Rampant industrial growth and urbanization have caused a wide range of hazardous contaminants to be released into the environment resulting in several environmental issues that could eventually lead to ecological disasters. The unscientific disposal of urban and industrial wastes is a critical issue as it can cause soil contamination, bioaccumulation in crops, groundwater contamination, and changes in soil characteristics. This article explores the impact of various industrial and urban wastes, including petroleum hydrocarbons (PHs), coal-fired fly ash, municipal solid waste (MSW) and wastewater (MWW), and biomedical waste (BMW) on various types of soil. The contamination and impact of each of these wastes on soil properties such as compaction characteristics, plasticity, permeability, consolidation characteristics, strength characteristics, pH, salinity, etc is studied in detail. Most of the studies indicate that these wastes contain heavy metals, organics, and other hazardous compounds. When applied to the soil, PHs tend to cause large settlements and reduction in plasticity, while the effect of coal-fired fly ash varies as it mainly depends on the type of soil. From the studies it was seen that the long-term application of MWW improves the soil health and properties for agricultural purposes. Significant soil settlements were observed in areas of MSW disposal, and studies show that MSW leachate also alters soil properties. While the impacts of direct BMW disposal have not been extensively studied, few researchers have concentrated on utilizing certain components of BMW, like face masks and nitrile gloves to enhance the geotechnical characteristics of weak soil. Soil remediation is required to mitigate the contamination caused by heavy metals and PHs from these wates to improve the soil quality for engineering and agricultural purposes, avert bioaccumulation in crops, and pose less environmental and public risks, and ecotoxicity. Coal-fired fly ash and biomedical waste ash contain compounds that promote pozzolanic reactions in soil, recycling and reuse as soil stabilizers offer an effective strategy for their reduction in the environment, thus complying to sustainable practices. In essence, this study offers a contemporary information on the above aspects by identifying the gaps for future research and mitigation strategies of contaminated soils.

Petroleum pollution in soil is very damaging to the areas affected by the accidental release of petroleum hydrocarbons and has destructive impacts on natural resources and environmental health. Therefore, its monitoring and analysis are critical, however, due to the cost and time associated with chemical approaches, finding a quick and cost-effective analytical method is valuable. This study was conducted to evaluate the potential of using visible near infrared (Vis-NIR) spectroscopy to predict total petroleum hydrocarbons (TPH) in polluted soils around the Shadegan ponds, in southern Iran. One hundred soil samples showing various degrees of pollution were randomly collected from topsoil (0-10 cm). The soil samples were analyzed for TPH using Vis-NIR reflectance spectroscopy in the laboratory and then following application of preprocessing transformation, partial least squares PLS regression as well as two machine learning models including random forest (RF), and support vector machine (SVM) were examined. The results showed that the reflectance values at 1725 nm and 2311 nm, respectively, served as indicative TPH reflectance features, exhibiting weaker reflection with rising TPH. Among the preprocessing methods, the baseline correction method indicated the highest performance than the others. According to the evaluation model criteria in the validation dataset, the efficiency of the three selected models was observed in the following order SVM > RF > PLS regression. The SVM model provided the best performance in the validation dataset with r(2) = 0.85, root mean of square (RMSEP = 1.59 %, and the ratio of prediction to deviation (RPD) = 2.6. Overall, this study provided strong evidence supporting the considerable potential of Visible-NIR spectroscopy as a rapid and cost-effective technique for estimating TPH levels in oil-contaminated soils, surpassing traditional chemical analytical methods. Applying the mid-infrared spectrum (MIR) in combination with Visible-NIR data is expected to provide more comprehensive and accurate results when assessing soils in polluted areas, thereby enhancing the accuracy and reliability of the results across a diverse range of soil types.

Kerosene is widely used in various types of anthropogenic activities. Its environmental safety is mainly discussed in the context of aerospace activities. At all stages of its life cycle, aerospace activity impacts the environment. In aviation, the pollution of atmospheric air and terrestrial ecosystems is caused, first of all, by jet fuel and the products of its incomplete combustion and is technologically specified for a number of models in the case of fuel leak during an emergency landing. In the rocket and space activities, jet fuel enters terrestrial ecosystems as a result of fuel spills from engines and fuel tanks at the crash sites of the first stages of launch vehicles. The jet fuel from the second and third stages of launch vehicles does not enter terrestrial ecosystems. The fuel components have been studied in sufficient detail. However, the papers with representative data sets and their statistical processing not only for the kerosene content, but also for the total petroleum hydrocarbons in the soils affected by aerospace activity are almost absent. Nevertheless, the available data and results of mathematical modeling allow us to assert that an acceptable level of hydrocarbons, not exceeding the assimilation potential, enters terrestrial ecosystems during a regular aerospace activity. Thus, the incoming amount of jet fuel disappears rapidly enough without causing any irreversible damage.

Featured Application Python application that uses data science and machine learning to estimate the main properties of acid tars. Its main advantage is that determinations for acid tar properties are no longer necessary, thus saving time and money. However, good machine learning estimations are highly dependent on the number and quality of the training data, meaning that the larger and more consistent the training database, the better the estimations.Abstract Hazardous petroleum wastes are an inevitable source of environmental pollution. Leachates from these wastes could contaminate soil and potable water sources and affect human health. The management of acid tars, as a byproduct of refining and petrochemical processes, represented one of the major hazardous waste problems in Romania. Acid tars are hazardous and toxic waste and have the potential to cause pollution and environmental damage. The need for the identification, study, characterization, and subsequently either the treatment, valorization, or elimination of acid tars is determined by the fact that they also have high concentrations of hydrocarbons and heavy metals, toxic for the storage site and its neighboring residential area. When soil contamination with acid tars occurs, sustainable remediation techniques are needed to restore soil quality to a healthy production state. Therefore, it is necessary to ensure a rapid but robust characterization of the degree of contamination with hydrocarbons and heavy metals in acid tars so that appropriate techniques can then be used for treatment/remediation. The first stage in treating these acid tars is to determine its properties. This article presents a software program that uses machine learning to estimate selected properties of acid tars (pH, Total Petroleum Hydrocarbons-TPH, and heavy metals). The program uses the Automatic Machine Learning technique to determine the Machine Learning algorithm that has the lowest estimation error for the given dataset, with respect to the Mean Average Error and Root Mean Squared Error. The chosen algorithm is used further for properties estimation, using the R2 correlation coefficient as a performance criterion. The dataset used for training has 82 experimental points with continuous, unique values containing the coordinates and depth of acid tar samples and their properties. Based on an exhaustive search performed by the authors, a similar study that considers machine learning applications was not found in the literature. Further research is required because the method presented therein can be improved because it is dataset dependent, as is the case with every ML problem.

The Loess Plateau, located in Gansu Province, is an important energy base in China because most of the oil and gas resources are distributed in Gansu Province. In the last 40 a, ecological environment in this region has been extremely destroyed due to the over-exploitation of crude-oil resources. Remediation of crude-oil contaminated soil in this area remains to be a challenging task. In this study, in order to elucidate the effects of organic compost and biochar on phytoremediation of crude-oil contaminated soil (20 g/kg) by Calendula officinalis L., we designed five treatments, i.e., natural attenuation (CK), planted C. officinalis only (P), planted C. officinalis with biochar amendment (PB), planted C. officinalis with organic compost amendment (PC), and planted C. officinalis with co-amendment of biochar and organic compost (PBC). After 152 d of cultivation, total petroleum hydrocarbons (TPH) removal rates of CK, P, PB, PC and PBC were 6.36%, 50.08%, 39.58%, 73.10% and 59.87%, respectively. Shoot and root dry weights of C. officinalis significantly increased by 172.31% and 80.96% under PC and 311.61% and 145.43% under PBC, respectively as compared with P (P<0.05). Total chlorophyll contents in leaves of C. officinalis under P, PC and PBC significantly increased by 77.36%, 125.50% and 79.80%, respectively (P<0.05) as compared with PB. Physical-chemical characteristics and enzymatic activity of soil in different treatments were also assessed. The highest total N, total P, available N, available P and SOM occurred in PC, followed by PBC (P<0.05). C. officinalis rhizospheric soil dehydrogenase (DHA) and polyphenol oxidase (PPO) activities in PB were lower than those of other treatments (P<0.05). The values of ACE (abundance-based coverage estimators) and Chao indices for rhizospheric bacteria were the highest under PC followed by PBC, P, PB and CK (P<0.05). However, the Shannon index for bacteria was the highest under PC and PBC, followed by P, PB and CK (P<0.05). In terms of soil microbial community composition, Proteiniphilum, Immundisolibacteraceae and Solimonadaceae were relatively more abundant under PC and PBC. Relative abundances of Pseudallescheria, Ochroconis, Fusarium, Sarocladium, Podospora, Apodus, Pyrenochaetopsis and Schizpthecium under PC and PBC were higher, while relative abundances of Gliomastix, Aspergillus and Alternaria were lower under PC and PBC. As per the nonmetric multidimensional scaling (NMDS) analysis, application of organic compost significantly promoted soil N and P contents, shoot length, root vitality, chlorophyll ratio, total chlorophyll, abundance and diversity of rhizospheric soil microbial community in C. officinalis. A high pH value and lower soil N and P contents induced by biochar, altered C. officinalis rhizospheric soil microbial community composition, which might have restrained its phytoremediation efficiency. The results suggest that organic compost-assisted C. officinalis phytoremediation for crude-oil contaminated soil was highly effective in the Loess Plateau, China.