Xanthan gels were assessed to control the reductive dechlorination of hexachlorocyclohexanes (HCHs) and trichlorobenzenes (TCBs) in a strong permeability contrast and high velocity sedimentary aquifer. An alkaline degradation was selected because of the low cost of NaOH and Ca(OH)₂. The rheology of alkaline xanthan gels and their ability to deliver alkalinity homogeneously, while maintaining the latter, were studied. Whereas the xanthan gels behaved like non-Newtonian shear-thinning fluids, alkalinity and Ca(OH)₂ microparticles had detrimental effects, yet, the latter decreased with the shear-rate. Breakthrough curves for the NaOH and Ca(OH)₂ in xanthan solutions, carried out in the lowest permeability soil (9.9 μm²), demonstrated the excellent transmission of alkalinity, while moderate pressure gradients were applied. Injection velocities ranging from 1.8 to 3.8 m h⁻¹ are anticipated in the field, given the permeability range from 9.9 to 848.7 μm². Despite a permeability contrast of 8.7 in an anisotropic aquifer model, the NaOH and the Ca(OH)₂ both in xanthan gels spread only 5- and 7-times faster in the higher permeability zone, demonstrating that the delivery was enhanced. Moreover, the alkaline gels which were injected into a high permeability layer under lateral water flow, showed a persistent blocking effect and longevity (timescale of weeks), in contrast to the alkaline solution in absence of xanthan. Kinetics of alkaline dechlorination carried out on the historically contaminated soil, using the Ca(OH)₂ suspension in xanthan solution, showed that HCHs were converted in TCBs by dehydrodechlorination, whereas the latter were then degraded by reductive hydrogenolysis. Degradation kinetics were achieved within 30 h for the major and most reactive fraction of HCHs.

As one of the most cost-effective and sustainable methods for contaminants' removal, sequestration and/or detoxification, phytoremediation has already captured comprehensive attention worldwide. Nevertheless, the accurate effects of various spatial pattern in enhancing phytoremediation efficiency is not yet clear, especially for the polluted mining areas. This study designed nine planting patterns (monocropping, double intercropping and triple intercropping) of three indigenous plant species (Setaria viridis (L.), Echinochloa crus-galli (L.) and Phragmites australis (Cav.) Trin. ex Steud.) to further explore the effects of plants spatial pattern on phytoremediation efficiency. Considering the uncertainties of the residual contaminants' concentration (RCC) caused by soil anisotropy, permeability and land types, the interval transformation was introduced into the plant uptake model to simulate the remediation efficiency. Then multi-criteria decision analysis (MCDA) were applied to optimal the planting patterns, with the help of criteria of (a) the amount of heavy metal absorption; (b) the concentration of residual contaminant in soil; (c) root tolerance of heavy metals; (d) the total investment cost. Results showed that (1) the highest concentrations of Zn, Cd, and Pb of the polluted area were 7320.02, 14.30, 1650.51 mg kg⁻¹ (2) During the 180 days simulation, the highest RMSE of residue trace metals in soil are 3.02(Zn), 2.67(Pb), 2.89(Cd), respectively. (3) The result of IMCDA shows that the planting patterns of Setaria viridis, Echinochloa crus-galli and Phragmites australis in alternative a9 (269 mg kg⁻¹ year⁻¹) had the highest absorption rate of heavy metals compared with a7 (235 mg kg⁻¹ year⁻¹) and a2 (240 mg kg⁻¹ year⁻¹). After 20 years of remediation, the simulated RCC in a9 is far below the national standard, and the root toxicity is 0.12 (EC ≤ EC₂₀). In general, the optimal alternative derived from interval residual contaminant concentration can effectively express the dynamic of contaminant distribution and then can be effectively employed to evaluate the sustainable remediation methods.

Air pollution is a complex process and is affected by meteorological conditions and other chemical components. Numerous studies have demonstrated that data-driven spatio-temporal prediction models of PM₂.₅ concentration are comparable with the model-driven model. However, data-driven models are usually depending on the statistical correlation between PM₂.₅ and other factors and have challenges in dealing with causality in complex systems. In this paper, we argue that domain knowledge should be incorporated into data-driven models to enhance prediction accuracy and make the model more physically realistic. We focus on the influence of dynamic wind-field on PM₂.₅ concentration distribution and fuse the pollution diffusion distance with the deep learning model based on a wind-field surface. In order to model spatial dependence between monitoring stations, which is dynamic and anisotropic because of the wind-field, we proposed a hybrid deep learning framework, dynamic directed spatio-temporal graph convolution networks (DD-STGCN). It expanded the ability to deal with space-time prediction in the continuous and dynamic wind-field. We used a directed graph time-series to describe the vertex state and topological relationship between vertices and replaced traditional Euclidean distance with wind-field diffusion distance to describe the proximity relationship between vertices. Our experiment results demonstrated that the DD-STGCN model achieved a better prediction ability than LSTM, GC-LSTM, and STGCN models. Compared to the best comparison model, MAPE, MAE, and RMSE were improved by 10.2%, 9.7%, and 9.6% in 12 h on an average, respectively. The performance of our model was further tested during a haze period. In the case that two models both considered the effect of wind, compared with the pure data-driven model, our model performed better in prediction distribution and showed the benefit of spatial interpretability provided by domain knowledge.

Air pollution exposure during early-life is associated with altered brain development, but the precise periods of susceptibility are unknown. We aimed to investigate whether there are periods of susceptibility of air pollution between conception and preadolescence in relation to white matter microstructure and brain volumes at 9–12 years old. We used data of 3515 children from the Generation R Study, a population-based birth cohort from Rotterdam, the Netherlands (2002–2006). We estimated daily levels of nitrogen dioxide (NO2), and particulate matter (PM2.5 and PM2.5absorbance) at participants’ homes during pregnancy and childhood using land-use regression models. Diffusion tensor and structural brain images were obtained when children were 9–12 years of age, and we calculated fractional anisotropy and mean diffusivity, and several brain structure volumes. We performed distributed lag non-linear modeling adjusting for socioeconomic and lifestyle characteristics. We observed specific periods of susceptibility to all air pollutants from conception to age 5 years in association with lower fractional anisotropy and higher mean diffusivity that survived correction for multiple testing (e.g., −0.85 fractional anisotropy (95%CI -1.43; −0.27) per 5 μg/m³ increase in PM2.5 between conception and 4 years of age). We also observed certain periods of susceptibility to some air pollutants in relation to global brain and some subcortical brain volumes, but only the association between PM2.5 and putamen survived correction for multiple testing (172 mm³ (95%CI 57; 286) per 5 μg/m³ increase in PM2.5 between 4 months and 1.8 year of age). This study suggested that conception, pregnancy, infancy, toddlerhood, and early childhood seem to be susceptible periods to air pollution exposure for the development of white matter microstructure and the putamen volume. Longitudinal studies with repeated brain outcome measurements are needed for understanding the trajectories and the long-term effects of exposure to air pollution.

Several works have been performed in order to understand seawater intrusion by simulating the Henry problem. Investigations were implemented by simulating the effect of many parameters (fractured aquifer, dispersion and diffusion, geochemical reactions, heterogeneity, anisotropy, boundary conditions) on the flow and transport. This paper focus on the concept of the “stagnation point,” and this concept plays an important role in modeling, management and characterization of coastal aquifer. A total of forty-eight simulations, including the base case, were made to explore the effect of molecular diffusion coefficient, dispersivity, and seawater density on the position of the stagnation point. It was found that the increase of the molecular diffusion coefficient or the dispersivity leads to a downward displacement of the stagnation to the aquifer bottom and the lowest point position is reached for a Pe value of 0.35. For the seawater density effect, numerical results predict a nonlinear behavior of the stagnation point position, where the downward displacement is detected only for a ρₛ ranging from 1025 to 1045 kg/m³.

CO₂ can be injected into deep saline aquifers for storage, thereby reducing the concentration of CO₂ in the atmosphere. When CO₂ is injected into the aquifer, salt precipitation may occur, which may impair the injectivity and affect storage safety. In this study, numerical simulation was used to study salt precipitation in heterogeneous anisotropic sandstone aquifers, the feedback effect of salt precipitation on the flow was considered, and the additional pressure increase caused by salt precipitation was evaluated. The results showed that the maximum decrease in formation permeability and the maximum additional pressure buildup caused by salt precipitation reached 88% and 4.91 MPa, respectively. The salinity of the formation water and the maximum additional pressure buildup is approximately proportional when the salinity is low. Once the salinity exceeds a certain value (approximately 20% in this study), the maximum additional pressure buildup increases sharply. As the permeability increases, the additional pressure buildup decreases. When permeability reaches a certain threshold (approximately 5×10⁻¹⁴ m² in this study), the maximum additional pressure buildup decreases rapidly and changes only slightly as permeability increases. The CO₂ injection rate is basically proportional to the maximum additional pressure buildup. When the vertical permeability increases, the additional pressure buildup due to salt precipitation shows a downward trend. The low-permeability interbeds above the CO₂ injection well will cause more local salt precipitation near it, which will further cause a greater and wider pressure buildup. The heterogeneity of the formation will greatly enhance salt precipitation, thereby promoting the formation pressure buildup. The formation heterogeneity must be considered in the study of the salt precipitation and its effect on CO₂ injection, especially when the formation permeability is low, the CO₂ injection rate is high, and the salinity of the formation water is high.

Vertical wells are commonly used for recirculating leachate into a landfill which can offer significant environmental and economic benefits. However, in some cases, the leachate collection and removal system (LCRS) at the bottom is overloaded and clogged due to biological and chemical processes. This results in a relatively high leachate level which could pose a threat to landfill slope stability. This study develops a three-dimensional landfill slope model with vertical recirculation wells and then investigates the effect of LCRS clogging on leachate recirculation and slope stability in terms of leachate saturation, pore water pressure, and factor of safety (FS) of a landfill slope. The results show that with an increase in clogging level that is characterized by an increased leachate level, the pore water pressure below the well injection screen is significantly increased by leachate recirculation, giving rise to a decreased slope FS value. In such conditions, the landfill slope formed by highly anisotropic waste is more likely to suffer instability. To prevent this kind of slope failure, a safe injection pressure of vertical recirculation wells is proposed for a wide range of parameter combinations involving waste anisotropy, clogging level, and the setback distance from the slope surface. This design guideline can be used to control the injection pressure in leachate recirculation applications and contributes to a better understanding of the slope stability of a bioreactor landfill.

The presence of nanoplastics (NPs) in various products and from the weathering of released plastic materials are of concern for the environment’s safety. The purpose of this study was to examine the biophysical effects of polystyrene NPs on freshwater mussels. Mussels were exposed to a range of concentrations of NPs (0.1, 0.5, 1, and 5 mg/L) for 24 h and allowed to depurate for 12 h in clean aquarium water. The digestive gland was isolated and analyzed for NPs, lipids, viscosity, protein aggregation, anisotropic changes (liquid crystals: LCs), and the oscillatory modulation in viscosity during the formation of self-organizing enzyme complex of fumarase, malate dehydrogenase, and citrate synthase. The results revealed that mussels accumulated NPs in the digestive gland and their levels were significantly correlated with lipids levels, LCs, the increase in the malate dehydrogenase/citrate synthase activity ratio, and oscillations in viscosity. Protein aggregation was also found to be correlated with lipid levels. The data suggests that the presence of NPs in the digestive gland involves changes in lipid content and LC formation and perturbs the normal oscillations in viscosity during sequential enzyme reactions of the above enzymes. It is concluded that the uptake of NPs in cells could disrupt the internal organization of cells which can interfere with the normal association of enzymes involved in energy metabolism.

Pore space characteristics of biochars may vary depending on the used raw material and processing technology. Pore structure has significant effects on the water retention properties of biochar amended soils. In this work, several biochars were characterized with three-dimensional imaging and image analysis. X-ray computed microtomography was used to image biochars at resolution of 1.14 μm and the obtained images were analysed for porosity, pore size distribution, specific surface area and structural anisotropy. In addition, random walk simulations were used to relate structural anisotropy to diffusive transport. Image analysis showed that considerable part of the biochar volume consist of pores in size range relevant to hydrological processes and storage of plant available water. Porosity and pore size distribution were found to depend on the biochar type and the structural anisotopy analysis showed that used raw material considerably affects the pore characteristics at micrometre scale. Therefore, attention should be paid to raw material selection and quality in applications requiring optimized pore structure.