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Soil moisture is an important indicator for monitoring land degradation and plays an important role in soil biogeochemistry . Mapping the spatial distribution of soil moisture provides fundamental information for soil management and agricultural production. In this study, 225 sampling points in the study area, Nanxiong basin, were investigated to map the spatial distribution of soil moisture in the typical ecological degradation red beds area. Four interpolation methods including inverse distance weighting (IDW), ordinary kriging (OK), radial basis function (RBF) and empirical bayesian kriging (EBF) were used to estimate the continuous soil moisture distribution. The results showed that the soil moisture of the study area ranged from 8.12% to 32.82% with an average of 18.42% and a median of 18.20%. The annual average temperature and soil bulk density had significantly negative correlations with soil moisture, of-0.53 and − 0.31, respectively. The regional soil moisture had a moderate variation with the variation coefficient of 21.66% and strong spatial dependence with the nugget-to-sill ratios of 32.23%. The IDW method obtained a more accurate estimation on the spatial variability of soil moisture in the Nanxiong basin. The spatial distribution of soil moisture in the study area was drawn by different interpolation methods. The results show that the red bed degradation in the eastern part of the Nanxiong basin is more severe than in other areas, in which the geomorphic characteristics of red bed desert have appeared with the lowest soil moisture in this study. In addition, the spatial heterogeneity of soil moisture is also pertinent to human disturbance and land use. The results of spatial soil moisture distribution are of great significance for monitoring land degradation and agricultural drought in the red bed area.

Predicting the distribution of engineered nanomaterials (ENMs) in the environment will provide critical information for risk assessment and policy development to regulate these emerging contaminants. The fate and transport of ENMs in natural subsurface environments are complicated by various factors, such as hydraulic gradient, initial release concentration, nanoparticle size, and collision efficiency factor. Based on advanced statistical methodologies (i.e., response surface methodology (RSM)), we explore simple relationships between key factors that control ENM transport (collision efficiency factor, particle size, hydraulic gradient, and initial release concentration) and key parameters that describe the ENM concentration distribution in porous media (maximum standardized concentration, the mass percentage of injected nanoparticle attached in the aquifer, the x-centroid of aqueous phase nC₆₀ plume, and the x-centroid of attached phase nC₆₀ distribution). Hypothetical scenarios for the release of nanoparticles into an aquifer were simulated numerically with randomly generated permeability fields that were based on mildly and highly heterogeneous sites. RSM was used to develop polynomial regression equations based on a statistical experimental design. High R-squared values (greater than 0.9) of the regression equations were obtained for all the models developed based on the mildly heterogeneous site. On the highly heterogeneous site, the R-squared value of the regression equation for the percentage of nanoparticles attached (by mass) was more than 0.9. The ability to accurately estimate aqueous phase ENM concentration distribution using simple regression equations is particularly critical for risk assessment. Even though the correlations developed in this study were site and scenario specific, this work represents a first effort of applying RSM for predicting the distribution of engineered nanomaterials in porous media.

The escalating production and consumption of engineered nanomaterials may lead to their increased release into groundwater. A number of studies have revealed the potential human health effects and aquatic toxicity of nanomaterials. Understanding the fate and transport of engineered nanomaterials is very important for evaluating their potential risks to human and ecological health. While there has been a great deal of research effort focused on the potential risks of nanomaterials, a limited amount of work has evaluated the transport of engineered nanomaterials under different release scenarios in a typical layered geological field setting. In this work, we simulated the transport of fullerene aggregates (nC60), a widely used engineered nanomaterial, in a multi-dimensional environment. A Modular Three-Dimensional Multispecies Transport Model (MT3DMS) was modified to evaluate the transport and retention of nC60 nanoparticles. Hypothetical scenarios for the introduction of nanomaterials into the subsurface environment were investigated, including the release from an injection well and the release from a waste site. Under the conditions evaluated, the mobility of nC60 nanoparticles was found to be very sensitive to the release scenario, release concentration, aggregate size, collision efficiency factor, and dispersivity of the nanomaterial.

Accurately predicting the transport of contaminants in the field is subject to multiple sources of uncertainty due to the variability of geological settings, the complexity of field measurements, and the scarcity of data. Such uncertainties can be amplified when modeling some emerging contaminants, such as engineered nanomaterials, when a fundamental understanding of their fate and transport is lacking. Typical field work includes collecting concentration at a certain location for an extended period of time, or measuring the movement of plume for an extended period time, which would result in a time series of observation data. This work presents an effort to evaluate the possibility of applying time series analysis, particularly, autoregressive integrated moving average (ARIMA) models, to forecast contaminant transport and distribution in the subsurface environment. ARIMA modeling was first assessed in terms of its capability to forecast tracer transport at two field sites, which had different levels of heterogeneity. After that, this study evaluated the applicability of ARIMA modeling to predict the transport of engineered nanomaterials at field sites, including field measured data of nanoscale zero valent iron and (nZVI) and numerically generated data for the transport of nano-fullerene aggregates (nC60). This proof-of-concept effort demonstrates the possibility of applying ARIMA to predict the contaminant transport in the subsurface environment. Like many other statistical models, ARIMA modeling is only descriptive and not explanatory. The limitation and the challenge associated with applying ARIMA modeling to contaminant transport in the subsurface are also discussed.