Release of Additives from Agricultural Plastic Films in Water: Experiment and Modeling.

Globally, more than 6 million metric tons of agricultural plastic films are used to increase crop yields and reduce the use of water and herbicides, resulting in the contamination of soil and water by plastic debris and additives. However, knowledge of the occurrence and release of additives from agricultural films is limited. In this study, suspect screening with high-resolution mass spectrometry, one-dimensional Fickian diffusion models, and linear free energy relationships (LFERs) were used to determine the occurrence and mass transfer of various additives from agricultural plastic films. A total of 89 additives were tentatively identified in 40 films, and 62 of them were further validated and quantified. The aqueous concentrations of 26 released additives reached mg L-1 after a 28 day incubation at 25 °C. Diffusion models and LFERs demonstrated that the film-water partition coefficient and the diffusivity in the polymer, the two critical parameters controlling the mass transfer, could be predicted using Abraham descriptors. The findings of this study highlighted the need for future research on the environmental fate and risk assessment of previously neglected additives in agricultural plastic films and other similar products.

A lifelong journey of lead in soil profiles at an abandoned e-waste recycling site: Past, present, and future.

Heavy metal pollutants resulting from human activities consistently move from the topsoil to the subsoil profiles under the influence of rainfall leaching. This study intends to predict the long-term transport of heavy metals at an abandoned e-waste recycling site with respect to historical pollution activities, land use, and metal pollutant dynamics. Our results showed that the site was seriously contaminated with heavy metals (Cd, Cu, Pb, and Zn) in the soil profiles. More specifically, Cu and Zn accumulated primarily in the upper layers of the soil profile owing to their weak mobility, while significant migration of Cd and Pb was observed in the deeper soil layers. Furthermore, to clarify the fate of Pb in soil profiles, Pb isotopes and the Hydrus model were used to trace the sources of Pb contamination and predict its long-term distribution. The Pb isotope results suggest that past e-waste recycling activities significantly contributed to the heavy metal concentration in the soil profiles; however, other anthropogenic sources such as vehicle exhaust had smaller impacts. Moreover, our model findings predicted that within the next 30 years, 60% of Pb contaminants will be concentrated in the surface soil. Together these results provide a theoretical foundation and scientific basis for evaluating, controlling, and remediating abandoned e-waste recycling sites.

A novel sampling-free algorithm for subsurface data assimilation using Gaussian process-derived sensitivities.

Accurate characterization of hydraulic parameters is vital for modeling subsurface flow and transport. In the past decade, ensemble-based methods have been widely applied in estimating unknown parameters from state measurements. However, these methods require sufficiently large ensemble sizes to guarantee the accuracy of the ensemble averaged parameter sensitivities, leading to heavy computational burdens especially in large-scale problems. Although different surrogates have been introduced to alleviate the computational burden, the sensitivity information therein is still calculated by sampling the surrogate. Therefore, the sampling error is still inevitable. In this study, we propose an adaptive Gaussian process (GP) based iterative smoother (GPIS) algorithm in which the parameter sensitivity indices are analytically derived from the GP surrogate. During the iterations, the GP surrogate is adaptively refined by taking the updated parameter realizations as new base points. Both numerical and experimental cases are conducted to test the effectiveness of GPIS. We also compare its performance in estimating the heterogeneous hydraulic conductivity field with that of its prototype iterative ensemble smoother (IES) and our previously developed GP based iterative ensemble smoother (GPIES). Results show that, using the GP-derived sensitivity indices, GPIS shows advantages over GPIES in terms of both estimation accuracy and computational efficiency. Although subsurface flow and transport problems are considered in this work, the proposed method can be equally applied in other hydrological problems.

Novel agricultural waste-based materials decrease the uptake and accumulation of cadmium by rice (Oryza sativa L.) in contaminated paddy soils.

Heavy metal pollution in paddy fields has caused widespread concerns due to the threat to food safety. The present study used low-cost sugarcane bagasse (SB) and two sugarcane bagasse materials modified with citric-acid (SSB) and citric-acid/Fe3O4 (MSB) to investigate their effects on the bioavailability of Cd in soil and Cd accumulations in rice in a pot experiment. The three organic amendments significantly decreased the Cd accumulation in plants by limiting its mobilization in soil. The MSB and SSB but not SB increased the soil pH and immobilized the Cd in soil significantly during the 120-day experiment. The amendments decreased Cd bioavailability through transforming to the stable fraction throughout the whole growth stage. The functional groups in the amendments (-OH, -COOH, C-O, -COO- and Fe-O) and precipitates [Cd(NO2)2K(NO2)2, Cd(OH)2 and Cd75Zn25Fe2O4] played active roles in Cd immobilization. Moreover, the three organic materials increased the content of Fe-Mn plaque on rice roots, which prevented its transport from soil to rice roots further. We also found that Fe competed with Cd for transporters and reduced potential Cd uptake and translocation in rice tissues. The addition of MSB and SB but not SSB inhibited the rice growth compared to the unamended control, indicating the potential of SSB in situ remediation. These results provide valuable information to use organic amendments for Cd passivation in soil and food safety.

Quantification of the sorption of organic pollutants to minerals via an improved mathematical model accounting for associations between minerals and soil organic matter.

The retention of organic pollutant (OP) in soils is commonly attributed to interactions with soil organic matter (SOM), perhaps overlooking substantial involvement of soil minerals. In this study, 36 soil samples with far-ranging ratios of clay to organic carbon were used to examine contribution of minerals on soil sorption of pentachlorophenol (PCP) and phenanthrene (PHE). Sorption isotherms (n = 216) were fit individually using three typical sorption models, with the most fitted Kd values screened out for quantification of the net mineral contribution to total sorption via development of mathematical model accounting for associations between minerals and SOM. Two mineral-relevant parameters [adsorption distribution coefficient (Kmin) and mineral contribution index (MCI)] were simultaneously defined. Previously reported soil sorption data of PCP, PHE and butachlor (13, 12 and 46, respectively) were also extracted and included to improve the credibility of mathematic model. The average MCI values were calculated as 0.421, 0.405 and 0.512 in PCP, PHE and butachlor treated soils, respectively, very close to or even over than the minerals dominant critical value (0.5). This suggested the significant, or even predominant, contribution of minerals - as compared to SOM. Significant dependence of MCI with four conventional parameters of soil property further offered the possibility to roughly evaluate mineral contributions based on estimated threshold values of soil property parameters (especially TOC). This study provides an accessible approach for predicting the contribution of minerals in soil OP retention, especially highlighting their predominant roles vs. SOM in regulating OP removal in most of subsurface soil or contaminated brownfields where organic carbon content of soil was very low, that was not like what previously believed.

Bayesian Monitoring Design for Streambed Heat Tracing: Numerical Simulation and Sandbox Experiments.

Heat tracing methods have been widely employed for subsurface characterization. Nevertheless, there were very few studies regarding the optimal monitoring design for heat tracing in heterogeneous streambeds. In this study, we addressed this issue by proposing an efficient optimal design framework to collect the most informative diurnal temperature signal for Bayesian estimation of streambed hydraulic conductivities. The data worth (DW) was measured by the expected relative entropy between the prior and posterior distributions of the conductivity field. An adaptively refined Gaussian process surrogate was employed to alleviate the computational burden, resulting in at least three orders of magnitude of speed-up. The applicability of the optimal experimental design framework was evaluated by both numerical and sandbox experimental cases. Results showed that the most informative locations centered in the transition zones among the main patterns of the hydraulic conductivity field, while the most informative times centered in a short period after the minimum/maximum temperature appeared. With the fixed number of measurements, extending the calibration period was more beneficial than increasing the monitoring frequency in improving the estimation results. To our best knowledge, this work is the first study on Bayesian monitoring design for streambed characterization with the heat tracing method. The method and results can provide guidance on selecting monitoring strategies under budget-limited conditions.

Investigating two-dimensional soil gas transport of trichloroethylene in vapor intrusion scenarios involving surface pavements using a pilot-scale tank.

In this study, we investigate the soil gas concentration attenuation with diffusive transport in lateral and vertical transport in cases with surface pavements with a pilot-scale tank. Three scenarios were investigated, one with a completely open soil surface and the other two involving partially paved soil surface. The results, on the one hand, indicate that the soil gas concentration generally decreases linearly and exponentially in the vertical and horizontal transport directions, respectively, generally in accordance with available modeling studies. On the other hand, our experiment shows that low-permeability ground covers can increase shallow soil gas concentrations beneath the pavement by at most 3-4 times, inducing higher subslab concentration than that below open ground surface, even if the latter is obtained at a closer location to vapor source. For cases with uniform soil properties, our study suggests exterior soil gas sample should be taken at a depth below the building foundation by half of the building footprint size, if the vapor source is laterally extensive relative to the building footprint, or by 1 m and 2-3 m for slab-on-grade and basement scenarios, respectively, if the vapor source is located laterally away from the building.

A process-based model for pentachlorophenol dissipation in a flooded paddy soil.

Process-based models have been widely used for predicting environmental fate of contaminants. Nevertheless, accurate modeling of pentachlorophenol (PCP) dissipation in soils at the millimeter-scale remains a challenge due to the scarcity of observation data and uncertainty associated with model assumptions and estimation of the model parameters. To provide quantitative analysis of PCP-dissipation at the anaerobic/aerobic interface of a rhizobox experiment, this study implemented Bayesian parameter estimation for a process-based reactive chemical transport model. The model considered the main transport and transformation processes of chemicals including diffusion, sorption and degradation. The contributions of the processes to PCP dissipation were apportioned both in space and time. Using the maximum-a-posteriori (MAP) estimation of parameters, our model fitted the experimental data better compared with the previous work. Our results indicated that the most reactive zone for PCP dissipation occurred in the layer of 0-2.4 mm where degradation in solid phase dominated the PCP dissipation, while upward diffusion was the main mechanism for the reduction of PCP concentration in deeper layer (2.4-4.8 mm). By considering the coupled reactive transport of PCP and Cl-, the average degrees of PCP dechlorination in each layer were estimated from corresponding total concentrations of PCP and Cl-. The degrees of PCP dechlorination in the ponding water and the top layer of soil profile were highest, while 2,3,4,5- TeCP and 3,4,5- TCP were identified as the main dechlorination products in the soil. This study demonstrated that combining Bayesian estimation with process-based reactive chemical transport model can provide more insights of PCP dissipation at the millimeter-scale. This approach can help to understand complex dissipation mechanisms for other contaminants.

A multi-medium chain modeling approach to estimate the cumulative effects of cadmium pollution on human health.

Cadmium is a highly persistent and toxic heavy metal that poses severe health risks to humans. Diet is the primary source of human exposure to cadmium, especially in China. Soil, as the main medium that transfers cadmium to rice, can be used as a helpful indicator to predict human exposure to cadmium in soils. There is, however, very little work that links a soil-rice transfer model with a biokinetic model to assess health risks. In this work, we introduce a multi-medium chain model based upon a soil-rice-human continuum to address this issue. The model consists of three basic steps: (i) development and validation of a soil-rice transfer model for cadmium based on 189 pairs of measured data in Wenling of Zhejiang province in Southeast China; (ii) calculation of weekly exposure based on the nationwide monitoring and survey results; (iii) linking the exposure model with a modified biokinetic model proposed with a classic biokinetic model to predict urinary cadmium, which is a biomarker to assess the health risks. Results indicated that the developed soil-rice-human transfer model predicted well the urinary cadmium levels in humans subjected to age and exposure uncertainties. We observed a maximum of 0.71 µg g-1 creatinine in males and 1.53 µg g-1 creatinine in females at 70 years old under median cadmium exposure, which was consistent with previous studies. Sensitive analysis was also conducted to detect the sensitive parameters that have the most significant influences on the output of the model. The new risk assessment strategy proposed in this work is beneficial for predicting the cumulative cadmium levels in various exposed populations so that we can quickly identify the critical areas from basic soil properties.

Bayesian inference for kinetic models of biotransformation using a generalized rate equation.

Selecting proper rate equations for the kinetic models is essential to quantify biotransformation processes in the environment. Bayesian model selection method can be used to evaluate the candidate models. However, comparisons of all plausible models can result in high computational cost, while limiting the number of candidate models may lead to biased results. In this work, we developed an integrated Bayesian method to simultaneously perform model selection and parameter estimation by using a generalized rate equation. In the approach, the model hypotheses were represented by discrete parameters and the rate constants were represented by continuous parameters. Then Bayesian inference of the kinetic models was solved by implementing Markov Chain Monte Carlo simulation for parameter estimation with the mixed (i.e., discrete and continuous) priors. The validity of this approach was illustrated through a synthetic case and a nitrogen transformation experimental study. It showed that our method can successfully identify the plausible models and parameters, as well as uncertainties therein. Thus this method can provide a powerful tool to reveal more insightful information for the complex biotransformation processes.

The effective migration of Massilia sp. WF1 by Phanerochaete chrysosporium and its phenanthrene biodegradation in soil.

Pollutant-degrading bacteria migrated by fungi may enhance the contacts between microorganisms and pollutants and improve the bioremediation efficiency of persistent organic pollutants in soil. Here, the migration of phenanthrene (PHE)-degrading bacteria Massilia sp. WF1 and Mycobacterium sp. WY10 by the hydrophobic fungi Phanerochaete chrysosporium (P. chrysosporium) and its effects on the PHE biodegradation in soil were investigated. Migration of the hydrophilic bacterium WF1 was better than that of the hydrophobic bacterium WY10 by P. chrysosporium mycelia since strain WF1 possesses flagellum and the type III secretion system. The interaction energy change of P. chrysosporium-WF1 was lower, but the interaction forces (van der Waals attractions, capillary forces, and cross-linking effects) were stronger than those of P. chrysosporium-WY10. Thus, the adhesive attraction between strain WF1 and P. chrysosporium was stronger, and consequently, strain WF1 was migrated by P. chrysosporium to a greater extent than WY10. The corresponding migration mechanism was inferred to be a bacterial 'passive' method: bacteria adhered to mycelia before they migrated with the growing mycelia. Moreover, migrated strain WF1 via P. chrysosporium showed effective PHE biodegradation in soil. Fungus-mediated migration of pollutant-degrading bacteria may play an important role in the bioremediation of pollutants in soil.

Expressing lead isotopic compositions by fractional abundances for environmental source apportionment.

Lead (Pb) isotope has been extensively used to identify sources of Pb and apportion their contributions in the environment. Conventionally, isotope ratios are used to express Pb isotopic composition. However, the linear combination of Pb isotope ratios is not consistent with mass balance. Moreover, the graphical presentations based on Pb isotope ratios are always inconsistent when different Pb isotope ratios are used. In this study, we proposed to use fractional abundance to express Pb isotopic composition to achieve more accurate and reliable source apportionment. A new method (rotation-projection method) based on fractional abundance was developed in this research. The new method compared favorably to the isotopic ratio-based method and to another fractional abundance based method using default 204Pb value (0) (Walraven's method). It allows to present four-dimensional (4-D) Pb isotope fractional abundance data in a 3-D plot. In the meantime, due to the low variation of the fractional abundance of 204Pb in the terrestrial ecosystem, the terrestrial Pb isotope fractional abundance data fell nearly on a plane, which further allows to plot the Pb isotope fractional abundance data on a two-dimensional diagram. Proper presentation of the isotopic composition data helps to achieve more accurate and reliable source identification and apportionment.

Effects of nitrogen fertilization on the acidity and salinity of greenhouse soils.

A greenhouse pot experiment was conducted to study the effects of conventional nitrogen fertilization on soil acidity and salinity. Three N rates (urea; N0, 0 kg N ha(-1); N1, 600 kg N ha(-1); and N2, 1,200 kg N ha(-1)) were applied in five soils with different greenhouse cultivation years to evaluate soil acidification and salinization rate induced by nitrogen fertilizer in lettuce production. Both soil acidity and salinity increased significantly as N input increased after one season, with pH decrease ranging from 0.45 to 1.06 units and electrolytic conductivity increase from 0.24 to 0.68 mS cm(-1). An estimated 0.92 mol H(+) was produced for 1 mol (NO2 (-) + NO3 (-))-N accumulation in soil. The proton loading from nitrification was 14.3-27.3 and 12.1-58.2 kmol H(+) ha(-1) in the center of Shandong Province under N1 and N2 rate, respectively. However, the proton loading from the uptake of excess bases by lettuces was only 0.3-4.5 % of that from nitrification. Moreover, the release of protons induced the direct release of base cations and accelerated soil salinization. The increase of soil acidity and salinity was attributed to the nitrification of excess N fertilizer. Compared to the proton loading by lettuce, nitrification contributed more to soil acidification in greenhouse soils.

County-scale temporal-spatial distribution and variability tendency of heavy metals in arable soils influenced by policy adjustment during the last decade: a case study of Changxing, China.

The widespread "pertinacious illness" for environmental management soil contamination by toxic metals has inevitably led to announcement of gradual adjustment for the local government to curb the notorious pollution. An extensive survey was conducted in Changxing County, Zhejiang Province, to investigate the contents of cadmium (Cd), arsenic (As), lead (Pb), and copper (Cu) and characterize the spatial-temporal change pattern to elucidate the current state and performance of policy adjustment. Geostatistic methods were applied in the mapping and characterizing processes to assess the concentrations of heavy metals at unsampled sites. Enrichment factor analysis indicated obvious Cd and As enrichment in the west and northwest of the county. Ordinary kriging interpolation combined with semivariogram revealed that the four heavy metals possessed distinct spatial distribution patterns. However, the distribution patterns for the elevated Cd and Pb concentrations exhibited a similar geographic drift, corresponding primarily to industrial distribution. Compared with the investigation findings in 2003, the results indicated an uptrend of Cd and As and downtrend of Cu, while Pb was a mixture of increase and decrease. However, strengthened actions aimed at restraining heavy metal contamination are still needed to establish a mechanism to secure residents' health and livelihood.

Positive matrix factorization as source apportionment of soil lead and cadmium around a battery plant (Changxing County, China).

Chemical compositions of soil samples are multivariate in nature and provide datasets suitable for the application of multivariate factor analytical techniques. One of the analytical techniques, the positive matrix factorization (PMF), uses a weighted least square by fitting the data matrix to determine the weights of the sources based on the error estimates of each data point. In this research, PMF was employed to apportion the sources of heavy metals in 104 soil samples taken within a 1-km radius of a lead battery plant contaminated site in Changxing County, Zhejiang Province, China. The site is heavily contaminated with high concentrations of lead (Pb) and cadmium (Cd). PMF successfully partitioned the variances into sources related to soil background, agronomic practices, and the lead battery plants combined with a geostatistical approach. It was estimated that the lead battery plants and the agronomic practices contributed 55.37 and 29.28%, respectively, for soil Pb of the total source. Soil Cd mainly came from the lead battery plants (65.92%), followed by the agronomic practices (21.65%), and soil parent materials (12.43%). This research indicates that PMF combined with geostatistics is a useful tool for source identification and apportionment.

Nitrate source apportionment in a subtropical watershed using Bayesian model.

Nitrate (NO3-) pollution in aquatic system is a worldwide problem. The temporal distribution pattern and sources of nitrate are of great concern for water quality. The nitrogen (N) cycling processes in a subtropical watershed located in Changxing County, Zhejiang Province, China were greatly influenced by the temporal variations of precipitation and temperature during the study period (September 2011 to July 2012). The highest NO3- concentration in water was in May (wet season, mean±SD=17.45±9.50 mg L(-1)) and the lowest concentration occurred in December (dry season, mean±SD=10.54±6.28 mg L(-1)). Nevertheless, no water sample in the study area exceeds the WHO drinking water limit of 50 mg L(-1) NO3-. Four sources of NO3(-) (atmospheric deposition, AD; soil N, SN; synthetic fertilizer, SF; manure & sewage, M&S) were identified using both hydrochemical characteristics [Cl-, NO3-, HCO3-, SO42-, Ca2+, K+, Mg2+, Na+, dissolved oxygen (DO)] and dual isotope approach (δ15N-NO3- and δ(18)O-NO3-). Both chemical and isotopic characteristics indicated that denitrification was not the main N cycling process in the study area. Using a Bayesian model (stable isotope analysis in R, SIAR), the contribution of each source was apportioned. Source apportionment results showed that source contributions differed significantly between the dry and wet season, AD and M&S contributed more in December than in May. In contrast, SN and SF contributed more NO3- to water in May than that in December. M&S and SF were the major contributors in December and May, respectively. Moreover, the shortcomings and uncertainties of SIAR were discussed to provide implications for future works. With the assessment of temporal variation and sources of NO3-, better agricultural management practices and sewage disposal programs can be implemented to sustain water quality in subtropical watersheds.