Gaussian Dispersion Research Articles

Vision-Centric BEV Perception: A Survey.

In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant interest from both industry and academia due to its inherent advantages, such as providing an intuitive representation of the world and being conducive to data fusion. The rapid advancements in deep learning have led to the proposal of numerous methods for addressing vision-centric BEV perception challenges. However, there has been no recent survey encompassing this novel and burgeoning research field. To catalyze future research, this paper presents a comprehensive survey of the latest developments in vision-centric BEV perception and its extensions. It compiles and organizes up-to-date knowledge, offering a systematic review and summary of prevalent algorithms. Additionally, the paper provides in-depth analyses and comparative results on various BEV perception tasks, facilitating the evaluation of future works and sparking new research directions. Furthermore, the paper discusses and shares valuable empirical implementation details to aid in the advancement of related algorithms.

Gaseous Object Detection.

Object detection, a fundamental and challenging problem in computer vision, has experienced rapid development due to the effectiveness of deep learning. The current objects to be detected are mostly rigid solid substances with apparent and distinct visual characteristics. In this paper, we endeavor on a scarcely explored task named Gaseous Object Detection (GOD), which is undertaken to explore whether the object detection techniques can be extended from solid substances to gaseous substances. Nevertheless, the gas exhibits significantly different visual characteristics: 1) saliency deficiency, 2) arbitrary and ever-changing shapes, 3) lack of distinct boundaries. To facilitate the study on this challenging task, we construct a GOD-Video dataset comprising 600 videos (141,017 frames) that cover various attributes with multiple types of gases. A comprehensive benchmark is established based on this dataset, allowing for a rigorous evaluation of frame-level and video-level detectors. Deduced from the Gaussian dispersion model, the physics-inspired Voxel Shift Field (VSF) is designed to model geometric irregularities and ever-changing shapes in potential 3D space. By integrating VSF into Faster RCNN, the VSF RCNN serves as a simple but strong baseline for gaseous object detection. Our work aims to attract further research into this valuable albeit challenging area.

Health impact assessment of exposure to road traffic noise and air pollution according to pre- and post-densification scenarios in Helsingborg, Sweden

A prevalent solution to accommodate population growth due to urbanization is densification. However, this often pushes new residential housing closer to roads, increasing exposure to both noise and air pollution. The present study’s aim was to estimate the health impacts of road traffic-related noise and air pollution for a low-income area (Drottninghög) in Helsingborg, Sweden, according to pre-densification (2012) and post-densification (2030) scenarios.Road traffic noise was simulated at the façade of residential buildings using the Nordic prediction method, and exposure was assessed using SoundPLAN. Exposure-response functions (ERF) from the WHO were utilized for the following health outcomes associated with noise: annoyance, adverse sleep disturbance, ischemic heart disease (IHD) incidence and IHD mortality. Air pollution (nitrogen dioxide, NO2) was assessed using a Gaussian dispersion model (AERMODE). Health outcomes associated with NO2 included natural cause mortality, pediatric asthma, respiratory hospitalizations and low birth weight (LBW). ERFs were derived from meta-analyses. Health impact assessments were then performed for both scenarios.Densifying Drottninghög according to the municipality’s planned strategy would lead to a 15 % unit increase in the proportion of residents exposed to road traffic noise above the WHO’s health-based guideline value (53 dB(A) Lden). This was estimated to markedly increase the proportion of residents highly annoyed by traffic noise (7.4–13.9 %) as well as those highly sleep disturbed (3.0–4.9 %). IHD incidence and IHD mortality attributed to noise would increase by an estimated 49 % and 44 %, respectively, post-densification. NO2 exposure was estimated to increase slightly (0.7 µg/m3) post-densification, which would contribute to an estimated 4–6 % increase in natural cause mortality, pediatric asthma, respiratory hospitalization and LBW.Urban planning initiatives need to consider these prevalent urban environmental exposures and integrate a public health perspective into densification strategies. Doing so can create synergies in the built environment that promote healthy, sustainable cities.

Forecasting Daily Air Quality Index and Early Warning System for Estimating Ambient Air Pollution on Road Networks Using Gaussian Dispersion Model with Deep Learning Algorithm

The rapid growth of the vehicle population is a major factor in heavy air pollution and public health issues. Traffic-related air pollutants (TRAPs) on roads are often much higher than ambient values, leading to high exposure levels in vehicles. This research proposes a hybrid forecasting model for early detection and early warning systems (EWS) of road networks during real-world travels. Data is collected from Kannur, Calicut, Palakkad, and Coimbatore using real-time sensors, including surrounding discussion information, activity information, vehicle speed, and stopping events. The study predicts ambient air quality (AAQ) levels on the road network using the Gaussian Dispersion model (GDM) and measures the risk sensitivity of PM10 and PM2.5 in selected regions. This helps formulate powerful prevention strategies and prevent negative health impacts. The air pollution module for predicting concentration has an innovative hybridization model that combines an improved cuckoo search (CS) and differential evolution (DE) algorithm with a stacked LSTM model to increase forecasting accuracy of six major environmental pollution levels. This model predicts the AAQ level and is effective and robust for warning one day before the pollutant event occurs based on the risk level of an ambient air pollutant from the RN.

Computational fluid dynamics simulation of ammonia leakage scenarios during ship-to-ship bunkering

Ammonia is emerging as a potential marine fuel, yet its toxicity and risk of accidental leaks pose significant safety challenges compared to other alternatives. This study focuses on the critical ship-to-ship bunkering process using Computational Fluid Dynamics (CFD) simulations to analyse diverse ammonia leakage scenarios considering various leak orientations relative to the wind and the ships. The numerical setup was initially validated against controlled experiments, demonstrating satisfactory agreement. Results indicate that atmospheric ammonia dispersion is significantly influenced by obstacles like the superstructure and hull in the near field, with diminishing effect in the far field. Conventional Gaussian plume models are inadequate within the first few hundred metres from the release source due to the complex near-field flow patterns and increased turbulence generated by the wakes of the hull and superstructure. These findings underscore the necessity for improved ship-scale dispersion models to accurately evaluate the impact of obstacles on ammonia dispersion. The consequence analysis reveals that upward leaks create larger risk zones with potential for irreversible health effects, while horizontal leaks may expose personnel to life-threatening concentrations. Vertical leaks in crosswind configurations generate lethal zones covering tens of metres on deck. These results highlight the importance of considering the exact location of the transfer hose relative to the ship and all potential wind orientations in risk assessments. Given the extensive range of potential dispersion patterns and the inadequacy of conventional Gaussian dispersion models in this context, we strongly recommend vessel-specific CFD simulations before certifying ammonia bunkering operations as safe. This approach is crucial for accurate risk assessment at the ship scale, where complex geometries and multi-directional flows invalidate the assumptions of traditional point-source, uni-directional dispersion models.

CALINE4 and AERMOD modelling for roadway vehicle-related air pollution: a recent review in India.

Modelling and prediction of air quality facilitates the drafting of efficient guidelines and, in turn, proper management of adversely affected areas. In order to depict the air pollutants in urban centres, this research analyses two modelling tools: AERMOD and CALINE4. Both technologies provide distinct capabilities in the modelling of air quality from vehicular and other emissions. CALINE4, a Gaussian dispersion model, specifies pollutant dispersion from mobile sources along roadways. It boasts a user-friendly interface and road-specific modelling capabilities, factoring in traffic speed and vehicle emissions. However, it simplifies intricate flow patterns and relies on primary meteorological data. On the other hand, AERMOD is a versatile model suitable for various emission sources, including both mobile and stationary sources. It excels at capturing diverse atmospheric processes but demands precise meteorological, terrain, and emission data. AERMOD is often preferred for regulatory compliance assessments, although it entails a steeper learning curve and higher computational requirements. The choice between CALINE4 and AERMOD hinges on study needs, data availability, and desired modelling precision. This review offers an overview to assist researchers in making informed model selections for assessing vehicle-related pollution, critical aspects of urban sustainability, and air quality management.

Quantifying CO2 emissions of power plants with Aerosols and Carbon Dioxide Lidar onboard DQ-1

Transparent, accurate, and affordable monitoring of CO2 emissions from power plants is crucial for realization of transition away from fossil fuels. Satellite remote sensing has emerged as a recent research focus, enabling emission estimation without the cooperation of emitters. In 2022, China launched the world's first CO2 monitoring satellite based on laser detection, DQ-1. Equipped with ACDL, it enables accurate detection even under nighttime and high aerosol load conditions, serving as a significant complement to existing optical satellite systems. However, the feasibility of laser detection with narrow swath width for monitoring power plant emissions remains uncertain. This work presents, for the first time, the practical capability of using spaceborne LIDAR to estimate CO2 emissions from power plants. We employ the Gaussian dispersion model as the transport model and quantify emission rates using genetic and trust-region algorithms. Through satellite observations from June to December 2022, we conducted emission estimation experiments for 15 globally distributed power plants. Results indicate that using ACDL's XCO2 yields approximately unbiased estimates (∼2%) of power plant emissions overall, despite a single-overpass uncertainty of 18%. This performance is comparable to OCO-2/3, suggesting consistent benchmarks for different satellite mechanisms in reporting power plant emissions. Consequently, combining their results enhances monitoring of global power plants. We advocate the joint use of diverse CO2 monitoring satellites employing various detection mechanisms to gather a more comprehensive XCO2 dataset, aiming to achieve routine satellite-based verification of power plant emissions.

The screening evaluation of environmental odors: a new dispersion modelling-based tool.

Odor pollution is the biggest source of complaints from citizens concerning environmental issues after noise. Often, the need for corrective actions is evaluated through simulations performed with atmospheric dispersion models. To save resources, air pollution control institutions perform a first-level odor impact assessment, for screening purposes. This is often based on Gaussian dispersion models (GDM), which does not need high computational power. However, their outputs tend to be conservative regarding the analyzed situation, rather than representative of the real in-site conditions. Hence, regulations and guidelines adopted at an institutional level for authorization/control purposes are based on Lagrangian particle dispersion models (LPDM). These models grant a more accurate simulation of the pollutants' dispersion even if they are more demanding regarding both technical skills and computing power. The present study aims to increase the accuracy of screening odor impact assessment by identifying the correlation function of the outputs derived from the two simulation models. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The obtained correlation functions allow the practitioner to have a more accurate first-level odor impact assessment, to save time for training, and to reduce the site-specific meteorological data before proceeding with the simulation. The identified functions could allow institutions to estimate the results that would have been forecasted with the application of the more complex LPDM, applying, however, the much simpler GDM. This solution grants an accurate tool which can be used to address citizens' concerns while saving workforce and technical resources. Limitations are related to the specificity of the method regarding type sources, orography, and meteorological conditions. Comparison with other screening tools is also presented and discussed.

Using metal oxide gas sensors to estimate the emission rates and locations of methane leaks in an industrial site: assessment with controlled methane releases

Abstract. Fugitive methane (CH4) emissions occur in the whole chain of oil and gas production, including from extraction, transportation, storage, and distribution. Such emissions are usually detected and quantified by conducting surveys as close as possible to the source location. However, these surveys are labour-intensive, are costly, and fail to not provide continuous emissions monitoring. The deployment of permanent sensor networks in the vicinity of industrial CH4 emitting facilities would overcome the limitations of surveys by providing accurate emission estimates, thanks to continuous sampling of emission plumes. Yet high-precision instruments are too costly to deploy in such networks. Low-cost sensors using a metal oxide semiconductor (MOS) are presented as a cheap alternative for such deployments due to their compact dimensions and to their sensitivity to CH4. In this study, we demonstrate the ability of two types of MOS sensors (TGS 2611-C00 and TGS 2611-E00) manufactured by Figaro® to reconstruct a CH4 signal, as measured by a high-precision reference gas analyser, during a 7 d controlled release campaign conducted by TotalEnergies® in autumn 2019 near Pau, France. We propose a baseline voltage correction linked to atmospheric CH4 background variations per instrument based on an iterative comparison of neighbouring observations, i.e. data points. Two CH4 mole fraction reconstruction models were compared: multilayer perceptron (MLP) and second-degree polynomial. Emission estimates were then computed using an inversion approach based on the adjoint of a Gaussian dispersion model. Despite obtaining emission estimates comparable with those obtained using high-precision instruments (average emission rate error of 25 % and average location error of 9.5 m), the application of these emission estimates is limited to adequate environmental conditions. Emission estimates are also influenced by model errors in the inversion process.

Lévy flights, optimal foraging strategies, and foragers with a finite lifespan

In some recent work, we have introduced some efficiency functionals to account for optimal dispersal strategies of predators in search of food. The optimization parameter in this framework is given by the L\'evy exponent of the dispersal of the predators. In this paper, we apply our model to the case of foragers with finite lifetime (i.e., foragers which need to eat a certain amount of food in a given time, otherwise they die). Specifically, we consider the case in which the initial distribution of the forager coincides with a stationary distribution of the targets and we determine the optimal L\'evy exponent for the associated efficiency functional. Namely, we show that if the Fourier transform of the prey distribution is supported in a sufficiently small ball, then the optimizer is given by a Gaussian dispersal, and if instead the Fourier transform of the prey distribution is supported in the complement of a suitable ball, then the ballistic diffusion provides an optimizer (precise conditions for the uniqueness of these optimizers are also given).

Detection and long-term quantification of methane emissions from an active landfill

Abstract. Landfills are a significant source of fugitive methane (CH4) emissions, which should be precisely and regularly monitored to reduce and mitigate net greenhouse gas emissions. In this study, we present long-term, in situ, near-surface, mobile atmospheric CH4 mole fraction measurements (complemented by meteorological measurements from a fixed station) from 21 campaigns that cover approximately 4 years from September 2016 to December 2020. These campaigns were utilized to regularly quantify the total CH4 emissions from an active landfill in France. We use a simple atmospheric inversion approach based on a Gaussian plume dispersion model to derive CH4 emissions. Together with the measurements near the soil surface, mainly dedicated to the identification of sources within the landfill, measurements of CH4 made on the landfill perimeter (near-field) helped us to identify the main emission areas and to provide some qualitative insights about the rank of their contributions to total emissions from the landfill. The two main area sources correspond, respectively, to a covered waste sector with infrastructure with sporadic leakages (such as wells, tanks, pipes, etc.) and to the last active sector receiving waste during most of the measurement campaigns. However, we hardly managed to extract a signal representative of the overall landfill emissions from the near-field measurements, which limited our ability to derive robust estimates of the emissions when assimilating them in the atmospheric inversions. The analysis shows that the inversions based on the measurements from a remote road further away from the landfill (far-field) yielded reliable estimates of the total emissions but provided less information on the spatial variability of emissions within the landfill. This demonstrates the complementarity between the near- and far-field measurements. According to these inversions, the total CH4 emissions have a large temporal variability and range from ∼ 0.4 to ∼ 7 t CH4 d−1, with an average value of ∼ 2.1 t CH4 d−1. We find a weak negative correlation between these estimates of the CH4 emissions and atmospheric pressure for the active landfill. However, this weak emission–pressure relationship is based on a relatively small sample of reliable emission estimates with large sampling gaps. More frequent robust estimations are required to better understand this relationship for an active landfill.

The contribution of residential wood combustion to the PM2.5 concentrations in the Helsinki metropolitan area

Abstract. This article has investigated the contribution of residential wood combustion (RWC) to the fine particulate matter (PM2.5) concentrations in the Helsinki metropolitan area (HMA) for 6 years, from 2009 to 2014. We have used the PM2.5 concentrations measured at eight air quality measurement stations. The dispersion of emissions on an urban scale was evaluated with multiple-source Gaussian dispersion models, UDM-FMI (urban dispersion model of the Finnish Meteorological Institute) and CAR-FMI (Contaminants in the Air from a Road model of the Finnish Meteorological Institute), and on a regional scale using the chemical transport model SILAM (System for Integrated modeLling of Atmospheric coMposition). The overall agreement of the predicted concentrations with measurements of PM2.5 was good or fairly good for all stations and years; e.g. at the permanent residential station the daily average values of the index of agreement ranged from 0.69 to 0.81, and the fractional bias values ranged from −0.08 to 0.11, for the considered 6 years. Both the measured and predicted daily averaged concentrations showed increasing trends towards the lower-temperature values. The highest predicted annual averaged concentrations in the region occurred in the vicinity of major roads and streets and in the suburban residential areas to the northwest, north, and northeast of the city centre. The average concentrations of PM2.5 attributed to RWC in winter were up to 10- or 15-fold, compared to the corresponding concentrations in summer. During the considered 6-yearly period, the spatially highest predicted fractions of RWC of the annual PM2.5 concentrations ranged from 12 % to 14 %. In winter, the corresponding contributions ranged from 16 % to 21 %. The RWC contribution was higher than the corresponding urban vehicular traffic contribution at all the residential stations during all years. The study has highlighted new research needs for the future, in particular (i) the modelling of the RWC emissions that would be explicitly based on the actual ambient temperatures and (ii) the modelling of the impacts of the most important holiday periods on the emissions from RWC.

The Impact of Urban Trees on Air Pollution in Kirkuk City: A Gaussian Dispersion Model Approach

The Impact of Urban Trees on Air Pollution in Kirkuk City: A Gaussian Dispersion Model Approach

NOAA’s Air Resources Laboratory—75 Years of Research Linking Earth and Sky: A Historical Perspective

Abstract For over 75 years, the National Oceanic and Atmospheric Administration’s Air Resources Laboratory (NOAA ARL) has been at the forefront of federal meteorological and climate research. As the Special Projects Section (SPS) of the U.S. Weather Bureau (USWB), the laboratory pioneered the development of atmospheric trajectory modeling, initially used in studies related to nuclear weapons following World War II. Model development was guided by observations following weapons tests, assisted by later experiments using a wide variety of atmospheric tracers. Today’s familiar Gaussian plume dispersion model, previously in nascent form, was developed and promoted with ARL research, as was the much later and widely used HYSPLIT model. Much of ARL’s early research was focused on the challenges presented by the complex terrain surrounding nuclear installations, often addressed with high-spatial-resolution meteorological measurements, atmospheric tracers, and site-specific models. ARL has since extended boundary layer research to increasingly complex landscapes, such as forests, agricultural lands, and urban areas, and has expanded its research scope to air quality, weather, and climate applications based on the knowledge and experience developed throughout its long history. Examples of these research endeavors include the establishment of the U.S. Climate Reference Network, fundamental contributions to the development of the National Air Quality Forecast Capability, and foundational participation in the National Atmospheric Deposition Program. ARL looks forward to continuing to refine scientific understanding from field experiments, including coupling ground-based experimentation with modeling, and sustained observations, in order to facilitate the transfer of knowledge into practical applications of societal relevance.

Modelling of Carbon Dioxide Venting in Carbon Capture and Storage Studies: Design and Safety Implications

The reduction of the impact of carbon dioxide on the environment is a key driver of the energy transition, which will progressively reduce the use of fossil fuels and will promote the development of viable technologies. This process will be based on renewable energy sources to reduce, to any possible extent, the production and the release of carbon dioxide into the atmosphere. CCS (Carbon Capture and Storage) projects are more and more an ineludible stage of this process. In the last decade, a significant segment of chemical engineering research has been devoted to the management of carbon dioxide release scenarios, which can result both from an accidental loss of containment or from an emergency venting of carbon dioxide streams. This gas is asphyxiating and toxic, depending on concentration levels. Consequently, acquiring familiarity with its dispersion modelling is a key task for process and process safety engineers. On the other hand, carbon dioxide presents a very peculiar behaviour, as it is a dense gas and, below the triple point, its release can produce solid formation. The article presents a relatively simple multi-stage validated model, covering the release scenario from the source, as a heavy gas, up to the neutral Gaussian dispersion, considering also the very low temperatures possibly resulting from the JT (Joule Thomson) effect. Two case studies, carried out through the Megaris Platform, illustrate the application of the methodology. It can be useful in the design phase and in the QRA studies, to properly size and locate the venting stack and to minimize any health upsets for the operators

Analisis Konsentrasi dan Dispersi Gas Carbon Monoksida (CO) pada Kegiatan Industri Pengasapan Ikan dengan Pendekatan Model Gaussian Multiple Source

This research was conducted at the fish smoking center in Desa Koto Mesjid, Kecamatan XIII Koto Kampar. The smoking activity uses firewood as fuel to produce smoke. The results of worker interviews from 45 respondents, as many as 18 people with sore eyes and 16 people with dizziness. It is necessary to conduct research to determine the concentration of CO gas and its distribution as well as the impact on workers and the environment. The study was conducted on 8 groups of fumigation businesses to calculate CO gas emissions with the fuel approach used, then analyze the dispersion and impact as well as measure the concentration of CO gas and compare it with quality standards. Calculation of pollutant concentrations using the Gaussian Multiple Source model, sampling of air quality in the field, testing the validity with the Wilmott's Index, and comparing the results of direct measurements with the PP No. Quality standard. No. 22 of 2021. Observations of the highest CO gas emission load were 8.511.314 g/sec, and the highest amount of fuel wood consumption was 17.696 kg/day in the Rezky Salai business group. The Gaussian dispersion approach obtained the results that the concentration of CO gas dispersed at the office, musholla, and canteen receptors was 1.287 μg/m3, 3.938 μg/m3, and 8.302 μg/m3, while the maximum concentration of actual CO gas measurements was 4.908 μg/m3, 9.077 μg/m3 and 9.204 μg/m3. The actual measurement results still meet the quality standards, with values ​​below 10.000 g/m3. The validation of the Gaussian modeling results with the Wilmott's Index resulted in the d value of the office = -0,140, the d value of the Musholla = -0,149, and the d value of the Canteen = -1,278. The value of d in Wilmott's Index indicates that the Gaussian model cannot be applied to the CO gas dispersion in this fish smoking center.

Innovative Model-Free Onboard Diagnostics for Diesel Particulate Filter

<div>Recent legislations require very low soot emissions downstream of the particulate filter in diesel vehicles. It will be difficult to meet the new more stringent OBD requirements with standard diagnostic methods based on differential sensors. The use of inexpensive and reliable soot sensors has become the focus of several academic and industrial works over the past decade. In this context, several diagnostic strategies have been developed to detect DPF malfunction based on the soot sensor loading time. This work proposes an advanced online diagnostic method based on soot sensor signal projection. The proposed method is model-free and exclusively uses soot sensor signal without the need for subsystem models or to estimate engine-out soot emissions. It provides a comprehensive and efficient filter monitoring scheme with light calibration efforts. The proposed diagnostic algorithm has been tested on an experimentally validated simulation platform. 2D signatures are generated from soot sensor signal for nominal and faulty configurations. Gaussian dispersions on soot estimator (30%) and sensor model (15%) have been considered. Based on a statistical analysis, a relevant threshold is defined satisfying a compromise between non-detection and false alarm rates. The selected threshold is then used for online DPF diagnostic using NEDC cycle. The obtained results are promising and clearly show the performance of the proposed method in terms of non-detection and false alarm rates. The resulting diagnostic scheme can be easily integrated in the ECU for onboard DPF monitoring.</div>

The Lévy flight foraging hypothesis: comparison between stationary distributions and anomalous diffusion *

We consider a stationary prey in a given region of space and we aim at detecting optimal foraging strategies. On the one hand, when the prey is uniformly distributed, the best possible strategy for the forager is to be stationary and uniformly distributed in the same region. On the other hand, in several biological settings, foragers cannot be completely stationary, therefore we investigate the best seeking strategy for Lévy foragers in terms of the corresponding Lévy exponent. In this case, we show that the best strategy depends on the region size in which the prey is located: large regions exhibit optimal seeking strategies close to Gaussian random walks, while small regions favor Lévy foragers with small fractional exponent. We also consider optimal strategies in view of the Fourier transform of the distribution of a stationary prey. When this distribution is supported in a suitable volume, then the foraging efficiency functional is monotone increasing with respect to the Lévy exponent and accordingly the optimal strategy is given by the Gaussian dispersal. If instead the Fourier transform of the distribution of a stationary prey is supported in the complement of a suitable volume, then the foraging efficiency functional is monotone decreasing with respect to the Lévy exponent and therefore the optimal strategy is given by a null fractional exponent (which in turn corresponds, from a biological standpoint, to a strategy of ‘ambush’ type). We will devote a rigorous quantitative analysis also to emphasize some specific differences between the one-dimensional and the higher-dimensional cases.

Atmospheric emissions estimation of polycyclic aromatic compounds from an oil sands tailings pond using passive air samplers

A mapping study targeting emissions of polycyclic aromatic compounds (PACs) from an oil sands tailings pond was undertaken in the Athabasca Oil sands Region (AOSR). Ten passive air samplers comprising polyurethane foam (PUF) disks were deployed around the perimeter of Suncor Tailings Pond 2/3 for a five-week period to generate time-integrated concentrations in air for PACs, which included ∑unsubstituted polycyclic aromatic hydrocarbons (PAHs), ∑alkylated PAHs (alk-PAHs), and ∑dibenzothiophenes (DBTs) (both unsubstituted and alkylated). Concentrations in air ranged from 13 to 70, 220–970, and 30–210 ng/m3, respectively, and were elevated in samplers downwind of the tailings pond. PAC emissions to air from the pond were estimated using only the air-side concentration information by applying a simplified Gaussian dispersion model and found to be 896 μg/m2/day. ∑alk-PAHs and ∑DBTs had the highest contribution to the total PAC fluxes (79% and 16%, respectively). This flux estimate for PACs is equivalent to 460 kg on an annual basis and 35 000 kg/year when scaled to represent all tailings ponds in the region. The results generally agree with fluxes estimated from coupled high volume air sampling data and tailings pond water concentrations from the same field study but which are complicated due to uncertainties associated with the use of pure water Henry's Law values for tailings pond water as well as the potential for surface oily films on the tailings ponds to impact water-air exchange of PACs. Overall, these findings support the use of relatively simple and electricity-free PUF disk samplers for mapping and estimating emissions from area sources such as tailings ponds, using only air-side concentration information.

Research on compound pollution characteristics and health risk evaluation of particulate matter and heavy metals in waste glass recycling process

Owing to the large amount of waste glass generated, the waste glass recycling base is an indispensable municipal supporting facility of a sustainable city. However, waste glass recycling is a complex process involving stages such as multiple-stage crushing and material sorting. Consequently, waste glass recycling base has a considerable impact on the surrounding environment, such as health risk of particulate matter on workers. In this study, we aimed to perform a comprehensive investigation and analysis of compound pollution characteristics and health risk evaluation of particulate matter and heavy metals generated from waste glass recycling process. Soil, particulate fallout, and glass samples were collected from inside and outside a recycling plant in eastern China. Our findings showed that the waste glass treatment process produces a large amount of air particulate matter, and the PM2.5 and PM10 concentrations can reach 3725 and 4055 μg/m3, respectively, in the production workshop during working hours. Meanwhile, the monitoring results show that the concentration of heavy metals on fine particles is higher compared to coarse particles. The high Zn and Pb concentrations detected in the soil and dustfall were proved to be derived from the glass raw materials. However, health risk assessment and particle deposition modeling in the human respiratory system revealed that heavy metals from the air particulate matter have no significant carcinogenicity or non-carcinogenic risk. The Gaussian dispersion model showed that the impact of particulate matter on the surrounding environment and health of the surrounding residents is minimal. Furthermore, transportation is the major emission link according to the particulate emission calculation, indicating that it is imperative to upgrade and reform the existing processes of waste glass recycling. Taken together, this study provides a scientific basis for the green development of waste glass recycling process and further environmental information regarding waste glass recycling plants.