Subsequent Remediation Research Articles

A Review on Remediation Technology and the Remediation Evaluation of Heavy Metal-Contaminated Soils.

With the rapid development of industry and agriculture, soil contamination has become a significant environmental issue, and the heavy metal contamination of soils is an important part of it. The main methods for the remediation of heavy metal-contaminated soils include physical methods, chemical methods, biological methods, and combined remediation methods have been proposed as research deepens. However, the standards and evaluation methods for the remediation of heavy metal-contaminated soils are still not well-established. This article discusses the sources and contamination status of heavy metals in soils, the advantages and disadvantages of remediation technology for heavy metal-contaminated soils, remediation standards, and post-remediation evaluation methods. It also proposes scientific issues to be addressed in future research and provides an outlook on future development, hoping to assist in subsequent remediation studies of heavy metal-contaminated soils.

Strong effect of often-overlooked initial spilled oil distribution on subsequent soil remediation: A pore-scale perspective

The removal of spilled oil within soil by water injection is essentially an immiscible fluid displacement process. Numerous studies have been conducted to investigate the way to achieve a higher displacement efficiency. However, these studies often assumed that the pore space was initially saturated by oil only, ignoring the effect of initial oil distribution on the remediation, which led to incorrectly computed flow field and biased analysis. This work aimed to reveal the effect of initial oil distribution on flow physics during the remediation. Based on a pore-scale model and the phase-field method, the spilled oil invasion and remediation in a heterogeneous porous media were investigated, and the dynamic evolution of the phase interface was examined in particular. Four different distribution patterns of spilled oil were obtained through various velocities during the oil invasion stage, which were considered as initial states for the subsequent remediation stage. The results showed that ignoring the oil invasion process led to a more than 10% reduction in the ultimate remediation efficiency. However, the maximum interfacial area of fluids was larger and the obtained relative permeability curve was much smoother for the scenario considering the oil invasion. Besides, a higher initial water saturation led to a lower starting pressure. This work provided new pore-scale insights into the often-overlooked roles of initial phase distribution in fluid dynamics behind the remediation of oil-contaminated soil by water injection, which was of fundamental importance to the development of effective response strategy for soil contamination.

Prediction of BTEX volatilization in polluted soil based on the sorption potential energy theory

Initial volatile concentration (Cs0) is a crucial parameter for the migration and diffusion of volatile organic pollutants (VOCs) from the soil to the atmosphere. The acquisition of Cs0 is, however, time-consuming and labor-intensive. This study developed a prediction model for Cs0 based on theoretical analysis and experimental simulations. The model was established by correlating the molecular kinetic and sorption potential energy. The pore structure and pore size distribution of the soil were analyzed based on the fractal theory of porous media, followed by calculating the sorption potential energy corresponding to each pore size. It was observed that the pore size distribution of soil influenced BTEX (benzene, toluene, ethylbenzene, and xylene) volatilization by impacting sorption potential energy. The soil parameters, such as organic matter and soil moisture content, and the initial concentration and physical properties of BTEX were coupled to the prediction model to ensure its practicability. Red soil was finally used to verify the accuracy and applicability of the model. The experimental and predicted values' maximum relative and root-mean-square errors were determined to be 24.2% and 11.7%, respectively. The model provides a simple, rapid, and accurate assessment of soil vapor emission content due to BTEX contamination. This study offers an economical and practical method for quantifying the amount of volatile BTEX in contaminated sites, providing a reference for its monitoring, control, and subsequent remediation.

Groundwater Contamination Source Recognition Based on a Two-Stage Inversion Framework with a Deep Learning Surrogate

Groundwater contamination source recognition is an important prerequisite for subsequent remediation efforts. To overcome the limitations of single inversion methods, this study proposed a two-stage inversion framework by integrating two primary inversion approaches—simulation-optimization and simulation-data assimilation—thereby enhancing inversion accuracy. In the first stage, the ensemble smoother with multiple data assimilation method (a type of simulation-data assimilation) conducted a global broad search to provide better initial values and ranges for the second stage. In the subsequent stage, a collective decision optimization algorithm (a type of simulation-optimization) was used for a refined deep search, further enhancing the final inversion accuracy. Additionally, a deep learning method, the multilayer perceptron, was utilized to establish a surrogate of the simulation model, reducing computational costs. These theories and methods were applied and validated in a hypothetical scenario for the synchronous identification of the contamination source and boundary conditions. The results demonstrated that the proposed two-stage inversion framework significantly improved search accuracy compared to single inversion methods, with a mean relative error and mean absolute error of just 4.95% and 0.1756, respectively. Moreover, the multilayer perceptron surrogate model offered greater approximation accuracy to the simulation model than the traditional shallow learning surrogate model. Specifically, the coefficient of determination, mean relative error, mean absolute error, and root mean square error were 0.9860, 9.72%, 0.1727, and 0.47, respectively, highlighting its significant advantages. The findings of this study can provide more reliable technical support for practical case applications and improve subsequent remediation efficiency.

Screening for Conscientiousness at Start of Residency: Early Detection of Professionalism Concerns.

Early identification of residents who may require remediation could help prevent problems for both individual residents and their programs. Our aim in this project was to identify whether residents prone to challenges with professionalism could be identified early. For 3 years we tracked onboarding tasks completed by residents between official match and the first day of residency to develop a tool that would help identify residents who may be at risk for problems with timely chart completion and subsequent remediation. We compared residents' "at risk" scores with in-training exam (ITE) scores to differentiate between professionalism (task completion issues) and medical knowledge. Our at-risk tool successfully predicted timely chart completion rates and need for remediation within our residency program (Fischer's exact P<.001), but was not predictive of ITE scores. Using readily accessible information, we can identify residents at risk for poor task completion performance, who may benefit from extra support and development in the area of organizational skill. Early identification may increase opportunities for early intervention.

Image based analytical approaches for study of particulate matter (PM) in air

Particulate matter (PM) stands as a predominant pollutant in developing countries, demanding effective source identification and remediation strategies. This review centers on the scanning electron microscopy (SEM) image-based methodology for PM analysis, particularly emphasizing the passive technique of utilizing plant leaves for PM capture. The SEM-image-based approach serves as a powerful tool for unraveling the morphological characteristics of PM, crucial for source identification. Additionally, SEM, when equipped with energy dispersive spectroscopy (EDS), enables chemical and mineralogical characterization, providing insights into the origin of PM. The first part of the review describes the plant as the best bio-sampler for PM. In this context, removal of PM from the environment through plant-based interventions is described. Subsequently, the application of SEM for size-based analysis using ImageJ and morphological analysis for source identification of PM is detailed. Following this, the PM chemical and mineralogical composition for source identification are described based on EDS analysis. Image-based techniques play a pivotal role in selecting the most effective plant species for PM removal from the air. The review comprehensively outlines the morphological, chemical, and mineralogical attributes utilized for PM source identification and their subsequent remediation by plants. Finally, the benefits of SEM-image-based techniques for PM analysis are elucidated. This review offers a holistic understanding of the SEM-EDS and plant-based approach, presenting a promising avenue for addressing PM pollution and enhancing environmental quality.

Is increased mutation driving genetic diversity in dogs within the Chornobyl exclusion zone?

Environmental contamination can have lasting impacts on surrounding communities, though the long-term impacts can be difficult to ascertain. The disaster at the Chornobyl Nuclear Power Plant in 1986 and subsequent remediation efforts resulted in contamination of the local environment with radioactive material, heavy metals, and additional environmental toxicants. Many of these are mutagenic in nature, and the full effect of these exposures on local flora and fauna has yet to be understood. Several hundred free-roaming dogs occupy the contaminated area surrounding the Chornobyl Nuclear Power Plant, and previous studies have highlighted a striking level of genetic differentiation between two geographically close populations of these dogs. With this work, we investigate mutation as a possible driver of this genetic differentiation. First, we consider large-scale mutation by assessing the karyotypic architecture of these dogs. We then search for evidence of mutation through short tandem repeat/microsatellite diversity analyses and by calculating the proportion of recently derived alleles in individuals in both populations. Through these analyses, we do not find evidence of differential mutation accumulation for these populations. Thus, we find no evidence that an increased mutation rate is driving the genetic differentiation between these two Chornobyl populations. The dog populations at Chornobyl present a unique opportunity for studying the genetic effects of the long-term exposures they have encountered, and this study expands and builds on previous work done in the area.

Pollution status and health risk assessment of a specific organophosphate pesticide-contaminated site

Organophosphate pesticide pollution is a severe global environmental issue, posing significant threats to human health. This study focuses on an organophosphate pesticide production site in North China. Controlled soil and groundwater were sampled using grid sampling and direct-push technology. Analytical methods such as headspace gas chromatography-mass spectrometry (GC-MS), gas chromatography (GC), and purge and trap GC-MS were employed to detect contaminants, including volatile organic compounds (VOCs), semi-volatile organic compounds (SVOCs), pesticides, and heavy metals. The results indicate that the maximum exceedances in the contaminated soil for benzene, xylene, tri-chloromethane, Parathion, phosphorodithioic acid, Phorate, and Terbufos were 490.63, 411.22, 3459.09, 507.89, 277.29, 1946.15, and 281.5 times the standard limits, respectively. In groundwater, benzene, dichloromethane, trichloromethane, and Phorate exceeded the standard limits by 220.83, 1374, 1853.33, and 806.67 times, respectively, severely contaminating the local soil and groundwater and damaging the eco-system. Additionally, the carcinogenic risk values in the contaminated soil for benzene, toluene, phenylethane, xylene, 1,3,5-trimethylbenzene, 1,2,4-trimethylbenzene, Parathion, 1,2,4-trichlorobenzene, tri-chloromethane, naphthalene, and aniline were 2.75E–3, 1.36E–2, 1.034E–2, 9.72E–1, 1.5E–3, 8.9E–3, 1.85E–3, 4E–3, 3.48E–2, 2.7E–2, 1.03E–3, respectively. In groundwater, toluene, xylene, dichloro-methane, and trichloromethane showed carcinogenic risk values of 8E–3, 1.86E–2, 1.23E–2, 3.07E–2, respectively, significantly exceeding the acceptable limits and posing severe threats to the health of nearby residents. Furthermore, the hazard quotient for non-carcinogenic effects of Cypermethrin in soil was 1.15, and 2,6-dichlorophenol in groundwater was 1.22, both slightly above the standard, indicating a mild impact on the health of nearby residents. Overall, this study provides a theoretical basis for subsequent remediation work at the contaminated site through pollutant analysis.

Reducing Ophthalmic Health Disparities Through Transfer Learning: A Novel Application to Overcome Data Inequality.

Race disparities in the healthcare system and the resulting inequality in clinical data among different races hinder the ability to generate equitable prediction results. This study aims to reduce healthcare disparities arising from data imbalance by leveraging advanced transfer learning (TL) methods. We examined the ophthalmic healthcare disparities at a population level using electronic medical records data from a study cohort (N = 785) receiving care at an academic institute. Regression-based TL models were usesd, transferring valuable information from the dominant racial group (White) to improve visual field mean deviation (MD) rate of change prediction particularly for data-disadvantaged African American (AA) and Asian racial groups. Prediction results of TL models were compared with two conventional approaches. Disparities in socioeconomic status and baseline disease severity were observed among the AA and Asian racial groups. The TL approach achieved marked to comparable improvement in prediction accuracy compared to the two conventional approaches as evident by smaller mean absolute errors or mean square errors. TL identified distinct key features of visual field MD rate of change for each racial group. The study introduces a novel application of TL that improved reliability of the analysis in comparison with conventional methods, especially in small sample size groups. This can improve assessment of healthcare disparity and subsequent remedy approach. TL offers an equitable and efficient approach to mitigate healthcare disparities analysis by enhancing prediction performance for data-disadvantaged group.

Successful Integration of UAV Aeromagnetic Mapping with Terrestrial Methane Emissions Surveys in Orphaned Well Remediation

Orphaned oil and gas wells represent an important environmental and economic development concern in areas where historical energy exploration and production activity pre-dated regulations on well abandonment documentation practices. From an economic development perspective, these wells pose a subsurface risk to infrastructure development, while the environmental impact of orphaned wells is largely associated with uncontrolled emissions of both fluids and gasses, most notably atmospheric methane. Often neglected in regulatory oversight, methane emissions from orphaned wells contribute to global greenhouse gas concentrations and without proper mitigation, these emissions contribute to and further exacerbate global climate change processes. An inherent challenge of remediation efforts targeting orphaned wells is the consistent observation that only a fraction of located and identified wells produce the majority of methane emissions, yet no methodology exists to effectively separate out heavy emitters without visiting and assessing each and every well. We demonstrate that emitting wells have certain defined characteristics largely pertaining to the presence and integrity of metal casing to the surface, which can be distinguished as an organized high intensity magnetic anomaly in low-altitude UAV-based aeromagnetic surveys. In this paper, we present a database of identified and ground-truthed wellsites correlated to their magnetic signatures and provide a roadmap to initial prioritization of wellsites for subsequent remediation activities that can be implemented in complex environments where other survey options are not feasible. In a regulatory environment where resources dedicated to wellsite remediation are limited, we propose implementing this methodology as a key element of a targeted approach to wellsite prioritization for subsequent remediation activity.

Isotope forensics of polycyclic aromatic compounds (PACs) in a contaminated shallow aquifer

Diesel was accidently released into the shallow subsurface at an industrial site in the province of Québec, Canada, in the late 1980s. Subsequent remediation efforts removed much of the contamination; however, traces of petroleum hydrocarbons continue to impact the local aquifer. In addition to the historical diesel spill, more recent yet unconfirmed accidental releases from ongoing on-site and neighbouring industrial activities may have potentially contributed to elevated levels of polycyclic aromatic compounds (PACs) in groundwater. To identify the main source(s) of contamination, compound-specific stable carbon isotope ratios (δ13C) of PACs in groundwater monitoring wells were compared to those in asphalt produced from a nearby plant and in fuel oil #6 oil being used by local industry. The δ13C values of five individual compounds (biphenyl, C2-naphthalene, C1-fluorene, dibenzothiophene and phenanthrene) and two groups of combined C1-phenanthrenes/anthracenes in all groundwater samples were within analytical uncertainty (±0.5‰). Moreover, the δ13CPAC values in groundwater samples were distinct from those in asphalt and fuel oil #6, indicating negligible contributions from these sources. The similarity in δ13CPAC values across monitoring wells, including one situated in the former source zone containing a floating hydrocarbon phase, pointed to a common source of subsurface contamination that was attributed to the historical diesel spill. These results thus demonstrate that δ13CPAC values can be used for source apportionment in shallow aquifers decades after the original spill event.

Polypyrrole-induced active-edge-S and high-valence-Mo reinforced composites with boosted electrochemical performance for the determination of 2,4,6-trichlorophenol in the aquatic environment

With the extensive application of halogenated aromatic compounds, including 2,4,6-Trichlorophenol (2,4,6-TCP), improper treatment or discharge contribute to persistently harmful effects on humans and the ecosystem, rendering the identification and monitoring of 2,4,6-TCP in the aquatic environment urgently required. In this study, a highly sensitive electrochemical platform was developed using active-edge-S and high-valence-Mo rich MoS2/polypyrrole composites. MoS2/PPy illustrates superior electrochemical performance and catalytic activity and has not been explored for detecting chlorinated phenols previously. The local environment of polypyrrole induces the richness of active edge S and a high oxidation state of Mo species in the composites, both of which endorse a sensitive anodic current response due to the favored oxidation of 2,4,6-TCP through nucleophilic substitution. Also, the higher complementarity between pyrrole and 2,4,6-TCP with respective electron-rich and electron-poor features through π-π stacking interactions enhances the specific detection capability of 2,4,6-TCP by the MoS2/polypyrrole-modified electrode. The MoS2/polypyrrole-modified electrode achieved a linear range of 0.1–260 μM with an ultralow limit of detection of 0.009 μM. Additionally, the structural stability boosted by the linkage of polypyrrole and MoS2 results in good resistance and satisfactory recovery in real water samples. The compiled results demonstrate that the proposed MoS2/polypyrrole composite opens up a new potential to advance a sensitive, selective, facile fabrication, and low-cost platform for the on-site determination of 2,4,6-TCP in aquatic systems. The sensing of 2,4,6-TCP is important to monitor its occurrence and transport, and can also serve to track the effectiveness and adjust subsequent remediation treatments applied to contaminated sites.

Rethinking governance through Samarco’s dam collapse in Brazil: a critique from the STS perspective

ABSTRACT This paper is based on a study of the Fundão dam collapse in Minas Gerais, Brazil, and it aims to analyze a widely used and rarely problematized concept in the corporate and, increasingly, in the public policy world: governance. Considering, in this case, the environmental governance that originated the disaster and the subsequent remediation governance, source of profound inequity, I try to extract its main problematics. In fact, the critical literature on governance systems points to the forced depoliticization of political decisions related to the transfer of public responsibilities to the private sector as the main issue. Based on the case, I intend to argue that the hegemony of techno-scientific solutions in eminently political contexts founds the aforementioned depoliticization, adding to and therefore aggravating the asymmetries of power already configured in terms of race and social class.

A magnetic sludge carbon combined persulfate-based ISCO system for leachate-contaminated groundwater remediation

Activation of persulfate by a green magnetic Steel Converter Slag-Sludge Carbon composite (SCS@SC) and subsequent remediation of actual leachate wastewater was studied in an intermittent reactor and column system to assess the potential of the persulfate-based in situ chemical oxidation (SR-ISCO) system for combined oxidative-reductive removal of organic contaminants from groundwater. In the batch experiment, an iron leaching concentration of 6942 mg/L, was higher than 223.2 and 124.4 times that of steel converter slag (31.14 mg/L) and sole sludge carbon (55.80 mg/L). In the column experiments, the total organic carbon (TOC) removal was maintained at 41.7 %, the loading rate reached 48.86 % with 15 g SCS@SC and 0.15 M persulfate after 400 min. The persulfate consumption, sulfuric acid generation, free radical generation, and iron ions flow out under acidic conditions, which are consistent with TOC removal. The mechanism of persulfate activation and organic matter removal was discussed and proposed by electron paramagnetic resonance (EPR) and energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), transmission electron microscopy (TEM), and gas chromatography-mass Spectrometry (GC–MS) to characterize the chemistry of solid and aqueous phases. A mechanism for contaminant removal was presented, which involves free radicals of persulfate activation by zero-valent iron (Fe0) and iron carbide (Fe3C) and precipitation removal by iron oxides. According to the findings, a viable alternative method for the inexpensive synthesis of catalysts from solid wastes such as sludge and steel converter slag was developed.

DEVELOPING SUSTAINABLE INNOVATION FROM MAIZE HUSKS FOR THE REMEDIATION OF SOIL AND WATER POLLUTED BY E-WASTES. CASE STUDY: THE EVACUATED LANDFILL SITE AT OWERRI, IMO STATE, NIGERIA

A novel and less cost effect remediation strategy was designed to meet the challenges posed by e-waste in parts of Owerri in Imo State of Nigeria. This followed the determination of the levels of contamination of the environment within a recently evacuated landfill site (dumpsite) in Owerri Imo state, Nigeria by the leaching of heavy metals from e-wastes. The analysis was necessitated as people were already settling very close to the site and cultivating plants for human consumption without prior examination of the vicinity and subsequent remediation of the polluted site. The Energy dispersive X-Ray Fluorescence (EDXRF), Atomic Absorption Spectroscopy (AAS) and the Pollution Index (PI) were used on randomly collected soil from the evacuated landfill site to obtain first, an overview, then the concentration of heavy metals present in the soil. The following heavy metals from the landfill site were highlighted: Pb, Ni, Zn, Cd, Cr, Cu, As and Fe and the level of the presence of each confirmed. Atomic Absorption Spectroscopy (AAS) was also used to determine the concentrations of the heavy metals in a shallow well located very close to the site. The concentration of lead and cadmium were hazardously found to fall above the World Health Organization (2003) standard in both the soil and shallow well. Lead for example was found to be 24.20mg/kg in the soil and 0.230mg/L in the shallow well as against 0.70mg/kg permissible in Nigeria. The lead and cadmium ions apparently leached from everyday electronic materials (e-wastes) dumped in the landfill site as these e-waste materials abound in the said site and area. A research was therefore carried out to assess the removal or reduction of lead(II) ion and Cadmium(II)ion in the polluted shallow well water by some maize wastes - from maize/corn husks biomass. The efficiency of this novel research is high as the biomass part before and after adsorption of Pb2+ and Cd2+using Fourier Transform Infrared Spectra showed that the waste maize part (husks) have available functional groups and as a result, active sites for the adsorption of Cd2+and Pb2+. This research is relevant particularly to all those living around the vicinity and to the Government Scientists who would need to speed up the remediation of the area and create a safe e-waste disposition method.

Tracking chlorinated contaminants in the subsurface using analytical, numerical and geophysical methods

In recent years, many research methods have been developed for the traceability of groundwater contamination source, in which the numerical simulation and analytical methods are the most common methods to study on groundwater flow and solute transport. However, the establishment and solution of an optimization model is a very complex inverse problem. Given that many decision variables are needed to be identified, two relatively simple analytical and numerical methods are applied for the prediction of chloride migration range and duration process in source area, then the geophysical prospecting and drilling sampling analysis are also used for the verification, moreover, the source center is determined based on the difference between predicted results and measured results. In addition, the influence of the observation points layout, hydrodynamic dispersion parameters and groundwater flow rate on the traceability effect are also analyzed. The results show that located observation points can reflect the chloride distribution accurately, hydrodynamic dispersion parameters and groundwater flow rate have more significant impacts on the traceability effect compared with other factors. Lastly, the proposed model application process is also discussed in the limited scale site, and it provides the reference for source traceability and subsequent remediation design under the similar hydrogeological conditions.

Cadmium, lead and arsenic contamination in an abandoned nonferrous metal smelting site in southern China: Chemical speciation and mobility

The investigation of chemical speciation of primary toxic metal(loid)s (Cd, Pb, and As) in soil profile in nonferrous metal smelting site is a key to the assessment of their mobility characteristics and formulation of subsequent remediation strategy. In this study, 74 soil samples were collected at 12 different soil profiles; soil physio-chemical properties and total content of Cd, Pb and As and corresponding chemical speciation were also determined. The results showed that the mean total concentration followed the order of Pb > As > Cd. A large proportion of Pb, Cd and As were accumulated in upper soil profiles (depth < 3 m). Heavy pollution of Pb, Cd and As were observed in the whole soil profile at the area of fuel oil storage tank (ZY6) and lead smelting area (ZY8). The dominant fraction of Cd was exchangeable fraction (F1); Pb was dominant in Fe/Mn oxides-bound fraction (F3) in most cases; Crystallized Fe/Al hydrous oxides bound fraction (F4) generally accounted for a large proportion of As. Mobility factor (MF) followed the order Cd > As > Pb, indicating that Cd was the most mobile element in soil profiles. Pearson correlation analysis found that MFCd was significantly positively correlated to soil silt; the F4 fraction percentage of As was significantly positively correlated to soil redox potential (Eh). Additionally, MFCd/Pb was found to be positively correlated to crystalline iron (Fec), while negatively correlated to amorphous iron (Feo). The findings reported in this study, on the basis of distribution characteristics of chemical speciation could provide a new solution for future soil remediation at the site. Long-term solutions to metal(loid)s pollution might be offered by microbial-assisted soil washing technique that promotes the transformation of Fe/Mn oxides-bound fraction and organic/sulfide-bound fraction.

Remediating sub-Saharan Airline challenges and limitations

Background: Air transport connectivity to and within Africa is provided by both African and non-African airlines. These carriers are confronted by both intrinsic and extrinsic challenges, which constrain their ability to provide the level of air transport connectivity demanded to fully enable economic growth for the continent. In addition, since April 2020, the reduction in air connectivity resulting from the coronavirus disease 2019 (COVID-19) pandemic has further constrained the capacity of the air transport industry to meet the passenger and freight demand.Objective: The primary objective of this study was to identify the challenges faced by the airline industry in sub-Saharan Africa in meeting the air transport demands of expected economic growth, and to rank the identified challenges in terms of their urgency and importance.Method: Initial data were collected from three sources: (1) a review of the literature, (2) industry reports, and (3) as a further but secondary resource, the key inputs into air connectivity model metrics were used to identify additional possible key result areas.From these data, the study derived open-ended questions as prompts for face-to-face (not virtual) interviews with chief executives of the airline industry. This enabled triangulation of the initial data for verification, and then prioritisation for its application in a possible subsequent project-based remediation strategy.Results: This study revealed that lack of route liberalisation, management weaknesses, state protection, lack of competition, connectivity challenges and the need for airline partnerships were the most important challenges faced by the industry. In this study, the identified challenges were ranked by thematic analysis.Conclusion: The key products of this study include the identification and ranking of the challenges impacting the sub-Saharan African air transport industry.

Selection of optimal models for predicting growth stress in Artemisia desertorum by comparison of linear regression and multiple neural networks: Take the construction of a green mine in the Bayan Obo mine as an example

In recent years, more and more countries are focusing on the control of mining sites and the surrounding ecological environment, and the new environmental concept of green mines has been proposed. By investigating the ecological background of a mine site, pollution and ecological imbalances in the mine can be predicted, managed or transformed. This study investigated the effects of rare earth elements on plant growth in the Baotou Bayan Obo Rare Earth Mine and evaluated soil contamination and subsequent remediation through the measured plant height. Using linear regression, BP(Back Propagation) neural networks, GA-BP(Genetic Algorithm- Back Propagation) neural networks, ELM(Extreme Learning Machine) and GA-ELM(Genetic Algorithm- Extreme Learning Machine) model prediction instruments, the different rare earth solution concentrations were set as input values and the heights of Artemisia desertorum, which as the model plant, were set as output values in the prediction. The results showed that the linear regression predicted the standard error of single La(III), Ce(III) solution and compound La(III) + Ce(III) solution for Artemisia desertorum growth stress was on the high side, 7.02%− 8.92%; the efficiency range of each group of models under BP neural network, GA-BP neural network and ELM neural network were 1.15%− 2.53%, 0.85%− 1.28%, 1.76%− 3.53%; while the efficiency range under GA-ELM neural network was 0.59%− 0.68%, with average error values and predicted values close to the true values. Among them, the MAPE of GA-ELM neural network are significantly lower than other models, and the error decreases with increasing concentration of the compound solution. So GA-ELM neural network can be used as an efficient, fast and reasonable optimal model for predicting the growth stress of Artemisia desertorum in Bayan Obo mining area. The experimental results can provide a theoretical basis for assessing the risk of soil rare earth contamination in the area, evaluating the expectation of later remediation, and provide a degree of new ideas for the construction of green mines.

Density-regulated remediation of dense non-aqueous phase liquids using colloidal biliquid aphrons (CBLA): Force model of transport and distribution

Using colloidal biliquid aphrons (CBLAs) for density control has been proved to a promising technology in dense non-aqueous phase liquids (DNAPLs) contaminated aquifer remediation. However, the transport and distribution of CBLAs in aquifer is an urgent issue for actual application in groundwater. Especially considering the fact that CBLAs have a lower density than water. In this work, the role of buoyancy force on CBLA transport in water-saturated sandbox was investigated, and the force model of CBLA in pore space was developed. Furthermore, the density regulation of trichloroethylene (TCE) in sandbox was studied using CBLA. We found that buoyancy plays a significant role compared with other interaction forces in the transport of CBLA, and the sine of the rising angle of CBLA has a significant correlation with the force on CBLA. CBLA at 5 times the volume of TCE displaced the TCE at the bottom of the tank by upward mobility and the maximum concentration dramatically decreased to 31.23 mg/L. These results can be used for predicting the transport of CBLA (as well as other remediation reagents that are less dense than water) in aquifer and are beneficial to the subsequent remediation application of CBLA in actual contaminated sites.