A predictive Bayesian data-derived multi-modal Gaussian model of sunken oil mass

2015 ◽  
Vol 69 ◽  
pp. 1-13 ◽  
Author(s):  
M. Angelica Echavarria-Gregory ◽  
James D. Englehardt
Keyword(s):  
Gaussian Model ◽  
Sunken Oil

Multidimensional, Nonthreshold Gaussian Model of Source Memory

2004 ◽  
Author(s):  
William P. Banks ◽  
Clayton Stephenson ◽  
Nisha Gottfredson ◽  
Andrew A. Sparrow
Keyword(s):  
Source Memory ◽  
Gaussian Model

scholarly journals Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 811
Author(s):  
Yaqin Hu ◽  
Yusheng Shi
Keyword(s):  
Power Plant ◽  
Co2 Emissions ◽  
Atmospheric Carbon Dioxide ◽  
Power Plants ◽  
Large Scale ◽  
Global Climate ◽  
Gaussian Model ◽  
Point Sources ◽  
Emission Inventories ◽  
Bottom Up

The concentration of atmospheric carbon dioxide (CO2) has increased rapidly worldwide, aggravating the global greenhouse effect, and coal-fired power plants are one of the biggest contributors of greenhouse gas emissions in China. However, efficient methods that can quantify CO2 emissions from individual coal-fired power plants with high accuracy are needed. In this study, we estimated the CO2 emissions of large-scale coal-fired power plants using Orbiting Carbon Observatory-2 (OCO-2) satellite data based on remote sensing inversions and bottom-up methods. First, we mapped the distribution of coal-fired power plants, displaying the total installed capacity, and identified two appropriate targets, the Waigaoqiao and Qinbei power plants in Shanghai and Henan, respectively. Then, an improved Gaussian plume model method was applied for CO2 emission estimations, with input parameters including the geographic coordinates of point sources, wind vectors from the atmospheric reanalysis of the global climate, and OCO-2 observations. The application of the Gaussian model was improved by using wind data with higher temporal and spatial resolutions, employing the physically based unit conversion method, and interpolating OCO-2 observations into different resolutions. Consequently, CO2 emissions were estimated to be 23.06 ± 2.82 (95% CI) Mt/yr using the Gaussian model and 16.28 Mt/yr using the bottom-up method for the Waigaoqiao Power Plant, and 14.58 ± 3.37 (95% CI) and 14.08 Mt/yr for the Qinbei Power Plant, respectively. These estimates were compared with three standard databases for validation: the Carbon Monitoring for Action database, the China coal-fired Power Plant Emissions Database, and the Carbon Brief database. The comparison found that previous emission inventories spanning different time frames might have overestimated the CO2 emissions of one of two Chinese power plants on the two days that the measurements were made. Our study contributes to quantifying CO2 emissions from point sources and helps in advancing satellite-based monitoring techniques of emission sources in the future; this helps in reducing errors due to human intervention in bottom-up statistical methods.

scholarly journals Surface Activity of Humic Acid and Its Sub-Fractions from Forest Soil

2021 ◽  
Vol 13 (15) ◽  
pp. 8122
Author(s):  
Shijie Tian ◽  
Weiqiang Tan ◽  
Xinyuan Wang ◽  
Tingting Li ◽  
Fanhao Song ◽  
...  
Keyword(s):  
Surface Tension ◽  
Particle Size ◽  
Humic Acid ◽  
Forest Soil ◽  
Surface Activity ◽  
Self Assembly ◽  
Average Particle Size ◽  
Gaussian Model ◽  
Exponential Model ◽  
Average Particle

Surface activity of humic acid (HA) and its six sub-fractions isolated from forest soil were characterized by surface tension measurements, dynamic light scattering, and laser doppler electrophoresis. The surface tension of HA and its sub-fractions reduced from 72.4 mN·m−1 to 36.8 mN·m−1 in exponential model with the increasing concentration from 0 to 2000 mg·L−1. The critical micelle concentration (CMC) and Z-average particle size ranged from 216–1024 mg·L−1 and 108.2–186.9 nm for HA and its sub-fractions, respectively. The CMC have related with alkyl C, O-alkyl C, aromatic C, and carbonyl C (p < 0.05), respectively, and could be predicted with the multiple linear regression equation of CMC, CMC = 18896 − 6.9 × C-296 × alkyl C-331 × aromatic C-17019 × H/C + 4054 × HB/HI (p < 0.05). The maximum particle size was 5000 nm after filtered by a membrane with pore size of 450 nm, indicating HA and its sub-fractions could progressed self-assembly at pH 6.86. The aggregate sizes of number-base particle size distributions were mainly in six clusters including 2 ± 1 nm, 5 ± 2 nm, 10 ± 3 nm, 21 ± 8 nm, 40 ± 10 nm, and >50 nm analyzed by Gaussian model that maybe due to the inconsistency of the components and structures of the HA sub-fractions, requiring further study. It is significance to explore the surface activity of HA and its sub-fractions, which is helpful to clarify the environmental behavior of HA.

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