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Electrosurgery produces surgical smoke. Different tissues produce different quantities and types of smoke, so we studied the particle characteristics of this surgical smoke in order to analyze the implications for the occupational health of the operation room personnel. We estimated the deposition of particulate matter (PM) from surgical smoke on the respiratory tract of operation room personnel using clinically relevant tissues from Finnish landrace porcine tissues including skeletal muscle, liver, subcutaneous fat, renal pelvis, renal cortex, lung, bronchus, cerebral gray and white matter, and skin. In order to standardize the electrosurgical cuts and smoke concentrations, we built a customized computer-controlled platform. The smoke particles were analyzed with an electrical low pressure impactor (ELPI), which measures the concentration and aerodynamic size distribution of particles with a diameter between 7 nm and 10 μm. There were significant differences in the mass concentration and size distribution of the surgical smoke particles depending on the electrocauterized tissue. Of the various tissues tested, liver yielded the highest number of particles. In order to better estimate the health hazard, we propose that the tissues can be divided into three distinct classes according to their surgical smoke production: 1) high-PM tissue for liver; 2) medium-PM tissues for renal cortex, renal pelvis, and skeletal muscle; and 3) low-PM tissues for skin, gray matter, white matter, bronchus, and subcutaneous fat.

Almost USD 3 billion per year is appropriated for wildfire management on public land in the United States. Recent studies have suggested that ongoing climate change will lead to warmer and drier conditions in the western United States, with a consequent increase in the number and size of wildfires, yet large uncertainty exists in these projections. To assess the influence of future changes in climate and land cover on lightning-caused wildfires in the national forests and parks of the western United States and the consequences of these fires on air quality, we link a dynamic vegetation model that includes a process-based representation of fire (LPJ-LMfire) to a global chemical transport model (GEOS-Chem). Under a scenario of moderate future climate change (RCP4.5), increasing lightning-caused wildfire enhances the burden of smoke fine particulate matter (PM), with mass concentration increases of ∼53 % by the late 21st century during the fire season in the national forests and parks of the western United States. In a high-emissions scenario (RCP8.5), smoke PM concentrations double by 2100. RCP8.5 also shows enhanced lightning-caused fire activity, especially over forests in the northern states.

The authors are grateful to the National Statistics Office of Mongolia, the Statistics Department of Ulaanbaatar, and the Health Development Center of the Ministry of Health and Sports which provided access to various demographics and health databases. (2017) Health assessment of future PM2.5 exposures from indoor, outdoor, and secondhand tobacco smoke concentrations under alternative policy pathways in Ulaanbaatar, Mongolia.

Addition of menthol to cigarettes may be associated with increased initiation of smoking. The potential mechanisms underlying this association are not known. Menthol, likely due to its effects on cold-sensing peripheral sensory neurons, is known to inhibit the sensation of irritation elicited by respiratory irritants. However, it remains unclear whether menthol modulates cigarette smoke irritancy and nicotine absorption during initial exposures to cigarettes, thereby facilitating smoking initiation. Using plethysmography in a C57Bl/6J mouse model, we examined the effects of L-menthol, the menthol isomer added to cigarettes, on the respiratory sensory irritation response to primary smoke irritants (acrolein and cyclohexanone) and smoke of Kentucky reference 2R4 cigarettes. We also studied L-menthol's effect on blood levels of the nicotine metabolite, cotinine, immediately after exposure to cigarette smoke. L-menthol suppressed the irritation response to acrolein with an apparent IC₅₀ of 4 ppm. Suppression was observed even at acrolein levels well above those necessary to produce a maximal response. Cigarette smoke, at exposure levels of 10 mg/m³ or higher, caused an immediate and marked sensory irritation response in mice. This response was significantly suppressed by L-menthol even at smoke concentrations as high as 300 mg/m³. Counterirritation by L-menthol was abolished by treatment with a selective inhibitor of Transient Receptor Potential Melastatin 8 (TRPM8), the neuronal cold/menthol receptor. Inclusion of menthol in the cigarette smoke resulted in roughly a 1.5-fold increase in plasma cotinine levels over those observed in mice exposed to smoke without added menthol. These findings document that, L-menthol, through TRPM8, is a strong suppressor of respiratory irritation responses, even during highly noxious exposures to cigarette smoke or smoke irritants, and increases blood cotinine. Therefore, L-menthol, as a cigarette additive, may promote smoking initiation and nicotine addiction.

Winter air pollution in Ulaanbaatar, Mongolia is among the worst in the world. The health impacts of policy decisions affecting air pollution exposures in Ulaanbaatar were modeled and evaluated under business as usual and two more-strict alternative emissions pathways through 2024. Previous studies have relied on either outdoor or indoor concentrations to assesses the health risks of air pollution, but the burden is really a function of total exposure. This study combined projections of indoor and outdoor concentrations of PM2.5 with population time-activity estimates to develop trajectories of total age-specific PM2.5 exposure for the Ulaanbaatar population. Indoor PM2.5 contributions from secondhand tobacco smoke (SHS) were estimated in order to fill out total exposures, and changes in population and background disease were modeled. The health impacts were derived using integrated exposure-response curves from the Global Burden of Disease Study. Annual average population-weighted PM2.5 exposures at baseline (2014) were estimated at 59 μg/m3. These were dominated by exposures occurring indoors, influenced considerably by infiltrated outdoor pollution. Under current control policies, exposures increased slightly to 60 μg/m3 by 2024; under moderate emissions reductions and under a switch to clean technologies, exposures were reduced from baseline levels by 45% and 80%, respectively. The moderate improvement pathway decreased per capita annual disability-adjusted life year (DALY) and death burdens by approximately 40%. A switch to clean fuels decreased per capita annual DALY and death burdens by about 85% by 2024 with the relative SHS contribution increasing substantially. This study demonstrates a way to combine estimated changes in total exposure, background disease and population levels, and exposure-response functions to project the health impacts of alternative policy pathways. The resulting burden analysis highlights the need for aggressive action, including the elimination of residential coal burning and the reduction of current smoking rates.

Smoke detectors face the challenges of increasing accuracy, sensitivity, and high reliability in complex use environments to ensure the timeliness, accuracy, and reliability of very early fire detection. The improvement in and innovation of the principle and algorithm of smoke particle concentration detection provide an opportunity for the performance improvement in the detector. This study is a new refinement of the smoke concentration detection principle based on capacitive detection of cell structures, and detection signals are processed by a multiscale smoke particle concentration detection algorithm to calculate particle concentration. Through experiments, it is found that the detector provides effective detection of smoke particle concentrations ranging from 0 to 10% obs/m; moreover, the detector can detect smoke particles at parts per million (PPM) concentration levels (at 2 and 5 PPM), and the accuracy of the detector can reach at least the 0.5 PPM level. Furthermore, the detector can detect smoke particle concentrations at better than 1 PPM accuracy even in an environment with 6% obs/m oil gas particles, 7% obs/m large dust interference particles, or 8% obs/m small dust interference particles.

During the fire season of 2023, extreme continuous wildfires in Canada exported smoke to distant areas. On June 6–8, record‐breaking smoke concentrations impacted human health and the environment in New York City (NYC) and its surroundings. In this work, for the first time, we incorporate Lagrangian airmass trajectories with Copernicus Atmospheric Monitoring Service (CAMS) forecasts to trace back the origin of the smoke in NYC and identify the weather systems governing its transport. We locate the main smoke plume which originated from fires in Quebec. The smoke traveled at a height of about 500 hPa southward and descended slantwise to NYC behind a deep cyclone over the east coast. A second peak in smoke concentration in NYC emerged by air that circulated around the cyclone back to the city, collecting smoke again from the fires in Quebec. Smoke from the major fires in western Canada did not contribute significantly to the NYC event but was transported at tropopause level toward Europe. The findings highlight the critical role of synoptic‐scale systems in the transport of wildfire smoke. During the 2023 fire season, smoke from intense wildfires in Canada reached New York City, causing a severe pollution event. This study employs Lagrangian airmass trajectories to trace the smoke's origin and transport. A deep cyclone over the east coast guided the smoke southward from Quebec, while descending airstreams to the west of the cyclone directed the smoke toward the city. The findings highlight the critical role of meso‐ and synoptic‐scale systems in the transport of wildfire smoke.

Indoor air pollution from cigarette smoke, poses significant threats to plant growth and development. This study investigates the impact of cigarette smoke on the indoor plant species Epipremnum aureum (money plant), focusing on morphological, biochemical, and physiological aspects, with an emphasis on photosynthetic efficiency. Plants were exposed to varying concentrations of cigarette smoke (0, 2, 4, 6, 8, 10 cigarettes/day) for 15 days in controlled conditions. Key findings include a significant reduction in the mean surface area (MSA) of leaves. Chlorophyll a fluorescence (ChlF) analysis revealed impaired PSII photochemistry, as evidenced by reduced fluorescence yields and a significant decrease in fluorescence intensity at higher smoke exposures. Specific energy fluxes, including absorption energy (ABS/RC), trapped energy (TR 0 /RC), and electron transport (ET 0 /RC), showed notable reductions, while dissipated energy (DI 0 /RC) increased significantly. The performance indices PI ABS and PI CS decreased with increasing smoke concentration and exposure duration, indicating a decline in overall photosynthetic efficiency. Principal component analysis demonstrated a distinct separation in the physiological response of plants based on exposure levels, with the highest cigarette smoke concentrations causing the most significant disruptions. The lethal dose calculations (LD 50 = 6.4 cigarettes/day; LD 90 = 9.9 cigarettes/day) highlight the severity of cigarette smoke’s impact on E. aureum . These results underscore the detrimental effects of cigarette smoke on indoor plants, suggesting that such pollutants can significantly impair plant health and stress the importance of managing indoor air quality.

Wildfires and prescribed fires produce emissions that degrade visibility and are harmful to human health. Smoke emissions and exposure monitoring is critical for public and environmental health protection; however, ground-level measurements of smoke from wildfires and prescribed fires has proven difficult, as existing (validated) monitoring technologies are expensive, cumbersome, and generally require line power. Few ground-based measurements are made during fire events, which limits our ability to assess the environmental and human health impacts of wildland fire smoke. The objective of this work was to develop and validate an Outdoor Aerosol Sampler (OAS) – a filter-based air sampler that has been miniaturized, solar powered, and weatherproofed. This sampler was designed to overcome several of the technical challenges of wildland fire monitoring by being relatively inexpensive and solar powered. The sampler design objectives were achieved by leveraging low-cost electronic components, open-source programming platforms, and in-house fabrication methods. A direct-reading PM2.5 sensor was selected and integrated with the OAS to provide time-resolved concentration data. Cellular communications established via short message service (SMS) technology were utilized in transmitting online sensor readings and controlling the sampling device remotely. A Monte Carlo simulation aided in the selection of battery and solar power necessary to independently power the OAS, while keeping cost and size to a minimum. Thirteen OAS were deployed to monitor smoke concentrations downwind from a large prescribed fire. Aerosol mass concentrations were interpolated across the monitoring network to depict smoke concentration gradients in the vicinity of the fire. Strong concentration gradients were observed (spatially and temporally) and likely present due to a combination of changing fire location and intensity, topographical features (e.g., mountain ridges), and diurnal weather patterns. Gravimetric filter measurements made by the OAS (when corrected for filter collection efficiency) showed relatively good agreement with measurements from an EPA federal equivalent monitor. However, the real-time optical sensor (Sharp GP2Y1023AU0F, Sharp Electronic Co.) within the OAS suffered from temperature dependence, drift, and imprecision.

Smoke detectors face the challenges of increasing accuracy, sensitivity, and high reliability in complex use environments to ensure the timeliness, accuracy, and reliability of very early fire detection. The improvement and innovation of the principle and algorithm for smoke particle concentration detection provide opportunities for improving the performance of the detector. This study represents a new refinement of the smoke concentration detection principle based on capacitive detection of cell structures, and detection signals are processed by a multiscale smoke particle concentration detection algorithm to calculate smoke concentration. Through experiments, it was found that the detector provides effective detection of smoke particle concentrations ranging from 0 to 10% obs/m; moreover, when the detection accuracy is greater than a certain number of parts per million (PPM), the sensitivity of the detector can reach the PPM level; furthermore, the detector can detect smoke particle concentrations higher than the PPM level accuracy even in an environment with a certain concentration of petroliferous and dust particles of different sizes.