Search Results (105)

Agriculture crop residue burning has become a major environmental problem facing the Indo-Gangetic plain, as well as contributing to global warming. This paper reports the results of a comprehensive study, examining the variations in aerosol optical, microphysical, and radiative properties that occur during biomass-burning events at Amity University Haryana (AUH), at a rural station in Gurugram (Latitude: 28.31° N, Longitude: 76.90° E, 285 m AMSL), employing ground-based observations of AERONET and Aethalometer, as well as satellite and model simulations during 7–16 November 2021. The smoke emissions during the burning events enhanced the aerosol optical depth (AOD) and increased the Angstrom exponent (AE), suggesting the dominance of fine-mode aerosols. A smoke event that affected the study region on 11 November 2021 is simulated using the regional NAAPS model to assess the role of smoke in regional aerosol loading that caused an atmospheric forcing of 230.4 W/m². The higher values of BC (black carbon) and BB (biomass burning), and lower values of AAE (absorption Angstrom exponent) are also observed during the peak intensity of the smoke-event period. A notable layer of smoke has been observed, extending from the surface up to an altitude of approximately 3 km. In addition, the observations gathered from CALIPSO regarding the vertical profiles of aerosols show a qualitative agreement with the values obtained from AERONET observations. Further, the smoke plumes that arose due to transport of a wide-spread agricultural crop residue burning are observed nationwide, as shown by MODIS imagery, and HYSPLIT back trajectories. Thus, the present study highlights that the smoke aerosol emissions during crop residue burning occasions play a critical role in the local/regional aerosol microphysical and radiation properties, and hence in the climate variability. Full article

The collapse of the World Trade Center (WTC) buildings in New York City generated a large plume of dust and smoke. WTC dust contained human carcinogens including metals, asbestos, polycyclic aromatic hydrocarbons (PAHs), persistent organic pollutants (POPs, including polychlorinated biphenyls (PCBs) and dioxins), and benzene. Excess levels of many of these carcinogens have been detected in biological samples of WTC-exposed persons, for whom cancer risk is elevated. As confirmed in this structured literature review (n studies = 80), all carcinogens present in the settled WTC dust (metals, asbestos, benzene, PAHs, POPs) have previously been shown to be associated with DNA methylation dysregulation of key cancer-related genes and pathways. DNA methylation is, therefore, a likely molecular mechanism through which WTC exposures may influence the process of carcinogenesis. Full article

Prescribed burning is a cost-effective method for reducing hazardous fuels in pine- and oak-dominated forests, but smoke emissions contribute to atmospheric pollutant loads, and the potential exists for exceeding federal air quality standards designed to protect human health. Fire behavior during prescribed burns influences above-canopy sensible heat flux and turbulent kinetic energy (TKE) in buoyant plumes, affecting the lofting and dispersion of smoke. A more comprehensive understanding of how enhanced energy fluxes and turbulence are related during the passage of flame fronts could improve efforts to mitigate the impacts of smoke emissions. Pre- and post-fire fuel loading measurements taken during 48 operational prescribed burns were used to estimate the combustion completeness factors (CC) and emissions of fine particulates (PM_2.5), carbon dioxide (CO₂), and carbon monoxide (CO) in pine- and oak-dominated stands in the Pinelands National Reserve of southern New Jersey. During 11 of the prescribed burns, sensible heat flux and turbulence statistics were measured by tower networks above the forest canopy. Fire behavior when fire fronts passed the towers ranged from low-intensity backing fires to high-intensity head fires with some crown torching. Consumption of forest-floor and understory vegetation was a near-linear function of pre-burn loading, and combustion of fine litter on the forest floor was the predominant source of emissions, even during head fires with some crowning activity. Tower measurements indicated that above-canopy sensible heat flux and TKE calculated at 1 min intervals during the passage of fire fronts were strongly influenced by fire behavior. Low-intensity backing fires, regardless of forest type, had weaker enhancement of above-canopy air temperature, vertical and horizontal wind velocities, sensible heat fluxes, and TKE compared to higher-intensity head and flanking fires. Sensible heat flux and TKE in buoyant plumes were unrelated during low-intensity burns but more tightly coupled during higher-intensity burns. The weak coupling during low-intensity backing fires resulted in reduced rates of smoke transport and dispersion, and likely in more prolonged periods of elevated surface concentrations. This research facilitates more accurate estimates of PM_2.5, CO, and CO₂ emissions from prescribed burns in the Pinelands, and it provides a better understanding of the relationships among fire behavior, sensible heat fluxes and turbulence, and smoke dispersion in pine- and oak-dominated forests. Full article

The fast prediction of the extent and impact of accidental air pollution releases is important to enable a quick and informed response, especially in cities. Despite this importance, only a small number of case studies are available studying the dispersion of air pollutants from fires in a short distance (O(1 km)) in urban areas. While monitoring pollution levels in Southampton, UK, using low-cost sensors, a fire broke out from an outbuilding containing roughly 3000 reels of highly flammable cine nitrate film and movie equipment, which resulted in high values of PM_2.5 being measured by the sensors approximately 1500 m downstream of the fire site. This provided a unique opportunity to evaluate urban air pollution dispersion models using observed data for PM_2.5 and the meteorological conditions. Two numerical approaches were used to simulate the plume from the transient fire: a high-fidelity computational fluid dynamics model with large-eddy simulation (LES) embedded in the open-source package OpenFOAM, and a lower-fidelity Gaussian plume model implemented in a commercial software package: the Atmospheric Dispersion Modeling System (ADMS). Both numerical models were able to quantitatively reproduce consistent spatial and temporal profiles of the PM_2.5 concentration at approximately 1500 m downstream of the fire site. Considering the unavoidable large uncertainties, a comparison between the sensor measurements and the numerical predictions was carried out, leading to an approximate estimation of the emission rate, temperature, and the start and duration of the fire. The estimation of the fire start time was consistent with the local authority report. The LES data showed that the fire lasted for at least 80 min at an emission rate of 50 g/s of PM_2.5. The emission was significantly greater than a ‘normal’ house fire reported in the literature, suggesting the crucial importance of the emission estimation and monitoring of PM_2.5 concentration in such incidents. Finally, we discuss the advantages and limitations of the two numerical approaches, aiming to suggest the selection of fast-response numerical models at various compromised levels of accuracy, efficiency and cost. Full article

High-volume space structures are characterized by high combustible loads, rapid fire development, difficulty in firefighting, and potential building collapse risks, making early fire detection particularly crucial. The effectiveness of early fire detection technologies relies on their ability to adapt to the characteristics of smoke-dominant combustion products in the protected space. However, there is a lack of targeted research on the characteristics of the smoke generated during the early low-power stages of fires in high-volume spaces, which has not supported the development of early fire detection technologies for such environments. To address this, this paper presents an experimental study that, for the first time, collects data on fire smoke parameters such as temperature, velocity, concentration, and particle size at heights ranging from 6.5 m to 18.5 m using lightweight sensors. The study analyzes the characteristic and correlations of these parameters and their impact on early fire detection in high-volume spaces for the first time, presenting variation patterns in the plume velocity and particle size distribution of early fire smoke with height. It identifies three patterns of particle size distribution, contrasting with previous studies, and offers a qualitative explanation for these findings. This research enhances the understanding of early fire smoke signals in large spaces and offers valuable insights for developing more accurate and efficient fire detection strategies and technologies. Full article

Previous works have shown the effectiveness of EfficientNet—a convolutional neural network built upon the concept of compound scaling—in automatically detecting smoke plumes at a distance of several kilometres in visible camera images. Building on these results, we have created enhanced EfficientNet models capable of precisely identifying the smoke location due to the introduction of a mosaic-like output and achieving extremely reduced false positive percentages due to using partial AUROC and applying class imbalance. Our EfficientNets beat InceptionV3 and MobileNetV2 in the same dataset and achieved a true detection percentage of 89.2% and a false positive percentage of only 0.306% across a test set with 17,023 images. The complete dataset used in this study contains 26,204 smoke and 51,075 non-smoke images. This makes it one of the largest, if not the most extensive, datasets reported in the scientific literature for smoke plume imagery. So, the achieved percentages are not only among the best reported for this application but are also among the most reliable due to the extent and representativeness of the dataset. Full article

In recent years, the role of the atmosphere in the formation of the chemical composition of water in Lake Baikal and its tributaries has been increasing. In this regard, the study of equivalent black carbon (eBC) in the air above the lake and its coast has an important practical application. This paper presents the results of the mass concentration of eBC and submicron aerosol in the air above the water area of Lake Baikal, which were obtained during expeditions onboard research vessels in the summer of 2019 and 2023. We analyzed the data from the coastal monitoring station Listvyanka. To measure eBC, an MDA-02 aethalometer was used in the water area of the lake, and a BAC-10 aethalometer at the Listvyanka station. The background level of the eBC concentration in the air at different areas of the lake ranged between 0.15 and 0.3 µg m⁻³. The results of the two expeditions revealed the influence of the coastal settlements and the air mass transport along the valleys of the lake’s large tributaries on the five- to twentyfold growth of the eBC concentration in the near-water atmosphere. In the diurnal dynamics of eBC near settlements, we recorded high values in the evening and at night. In background areas, the diurnal dynamics were poorly manifested. In the summer of 2019, there were smoke plumes in the water area of Lake Baikal from distant wildfires and a local fire site on the east coast of the lake. The eBC concentration increased to 5–6 µg m⁻³, which was 10 to 40 times higher than the background. The long-range transport of plumes from coal-fired thermal power plants in large cities of the region made the major contribution to the eBC concentration at «Listvyanka» in winter, which data on aerosol, gas impurities, and meteorological parameters confirmed. Full article

The integration of visual algorithms with infrared imaging technology has become an effective tool for industrial gas leak detection. However, existing research has mostly focused on simple scenarios where a gas plume is clearly visible, with limited studies on detecting gas in complex scenes where target contours are blurred and contrast is low. This paper uses a cooled mid-wave infrared (MWIR) system to provide high sensitivity and fast response imaging and proposes the MWIRGas-YOLO network for detecting gas leaks in mid-wave infrared imaging. This network effectively detects low-contrast gas leakage and segments the gas plume within the scene. In MWIRGas-YOLO, it utilizes the global attention mechanism (GAM) to fully focus on gas plume targets during feature fusion, adds a small target detection layer to enhance information on small-sized targets, and employs transfer learning of similar features from visible light smoke to provide the model with prior knowledge of infrared gas features. Using a cooled mid-wave infrared imager to collect gas leak images, the experimental results show that the proposed algorithm significantly improves the performance over the original model. The segment mean average precision reached 96.1% (mAP50) and 47.6% (mAP50:95), respectively, outperforming the other mainstream algorithms. This can provide an effective reference for research on infrared imaging for gas leak detection. Full article

Fire is a significant cause of fatalities and property loss. In tall spaces, early smoke dispersion is hindered by thermal barriers, and initial flames with limited smoke production may be obscured by ground-level structures. Consequently, smoke, temperature, and other fire sensor signals are weakened, leading to delays in fire detection by sensor networks. This paper proposes a multi-height and heterogeneous fusion discriminant model with a multilayered LSTM structure for the robust detection of weak fire signals in such challenging situations. The model employs three LSTM structures with cross inputs in the first layer and an input-weighted LSTM structure in the second layer to capture the temporal and cross-correlation features of smoke concentration, temperature, and plume velocity sensor data. The third LSTM layer further aggregates these features to extract the spatial correlation patterns among different heights. The experimental results demonstrate that the proposed algorithm can effectively expedite alarm response during sparse smoke conditions and mitigate false alarms caused by weak signals. Full article

Tunnels with natural ventilation and extraction have become the focus of ventilation research in recent years. It is significant to study the characteristics of smoke in tunnel fires to ensure the safety of people and the tunnel structure. Previous research has mainly focused on natural ventilation in horizontal tunnels, and there are few studies on sloped tunnels. In this paper, we studied the smoke characteristics of natural ventilation extraction in slope tunnel fires both experimentally and theoretically. The small-scale experimental results showed that the position of the fire source, heat release rate (HRR), and the size of the shaft had little effect on the deflection angle of the fire plume. The deflection angle of fire plume was only related to the tunnel slope and increased with the tunnel slope. The slope had no effect on the smoke temperature distribution on the downside of the tunnel, while the smoke temperature on the upside decreased with the increase in the slope. The calculation models of the maximum smoke temperature rise and the smoke temperature distribution were obtained based on the experimental results and theoretical analysis. Compared with the experimental data, the developed semi-empirical models could provide a reliable prediction of smoke temperature. Full article

Forest fires require rapid and precise early smoke detection to minimize damage. This study focuses on employing smoke recognition methods for early warning systems in forest fire detection, identifying smoke as the primary indicator. A significant hurdle lies in the absence of a large-scale dataset for real-world early forest fire smoke detection. Early smoke videos present characteristics such as smoke plumes being small, slow-moving, and/or semi-transparent in color, and include images where there is background interference, posing critical challenges for practical recognition algorithms. To address these issues, this paper introduces a real-world early smoke monitoring video dataset as a foundational resource. The proposed 4D attention-based motion target enhancement network includes an important frame sorting module which adaptively selects essential frame sequences to improve the detection of slow-moving smoke targets. Additionally, a 4D attention-based motion target enhancement module is introduced to mitigate interference from smoke-like objects and enhance recognition of light smoke during the initial stages. Moreover, a high-resolution multi-scale fusion module is presented, incorporating a small target recognition layer to enhance the network’s ability to detect small smoke targets. This research represents a significant advancement in early smoke detection for forest fire surveillance, with practical implications for enhancing fire management. Full article

Worsening wildfire seasons in recent years are reversing decadal progress on the reduction of harmful air pollutants in the US, particularly in Western states. Measurements of the contributions of wildfire smoke to ambient air pollutants, such as fine particulate matter (PM_2.5), at fine resolution scales would be valuable to public health research on climate vulnerable populations and compound climate risks. We estimate the influence of wildfire smoke emissions on daily PM_2.5 at fine-resolution, 3 km, for California 2011–2020, using a geostatistical modeled ambient PM_2.5 estimate and wildfire smoke plume data from NOAA Hazard Mapping System. Additionally, we compare this product with the US Environmental Protection Agency (EPA) daily and annual standards for PM_2.5 exposure. Our results show wildfires significantly influence PM_2.5 in California and nearly all exceedances of the daily US EPA PM_2.5 standard were influenced by wildfire smoke, while annual exceedances were increasingly attributed to wildfire smoke influence in recent years. This wildfire-influenced PM_2.5 product can be applied to public health research to better understand source-specific air pollution impacts and assess the combination of multiple climate hazard risks. Full article

Interactions between urban and wildfire pollution emissions are active areas of research, with numerous aircraft field campaigns and satellite analyses of wildfire pollution being conducted in recent years. Several studies have found that elevated ozone and particulate pollution levels are both generally associated with wildfire smoke in urban areas. We measured pollutant concentrations at two Utah Division of Air Quality regulatory air quality observation sites and a local hot spot (a COVID-19 testing site) within a 48 h period of increasing wildfire smoke impacts that occurred in Salt Lake City, UT (USA) between 20 and 22 August 2020. The wildfire plume, which passed through the study area during an elevated ozone period during the summer, resulted in increased criteria pollutant and greenhouse gas concentrations. Methane (CH₄) and fine particulate matter (PM_2.5) increased at comparable rates, and increased NO_x led to more ozone. The nitrogen oxide/ozone (NO_x/O₃) cycle was clearly demonstrated throughout the study period, with NO_x titration reducing nighttime ozone. These findings help to illustrate how the compounding effects of urban emissions and exceptional pollution events, such as wildfires, may pose substantial health risks. This preliminary case study supports conducting an expanded, longer-term study on the interactions of variable intensity wildfire smoke plumes on urban air pollution exposure, in addition to the subsequent need to inform health and risk policy in these complex systems. Full article

The characteristics of South American biomass burning (BB) aerosols transported over northern and central Argentina were investigated from July to December 2019. This period was chosen due to the high aerosol optical depth values found in the region and because simultaneously intensive biomass burning took place over the Amazon. More specifically, a combination of remote sensing observations with simulated air parcel back trajectories was used to link the optical and physical properties of three BB aerosol events that affected Pilar Observatory (PO, Argentina, 31°41′S, 63°53′W, 338 m above sea level), with low-level atmospheric circulation patterns and with types of vegetation burned in specific fire regions. The lidar observations at the PO site were used for the first time to characterize the vertical extent and structure of BB aerosol plumes as well as their connection with the planetary boundary layer, and dust particles. Based mainly on the air-parcel trajectories, a local transport regime and a long transport regime were identified. We found that in all the BB aerosol event cases studied in this paper, light-absorbing fine-mode aerosols were detected, resulting mainly from a mixture of aging smoke and dust particles. In the remote transport regime, the main sources of the BB aerosols reaching PO were associated with Amazonian rainforest wildfires. These aerosols were transported into northern and central Argentina within a strong low-level jet circulation. During the local transport regime, the BB aerosols were linked with closer fires related to tropical forests, cropland, grassland, and scrub/shrubland vegetation types in southeastern South America. Moreover, aerosols carried by the remote transport regime were associated with a high aerosol loading and enhanced aging and relatively smaller particle sizes, while aerosols associated with the local transport pattern were consistently less affected by the aging effect and showed larger sizes and low aerosol loading. Full article

Satellite remote sensing plays a significant role in the detection of smoke from forest fires. However, existing methods for detecting smoke from forest fires based on remote sensing images rely solely on the information provided by the images, overlooking the positional information and brightness temperature of the fire spots in forest fires. This oversight significantly increases the probability of misjudging smoke plumes. This paper proposes a smoke detection model, Forest Smoke-Fire Net (FSF Net), which integrates wildfire smoke images with the dynamic brightness temperature information of the region. The MODIS_Smoke_FPT dataset was constructed using a Moderate Resolution Imaging Spectroradiometer (MODIS), the meteorological information at the site of the fire, and elevation data to determine the location of smoke and the brightness temperature threshold for wildfires. Deep learning and machine learning models were trained separately using the image data and fire spot area data provided by the dataset. The performance of the deep learning model was evaluated using metric MAP, while the regression performance of machine learning was assessed with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected machine learning and deep learning models were organically integrated. The results show that the Mask_RCNN_ResNet50_FPN and XGR models performed best among the deep learning and machine learning models, respectively. Combining the two models achieved good smoke detection results (Precisionsmoke=89.12%). Compared with wildfire smoke detection models that solely use image recognition, the model proposed in this paper demonstrates stronger applicability in improving the precision of smoke detection, thereby providing beneficial support for the timely detection of forest fires and applications of remote sensing. Full article