Impacts of Spatial Resolution and XCO2 Precision on Satellite Capability for CO2 Plumes Detection.

Greenhouse gas satellites can provide consistently global CO2 data which are important inputs for the top-down inverse estimation of CO2 emissions and their dynamic changes. By tracking greenhouse gas emissions, policymakers and businesses can identify areas where reductions are needed most and implement effective strategies to reduce their impact on the environment. Monitoring greenhouse gases provides valuable data for scientists studying climate change. The requirements for CO2 emissions monitoring and verification support capacity drive the payload design of future CO2 satellites. In this study, we quantitatively evaluate the performance of satellite in detecting CO2 plumes from power plants based on an improved Gaussian plume model, with focus on impacts of the satellite spatial resolution and the satellite-derived XCO2 precision under different meteorological conditions. The simulations of CO2 plumes indicate that the enhanced spatial resolution and XCO2 precision can significantly improve the detection capability of satellite, especially for small-sized power plants with emissions below 6 Mt CO2/yr. The satellite-detected maximum of XCO2 enhancement strongly varies with the wind condition. For a satellite with a XCO2 precision of 0.7 ppm and a spatial resolution of 2 km, it can recognize a power plant with emissions of 2.69 Mt CO2/yr at a wind speed of 2 m/s, while its emission needs be larger than 5.1 Mt CO2/yr if the power plant is expected to be detected at a wind speed of 4 m/s. Considering the uncertainties in the simulated wind field, the satellite-derived XCO2 measurements and the hypothesized CO2 emissions, their cumulative contribution to the overall accuracy of the satellite's ability to identify realistic enhancement in XCO2 are investigated in the future. The uncertainties of ΔXCO2 caused by the uncertainty in wind speed is more significant than those introduced from the uncertainty in wind direction. In the case of a power plant emitting 5.1 Mt CO2/yr, with the wind speed increasing from 0.5 m/s to 4 m/s, the simulated ΔXCO2 uncertainty associated with the wind field ranges from 3.75 ± 2.01 ppm to 0.46 ± 0.24 ppm and from 1.82 ± 0.95 ppm to 0.22 ± 0.11 ppm for 1 × 1 km2 and 2 × 2 km2 pixel size, respectively. Generally, even for a wind direction with a higher overall uncertainty, satellite still has a more effective capability for detecting CO2 emission on this wind direction, because there is more rapid growth for simulated maximal XCO2 enhancements than that for overall uncertainties. A designed spatial resolution of satellite better than 1 km and a XCO2 precision higher than 0.7 ppm are suggested, because the CO2 emission from small-sized power plants is much more likely be detected when the wind speed is below 3 m/s. Although spatial resolution and observed precision parameters are not sufficient to support the full design of future CO2 satellites, this study still can provide valuable insights for enhancing satellite monitoring of anthropogenic CO2 emissions.

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.

Simulations of dispersion through an irregular urban building array.

Following the release of a harmful substance within an urban environment, buildings and street canyons create complex flow regimes that affect dispersion and localized effluent concentrations. While some fast-response dispersion models can capture the effects caused by individual buildings, further research is required to refine urban characterizations such as plume channeling and spreading, and initial dispersion, especially within the presence of a nonhomogeneous array of structures. Field, laboratory, and modeling experiments that simulate urban or industrial releases are critical in advancing current dispersion models. This project leverages the configuration of buildings used in a full-scale, mock urban field study to examine the concentrations of a neutrally buoyant tracer in a series of wind tunnel and Embedded Large Eddy Simulation (ELES) experiments. The behavior, propagation, and magnitude of the plumes were examined and compared to identify microscale effects. After demonstrating excellent quantitative and qualitative comparisons between the wind tunnel and ELES via lateral and vertical concentration profiles, we show that a nonlinear least squares fit of the Gaussian plume equation well represents these profiles, even within the array of buildings and network of street canyons. The initial plume dispersion depended strongly on the structures immediately adjacent to the release, and consequently, the near-surface plume spread very rapidly in the first few street canyons downwind of the source. The ELES modeling showed that under slightly oblique incoming wind directions of 5° and 15°, an additional 5° and 14° off-axis channeling of the plume occurred at ground level, respectively. This indicates how building structures can cause considerable plume drift from the otherwise expected centerline axis, especially with greater wind obliquity. Additionally, AERMOD was used to represent the class of fast-running, Gaussian dispersion models to inform where these types of models may be usefully applied within urban areas or groups of buildings. Using an urban wind speed profile and other parameters that may be locally available after a release, AERMOD was shown to qualitatively represent the ground-level plume while somewhat underestimating peak concentrations. It also overestimated the lateral plume spread and was challenged in the very near-field to the source. Adding a turbulence profile from the ELES data into AERMOD's meteorological input improved model estimates of lateral plume spread and centerline concentrations, although peak concentration values were still underestimated in the far field. Finally, we offer some observations and suggestions for Gaussian dispersion modeling based on this mock urban modeling exercise.

A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization Algorithm.

Chemical industrial parks, which act as critical infrastructures in many cities, need to be responsive to chemical gas leakage accidents. Once a chemical gas leakage accident occurs, risks of poisoning, fire, and explosion will follow. In order to meet the primary emergency response demands in chemical gas leakage accidents, source tracking technology of chemical gas leakage has been proposed and evolved. This paper proposes a novel method, Outlier Mutation Optimization (OMO) algorithm, aimed to quickly and accurately track the source of chemical gas leakage. The OMO algorithm introduces a random walk exploration mode and, based on Swarm Intelligence (SI), increases the probability of individual mutation. Compared with other optimization algorithms, the OMO algorithm has the advantages of a wider exploration range and more convergence modes. In the algorithm test session, a series of chemical gas leakage accident application examples with random parameters are first assumed based on the Gaussian plume model; next, the qualitative experiments and analysis of the OMO algorithm are conducted, based on the application example. The test results show that the OMO algorithm with default parameters has superior comprehensive performance, including the extremely high average calculation accuracy: the optimal value, which represents the error between the final objective function value obtained by the optimization algorithm and the ideal value, reaches 2.464e-15 when the number of sensors is 16; 2.356e-13 when the number of sensors is 9; and 5.694e-23 when the number of sensors is 4. There is a satisfactory calculation time: 12.743 s/50 times when the number of sensors is 16; 10.304 s/50 times when the number of sensors is 9; and 8.644 s/50 times when the number of sensors is 4. The analysis of the OMO algorithm's characteristic parameters proves the flexibility and robustness of this method. In addition, compared with other algorithms, the OMO algorithm can obtain an excellent leakage source tracing result in the application examples of 16, 9 and 4 sensors, and the accuracy exceeds the direct search algorithm, evolutionary algorithm, and other swarm intelligence algorithms.

Field Deployment of a Portable Optical Spectrometer for Methane Fugitive Emissions Monitoring on Oil and Gas Well Pads.

We present field deployment results of a portable optical absorption spectrometer for localization and quantification of fugitive methane (CH4) emissions. Our near-infrared sensor targets the 2ν3 R(4) CH4 transition at 6057.1 cm-1 (1651 nm) via line-scanned tunable diode-laser absorption spectroscopy (TDLAS), with Allan deviation analysis yielding a normalized 2.0 ppmv∙Hz-1/2 sensitivity (4.5 × 10-6 Hz-1/2 noise-equivalent absorption) over 5 cm open-path length. Controlled CH4 leak experiments are performed at the METEC CSU engineering facility, where concurrent deployment of our TDLAS and a customized volatile organic compound (VOC) sensor demonstrates good linear correlation (R2 = 0.74) over high-flow (>60 SCFH) CH4 releases spanning 4.4 h. In conjunction with simultaneous wind velocity measurements, the leak angle-of-arrival (AOA) is ascertained via correlation of CH4 concentration and wind angle, demonstrating the efficacy of single-sensor line-of-sight (LOS) determination of leak sources. Source magnitude estimation based on a Gaussian plume model is demonstrated, with good correspondence (R2 = 0.74) between calculated and measured release rates.

An Application of the Gaussian Plume Model to Localization of an Indoor Gas Source with a Mobile Robot.

The source localization of gas leaks is important to avoid any potential danger to the surroundings or the probable waste of resources. Currently there are several localization methods using robotic systems that try to find the origin of a gas plume. Many of these methods require wind velocity information involving the use of commercial anemometric systems which are extremely expensive compared to metal oxide gas sensors. This article proposes the validation of the Gaussian plume model inside an empty room and its application to localize the source of a gas plume without employing anemometric sensors, exclusively using concentration data. The model was selected due to its simplicity and since it easily admits variants closer to reality, explaining the behavior of pollutants transported by the wind. An artificial gas source was generated by a conventional fan and liquid ethanol as contaminant. We found that the physical fan, far from making the model impossible to implement, enriched the information and added realism. The use of a robotic system capable of autonomously mapping the room concentration distribution is described. The results showed that the Gaussian plume model is applicable to localize our experimental gas source. An estimated position of the source with a deviation of 14 cm (6.1%) was obtained.

Diffusion behavior and transport risk of bioaerosol particles in a domestic waste landfill site in an arid and cold region of northwestern China.

Bioaerosols have attracted increasing attention as novel contaminants because of their potential role in the spread of disease. In this study, sampling sites were established in a landfill in northwestern China with the aim of investigating the emission and diffusion characteristics of bioaerosols. The results revealed that the counts of airborne bacteria released by landfill cover area (LCA) and waste dumping area (WDA) located in the landfill area reached 18 193 ± 30 CFU/m3 and 10 948 ± 105 CFU/m3, respectively. These two aeras were the main sources of bioaerosol generation. Meanwhile, Corynebacterium spp., Bacteroidetes spp., and Pseudomonas spp. were identified as potential pathogens. A Gaussian model was applied to simulate the diffusion of the bioaerosols; the influence distance was calculated as 12 km from the boundary of the landfill site. The potential health risks of bioaerosol exposure to on-site workers and nearby residents were calculated and evaluated in terms of aerosol concentration, particle size, and pathogenic bacteria. The present study promotes the recognition of the emission behavior of microorganisms in aerosol particles and provides a basis for controlling bioaerosol contamination from landfill sites, particularly those located in cold and arid northwestern regions of China.

Quantifying instantaneous nitrogen oxides emissions from power plants based on space observations.

Thermal power plants are significant contributors to nitrogen oxides (NOx), impacting global atmospheric conditions and human health. Satellite observations, known for their continuity and global coverage, have become an effective means of quantifying power plant emissions. Previous studies, often accumulating long temporal data into integrated plumes, resulted in substantial errors in annual emissions at the individual power plant level due to neglecting variations in emissions and diffusion conditions. This study presents, for the first time, the quantification of instantaneous NOx emissions based on single overpass observations from the Tropospheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite. By addressing the temporal variability of power plant emissions, it effectively reduces annual estimation errors. Comparative analysis between the Exponentially-Modified Gaussian (EMG) and Gaussian Plume Model (GPM) simulations demonstrates the capability of EMG to provide instantaneous emission estimates based on actual plumes, exhibiting closer proximity to actual monitoring values than GPM. Applying the EMG method, we quantify the instantaneous emission rates of six power plants in the United States. Comparing annual emission estimations at individual power plants with traditional integrated plume results, our method demonstrates a 63.7 % improvement in annual emission estimations. This study offers more detailed data on power plant emissions, providing a new avenue for better understanding the emission behavior of thermal power plants.

Improved estimation of CO2 emissions from thermal power plants based on OCO-2 XCO2 retrieval using inline plume simulation.

CO2 emissions from power plants are the dominant source of global CO2 emissions, thus in the context of global warming, accurate estimation of CO2 emissions from power plants is essential for the effective control of carbon emissions. Based on the XCO2 retrievals from the Orbiting Carbon Observatory 2 (OCO-2) and the Gaussian Plume Model (GPM), a series of studies have been carried out to estimate CO2 emission from power plants. However, the GPM is an ideal model, and there are a number of assumptions that need to be made when using this model, resulting in large uncertainties in the inverted emissions. Here, based on 6 cases of power plant plumes observed by the OCO-2 satellite over the Yangtze River Delta, China, we use an inline plume rise module coupled in the Community Multi-scale Air Quality model (CMAQ) to simulate the plumes and invert the emissions, and compare the simulated plumes and inverted emissions using the GPM model. We found that CO2 emissions can be significantly overestimated or underestimated based on the GPM simulations, and that the CMAQ inline plume simulation could significantly improve the estimates. However, the simulation bias in wind speed can significantly affect the inversion results. These results indicate that accurate meteorological field and plume simulations are critical for future inversion of point source 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.

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 sulfur­carbon 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.

Development of a numerical model for sector-average plume gamma dose and its validation with dose rate measurements at Kalpakkam NPP site, India.

A Gaussian Plume based simple numerical model, named DIFFUSE is developed to simulate the long-term sector-average plume gamma dose due to radioactive plume released during normal operation of nuclear facilities. DIFFUSE calculates site specific joint frequency distributions of wind speed, wind direction and atmospheric stability using micrometeorological observations. It performs the finite sector-average dose integration for any stack height and gamma energy using Simpson's 1/3rd method with sufficient computational efficiency within the site boundary up to 2 km. Plume dose contribution to the main plume sector from nearest and next nearest side plume sectors is also calculated. DIFFUSE is validated with a 3-month long, starting from February 2021 to April 2021, dose rate observation data during operational releases from 100 m stack of Madras Atomic Power Station, Kalpakkam, India. Meteorological data from onsite 50 m tower and continuous dose rate observation from two sets of Autonomous Gamma Dose Logger (AGDL) detectors, namely n-AGDLs and r-AGDLs, placed in two different configurations along the geometric arcs of wind sectors around the stack are used. Simulated doses are compared with look-up table based dose estimates by Hukkoo et al. (1988). Linear spatial averaging of cumulative AGDL doses on a sector arc is used as measured sector-average dose for model validation. Simulations performed for both n-AGDL and r-AGDL configurations show DIFFUSE estimated doses are ∼37% lower and Hukkoo estimated doses are at least ∼50% lower than the measured doses. Statistical analysis of DIFFUSE simulated doses shows a statistical correlation of R2∼98.3%, slope of the fit ∼1.36 for n-AGDL setup and R2∼75.3%, slope of the fit ∼1.57 for r-AGDL setup. Overall, DIFFUSE produces conservative doses compared to look-up table based doses as required by regulatory bodies.

Burden of Disease (BoD) Assessment to Estimate Risk Factors Impact in a Real Nanomanufacturing Scenario.

An industrial nanocoating process air emissions impact on public health was quantified by using the burden of disease (BoD) concept. The health loss was calculated in Disability Adjusted Life Years (DALYs), which is an absolute metric that enables comparisons of the health impacts of different causes. Here, the health loss was compared with generally accepted risk levels for air pollution. Exposure response functions were not available for Ag nanoform. The health loss for TiO2 nanoform emissions were 0.0006 DALYs per 100,000 persons per year. Moreover, the exposure risk characterization was performed by comparing the ground level air concentrations with framework values. The exposure levels were ca. 3 and 18 times lower than the derived limit values of 0.1 µg-TiO2/m3 and 0.01 µg-Ag/m3 for the general population. The accumulations of TiO2 and Ag nanoforms on the soil top layer were estimated to be up to 85 µg-TiO2/kg and 1.4 µg-Ag/kg which was considered low as compared to measured elemental TiO2 and Ag concentrations. This assessment reveals that the spray coating process air emissions are adequately controlled. This study demonstrated how the BoD concept can be applied to quantify health impacts of nanoform outdoor air emissions from an industrial site.

Key Points in Air Pollution Meteorology.

Although emissions have a direct impact on air pollution, meteorological processes may influence inmission concentration, with the only way to control air pollution being through the rates emitted. This paper presents the close relationship between air pollution and meteorology following the scales of atmospheric motion. In macroscale, this review focuses on the synoptic pattern, since certain weather types are related to pollution episodes, with the determination of these weather types being the key point of these studies. The contrasting contribution of cold fronts is also presented, whilst mathematical models are seen to increase the analysis possibilities of pollution transport. In mesoscale, land-sea and mountain-valley breezes may reinforce certain pollution episodes, and recirculation processes are sometimes favoured by orographic features. The urban heat island is also considered, since the formation of mesovortices determines the entry of pollutants into the city. At the microscale, the influence of the boundary layer height and its evolution are evaluated; in particular, the contribution of the low-level jet to pollutant transport and dispersion. Local meteorological variables have a major influence on calculations with the Gaussian plume model, whilst some eddies are features exclusive to urban environments. Finally, the impact of air pollution on meteorology is briefly commented on.

Assessment of airborne enteric viruses emitted from wastewater treatment plant: Atmospheric dispersion model, quantitative microbial risk assessment, disease burden.

From a health prospective, it is critical to provide a comprehensive model which integrates all the parameters involved in virus transmission and its consequences on human body. In order to estimate the health risks, for workers and residents, associated with an exposure airborne viruses emitted from a wastewater treatment (WWTP), the concentration levels of viruses in emitted bioaerosols over a twelve-month period were measured by real-time polymerase chain reaction (RT-PCR). A combined Gaussian plum dispersion model and quantitative microbial risk assessment (QMRA) with Monte-Carlo simulation served as suitable explanatory tools to estimate the risk of acquiring gastrointestinal illness (GI) due to exposure to air containing Rotavirus (RoV) and Norovirus (NoV) bioaerosols. Additionally, DALY metric was applied to quantify the disability and mortality for workers and residents. RoV and NoV were detected above aeration tank with annual mean concentration 27 and 3099 (Viruses/m3.h), respectively. The medium calculated DALY indicator based on viral loads in contaminant source (RoV:5.76 × 10-2 and NoV:1.23 × 10-1) and estimated in different distances away (300-1000 m) (RoV:2.87 × 10-2- 2.75 × 10-2 and NoV:1.14 × 10-1-1.13 × 10-1) were markedly higher than the threshold values recommended by US EPA (10-4 DALY pppy) and WHO (10-6 DALY pppy). The sensitivity analysis highlighted dose exposure and disease burden per case (DBPC) as two most influential factors for both workers and residents following exposure to two pathogens of concern. Due to high resistance and high concentration in the environment, the presence of RoV and NoV can intensify the consequences of diarrhea especially for children under five years of age; A comprehensible and transparent presentation of DALYs and QMRA can help decision makers and responsibilities to justify the priorities of exposure to wastewater in comparison with other risks of daily life.

Optimisation of dispersion parameters of Gaussian plume model for CO2 dispersion.

The carbon capture and storage (CCS) and enhanced oil recovery (EOR) projects entail the possibility of accidental release of carbon dioxide (CO2) into the atmosphere. To quantify the spread of CO2 following such release, the 'Gaussian' dispersion model is often used to estimate the resulting CO2 concentration levels in the surroundings. The Gaussian model enables quick estimates of the concentration levels. However, the traditionally recommended values of the 'dispersion parameters' in the Gaussian model may not be directly applicable to CO2 dispersion. This paper presents an optimisation technique to obtain the dispersion parameters in order to achieve a quick estimation of CO2 concentration levels in the atmosphere following CO2 blowouts. The optimised dispersion parameters enable the Gaussian model to produce quick estimates of CO2 concentration levels, precluding the necessity to set up and run much more complicated models. Computational fluid dynamics (CFD) models were employed to produce reference CO2 dispersion profiles in various atmospheric stability classes (ASC), different 'source strengths' and degrees of ground roughness. The performance of the CFD models was validated against the 'Kit Fox' field measurements, involving dispersion over a flat horizontal terrain, both with low and high roughness regions. An optimisation model employing a genetic algorithm (GA) to determine the best dispersion parameters in the Gaussian plume model was set up. Optimum values of the dispersion parameters for different ASCs that can be used in the Gaussian plume model for predicting CO2 dispersion were obtained.