Gaussian Plume Research Articles

Risk Propagation Mechanism and Prediction Model for the Highway Merging Area

The merging area is one of the most accident-prone areas on highways. After an accident occurs, the risk will propagate along the main road over a certain range and time. Therefore, the study of the propagation mechanism of accident risk will help to quantify the driving risk in this region. An effective risk prediction model is important for improving traffic control measures in this specific area. In this study, simulation experiments were conducted in SUMO (Simulation of Urban Mobility) to obtain the accident and risk propagation data in merging areas. Firstly, the Gaussian plume model was optimized for the merging area situation to determine and divide the impact range of the accidents. Then, different accident scenarios in the merging area and downstream were simulated with different input flow rates to study the time and speed of risk propagation in the three-level affected areas. Finally, LSTM (long short-term memory) and RNN (recurrent neural network) models were built to predict the accident risk in the merging area. The results showed that the LSTM model had higher accuracy. This study provides an innovative insight into the propagation process of merging area accidents. It is of benefit to the development of post-accident control measures.

Leak Detection and Localization in Multi-Grid Space Using Improved Gaussian Plume Model.

Leak detection and localization of liquid or gas is of great significance to avoid potential danger and reduce the waste of resources. Leak detection and localization methods are varied and uniquely suited to specific application scenarios. The existing methods are primarily applied to conventional pressurized pipelines and open areas, and there are few methods suitable for multi-grid spaces. In this paper, a gas diffusion model applied to multi-grid space is constructed, and a method for leak detection and localization using the concentration gradient of characteristic gas is proposed according to the prediction behavior. The Gaussian plume model is selected due to its advantages of simplicity and the interpretation of gas diffusion behavior is closer to reality; the expression of the improved model is also obtained. To verify the correctness of the model and the applicability of the localization method, taking the coolant leakage in the circuit system as an example, three experiments with different source strengths were repeated. The fitting correlation coefficients between the gas concentration data of the three experiments and the model are 0.995, 0.997 and 0.997, respectively. The experimental results show that the model has a strong correlation with the real plume behavior, and it is reasonable to use the gas concentration gradient for the localization of the leak source. This study provides a reference for future research on the leak detection and localization of gas- or liquid-containing volatile substances in a complex multi-grid space.

Study Gaussian plume model and the Gradient Transport (K) of the advection-diffusion equation and its applications

In this paper, one dimension time dependent and the steady state three dimensions of advection-diffusion equation (ADE) have been solved analytically to estimate the concentration in the atmospheric boundary layer (ABL) taking into account the assumption that the ABL height (h) is divided into sub-layers and the downwind distance is also divided into intervals within each rectangular area the ADE is estimated by using the Laplace transform method assuming that the mean values of wind speed and eddy diffusivity. The proposed model, Gaussian plume model and previous work (Essa et al.2019) was compared with the observed concentration of Iodine-135 which was measured at Egyptian Atomic Energy Authority, Nuclear Research Reactor, Inshas, Cairo Egypt. The statistical analysis shows that there is a good agreement between the proposed and experimental values of concentration.

Quantification of methane emissions in Hamburg using a network of FTIR spectrometers and an inverse modeling approach

Abstract. Methane (CH4) is a potent greenhouse gas, and anthropogenic CH4 emissions contribute significantly to global warming. In this study, the CH4 emissions of the second most populated city in Germany, Hamburg, were quantified with measurements from four solar-viewing Fourier transform infrared (FTIR) spectrometers, mobile in situ measurements, and an inversion framework. For source type attribution, an isotope ratio mass spectrometer was deployed in the city. The urban district hosts an extensive industrial and port area in the south as well as a large conglomerate of residential areas north of the Elbe River. For emission modeling, the TNO GHGco (Netherlands Organisation for Applied Scientific Research greenhouse gas and co-emitted species emission database) inventory was used as a prior for the inversion. In order to improve the inventory, two approaches were followed: (1) the addition of a large natural CH4 source, the Elbe River, which was previously not included in the inventory, and (2) mobile measurements were carried out to update the spatial distribution of emissions in the TNO GHGco gridded inventory and derive two updated versions of the inventory. The addition of the river emissions improved model performance, whereas the correction of the spatial distribution with mobile measurements did not have a significant effect on the total emission estimates for the campaign period. A comparison of the updated inventories with emission estimates from a Gaussian plume model (GPM) showed that the updated versions of the inventory match the GPM emissions estimates well in several cases, revealing the potential of mobile measurements to update the spatial distribution of emission inventories. The mobile measurement survey also revealed a large and, at the time of the study, unknown point source of thermogenic origin with a magnitude of 7.9 ± 5.3 kg h−1 located in a refinery. The isotopic measurements show strong indications that there is a large biogenic CH4 source in Hamburg that produced repeated enhancements of over 1 ppm which correlated with the rising tide of the river estuary. The CH4 emissions (anthropogenic and natural) of the city of Hamburg were quantified as 1600 ± 920 kg h−1, 900 ± 510 kg h−1 of which is of anthropogenic origin. This study reveals that mobile street-level measurements may miss the majority of total methane emissions, potentially due to sources located within buildings, including stoves and boilers operating on natural gas. Similarly, the CH4 enhancements recorded during the mobile survey from large-area sources, such as the Alster lakes, were too small to generate GPM emission estimates with confidence, but they could nevertheless influence the emission estimates based on total column measurements.

Monitoring and quantifying CO2emissions of isolated power plants from space

Abstract. Top-down CO2 emission estimates based on satellite observations are of great importance for independently verifying the accuracy of reported emissions and emission inventories. Difficulties in verifying these satellite-derived emissions arise from the fact that emission inventories often provide annual mean emissions, while estimates from satellites are available only for a limited number of overpasses. Previous studies have derived CO2 emissions for power plants from the Orbiting Carbon Observatory-2 and 3 (OCO-2 and OCO-3) satellite observations of their exhaust plumes, but the accuracy and the factors affecting these emissions are uncertain. Here we advance monitoring and quantifying point source carbon emissions by focusing on how to improve the accuracy of carbon emission using different wind data estimates. We have selected only isolated power plants for this study, to avoid complications linked to multiple sources in close proximity. We first compared the Gaussian plume model and cross-sectional flux methods for estimating CO2 emission of power plants. Then we examined the sensitivity of the emission estimates to possible choices for the wind field. For verification we have used power plant emissions that are reported on an hourly basis by the Environmental Protection Agency (EPA) in the US. By using the OCO-2 and OCO-3 observations over the past 4 years we identified emission signals of isolated power plants and arrived at a total of 50 collocated cases involving 22 power plants. We correct for the time difference between the moment of the emission and the satellite observation. We found the wind field halfway the height of the planetary boundary layer (PBL) yielded the best results. We also found that the instantaneous satellite estimated emissions of these 50 cases, and reported emissions display a weak correlation (R2=0.12). The correlation improves with averaging over multiple observations of the 22 power plants (R2=0.40). The method was subsequently applied to 106 power plant cases worldwide and yielded a total emission of 1522 ± 501 Mt CO2 yr−1, estimated to be about 17 % of the power sector emissions of our selected countries. The improved correlation highlights the potential for future planned satellite missions with a greatly improved coverage to monitor a significant fraction of global power plant emissions.

Improved Gaussian plume model for atmospheric dispersion considering buoyancy and gravitational deposition: The case of multi-form tritium

Various types of radionuclides have different atmospheric dispersion characteristics, such as buoyancy and gravitational deposition phenomenon of light gas and heavy particles, respectively. Gaussian plume model was widely used to describe atmospheric dispersion behaviors of radioactive effluents, particularly for the purpose of engineering environmental impact assessment or nuclear emergency support. Nonetheless, buoyancy and gravitational deposition were rarely reported in previous work for tritium in particular, which might cause a deviation in evaluating near-surface concentration distribution and radiation dose to the public. Based on the multi-form tritium case, we made a quantitative description for the buoyancy and gravitational deposition phenomenon and discussed the feasibility of developing an improved Gaussian plume model to predict near-surface concentration distribution. Firstly, tritium concentration distribution near to the surface was predicted by using computational fluid dynamics method (CFD) and standard Gaussian plume model to reach consistency without consideration of buoyancy and gravitational deposition effects. Secondly, effects of buoyancy and gravitational deposition were identified by species transport model for gaseous tritium and discrete phase model for droplet tritium with integrating the buoyancy force caused by density variation of gaseous tritium and gravitational force of droplet tritium with enough size. Thirdly, buoyancy and gravitational deposition correction factors were obtained to modify the standard Gaussian plume model. Lastly, predictive results by improved Gaussian plume model were compared with CFD method. It was proved the improved correction method enables higher accuracy in predicting the atmospheric concentration distribution of gaseous pollutants with density variation or particles with gravitational deposition properties.

Study the effect of different shapes of plume rise on Gaussian plume models and its maximum in unstable conditions

Exposure to radioactive pollutants such as Iodine-135 I135 seriously threatens public health and environmental balance. Monitoring and managing these pollutants require expensive economic equipment that is not suitable for low-income countries such as Egypt. Therefore, trying to derive a mathematical model that estimates the concentrations of these radioactive pollutants with high accuracy and a low relative error coefficient compared to the actually measured values ​​is very important. Therefore a mathematical Gaussian model was received to estimate the concentrations of I135 emitted from the research nuclear reactor in the Inshas region in Egypt using different shapes of plume rise in unstable conditions. A comparison between the derived model and its maximum values with observed concentrations data measuring on Egyptian Atomic Energy Authority for I135 in unstable conditions has been found. The derived model may be applied to estimate and predict the emissions of any radioactive pollutant for any similar area and similar type of used reactor, which provides high-precision technology with zero economic cost. The statistical technique shows that the entire Gaussian model and its maximum inside a factor of two with observed concentration data achieved 98%. In addition, the statistics show that all the Gaussian plume models and their maximum have a correlation coefficient of about 0.95%. Also, the normalized mean square error. And the fraction bias. are near-zero values in all Gaussian models and their maximum.

Validation of a Contrail Life-Cycle Model in Central Europe

In an industry beset by economic and environmental crises, air transport, the safest and most efficient long-haul mode of transport, is confronted daily with multi-criteria challenges to improve its environmental performance. The formation of contrails through the emission of water vapor and condensation nuclei in what are actually dry and clean atmospheric layers represents one of the most unpredictable, or measurable, environmental impacts of air traffic. Following the bottom-up principle to evaluate individual contrails in order to derive recommendations for trajectory optimization, not only the calculation of the radiative forcing of the contrails but also the modeling of their life cycle is burdened with uncertainties. In former studies for modeling the microphysical life cycle of contrails based on a 3-D Gaussian plume model, the atmospheric conditions, specifically the turbulence, were often unknown and had to be considered as a free input variable. In this study, an innovative photographic method for identifying and tracking contrails in Central Europe, connected with database access to Automatic Dependent Surveillance—Broadcast (ADS-B) data (i.e., aircraft type, speed, altitude, track, etc.), and a combination of measured and modeled weather data are used to validate the contrail life-cycle model (i.e., the assumed Gaussian plume behavior). We found that it is challenging to model the position of ice-supersaturated layers with global forecast models, but they have the most significant impact on the contrail lifetime. On average, the contrail’s lifespan could be modeled with an error margin of 10%. Sometimes, we slightly underestimated the lifetime. With the validated and plausible contrail life-cycle model, we can apply the climate effectiveness of individual contrails with higher certainty in trajectory optimization and compare it, for example, with economic aspects such as delay costs or fuel costs.

Solving partial differential equation for atmospheric dispersion of radioactive material using physics-informed neural network

The governing equations of atmospheric dispersion most often taking the form of a second-order partial differential equation (PDE). Currently, typical computational codes for predicting atmospheric dispersion use the Gaussian plume model that is an analytic solution. A Gaussian model is simple and enables rapid simulations, but it can be difficult to apply to situations with complex model parameters. Recently, a method of solving PDEs using artificial neural networks called physics-informed neural network (PINN) has been proposed. The PINN assumes the latent (hidden) solution of a PDE as an arbitrary neural network model and approximates the solution by optimizing the model. Unlike a Gaussian model, the PINN is intuitive in that it does not require special assumptions and uses the original equation without modifications. In this paper, we describe an approach to atmospheric dispersion modeling using the PINN and show its applicability through simple case studies. The results are compared with analytic and fundamental numerical methods to assess the accuracy and other features. The proposed PINN approximates the solution with reasonable accuracy. Considering that its procedure is divided into training and prediction steps, the PINN also offers the advantage of rapid simulations once the training is over.

A quasi-one-dimensional model for ion-aerosol interactions and aerosol charge state downwind of corona-producing alternating current (AC) HVPL under stable atmospheric conditions

Corona ions produced by high voltage power lines (HVPL) can alter the local atmospheric electrical environment downwind, potentially increasing electrostatic charge on airborne particulates via ion-aerosol attachment. However, previous epidemiological assessments attempting to assess this ‘corona ion hypothesis’ have used proxies e.g. ion concentration or distance from HVPL, rather than aerosol charge state directly, due to difficulties in modeling this quantity. We present a quasi-1D model incorporating both Gaussian plume dynamics and ion-aerosol and ion-ion interaction microphysics which could be applied to future studies of charged aerosol near HVPL. The response of the model to changes in a range of input parameters is characterized, and validation is attempted by means of comparison with previous work where ion- and aerosol concentrations and properties (including electrical mobility and electric charge states) upwind and downwind of HVPL are measured.

Constraining industrial ammonia emissions using hyperspectral infrared imaging

Atmospheric emissions of reactive nitrogen in the form of nitrogen dioxide (NO2) and ammonia (NH3) worsen air quality and upon deposition, dramatically affect the environment. Recent infrared satellite measurements have revealed that NH3 emitted by industries are an important and underestimated emission source. Yet, to assess these emissions, current satellite sounders are severely limited by their spatial resolution. In this paper, we analyse measurement data recorded in a series of imaging surveys that were conducted over industries in the Greater Berlin area (Germany). On board the aircraft were the Telops Hyper-Cam LW, targeting NH3 measurements in the longwave infrared at a resolution of 4 m and the SWING+ spectrometer targeting NO2 measurements in the UV–Vis at a resolution of 180 m.Two flights were carried out over German’s largest production facility of synthetic NH3, urea and other fertilizers. In both cases, a large NH3 plume was observed originating from the factory. Using a Gaussian plume model to take into account plume rise and dispersion, coupled with well-established radiative transfer and inverse methods, we retrieve vertical column densities. From these, we calculate NH3 emission fluxes using the integrated mass enhancement and cross-sectional flux methods, yielding consistent emissions of the order of 2200 t yr−1 for both flights, assuming constant fluxes across the year. These estimates are about five times larger than those reported in the European Pollutant Release and Transfer Register (E-PRTR) for this plant. In the second campaign, a co-emitted NO2 plume was measured, likely related to the production of nitric acid at the plant.A third flight was carried out over an area comprising the cities of Staßfurt and Bernburg. Several small NH3 plumes were seen, one over a production facility of mineral wool insulation, one over a sugar factory and two over the soda ash plants in Staßfurt and Bernburg. A fifth and much larger plume was seen to originate from the sedimentation basins associated with the soda ash plant in Staßfurt, indicating rapid volatilization of ammonium rich effluents. We use the different measurement campaigns to simulate measurements of Nitrosat, a potential future satellite sounder dedicated to the sounding of reactive nitrogen at a resolution of 500 m. We demonstrate that such measurements would allow accurately constraining emissions in a single overpass, overcoming a number of important drawbacks of current satellite sounders.

STUDI POLA DISPERSI EMISI GAS SO2 DARI CEROBONG KILANG PT PERTAMINA (PERSERO) RU V BALIKPAPAN

Modeling of exhaust emissions (SO2) from refinery chimneys was carried out using the Gaussian Plume model through Screen3 software. The results showed that there was an influence of wind speed and atmospheric stability on the variation in modeling results. The higher the wind speed and the more stable an atmosphere, the lower the resulting concentration value. Modeling on the worst case scenario, produces the largest possible concentration value that might occur somewhere. The distribution map of the model results is displayed following the dominant wind direction, namely to the South and North of the refinery. The difference in the magnitude of the distribution of concentration values is depicted in the color difference. The more stable the conditions of atmospheric stability, the farther the high concentration will be from the refinery. The overall value of the model results shows a concentration value that is below the quality standard value when based on PP No. 41 of 1999. Mitigation of risk management includes regular training by industry and the government regarding air emissions and ambient air at refineries; through policies to eliminate the risk of industrial accidents.

Air Quality Impact Estimation Due to Uncontrolled Emissions from Capuava Petrochemical Complex in the Metropolitan Area of São Paulo (MASP), Brazil

In the second quarter of 2021, the companies at the Capuava Petrochemical Complex (CPC, Santo André, Brazil) carried out a 50-day scheduled shutdown for the maintenance and installation of new industrial equipment. This process resulted in severe uncontrolled emissions of particulate matter (PM) and volatile organic compounds (VOCs) in a densely populated residential area (~3400 inhabitants/km2). VOCs can be emitted directly into the atmosphere in urban areas by vehicle exhausts, fuel evaporation, solvent use, emissions of natural gas, and industrial processes. PM is emitted by vehicle exhausts, mainly those powered by diesel, industrial processes, and re-suspended soil dust, in addition to that produced in the atmosphere by photochemical reactions. Our statistical analyses compared the previous (2017–2020) and subsequent (2021–2022) periods from this episode (April–May 2021) from the official air quality monitoring network of the PM10, benzene, and toluene hourly data to improve the proportion of this period of uncontrolled emissions. Near-field simulations were also performed to evaluate the dispersion of pollutants of industrial origin, applying the Gaussian plume model AERMOD (steady-state plume model), estimating the concentrations of VOC and particulate matter (PM10) in which the population was exposed in the region surrounding the CPC. The results comparing the four previous years showed an increase in the mean concentrations by a factor of 2 for PM10, benzene, and toluene, reaching maximum values during the episode of 174 µg m−3 (PM10), 79.1 µg m−3 (benzene), and 58.7 µg m−3 (toluene). Meanwhile, these higher concentrations continued to be observed after the episode, but their variation cannot be fully explained yet. However, it is worth highlighting that this corresponds to the post-pandemic period and the 2022 data also correspond to the period from January to June, that is, they do not represent the annual variation. A linear correlation indicated that CPC could have been responsible for more than 60% of benzene measured at the Capuava Air Quality Station (AQS). However, the PM10 behavior was not fully explained by the model. AERMOD showed that the VOC plume had the potential to reach a large part of Mauá and Santo André municipalities, with the potential to affect the health of more than 1 million inhabitants.

Stationary and drone-assisted methane plume localization with dispersion spectroscopy

This work presents stationary and mobile retroreflector-based remote sensing techniques for methane leak localization and quantification using chirped laser dispersion spectroscopy equipped with a custom laser transceiver capable of continuous tracking of a flying drone and coupled with inverse atmospheric gas dispersion modeling. The techniques demonstrate the ability to localize leaks as low as 0.13 g CH4·s−1, which are up to 25 times smaller than those typically observed at natural gas facilities, as well as actively track a moving retroreflector mounted on a lightweight (∼250 g) drone to enable spatial plume reconstruction. This system exhibited a 2.3 ppm-m sensitivity over pathlengths of 40–150 m. Source localization to within ±7 m is demonstrated using a modified horizontal radial plume mapping technique with a stationary retroreflector grid. Meanwhile, the mobile system utilizing a drone-mounted retroreflector is able to localize a controlled release within ±1 m of its source location and estimate leak rates using inversion techniques assuming type B Gaussian plume stability class within ±30% error with respect to the actual low flow rate releases.

Road traffic-induced particle matter dispersion in a calm wind environment at the main roundabout in Douala, central Africa


 
 
 
 Introduction: Road traffic emissions are among the most significant sources of pollution in Douala, Cameroon's economic town, alongside industrial operations. The morning and the evening are two times of the day when traffic is heavier and the winds are also at their calmest. The majority of the non-exhaust Particulate Matters (PMs) produced by autos is re-suspended road contaminants. The purpose of this research is to estimate fine particle dispersion in conditions of calm winds.
 Materials and methods: In one of Douala's roundabouts, the Gaussian Plume model is employed to calculate the PM concentration under calm winds conditions. Different vehicle classes (HDV: Heavy Duty Vehicles, LDV: Light Duty Vehicles, PC: Passenger Cars) are used to figure out the amount of PMs they produce. Measurements of ambient fine particle concentrations are made with the OC-300 laser dust particle detector.
 Results: The results made it possible to compare actual measurements of PM2.5, PM10 (300±150 μg/m3 and 650±150 μg/m3, respectively) with simulated values (PM2.5, PM10: 310 μg/m3 and 631 μg/m3, respectively). The difference between in-situ and computed values can range from 10 to 132 μg/m3. From 6 to 10 AM, the population's exposure to PM pollution is more severe. It has also been demonstrated that there is a significant association between traffic flow and PM Concentration during the dry season (R2=0.921). With increased traffic flow intensity, particle concentration levels rise.
 Conclusion: The concentration threshold stays above the World Health Organization (WHO) limits regardless of the approach. Furthermore, this paper provides important information about Douala's pollution levels.
 
 
 

CO2 emissions retrieval from coal-fired power plants based on OCO-2/3 satellite observations and a Gaussian plume model

The CO2 emissions from coal-fired power plants are a major source of carbon emissions in China. However, the existing emission inventory does not accurately reflect current emissions because of a lag in the statistical data and the accuracy limit for emissions factor. This study aims to provide a method for estimating CO2 emissions from coal-fired power plants based on an improved Gaussian plume model and the Orbiting Carbon Observatory 2/3 satellite. We quantified CO2 emissions from six coal-fired power plants (>3000 MW) in China using 14 plumes from the OCO-2 (2014.09.06–2021.10.01) and OCO-3 (2019.08.06–2021.10.01) datasets. Four of the plumes are in good agreement with those from co-located the Tropospheric Monitoring Instrument measurements of co-emitted NO2. The estimated CO2 emissions ranged from 31.81 to 99.71 kt/d with correlation coefficient of 0.47–0.95. The uncertainty of individual plumes varied from 8% to 42% (1σ), with wind speed being the greatest source of uncertainty, and the average XCO2 enhancement of all power plants in this study is less than 1 ppm when the wind speed increases to about 10 m/s. After validation, our results show high agreement with the existing emission inventory. However, this study found that the Carbon Monitoring for Action may have underestimated the CO2 emissions from Tuoketuo due to the timing of the update, and carbon brief may have underestimated the emissions from Wujiaqu by ignoring the high emission factor of lignite. This study helps monitor and estimate important point source carbon emissions, which is not only a prerequisite for the power industry to carry out carbon reduction efforts, but also helps develop specific regional carbon reduction policies, which are important for reducing anthropogenic carbon emissions.

Decadal cyclical geological atmospheric emissions for a major marine seep field, offshore Coal Oil Point, Southern California

The greenhouse gas, methane, budget has significant uncertainty for many sources, including natural geological emissions. A major uncertainty of geological methane emissions, including onshore and offshore hydrocarbon seepage from subsurface hydrocarbon reservoirs is the gas emissions’ temporal variability. Current atmospheric methane budget models assume seepage is constant; nevertheless, available data and seepage conceptual models suggest gas seepage can vary considerably on timescales from second to century. The assumption of steady-seepage is used because long-term datasets to characterize these variabilities are lacking. A 30-year air quality dataset downwind of the Coal Oil Point seep field, offshore California found methane, CH4, concentrations downwind of the seep field increased from a 1995 minimum to a 2008 peak, decreasing exponentially afterward with a 10.2-year timescale (R2 = 0.91). Atmospheric emissions, EA, were derived by a time-resolved Gaussian plume inversion model of the concentration anomaly using observed winds and gridded sonar source location maps. EA increased from 27,200 to 161,000 m3 day−1 (corresponding to 6.5–38 Gg CH4 year−1 for 91% CH4 content) for 1995–2009, respectively, with 15% uncertainty, then decreased exponentially from 2009 to 2015 before rising above the trend. 2015 corresponded to the cessation of oil and gas production, which affects the western seep field. EA varied sinusoidally with a 26.3-year period (R2 = 0.89) that largely tracked the Pacific Decadal Oscillation (PDO), which is driven on these timescales by an 18.6-year earth-tidal cycle (27.9-year beat). A similar controlling factor may underlie both, specifically varying compressional stresses on migration pathways. This also suggests the seep atmospheric budget may exhibit multi-decadal trends.

EASIUR-HR: A Model To Evaluate Exposure Inequality Caused by Ground-Level Sources of Primary Fine Particulate Matter.

People of color disproportionately bear the health impacts of air pollution, making air quality a critical environmental justice issue. However, quantitative analysis of the disproportionate impacts of emissions is rarely done due to a lack of suitable models. Our work develops a high-resolution reduced-complexity model (EASIUR-HR) to evaluate the disproportionate impacts of ground-level primary PM2.5 emissions. Our approach combines a Gaussian plume model for near-source impacts of primary PM2.5 with a previously developed reduced-complexity model, EASIUR, to predict primary PM2.5 concentrations at a spatial resolution of 300 m across the contiguous United States. We find that low-resolution models underpredict important local spatial variation of air pollution exposure to primary PM2.5 emissions, potentially underestimating the contribution of these emissions to national inequality in PM2.5 exposure by more than a factor of 2. We apply EASIUR-HR to analyze the impacts of vehicle electrification on exposure disparities. While such a policy has small aggregate air quality impacts nationally, it reduces exposure disparity for race/ethnic minorities. Our high-resolution RCM for primary PM2.5 emissions (EASIUR-HR) is a new, publicly available tool to assess inequality in air pollution exposure across the United States.

Investigation on the concentration prediction model and personnel hazard range of LNG leakage from tankers in the tunnel

Liquid natural gas (LNG) leaks from tanker in the tunnel could result in more severe fire and explosion catastrophes than those that occur in unconfined space. Investigating the gas concentration distribution law and the accompanying personnel hazard range is crucial to provide guidelines for personnel escape and fire rescue in such accidents. In this paper, to provide a rapid technique for concentration estimation, the virtual source approach and the fluid settlement theory are applied to put forward a new model, which is based on the Gaussian plume model. Three batches of LNG diffusion tests were carried out in a 5 m long wind tunnel with a cross-section of 1 m × 1 m. The comparison results show that the prediction accuracy of the proposed model in the far region of the leakage source is better than that in the near region, with the error within ±16%, which demonstrated an accurate prediction performance of the proposed model. The numerical model of LNG leakage from tanker in the tunnel is established, and the influence of leakage direction, leakage aperture, and tunnel cross-section on concentration distribution law is discussed. The findings indicate that when the leakage direction changes from horizontal to vertical, the gas is more likely to accumulate in the tunnel due to the greater attenuation of the initial momentum of the gas, which causes the combustible range to increase by about 2 times. When the leakage aperture is 50 mm, the mass of combustible gas reaches the maximum value of 320.91 kg in 85.9 s, which indicates that the explosion intensity is not proportional to the leakage aperture. Excessive leakage aperture causes the concentration to surpass the Upper Flammable Limit, and the similar trends occur when the tunnel cross-section reduces. Based on the numerical results, the calculation model of the personnel hazard range suitable for various tunnel cross-sections and leakage apertures is derived by data fitting.

Development of a spectrum-based ship fuel sulfur content real-time evaluation method

Although maritime transport plays an essential role in the global economy, it inevitably imposes negative impacts on our living environment, especially for ships using fuel with high sulfur content. Nowadays, ship emission monitoring highly depends on manual inspection, which is time-consuming and labor-intensive. This study proposes a decision framework based on spectrum technology and the sulfur‑carbon ratio method to measure the ship fuel sulfur content. Specifically, after the Gaussian plume model optimization from four aspects, a multistep-based emission contribution evaluation method is developed to improve the evaluation accuracy. The proposed framework is validated by a suspected ship and a series of exempted ships from the Maritime Safety Administration in Nanjing, China. The validation results imply that the proposed framework has a certain enhancement in detection rate, evaluation accuracy and extensibility. It may provide an efficient and accurate supervision approach for the Maritime Safety Administration on ship fuel sulfur content measurement.