Potential of volatile organic compounds as markers of entrapped humans for use in urban search-and-rescue operations

Volatile organic compounds emitted by a human body form a chemical signature capable of providing invaluable information on the physiological status of an individual and, thereby, could serve as signs-of-life for detecting victims after natural or man-made disasters. In this review a database of potential biomarkers of human presence was created on the basis of existing literature reports on volatiles in human breath, skin emanation, blood, and urine. Approximate fluxes of these species from the human body were estimated and used to predict their concentrations in the vicinity of victims. The proposed markers were classified into groups of different potential for victim detection. The major classification discriminants were the capability of detection by portable, real-time analytical instruments and background levels in urban environment. The data summarized in this review are intended to assist studies on the detection of humans via chemical analysis and accelerate investigations in this area of knowledge.

💡 Research Summary

The paper presents a comprehensive review of volatile organic compounds (VOCs) emitted by the human body and evaluates their potential as chemical markers for locating entrapped victims in urban search‑and‑rescue (USAR) operations. By compiling data from breath, skin emanation, blood, and urine studies, the authors assembled a database of roughly one hundred candidate VOCs. For each compound, they estimated an average emission flux based on literature values, typical body surface area, and respiration rates, then used these fluxes to predict steady‑state concentrations in a 1 m³ volume surrounding a single victim.

A central contribution of the work is the systematic comparison of these predicted concentrations with typical urban background levels. The authors argue that a viable marker must satisfy two key criteria: (1) its ambient concentration near a victim should be significantly above the background, and (2) it must be detectable by portable, real‑time analytical instruments that can be deployed in the field. Using these discriminants, the candidates are divided into three groups.

The “high‑sensitivity/low‑background” group includes compounds such as methanol, isopropanol, 2‑methyl‑propane‑1‑ol, and 1‑propanol. These substances are present at low levels in city air but are emitted by human metabolism at rates that allow detection at parts‑per‑billion (ppb) concentrations with electrochemical sensors or miniaturized gas chromatography‑mass spectrometry (GC‑MS). Because they are directly linked to metabolic pathways (e.g., alcohol metabolism, fatty‑acid oxidation), they provide a reliable indication of human presence.

The “moderate‑sensitivity/moderate‑background” group comprises acetone, ethanol, isobutane, dimethyl sulfide, and similar volatiles. While these compounds are also emitted by humans, they are common in urban air due to vehicle exhaust, industrial processes, and household products. The authors suggest that a multi‑sensor array combined with pattern‑recognition algorithms (e.g., machine‑learning classifiers) can differentiate the human‑specific ratio of these volatiles (for instance, the acetone‑to‑ethanol ratio) from the ambient mixture. Moreover, physiological stress or injury can cause rapid changes in their emission rates, offering a dynamic signal of victim status.

The “low‑sensitivity/high‑background” group contains aromatic hydrocarbons such as benzene, toluene, and xylene. Their high ambient concentrations in many urban settings render them unsuitable as primary markers, though they could serve as auxiliary indicators in environments where background levels are relatively stable (e.g., industrial zones).

Beyond chemical selection, the review critically assesses the current state of portable analytical technologies. Electrochemical sensors are lightweight and low‑power but suffer from cross‑sensitivity; this can be mitigated by sensor fusion and temperature/humidity compensation. Portable GC‑MS offers high selectivity and sensitivity but is limited by weight, power consumption, and the need for rapid sample introduction, prompting interest in thermal desorption modules. Fourier‑transform infrared (FTIR) and Raman spectrometers provide non‑contact measurement but have higher detection limits, making them useful only when VOC concentrations are relatively high.

The authors propose an integrated detection strategy: a network of heterogeneous sensors (electrochemical, mini‑GC‑MS, FTIR) deployed on robots, drones, or handheld units, feeding real‑time data to a central processing node. Machine‑learning models would continuously compare measured VOC patterns against the background model, flagging a “human signature” when statistical thresholds are exceeded. The system could then transmit GPS‑tagged alerts to rescue teams, dramatically reducing the time needed to locate survivors.

Future research directions identified include: (1) expanding the VOC emission database to cover diverse demographics (age, gender, health status), (2) conducting field trials in realistic disaster simulations to validate detection limits and false‑positive rates, (3) improving sensor durability and battery life for extended operations, and (4) developing adaptive background‑correction algorithms that account for temporal and spatial variability in urban air quality.

In summary, the paper demonstrates that a carefully selected set of VOCs—particularly those with low urban background and high human‑specific emission—combined with portable, real‑time analytical platforms and advanced data‑fusion techniques, can provide a powerful, complementary “chemical nose” for USAR missions. This approach has the potential to augment traditional auditory, visual, and thermal detection methods, thereby increasing the likelihood of rapid victim localization and rescue success.