Explore the vast range of titles available.

Covering the application of different spectroscopic methods to characterize crude oil and related products, this book outlines the basic theory, applications and recent trends in the field.

In this randomized trial involving pregnant women in low- and middle-income countries, birth weight was not higher among infants born to women who used LPG stoves than among those born to women who used biomass stoves.

Petroleum hydrocarbons represent the most frequent environmental contaminant. The introduction of petroleum hydrocarbons into a pristine environment immediately changes the nature of that environment, resulting in reduced ecosystem functionality. Natural attenuation represents the single, most important biological process which removes petroleum hydrocarbons from the environment. It is a process where microorganisms present at the site degrade the organic contaminants without the input of external bioremediation enhancers (i.e., electron donors, electron acceptors, other microorganisms or nutrients). So successful is this natural attenuation process that in environmental biotechnology, bioremediation has developed steadily over the past 50 years based on this natural biodegradation process. Bioremediation is recognized as the most environmentally friendly remediation approach for the removal of petroleum hydrocarbons from an environment as it does not require intensive chemical, mechanical, and costly interventions. However, it is under-utilized as a commercial remediation strategy due to incomplete hydrocarbon catabolism and lengthy remediation times when compared with rival technologies. This review aims to describe the fate of petroleum hydrocarbons in the environment and discuss their interactions with abiotic and biotic components of the environment under both aerobic and anaerobic conditions. Furthermore, the mechanisms for dealing with petroleum hydrocarbon contamination in the environment will be examined. When petroleum hydrocarbons contaminate land, they start to interact with its surrounding, including physical (dispersion), physiochemical (evaporation, dissolution, sorption), chemical (photo-oxidation, auto-oxidation), and biological (plant and microbial catabolism of hydrocarbons) interactions. As microorganism (including bacteria and fungi) play an important role in the degradation of petroleum hydrocarbons, investigations into the microbial communities within contaminated soils is essential for any bioremediation project. This review highlights the fate of petroleum hydrocarbons in tertial environments, as well as the contributions of different microbial consortia for optimum petroleum hydrocarbon bioremediation potential. The impact of high-throughput metagenomic sequencing in determining the underlying degradation mechanisms is also discussed. This knowledge will aid the development of more efficient, cost-effective commercial bioremediation technologies.

The 2010 Deepwater Horizon disaster introduced an unprecedented discharge of oil into the deep Gulf of Mexico. Considerable uncertainty has persisted regarding the oil’s fate and effects in the deep ocean. In this work we assess the compound-specific rates of biodegradation for 125 aliphatic, aromatic, and biomarker petroleum hydrocarbons that settled to the deep ocean floor following release from the damaged Macondo Well. Based on a dataset comprising measurements of up to 168 distinct hydrocarbon analytes in 2,980 sediment samples collected within 4 y of the spill, we develop a Macondo oil “fingerprint” and conservatively identify a subset of 312 surficial samples consistent with contamination by Macondo oil. Three trends emerge from analysis of the biodegradation rates of 125 individual hydrocarbons in these samples. First, molecular structure served to modulate biodegradation in a predictable fashion, with the simplest structures subject to fastest loss, indicating that biodegradation in the deep ocean progresses similarly to other environments. Second, for many alkanes and polycyclic aromatic hydrocarbons biodegradation occurred in two distinct phases, consistent with rapid loss while oil particles remained suspended followed by slow loss after deposition to the seafloor. Third, the extent of biodegradation for any given sample was influenced by the hydrocarbon content, leading to substantially greater hydrocarbon persistence among the more highly contaminated samples. In addition, under some conditions we find strong evidence for extensive degradation of numerous petroleum biomarkers, notably including the native internal standard 17α(H),21β(H)-hopane, commonly used to calculate the extent of oil weathering.

The Deepwater Horizon (DWH) accident in the northern Gulf of Mexico occurred on April 20, 2010 at a water depth of 1525 meters, and a deep-sea plume was detected within one month. Oil contacted and persisted in parts of the bottom of the deep-sea in the Gulf of Mexico. As part of the response to the accident, monitoring cruises were deployed in fall 2010 to measure potential impacts on the two main soft-bottom benthic invertebrate groups: macrofauna and meiofauna. Sediment was collected using a multicorer so that samples for chemical, physical and biological analyses could be taken simultaneously and analyzed using multivariate methods. The footprint of the oil spill was identified by creating a new variable with principal components analysis where the first factor was indicative of the oil spill impacts and this new variable mapped in a geographic information system to identify the area of the oil spill footprint. The most severe relative reduction of faunal abundance and diversity extended to 3 km from the wellhead in all directions covering an area about 24 km(2). Moderate impacts were observed up to 17 km towards the southwest and 8.5 km towards the northeast of the wellhead, covering an area 148 km(2). Benthic effects were correlated to total petroleum hydrocarbon, polycyclic aromatic hydrocarbons and barium concentrations, and distance to the wellhead; but not distance to hydrocarbon seeps. Thus, benthic effects are more likely due to the oil spill, and not natural hydrocarbon seepage. Recovery rates in the deep sea are likely to be slow, on the order of decades or longer.

We use a spatially explicit biogeochemical end-to-end ecosystem model, Atlantis, to simulate impacts from the Deepwater Horizon oil spill and subsequent recovery of fish guilds. Dose-response relationships with expected oil concentrations were utilized to estimate the impact on fish growth and mortality rates. We also examine the effects of fisheries closures and impacts on recruitment. We validate predictions of the model by comparing population trends and age structure before and after the oil spill with fisheries independent data. The model suggests that recruitment effects and fishery closures had little influence on biomass dynamics. However, at the assumed level of oil concentrations and toxicity, impacts on fish mortality and growth rates were large and commensurate with observations. Sensitivity analysis suggests the biomass of large reef fish decreased by 25% to 50% in areas most affected by the spill, and biomass of large demersal fish decreased even more, by 40% to 70%. Impacts on reef and demersal forage caused starvation mortality in predators and increased reliance on pelagic forage. Impacts on the food web translated effects of the spill far away from the oiled area. Effects on age structure suggest possible delayed impacts on fishery yields. Recovery of high-turnover populations generally is predicted to occur within 10 years, but some slower-growing populations may take 30+ years to fully recover.

The potentials of the invasive duckweed species, Lemna paucicostata to remove pollutants from aquatic environment was tested in a constructed wetlands as an ecological based system for the phytoremediation of petroleum hydrocarbons in crude oil-contaminated waters within 120 days. Total petroleum hydrocarbons in wetlands and tissues of duckweed were analyzed using gas chromatography with flame ionization detector following established methods while the experimental data were subjected to the first-order kinetic rate model to understand the remediation rate of duckweed in wetlands. L. paucicostata effected a significant (F = 253.405, P < 0.05) removal of hydrocarbons from wetlands reaching 97.91% after 120 days. Assessment on the transport and fate of hydrocarbons in duckweed indicated that L. paucicostata bioaccumulated less than 1% and significantly biodegraded 97.74% of hydrocarbons in wetlands at the end of the study. The experimental data reasonably fitted (r 2  = 0.938) into the first-order kinetic rate model. From the result of the study, it is reasonable to infer that L. paucicostata is an effective aquatic macrophyte for the removal of petroleum hydrocarbons in moderately polluted waters.

Over the past two decades, oil spills have been one of the most serious ecological disasters, causing massive damage to the aquatic and terrestrial ecosystems as well as the socio-economy. In view of this situation, several methods have been developed and utilized to analyze oil samples. Among these methods, laser-induced fluorescence (LIF) technology has been widely used in oil spill detection due to its classification method, which is based on the fluorescence characteristics of chemical material in oil. This review systematically summarized the LIF technology from the perspective of excitation wavelength selection and the application of traditional and novel machine learning algorithms to fluorescence spectrum processing, both of which are critical for qualitative and quantitative analysis of oil spills. It can be seen that an appropriate excitation wavelength is indispensable for spectral discrimination due to different kinds of polycyclic aromatic hydrocarbons’ (PAHs) compounds in petroleum products. By summarizing some articles related to LIF technology, we discuss the influence of the excitation wavelength on the accuracy of the oil spill detection model and proposed several suggestions on the selection of excitation wavelength. In addition, we introduced some traditional and novel machine learning (ML) algorithms and discussed the strengths and weaknesses of these algorithms and their applicable scenarios. With an appropriate excitation wavelength and data processing algorithm, it is believed that laser-induced fluorescence technology will become an efficient technique for real-time detection and analysis of oil spills.

Oil spills and tarballs are significant pollutants in marine environments, and identifying their sources is crucial for mitigating environmental impacts. This study aims to determine the primary sources of petroleum spills and tarballs along the shores of Genaveh (northwest of the Persian Gulf) by employing chemical fingerprinting techniques and biomarkers. Specifically, petroleum hydrocarbons were analyzed in 19 tarball samples, 13 surface sediment samples from the Genaveh coast, and reference oil samples from 21 offshore oil platforms located in the Bahregan, Khark, Lavan, Siri, and Hendurabi regions. Samples were extracted using the Soxhlet extraction method, followed by two-step column chromatography, and the compositions of n -alkanes, polycyclic aromatic hydrocarbons (PAHs), hopanes, and steranes were determined using gas chromatography–mass spectrometry (GC–MS). The concentration of PAHs in coastal sediment samples from Genaveh ranged from 345 (S841) to 27,374 ng/g-dw (S852), indicating moderate to very high pollution levels. The total concentration of aliphatic hydrocarbons in coastal sediment samples from Genaveh ranged from 8,694 (S842) to 27,374 µg/g-dw (S851), significantly exceeding n- alkane concentrations reported in surface sediments from many other regions worldwide, suggesting considerable pollution levels. For the 19 collected tarball samples, PAH concentrations varied from 597.5 to 10,173 ng/g-dw, while n -alkane concentrations ranged from 27,136 to 66,341 µg/g-dw, which may indicate differences in age or freshness. Diagnostic indicators, such as undifferentiated complex mixtures (UCM), a carbon preference index (CPI) close to 1 for n- alkanes, diagnostic ratios of PAHs, and ratios of hopanes and steranes, pointed to a predominantly petrogenic origin of hydrocarbons in the coastal sediments of Genaveh. The principal component analysis (PCA) results revealed a distinction between oil samples from the Lavan, Hendurabi, and Siri platforms, indicating oil leakage from the Khark oilfield pipelines (Doroud and Forouzan) and Bahregan oilfields (Bahregansar and Soroosh) as a major pollution source. This implies that both oil spills, observed in the coastal sediments of Genaveh, and episodic spills, represented by tarballs, originate from a similar, homogeneous source. Only sediment samples from stations S841 and S812 were not influenced by tarballs, suggesting pollution from a different source compared to other sediment samples. Graphical Abstract