Landscape fire smoke airway exposure impairs respiratory and cardiac function and worsens experimental asthma.

BACKGROUND: Millions of people are exposed to landscape fire smoke (LFS) globally, and inhalation of LFS particulate matter (PM) is associated with poor respiratory and cardiovascular outcomes. However, how LFS affects respiratory and cardiovascular function is less well understood. OBJECTIVE: We aimed to characterize the pathophysiologic effects of representative LFS airway exposure on respiratory and cardiac function and on asthma outcomes. METHODS: LFS was generated using a customized combustion chamber. In 8-week-old female BALB/c mice, low (25 µg/m3, 24-hour equivalent) or moderate (100 µg/m3, 24-hour equivalent) concentrations of LFS PM (10 µm and below [PM10]) were administered daily for 3 (short-term) and 14 (long-term) days in the presence and absence of experimental asthma. Lung inflammation, gene expression, structural changes, and lung function were assessed. In 8-week-old male C57BL/6 mice, low concentrations of LFS PM10 were administered for 3 days. Cardiac function and gene expression were assessed. RESULTS: Short- and long-term LFS PM10 airway exposure increased airway hyperresponsiveness and induced steroid insensitivity in experimental asthma, independent of significant changes in airway inflammation. Long-term LFS PM10 airway exposure also decreased gas diffusion. Short-term LFS PM10 airway exposure decreased cardiac function and expression of gene changes relating to oxidative stress and cardiovascular pathologies. CONCLUSIONS: We characterized significant detrimental effects of physiologically relevant concentrations and durations of LFS PM10 airway exposure on lung and heart function. Our study provides a platform for assessment of mechanisms that underpin LFS PM10 airway exposure on respiratory and cardiovascular disease outcomes.

Assessment of Left Lung Remodeling With Magnetic Resonance Imaging in a Murine Model Following Exposure to Douglas Fir Smoke.

Wildland firefighters (WLFFs) experience lung function decline due to occupational exposure to fire smoke. WLFFs typically do not wear respiratory personal protective equipment, and if they do, it is a simple bandana, which is not effective at filtering smoke. To pinpoint the biological underpinnings of abnormal respiratory function following 3-7 years of WLFF service, we exposed mice to Douglas fir smoke (DFS) over 8 weeks. Following exposure, we assessed changes in lung structure through Magnetic Resonance Imaging (MRI) and histological analysis, which was supported by immunohistochemistry staining. With MRI, we found that the signal decay time, T2*, from ultrashort echo time (UTE) images was significantly shorter in mice exposed to DFS compared to air controls. In addition, the variation in T2* was more heterogeneously distributed throughout the left lung in DFS-exposed mice, compared to air controls. As confirmed by histological analysis, shorter T2* was caused by larger parenchyma airspace sizes and not fibrotic remodeling. Destruction of the alveolar spaces was likely due to inflammation, as measured by an influx of CD68+ macrophages and destruction due to enhanced neutrophil elastase. In addition, measurements of airspace dimensions from histology were more heterogeneously distributed throughout the lung, corroborating the enhanced relative dispersion of T2*. Findings from this study suggest that the decline in lung function observed in WLFFs may be due to emphysema-like changes in the lung, which can be quantified with MRI.

FSH-DETR: An Efficient End-to-End Fire Smoke and Human Detection Based on a Deformable DEtection TRansformer (DETR).

Fire is a significant security threat that can lead to casualties, property damage, and environmental damage. Despite the availability of object-detection algorithms, challenges persist in detecting fires, smoke, and humans. These challenges include poor performance in detecting small fires and smoke, as well as a high computational cost, which limits deployments. In this paper, we propose an end-to-end object detector for fire, smoke, and human detection based on Deformable DETR (DEtection TRansformer) called FSH-DETR. To effectively process multi-scale fire and smoke features, we propose a novel Mixed Encoder, which integrates SSFI (Separate Single-scale Feature Interaction Module) and CCFM (CNN-based Cross-scale Feature Fusion Module) for multi-scale fire, smoke, and human feature fusion. Furthermore, we enhance the convergence speed of FSH-DETR by incorporating a bounding box loss function called PIoUv2 (Powerful Intersection of Union), which improves the precision of fire, smoke, and human detection. Extensive experiments on the public dataset demonstrate that the proposed method surpasses state-of-the-art methods in terms of the mAP (mean Average Precision), with mAP and mAP50 reaching 66.7% and 84.2%, respectively.

Embedded Spatial-Temporal Convolutional Neural Network Based on Scattered Light Signals for Fire and Interferential Aerosol Classification.

Photoelectric smoke detectors are the most cost-effective devices for very early warning fire alarms. However, due to the different light intensity response values of different kinds of fire smoke and interference from interferential aerosols, they have a high false-alarm rate, which limits their popularity in Chinese homes. To address these issues, an embedded spatial-temporal convolutional neural network (EST-CNN) model is proposed for real fire smoke identification and aerosol (fire smoke and interferential aerosols) classification. The EST-CNN consists of three modules, including information fusion, scattering feature extraction, and aerosol classification. Moreover, a two-dimensional spatial-temporal scattering (2D-TS) matrix is designed to fuse the scattered light intensities in different channels and adjacent time slices, which is the output of the information fusion module and the input for the scattering feature extraction module. The EST-CNN is trained and tested with experimental data measured on an established fire test platform using the developed dual-wavelength dual-angle photoelectric smoke detector. The optimal network parameters were selected through extensive experiments, resulting in an average classification accuracy of 98.96% for different aerosols, with only 67 kB network parameters. The experimental results demonstrate the feasibility of installing the designed EST-CNN model directly in existing commercial photoelectric smoke detectors to realize aerosol classification.

Lessons learned and recommendations in conducting solutions-driven environmental and public health research.

Solutions-driven research is a transdisciplinary approach that incorporates diverse forms of expertise to identify solutions to stakeholder-identified environmental problems. This qualitative evaluation of early solutions-driven research projects provides transferable recommendations to improve researcher and stakeholder experiences and outcomes in transdisciplinary environmental research projects. Researchers with the U.S. Environmental Protection Agency (EPA) Office of Research and Development recently piloted a solutions-driven research approach in two parallel projects; one addressing nutrient management related to coastal waters and another studying wildland fire smoke impacts on indoor air quality. Studying the experiences of those involved with these pilots can enhance the integration of researcher and experiential expertise, improving solutions-driven research outcomes. Data collection included semi-structured interviews with 17 EPA researchers and 12 other stakeholders and reflective case narratives from the authors. We used conventional content analysis to qualitatively analyze perspectives on implementing innovative engagement and research approaches in a solutions-driven process. Findings that reflect common perspectives include the importance of continuous engagement, the challenges of differing timelines and priorities for researchers and stakeholders, and the need to define consistent markers of success across researchers and stakeholders. Key lessons to improve transdisciplinary research identified from the analysis are (1) improving clarity of roles and responsibilities; (2) planning to provide sufficient, continuous project funding over multiple years; (3) expecting research needs and plans to adapt to evolving circumstances; and (4) clearly defining the end of the project.

Asthma and landscape fire smoke: A Thoracic Society of Australia and New Zealand position statement.

Landscape fires are increasing in frequency and severity globally. In Australia, extreme bushfires cause a large and increasing health and socioeconomic burden for communities and governments. People with asthma are particularly vulnerable to the effects of landscape fire smoke (LFS) exposure. Here, we present a position statement from the Thoracic Society of Australia and New Zealand. Within this statement we provide a review of the impact of LFS on adults and children with asthma, highlighting the greater impact of LFS on vulnerable groups, particularly older people, pregnant women and Aboriginal and Torres Strait Islander peoples. We also highlight the development of asthma on the background of risk factors (smoking, occupation and atopy). Within this document we present advice for asthma management, smoke mitigation strategies and access to air quality information, that should be implemented during periods of LFS. We promote clinician awareness, and the implementation of public health messaging and preparation, especially for people with asthma.

Real-Time Fire Smoke Detection Method Combining a Self-Attention Mechanism and Radial Multi-Scale Feature Connection.

Fire remains a pressing issue that requires urgent attention. Due to its uncontrollable and unpredictable nature, it can easily trigger chain reactions and increase the difficulty of extinguishing, posing a significant threat to people's lives and property. The effectiveness of traditional photoelectric- or ionization-based detectors is inhibited when detecting fire smoke due to the variable shape, characteristics, and scale of the detected objects and the small size of the fire source in the early stages. Additionally, the uneven distribution of fire and smoke and the complexity and variety of the surroundings in which they occur contribute to inconspicuous pixel-level-based feature information, making identification difficult. We propose a real-time fire smoke detection algorithm based on multi-scale feature information and an attention mechanism. Firstly, the feature information layers extracted from the network are fused into a radial connection to enhance the semantic and location information of the features. Secondly, to address the challenge of recognizing harsh fire sources, we designed a permutation self-attention mechanism to concentrate on features in channel and spatial directions to gather contextual information as accurately as possible. Thirdly, we constructed a new feature extraction module to increase the detection efficiency of the network while retaining feature information. Finally, we propose a cross-grid sample matching approach and a weighted decay loss function to handle the issue of imbalanced samples. Our model achieves the best detection results compared to standard detection methods using a handcrafted fire smoke detection dataset, with APval reaching 62.5%, APSval reaching 58.5%, and FPS reaching 113.6.

Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning.

To date, most existing forest fire smoke detection methods rely on coarse-grained identification, which only distinguishes between smoke and non-smoke. Thus, non-fire smoke and fire smoke are treated the same in these methods, resulting in false alarms within the smoke classes. The fine-grained identification of smoke which can identify differences between non-fire and fire smoke is of great significance for accurate forest fire monitoring; however, it requires a large database. In this paper, for the first time, we combine fine-grained smoke recognition with the few-shot technique using metric learning to identify fire smoke with the limited available database. The experimental comparison and analysis show that the new method developed has good performance in the structure of the feature extraction network and the training method, with an accuracy of 93.75% for fire smoke identification.

COPD in Firefighters: A Specific Event-Related Condition Rather than a Common Occupational Respiratory Disorder.

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. Smoking remains the most important risk factor, but occupational exposures may play an essential role as well. Firefighters are among occupations regularly exposed to a variety of irritative inhalational products, and they may be expected to develop respiratory health problems because of such an occupational exposure. To better understand and characterize this relationship, we performed an extensive search of the scientific literature, and we identified two major research areas: firefighters exposed to wildland fire smoke and firefighters involved in the World Trade Centre disaster-related operations. Most of the studies did not report a significant increase in COPD diagnosis in firefighters. An accelerated rate of decline in lung function was seen, a short time after major exposure events. This is the reason for an increased rate of exacerbations observed in individuals already diagnosed with obstructive respiratory disorders. A limited number of studies not covering these specific circumstances of exposure were found. They reported long-term morbidity and mortality data, and the results are controversial. Major confounding factors for most of the studies were the "healthy worker effect" and the lack of useful data regarding smoking habits. Efforts should be made in the future to better characterize specific biomarkers for the progression of COPD; to establish exposure limits; and to implement preventive strategies like rotation of workers, smoking cessation programs, and long-term monitoring programs for respiratory disorders.

Impact of exposure to mine fire emitted PM2.5 on ambulance attendances: A time series analysis from the Hazelwood Health Study.

BACKGROUND: For six weeks from February 9, 2014, smoke and ash from a fire in the Morwell open cut brown coal mine adjacent to the Hazelwood power station covered parts of the Latrobe Valley, in south eastern Australia. AIMS: To evaluate the health impact of the mine fire, daily counts of ambulance attendances from July 2010 to March 2015 were analysed. METHODS: Time series models were used to evaluate the relative risk of ambulance attendances during the mine fire, in comparison with the remainder of the analysis period, and to also assess the risk of ambulance attendances associated with lagged effects of exposure to mine fire-related PM2.5 levels. The models controlled for factors likely to influence ambulance attendances including seasonality, long-term temporal trends, day of the week, daily maximum temperature and public holidays. RESULTS: A 10 µg/m3 increase in fire-related PM2.5 was found to be associated with a 42% (95%CI: 14-76%) increase in ambulance attendances for respiratory conditions and a 7% (0-14%) increase in all ambulance attendances over a 20-day lag period. A smaller effect associated with exposure to fire-related PM2.5 was identified when assuming shorter lag effects. Similar results were identified when assessing whether ambulance attendances increased during the 30-day mine fire period. There was a 15% (8-21%) increased risk of ambulance attendances for all conditions and a 47% (19-81%) increased risk for respiratory conditions during the mine fire period. CONCLUSIONS: Exposure to smoke and ash from a fire in an open cut brown coal mine was associated with increased ambulance attendances, particularly for respiratory conditions. These findings guide the development and implementation of effective and timely strategies and health service planning to respond and mitigate health risks that arise in affected communities during future major air pollution episodes.

Characterization of occupational smoke exposure among wildland firefighters in the midwestern United States.

Wildland firefighters are repeatedly exposed to elevated levels of wildland fire smoke (WFS) while protecting lives and properties from wildland fires. Studies reporting personal exposure concentrations of air pollutants in WFS during fire suppression or prescribed burn activities have been geographically limited to the western and southeastern United States. The objective of this study is to characterize exposure concentrations of air pollutants in WFS emissions among wildland firefighters who conducted prescribed burns in the Midwest. Between 2016 and 2019, a total of 35 firefighters (31 males and 4 females, age of 35.63 ± 9.31 years) were recruited to participate in this study. Personal particulate matter 2.5 (PM2.5) and carbon monoxide (CO) exposure concentrations were measured during prescribed burns. The level of black carbon (BC) in WFS particulates was determined using the light transmission technique, while trace metal composition was analyzed using inductively coupled plasma mass spectrometry (ICP-MS). The results showed geometric means for PM2.5, CO, and BC concentrations were 1.43 ± 0.13 mg/m3, 7.02 ± 0.69 ppm, and 58.79 ± 5.46 µg/m3, respectively. Although no occupational exposure limits (OELs) were exceeded by 8-h time-weighted average (TWA) exposure concentration observed in the firefighters, a total of 28 personal CO exposure concentrations were above the National Institute for Occupational Safety and Health (NIOSH) Recommended Exposure Limit (REL) Ceiling (200 ppm) for CO. PM2.5 and CO concentrations were about 2-7 times higher in the Midwest than the other regions. Firefighters who performed holding had higher CO exposure concentrations compared to firefighters who performed lighting (p < 0.01), while lighters were exposed to higher level of BC in the smoke particulates (p < 0.01), possibly due to the domination of exposure by different combustion sources and stages. The levels of trace metals in WFS particulates were well below the corresponding OELs and no task-related difference was observed except for manganese. Our results suggest that wildland firefighters in the midwestern region have higher WFS exposures while working at prescribed burns compared to those western and southeastern United States.

The U.S. EPA wildland fire sensor challenge: Performance and evaluation of solver submitted multi-pollutant sensor systems.

Wildland fires can emit substantial amounts of air pollution that may pose a risk to those in proximity (e.g., first responders, nearby residents) as well as downwind populations. Quickly deploying air pollution measurement capabilities in response to incidents has been limited to date by the cost, complexity of implementation, and measurement accuracy. Emerging technologies including miniaturized direct-reading sensors, compact microprocessors, and wireless data communications provide new opportunities to detect air pollution in real time. The U.S. Environmental Protection Agency (EPA) partnered with other U.S. federal agencies (CDC, NASA, NPS, NOAA, USFS) to sponsor the Wildland Fire Sensor Challenge. EPA and partnering organizations share the desire to advance wildland fire air measurement technology to be easier to deploy, suitable to use for high concentration events, and durable to withstand difficult field conditions, with the ability to report high time resolution data continuously and wirelessly. The Wildland Fire Sensor Challenge encouraged innovation worldwide to develop sensor prototypes capable of measuring fine particulate matter (PM2.5), carbon monoxide (CO), carbon dioxide (CO2), and ozone (O3) during wildfire episodes. The importance of using federal reference method (FRM) versus federal equivalent method (FEM) instruments to evaluate performance in biomass smoke is discussed. Ten solvers from three countries submitted sensor systems for evaluation as part of the challenge. The sensor evaluation results including sensor accuracy, precision, linearity, and operability are presented and discussed, and three challenge winners are announced. Raw solver submitted PM2.5 sensor accuracies of the winners ranged from ~22 to 32%, while smoke specific EPA regression calibrations improved the accuracies to ~75-83% demonstrating the potential of these systems in providing reasonable accuracies over conditions that are typical during wildland fire events.

Evaluation of small form factor, filter-based PM2.5 samplers for temporary non-regulatory monitoring during wildland fire smoke events.

Wildland fire activity and associated emission of particulate matter air pollution is increasing in the United States over the last two decades due primarily to a combination of increased temperature, drought, and historically high forest fuel loading. The regulatory monitoring networks in the Unites States are mostly concentrated in larger population centers where anthropogenic air pollution sources are concentrated. Smaller population centers in areas more likely to be impacted by wildland fire smoke in many instances lack adequate observational air quality data. Several commercially available small form factor filter-based PM2.5 samplers (SFFFS) were evaluated under typical ambient and simulated near-to mid-field wildland fire smoke conditions to evaluate their accuracy for use in temporary deployments during prescribed and wildfire events. The performance of all the SFFFS tested versus the designated federal reference methods (FRM) was acceptable in determining PM2.5 concentration in both ambient (2.7-14.0 µg m-3) and chamber smoke environments (24.6-3044.6 µg m-3) with accuracies ranging from ~92 to 98%. However, only the ARA Instruments model N-FRM Sampler was found to provide PM2.5 mass measurement accuracies that meet FRM guideline performance specifications under both typical ambient (97.3 ± 1.9%) and simulated wildland fire conditions (98.2 ± 1.4%).

The role of fuel type and combustion phase on the toxicity of biomass smoke following inhalation exposure in mice.

The characteristics of wildland fire smoke exposures which initiate or exacerbate cardiopulmonary conditions are unclear. We previously reported that, on a mass basis, lung toxicity associated with particulate matter (PM) from flaming smoke aspirated into mouse lungs is greater than smoldering PM. In this study, we developed a computer-controlled inhalation system which can precisely control complex biomass smoke emissions from different combustion conditions. This system was used to examine the toxicity of inhaled biomass smoke from peat, eucalyptus, and oak fuels generated under smoldering and flaming phases with emissions set to the same approximate concentration of carbon monoxide (CO) for each exposure (60-110 ppm), resulting in PM levels of ~ 4 mg/m3 for flaming and ~ 40 mg/m3 for smoldering conditions. Mice were exposed by inhalation 1 h/day for 2 days, and assessed for lung toxicity at 4 and 24 h after the final exposure. Peat (flaming and smoldering) and eucalyptus (smoldering) smoke elicited significant inflammation (neutrophil influx) in mouse lungs at 4 h with the peat (flaming) smoke causing even greater lung inflammation at 24-h post-exposure. A significant alteration in ventilatory timing was also observed in mice exposed to the peat (flaming) and eucalyptus (flaming and smoldering) smoke immediately after each day of exposure. No responses were seen for exposures to similar concentrations of flaming or smoldering oak smoke. The lung toxicity potencies (neutrophil influx per PM mass) agreed well between the inhalation and previously reported aspiration studies, demonstrating that although flaming smoke contains much less PM mass than smoldering smoke, it is more toxic on a mass basis than smoldering smoke exposure, and that fuel type is also a controlling factor.

Urinary mutagenicity and other biomarkers of occupational smoke exposure of wildland firefighters and oxidative stress.

Background: Wildland firefighters conducting prescribed burns are exposed to a complex mixture of pollutants, requiring an integrated measure of exposure. Objective: We used urinary mutagenicity to assess if systemic exposure to mutagens is higher in firefighters after working at prescribed burns versus after non-burn work days. Other biomarkers of exposure and oxidative stress markers were also measured. Methods: Using a repeated measures study design, we collected urine before, immediately after, and the morning after a work shift on prescribed burn and non-burn work days from 12 healthy subjects, and analyzed for malondialdehyde (MDA), 8-isoprostane, 1-hydroxypyrene (OH-pyrene), and mutagenicity in Salmonella YG1041 +S9. Particulate matter (PM2.5) and carbon monoxide (CO) were measured by personal monitoring. Light-absorbing carbon (LAC) of PM2.5 was measured as a surrogate for black carbon exposure. Linear mixed-effect models were used to assess cross-work shift changes in urinary biomarkers. Results: No significant differences occurred in creatinine-adjusted urinary mutagenicity across the work shift between burn days and non-burn days. Firefighters lighting fires had a non-significant, 1.6-fold increase in urinary mutagenicity for burn versus non-burn day exposures. Positive associations were found between cross-work shift changes in creatinine-adjusted urinary mutagenicity and MDA (p = 0.0010), OH-pyrene (p = 0.0001), and mass absorption efficiency which is the LAC/PM2.5 ratio (p = 0.2245), respectively. No significant effect of day type or work task on cross-work shift changes in MDA or 8-isoprostane was observed. Conclusion: Urinary mutagenicity may serve as a suitable measure of occupational smoke exposures among wildland firefighters, especially among those lighting fires for prescribed burns.

Impact of Climate Change on Pollen and Respiratory Disease.

PURPOSE OF REVIEW: A warming world will impact everyone and everything. The practice of allergic and respiratory disease will not be excepted. All the impacts will be impossible to anticipate. This review is intended to discuss significant factors related to individuals with allergic and respiratory disease. RECENT FINDINGS: Recent findings include the increased growth of allergenic plants in response to higher carbon dioxide levels and warmer temperatures. This also contributes to the increased production of pollen as well as the appearance of allergenic species in new climactic areas. Stinging insects will extend their ranges into northern areas where they have not previously been a problem. The shift and extension of pollen seasons with warmer springs and later frosts have already been observed. Recent severe hurricanes and flooding events may be just the harbinger of increasing damp housing exposure related to sea level rise. Evidence is accumulating that indicates the expected higher number of ozone alert days and increased pollution in populated areas is bringing increases in pollen potency. Finally, increased exposure to smoke and particles from wild fires, resulting from heat waves, will contribute to the general increase in respiratory disease. The practice of allergy being closely aligned with environmental conditions will be especially impacted. Allergists should consider increasing educational activities aimed at making patients more aware of air quality conditions.

A Video Based Fire Smoke Detection Using Robust AdaBoost.

This work considers using camera sensors to detect fire smoke. Static features including texture, wavelet, color, edge orientation histogram, irregularity, and dynamic features including motion direction, change of motion direction and motion speed, are extracted from fire smoke to train and test with different combinations. A robust AdaBoost (RAB) classifier is proposed to improve training and classification accuracy. Extensive experiments on well known challenging datasets and application for fire smoke detection demonstrate that the proposed fire smoke detector leads to a satisfactory performance.

Where there's smoke, there's fire: focal points for risk communication.

Large fires involving hazardous materials are often characterized by failing crisis communication. In this study, we compared opinions of experts regarding the risks of major fires to lay beliefs using a mental models approach. Amongst lay people this revealed relevant knowledge gaps and beliefs in opposition to those held by experts. While, experts considered the chance of getting cancer from inhaling smoke from a chemical fire extremely small, most lay people thought that even at a great distance, the chance of getting cancer to be large. To improve crisis communication about risk in a case of large chemical fires, and reduce the potential for messages to be misunderstood, distrusted or dismissed, we recommend a clarification of cancer risk in communications about public health emergencies such as chemical fires, for which lay people equate even small exposures to carcinogenic chemicals make one more likely to get cancer later in life.

Portable air cleaners should be at the forefront of the public health response to landscape fire smoke.

Landscape fires can produce large quantities of smoke that degrade air quality in both remote and urban communities. Smoke from these fires is a complex mixture of fine particulate matter and gases, exposure to which is associated with increased respiratory and cardiovascular morbidity and mortality. The public health response to short-lived smoke events typically advises people to remain indoors with windows and doors closed, but does not emphasize the use of portable air cleaners (PAC) to create private or public clean air shelters. High efficiency particulate air filters and electrostatic precipitators can lower indoor concentrations of fine particulate matter and improve respiratory and cardiovascular outcomes. We argue that PACs should be at the forefront of the public health response to landscape fire smoke events.

Urine proteome profile of firefighters with exposure to emergency fire-induced smoke: A pilot study to identify potential carcinogenic effects.

Firefighters are frequently exposed to a variety of chemicals formed from smoke, which pose a risk for numerous diseases, including cancer. Comparative urine proteome profiling could significantly improve our understanding of the early detection of potential cancer biomarkers. In this study, for the first time, we conducted a comparative protein profile analysis of 20 urine samples collected from ten real-life firefighters prior to and following emergency fire-induced smoke. Using a label-free quantitative proteomics platform, we identified and quantified 1325 unique protein groups, of which 45 proteins showed differential expressions in abundance in response to fire-smoke exposure (post) compared to the control (pre). Pathway analysis showed proteins associated with epithelium development (e.g., RHCG, HEG1, ADAMTSL2) and Alzheimer's disease (SORL1) were significantly increased in response to smoke exposure samples. A protein-protein-network study showed a possible link between these differentially abundant proteins and the known cancer gene (TP53). Moreover, a cross-comparison analysis revealed that seven proteins-ALDH1A1, APCS, POMC, COL2A1, RDX, DDAH2, and SDC4 overlapped with the previously published urine cancer proteome datasets, suggesting a potential cancer risk. Our findings demonstrated that the discovery proteomic platform is a promising analytical technique for identifying potential non-invasive biomarkers associated with fire-smoke exposure in firefighters that may be related to cancer.