Search Results (6)

The environment, the climate and human health are largely exposed to gas flaring (GF) effects, releasing significant dangerous gases into the atmosphere. In the last few decades, remote sensing technology has received great attention in gas flaring investigation. The Pars Special Economic Energy Zone (PSEEZ), located in the south of Iran, hosts many natural oil/gas processing plants and petrochemical industries, making this area one of the most air-polluted zones of Iran. The object of this research is to detect GF-related thermal anomalies in the PSEEZ by applying, for the first time, the Reed-Xiaoli Detector (RXD), distinguished as the benchmark algorithm for spectral anomaly detection. The RXD performances in this research field have been tested and verified using the shortwave infrared (SWIR) bands of OLI-Landsat 8 (L8), acquired in 2018 and 2019 on the study area. Preliminary results of this automatic unsupervised learning algorithm demonstrated an exciting potential of RXD for GF anomaly detection on a monthly scale (75% success rate), with peaks in the months of January and February 2018 (86%) and December 2019 (84%). The lowest detection was recorded in October 2019 (48%). Regarding the spatial distribution of GF anomalies, a qualitatively analysis demonstrated the RXD capability in mapping the areas affected by gas flaring, with some limitations (i.e., false positives) due to possible solar radiation contribution. Further analyses will be dedicated to recalibrate the algorithm to increase its reliability, also coupling L8 and Landsat 9, as well as exploring Sentinel 2 SWIR imagery, to overcome some of the observed RXD drawbacks. Full article

The index mechanical properties, strength, and stiffness parameters of rock materials (i.e., uniaxial compressive strength, c, ϕ, E, and G) are critical factors in the proper geotechnical design of rock structures. Direct procedures such as field surveys, sampling, and testing are used to estimate these properties, and are time-consuming and costly. Indirect methods have gained popularity in recent years due to their time-saving and highly accurate results, which are comparable to those obtained through direct approaches. This study presents a procedure for establishing a deep learning-based predictive model (DNN) for obtaining the geomechanical characteristics of marlstone samples that have been recovered from the South Pars region of southwest Iran. The model was implemented on a dataset resulting from the execution of numerous geotechnical tests and the evaluation of the geotechnical parameters of a total of 120 samples. The applied model was verified by using benchmark learning classifiers (e.g., Support Vector Machine, Logistic Regression, Gaussian Naïve Bayes, Multilayer Perceptron, Bernoulli Naïve Bayes, and Decision Tree), Loss Function, MAE, MSE, RMSE, and R-square. According to the results, the proposed DNN-based model led to the highest accuracy (0.95), precision (0.97), and the lowest error rate (MAE = 0.13, MSE = 0.11, and RMSE = 0.17). Moreover, in terms of R², the model was able to accurately predict the geotechnical indices (0.933 for UCS, 0.925 for E, 0.941 for G, 0.954 for c, and 0.921 for φ). Full article

The diffuse attenuation coefficient of photosynthetically active radiation is an important inherent optical property of the subaquatic light field. This parameter, as a measure of the transparency of the medium, is a good indicator of water quality. Degradation of the optical properties of water due to anthropogenic disturbances is a common phenomenon in freshwater ecosystems. In this study, we used four algorithm-based Landsat 8 OLI and Sentinel-2A/B MSI images to estimate the diffuse attenuation coefficient of photosynthetically active radiation in Lake Villarrica located in south-central Chile. The algorithms’ estimated data from the ACOLITE module were validated with in situ measurements from six sampling stations. Seasonal and intralake variations of the light attenuation coefficient were studied. The relationship between the diffuse attenuation coefficient of photosynthetically active radiation, meteorological parameters, and an optical classification was also explored. The best results were obtained with QAA v6 KdPAR Nechad (R² = 0.931, MBE = 0.023 m⁻¹, RMSE = 0.088 m⁻¹, and MAPE = 35.9%) for spring and QAA v5 Kd490 algorithms (R² = 0.919, MBE = −0.064 m⁻¹, RMSE = −0.09 m⁻¹, and MAPE = 30.3%) for summer. High KdPAR values are associated with the strong wind and precipitation events suggest they are caused by sediment resuspension. Finally, an optical classification of freshwater ecosystems was proposed for this lake. The promising results of this study suggest that the combination of in situ data and observation satellites can be useful for assessing the bio-optical state of water and water quality dynamics in Chilean aquatic systems. Full article

This study aims to increase our current knowledge on the concentration of particulate-bound mercury (PBM) in urban environments of three Iranian cities, where high concentrations of dust particles can act as carriers for mercury transport and deposition. A total of 172 dust samples were collected from Ahvaz, Asaluyeh, and Zabol residential houses and in outdoor air and were analyzed for total mercury content. Ahvaz is a highly industrialized city with large metallurgical plants, refineries, and major oil-related activities, which were assumed to contribute to elevated contents of PBM in this city. Very high levels of Hg contamination in Ahvaz indoor dust samples were calculated (Contamination Factor: CF > 6). Sampling sites in Asaluyeh are influenced by Hg emissions from the South Pars Gas Field. However, the results revealed a relatively lower concentration of PBM in Asaluyeh, with a low-to-moderate level of Hg contamination. This is likely ascribed to the lower content of total mercury in hydrocarbon gases than crude oil, in addition to the absence of metal smelting plants in this city compared to Ahvaz. Zabol, as a city devoid of industrial activity, presented the lowest levels of PBM concentration and contamination. Indoor dust in Ahvaz showed considerable potential to cause a non-carcinogenic health risk for children, mainly through the inhalation of PBM, while the health risk for other cities was below safe limits. The trend of health risk was found in the order of indoor > outdoor and children > adults in all studied cities. Full article

Rapid and widespread monitoring of inland and coastal water quality occurs through the use of remote sensing and near-surface water quality sensors. A new addition is the development of smartphone applications (Apps) to measure and record surface reflectance, water color and water quality parameters. In this paper, we present a field study of the HydroColor (HC, measures RGB reflectance and suspended particulate matter (SPM)) and EyeOnWater (EoW, determines the Forel–Ule scale—an indication to the visual appearance of the water surface) smartphone Apps to evaluate water quality for inland waters in Eastern Australia. The Brisbane river, multiple lakes and reservoirs and lagoons in Queensland and New South Wales were visited; hyperspectral reflection spectra were collected and water samples were analysed in the laboratory as reference. Based on detailed measurements at 32 sites, covering inland waters with a large range in sediment and algal concentrations, we find that both water quality Apps are close, but not quite on par with scientific spectrometers. EoW is a robust application that manages to capture the color of water with accuracy and precision. HC has great potential, but is influenced by errors in the observational procedure and errors in the processing of images in the iPhone. The results show that repeated observations help to reduce the effects of outliers, while implementation of camera response functions and processing should help to reduce systematic errors. For both Apps, no universal conversion to water quality composition is established, and we conclude that: (1) replicated measurements are useful; (2) color is a reliable monitoring parameter in its own right but it should not be used for other water quality variables, and; (3) tailored algorithms to convert reflectance and color to composition could be developed for lakes individually. Full article

There is a recognized need for the determination of total organic carbon (TOC) as an essential factor in the evaluation of source rocks. In this study, the ∆LogR technique was coupled with logging curves of sonic, resistivity, spectral gamma-ray (SGR), and computed gamma-ray (CGR) to determine an accurate content of TOC in the Gadvan Formation. Multiple linear regression analysis was also applied to the ∆LogR technique. To this aim, 14 samples of the Gadvan Formation were taken from Wells B and C in the South Pars field and analyzed using Rock-Eval pyrolysis. Results from the ∆LogR technique and multiple linear regression analysis, well logs, and Rock-Eval were compared to calculate the accurate content of TOC in the Gadvan Formation. Geochemical data confirmed that the Gadvan Formation was a relatively poor source rock in the South Pars field, as average TOC and T_max values of the samples were 0.79 and less than 430 °C, respectively. Also, the content of potassium (K < 0.1%) confirmed the origin of the source rock as a pure carbonate, whereas the low content of thorium (Th < 5 ppm) was indicative of the percentage of clays. There was a moderate content of uranium (U < 10 ppm), suggesting that the Gadvan Formation was not deposited in an excellent reducing environment to conserve the organic matter. Moreover, the results from the integration of the multiple linear regression model with SGR and CGR showed that the value of R² was higher than the results obtained without SGR and CGR. Findings from this study could help the exploration and production team to determine the accurate content of TOC using the ∆LogR technique in association with logging curves. Full article