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Impacts of Nanobubbles in Pore Water on Heavy Metal Pollutant Release from Contaminated Soil Columns

Department of Civil and Environmental Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA
Author to whom correspondence should be addressed.
Nanomaterials 2023, 13(10), 1671;
Submission received: 18 April 2023 / Revised: 15 May 2023 / Accepted: 16 May 2023 / Published: 18 May 2023
(This article belongs to the Special Issue Nanobubbles and Their Applications)


This study investigated the release of heavy metals from polluted soil under the pore water flow containing nanobubbles (NBs) to simulate natural ebullition. Three types of NBs (CH4, H2, and CO2) were generated in water and characterized, including bubble size, zeta potential, liquid density, and tension. The flow rate used in column tests was optimized to achieve proper soil fluidization and metal desorption or release. The leachate chemistries were monitored to assess the effect of NBs on conductivity, pH, oxidation–reduction potential (ORP), and dissolved oxygen (DO). The results showed that NBs in the pore water flow were significantly more effective in releasing Pb compared to DI water, with CO2 NB water being the most effective and H2 NB water being the least effective. CO2 NB water was also used to rinse column soil contaminated with four different metals (Pb, Cu, Zn, and Cr), which exhibited different leaching kinetics. Moreover, a convective–dispersion–deposition equation (CDDE) model accurately simulated the leaching kinetics and explained the effects of NBs on the key parameters, such as the deposition rate coefficient (Kd), that affect the released metal transport. The findings could provide new insights into soil pollutant release under ebullition and soil remediation using water wash containing NBs.

1. Introduction

Due to past industrial and agricultural activities, numerous urban rivers, estuaries, and watersheds have become polluted with various contaminants, including synthetic organic compounds and heavy metals. Because of their hydrophobicity and recalcitrance to biodegradation, many organic pollutants tend to persist and accumulate in soils, sediments, and suspended solids, as well as in biological matrices [1]. Heavy metals could also bind to sediment matrices due to the porous nature and absorptive ability of sediment or soil organic matter. The sediment pollution has persisted over time and poses a significant challenge for the restoration of these water bodies [2,3]. The Environmental Protection Agency (US, EPA) has estimated that there are 1.2 billion cubic meters of contaminated surficial sediments in the US that could pose a risk to surface water resources, pollutant transport, public exposure, and even the security of drinking water supply [4,5]. With the implementation of the Clean Water Act (CWA), contaminated sediments have converted from being a sink to a source and thus deserve remediation [6].
Natural ebullition is a result of digenetic processes from certain aquatic sediments [7], which affects sediment contaminant transport [8,9]. Fine bubble formation in the sediment could be attributed to methanogenesis and fermentation, which produces gases such as CO2, CH4, and H2 [10]. The natural ebullition process creates a unique multi-phase flow phenomenon that affects sediment structures and sediment pollutant migration (e.g., desorption and release) [11]. The pore transport and rise could open up pore sizes and desorb contaminants in sediment. The release of sediment metals could be ebullition-facilitated [3]. For instance, arsenic (As) is released from soil into the aqueous solution under reductive conditions, whereas cadmium (Cd) becomes soluble under oxidative environments. Ebullition-induced mercury release from sediments has been observed in various aquatic environments [12], including lakes, reservoirs, and rivers [13]. Ebullition can also facilitate the release of lead and zinc from mining-contaminated sediments in rivers and streams [14]. Thus, the formation of microbubbles or nanobubbles in sediment may shift the local soil redox levels, altering the solubility and speciation of different heavy metals [15,16]. Moreover, CO2 NBs in water can suppress the pH down to 5.6 and may significantly increase the mobility of metal colloids or metal ions in the soil [17].
However, the bubble formation and blowout processes through sediment pores have not been sufficiently investigated. Many previous studies largely focused on sediment contaminant transport within the pore water; however, there is a limited understanding of the fine bubble transport characteristics and the associated impacts on sediment contaminant transport or migration [8]. Based on the existing understanding, the introduction of fine bubbles can create pressure gradients that can cause the transport of pollutants in soil to increase significantly [18]. The bubbles can increase the rate of diffusion of pollutants from the soil to the gas phase, which can then lead to atmospheric transport [19]. Additionally, the bubbles can create pathways for the movement of pollutants, increasing the likelihood of contamination of groundwater resources [20]. McLinn et al. reported that ebullition was employed to transfer coal tar from a synthetic polluted sediment through a sand cap under laboratory settings, which repeated the observed field transport phenomena reported by (US EPA, 2006) [21]. Exploring the effect of gas ebullition on the contaminant kinetics will provide an insight into the transport mechanism that governs pollutant release and resuspension.
Based on the above-mentioned knowledge gaps and challenges, the present study evaluated the release of heavy metals from polluted sediment when the pore water flow was filled with NBs to mimic the natural ebullition. Different NBs (CO2, CH4, and H2) were generated in water and characterized to examine the influences of the NB water flow on the soil chemical properties, structural expansion, and metal leaching from spiked soil to the elute water phase. Among different typical sediment pollutants, heavy metals (e.g., Cu Fe, Pb, and Zn) were selected as model pollutants. The heavy metal release indicated by the calculated heavy metal fluxes (µg·L−1·min−1) was measured under different conditions. Finally, leaching kinetics’ modeling analysis was conducted to reveal the impacts of NBs on key parameters related to the transport behavior. Understanding and quantifying the influence of the fine bubble flows on sediment contaminant fate and transport could provide new insights into the effects of natural ebullition on soil pollutant fate and soil pollution remediation and prevention [19].

2. Materials and Methods

2.1. Preparation of Heavy Metal Contaminated Soil

Miracle Gro Garden Soil was obtained from the Rutgers Soil Testing Laboratory, North Brunswick Township, NJ, USA and stored in precleaned brown glass bottles sealed with covers to avoid light and evaporation at 4 °C. These pristine soil samples were characterized and certified to be free of any known pollutants [22]. The major soil properties are provided in Table S1 in the Supplementary Materials (SM). To prepare for the experiments, the soil samples were dried in the oven at 104 ± 1 °C for 8 h to obtain a constant weight and sieved with a pore size of 2 mm. The contaminated soil was prepared to reach the reported heavy metal levels for polluted sediment in New Jersey and New York states [23]. Briefly, contaminated soil with 30 mg-Pb∙kg−1 was prepared by adding 30 mL of the 226 mg∙L−1 PbCl2 solution to 200 g air dry soil, which was mixed for 3 h with a Compact Digital Mixer (UX-50006-01, Cole-Parmer, Vernon Hills, IL, USA) and left in the atmosphere until the methanol completely evaporated. Similarly, another group of contaminated soil samples was prepared by adding the same volume of the four heavy metal solutions with the same molar concentration of 2 mmol∙L−1. Correspondingly, the soil samples contained 164 mg∙kg−1 of Pb, 128 mg∙kg−1 of Cu, and 130 mg∙kg−1 of Zn, and 104 mg∙kg−1 was prepared with solutions of PbCl2, Cu(NO3)2, Zn(NO3)2, and CrCl3 [24]. To confirm the contents of heavy metals in the spiked soil samples, these samples were digested by nitric acid (60–70% Trace Metal grade) at 85–95 °C to measure the leached metal concentration using ICP-MS, as detailed in Supplementary Materials Section S2.

2.2. Generation and Characterization of NBs

NBs were produced in deionized (DI) water as suspensions following our reported membrane bubbling method [25]. As illustrated in Figure S1, compressed gases (i.e., CO2, CH4, and H2) were injected under a pressure of 414 kPa and a gas flow of 0.45 L·m−1 into a ceramic tubular membrane (model WFA0.1-Refractron, Newark, NY, USA). The air bubbles were dispensed into the flowing water that was pumped to circulate between the membrane module and the reservoir tank for 60 min to reach the maximum stable bubble concentration near 1.5 × 108·mL−1 in 3-L water. The density of different NB saturated water was determined by measuring the weight of the aqueous solution with the same volume (20 mL). The temperature of the bubble suspension was also monitored at the same time [12]. The bubble size distribution and zeta potential of the water suspension of NBs were measured immediately after preparation using dynamic light scattering (DLS) on a Zetasizer Nano ZS instrument (Malvern Instruments, Malvern, UK). Each result was obtained from the average of five measurements. Furthermore, the mean concentration of NBs was determined using a nanoparticle tracking analysis (NTA) instrument (ViewSizer 3000, Horiba, Kyoto, Japan). The NTA graphs presented the standard deviations of five different measurements for each sample as error bars. A stable bubble size distribution and concentration (approximately 1.5 × 108·mL−1) in water were obtained and were ready for use in the following experiments. The surface tension of all the NB water mentioned above was evaluated using the pendant drop method [26].

2.3. Column Setup and Characterization under a Bubble Water Flow

Column tests were conducted in a Perspex cylinder (Figure 1) with a 3 cm inner diameter and 30 cm inner height [27]. Each column was filled with 37 g of glass beads of 0.6 cm in diameter from the bottom up to form a 5-cm bottom layer as a support base and then with the 50 g contaminated soil to form the 5 cm soil layer that corresponds to a packing density of 1.40 g·cm−3, as suggested elsewhere [28]. On top of the soil layer, there was a 10 cm layer of glass beads to prevent soil swelling. The effluent exited the column 10 cm above the topsoil surface. To mimic the natural pore water flows or gas ebullition [29,30,31,32], different flow rates (5–20 mL·min−1) or fluxes from 0.71 to 2.83 mL·cm−2·min−1 were injected into the soil column from the bottom using a peristaltic pump (Masterflex Model 77200-62, Cole-Parmer Instrument Company, Vernon Hills, IL, USA). The upflow water flow was expected to result in a moderate fluidization level without causing any turbulence or soil mixing.
The bulk density and porosity of the packed column were measured gravimetrically [33]. The porosity was calculated as the difference between the bulk density and the packing density of the soil [34]. Additional to the soil structures, the eluate was collected to measure turbidity (Ratio XR Turbidimeter model 43900, HACH, Loveland, CO, USA), conductivity (conductivity sensor, PS-3210, PASCO, Portland, OR, USA), pH, and ORP using PASCO Xplorer GLX sensors (PASCO, Roseville, CA, USA).

2.4. Evaluation of the Metal Release from the Spiked Soil under a Bubble Water Flow

To evaluate the effect of different NBs on Pb release, the different types of bubble water (CO2, CH4, and H2) were pumped into the soil column at a fixed flow flux of 0.71 mL·min−1·cm−2. The Pb-spiked soil columns underwent two parallel soil-washing tests. The column was sealed by a rubber plug at the top to keep the drainage from being exposed to air. Effluent that was not exposed to air was collected from a sidewall port 20 cm above the bottom of the cylinder. Then, 10 mL of the effluent samples was taken directly from the effluent collection point every 10 min for the first 60 min and every 30 min for the following 60 min to analyze the leached heavy metal concentration. Deionized (DI) water without NBs was pumped into the column at the same flux for comparison as a negative control group. For the soil spiked with multiple metals (i.e., Pb, Cu, Zn, and Cr), CO2 NB water was used to rinse the column soil and assess the leaching efficiencies. To determine significant differences between the control and experimental data, one-way analysis of variance (t-test, two-sided, with a significance level of α = 0.05) was employed to assess the data statistics of heavy metal concentrations in the leachate. All statistical analysis and data plotting were carried out using Excel 2016 and Origin version 2020b [35]. All the standard deviations (error bars) were calculated from two parallel experiments’ data, which are subjected to uncertainties in sampling and instrument analysis.

2.5. Modeling Analysis of the Metal Leaching Kinetics

The leached heavy metal concentration, C(t), often varies with the elapsed or leaching time, t (min), in a first-order kinetic deposition term [36,37]. Recently, Zhang et al. developed a time fractional convective–dispersion–deposition equation (CDDE) model in Equation (1) [38], which described the tailing property of the pollutant leaching kinetics of different heavy metals such as Zn, Mn, Ag, and Ga from contaminated soil.
R f α C t α = D 2 C x 2 v C x K d C
where Rf (dimensionless) is the retardation coefficient and D is the hydrodynamic dispersion coefficient, both of which are calculated in detail in Table S2. In addition, v is the average linear water velocity (cm·min−1), Kd is the deposition rate coefficient that describes the adhesion strength of target pollutants with soil media or surface, and 𝛼 is the time fractional derivative order (between 0 and 1), which reflects the retention effect of the soil column on pollutants. Generally, a strong tailing effect occurs to the leaching process of pollutants when α becomes small. This CDDE model in Equation (2) reduces to a CDE model when α = 1, as follows:
R f C t = D 2 C x 2 v C x K d C
The above two models indicate five parameters may affect the transport of heavy metal ions in a porous soil column under washing, including the time fractional derivative order (α), retardation factor (Rf), hydrodynamic dispersion coefficient (D), flow velocity (v), and deposition rate coefficient (Kd). The above two models were employed to fit the experiment data of the leached concentrations of heavy metals under different rinsing conditions. Our assumption is that the presence of NBs could affect α or Kd, which were used as the fitting parameters. The detailed solutions of the above CDDE or CDE model are provided in Section S6.

3. Results and Discussions

3.1. Soil Characterization

Table S1 summarizes the major soil properties, such as the organic matter content of 3.0%, the bulk-density of 0.4 kg·L−1, soil porosity of 41%, and a pH of 5.76. The measured soil properties represent a sandy loam that is composed primarily of gravel and sand with very high organic-carbon content of 3.0%, which may strongly bind with Pb2+ and other heavy metals [39]. Soil organic matter plays an essential role in heavy metal adsorption (e.g., Pb and Cd) and sequestration as a result of the formation of metal-organic complexes [40,41].

3.2. Bubble Characterization

Figure 2a shows the bubble sizes and zeta potentials of CH4, CO2, and H2 NBs in tap water. These NBs had hydrodynamic diameters from 300 to 500 nm and negative surface charges. H2 NBs had the greatest zeta potential of −38.2 mV, while CH4 NBs has the highest zeta potential of −5.85 mV. Figure 2b indicates that the water suspension densities for the three types of NBs are consistently lower than the water density (1 g·mL−1). According to the NTA measurement, the concentration of the original water suspension of these NBs was approximately 1.5 × 108 #·mL−1. Clearly, the presence of NBs slightly reduced the density of the water suspension. The surface tension of NB water was almost the same for the three types of NBs. For example, the surface tension of CH4 NB water was shown to be 70.20 mN·m−1, which is slightly lower than the water surface tension (72 mN·m−1 at room temperature). Figure S2.

3.3. Effects of Nanobubble Water on Soil Structures and Soil Chemistries

3.3.1. Soil Fluidization

To compare the effects of the water flow with/without NBs on soil structures, the bubble water and DI water were purged separately in the soil column without placing the 10 cm glass bead layer on the top. Figure 3a shows the height of the expanded soil layer, which was consistently greater for the groups treated by the NB water compared to that by DI water under different flow rates. This result indicates that the presence of NBs could significantly increase the expansion and fluidization of the packed soil by 20–40% compared to the negligible expansion under DI water. Correspondingly, the calculated soil porosity in Figure 3b was shown to rise from 40% to 70% as the flow rate of NB water increased up to 2.12 mL·min−1·cm−2. At the flow rate of 2.83 mL·min−1·cm−2, there was a sudden “burst out” of the expanded soil column that ejected a vigorous jet of water and sediment into the overlying water and caused significant turbulent mixing, as illustrated in the inset photos of Figure 3b. Thus, for the following heavy metal leaching tests, a constant flow rate of 0.71 mL·min−1·cm−2 was chosen unless indicated otherwise. Figure S3.

3.3.2. Water Chemistries of the Soil Elute

Figure 4a compares the elute conductivity under the purging flow of DI water and NB water, which resulted in significant differences of ionic species release. The DI water caused a high initial conductivity due to the rapid leaching of ionic species from the washed soil, which after approximately 20 min reached a plateau near zero. Though the flow rate of DI water did not vary the elute conductivity significantly, the elute conductivity appeared to increase slightly when a flow rate of DI water at 0.71 mL·min−1·cm−2 was used, probably because a longer water retention time permitted more effective leaching of ionic species from the soil. However, the wash process by different NB waters extended the leaching time significantly (e.g., from 20 min to nearly 60 min) besides the rapid leaching at the beginning. For example, CO2 NBs caused a consistently high conductivity in elute, indicating that CO2 NBs appeared to mobilize and leach out higher amounts of ionic species from the soil than DI water or other types of NBs. All NBs in soil water are expected to carry negative charges (between −5 mV and −35 mV) and, therefore, may attract positively charged cations from the soil, which explains the higher conductivity in the elute water from the NB water treatment groups compared to the DI water treatment group.
Figure 4b–d compares the changes of the elute pH, ORP, and DO over the sampling time under the same flux of DI water or the NB water flux at 0.71 mL·min−1·cm−2. Figure 4b shows that CO2 NBs and DI water did not result in significant changes of the elute pH, whereas CH4 and H2 NBs both increased the elute pH appreciably to 7 and 6, respectively. This pH rise is likely due to the stripping of the dissolved CO2 from ambient air by CH4 and H2 NBs. Figure 4c shows that the elute ORP levels for CH4 and H2 NBs were progressively reduced from 200 mV to 150 mV or nearly 30 mV after 120 min, which partly results from the reduction of the dissolved oxygen (e.g., from 8 mg·L−1 to 6.5 mg·L−1), as shown in Figure 4d. Due to the linear relationship between ORP and the logarithm of oxygen concentration, an ORP of −96 mV in the aqueous phase is equivalent to a DO of 0.1 mg·L−1 [42]. In addition, the existence of CH4 in water can cause a micro-oxygen environment such that the ORP value is in the range from 0 to −470 mV [43]. The generation of hydrogen bubbles in water for 30 min can reduce the ORP to −115 mV, and after boiling for 15 min, in contrast, the oxidation–reduction potential is reduced to −79 mV [44]. However, despite the low DO caused by CO2 NB, ORP did not vary significantly with the rinsing time of CO2 NBs. According to Equation (S4), as CO2 NBs are purged into the soil column, the CO2 vapor pressure in the elute will increase, which will linearly increase ORP or EH if pH remains constant; this in fact dropped slightly (red circle data) according to Figure 4c and thus further caused the increase of ORP.

3.4. Release of Soil Heavy Metals under the Soil Washing by Different NB Water

Figure 5 shows that the release kinetics of the Pb concentrations from the soil was highly dependent on the types of NBs. The released Pb concentrations started to decline over time due to the elution or desorption from the spiked soil within the first 20 PVs. The leaching rate reduced significantly after 20 PVs. The effluent Pb concentrations tended to stabilize and reached a relatively low level after 30 PVs. The initial leached concentration of Pb under DI water wash is significantly lower than that under the NB water wash, suggesting an enhanced leaching process of heavy metal in soil by the presence of NBs.
Among different NBs, CO2 NB water led the highest or fastest rate of the Pb release (nearly 5.12 mg·L−1 at the time of 10 min) within the first 2 h (p < 0.05). By comparison, the DI water wash only led to an immediate concentration of 3.05 mg·L−1. H2 or CH4 NBs did not result in significant differences. Again, the enhanced Pb release under the pore flow water of NBs should be attributed to the negative surface charges of NBs that enabled electrostatic attractions towards positively charged Pb, among other possible mechanisms [25]. In particular, for CO2 NBs, the enhanced Pb release could also be attributed to the soil pH drop or slight acidification. Ionic Pb in soil may be absorbed by organic matter or coprecipitate with oxygen-containing groups such as carboxyl (-COOH) and hydroxyl (-OH) [45]. When the soil pH decreases, the binding strength between Pb and oxygen-containing anions will be reduced [46], which remarkably promotes the leaching process. According to Yang et al. [39], when the pH of the red soil was below 2, the desorption rate of Pb was found to be greater than 80%. However, the rate decreased to approximately 50% when the pH was between 2.9 and 3.4.
Thus, we further compared the leached concentrations of Pb, Cu, Zn, and Cr using CO2 NB water in Figure 5b–e. The soil sample was prepared by adding the same volume of the four heavy metal solutions with the same molar concentration of 2 mmol∙L−1. Compared to DI water, the water rinse with CO2 NBs consistently caused higher leached concentrations of heavy metals, especially at the sampling time of 10 min. Moreover, the leached levels of Cu (288.55 µg·L−1) and Zn (2433 µg·L−1) were substantially higher than Pb (35.86 µg·L−1) and Cr (71.94 µg·L−1), which indicates that Cu and Zn leached faster than Pb and Cr under the same CO2 NB water wash. The different release rates of different heavy metals may result from their interactions with NBs, soil binding, and desorption characteristics under the pore water rinse. Finally, we calculated the total released Pb amount by integration of the time-dependent concentration and volume of elute, which suggested that only 3–8% of Pb was leached in the 2 h experiments. A higher or complete removal of the spiked heavy metals requires further optimization of operations, such as increasing the washing time, choosing the washing liquid/soil (L/S) ratio, and adding chemical surfactants or chelate, which are not the focuses of this study.

3.5. Leaching Kinetics Modeling Analysis

The smooth curves of different lines in Figure 5 are the model fits generated by the CDDE model by varying the two fitting parameters (α and Kd), which yielded an excellent match with experimental data points. Figure S4 further compares the model fit and experimental data using the CDDE and CDE models. In the model fitting process with CDE, only one fitting parameter (Kd) can be varied, whereas the CDDE model fit could generate the potential changes of the time fractional derivative (α) under different rinsing solutions. Clearly, both models achieved excellent fitting, with their fitting parameters summarized in the caption of Figure S4, which shows that the fitting values of α remain constant at 0.97 or 1 for the two models and Kd reduced when NBs were present. In particular, the curve fit for the data of CO2 NBs resulted in a Kd value of 0.046, much lower than that of DI water (0.0925), which suggests that NBs reduced the adhesion strength of Pb on the soil surface. Moreover, the model fitting results reveal that the retention effect of the soil column on Pb is low without a significant tailing effect, as 𝛼 is almost 1. Thus, after the majority of the pollutant has been transported or leached, a very small residual amount of pollutant remains trapped or bound within the soil matrix. By contrast, significant tailing was reported for the release of iron and mercury from the contaminated soils that were barren, alkaline, and deficient with organic matters [47]. The soil sample used in this study was rich in organic matter and had a pH of 5.76, which may explain the monitor tailing effect on the Pb release.
Figures S5–S8 show the simulation results using the CDDE and CDE models to evaluate the impacts of the four parameters, retardation factor (Rf), hydrodynamic dispersion coefficient (D), flow velocity (v), and deposition rate coefficient (Kd), on the leaching kinetics of Pb under the influences of three types of NBs and DI water. For instance, Figure S5a shows the calculated concentration of leached Pb under elution with the CH4 NB water when varying Rf from 1 to 10 with the two models, where α = 0.97 and Kd = 0.07974 were used. Clearly, the CDE model shows that increasing Rf led to a slower release or leaching of Pb due to the retardation effect, whereas the CDDE model simulation shows that Rf barely affects the leaching kinetics. Similarly, the model simulation in Figure S5b shows that the leaching kinetics does not depend on the variations of D in the range (1.45 × 10−5 to 1.45 × 10−3 cm2·min−1). Likewise, the influence of v on the leaching kinetics of Pb is also negligible. By contrast, the impact of Kd on the leaching process of Pb is obvious. Increasing Kd from 0.04620 to 0.7974 min−l also slowed down the release of Pb, as a higher deposition rate coefficient means that Pb tends to adhere to the soil and thus leach less efficiently.
Figure S9 shows the simulation results using the CDDE model to evaluate the impact of the time fractional derivative (α). When α increases from 0.97 to 1.0, the leaching velocity slightly increases at a later stage after 10 PV. The time fractional derivative is used to account for the non-integer order of diffusion that occurs when solute moves through a porous medium. When α increases, the diffusion process will become more anomalous and therefore the leaching process is more sensitive to changes in the concentration of the solute in the liquid phase. In other words, the leaching process becomes more efficient and the rate of extraction of the solute from the solid material increases.

4. Conclusions

The presented study investigated the release of heavy metals from polluted sediment under the pore water flow containing NBs made with H2, CO2, and CH4. The sandy loam soil had a high organic-carbon content and a pH of 5.76. Three types of NBs (CO2, CH4, and H2) in the pore water caused soil expansion, as indicated by soil column height increase. A constant pore flow flux of 0.71 mL·min−1·cm−2 was chosen for proper soil fluidization and heavy metal leaching. The conductivity of leachate using DI water under different flow rates decreased rapidly and reached a plateau near zero after 20 min, while different NBs water extended the washing process and CO2 NBs caused a comparatively higher conductivity; CH4 and H2 NBs increased the elute pH to 7 and 6, respectively, while CO2 NBs decreased the elute pH to 5.5; ORP levels for CH4 NBs and H2 NBs were reduced from 200 mV to 150 mV or nearly 30 mV, while CO2 NBs increased the ORP slightly. All three types of NBs decreased the elute DO to 5.5–6.8 mg·L−1. The presence of NBs in water wash increased the Pb release by 57.15–198.77% compared to DI water, probably because the negative surface charges and high specific surface areas enabled electrostatic attraction of soil cations. In particular, CO2 NBs achieved the highest leaching rate of Pb, followed by CH4 and H2 NBs that led to similar leaching rates of Pb. The rinse of the soil column spiked with four different metals (Pb, Cu, Zn, and Cr) by CO2 NB water resulted in different leached concentrations of Cu (288.55 µg·L−1) and Zn (2.433 mg·L−1) at 10 min, which were much higher than Pb (35.86 µg·L−1) and Cr (71.94 µg·L−1). Clearly, the interactions of heavy metal cations and NBs, soil binding, and desorption characteristics may affect the release kinetics. To provide insights into the effects of NBs on soil pollutant transport behavior, a convective–dispersion–deposition equation (CDDE) and a convective–dispersion equation (CDE) model were employed to fit the leaching data to obtain key parameters such as retardation factor (Rf), hydrodynamic dispersion coefficient (D), flow velocity (v), and deposition rate coefficient (Kd). Results showed that leaching kinetics does not depend on the variations of D in the range 1.45 × 10−5 to 1.45 × 10−3 cm2·min−1 nor v in the range 0.71 to 2.84 mL·min−1·cm−2. The CDE model demonstrates that as Rf increases, the release of Pb slows down because of the retardation effect, while the CDDE model simulation indicates that the leaching kinetics of Pb is barely affected by Rf, while increasing Kd from 0.04620 to 0.7974 min−l also led to a slow and reduced release of Pb in both models.

Supplementary Materials

The following supporting information can be downloaded at:, Figure S1: (a) Photo of the nanobubble generator system; (b) Schematic of the generation process; Figure S2: (a) Experimental apparatus of the pendant drop method; (b) Photo of the drop captured at the tip of the needle; Figure S3: (a) Particle size distribution of the soil grains determined by sieving; (b) Minimum fluidization velocity as a function of grain size or d90; Figure S4: Breakthrough curves of Pb under a water flow containing: (a) CH4 NBs; (b) CO2 NBs; (c) H2 NBs; (d) DI water; Figure S5: Model prediction of the leached Pb concentrations under CH4 NB water wash using the CDDE model. (a) The results when varying Rf; (b) The results when varying D; (c) The results when varying v; and (d) The results when varying Kd.; Figure S6: Model prediction of the leached Pb concentrations under CO2 NB water wash using the CDDE model. (a) The results when varying Rf; (b) The results when varying D; (c) The results when varying v; and (d) The results when varying Kd; Figure S7: Model prediction of the leached Pb concentrations under H2 NB water wash using the CDDE model. (a) The results when varying Rf; (b) The results when varying D; (c) The results when varying v; and (d) The results when varying Kd; Figure S8: Model prediction of the leached Pb concentrations under DI water wash using the CDDE model. (a) The results when varying Rf; (b) The results when varying D; (c) The results when varying v; and (d) The results when varying Kd; Figure S9: Model prediction of the leached Pb concentrations under CH4 NB water wash using the CDDE model. (Rf = 10, D = 1.45 × 10−5 cm2·min−1, v = 0.71 mL·min−1·cm−2, and Kd = 0.07974 min−1); Table S1: The basic properties of the Miracle Gro Garden Soil; Table S2: The parameters of the CDE and CDDE models for fitting the experimentally measured concentrations of Pb or other metals. References [13,22,26,38,48,49,50,51,52,53,54,55,56,57,58,59] are cited in the Supplementary Materials.

Author Contributions

Conceptualization, Y.Z.; methodology, Y.Z.; software, Z.S. and K.S.; validation, S.X. and W.Z.; formal analysis, Y.Z.; investigation, Y.Z.; writing—original draft preparation, Y.Z.; writing—review and editing, Y.Z. and W.Z.; supervision, W.Z. All authors have read and agreed to the published version of this manuscript.


This research was funded by New Jersey Water Resources Research Institute (NJWRRI) Grant (Project Number: 2020NJ027B).

Data Availability Statement

The data presented in this study are available on request from the corresponding authors.


The authors thank Stephanie Murphy from the Rutgers Soil Testing Laboratory for providing soil samples and Ying Yao from the Meadowlands Research & Restoration Institute for the dissolved metal detection using ICP-MS.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of this manuscript; or in the decision to publish the results.


  1. Lai, Y.; Xia, X.; Dong, J.; Lin, W.; Mou, X.; Zhao, P.; Jiang, X.; Li, Z.; Tong, Y.; Zhao, Y. Equilibrium state of PAHs in bottom sediment–water–suspended sediment system of a large river considering freely dissolved concentrations. J. Environ. Qual. 2015, 44, 823–832. [Google Scholar] [CrossRef] [PubMed]
  2. Viana, P.; Yin, K.; Rockne, K. Comparison of direct benthic flux to ebullition-facilitated flux of polycyclic aromatic hydrocarbons and heavy metals measured in the field. J. Soils Sediments 2018, 18, 1729–1742. [Google Scholar] [CrossRef]
  3. Loganathan, B.G.; Lam, P.K.-S. Global Contamination Trends of Persistent Organic Chemicals; CRC Press: Boca Raton, FL, USA, 2011. [Google Scholar]
  4. Jung, H. Nutrients and heavy metals contamination in an urban estuary of northern new jersey. Geosciences 2017, 7, 108. [Google Scholar] [CrossRef]
  5. Yuan, Q. Experimental and Modeling Studies of Contaminant Transport in Capped Sediments during Gas Bubble Ebullition. Ph.D. Dissertation, Louisiana State University, Baton Rouge, LA, USA, 2007. [Google Scholar]
  6. Birks, S.; Cho, S.; Taylor, E.; Yi, Y.; Gibson, J. Characterizing the PAHs in surface waters and snow in the Athabasca region: Implications for identifying hydrological pathways of atmospheric deposition. Sci. Total Environ. 2017, 603, 570–583. [Google Scholar] [CrossRef] [PubMed]
  7. Huttunen, J.T.; Lappalainen, K.M.; Saarijärvi, E.; Väisänen, T.; Martikainen, P.J. A novel sediment gas sampler and a subsurface gas collector used for measurement of the ebullition of methane and carbon dioxide from a eutrophied lake. Sci. Total Environ. 2001, 266, 153–158. [Google Scholar] [CrossRef] [PubMed]
  8. Yuan, Q.; Valsaraj, K.T.; Reible, D.D. A model for contaminant and sediment transport via gas ebullition through a sediment cap. Environ. Eng. Sci. 2009, 26, 1381–1391. [Google Scholar] [CrossRef]
  9. Yuan, Q.; Valsaraj, K.T.; Reible, D.D.; Willson, C.S. A laboratory study of sediment and contaminant release during gas ebullition. J. Air Waste Manag. Assoc. 2007, 57, 1103–1111. [Google Scholar] [CrossRef]
  10. Wang, C.; Xu, D.; Bai, L.; Bai, C.; Huang, L.; Jiang, H. Important Role of Biogas and Secretions in the Formation of Biological Fluid Sediment under Cyanobacterial Bloom Biomass Degradation. ACS EST Water 2023, 3, 773–782. [Google Scholar] [CrossRef]
  11. Malyan, S.K.; Singh, O.; Kumar, A.; Anand, G.; Singh, R.; Singh, S.; Yu, Z.; Kumar, J.; Fagodiya, R.K.; Kumar, A. Greenhouse gases trade-off from ponds: An overview of emission process and their driving factors. Water 2022, 14, 970. [Google Scholar] [CrossRef]
  12. Ohgaki, K.; Khanh, N.Q.; Joden, Y.; Tsuji, A.; Nakagawa, T. Physicochemical approach to nanobubble solutions. Chem. Eng. Sci. 2010, 65, 1296–1300. [Google Scholar] [CrossRef]
  13. Razavi, N.R.; Ridal, J.J.; de Wit, W.; Hickey, M.B.; Campbell, L.M.; Hodson, P.V. Ebullition rates and mercury concentrations in St. Lawrence river sediments and a benthic invertebrate. Environ. Toxicol. Chem. 2013, 32, 857–865. [Google Scholar] [CrossRef] [PubMed]
  14. Delwiche, K.; Gu, J.; Hemond, H.; Preheim, S.P. Vertical transport of sediment-associated metals and cyanobacteria by ebullition in a stratified lake. Biogeosciences 2020, 17, 3135–3147. [Google Scholar] [CrossRef] [PubMed]
  15. Pan, Y.; Koopmans, G.F.; Bonten, L.T.; Song, J.; Luo, Y.; Temminghoff, E.J.; Comans, R.N. Temporal variability in trace metal solubility in a paddy soil not reflected in uptake by rice (Oryza sativa L.). Environ. Geochem. Health 2016, 38, 1355–1372. [Google Scholar] [CrossRef] [PubMed]
  16. Pardue, J.H.; Patrick, W.H. Changes in metal speciation following alteration of sediment redox status. In Metal Contaminated Aquatic Sediments; Routledge: Oxfordshire, UK, 2018; pp. 169–185. [Google Scholar]
  17. Pan, Y.; Koopmans, G.F.; Bonten, L.T.; Song, J.; Luo, Y.; Temminghoff, E.J.; Comans, R.N. Influence of pH on the redox chemistry of metal (hydr) oxides and organic matter in paddy soils. J. Soils Sediments 2014, 14, 1713–1726. [Google Scholar] [CrossRef]
  18. Yang, C.; Jing, Y.; Zhang, F.; Shen, S. Effects of fine bubble aeration on sediment-water interface on release potential of heavy metals in the sediment from a heavily polluted urban river. J. Tongji Univ. 2015, 43, 6. [Google Scholar]
  19. Viana, P.Z.; Yin, K.; Rockne, K.J. Field measurements and modeling of ebullition-facilitated flux of heavy metals and polycyclic aromatic hydrocarbons from sediments to the water column. Environ. Sci. Technol. 2012, 46, 12046–12054. [Google Scholar] [CrossRef]
  20. Elrahmani, A.; Al-Raoush, R.I.; Abugazia, H.; Seers, T. Pore-scale simulation of fine particles migration in porous media using coupled CFD-DEM. Powder Technol. 2022, 398, 117130. [Google Scholar] [CrossRef]
  21. McLinn, E.L.; Stolzenburg, T.R. Ebullition-facilitated transport of manufactured gas plant tar from contaminated sediment. Environ. Toxicol. Chem. Int. J. 2009, 28, 2298–2306. [Google Scholar] [CrossRef]
  22. Brusseau, M.L. Assessing the potential contributions of additional retention processes to PFAS retardation in the subsurface. Sci. Total Environ. 2018, 613, 176–185. [Google Scholar] [CrossRef]
  23. Potapova, M.; Desianti, N.; Enache, M. Potential effects of sediment contaminants on diatom assemblages in coastal lagoons of New Jersey and New York States. Mar. Pollut. Bull. 2016, 107, 453–458. [Google Scholar] [CrossRef]
  24. Zhai, X.; Li, Z.; Huang, B.; Luo, N.; Huang, M.; Zhang, Q.; Zeng, G. Remediation of multiple heavy metal-contaminated soil through the combination of soil washing and in situ immobilization. Sci. Total Environ. 2018, 635, 92–99. [Google Scholar] [CrossRef] [PubMed]
  25. Khaled Abdella Ahmed, A.; Sun, C.; Hua, L.; Zhang, Z.; Zhang, Y.; Marhaba, T.; Zhang, W. Colloidal Properties of Air, Oxygen, and Nitrogen Nanobubbles in Water: Effects of Ionic Strength, Natural Organic Matters, and Surfactants. Environ. Eng. Sci. 2018, 35, 720–727. [Google Scholar] [CrossRef]
  26. Berry, J.D.; Neeson, M.J.; Dagastine, R.R.; Chan, D.Y.; Tabor, R.F. Measurement of surface and interfacial tension using pendant drop tensiometry. J. Colloid Interface Sci. 2015, 454, 226–237. [Google Scholar] [CrossRef] [PubMed]
  27. Hu, L.; Xia, Z. Application of ozone micro-nano-bubbles to groundwater remediation. J. Hazard. Mater. 2018, 342, 446–453. [Google Scholar] [CrossRef]
  28. Avnimelech, Y.; Ritvo, G.; Meijer, L.E.; Kochba, M. Water content, organic carbon and dry bulk density in flooded sediments. Aquac. Eng. 2001, 25, 25–33. [Google Scholar] [CrossRef]
  29. Tanner, C.C.; Adams, D.D.; Downes, M.T. Methane emissions from constructed wetlands treating agricultural wastewaters. J. Environ. Qual. 1997, 26, 1056–1062. [Google Scholar] [CrossRef]
  30. Rothfuss, F.; Conrad, R. Effect of gas bubbles on the diffusive flux of methane in anoxic paddy soil. Limnol. Oceanogr. 1998, 43, 1511–1518. [Google Scholar] [CrossRef]
  31. Henry, P.; Thomas, M.; Clennell, M.B. Formation of natural gas hydrates in marine sediments: 2. Thermodynamic calculations of stability conditions in porous sediments. J. Geophys. Res. Solid Earth 1999, 104, 23005–23022. [Google Scholar] [CrossRef]
  32. Himmelheber, D.; Hughes, J. Complete tetrachloroethene dechlorination in Anacostia River sediment. In Proceedings of the SETAC 26th Annual Meeting in North America, Baltmore, MD, USA, 13–17 November 2005; pp. 13–17. [Google Scholar]
  33. Costanza, J.; Abriola, L.M.; Pennell, K.D. Aqueous Film-Forming Foams Exhibit Greater Interfacial Activity than PFOA, PFOS, or FOSA. Environ. Sci. Technol. 2020, 54, 13590–13597. [Google Scholar] [CrossRef]
  34. Fu, Y.; Tian, Z.; Amoozegar, A.; Heitman, J. Measuring dynamic changes of soil porosity during compaction. Soil Tillage Res. 2019, 193, 114–121. [Google Scholar] [CrossRef]
  35. Xue, S.; Marhaba, T.; Zhang, W. Nanobubble watering affects nutrient release and soil characteristics. ACS Agric. Sci. Technol. 2022, 2, 453–461. [Google Scholar] [CrossRef]
  36. Kretzschmar, R.; Barmettler, K.; Grolimund, D.; Yan, Y.-D.; Borkovec, M.; Sticher, H. Experimental determination of colloid deposition rates and collision efficiencies in natural porous media. Water Resour. Res. 1997, 33, 1129–1137. [Google Scholar] [CrossRef]
  37. Tufenkji, N.; Redman, J.A.; Elimelech, M. Interpreting Deposition Patterns of Microbial Particles in Laboratory-Scale Column Experiments. Environ. Sci. Technol. 2003, 37, 8. [Google Scholar] [CrossRef] [PubMed]
  38. Zhang, M.; Wei, S.; Dong, S.; Wei, W.; Zhang, Y. Effects of sodium dodecyl sulfate and solution chemistry on retention and transport of biogenic nano-hydroxyapatite in saturated porous media. Colloids Surf. A Physicochem. Eng. Asp. 2023, 661, 130956. [Google Scholar] [CrossRef]
  39. Yang, J.Y.; Yang, X.E.; He, Z.L.; Li, T.Q.; Shentu, J.L.; Stoffella, P.J. Effects of pH, organic acids, and inorganic ions on lead desorption from soils. Environ. Pollut. 2006, 143, 9–15. [Google Scholar] [CrossRef]
  40. Riffaldi, R.; Levi-Minzi, R.; Soldatini, G. Pb adsorption by soils. Water Air Soil Pollut. 1976, 6, 10. [Google Scholar] [CrossRef]
  41. Somrani, A.; Shabani, M.; Mohamed, Z.; Ghaffour, N.; Seibel, F.; Briao, V.B.; Pontié, M. Transforming an end-of-life reverse osmosis membrane in a cationic exchange membrane and its application in a fungal microbial fuel cell. Ionics 2021, 27, 3169–3184. [Google Scholar] [CrossRef]
  42. Nguyen, D.; Wu, Z.; Shrestha, S.; Lee, P.-H.; Raskin, L.; Khanal, S.K. Intermittent micro-aeration: New strategy to control volatile fatty acid accumulation in high organic loading anaerobic digestion. Water Res. 2019, 166, 115080. [Google Scholar] [CrossRef]
  43. Wang, X.; Yuan, T.; Lei, Z.; Kobayashi, M.; Adachi, Y.; Shimizu, K.; Lee, D.-J.; Zhang, Z. Supplementation of O2-containing gas nanobubble water to enhance methane production from anaerobic digestion of cellulose. Chem. Eng. J. 2020, 398, 125652. [Google Scholar] [CrossRef]
  44. Asada, R.; Saitoh, Y.; Miwa, N. Effects of hydrogen-rich water bath on visceral fat and skin blotch, with boiling-resistant hydrogen bubbles. Med. Gas Res. 2019, 9, 68. [Google Scholar] [CrossRef]
  45. Bu, H.; Wang, W.; Song, X.; Zhang, Q. Characteristics and source identification of dissolved trace elements in the Jinshui River of the South Qinling Mts., China. Environ. Sci. Pollut. Res. Int. 2015, 22, 14248–14257. [Google Scholar] [CrossRef] [PubMed]
  46. Borggaard, O.K.; Holm, P.E.; Strobel, B.W. Potential of dissolved organic matter (DOM) to extract As, Cd, Co, Cr, Cu, Ni, Pb and Zn from polluted soils: A review. Geoderma 2019, 343, 235–246. [Google Scholar] [CrossRef]
  47. Li, S.; Wu, J.; Huo, Y.; Zhao, X.; Xue, L. Profiling multiple heavy metal contamination and bacterial communities surrounding an iron tailing pond in Northwest China. Sci. Total Environ. 2021, 752, 141827. [Google Scholar] [CrossRef]
  48. Stoppa, F.; Schiazza, M.; Pellegrini, J.; Ambrosio, F.A.; Rosatelli, G.; D’Orsogna, M.R. Phthalates, heavy metals and PAHs in an overpopulated coastal region: Inferences from Abruzzo, central Italy. Marine pollution bulletin 2017, 125, 501–512. [Google Scholar] [CrossRef] [PubMed]
  49. Church, C.; Spargo, J.; Fishel, S. Strong acid extraction methods for “total phosphorus” in soils: EPA Method 3050B and EPA Method 3051. Agric. Environ. Lett. 2017, 2, 160037. [Google Scholar] [CrossRef]
  50. EPA. Method 3051A Microwave Assisted Acid Digestion of Sediments, Sludges, Soils, and Oils; EPA: Washington, DC, USA, 1996. [Google Scholar]
  51. Teng-Fei, S.; Xiang, L.; Lei, C.; Tao, X.; Ce-Hui, M.; Yan-Wen, L.; Quan-Ying, C.; Guo-Cheng, H.; De-Chun, H. Research progresses of determination of perfluorinated compounds in environmental water and solid samples. Chin. J. Anal. Chem. 2017, 45, 601–610. [Google Scholar]
  52. Bashforth, F.; Adams, J.C. An Attempt to Test the Theories of Capillary Action by Comparing the Theoretical and Measured Forms of Drops of Fluid; University Press: Oxford, UK, 1883. [Google Scholar]
  53. Morita, A.; Carastan, D.; Demarquette, N. Influence of drop volume on surface tension evaluated using the pendant drop method. Colloid Polym. Sci. 2002, 280, 857–864. [Google Scholar] [CrossRef]
  54. Wen, C.Y.; Yu, Y.H. Mechanics of fluidization. Chem. Eng. Prog. Symp. Ser. 1966, 62, 100–111. [Google Scholar]
  55. New York State Department of Environmental Conservation. Guidelines for Conducting Bird and Bat Studies at Commercial Wind Energy Projects; Division of Fish Wildlife and Marine Resources, Ed.: Albany, NY, USA, 2009. [Google Scholar]
  56. Fernandez, J.F.; Jastorff, B.; Stormann, R.; Stolte, S.; Thoming, J. Thinking in Terms of Structure-Activity-Relationships (T-SAR): A Tool to Better Understand Nanofiltration Membranes. Membranes 2011, 1, 162–183. [Google Scholar] [CrossRef]
  57. Kim, H.; Rao, P.S.C.; Annable, M.D. Determination of effective air-water interfacial area in partially saturated porous media using surfactant adsorption. Water Resour. Res. 1997, 33, 2705–2711. [Google Scholar] [CrossRef]
  58. Nakamura, K.; Yasutaka, T.; Kuwatani, T.; Komai, T. Development of a predictive model for lead, cadmium and fluorine soil-water partition coefficients using sparse multiple linear regression analysis. Chemosphere 2017, 186, 501–509. [Google Scholar] [CrossRef] [PubMed]
  59. Ding, G.; Peijnenburg, W.J.G.M. Physicochemical Properties and Aquatic Toxicity of Poly- and Perfluorinated Compounds. Crit. Rev. Environ. Sci. Technol. 2013, 43, 598–678. [Google Scholar] [CrossRef]
Figure 1. (a) Schematic of the soil column and upflow soil washing to leach heavy metals; (b) Photo of the soil column.
Figure 1. (a) Schematic of the soil column and upflow soil washing to leach heavy metals; (b) Photo of the soil column.
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Figure 2. (a) The bubble diameter and zeta potentials in tap water; (b) Liquid density and surface tension of three different NBs water.
Figure 2. (a) The bubble diameter and zeta potentials in tap water; (b) Liquid density and surface tension of three different NBs water.
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Figure 3. The effects of DI water and NBs water flows on soil structures: (a) Soil layer height; (b) Soil column porosity (the solid lines are a guide to the eye).
Figure 3. The effects of DI water and NBs water flows on soil structures: (a) Soil layer height; (b) Soil column porosity (the solid lines are a guide to the eye).
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Figure 4. (a) Conductivity of elute using different flow rates of DI water or at the NB water flux of 0.71 mL·min−1·cm−2; (bd) The elute pH, ORP, and DO changes under different purging time by the NB water.
Figure 4. (a) Conductivity of elute using different flow rates of DI water or at the NB water flux of 0.71 mL·min−1·cm−2; (bd) The elute pH, ORP, and DO changes under different purging time by the NB water.
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Figure 5. (a) Pb concentration in the effluent using DI water and three types of NB water under the same water flux of 0.71 mL·min−1·cm−2 . The data points correspond to the experimental measured Pb concentrations at different sampling times. The lines represent the model fits using the CDDE model in Equation (1). (be) The leached concentrations of four heavy metals (Pb, Cu, Zn, Cr) from the soil with mixed heavy metal spike using DI water and CO2 NB water; * indicates that the difference between the two treated groups is significant (p < 0.05). Pore volume (PV) = (Q·t)/(VT·φ), where the flow rate (Q) is 5 mL·min−1, t is the sampling time (min), the soil layer volume (VT) is 35.34 cm3, and the porosity (φ) is 0.41.
Figure 5. (a) Pb concentration in the effluent using DI water and three types of NB water under the same water flux of 0.71 mL·min−1·cm−2 . The data points correspond to the experimental measured Pb concentrations at different sampling times. The lines represent the model fits using the CDDE model in Equation (1). (be) The leached concentrations of four heavy metals (Pb, Cu, Zn, Cr) from the soil with mixed heavy metal spike using DI water and CO2 NB water; * indicates that the difference between the two treated groups is significant (p < 0.05). Pore volume (PV) = (Q·t)/(VT·φ), where the flow rate (Q) is 5 mL·min−1, t is the sampling time (min), the soil layer volume (VT) is 35.34 cm3, and the porosity (φ) is 0.41.
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Zhang, Y.; Song, Z.; Sugita, K.; Xue, S.; Zhang, W. Impacts of Nanobubbles in Pore Water on Heavy Metal Pollutant Release from Contaminated Soil Columns. Nanomaterials 2023, 13, 1671.

AMA Style

Zhang Y, Song Z, Sugita K, Xue S, Zhang W. Impacts of Nanobubbles in Pore Water on Heavy Metal Pollutant Release from Contaminated Soil Columns. Nanomaterials. 2023; 13(10):1671.

Chicago/Turabian Style

Zhang, Yihan, Zimu Song, Kosuke Sugita, Shan Xue, and Wen Zhang. 2023. "Impacts of Nanobubbles in Pore Water on Heavy Metal Pollutant Release from Contaminated Soil Columns" Nanomaterials 13, no. 10: 1671.

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