Semi-empirical Algorithm Research Articles

Salinity Measurement with a Plasmonic Sensor Based on Doubly Deposited Tapered Optical Fibers.

Salinity is a very important parameter from an environmental perspective, and therefore, efficient and accurate systems are required for marine environmental monitoring and productive industries. A plasmonic sensor based on doubly deposited tapered optical fibers (DLUWTs-double-layer uniform-waist tapers) for the measurement of salinity is presented. The physical principle of the sensor, as well as its structure, is discussed, and its performance is experimentally demonstrated, obtaining very good sensitivities. The possibility of shifting towards higher wavelength measuring ranges associated with DLUWTs is also exploited. At the same time, we have considered the necessity of an extensive characterization of the behavior of the refractive index of salty water, both with variations in temperature and the composition of the salts dissolved. This is important due to the somehow changing reality of salinity measurements and the possibility of establishing new approaches for the determination of absolute salinity as opposed to practical salinity based on electrical conductivity measurements. The results obtained, which show high sensitivity and a good performance in general without the need for the use of semi-empirical algorithms, permit, in our opinion, an advance in the tendency towards refractometric determination of salinity with optical sensors apt for in situ, real-time, accurate measurements in realistic measuring conditions.

ANN_ITU: Predicting rain attenuation with a hybrid model for earth-space links

Rain attenuation prediction of earth-space links is of vital significance for the application and development of satellite communication. Recently, most rain attenuation prediction methods are based on semi-empirical models or data-driven models, the former suffering from incompleteness problem, the latter faced with limited performance due to scarce data. In order to realize higher rain attenuation prediction performance, we propose a novel hybrid model ANN_ITU that combines advantages of the semi-empirical model and the artificial neural network. In ANN_ITU framework, the semi-empirical model ITU-R P.618-12 is leveraged to predict rain attenuation, and a six-layer artificial neural network is utilized to correct the rain attenuation predicted by ITU-R P.618-12, thus generating the final rain attenuation value. What’s more, we also present theories of two machine-learning based rain attenuation prediction methods, namely, random forest and support vector regression. Last but not least, we expound on processes of DBSG3 dataset filtering and data preprocessing. Experiments on DBSG3 dataset are carried out. Experimental results demonstrate that the hybrid ANN_ITU algorithm outperforms purely semi-empirical algorithms and data-driven algorithms. The evaluation indexes mean value, standard deviation, and root mean square value are 0.0355%, 19.63%, and 19.63%, respectively, which prove the effectiveness and precision of our rain attenuation prediction model ANN_ITU.

A semi-empirical algorithm for determining the impulse characteristics of gas-insulated two-electrode systems in the vicinity of Paschen minimum with and without taking into account the spark channel thermoionization time

The impulse characteristic represents the response of the gas insulation to the impulse load. There is no rational way to determine it, since it must include all pulse shapes and all probability quantiles of a random breakdown event. Near Paschen minimum is the operating point of gas surge arresters. For this reason, the results presented in this paper are important for the coordination of insulation at the low-voltage level. The semi-empirical algorithm derived in this paper is simple and allows obtaining impulse characteristics based on a small number of measurements. The paper presents two variants of the derived algorithm. One with and one without taking into account the thermoionization time of the streamer channel. Both variants were experimentally verified in well-controlled laboratory conditions and good results were obtained from the point of view of engineering practice. Of course, the algorithm which includes the thermoionization time of the spark channel gave more exact results. However, even in that case, an improvement can be made by observing the thermoionization process as a stochastic phenomenon. In this sense, suggestions for further improvement of the proposed algorithm are given.

Performance study of an active-passive combined anti-frosting method for fin-tube heat exchanger

Superhydrophobic surface has demonstrated excellent anti-frosting and defrosting properties, but its effect is limited by monotonous passive application styles. In a novel active-passive combined frost suppression method for air source heat pumps, superhydrophobic modification and high-speed airflow (SMAF) are employed for frost suppression. In this work, the frosting-defrosting characteristics were experimentally studied for a fin-tube heat exchanger with the SMAF method applied under typical frosting conditions, and the results were compared with bare, hydrophilic, and superhydrophobic control groups. The influence of airflow parameters on the frost suppression effect was also revealed. Based on the experimental results, transient models for airflow parameters and frosting are established, a semi-empirical algorithm for heat exchanger performance under different anti-frosting methods is developed, and the adaptability of SMAF method to ambient temperature and humidity is revealed. The results show that the maximum net yield of the SMAF method can reach 97.9% and 18.7% respectively, compared with bare and superhydrophobic modification methods.

Semi-empirical algorithm for estimation of calorimetric properties in binary liquid mixtures from acoustic and volumetric data

In the present work, a semi-empirical algorithm is proposed for estimating the adiabatic coefficient (γ), the isothermal compressibility (κτ), the heat capacity at constant pressure (cp), and constant volume (cv), for liquid mixtures of organic compounds as a function of temperature and concentration. The algorithm was applied to the binary systems: 1,6-Dichlorohexene (x) + Dodecane (1-x) and 1,5-Dichloropentane (x) + Dodecane (1-x) reported in literature. The obtaining values for γ, cp,cv and κτ are reported at all concentrations and temperatures. The implementation of the algorithm required experimental data of the adiabatic coefficients of the pure components (γi0) to 298.15 K, acoustic and volumetric data of the binary mixture in the entire concentration and temperature range between (278.15–328.15) K every 5 K. The results obtained are in excellent agreement with the experimental results.

Quantification of Phycocyanin in Inland Waters through Remote Measurement of Ratios and Shifts in Reflection Spectral Peaks

This study introduces a semi-empirical algorithm to estimate the extent of the phycocyanin (PC) concentration in eutrophic freshwater bodies; this is achieved by studying the reflectance characteristics of the red and near-red spectral regions, especially the shifting of the peak near 700 nm towards longer wavelengths. Spectral measurements in a darkroom environment over the pure-cultured cyanobacteria Microcystis showed that the shift is proportional to the algal biomass. A similar proportional trend was found from extensive field measurement data. The data also showed that the correlation of the magnitude of the shift with the PC concentration was greater than that with chlorophyll-a. This indicates that the characteristic can be a useful index to quantify cyanobacterial biomass. Based on these observations, a new PC algorithm was proposed that uses the remote sensing reflectance of the peak band around 700 nm and the trough band around 620 nm, and the magnitude of the peak shift near 700 nm. The efficacy of the algorithm was tested with 300 sets of field data, and the results were compared to select algorithms for the PC concentration prediction. The new algorithm performed better than the other algorithms with respect to most error indices, especially the mean relative error, indicating that the algorithm can reduce errors when PC concentrations are low. The algorithm was also applied to a hyperspectral dataset obtained through aerial imaging, in order to predict the spatial distribution of the PC concentration in an approximately 86 km long reach of the Nakdong River.

Monitoring Total Suspended Sediment Concentration in Spatiotemporal Domain over Teluk Lipat Utilizing Landsat 8 (OLI)

Total suspended sediment (TSS) is a water quality parameter that is used to understand sediment transport, aquatic ecosystem health, and engineering problems. The majority of TSS in water bodies is due to natural and human factors such as brought by river runoff, coastal erosion, dredging activities, and waves. It is an important parameter that should be monitored periodically, particularly over the dynamic coastal region. This study aims to monitor spatiotemporal TSS concentration over Teluk Lipat, Malaysia. To date, there are two commonly used methods to monitor TSS concentration over wide water regions. Firstly, field sampling is known very expensive and time-consuming method. Secondly, the remote sensing technology that can monitor spatiotemporal TSS concentration freely. Although remote sensing technology could overcome these problems, universal empirical or semiempirical algorithms are still not available. Most of the developed algorithms are on a regional basis. To measure TSS concentration over the different regions, a new regional algorithm needs to develop. To do so, two field trip was conducted in the study area concurrent with the passing of Landsat 8. A total of 30 field samples were collected from 30 sampling points during the first field trip and 30 samples from 30 samplings from the second field trip. The samples were then analyzed using an established method to develop the TSS algorithm. The data obtained from the first field trip were then used to develop a regional TSS algorithm using the regression analysis technique. The developed algorithm was then validated by using data obtained from the second field trip. The results demonstrated that TSS in the study area is highly correlated with three Landsat 8 bands, namely green, near-infrared (NIR), and short-wavelength (SWIR) bands, with R2 = 0.79. The TSS map is constructed using the algorithm. Analyses of the image suggest that the highest TSSs are mainly observed along the coastal line and over the river mouth. It suggested that the main contributing factors over the study area are river runoff and wave splash.

Optimizing the Empirical Parameters of the Data-Driven Algorithm for SIF Retrieval for SIFIS Onboard TECIS-1 Satellite.

Space-based solar-induced chlorophyll fluorescence (SIF) has been widely demonstrated as a great proxy for monitoring terrestrial photosynthesis and has been successfully retrieved from satellite-based hyperspectral observations using a data-driven algorithm. As a semi-empirical algorithm, the data-driven algorithm is strongly affected by the empirical parameters in the model. Here, the influence of the data-driven algorithm’s empirical parameters, including the polynomial order (np), the number of feature vectors (nSV), the fluorescence emission spectrum function, and the fitting window used in the retrieval model, were quantitatively investigated based on the simulations of the SIF Imaging Spectrometer (SIFIS) onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1). The results showed that the fitting window, np, and nSV were the three main factors that influenced the accuracy of retrieval. The retrieval accuracy was relatively higher for a wider fitting window; the root mean square error (RMSE) was lower than 0.7 mW m−2 sr−1 nm−1 with fitting windows wider than 735–758 nm and 682–691 nm for the far-red band and the red band, respectively. The RMSE decreased first and then increased with increases in np range from 1 to 5 and increased in nSV range from 2 to 20. According to the specifications of SIFIS onboard TECIS-1, a fitting window of 735–758 nm, a second-order polynomial, and four feature vectors are the optimal parameters for far-red SIF retrieval, resulting in an RMSE of 0.63 mW m−2 sr−1 nm−1. As for red SIF retrieval, using second-order polynomial and seven feature vectors in the fitting window of 682–697 nm was the optimal choice and resulted in an RMSE of 0.53 mW m−2 sr−1 nm−1. The optimized parameters of the data-driven algorithm can guide the retrieval of satellite-based SIF and are valuable for generating an accurate SIF product of the TECIS-1 satellite after its launch.

A Semi-Empirical Chlorophyll-a Retrieval Algorithm Considering the Effects of Sun Glint, Bottom Reflectance, and Non-Algal Particles in the Optically Shallow Water Zones of Sanya Bay Using SPOT6 Data

Chlorophyll-a (Chl-a) concentration retrieval is essential for water quality monitoring, aquaculture, and guiding coastline infrastructure construction. Compared with common ocean color satellites, land observation satellites have the advantage of a higher resolution and more data sources for retrieving the concentration of Chl-a from optically shallow waters. However, the sun glint (Rsg), bottom reflectance (Rb), and non-algal particle (NAP) derived from terrigenous matter affect the accuracy of Chl-a concentration retrieval using land observation satellite image data. In this paper, we propose a semi-empirical algorithm based on the remote sensing reflectance (Rrs) of SPOT6 to retrieve the Chl-a concentration in Sanya Bay (SYB), considering the effect of Rsg, Rb, and NAP. In this semi-empirical algorithm, the Cox–Munk anisotropic model and radiative transfer model (RTM) were used to reduce the effects of Rsg and Rb on Rrs, and the Chl-a concentration was retrieved by the Chl-a absorption coefficient at 490 nm (aphy(490)) to remove the effect of NAP. The semi-empirical algorithm was in the form of Chl-a = 43.3[aphy(490)]1.454, where aphy (490) was calculated by the total absorption coefficient and the absorption coefficients of each component by empirical algorithms. The results of the Chl-a concentration retrieval show the following: (1) SPOT6 data are available for Chl-a retrieval using this semi-empirical algorithm in oligotrophic or mesotrophic coastal waters, and the accuracy of the algorithm can be improved by removing the effects of Rsg, Rb, and NAP (R2 from 0.71 to 0.93 and root mean square error (RMSE) from 0.23 to 0.11 ug/L); (2) empirical algorithms based on the blue-green band are suitable for oligotrophic or mesotrophic coastal waters, and the algorithm based on the blue-green band difference Chl-a index (DCI) has stronger anti-interference in terms of the effects of sun glint and bottom reflectance than the algorithm based on the blue-green ratio (BGr); (3) in the case of ignoring Rsg unrelated to inherent optical properties (IOPs), NAP is the biggest interference factor when >9.5 mg/L and the effect of bottom reflectance should be considered when the water depth (H) <5 m in SYB; and (4) the inherent optical properties of the waters in SYB are dominated by NAP (Chl-a = 0.2–2.6 ug/L and NAP = 2.2–30.1 mg/L), and the nutrients are concentrated by enclosed terrain and southeast current. This semi-empirical algorithm for Chl-a concentration retrieval has the potential to monitor Chl-a in oligotrophic and mesotrophic coastal waters using other land observation satellites (e.g., Landsat8 OLI, ASTER, and GaoFen2).

Hyperspectral Satellite Remote Sensing of Water Quality in Lake Atitlán, Guatemala

In this study we evaluated the applicability of a space-borne hyperspectral sensor, Hyperion, to resolve for chlorophyll a (Chl a) concentration in Lake Atitlan, a tropical mountain lake in Guatemala. In situ water quality samples of Chl a concentration were collected and correlated with water surface reflectance derived from Hyperion images, to develop a semi-empirical algorithm. Existing operational algorithms were tested and the continuous bands of Hyperion were evaluated in an iterative manner. A third order polynomial regression provided a good fit to model Chl a. The final algorithm uses a blue (467 nm) to green (559 nm) band ratio to successfully model Chl a concentrations in Lake Atitlan during the dry season, with a relative error of 33%. This analysis confirmed the suitability of hyperspectral imagers like Hyperion, to model Chl a concentrations in Lake Atitlan. This study also highlights the need to test and update this algorithm with operational multispectral sensors such as Landsat and Sentinel-2.

Quantitative structure activity relationship (qsar) study and biological evaluation on mono-ketone analogs of curcumin as antioxidant

The objective of this study was to design new mono-ketone analogs of curcumin and determine its lipid peroxidation inhibition. Molecular modeling studies were carried out by using the semi-empirical quantum chemical algorithm AM-1 method in MOE 2018.01.01 software. Build QSAR was used to generate an equation model that will be used to design new mono-ketone analogs of curcumin. Synthesis of new compounds produced from an aldol condensation reaction. Thiobarbituric acid reactive substance (TBARS) method was used to determine antioxidant activity by measuring lipid peroxide concentration. Promising predictions values of lipid peroxidation the QSAR study obtained inhibitory activity. The in vitro lipid peroxidation inhibition of 2,6-bis-(3’-ethoxy, 4’-hydroxybenzylidene)-cyclohexanone; 2,6-bis-(3’-Bromo,4’-methoxybenzylidene)-cyclohexanone; and 2,6-bis-(3’,4’-dimethoxybenzylidene)-cyclohexanone indicates good inhibitory with IC50 values of 2.95; 0.95; and 2.45μM respectively. The new mono-ketone analogs of curcumin compound from the QSAR study has been shown to have antioxidant activity by inhibiting lipid peroxidation by scavenging free radicals.

Atmospheric Phase Compensation in Extreme Weather Conditions for Ground-Based SAR

Herein, a semiempirical model is proposed to remove the atmospheric phase screen (APS) that occurs during ground-based synthetic aperture radar (GB-SAR) monitoring in steep mountainous areas with extreme weather conditions. The proposed method is based on a model-based statistical technique, which combines the topographical information and the estimated phase of interferograms. A 3-D geographical model was designed to investigate the effect of topographical irregularities, such as elevation, slope, and their correlation with the APS. The observed phases were then modeled according to the altitude and range of the 3-D topographical structure seen by the radar. A two-stage semiempirical algorithm is proposed to compensate for the APS in the spatial domain. Herein, the temporal changes in meteorological parameters, such as the atmospheric temperature, pressure, or humidity, were not considered for phase correction, drastically reducing the model background information and providing faster data processing for real-time GB-SAR monitoring. The proposed model was applied to the mountainous environment of a road reconstruction site in Minami-Aso, Kumamoto, Japan, where large-scale landslides were triggered after the Kumamoto earthquake in April 2016.

A Semi-Empirical Split-Window Algorithm for Retrieving near Surface Air Temperature from MODIS Data

This study attempts to develop an effective algorithm to directly derive near surface air temperature from EOS/MODIS data. From a theoretical viewpoint, the split-window algorithm for retrieving near surface air temperature was developed based on the radiative transfer equation, which includes the calculation of atmospheric thermal radiance, the linearization of Planck functions, the transformation from effective atmospheric mean temperature to near surface air temperature and other derivation processes. Considering that the coefficients of the theoretical algorithm are highly dependent on the atmospheric profile, which is difficult to acquire in practical applications, a semi-empirical split-window algorithm is generated on the basis of the theoretical algorithm to improve the practicality. The semi-empirical algorithm was applied and validated in the Jing-Jin-Ji (JJJ) Region and the Jiang-Zhe-Hu-Wan (JZHW) Region in China. Results indicate that the algorithm achieves an MAE of 2.11 °C in the JJJ Region and an MAE of 2.22 °C in the JZHW Region. The semi-empirical split-window algorithm also shows better stability than linear regression and machine learning methods when being applied to other data periods. Due to its accuracy and simplicity, the semi-empirical split-window algorithm is a novel method for retrieving near surface air temperature from MODIS thermal bands.

Semi-Empirical Algorithm for Wind Speed Retrieval from Gaofen-3 Quad-Polarization Strip Mode SAR Data

Synthetic aperture radar (SAR) is a suitable tool to obtain reliable wind retrievals with high spatial resolution. The geophysical model function (GMF), which is widely employed for wind speed retrieval from SAR data, describes the relationship between the SAR normalized radar cross-section (NRCS) at the copolarization channel (vertical-vertical and horizontal-horizontal) and a wind vector. SAR-measured NRCS at cross-polarization channels (horizontal-vertical and vertical-horizontal) correlates with wind speed. In this study, a semi-empirical algorithm is presented to retrieve wind speed from the noisy Chinese Gaofen-3 (GF-3) SAR data with noise-equivalent sigma zero correction using an empirical function. GF-3 SAR can acquire data in a quad-polarization strip mode, which includes cross-polarization channels. The semi-empirical algorithm is tuned using acquisitions collocated with winds from the European Center for Medium-Range Weather Forecasts. In particular, the proposed algorithm includes the dependences of wind speed and incidence angle on cross-polarized NRCS. The accuracy of SAR-derived wind speed is around 2.10 m s-1 root mean square error, which is validated against measurements from the Advanced Scatterometer onboard the Metop-A/B and the buoys from the National Data Buoy Center of the National Oceanic and Atmospheric Administration. The results obtained by the proposed algorithm considering the incidence angle in a GMF are relatively more accurate than those achieved by other algorithms. This work provides an alternative method to generate operational wind products for GF-3 SAR without relying on ancillary data for wind direction.

An efficient algorithm for computation of spatial heat generation during interaction of high energy proton beam with target materials

The interaction of a proton beam with target material leads to heat generation and subsequently temperature rise in the target materials. Conventionally, the mechanical design of target materials involves thermal analysis with heat input through proton energy deposition using Monte Carlo based particle transport codes. This computation involves two step process: simulation of proton energy deposition and thermal analysis. In the present work, a single step Semi Empirical heat generation algorithm (SEHG) is developed that enhances the capability of a thermal solver to compute energy deposition by proton beam on target materials. The results of SEHG algorithm is benchmarked with, ‘FLUKA’ up to 1 GeV beam energy and is valid till the onset of nucleon disintegration phase. The developed algorithm paved ways for a simple and efficient investigation for thermal effects of beam – material interactions, without compromising the quality of mechanical design performance.

Removal of Chlorophyll-a Spectral Interference for Improved Phycocyanin Estimation from Remote Sensing Reflectance

Monitoring cyanobacteria is an essential step for the development of environmental and public health policies. While traditional monitoring methods rely on collection and analysis of water samples, remote sensing techniques have been used to capture their spatial and temporal dynamics. Remote detection of cyanobacteria is commonly based on the absorption of phycocyanin (PC), a unique pigment of freshwater cyanobacteria, at 620 nm. However, other photosynthetic pigments can contribute to absorption at 620 nm, interfering with the remote estimation of PC. To surpass this issue, we present a remote sensing algorithm in which the contribution of chlorophyll-a (chl-a) absorption at 620 nm is removed. To do this, we determine the PC contribution to the absorption at 665 nm and chl-a contribution to the absorption at 620 nm based on empirical relationships established using chl-a and PC standards. The proposed algorithm was compared with semi-empirical and semi-analytical remote sensing algorithms for proximal and simulated satellite sensor datasets from three central Indiana reservoirs (total of 544 sampling points). The proposed algorithm outperformed semi-empirical algorithms with root mean square error (RMSE) lower than 25 µg/L for the three analyzed reservoirs and showed similar performance to a semi-analytical algorithm. However, the proposed remote sensing algorithm has a simple mathematical structure, it can be applied at ease and make it possible to improve spectral estimation of phycocyanin from space. Additionally, the proposed showed little influence from the package effect of cyanobacteria cells.

A semi-empirical multi-degree of freedom body force propeller model

Efficient and accurate modelling of a self-propelled vessel in a large amplitude seaway with CFD-based numerical methods is a challenging task and is prohibitively expensive for most designers and engineers. Viscous CFD methods can accurately model viscous effects, but the small numerical time step required to analyze the rotating propeller leads to a large computational cost. A method for efficiently and accurately modelling the six degree of freedom force on the propeller is required to feasibly model a self propelled vessel maneuvering in a high amplitude seaway. This paper outlines the framework for developing an unsteady body force propeller model for unsteady conditions. The purpose of this study is to train a semi-empirical algorithm to accurately prescribe the unsteady body force to model the propeller. The MOERI Container ship propeller is analyzed with RANS CFD. Open water data is compared to the RANS CFD results of a steady Moving Reference Frame approach. Unsteady surge and propeller revolution rate are applied to a transient rotating mesh model in open water and behind condition. The predictions of the algorithm match well with the CFD results.

Modeling of Fast-Permeant Component Removal from Gas Mixture in a Membrane Module with Pulsed Retentate

The process of unsteady-state membrane gas separation (fast-permeant impurity removal) with a pulsed retentate flow operation was considered. A semiempirical mathematical algorithm was developed to describe this process taking into account its kinetic characteristics (total cycle time, stripping time and withdrawal time, withdrawal velocity) using the MathCad® software package. Based on the developed algorithm, the basic operational parameters that affect the separation efficiency of the unsteady-state process were analyzed. It was shown that the optimal ratio of the stripping time and the withdrawal one determined by the maximum efficiency criterion more corresponds to the minimum retentate concentration than to the maximum productivity. However, the developed algorithm allows to set the productivity minimum limit by introducing additional initial data into the calculation procedure. The mathematical modeling results correlate well with the experimental data.

Comparison of Cloudy‐Sky Downward Longwave Radiation Algorithms Using Synthetic Data, Ground‐Based Data, and Satellite Data

AbstractCloud plays a crucial role in surface downward longwave radiation (DLR). To determine optimal algorithms for cloud‐sky DLR calculation under various climates, three types of algorithms were assessed, (i) four empirical algorithms determining cloudy DLR by simple cloud correction using the cloud fraction, (ii) three parameterized algorithms determining the cloud contribution by cloud temperature, and (iii) a semiempirical algorithm, Zhou‐Cess, parameterized by the cloud water path. A sensitivity study was conducted using a Moderate Resolution Transmittance (MODTRAN) code to first determine the sensitive cloud parameters of DLR. Then, these algorithms were validated using synthetic data simulated by MODTRAN, ground‐observed data, and satellite‐observed data. When all the input parameters were accurate, the cloud‐correction algorithms showed poor performance. Cloud‐temperature‐based algorithms showed much better results but exhibited positive systematic biases. The Zhou‐Cess algorithm performed best but could not precisely describe the effect caused by cloud variations. When these algorithms were applied to ground‐ and satellite‐based data, the accuracies of DLR calculations were affected by the uncertainty in the atmospheric and cloud parameters. The simple empirical algorithms showed the poorest results. The cloud‐temperature‐based algorithms were greatly influenced by the uncertainty in cloud base temperature and cloud fraction and showed acceptable results when cloud fractions were accurate. The Zhou‐Cess algorithm revealed the best results at most sites and was less impacted by cloud parameter uncertainties; therefore, this algorithm is suggested for cloudy‐sky DLR calculation with poor‐quality cloud parameters.

Emissions of organic compounds from produced water ponds III: Mass-transfer coefficients, composition-emission correlations, and contributions to regional emissions

A common method for treating the aqueous phase (produced water) brought to the surface along with oil and natural gas is to discharge it into surface impoundments, also known as produced water ponds. Here we analyze data on the concentration of organic compounds in the water and on the flux of the same compounds into the atmosphere. Flux data extending from about 5 × 10−2 to 10+3 mg m−2 h−1 are consistent with mass-transfer laws given by the WATER9 semi-empirical algorithm, although empirical data display a noise level of about one order of magnitude and predictions by WATER9 are biased high. The data suggest partitioning between hydrocarbons in aqueous solution and in suspension, especially at higher overall concentrations. Salinity of the produced water does not have a detectable effect on hydrocarbon fluxes. Recently impounded waters are stronger emitters of hydrocarbons, while emissions of older waters are dominated by CO2. This aging effect can be explained by assuming, first, poor vertical mixing in the ponds, and second, gradual oxidation of hydrocarbons to CO2. Our measurements account for about 25% of the produced water ponds in the Uinta Basin, Eastern Utah, and when extrapolated to all ponds in the basin, account for about 4% to 14% of all organic compound emissions by the oil and natural gas sector of the basin, depending on the emissions inventory, and about 13% and 58%, respectively, of emissions of aromatics and alcohols.