Response of environmental factors to attenuation of toluene in vadose zone

Comprehensive characterization of volatile organic compounds in Chinese chemical industry park soils: Spatial variation, source identification, and health risk assessment

Ubiquitous contamination of the soil environment with volatile organic compounds (VOCs) has raised considerable concerns. However, there is still limited comprehensive surveying of soil VOCs on a national scale. Herein, 65 species of VOCs were simultaneously determined in surface soil samples collected from 63 chemical industrial parks (CIPs) across China. The results showed that the total VOC concentrations ranged from 7.15 to 1842 ng/g with a mean concentration of 326 ng/g (median: 179 ng/g). Benzene homologs and halogenated hydrocarbons were identified as the dominant contaminant groups. Positive correlations between many VOC species indicated that these compounds probably originated from similar sources. Spatially, the hotspots of VOC pollution were located in eastern and southern China. Soils with higher clay content and a higher fraction of total organic carbon (TOC) content were significantly associated with higher soil VOC concentrations. Precipitation reduces the levels of highly water-soluble substances in surface soils. Both positive matrix factorization (PMF) and principal component analysis-multiple linear regression (PCA-MLR) identified a high proportion of industrial sources (PMF: 59.2 % and PCA-MLR: 66.5 %) and traffic emission sources (PMF: 32.3 % and PCA-MLR: 33.5 %). PMF, which had a higher R2 value (0.7892) than PCA-MLR (0.7683), was the preferred model for quantitative source analysis of soil VOCs. The health risk assessment indicated that the non-carcinogenic and carcinogenic risks of VOCs were at acceptable levels. Overall, this study provides valuable data on the occurrence of VOCs in soil from Chinese CIPs, which is essential for a comprehensive understanding of their environmental behavior.

Soil processes modify the composition of volatile organic compounds (VOCs) from CO2- and CH4-dominated geogenic and landfill gases: A comprehensive study

Degradation mechanisms affecting non-methane volatile organic compounds (VOCs) during gas uprising from different hypogenic sources to the surface were investigated through extensive sampling surveys in areas encompassing a high enthalpy hydrothermal system associated with active volcanism, a CH4-rich sedimentary basin and a municipal waste landfill. For a comprehensive framework, published data from medium-to-high enthalpy hydrothermal systems were also included. The investigated systems were characterised by peculiar VOC suites that reflected the conditions of the genetic environments in which temperature, contents of organic matter, and gas fugacity had a major role. Differences in VOC patterns between source (gas vents and landfill gas) and soil gases indicated VOC transformations in soil. Processes acting in soil preferentially degraded high-molecular weight alkanes with respect to the low-molecular weight ones. Alkenes and cyclics roughly behaved like alkanes. Thiophenes were degraded to a larger extent with respect to alkylated benzenes, which were more reactive than benzene. Furan appeared less degraded than its alkylated homologues. Dimethylsulfoxide was generally favoured with respect to dimethylsulfide. Limonene and camphene were relatively unstable under aerobic conditions, while α-pinene was recalcitrant. O-bearing organic compounds (i.e., aldehydes, esters, ketones, alcohols, organic acids and phenol) acted as intermediate products of the ongoing VOC degradations in soil. No evidence for the degradation of halogenated compounds and benzothiazole was observed. This study pointed out how soil degradation processes reduce hypogenic VOC emissions and the important role played by physicochemical and biological parameters on the effective VOC attenuation capacity of the soil.

Predictive modeling of pharmaceutical product removal by a managed aquifer recharge system: Comparison and optimization of models using ensemble learners

Pharmaceutical products (PPs) are emerging water pollutants with adverse environmental and health-related impacts, owing to their toxic, persistent, and undetectable microscopic nature. Globally, increasing scientific knowledge and advanced technologies have allowed researchers to study PP-associated problems and their removal for water reuse. Experimental modeling methods require laborious, lengthy, expensive, and environmentally hazardous lab-work to optimize the process. On the other hand, predictive machine learning (ML) models can trace the complex input-output relationship of a process using available datasets. In this study, ensemble ML techniques, including decision tree (DT), random forest (RF), and Xtreme gradient boost (XGB), were used to explore PP (diclofenac, iopromide, propranolol, and trimethoprim) removal by a managed aquifer recharge (MAR) system. The model input parameters included characteristics of reclaimed water and soil used in the columns, pH, dissolved organic carbon, operating time, nitrogen dioxide, sulfate, nitrate, electrical conductivity, manganese, and iron. The selected PP removal was the model output. Datasets were collected through a one-year experimental study of continuous MAR system operation to predict the removal of PPs. DT, RF, and XGB models were then developed for one of the selected compounds and tested for the others to check the reliability of the ML model results. The developed models were assessed using statistical performance matrices. The experimental results showed >80% removal of propranolol and trimethoprim; however, removal of diclofenac and iopromide was only ≈50% by the MAR system. The proposed DT and RF models presented higher coefficients of determination (R² ≥ 0.92) for diclofenac, propranolol, and trimethoprim than for iopromide (R² ≤ 0.63). In contrast, the XGB model showed better results for diclofenac, iopromide, propranolol, and trimethoprim, with R² values of 0.92, 0.72, 0.96, and 0.97, respectively. Therefore, XGB could be the best predictive model to provide insight into the adaptation of ML models to predict PP removal by the MAR system, thereby minimizing experimental work.

Response relationship of environmental factors caused by toluene concentration during leaching of capillary zone

Due to the leaching of capillary water, the petroleum pollutants initially trapped in vadose zone may migrate to lower aquifer, thus increasing the risk of groundwater pollution. In order to explore the effect of capillary leaching on toluene-contaminated soil and the relationship between toluene concentration (TC) and environmental factors (EFs) during the leaching process, the sterilized and non-sterilized soil column experiments were designed. The EFs were used to estimate TC. The results showed that the difference between leaching and volatilization rates directly determined the changing trend of toluene concentration in capillary water. The toluene concentration in the medium always showed decreasing trend due to leaching. The indigenous microbial community structure of the non-sterilized soil column was analyzed by 16S rRNA sequencing. It was found that indigenous microorganisms could degrade toluene after 33.0 days of acclimatation. The microbial population was dominated by bacteria, among them the Ellin6055 strain and Pseudomonas, Pseudoxanthomonas, Cupriavidus, Bdellovibrio, Sphingobium, Phenylobacterium, Ramlibacter, Bradyrhizobium, Shinella genera. The Pseudomonas was the most crucial bacterial genus that degraded toluene. Indigenous microbial degradation was the fundamental reason for strong response relationship. Furthermore, we suggested a relationship of function between environmental factors (pH, DO, ORP) and time (t) for toluene attenuation: C₀+ln(e^Atα^Bγ^Cβ)=C_Toluene, (α, β, γ represent the pH, DO, and ORP in leaching capillary water, respectively; A, B, and C represent undetermined coefficients), and the fitting coefficient R² > 0.950. This relationship can only characterize the attenuation process of capillary zone leaching on toluene. However, it may still be utilized to give a theoretical foundation for understanding the dynamic of pollutant concentration change processes under specific environmental factors.

A sustainable Decision Support System for soil bioremediation of toluene incorporating UN sustainable development goals

Decision Support System (DSS) is a novel approach for smart, sustainable controlling of environmental phenomena and purification processes. Toluene is one of the most widely used petroleum products, which adversely impacts on human health. In this study, Fusarium Solani fungi are utilized as the engine of the toluene bioremediation procedure for the monitoring part of DSS. Experiments are optimized by Central Composite Design (CCD) - Response Surface Methodology (RSM), and the behavior of the mentioned fungi is estimated by M5 Pruned model tree (M5P), Gaussian Processes (GP), and Sequential Minimal Optimization (SMOreg) algorithms as the prediction section of DSS. Finally, the control stage of DSS is provided by integrated Petri Net modeling and Failure Modes and Effects Analysis (FMEA). The findings showed that Aeration Intensity (AI) and Fungi load/Biological Waste (F/BW) are the most influential mechanical and biological factors, with P-value of 0.0001 and 0.0003, respectively. Likewise, the optimal values of main mechanical parameters include AI, and the space between pipes (S) are equal to 13.76 m³/h and 15.99 cm, respectively. Also, the optimum conditions of biological features containing F/BW and pH are 0.001 mg/g and 7.56. In accordance with the kinetic study, bioremediation of toluene by Fusarium Solani is done based on a first-order reaction with a 0.034 s-1 kinetic coefficient. Finally, the machine learning practices showed that the GP (R2 = 0.98) and M5P (R2 = 0.94) have the most precision for predicting Removal Percentage (RP) for mechanical and biological factors, respectively. At the end of the present research, it is found that by controlling seven possible risk factors in bioremediation operation through the FMEA- Petri Net technique, efficiency of the process can be adjusted to optimum value.