Coupled Kinetics Model for Microbially Mediated Arsenic Reduction

Jun 17, 2019 - Microbial reduction of As(V) in the presence of iron oxides has been ... (14,30) Under those conditions, As kinetic behavior was contro...
1 downloads 0 Views 2MB Size
Download PDF
Article Cite This: Environ. Sci. Technol. XXXX, XXX, XXX−XXX

pubs.acs.org/est

Coupled Kinetics Model for Microbially Mediated Arsenic Reduction and Adsorption/Desorption on Iron Oxides: Role of Arsenic Desorption Induced by Microbes Jingyi Lin,†,‡ Shiwen Hu,†,‡ Tongxu Liu,§ Fangbai Li,§ Lanfang Peng,†,‡ Zhang Lin,†,‡ Zhi Dang,†,‡ Chongxuan Liu,⊥ and Zhenqing Shi*,†,‡

Downloaded via BUFFALO STATE on July 17, 2019 at 08:07:06 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

†

School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, People’s Republic of China ‡ The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, Guangdong 510006, People’s Republic of China § Guangdong Key Laboratory of Agricultural Environment Pollution Integrated Control, Guangdong Institute of Eco-Environmental Science and Technology, Guangzhou, Guangdong 510650, People’s Republic of China ⊥ State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, People’s Republic of China S Supporting Information *

ABSTRACT: The dynamic behavior of arsenic (As) species is closely associated with iron mineral dissolution/transformation in the environment. Bacterially induced As(V) desorption from iron oxides may be another important process that facilitates As(V) release from iron oxides without significant reductive dissolution of iron oxides. Under the impact of bacterially induced desorption, As kinetic behavior is controlled by both the microbial reduction of As(V) and the As(III)&As(V) reactions on iron oxide surfaces. However, there is still a lack of quantitative understanding on the coupled kinetics of these processes in complex systems. We developed a quantitative model that integrated the timedependent microbial reduction of As(V) with nonlinear As(III)&As(V) adsorption/desorption kinetics on iron oxides under the impact of bacterially induced As(V) desorption. We collected and modeled literature data from 11 representative studies, in which microbial reduction reactions occurred with minimal iron oxide dissolution/transformation. Our model highlighted the significance of microbially induced As(V) desorption and time-dependent changes of microbial reduction rates. The model can quantitatively assess the roles and the coupling of individual reactions in controlling the overall reaction rates. It provided a basis for developing comprehensive models for As cycling in the environment by coupling with other chemical, physical, and microbial processes.

■

INTRODUCTION

microbial reduction) involving As(III)&As(V) species, iron oxides, and microbes, experimentally observed reactions rates are usually controlled by multiple coupled kinetic reactions. Therefore, a quantitative model accounting for the coupled kinetic reactions in As-iron oxide-bacteria systems will help to develop predictive models for describing As redox cycling in the environment. Microbial reduction of As(V) in the presence of iron oxides has been extensively investigated to elucidate the mechanisms

In natural environments the fate of arsenic (As) is significantly affected by the coupled kinetic processes of As(V) reduction and As(III)&As(V) reactions with minerals, such as adsorption/desorption, reductive dissolution, and incorporation.1−3 Iron oxides are among the most important mineral adsorbents for As in the environment,4−6 and the As desorption from iron oxides may be a slow process.7,8 Microbial reduction is one of the key driving forces for As release from iron oxides,9−12 and the kinetics of microbial reduction is affected by chemical conditions and types of microbes in the reaction systems.11−15 Owing to the complexity of both abiotic reactions (e.g., adsorption/ desorption and dissolution) and biotic reactions (e.g., © XXXX American Chemical Society

Received: Revised: Accepted: Published: A

January 7, 2019 May 28, 2019 June 17, 2019 June 17, 2019 DOI: 10.1021/acs.est.9b00109 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology Table 1. Detailed Experimental Information

a

Fe oxidesa

Fe oxide concentration (g L−1)

data set

pH

D1

7

D2

7

D3 D4

7 7

Fh Gt Fh Gt Gt Fh

D5

7

Fh

0.2 2 0.2 0.2/2/20 2 0.33 0.166 6.63

D6 D7 D8 D9

8.8 7.5 7.3 7.2

Fh Fh Fh Fh

0.26 4.45 0.45 2

0.44 mM 5 mM 1 mM 3.5 mM

D10

8 7

Fh

2.8 2.13

2.8 mM 0.24 mM

D11

7.3

Fh

4.45

0.5 mM

total As concentration 10 μM 10 μM 10 μM 20/200 μM 20 μM 0.48 mM

initial cell density (cells mL−1)

bacteria Inactivatedb S. putrefaciens CN-32 S. putrefaciens CN-32 S. putrefaciens CN-32 S. putrefaciens CN-32 S. putrefaciens ANA-3/S. putrefaciens FERM (ANA-3 mutant) Pantoea sp. IMH S. putrefaciens 200 S. barnesii SES-3 sediment bacteria S. putrefaciens ANA-3 S. putrefaciens ANA-3/S. putrefaciens CN-32 soil bacteria

carbon source

ref

5 × 109

25 μM lactate

Huang et al. (2011)12

5 × 109

25 μM lactate

Huang et al. (2011)14

1 × 109 5 × 109 7.5 × 109 1 × 106

25 μM lactate 2500 μM lactate 25/2500 μM lactate 20 mM lactate

Huang (2018)17 Huang (2018)32

1 × 109 OD600 = 2.0 2.5 × 109 unknown

Tian et al. (2015)13 Jiang et al. (2013)34 Zobrist et al. (2000)36 Campbell et al. (2006)30

500 1 × 108

20 mM lactate 20 mM pyruvate 20 mM acetate no carbon/19 mM lactate/17 mM acetate 14 mM lactate 20 mM lactate

unknown

10 mM lactate

Zhang et al. (2012)35

Reyes et al. (2010)21

Revesz et al. (2016)18

b

Notation: Fh, ferrihydrite; Gt, goethite. Inactivated cells: Shewanella cells were deactivated by treatment with 4% paraformaldehyde to avoid microbial As(V) reduction in D1.

of As release,12,16,17 iron mineral transformation (e.g., reductive dissolution and recrystallization),18−20 and microbial activities (e.g., microbially induced As desorption, genetic responses, and electron shuttles).12,21−24 The long-term As kinetic behavior was observed to be closely associated with the reductive dissolution/transformation of iron oxides in the environment, and As release may be controlled by multiple coexistent iron minerals and reactions (e.g., dissolution and desorption) involving different chemical species (e.g., adsorbed and incorporated).19,25−28 Kocar and Fendorf29 have thoroughly analyzed the thermodynamic constraints on reductive reactions involving As and Fe, and concluded that the general sequence of microbial reduction should be As(V) followed by Fe(III). As also shown in a number of previous studies, under nutrient-limiting conditions or early stages of the reactions with low Fe(III) reduction, microbial As(V) reduction may proceed without significant changes of iron oxides.14,30 Under those conditions, As kinetic behavior was controlled by both As(V) reduction, and As(III)&As(V) adsorption/desorption kinetic reactions under the impact of bacterially induced As(V) desorption.14 As(V) desorption from iron oxides under typical environmental pH conditions was usually slow.8,31 In a number of previous studies by Huang and coauthors,12,14,17,32 bacterially induced desorption was proposed to be one of the crucial processes enhancing As release and reduction in soils and sediments, in which adsorbed As(V) desorbed from iron oxides first and was then reduced by microbes in the solution. In comparison, a couple of other studies reported that bacteria did not enhance As(V) release from iron oxides. 13,33 Therefore, As(V) desorption induced by microbes was likely dependent on specific bacterial strains and reaction conditions. In these As−iron oxide−bacteria systems, As(III)&As(V) adsorption/desorption on iron oxides and the reduction of As(V) by microbes in solution may play different roles in controlling the overall kinetic processes. A quantitative understanding of the contribution of each reaction process to

the overall reaction rates and the dynamic coupling between different reactions is essential for predicting As kinetic behavior. Developing a quantitative model for microbially mediated As reduction and adsorption/desorption on iron oxides requires simultaneous consideration of nonlinear As(III)&As(V) binding to heterogeneous iron oxide binding sites and timedependent microbial reduction of As(V). In our previous work, we have developed a model for describing the kinetics of As(V) adsorption and desorption on iron oxides,8,31 which can account for the variations of solution chemistry conditions and heterogeneity of binding sites of iron oxides. This provided a solid basis for further developing kinetics model involving both adsorption/desorption and redox reactions. The ability of bacteria to reduce As(V) usually varies significantly among different species of microbes and reaction conditions.23,29 Microbial reduction of As(V) can be described by the firstorder reaction kinetics, but in the iron oxide-bacteria systems, the ability of bacteria to reduce As(V) may decrease with the presence of iron oxides.14 Furthermore, bacterial cells, metabolic products, and other anions may also compete with As ions for the adsorption sites on iron oxides, which may promote As(V) release and its reduction in the solution.12,17 To our best knowledge, no kinetics models are available for the coupled kinetic processes in the As−iron oxide−microbe systems, which can simultaneously consider As(III) and As(V) kinetic reactions with both iron oxides and microbes that can be used to quantify the effects of bacterially induced desorption and time-dependent microbial reduction on As kinetic behavior. The objectives of this study were to develop a model for describing the coupled kinetics of microbial reduction of As(V) and As(III)&As(V) adsorption/desorption on iron oxides in As−iron oxide−bacteria systems, with a focus on the role of bacterially induced As(V) desorption. We reviewed kinetic data published during the last two decades and selected most relevant data for model analysis. We specifically B

DOI: 10.1021/acs.est.9b00109 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology quantified and assessed the contribution of each individual kinetic reaction to the overall rates of coupled reactions under various conditions. We elucidated the dynamic coupling between microbial reduction and adsorption/desorption kinetic reactions and discussed its implication on predicting As kinetic behavior in the environment. The kinetic model developed in this study will advance our quantitative understanding on the kinetic reactions occurring in As−iron mineral−microbe systems, which in turn will contribute to the development of more comprehensive models for predicting As cycling in the environment by coupling with other chemical and physical processes.

■

The model includes the following key reactions: (i) dissolved As(III) and As(V) (Cion,As(III) and Cion,As(V) (μmol L−1)) adsorbed/desorbed on Fh or Gt (Cpi,As(III) and Cpi,As(V) (μmol g−1)), with adsorption rate coefficients kai,As(III) and kai,As(V) (L (g h)−1) and desorption rate coefficients kdi,As(III) and kdi,As(V) (h−1), respectively; (ii) dissolved As(V) was reduced to As(III) by bacteria in solution, with microbial reduction coefficients kred (h−1) varying with time; (iii) bacteria in solution competed with both As(III) and As(V) for the binding sites on Fh or Gt, which induced As desorption through a competing ligand RO− with binding constants KRO−. We considered that bacteria, whether adsorbed on iron oxides or staying in aqueous solutions, were capable of reducing As(V) in solution. While it is possible that adsorbed As(V) may be reduced on iron oxide surfaces,40 As(V) reduction in solution was considered to be the most favorable under the conditions in which the experimental data were collected.14 In addition, in goethite suspensions, As(III) oxidation by Fe(II)-activated goethite41 was not considered to simplify the model. The rates of As(III) and As(V) adsorption/desorption on the specific binding site i of Fh or Gt can be described as

MATERIALS AND METHODS

Data Description. The kinetic data analyzed in this study were obtained from 11 previous studies, in which As reduction and adsorption/desorption in the presence of both iron oxides and bacteria were investigated in the batch reactors. There were two reaction systems. One is As−ferrihydrite (Fh)− bacteria, and the other is As−goethite (Gt)−bacteria. The bacteria included different As(V) reducing strains (e.g., CN-32, ANA-3, IMH, etc.), with Shewanella putrefaciens (S. putrefaciens) being the majority. Initial cell concentrations varied significantly among different studies. The bacteria in different studies were grown with varying concentrations of nutrients, ranging from 25 μM to 20 mM lactate or other carbon sources (Table 1). Overall, the kinetic data were obtained under conditions of varying solution pH (7.0−8.8), total As concentrations (10 μM − 5 mM), iron oxide concentrations (0.166−20 g L−1), and types of arsenic-reducing bacteria (Table 1). All experimental data were digitized from the literature using the OriginPro 9 program and then tabulated in Microsoft Excel spreadsheets. In the 11 experimental cases selected for this modeling analysis, some experiments were conducted in the conditions to maintain minimal Fe(III) reduction,12,14,17,32 while others found minimal Fe(II) production during the initial stages of the reactions.13,18,21,30,34−36 The extent of Fe(III) reduction determined based on the Fe(II) data in all of the 11 cases (S1 section of Supporting Information (SI)) ranged from negligible to about 8% of total Fe within the first a few days (Table S1, SI). Therefore, the contributions of iron reductive dissolution and mineral transformation to As(V) release and sequestration was considered to be minimal. The data with less than 8% Fe(III) reduction were selected for our kinetic model development. Modeling Methods. Conceptual Model and Kinetic Equations. Most previous modeling work focused on the reductive dissolution of iron oxides and associated As release, which generally showed varying dissolution rates of iron oxides.25,28,37−39 In this study, we focused on modeling the kinetics of As(V) reduction while considering the adsorption and desorption of both As(V) and As(III) on iron oxides in the presence of active As-reducing bacteria. Since iron oxide dissolution was minimal as explained above, only bacterially induced As desorption was considered to account for As release. The conceptual model for the coupled kinetics of As(V) reduction, bacterially induced As desorption, and As adsorption in the presence of iron oxides (Fh or Gt) and bacteria can be described as follows (eq 1),

dCpi ,As(V) dt

= −kdi ,As(V)Cpi ,As(V) + kai ,As(V)Cion,As(V)

(2)

= −kdi ,As(III)Cpi ,As(III) + kai ,As(III)Cion,As(III)

(3)

dCpi ,As(III) dt

and the changes of solution As(III) and As(V) concentrations are as follows, dCion,As(V) dt

=

∑ kdi ,As(V)mCpi ,As(V) −

∑ kai ,As(V)mCion,As(V) − kredCion,As(V) (4)

dCion,As(III) dt

=

∑ kdi ,As(III)mCpi ,As(III) −

∑ kai ,As(III)mCion,As(III) + k redCion,As(V) (5)

where m (g L−1) is the particle concentrations of Fh or Gt in the reaction. The total concentrations of As(III) and As(V), Ctotal, equal the sum of the dissolved As concentrations and the adsorbed As concentrations. Relationships between Equilibrium Parameters and Kinetic Parameters. In the coupled kinetics model, one key issue is to specifically consider the variations of As(III)&As(V) adsorption/desorption rate coefficients, which controlled the solution As(V) concentrations available for microbial reduction. To account for the impact of the solution chemistry conditions (e.g., pH and As concentrations) and nonlinear As(III)&As(V) binding to the heterogeneous sites of iron oxides during the kinetic reactions, we employed the kinetic modeling approaches developed in our previous kinetic studies,8,42,43 which included two key relationships between the adsorption/desorption rate coefficients and the equilibrium binding properties of As(III)&As(V) on iron oxides, C

DOI: 10.1021/acs.est.9b00109 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology

Figure 1. Kinetics of As(V) reduction by bacteria and As(III) and As(V) adsorption/desorption on goethite (RMSE = 0.48, 0.53, 0.04, 0.47, 0.28, 0.98, 0.15 μM from panel a to panel g). For each plot, the left panel showed the kinetic data and model fits of the solution As(III) and As(V) concentrations, and the right panel showed model calculations of the adsorbed As(III) and As(V) concentrations on iron oxides and the changes of microbial reduction rate coefficients kred with reaction time. Total [As(V)] = 10 μM and the bacteria are S. putrefaciens CN-32. Other key experimental conditions are presented in the legends. Symbols represent the experimental data and lines are model calculations: (a−c) experiments in D2 with varying goethite concentrations; (d−g) experiments in D3 with varying phosphate concentrations.

kai = kdiK pi

(6)

log kdj − log kdi = 0.5(log KMi − log KMj)

(7)

In this study, we employed the CD-MUSIC model44 to predict the equilibrium of As(III)&As(V) binding to Fh and Gt (Kpi values), which was available in Visual MINTEQ 3.1.45 For As(V), the CD-MUSIC model46,47considers two bidentate complexes and one monodentate complex on both Fh and Gt, and for As(III), it considers one bidentate complex on Fh, and one monodentate and another bidentate complexes on Gt. Table S3 summarized different surface complexation reactions and the values of As(III)&As(V) binding constants (KMi). Incorporation of the Time-Dependent Microbial Reduction Kinetics and Competition of Bacterial Functional Groups for As Binding. Another central issue of the coupled kinetics model is to integrate the microbial reduction kinetics into the adsorption/desorption kinetics model. Considering that the ability of bacteria to reduce As(V) may decline with reaction time, the following equation was used to describe the microbial reduction rate coefficients that decay with time,

Derivations of both eqs 6 and 7 are presented in the Supporting Information. Equation 6 indicated that, for one specific binding site, As(III) or As(V) adsorption and desorption rate coefficients were constrained by the local equilibrium partition coefficient Kpi (L g−1), which was not a constant but a function of Cpi, pH, and microbial competing ligand RO− during the reaction. Equation 7 demonstrated that the desorption rate coefficients of two binding sites with similar complexation mechanisms were constrained by their metal binding constants KM. Therefore, when the kdi value of one specific binding site was determined, the kai value of the same binding site can be calculated using eq 6 with known Kpi values under specific reaction conditions and the kdi values of other sites can be calculated using eq 7 with known KM values of different binding sites.

k red = k 0M(t ) D

(8) DOI: 10.1021/acs.est.9b00109 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology

Figure 2. Kinetics of As(V) reduction by bacteria and As(III) and As(V) adsorption/desorption on ferrihydrite (RMSE = 0.05, 0.24, 0.32, 1.88, 76.7, 24.3, 103, and 35.1 μM from panel a to panel h). For each plot, the left panel showed the kinetic data and model fits of the solution As(III) and As(V) concentrations, and the right panel showed model calculations of the adsorbed As(III) and As(V) concentrations on iron oxides and the changes of microbial reduction rate coefficients kred with reaction time. Key experimental conditions are presented in the legends. Symbols represent the experimental data and lines are model calculations.

where k0 (h−1) was the initial As(V) reduction rate constant and M(t) was a function reflecting the combined effects of microbes and minerals on kred. M(t) may be affected by the microbial parameters (e.g., cell density, carbon sources), the impact of mineral phases, and the solution compositions. Usually, microbial biomass alone was not a satisfactory parameter to explain the variations of microbial reduction rates during kinetic experiments,48 because microbial activities were also affected by reactants (e.g., As concentrations) and electron donors (e.g., carbon sources). Ideally, both k0 and M(t) can be determined based on the microbial genetic functions with reaction time, which directly controlled As(V) reduction by microbes. However, the conditions of bacteria were not documented with time in most studies, and we used an exponential function to represent M(t) to simplify the model, k red = k 0 × exp( −bt )

To account for the bacterially induced As desorption due to bacteria and their metabolic products, we introduced a reactive component RO− to account for the competition effects of the functional groups on bacterial cell surfaces and their metabolic products on As(III)&As(V) binding to Fh and Gt, an approach developed previously.4 We added the component RO− to the CD-MUSIC model in order to describe the reactions of microbial adsorption on the mineral surfaces (Table S3). The binding constant of RO− was the same for all experiments, while the varying concentrations of RO− reflected the different degrees of the competition effects on As binding. Note that bacterial biomass, their metabolic products, and carbon sources may vary during the kinetic experiments. Since there was no detailed information on these aspects in original studies, in the kinetics model we fixed RO− concentration for each experiment to represent an average competition effect of the bacterial functional groups for As binding to simplify the model and to reduce the model fitting parameters. The competitive binding of other anions, including phosphate and bicarbonate, was considered based on the solution chemistry conditions.

(9)

where b was a constant. E

DOI: 10.1021/acs.est.9b00109 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology Model Calculations. For CD-MUSIC calculations, we input all parameters into Visual MINTEQ 3.1, including solution chemical compositions, concentrations of iron oxides, and the microbial component RO−. Details of the Visual MINTEQ input parameters are described in the Supporting Information. On the basis of the output of Visual MINTEQ calculations, we tabulated As(III) and As(V) adsorption isotherms and calculated the equilibrium partition coefficients Kpi for each binding site at different adsorbed As(III) or As(V) concentrations. Model equations were solved in Excel spreadsheets using an implicit numerical method. During the numerical calculations, temporal variations of parameter Kpi were obtained as a function of pH and Cpi using the VBA codes in Excel or interpolated based on the calculated Kpi. In the kinetic model, kdi values of different binding sites remained constant during the reactions, which suggested a disjunctive reaction pathway for As desorption from iron oxides,8 and kai values of different binding sites at each reaction time were calculated through eq 6. For each data set, we calculated the differences between experimental data and model calculations. The differences were then squared and summed to obtain the total squared errors (TSE). Then we used the SOLVER program in Excel to optimize the model fitting parameters (kdi, k0, and b) by minimizing TSE. For each data set the root-mean-squarederror (RMSE) was calculated to assess the performance of model fits. A detailed description of the model fitting procedures is presented in the Supporting Information.

summarized in Table S1, we analyzed kinetic data in which iron reductive dissolution was negligible or less than 8% of the total Fe. Iron oxide binding sites adsorbed with As(V) was much less likely to be reduced than those without As(V) binding, as shown in previous studies in which Fe(II), As(V), or other oxyanion adsorption significantly inhibited iron oxide reduction or transformation.31,38,49,50 Therefore, on the basis of the total mass balance, less than 8% of Fe(III) reduction and dissolution was not likely to significantly affect the total As binding sites on iron oxide surfaces and thus had minor impact on As(III)&As(V) adsorption/desorption reactions. The Roles of Major Chemical Factors in Coupled Kinetic Processes. Various factors such as iron oxide concentrations, competing anions, and initial As(V) concentrations affected the coupled kinetics and the dynamic distribution of As(III)&As(V) between the iron oxides and the solutions. The results in As−goethite−bacteria systems specifically demonstrated the impact of goethite concentrations and phosphate concentrations on the coupled kinetic reactions (Figure 1). Note that all the data related to As−goethite− bacteria systems were from the work by Huang et al.,14,17 with negligible observed Fe(III) reduction (Table S1), which provided an ideal condition to test our kinetics model. At 0.2 and 2 g L−1 goethite, the kinetics model showed that microbially induced As(V) desorption was significant over reaction time (Figure 1a,b). At the highest Gt concentration (20 g L−1) (Figure 1c), model calculations showed that most As(V) was tightly bound by goethite with little As(V) desorption and little As(V) reduction occurring under this condition. The kinetic model was able to account for the competition effect of phosphate within a range of phosphate concentrations (0, 10, 50, and 100 μM) (Figure 1d−g), with the same model parameters but varying phosphate concentrations in model calculations. The addition of phosphate in the model virtually decreased both As(III) and As(V) adsorption (Kpi) due to the competition of phosphate ions for As binding, which decreased As adsorption rates (eq 6) and enhanced overall As(V) release rates (eqs 2 and 3). The kred values remained the same at different phosphate concentrations. The As−ferrihydrite−bacteria system had more data than the As−goethite−bacteria system. Overall initial As(V) concentrations had significant impact on the subsequent coupled kinetic processes. In some experiments, the dissolved As(V) concentrations at the beginning of the experiments were low due to the strong binding of As(V) to ferrihydrite. The dissolved As(V) concentrations increased with reaction time under most conditions due to microbially induced As(V) desorption (Figure 2a−d). In comparison, Figure 2e−h showed experimental and modeling results with high initial As(V) concentrations, in which dissolved As(V) concentrations decreased with time due to the microbial reduction. The dissolved As(III) concentrations increased with time due to the As(V) reduction in solution and adsorption of As(III) to iron oxides. The Role of Bacterial Reduction in Coupled Kinetic Processes. Our modeling results suggested that the overall microbial reduction rates of As(V) decreased with the presence of iron oxides and with reaction time. According to eqs 2−5, the rates of the total As(V) or As(III) concentration were

■

RESULTS AND DISCUSSION Modeling Coupled Kinetics of As Reactions: Model Utility. Representative modeling results of the kinetics of As(V) reduction and As(III)&As(V) adsorption/desorption are presented in Figure 1 for As−goethite−bacteria systems and Figure 2 for As−ferrihydrite−bacteria systems. Additional figures are shown in Figures S1 and S2. Overall, the coupled kinetic model reproduced most of the experimental data reasonably well under conditions of a wide range of solution chemical compositions, iron oxide concentrations, and bacterial strains. Most RMSEs of model fits as shown in the figure captions were less than 10% of the total As concentrations. The modeling results generally validated the modeling approach, in which the same modeling framework was applicable to different reaction systems. The variations of solution chemical conditions and iron oxides were accounted for by the CD-MUSIC model (Table S2), which effectively determined the adsorption rate coefficients (eq 6) because Kpi was calculated by the CD-MUSIC model (Figure S3). The variations of the microbes affected the reaction rates through the time-dependent microbial reduction rate coefficients kred (Figures 1 and 2 and Figure S2), which was an empirical function incorporating multiple effects discussed later. On the basis of the modeling results, we found that the microbially induced As(V) desorption was a key process accounting for As release in most experiments we analyzed (see discussion later), as proposed previously.12,14,17,32 Figure S1 clearly showed the ability of bacteria and their metabolic products to induce As(V) desorption from iron oxides, since bacterial cells were deactivated with no As(V) and Fe(III) reduction.12 It is known that As release from iron minerals was closely associated with iron reductive dissolution, and Fe(III) reduction may also change the adsorption capacity of iron oxides and form new iron mineral phases for As adsorption. As

dC

determined by the equation of T,As(V) = k redCion,As(V). Theredt fore, the observed decrease of microbial reduction rates during F

DOI: 10.1021/acs.est.9b00109 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology

Figure 3. Variations of adsorption, desorption, and reduction rates of As(V) and As(III) in various studies: (a) systems with low initial As(V) concentrations (from top to bottom: D2, [As] = 10 μM, [Fh] = 0.2 g L−1, CN-32; D4, [As] = 20 μM, [Fh] = 0.33 g L−1, CN-32; D4, [As] = 20 μM, [Fh] = 0.17 g L−1, CN-32); (b) systems with high initial As(V) concentrations (from top to bottom: D5: [As] = 0.48 mM, [Fh] = 6.63 g L−1, ANA-3; D7: [As] = 5 mM, [Fh] = 4.45 g L−1, S. putrefaciens 200; D9: [As] = 3.5 mM, [Fh] = 2 g L−1, sediment bacteria).

toxicity effect of As on microbes,33,48,52 etc. This may result in varying degrees of the decrease in kred during the kinetic experiments (Figures 1 and 2 and Figure S2). The decrease of biomass may not be significant in this study considering the short time scales. A significant decrease of kred was generally observed in low nutrient conditions employed in some studies (Figures 1 and 2).14,17,32 In a few other cases, very small changes of kred were observed, which corresponded to a low microbial reduction activity as observed in the experiments (e.g., Figure 2c,d). Overall, the modeling results highlighted the importance of considering the time-dependent changes of microbial reduction rates when predicting the dynamic behavior of As(III) and As(V) during the coupled kinetic processes. The Role of Microbially Induced As(V) Desorption in Coupled Kinetic Processes. In most experiments with low As concentrations and with bacteria CN-32, the kinetics model highlighted the importance of microbially induced desorption as an important driving force to promote the As(V) release and reduction in As(V)−iron oxide systems. In the model, microbially induced As(V) desorption was accounted for by the competitive adsorption of RO− to iron oxides. The variations of the RO− concentrations indicated that the competition of bacterial functional groups with As(V) binding varied among different bacterial strains (Table S4). Competitive binding from bacterial functional groups significantly increased the overall rates of As(V) release due to the reduced As(V) readsorption reactions (eq 2), an important but underappreciated mechanism responsible for the enhanced release rates.53,54 It is possible that As may also adsorb on the surfaces of bacterial cells,55 which was not considered in the model but might be important at low iron oxide concentrations. In four studies (D5, D6, D7, and D9), mostly with

the kinetic processes can be explained by two distinct mechanisms, As(V) adsorption by iron oxides, which reduced the dissolved As(V) concentrations, and the decline of kred with time. The impact of As(V) adsorption on iron oxides on overall As(V) reduction rates was clearly demonstrated in Figure 1. With an increase in goethite concentration, more As(V) was bound to goethite during the kinetic experiments, and less As(V) was reduced. At the highest goethite concentration (20 g L−1), little As(V) desorption and reduction occurred. The modeling results have quantitatively demonstrated that the time-dependent changes of microbial reduction coefficients kred played a key role of regulating the changes of As concentrations in the solution (Figures 1 and 2, Figure S2). A fast decline of kred from large k0 values explained that dissolved As(III) concentrations increased quickly and then approached a plateau (Figure 2a). In comparison, a slower decline of kred values from medium k0 values contributed to the steady increase in the dissolved As(III) concentrations (Figure 2b), and small kred values during the experiments accounted for the slow but steady increase in the dissolved As(III) concentrations (Figure 2c,d). For the adsorbed phases, in most cases, the adsorbed As(V) concentrations slowly decreased with time due to the microbially induced desorption of As(V). The adsorbed As(III) concentrations remained low or slowly increased with time due to the lower affinity of As(III) with iron oxides,45 and, in some cases with high As concentrations, adsorbed As(III) may become higher than adsorbed As(V) (Figure S2).30 The time-dependent changes of kred values could be due to multiple combined factors during the kinetic processes, such as the consumption of nutrients,25 decrease of bacterial biomass,51 bacterial interactions with minerals,12 possible G

DOI: 10.1021/acs.est.9b00109 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology relatively high As(V) concentrations, little microbially induced As(V) desorption was observed, with no RO− needed in the model to fit the data. Furthermore, at high iron oxide concentrations, microbially induced desorption was not strong enough to release a significant amount of As(V) into the solution, which inhibited the overall As(V) reduction rates (Figure 1c). In the model calculations, the RO− concentrations were adjusted with a trial-and-error method until the model calculations were close to the experimental data. The sensitivity of RO − was tested by changing the RO − concentrations in model calculations under typical conditions (Figure S4). In the As−goethite−bacteria systems (Figure S4a), and most As−ferrihydrite−bacteria systems (Figure S4b,c), kinetic model results were sensitive to the RO− competition, and the RO− concentrations used in the model calculations provided optimal model performance. In the four studies with little RO− competition, adding the range of RO− concentrations as shown in Table S4 had little impact on the overall model performance (Figure S4d), presumably due to high As(V) concentrations present in the systems. At high As(V) concentrations, very high RO− concentrations would be required to result in any significant competitive adsorption from bacteria. Since different bacterial strains have shown varying ability to induce As(V) desorption, further studies were desired to accurately quantify the microbially induced As(V) desorption due to bacteria and their metabolic products (e.g., exopolysaccharides).12 In addition, the current model employed a constant RO− concentration for each kinetic experiment, which appeared to be able to account for the competition effects of bacteria and their metabolic products on As adsorption on iron oxides. Future work on quantifying the time-dependent changes of RO− concentrations will help to calibrate the model parameters and to improve the accuracy of the kinetics model. Contribution of As Reduction and Adsorption/ Desorption to Overall Reaction Rates. One of the fundamental aspects of the coupled kinetic model is to quantitatively assess the contribution of each individual reaction rate to the overall reaction rates at different times, which is usually difficult to determine experimentally but essential for predicting As kinetic behavior. Figure 3 shows representative results of the changes of reduction, adsorption, and desorption rates as a function of time for both As(V) and As(III). At low initial As(V) concentrations in the solution, microbially induced desorption of As(V) from iron oxides dominated the overall reaction rates in the beginning, and the reduction and/or adsorption reactions later became significant (Figure 3a). At high initial As(V) concentrations in the solution, As(V) reduction reactions dominated the overall reaction rates in the beginning, and the microbially induced desorption of As(V) was not able to sustain the As(V) reduction in the solution (Figure 3b). Simulations were performed to assess the effects of varying initial reduction rate constants k0 (e.g., due to varying bacteria strains, growth conditions, initial cell counts, etc.) and varying desorption rate coefficients of As(V) for the nonprotonated bidentate sites kd2,As(V) (e.g., due to different morphology of iron oxides and mixing conditions) on the reaction rates of the reduction, adsorption, and desorption processes (Figure 4). The simulations were run under typical reaction conditions as used in the previous study by Huang et al. (2011),14 which represented a carbon-limiting condition with low concen-

Figure 4. Variations of adsorption, desorption, and reduction rates of As(V) and As(III) with changing (a) k0 (0.01−0.5 h−1) or (b) kd2,As(V) (0.0025−0.005 h−1). The direction of the arrow represents an increase of kd2,As(V) or k0 values. The experimental conditions for model simulations were from data set D2: k0, initial As(V) reduction rate constant; kd2,As(V), the desorption rate coefficients of the nonprotonated bidentate sites for As(V). Note that the nonprotonated bidentate sites were the dominant sites for As(V) binding on ferrihydrite.

trations of carbon sources. The microbial reduction rates were highly dependent on k0, which corresponded to varying microbial reduction ability of different bacteria. The As(V) adsorption rates decreased with increasing k0 values due to increasing reduction of the dissolved As(V) to As(III). However, the variations of k0 values had little impact on the desorption rates of As(V) since the desorption rates were controlled by the adsorbed As(V) concentrations and the desorption rate coefficients (Figure 4a). In comparison, variations of kd2,As(V) values had significant impact on all three reactions of As(V) (Figure 4b), indicating the importance of As(V) desorption from iron oxides in controlling the coupled kinetic processes. Larger kd led to larger ka for As(V) according to eq 6, which directly enhanced both As(V) adsorption and desorption rates. Enhanced As(V) desorption rates further increased As(V) reduction rates in the solution. Overall, the model calculations highlighted the dynamic coupling of the reduction, adsorption, and desorption processes in the As-iron oxides-bacteria systems. Model Assessment. The desorption rate coefficients of both As(III) and As(V) for different binding sites of ferrihydrite and goethite are presented in Figure S5. Since ferrihydrite had large data sets, the variations of kdi values were about 2 orders of magnitude (Figure S5a). The variations of kdi values could be due to different experimental settings. Different experimental conditions and microbial cell physiology may cause different morphological changes of iron minerals and conditions on the mineral surfaces. Different shaking speeds may also affect the overall As adsorption/desorption kinetics. All these factors may contribute to different kdi values acquired from different experiments.8,56 There were fewer data sets for goethite, and one single set of kdi values can be applied to different studies (Figure S5b). This is consistent with the relatively stable morphology of goethite compared with that of ferrihydrite. Adsorption of As and Fe ions by the functional groups of bacterial cell surfaces57 or the formation of the cell− Fe(II)−As(V) ternary complexes55 was not considered in this study, which can be further included into the current model. H

DOI: 10.1021/acs.est.9b00109 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology

results had significance in predicting As cycling in the environment. On the basis of the desorption rate coefficients of As(V), microbially induced desorption may control As(V) slow release within the time scales of days to months. Surface soils may contain abundant microbes and the associations among plant roots, microbes, and minerals may also result in substantial extracellular polymeric substances, which are capable of inducing As desorption from minerals. The rates of microbial reductive dissolution of iron oxides were highly dependent on the concentrations of carbon sources and iron oxides, surface properties of iron oxides, types of bacteria, and solution chemistry conditions, which resulted in a wide range of iron reductive dissolution rates.25,28,37−39,61 Therefore, it is likely that both desorption and dissolution processes contributed to As release from minerals. Iron oxide transformation is another important process to control the longterm As cycling in the environment. Incorporating the kinetic processes driven by microbially induced As desorption into the kinetics models including iron mineral dissolution/transformation will improve our ability to predict As behavior across a wide range of environmental conditions and time scales. Considering the importance of microbially induced As desorption, future work should specifically elucidate the competition effects of various microbes and their metabolic products on As binding to iron minerals, which will help to calibrate the kinetics model developed in this study. Furthermore, there is still a poor understanding on quantifying the time-dependent microbial reduction rates, and future experimental work should focus on quantifying the As(V) reduction constants based on the genetic information on microbes during the kinetic experiments. Our modeling results have provided a basis for further developing comprehensive models in field conditions by coupling other chemical, physical, and microbial processes. A complete kinetics model combining both short-term As reaction kinetics on iron oxides and long-term As transfer among different mineral phases will enable us to accurately predict As mobilization and reduction rates.

The As(V) reduction rate coefficients (kred) for different studies are shown in Figure 1, Figure 2, and Figure S2, and the k0 and b values are summarized in Table S4. In the literature data analyzed in this study, the As(V) reducing microbial species contained varying types of the bacteria such as CN-32, ANA-3, and some other bacterial strains. The initial As(V) reduction rate constants k0 of the different types of bacteria varied from 0.006 to 0.9 h−1 (Table S4), which is presumably due to the difference in bacterial strains, cell counts, nutrients, and experimental conditions. In addition, the constant b, which reflected the rates of k0 decaying during the kinetic processes, also varied. Even for the same type of bacteria (S. putrefaciens CN-32 in D2−D4), both k0 and b values varied significantly due to different initial bacterial concentrations, carbon sources, and initial As concentrations, suggesting that the bacterial and solution factors together determined the changes in the microbial reduction rates. It is likely that both As and carbon sources generated different genetic responses of bacteria and thus varying As reduction ability. Future studies are desired to elucidate the mechanisms accounting for the variations and the time-dependent changes of As reduction rates. We conducted sensitivity and uncertainty tests on model fitting parameters, which are summarized in the S2 section of the Supporting Information. Representative results are presented in Figures S6−S11. Generally, model performance had different sensitivities to different model parameters (Figures S6−S8). The uncertainties of the kinetics model and their impact on modeling results also varied among different studies (Figures S9−S11). Overall, our sensitivity and uncertainty tests indicated that most model uncertainties and errors associated with model calculations were mainly due to the lack of sufficient experimental data that can specifically constrain the model fitting parameters. This highlighted the importance of employing large data sets from well-designed experiments in order to fully calibrate the model parameters and improve the accuracy of model predictions. The microbial reduction kinetics was simplified in the current study and the model describing microbial reduction kinetics can be further improved in future studies by incorporating the microbial functions into the reduction kinetics.23,58 For example, future studies can incorporate the time-dependent profiles of genes responsible for As(V) reduction into the kinetics model, which is likely affected by both the types of bacteria and the reaction conditions as mentioned above. This will allow us to build a more mechanistic-based kinetics model applicable to different bacteria−iron mineral systems. In conditions for which the direct reduction of As(V) on the solid surfaces may be significant,40 the kinetics model can be extended to include the reduction reactions on iron oxide surfaces. When iron mineral dissolution/transformation is significant,27,59,60 reductive dissolution processes, the variations of iron oxide compositions, and the possible structure incorporation of As into iron oxides should be specifically considered. This can be done based on the modeling approach employed in recent studies.25,28,31 In addition, competitive binding from dissolved organic matter may also play important roles in affecting As kinetic reactions with iron oxides, and it should be specifically considered in future models using a similar modeling approach for considering phosphate competition. Environmental Implications. The modeling results in this study showed the important roles of microbially induced desorption in controlling As(V) release and reduction. The

■

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.9b00109. Additional details of literature data; description of model calculations and parameters; additional figures (PDF)

■

AUTHOR INFORMATION

Corresponding Author

*Phone: 86-20-39380503; fax: 86-20-39380508; e-mail: [email protected]. ORCID

Tongxu Liu: 0000-0002-2348-3952 Fangbai Li: 0000-0001-9027-9313 Zhang Lin: 0000-0002-6600-2055 Chongxuan Liu: 0000-0002-7403-6001 Zhenqing Shi: 0000-0003-1721-5369 Notes

The authors declare no competing financial interest. I

DOI: 10.1021/acs.est.9b00109 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology

■

(16) Kocar, B. D.; Polizzotto, M. L.; Benner, S. G.; Ying, S. C.; Ung, M.; Ouch, K.; Samreth, S.; Suy, B.; Phan, K.; Sampson, M.; Fendorf, S. Integrated biogeochemical and hydrologic processes driving arsenic release from shallow sediments to groundwaters of the Mekong delta. Appl. Geochem. 2008, 23 (11), 3059−3071. (17) Huang, J. H. Impact of competitive adsorption on microbial arsenate reduction at the water-goethite interface. Appl. Geochem. 2018, 88, 59−67. (18) Revesz, E.; Fortin, D.; Paktunc, D. Reductive dissolution of arsenical ferrihydrite by bacteria. Appl. Geochem. 2016, 66, 129−139. (19) Muehe, E. M.; Morin, G.; Scheer, L.; Pape, P. L.; Esteve, I.; Daus, B.; Kappler, A. Arsenic(V) incorporation in vivianite during microbial reduction of arsenic(V)-bearing biogenic Fe(III) (oxyhydr)oxides. Environ. Sci. Technol. 2016, 50 (5), 2281−2291. (20) Kocar, B. D.; Borch, T.; Fendorf, S. Arsenic repartitioning during biogenic sulfidization and transformation of ferrihydrite. Geochim. Cosmochim. Acta 2010, 74 (3), 980−994. (21) Reyes, C.; Murphy, J. N.; Saltikov, C. W. Mutational and gene expression analysis of mtrDEF, omcA and mtrCAB during arsenate and iron reduction in Shewanella sp. ANA-3. Environ. Microbiol. 2010, 12 (7), 1878−88. (22) Yamamura, S.; Sudo, T.; Watanabe, M.; Tsuboi, S.; Soda, S.; Ike, M.; Amachi, S. Effect of extracellular electron shuttles on arsenicmobilizing activities in soil microbial communities. J. Hazard. Mater. 2018, 342, 571−578. (23) Zhu, Y.-G.; Xue, X.-M.; Kappler, A.; Rosen, B. P.; Meharg, A. A. Linking genes to microbial biogeochemical cycling: lessons from arsenic. Environ. Sci. Technol. 2017, 51 (13), 7326−7339. (24) Kulkarni, H. V.; Mladenov, N.; McKnight, D. M.; Zheng, Y.; Kirk, M. F.; Nemergut, D. R. Dissolved fulvic acids from a high arsenic aquifer shuttle electrons to enhance microbial iron reduction. Sci. Total Environ. 2018, 615, 1390−1395. (25) Rawson, J.; Prommer, H.; Siade, A.; Carr, J.; Berg, M.; Davis, J. A.; Fendorf, S. Numerical modeling of arsenic mobility during reductive iron-mineral transformations. Environ. Sci. Technol. 2016, 50 (5), 2459−2467. (26) Muehe, E. M.; Scheer, L.; Daus, B.; Kappler, A. Fate of arsenic during microbial reduction of biogenic versus abiogenic As−Fe(III)− mineral coprecipitates. Environ. Sci. Technol. 2013, 47 (15), 8297− 8307. (27) Sun, J.; Chillrud, S. N.; Mailloux, B. J.; Bostick, B. C. In situ magnetite formation and long-term arsenic immobilization under advective flow conditions. Environ. Sci. Technol. 2016, 50 (18), 10162−71. (28) Sun, J.; Prommer, H.; Siade, A. J.; Chillrud, S. N.; Mailloux, B. J.; Bostick, B. C. Model-based analysis of arsenic immobilization via iron mineral transformation under advective flows. Environ. Sci. Technol. 2018, 52 (16), 9243−9253. (29) Kocar, B. D.; Fendorf, S. Thermodynamic constraints on reductive reactions influencing the biogeochemistry of arsenic in soils and sediments. Environ. Sci. Technol. 2009, 43 (13), 4871−4877. (30) Campbell, K. M.; Malasarn, D.; Saltikov, C. W.; Newman, D. K.; Hering, J. G. Simultaneous microbial reduction of iron(III) and arsenic(V) in suspensions of hydrous ferric oxide. Environ. Sci. Technol. 2006, 40 (19), 5950−5955. (31) Hu, S.; Lu, Y.; Peng, L.; Wang, P.; Zhu, M.; Dohnalkova, A. C.; Chen, H.; Lin, Z.; Dang, Z.; Shi, Z. Coupled kinetics of ferrihydrite transformation and As(V) sequestration under the effect of humic acids: a mechanistic and quantitative study. Environ. Sci. Technol. 2018, 52 (20), 11632−11641. (32) Huang, J.-H. Characterising microbial reduction of arsenate sorbed to ferrihydrite and its concurrence with iron reduction. Chemosphere 2018, 194, 49−56. (33) Langner, H. W.; Inskeep, W. P. Microbial reduction of arsenate in the presence of ferrihydrite. Environ. Sci. Technol. 2000, 34 (15), 3131−3136. (34) Jiang, S.; Lee, J. H.; Kim, D.; Kanaly, R. A.; Kim, M. G.; Hur, H. G. Differential arsenic mobilization from As-bearing ferrihydrite by

ACKNOWLEDGMENTS We thank the National Key Research and Development Program of China (No. 2017YFD0801000), Guangdong Innovative and Entrepreneurial Research Team Program (No. 2016ZT06N569), and the Fundamental Research Funds for the Central Universities (No. 2018PY10) for financial support. This manuscript benefits from discussions with Prof. Zimeng Wang at Fudan University.

■

REFERENCES

(1) Borch, T.; Kretzschmar, R.; Kappler, A.; Cappellen, P. V.; Ginder-Vogel, M.; Voegelin, A.; Campbell, K. Biogeochemical redox processes and their impact on contaminant dynamics. Environ. Sci. Technol. 2010, 44 (1), 15−23. (2) Weber, F.-A.; Hofacker, A. F.; Voegelin, A.; Kretzschmar, R. Temperature Dependence and Coupling of Iron and Arsenic Reduction and Release during Flooding of a Contaminated Soil. Environ. Sci. Technol. 2010, 44 (1), 116−122. (3) Fendorf, S.; Michael, H. A.; van Geen, A. Spatial and Temporal Variations of Groundwater Arsenic in South and Southeast Asia. Science 2010, 328 (5982), 1123−1127. (4) Gustafsson, J. P. Arsenate adsorption to soils: Modelling the competition from humic substances. Geoderma 2006, 136 (1−2), 320−330. (5) Gustafsson, J. P.; Bhattacharya, P. Geochemical modelling of arsenic adsorption to oxide surfaces. Arsenic in Soil and Groundwater EnvironmentBiogeochemical Interactions, Health Effects and Remediation; Elsevier, 2007; Vol. 9, pp 159−206, . (6) Weng, L. P.; Van Riemsdijk, W. H.; Hiemstra, T. Effects of fulvic and humic acids on arsenate adsorption to goethite: Experiments and modeling. Environ. Sci. Technol. 2009, 43 (19), 7198−7204. (7) Farrell, J.; Chaudhary, B. K. Understanding arsenate reaction kinetics with ferric hydroxides. Environ. Sci. Technol. 2013, 47 (15), 8342−8347. (8) Tian, L.; Shi, Z.; Lu, Y.; Dohnalkova, A.; Lin, Z.; Dang, Z. Kinetics of cation and oxyanion adsorption and desorption on ferrihydrite: roles of ferrihydrite binding sites and a unified model. Environ. Sci. Technol. 2017, 51, 10605−10614. (9) Tufano, K. J.; Reyes, C.; Saltikov, C. W.; Fendorf, S. Reductive processes controlling arsenic retention: revealing the relative importance of iron and arsenic reduction. Environ. Sci. Technol. 2008, 42 (22), 8283−8289. (10) Islam, F. S.; Gault, A. G.; Boothman, C.; Polya, D. A.; Charnock, J. M.; Chatterjee, D.; Lloyd, J. R. Role of metal-reducing bacteria in arsenic release from Bengal delta sediments. Nature 2004, 430, 68. (11) Zobrist, J.; Dowdle, P. R.; Davis, J. A.; Oremland, R. S. Mobilization of arsenite by dissimilatory reduction of adsorbed arsenate. Environ. Sci. Technol. 2000, 34, 4747−4753. (12) Huang, J. H.; Elzinga, E. J.; Brechbuehl, Y.; Voegelin, A.; Kretzschmar, R. Impacts of Shewanella putrefaciens strain CN-32 cells and extracellular polymeric substances on the sorption of As(V) and As(III) on Fe(III)-(hydr)oxides. Environ. Sci. Technol. 2011, 45 (7), 2804−10. (13) Tian, H.; Shi, Q.; Jing, C. Arsenic biotransformation in solid waste residue: comparison of contributions from bacteria with arsenate and iron reducing pathways. Environ. Sci. Technol. 2015, 49 (4), 2140−6. (14) Huang, J. H.; Voegelin, A.; Pombo, S. A.; Lazzaro, A.; Zeyer, J.; Kretzschmar, R. Influence of arsenate adsorption to ferrihydrite, goethite, and boehmite on the kinetics of arsenate reduction by Shewanella putrefaciens strain CN-32. Environ. Sci. Technol. 2011, 45 (18), 7701. (15) Burnol, A.; Garrido, F.; Baranger, P.; Joulian, C.; Dictor, M. C.; Bodenan, F.; Morin, G.; Charlet, L. Decoupling of arsenic and iron release from ferrihydrite suspension under reducing conditions: a biogeochemical model. Geochem. Trans. 2007, 8, 12. J

DOI: 10.1021/acs.est.9b00109 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology iron-respiring Shewanella strains with different arsenic-reducing activities. Environ. Sci. Technol. 2013, 47 (15), 8616−23. (35) Zhang, X.; Jia, Y.; Wang, S.; Pan, R.; Zhang, X. Bacterial reduction and release of adsorbed arsenate on Fe(III)-, Al- and coprecipitated Fe(III)/Al-hydroxides. J. Environ. Sci. 2012, 24 (3), 440−448. (36) Zobrist, J.; Dowdle, P. R.; Davis, J. A.; Oremland, R. S. Mobilization of arsenite by dissimilatory reduction of adsorbed arsenate. Environ. Sci. Technol. 2000, 34 (22), 4747−4753. (37) Fredrickson, J. K.; Kota, S.; Kukkadapu, R. K.; Liu, C.; Zachara, J. M. Influence of electron donor/acceptor concentrations on hydrous ferric oxide (HFO) bioreduction. Biodegradation 2003, 14 (2), 91− 103. (38) Liu, C.; Kota, S.; Zachara, J. M.; Fredrickson, J. K.; Brinkman, C. K. Kinetic analysis of the bacterial reduction of goethite. Environ. Sci. Technol. 2001, 35 (12), 2482−2490. (39) Roden, E. E.; Zachara, J. M. Microbial reduction of crystalline iron(III) oxides: influence of oxide surface area and potential for cell growth. Environ. Sci. Technol. 1996, 30 (5), 1618−1628. (40) Ohtsuka, T.; Yamaguchi, N.; Makino, T.; Sakurai, K.; Kimura, K.; Kudo, K.; Homma, E.; Dong, D. T.; Amachi, S. Arsenic dissolution from Japanese paddy soil by a dissimilatory arsenatereducing bacterium Geobacter sp. OR-1. Environ. Sci. Technol. 2013, 47 (12), 6263−6271. (41) Amstaetter, K.; Borch, T.; Larese-Casanova, P.; Kappler, A. Redox transformation of arsenic by Fe(II)-activated goethite (αFeOOH). Environ. Sci. Technol. 2010, 44 (1), 102−108. (42) Peng, L.; Liu, P.; Feng, X.; Wang, Z.; Cheng, T.; Liang, Y.; Lin, Z.; Shi, Z. Kinetics of heavy metal adsorption and desorption in soil: Developing a unified model based on chemical speciation. Geochim. Cosmochim. Acta 2018, 224, 282−300. (43) Shi, Z.; Wang, P.; Peng, L.; Lin, Z.; Dang, Z. Kinetics of heavy metal dissociation from natural organic matter: roles of the carboxylic and phenolic Sites. Environ. Sci. Technol. 2016, 50 (19), 10476− 10484. (44) Hiemstra, T.; Van Riemsdijk, W. H. A surface structural approach to ion adsorption: The charge distribution (CD) Model. J. Colloid Interface Sci. 1996, 179 (2), 488−508. (45) Gustafsson, J. P. Visual MINTEQ version 3.1, 2018. http:// vminteq.lwr.kth.se/download/ (accessed 20 March 2018). (46) Tiberg, C.; Sjöstedt, C.; Persson, I.; Gustafsson, J. P. Phosphate effects on copper(II) and lead(II) sorption to ferrihydrite. Geochim. Cosmochim. Acta 2013, 120, 140−157. (47) Weng, L. P.; Temminghoff, E. J. M.; van Riemsdijk, W. H. Contribution of individual sorhents to the control of heavy metal activity in sandy soil. Environ. Sci. Technol. 2001, 35 (22), 4436−4443. (48) Liu, T.; Li, X.; Li, F.; Han, R.; Wu, Y.; Yuan, X.; Wang, Y. In situ spectral kinetics of Cr(VI) reduction by c-type cytochromes in a suspension of living Shewanella putrefaciens 200. Sci. Rep. 2016, 6, 29592. (49) Das, S.; Hendry, M. J.; Essilfie-Dughan, J. Effects of adsorbed arsenate on the rate of transformation of 2-Line ferrihydrite at pH 10. Environ. Sci. Technol. 2011, 45 (13), 5557−5563. (50) Borch, T.; Masue, Y.; Kukkadapu, R. K.; Fendorf, S. Phosphate imposed limitations on biological reduction and alteration of ferrihydrite. Environ. Sci. Technol. 2007, 41 (1), 166−172. (51) Wu, S.; Fang, G.; Wang, Y.; Zheng, Y.; Wang, C.; Zhao, F.; Jaisi, D. P.; Zhou, D. Redox-Active Oxygen-Containing Functional Groups in Activated Carbon Facilitate Microbial Reduction of Ferrihydrite. Environ. Sci. Technol. 2017, 51 (17), 9709−9717. (52) Han, J.-C.; Zhang, F.; Cheng, L.; Mu, Y.; Liu, D.-F.; Li, W.-W.; Yu, H.-Q. Rapid release of arsenite from roxarsone bioreduction by exoelectrogenic bacteria. Environ. Sci. Technol. Lett. 2017, 4 (8), 350− 355. (53) Shi, Z.; Di Toro, D. M.; Allen, H. E.; Ponizovsky, A. A. Modeling kinetics of Cu and Zn release from soils. Environ. Sci. Technol. 2005, 39, 4562−4568.

(54) Shi, Z.; Di Toro, D. M.; Allen, H. E.; Sparks, D. L. A general model for kinetics of heavy metal adsorption and desorption on soils. Environ. Sci. Technol. 2013, 47 (8), 3761. (55) Hohmann, C.; Winkler, E.; Morin, G.; Kappler, A. Anaerobic Fe(II)-oxidizing bacteria ahow As resistance and immobilize As during Fe(III) mineral precipitation. Environ. Sci. Technol. 2010, 44 (1), 94−101. (56) Tian, L.; Liang, Y.; Lu, Y.; Peng, L.; Wu, P.; Shi, Z. Pb(II) and Cu(II) Adsorption and Desorption Kinetics on Ferrihydrite with Different Morphologies. Soil Science Society of America Journal 2018, 82 (1), 96. (57) Fein, J. B.; Daughney, C. J.; Yee, N.; Davis, T. A. A chemical equilibrium model for metal adsorption onto bacterial surfaces. Geochim. Cosmochim. Acta 1997, 61 (16), 3319−3328. (58) Li, M.; Qian, W.-j.; Gao, Y.; Shi, L.; Liu, C. Functional enzymebased approach for linking microbial community functions with biogeochemical process kinetics. Environ. Sci. Technol. 2017, 51 (20), 11848−11857. (59) Wu, S.; Fang, G.; Wang, D.; Jaisi, D. P.; Cui, P.; Wang, R.; Wang, Y.; Wang, L.; Sherman, D. M.; Zhou, D. Fate of As(III) and As(V) during Microbial Reduction of Arsenic-Bearing Ferrihydrite Facilitated by Activated Carbon. ACS Earth and Space Chemistry 2018, 2 (9), 878−887. (60) Han, R.; Liu, T.; Li, F.; Li, X.; Chen, D.; Wu, Y. Dependence of Secondary Mineral Formation on Fe(II) Production from Ferrihydrite Reduction by Shewanella oneidensis MR-1. ACS Earth and Space Chemistry 2018, 2 (4), 399−409. (61) Urrutia, M. M.; Roden, E. E.; Fredrickson, J. K.; Zachara, J. M. Microbial and surface chemistry controls on reduction of synthetic Fe(III) oxide minerals by the dissimilatory iron-reducing bacterium Shewanella alga. Geomicrobiol. J. 1998, 15 (4), 269−291.

K

DOI: 10.1021/acs.est.9b00109 Environ. Sci. Technol. XXXX, XXX, XXX−XXX