior of black hair and delving into the morphological characteristics of hair exhibiting slight thermal damage. At temperatures exceeding 240 °C, the proteins within the hair began to degrade. This degradation, in conjunction with tension along the hair shaft resulting from water loss, led to the formation of microcracks that could be detected through scanning electron microscopy (SEM) but eluded observation with an optical microscope (OM). It is noteworthy that the initial slight thermal damage was regularly located at the hair shaft but not the hair tip, which should be the key parts when exanimating hairs without obvious thermal damage. Additionally, during very short exposure, the appearance of typical slight thermal damage on fire is probabilistic events. Along with the increase of temperature, the organic compounds in hair were thermally degraded into NH3, SCO and carbon CO2, resulting in the typical traces of discoloration, expansion, blistering, and cracking presented at hair shafts and tips. The probability of encountering both slight and obvious thermal damage on hair increased with rising temperatures. By observing the traces on the easily overlooked part of the hair shaft, the research established a method to analyze and discriminate the slight thermal damage to hair at fire scene, which provide valuable references for confirming arson suspects.

Research on species categorical authentication of accelerants based on flame characteristics analysis

Species categorical authentication of accelerants has traditionally relied on fire debris analysis. To explore a novel method for identifying the accelerants species, four commonly used accelerants for arson were loaded onto different substrates and ignited at different locations. The entire combustion process was recorded and flame characteristics were analyzed. The results showed that the probability density function (PDF) of flame apex angle counts within a certain period after ignition can be used to distinguish accelerant species, and this method is not affected by accelerant loading amount, ignition location, and substrate, demonstrating strong stability and universality, while the temporal variation of flame area and the value obtained by dividing half of the flame width by the flame height (tangent of flame cone angle) can effectively differentiate gasoline and diesel. The utilization of flame characteristics for identifying accelerants species holds significant implications for arson investigation.

The Relevance of Sr–O–C Isotope Analysis on Burnt Human Skeletal Remains in Archeological and Forensic Contexts: A Review and Future Directions

AbstractRecovery and identification of heavily burnt and fragmented skeletal remains pose significant challenges in fire and arson investigations. The ability to identify individuals is crucial in such cases, prompting extensive research into various techniques to aid in the identification process, including isotopic analysis. Although direct personal identification is not achievable through isotopic analysis of skeletal remains, it serves as a valuable tool for obtaining chemical elements of the biological profile. Strontium (Sr), oxygen (O), and carbon (C) isotope analyses have been widely used in archeological contexts but are relatively new approaches in the medicolegal domain. Oxygen isotope analysis, for instance, offers insights into the burning temperature, while strontium isotope analysis provides valuable information for tracing the individual's movement across geologically distinct landscapes. Consequently, the investigation of the impact of heat on the biogenic isotope composition of human bones in forensic contexts has gained significant attention. This article presents a comprehensive overview of the key archeological and forensic questions addressed by Sr–O–C isotope analysis of burnt skeletal remains. By examining the current state of the art and prospects, we aim to contribute to a broader understanding and potential advancements in the field of forensic isotope research.This article is categorized under: Forensic Biology > Interpretation of Biological Evidence Forensic Anthropology > Trauma Analysis

Detection of Various Accelerants Found in Arson Cases: Implications for Fire Investigation

Abstract: Arson cases pose significant challenges to fire investigators due to the deliberate use of accelerants, which are substances that promote and expedite the spread of fire. This research paper presents an analysis of various accelerants commonly found in arson cases and explores their implications for fire investigation. The study involved a comprehensive review of existing literature, case studies, and experimental analysis of accelerant samples collected from real arson incidents. The research aims to provide a deeper understanding of the characteristics, detection methods, and forensic significance of accelerants. The analysis includes a wide range of accelerants, such as gasoline, kerosene, alcohols, and other flammable liquids commonly utilized in arson incidents. Factors affecting the selection, application, and identification of accelerants are examined, including their volatility, residue patterns, and the influence of environmental conditions. Key findings reveal the importance of accurate and timely identification of accelerants in arson investigations. The study highlights the significance of forensic techniques like gas chromatography-mass spectrometry (GC-MS) and Fourier-transform infrared spectroscopy (FTIR) in identifying and analysing accelerant residues. Moreover, the paper emphasizes the crucial role of collaboration between fire investigators, forensic experts, and law enforcement agencies to effectively interpret and present the findings in a court of law.

Rapid Detection of Accelerants in Fire Debris Using a Field Portable Mid-Infrared Quantum Cascade Laser Based Analyzer

Arson presents a challenging crime scene for fire investigators worldwide. Key to the investigation of suspected arson cases is the analysis of fire debris for the presence of accelerants or ignitable liquids. This study has investigated the application and method development of vapor phase mid-Infrared (mid-IR) spectroscopy using a field portable quantum cascade laser (QCL) based system for the detection and identification of accelerant residues such as gasoline, diesel, and ethanol in fire debris. A searchable spectral library of various ignitable fluids and fuel components measured in the vapor phase was constructed that allowed for real-time identification of accelerants present in samples using software developed in-house. Measurement of vapors collected from paper material that had been doused with an accelerant followed by controlled burning and then extinguished with water showed that positive identification could be achieved for gasoline, diesel, and ethanol. This vapor phase mid-IR QCL method is rapid, easy to use, and has the sensitivity and discrimination capability that make it well suited for non-destructive crime scene sample analysis. Sampling and measurement can be performed in minutes with this 7.5 kg instrument. This vibrational spectroscopic method required no time-consuming sample pretreatment or complicated solvent extraction procedure. The results of this initial feasibility study demonstrate that this portable fire debris analyzer would greatly benefit arson investigators performing analysis on-site.

A Meticulous Review on Arson Inquest Using Gas Chromatography

Arson investigation is hands down the most tedious and the robust form of investigation to go with. It is by far the multifarious and endearing type of investigation as investigating at the arson scene demands high endeavour in order to withstand from all the challenges that stood up at the scene of crime. Stringent norms have been framed by the government that defines it under section 435, 436 and 438 of IPC. In such cases, the chain of custody plays a dynamic and a crucial role as from the procurement of the most fragile samples to transporting it in the most desired form to follow the best sample preparation method before subjecting to GC analysis in order to avail the quality result. Keeping in mind the susceptibility of fire evidences American Society for Testingand Materials International has established standard guidelines for subjecting sample before GC-MS introducing solid phase micro-extraction technique linking to various GC detectors. New minds and innovation has bumped into better and more approachable forms of GC by making hybrids of it such as E-nose i.e. headspace-mass spectrometry, a strategic mass spectrometry based instrument with both qualitative and quantitative applications surpassing the bygone separation process. Revolution in the arson and analytical world is making way for non destructible techniques to serve as a better alternative to any of the GC detectors.

Идентификация источников нефтяных загрязнений методом инфракрасной спектроскопии

The article demonstrates the importance of using reliable methods for identifying oil pollution in relation to accidents and incidents at hazardous production facilities of oil and gas production. Examples are given and prospects for possible negative consequences of biosocial emergencies at drilling and production facilities of the oil and gas complex and related infrastructure are shown. The processes of temporary and biodegradation of petroleum hydrocarbons in soils and aquatic environment are described. The necessity of considering the processes of transformation of oils and oil products in the environment during the production of identification eco-forensic studies in relation to them is shown. The advantages of Fourier spectrometers are revealed, which allow reliably distinguish between oil samples with a difference in optical density in the spectra at the level of 0.01 (0.3–1 % transmission), compared with diffraction infrared spectrometers. Fourier spectrometers were used to study seven oil samples from various fields of the Khanty-Mansi Autonomous Okrug. The influence was assessed related to artificial aging on the possibility of identifying the source of oil origin in relation to samples collected in the places of pollution. The results of the study of samples extracted from oil-contaminated soil were carried out and presented. The results of the research confirm the possibility of creating a reliable method for identifying oil pollution based on the method of infrared spectroscopy, as well as new methodological approaches to the problem of identifying samples of oil products in the investigation of arson. It was determined that in the case of using modern technology based on Fourier spectrometers equipped with systems for automatic processing and comparison of spectra, the method of infrared spectroscopy can be used as not only a preliminary, but also one of the basic means of identification.

L2 regularized deep convolutional neural networks for fire detection

Fire calamity is one of the worst adversarial events that can happen to the human race. Fire disaster can happen as a manmade disaster or even naturally, and it may cause environmental, social, and financial damages as well. In order to minimalize the unwanted fire calamity, early detection of fire eruptions coupled with immediate and effective response is extremely vital to disaster management systems. The classification of forest fire and non fire images using deep learning techniques has recently received popularity. Detection and prevention of forest fire have lot of significance from the perspective of the forest fire department, specially for the fire and arson investigators. There are shortcomings in the current mechanisms of forest fire detection in terms of accuracy. Hence, we propose a fire detection model using LeNet5 convolutional neural networks (CNN), which can spot fire in outdoor environments by classifying fire and non fire images. L2 regularization is critical technique that manipulates the complexity of the convolutional neural network model. In our work fire images have certain features that decide if the image is fire or non fire.A weight is assigned to every feature. Regularization used to help to reduce the over fitting that used to caused by plenty of weights. Our proposed provides the directiontowards developing a system that detects the early stages of forest fire.This model can further be utilized to prevent the damage caused by the fire. A CNN is a deep learning method, which has been adopted in order to detect the images of fire and non-fire. With the non sparse solution of L2 regularization we have obtained around 87% of train accuracy, 71% of validation accuracy and 70% of test accuracy after running 10 epochs.

Relighting the Charitable Deduction: A Proposed Public Benefit Exception

"Let no man’s ghost say his training ever let him down."Throughout the country, firefighters risk their lives on a daily basis to keep the public safe. It is, therefore, imperative they receive the best possible training. One invaluable training method is live burn training, in which a structure is set on fire providing firefighters with “a level of realism that is unsurpassed.” Fire departments, both career and volunteer, as well as numerous municipal departments conduct various training exercises, including roof ventilation, domestic violence exercises, simulated meth lab explosions, room-to-room fire practice, firefighter survival techniques, arson investigation training, rescue techniques, and/or firefighter down techniques, through the donation of homes that are finally burned to the ground as part of live burn training.For over thirty-five years, relying on a United States Tax Court case, many believed a taxpayer could claim a charitable deduction for the donation of the taxpayer’s home to the local municipality for live burn training, while still retaining ownership of the underlying land (live burn donation). In 2004, however, the Internal Revenue Service (the Service) established a task force, charged with the sole purpose of extinguishing live burn donations, and requested that local municipalities not cooperate with taxpayers in executing the requisite paperwork in claiming a charitable deduction for a live burn donation. In 2009, the deductibility of a live burn donation was thrust into the public spotlight as the media publicized the Service’s attack on the charitable deduction claimed by Kirk Herbstreit, an ESPN commentator and former Ohio State quarterback, for the donation of his home to the local fire department. Soon after, in 2010, the live burn donation was back in the headlines as it was exposed that Oregon gubernatorial candidate, Chris Dudley, claimed a deduction on his 2004 federal income tax return for a live burn donation so he could build a new residence on the land. Consequently, in recent years, there has been much confusion and debate on whether live burn donations qualify for charitable deductions.

Recognition of gasoline in fire debris using machine learning: Part II, application of a neural network

The recognition of ignitable liquid (IL) residues in fire debris is a resource intensive but key part of an arson investigation. Due to the highly diverse and heavily loaded chemical matrix of fire debris samples, combined with the broad chemical composition of IL, the interpretation of the laboratory analysis results is a very challenging task for the forensic examiner. Fire debris samples are commonly analyzed using gas chromatography coupled to mass spectrometry (GC-MS). This method delivers both the total ion chromatogram (TIC) with the individually separated compounds and the underlying mass spectrum of each of the separated compounds. In this study, a completely new approach for the recognition of gasoline in fire debris samples is presented. First, the GC-MS data, including retention time, signal intensity, and mass spectrum is converted into a bitmap image. Five different data-to-image conversion approaches are tested, and their advantages and limitations are discussed. Subsequently, a convolutional neural network (CNN) is utilized to allocate the generated images to the classes “with gasoline” or “without gasoline”. The applied approaches to generate a digital image and the pattern recognition of the CNN perform very well in the classification of unknown test samples. Depending on the data-to-image generation approach used, the rate of correct sample classification in the test dataset is between 95% and 98%. The machine learning approach in this study, as well as the complementary method presented in an accompanying article, are not only useful for the recognition of gasoline in fire debris but are equally applicable to any additional areas in which the interpretation of complex chromatographic and mass spectrometric is required.

Development of Crime Scene Intelligence Using a Hand-Held Raman Spectrometer and Transfer Learning.

The classification of ignitable liquids, such as gasoline, is critical crime scene intelligence to assist arson investigations. Rapid field gasoline classification is challenging because the current forensic testing standard requires gas chromatography-mass spectrometry analysis of evidence in an accredited laboratory. In this work, we reported a new intelligent analytical platform for field identification and classification of gasoline evidence. A hand-held Raman spectrometer was utilized to collect Raman spectra of reference gasoline samples with various octane numbers. The Raman spectrum pattern was converted into image presentations by continuous wavelet transformation (CWT) to facilitate artificial intelligence development using the transfer learning technique. GoogLeNet, a pretrained convolutional neural network (CNN), was adapted to train the classification model. Six different classification models were also developed from the same data set using conventional machine learning algorithms to evaluate the performance of our new approach. The experimental results indicated that the pretrained CNN model developed by our new data workflow outperformed other models in several performance benchmarks, such as accuracy, precision, recall, F1, Cohen's Kappa, and Matthews correlation coefficient. When the transfer learning model was challenged with the data collected from weathered gasoline samples, the classifier could still offer 73 and 53% accuracy for 50 and 25% weathered gasoline samples, respectively. In conclusion, wavelet transforms combined with transfer learning successfully processed and classified complex Raman spectral data without feature engineering. We envision that this nondestructive, automated, and accurate platform will accelerate crime scene intelligence development based on evidence's chemical signatures detected by hand-held Raman spectrometers.

A Forensic Approach to Evaluate the Effect of Different Matrices and Extraction Solvents for the Identification of Diesel Residue in Simulated Arson by GC–MS

Investigation of an arson case presents several challenges to the forensic investigator. Identification of the ignitable liquid residues in the fire debris is one of the crucial tasks that may help to determine the nature and cause of fire. Detection of the ignitable liquids using different analytical methods has been practiced for a long period across the globe. Diesel is a petroleum product that shows stable burning than gasoline and it is frequently used as accelerant also. This study was conducted to diagnose the efficiency of solvent as well as of the matrices for the detection of diesel residue. In this study, four different matrices (cotton cloth, wood, glaze tile, and PVC) were selected to create simulated miniature version of arson with diesel as an accelerant. After extinguishing the fire, diesel residues were extracted from the fire debris using hexane and diethylether as extraction solvents under ultrasonication. Gas Chromatography coupled with mass spectrometry was used to identify the major compounds from standard and burnt diesel. Diethylether extracted higher amount of diesel residues from the burnt matrices than hexane. Tile and cotton proved to be the better matrices for the extraction in hexane solvent. However, diethylether present advantageous results and significantly detected the diesel residues in tile, wood and cotton. Findings of this study may help an investigator to select the suitable matrices and solvent for the extraction of diesel residues and may efficaciously contribute in the forensic arson investigation.

Use of Detection Dogs in Forensic Investigations: The Italian Scenario

This paper describes the latest advancements of forensic odorology, a technique that uses canine units to detect forensic evidence at a crime scene. For a long time, dogs’ olfaction has been used to find missing persons, dead bodies, and explosive substances. These dog skills have been further de-veloped to identify olfactory fingerprints at crime scenes. Currently, forensic odorology plays an important role in pyromania and arson investigations, computer and electronic material searches, fugitive location, currency, tobacco and weapon recovery, as well as poaching fighting. During the Scent Detection activity, the dog is trained to detect the smell of a previously imprinted substance (substance odor imprinting). When the dog sniffs traces of a specific substance, it focuses on the track and follows itn to identify the odor source. Thus, the dog will reproduce a previously ac-quired behavioral response to signal the handler the presence of the substance. In this review, we touch upon canine forensic odorology, highlight the most innovative forensic activities carried out with dogs, and discuss how these investigative tools can be used in criminal trials.

PECULIARITIES OF INVESTIGATION OF CRIMINAL CASES ASSOCIATED WITH ARSONS

The article discusses some of the features of the investigation of criminal cases involving arson. Recommendations are given for the officers of the investigative-operational groups when planning further actions at the initial stage of the investigation of arson. The questions about the influence on the detection rate of the promptness of arrival to the place of the fire of the investigator (interrogator), about the features of collecting information at the place of the fire are touched upon. Obtaining information on the basis of special knowledge, allowing to formulate a search plan for the alleged offender and his criminological personality portrait. With the development of new technologies, for the efficiency of obtaining information from a specialist (expert) in the investigation of arson, it is necessary to use the technology of immersive telepresence (video presence). This allows for a better inspection of the crime scene in the absence of an expert (specialist) directly on the spot. The use of electronic databases and the use of modern means of communication also contribute to solving crimes related to arson hot on the trail.

Development of retention time indices for comprehensive multidimensional gas chromatography and application to ignitable liquid residue mapping in wildfire investigations

In this study, we introduce a simple three-step workflow for a universally applicable RI system, to be used in GC×GC analysis of ignitable liquid residue (ILR) for arson investigations. The proposed RI system applies a combination of two well-established GC RI systems: non-isothermal Kovats (K) index in the first dimension and Lee (L) index in the second dimension. The proposed KLI RI system showed very good correlations when compared with predicted values and existing RI systems (r2 = 0.97 in first dimension, r2 = 0.99 in second dimension) and was valid for a wide range of analyte concentrations and operational settings (coefficient of variance (CV) < 1% in first dimension, < 10% in second dimension). Using the KLI RI, an ILR classification contour map was created to assist with the identification of ILR types within ASTM E1618. The contour map was successfully applied to neat fuels and a fire scene sample, highlighting the application to wildfire investigation. Standardizing the RI process and establishing acceptable error margins allows the exploration and comparison of comprehensive data generated from GC×GC analysis of ILRs regardless of location, time, or system, further enhancing comprehensive and tenable chemometric analyses of samples. Overall, the KLI workflow was inexpensive, quick to apply, and user-friendly with its simple 3-step design.

Chemical fingerprinting of petrochemicals for arson investigations using two-dimensional gas chromatography - flame ionisation detection and multivariate analysis

Two-dimensional gas chromatography is a mature, yet underutilised, separation technique able to provide the high resolution and peak capacity required for the study of complex samples such as oils. This paper presents the development of a comprehensive two-dimensional gas chromatography method with flame ionisation detection to profile easily available ignitable liquids commonly found in arson cases. The use of 2D chromatograms to profile different potential ignitable liquids was also explored for classification purposes. The chemical fingerprints produced were visually different and allowed the distinction of all the petroleum products tested. How the chemical fingerprints of each fuel changed over time was also assessed. Each sample was subjected to weathering with aliquots (1 mL) being collected every half hour for a five-hour period. Principal component analysis of the resulting data was able to demonstrate the effect of weathering for all fuels tested and established that it was still possible to differentiate between the various petrochemicals even after weathering. The work demonstrates an optimised analytical method for petrochemical product analysis that provides forensic scientists with a robust, fast and sensitive technique that can be used to determine not only which ignitable liquid was used in a fire (even after the fact) but also provide information on the specific fuel used.

Forensic investigation of arson residue by infrared and Raman spectroscopy: From conventional to non-destructive techniques.

Arson can result in highly challenging and complicated crime scenes. Much physical evidence undergoes chemical degradation because of the destructive nature of fire, while accelerants either completely burn or evaporate, and may be present in traces within any of the decomposed materials. To identify the original material and the accelerant involved, it is necessary to use advanced analytical techniques. Gas chromatography, with different detectors, is one of the most frequently used instruments in fire debris and accelerant analysis. Among other instruments, capillary electrophoresis and laser-induced thermal desorption Fourier transform mass spectrometry are two major contributors. Vibrational spectroscopy, including infrared absorption and Raman scattering, is one of the major non-destructive tools for the analysis of evidence because of its advantages over other spectroscopic techniques. Most studies involving vibrational spectroscopy (i.e. infrared and Raman spectroscopy) have focused on the identification of commonly found household materials, while very few studies have considered the identification of ignitable liquids. This article reviews studies based on an analysis of fire debris and accelerants by vibrational spectroscopic techniques and considers the limitations and future perspectives of arson investigations in forensic science.

An Environmental Forensic Chemistry Experiment Involving Abandoned Mine Drainage Remediation

Most forensic chemistry experiments focus on the usual suspects involving criminal scenarios: illicit drug identification, gunshot residue detection, blood and fingerprint analysis, and arson investigations. However, environmental forensics involves chemical techniques that can expand the breadth of experience for forensic chemistry students. An experimental case study was designed to determine the potential source of groundwater contamination along a series of treatment steps designed to mitigate the environmental effects of an abandoned mine drainage site. Simulated drainage samples representing each treatment step are analyzed by atomic absorption spectroscopy to quantify the metals present, a gravimetric method to determine the concentration of sulfate, and a pH meter to measure relative acidity of the drainage water. Using the data obtained from the simulated samples, students can determine the effectiveness of each treatment step as well as identify the location along the drainage treatment series that is the most likely source of the groundwater contamination.

Study of Characteristic Burn Patterns Formed by Three Different Accelerants on Plastered Wall

One of the important aspect of arson investigation is to study the burn patterns or characteristics resulting from various accelerants. In India, the basic accelerants which are used to set fire to a scene include petrol, diesel and kerosene. This research article is based on the different types of burn patterns on plastered wall resulting from the use of three different accelerants which are being commonly used to set fire to a scene. Fire was initiated by spilling the accelerant on the plastered wall to simulate the scene of arson. An equal amount of each accelerant was taken for experimental purposes. The burn patterns provided by the three accelerants namely petrol, diesel and kerosene on the plastered wall shows some varying characteristics which help in differentiating the patterns caused by these accelerants. It is expected that results provided by this research will help the arson investigators in the investigation process.

Testing of skeletonized human remains using GC/MS – development of a personal environmental profile

ABSTRACTGas chromatography/mass spectrometry (GC/MS) is well used in forensic science for the identification of materials present in toxicology, pharmacology and arson investigation. However, GC/MS has been underused in human identification. In this study, highly commingled skeletonized remains from a mass fatality incident involving over 400 individuals were examined. Environmental compounds and those present in the remains themselves were extracted from osseous materials removed from the surface of the skeletal elements using two different solvents, dichloromethane and acetonitrile. Solvent extracts were concentrated and resuspended in methanol prior to injection on the GC/MS instrument. Compounds present from the environment surrounding the individual post-mortem, as well as biological materials present in the individual ante-mortem, are detectable. Traces generated from the GC/MS instrument provide distinctive images, and analysis of those materials shows patterns that are specific to the individual and may be used for individuation. Results indicate that GC/MS analysis of skeletonized remains may be a new tool for human identification.