Online Conductimetric Flow-Through Analyzer Based on Membrane

Article Cite This: ACS Sens. 2019, 4, 1881−1888

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Online Conductimetric Flow-Through Analyzer Based on Membrane Diffusion for Ammonia Control in Wastewater Treatment Process Tianling Li,†,‡ Ming Zhou,‡ Zhaoyi Fan,† Xiaoxiao Li,† Jianyin Huang,‡,§ Yonghong Wu,∥ Huijun Zhao,*,‡ and Shanqing Zhang*,‡

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Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Reading Academy, Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, P. R. China ‡ Centre for Clean Environment and Energy, School of Environment, Griffith University, Gold Coast Campus, Southport, Queensland 4222, Australia § Division of Information Technology, Engineering and Environment, School of Natural and Built Environment, Mason Lakes Campus, University of South Australia, Adelaide, South Australia 5095, Australia ∥ State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, 71 East Beijing Road, Nanjing, Jiangsu 210008, P. R. China S Supporting Information *

ABSTRACT: Ammonia is a necessary monitoring parameter that should be controlled within an optimum range in the whole process of wastewater treatment and recycling, but few reliable real-time monitoring technologies are available currently under such harsh water conditions. This study proposes a continuous conductometric flow-through analyzer for ammonia monitoring (CFAA) in the wastewater treatment process. It is developed based on the gas diffusion mechanisms, and the proposed analytical principle has been validated in which the real-time conductivity increment rate is linearly proportional to the real-time ammonia concentration in the sample. This method could be generally applicable in monitoring a wide ammonia concentration range (10.2 μg L−1 to 500 mg L−1), and it is capable of achieving long-term ammonia monitoring by periodic renewal of the receiving solution. The potential impact factors and corresponding calibration principles are also developed to avoid tedious ongoing calibration. The field application results demonstrate that CFAA can effectively and directly achieve real-time and average ammoniacal nitrogen monitoring at different treatment stages regardless of the complexity of wastewater, not requiring any sample pretreatment. Compared with other ammonia online monitoring technologies, the proposed CFAA shows remarkable advantages in high reliability, durability, and accuracy, especially under severe monitoring condition. It can be a useful monitoring tool for continuous ammonia control in the wastewater treatment process. KEYWORDS: ammonia, flow-through electrochemical sensor, membrane diffusion, online monitoring, real-time detection, wastewater

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toxicity to aquatic organisms, as well as poor ecosystem performance.9−12 As a result, strict ammonia control of effluent in WWTP is of great importance for aquatic environments protection and human health. Besides the effluent, ammonia also plays a crucial role in some vital wastewater treatment and recycle processes. It is thus regarded as a necessary monitoring parameter and should be controlled within an optimum range. For example, anaerobic digestion is a vital step to produce biogas from waste sludge in wastewater treatment and recycle process,13−16 but excess ammonia could be an active inhibitor for this

mmonia is considered the most common pollutant in various water streams due to its notorious toxicity and high solubility;1,2 it is therefore monitored in all sorts of waters as an essential indicator of water quality.3,4 Generally, ammonia comes from numerous industries (e.g., food industry, oil refining and steel industry, fertilizer manufacture, textile and leather manufacture), agriculture (e.g., agricultural fertilizer), and municipal effluent (e.g., gray and black water).5−8 Every day, the ammonia-containing wastewaters from these sources are treated in a wastewater treatment plant (WWTP) and finally released to aquatic environments. Although a certain amount of ammonia presents in aquatic ecosystems as a critical biological nutrient,3 high levels of ammonia in natural waters will degrade the water quality, resulting in severe eutrophication of lakes and other water recipients, acute or chronic © 2019 American Chemical Society

Received: April 26, 2019 Accepted: June 7, 2019 Published: June 7, 2019 1881

DOI: 10.1021/acssensors.9b00768 ACS Sens. 2019, 4, 1881−1888

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ACS Sensors

Figure 1. (a) Schematic diagram of CFAA configuration. (b) Conceptual process of the ammonia sensing principle (GPM: gas permeable membrane).

process. 17,18 Hansen et al. also found that ammonia concentration of 100 mg-N L−1 could deeply restrict an unadapted anaerobic digestion process.19 In this way, ammonia monitoring is significantly important to maintain the stability of anaerobic digestion of waste sludge in the wastewater treatment process. On the contrary, in aerobic activated sludge treatment processes, the deficiency of ammonia might bring about the growth of filamentous bacteria thereby causing sludge bulking, reducing wastewater treatment efficiency.20 In this circumstance, real-time ammonia monitoring is also imperative to guarantee the normal operation of WWTP. Above all, the rigid ammonia control in all treatment sectors is of paramount importance for tertiary wastewater treatment. Many technologies in terms of sustainability, economy, and efficiency have been developed for ammonia removal and monitoring in the past decades.2,3,10,11,21,22 On one hand, the valid approaches to remove ammonia from wastewater mostly contain physical methods (e.g., filtration, screening, sedimentation, and membrane separation), chemical methods (e.g., chemical precipitation, ion exchange, coagulation, solvent extraction, and photocatalytic oxidation), and biological methods (e.g., aerobic and anaerobic reaction).2 On the other hand, various detection methods also have been developed for quantitative measurements of ammonia, including colorimetric methods (Nessler, Phenate, and Salicylate methods),23−27 ion-selective electrodes methods,28 electrochemical sensors,29−32 and some optical methods.33−36 Although these methods have some advantages such as simple operation, rapid response, and good repeatability, some main limitations such as needing extra sample pretreatment (e.g., filtration and dilution), high cost, and poor suitability for realtime monitoring due to frequent calibration or fragile design significantly restrict their full application. It is also worth mentioning that most current ammonia detection methods only allow for lab analysis of off-line samples from periodic spot sampling (i.e., the collection of discrete samples), and time-integrated sampling, i.e., the collection of the analyte from the aquatic environment over extended periods of time by some samplers, such as polar organic chemical integrative samplers (POCIS), semipermeable membrane devices (SPMD), and diffusion gradients in thin-film (DGT) samplers.3,11,37−39 Even if employing the fast sampling frequency, these sampling methods still miss some variational information on episodic pollution events compared with online continuous sampling, resulting in a poor understanding of the pollution source and its dynamic process. Not only that, but the overall analytical process based on these

off-line sampling methods often involves sample collection, preservation, and storage procedures, which are laborious, expensive, manpower wasting, and time-consuming.3 Apparently, online continuous monitoring techniques integrating real-time sampling and real-time detection are most desirable and necessary. Moreover, as the wastewater often shows high complexity, the ideal monitoring techniques should be capable of presenting robustness and durability for long-term operation in a hostile environment. To the best of our knowledge, few studies could effectively meet the imperative demands on developing online ammonia monitoring techniques for all wastewater treatment sectors. Therefore, development of an alternative reliable, real-time, and environmentally friendly ammonia monitoring analyzer is urgently needed for achieving comprehensive ammonia control in the wastewater treatment process. Due to these practical requirements, an online conductimetric flow-through ammonia analyzer (CFAA) based on membrane diffusion is developed and presented in this study. It employs a hydrophobic gas permeable membrane to ensure the selectivity and makes good use of instantaneity and sustainability of neutralization reaction to achieve real-time detection. To begin with, the analytical principle has been experimentally validated. Then, the potential impact factors and corresponding calibration principles are also developed. Additionally, related characteristics such as detection range and long-term monitoring reliability have been discussed. Furthermore, the performance of the developed CFAA is evaluated in the field application in this study.

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EXPERIMENTAL SECTION

Chemicals and Materials. All the chemicals used in this study were of AR grade or higher and were purchased from Merck. Working solutions were prepared daily from ammonia stock solution (1020 mg L−1 ammonia). Purified deionized water (Millipore Corp., 18 MΩ cm) was used for preparing all the solutions. 0.5 mol L−1 of boric acid solution was usually used as receiving solution unless otherwise stated. Hydrophobic PTFE Millipore membrane (Fluoropore, 0.22 μm, ø = 47 mm, thickness = 150 μm, porosity = 85%, white, plain) was employed as gas permeable membrane in CFAA. pH meter (MettlerToledo AG, Switzerland) and electrical conductivity meter (Model 3084, Amber Science, Oregon, USA) were used for calibration. All the experiments were carried out in an air-conditioned lab (25 °C) in this study unless otherwise stated. CFAA Assembly. The configuration and assembly of CFAA is schematically illustrated in Figure 1a. It is fabricated from acrylic and consists of top chamber, bottom chamber, and a gas permeable membrane in the middle. Four-point conductivity detector (EC detector) constructed by directly inserting four stainless steel rods 1882


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Figure 2. (a) σ−t profiles obtained from monitoring different [NH3]s ranges from 1.02 to 40.8 mg L−1 with three replications (b) RCI− [NH3]s plot derived from Figure 2a.

{NH3}g + H3BO3 + H 2O → NH4 + + B(OH)4 −

(1.5 mm in diameter) into the top chamber and a temperature sensor (LM335A) is located in direct contact of the receiving solution. The control electronics are located on the top of the sensing unit, which can convert sensor signal into a digital signal that is sent to a computer either via USB or wireless. Data is recorded every second and logged using in-house designed logger software. Sensing System Setup. Figure S1 shows a typical sensing system setup. A peristaltic pump (Pharmacia L KB, Sweden) is used for delivering the sample solution to CFAA. Sample solution is adjusted to pH > 12 by adding 0.5 mol L−1 NaOH to make sure all the ammonium is converted to ammonia, and a pH sensing unit is built for real-time pH detection. The real-time pH signal is also converted to a digital signal by control electronics and recorded by the selfdesigned software. The software has an automatic temperature and flow rate correction function. Field Experiments and Samples Analysis. Field experiments were carried out in one WWTP, Queensland, Australia. This WWTP employs a 3-stage treatment process, i.e., primary treatment, secondary treatment, and tertiary treatment, and the total daily peak design capacity is ca. 100,000 EP. The effluent discharged via a submerged outfall pipe to one nearby river at Queensland. CFAA sensing systems were deployed at different functioning sites in this WWTP, including suburban pump station, influent, primary sedimentation tank, and effluent. For each site, CFAA kept continuously working for 6 h and the grab samples were collected ca. every 30 min for lab analysis. All the grab samples after filtering and diluting were measured by Segmented Flow Analyzer (SFA, Seal AA3) conducted by an automated colorimetric method (APHA 4500NH3) to obtain ammonia nitrogen (NH3−N) concentration.40

(1)

As our previous research demonstrates,10 for a given experimental condition, the process of conversing {NH3}aq into {NH3}g is the control step of the membrane permeation process. The conversion rate is exactly the flux of ammonia (J) permeating through GPM, which is directly proportional to the ammonia concentration in the sample solution ([NH3]s). Additionally, because the permeated ammonia instantaneously reacts with boric acid (eq 1) to continuously increase the conductivity in the receiving solution by producing the strong electrolyte NH4+ and B(OH)4−, and the rate of conductivity increase (RCI) in the receiving phase can be finally described as eq 2, and the average ammonia concentration over a given deployment period of t can be expressed as eq 3: R CI = K [NH3]s [NH3]s =

R CI K

(2)

(3)

where K is an analyzer-specific proportional constant under a given condition, such as exposed area of the GPM, receiving phase volume, etc. For a given CFAA, K can be experimentally obtained; therefore, the real-time and average ammonia concentration in sample solution can be gained by readily measuring the real-time and average RCI in the receiving solution. More importantly, as K is an analyzer-specific constant, an ongoing calibration is not demanded for a given CFAA once the K is predetermined experimentally. For the purpose of in situ monitoring, no ongoing calibration is a significant advantage that would greatly save maintenance costs and manpower resource.36,43 Analytical Principle Validation. Selection of Receiving Solution and Selectivity. Boric acid is selected as receiving solution due to its hypotoxicity, low volatility, and weak dissociation capacity compared to other potential acidic receiving solutions (Table S1). Specifically, boric acid is a weak acid with a very low dissociation constant (5.80 × 10−10), presenting a low conductivity background (a thousand times lower than that of other acids), which makes it is preferred to achieve sensitive detection (i.e., the slight increase in conductivity can be accurately detected) and fast response. As deionized water was also considered a receiving solution due to its ideal low dissociation constant in some previous ammonia detection studies,44,45 we first carefully estimated the performance when using boric acid and deionized water as receiving solution, respectively. Figure S2 indicated that boric

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RESULTS AND DISCUSSION Analytical Principle. In CFAA, a gas permeable membrane (GPM) is employed to separate the boric acid receiving solution from the ammonia-containing sample solution. The ammonia transportation process can be described as a membrane distillation process.41,42 When the sample solution flows through the membrane surface, the dissolved ammonia ({NH3}aq) diffuses to the outer interface of GPM, converts into gas phase ammonia ({NH3}g) via evaporation, and passes the inner interface of GPM quickly. Then, the permeated {NH3}g instantaneously and stoichiometrically reacts with H3BO3 receiving solution to produce NH4+ and B(OH)4− as soon as it reaches the receiving solution (eq 1). The rapidity of the acid−base reaction efficiently maintains the {NH3}g concentration in receiving phase at essentially zero ([NH3]R ≈ 0), which continuously drives the ammonia to transport across the GPM. Overall, the ammonia transportation process is essentially driven by the different ammonia concentrations in the sample phase ([NH3]s) and in the receiving phase ([NH3]R) (Figure 1b). 1883


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ACS Sensors acid was more sensitive and accurate than deionized water when they were used in CFAA. Notably, the reaction between water and ammonia is highly reversible and incomplete. By contrast, the sufficient boric acid ensures that the equilibrium of the neutralization reaction (eq 1) continuously favors the forward direction, ensuring all permeated NH3 are continuously converted to NH4+, further giving rise to the highly proportional relationship between conductivity and time (R2 = 0.999). More importantly, the employment of boric acid and gas permeable membrane ensures the high selectivity of CFAA. To be specific, the gas permeable membrane only allows gaseous species to pass through, and the boric acid receiving solution further selects the reaction with basic gases, which has been confirmed in our previous study.10 As the ammonia is the only dissolved alkaline gas in aquatic environments with an appreciable solubility, the developed CFAA can be considered to have a specific selectivity for ammonia. Validation of Real-Time Ammonia Detection Principle. All the validation experiments were conducted using the CFAA shown in Figure 1 at a constant temperature of 25 °C and a flow rate of 0.75 mL min−1 unless otherwise stated. Figure 2a showed the σ−t profiles obtained from monitoring different [NH3]s ranges from 1.02 to 40.8 mg L−1 with three replications. Perfect linear curves and good repeatability were observed for all the investigated cases with RSD < 1.71%. RCI can be derived from the slope of the corresponding σ−t curve for each [NH3]s. The united linear relationship (Figure 2b) between RCI and ammonia concentration (R2 = 0.998) validated the detection principle proposed in eq 2 that RCI in receiving solution is directly proportional to the ammonia concentration in the sample solution. According to eq 2, the CFAA constant (K) equals the slope of the RCI− [NH3]s curve, so the CFAA used for these experiments has a value of K (25 °C) = 2.55 × 10−2 μS cm−1 sec−1 mg−1 L under current experimental conditions. It is important to note that the real-time ammonia concentration in the sample is proportional to the rate of conductivity increase (i.e., “relative” conductivity value) rather than the “absolute” conductivity value, which indicates that renewal of the receiving solution cannot affect the continuous monitoring, and by this way it can achieve long-term monitoring. Detection Range and Sensitivity. Detection range is an important parameter for the online monitoring technique, especially for wastewater monitoring in which the sample matrixes are highly diverse and complex and contain high concentration ammonia. Eighteen samples with different ammonia concentrations ranges from 10.2 μg L−1 to 816 mg L−1 were investigated to evaluate the detection linear range of CFAA, and the recorded σ−t profiles are shown in Figure S3. Figure 3 illustrated a plot of RCI− [NH3]s derived from Figure S3. Under current CFAA design and experimental conditions, a good linear range can be found when measuring ammonia concentration from 10.2 μg L−1 to 500 mg L−1, and the obtained K (25 °C) was confirmed as in the preceding section, i.e., 2.55 × 10−2 μS cm−1 sec−1 mg−1 L. It suggests that the CFAA is capable of directly determining a wide concentration range of ammonia, from as low as 10.2 μg L−1 to as high as 500 mg L−1. In fact, K also defines the sensitivity, i.e., the presence of 1 mg L−1 ammonia will lead to an increase of 0.0255 μS cm−1 within a second under current experimental condition. It is worth mentioning that, for different ammonia monitoring purposes, the detection range and sensitivity can be improved

Figure 3. RCI− [NH3]s plot derived from Figure S3.

by simply changing the sampling residence time due to the accumulative nature of CFAA. Furthermore, altering the CFAA design, such as the exposed membrane area and the volume of receiving phase, also can tune the sensitivity and detection linear range. Figure S4a,b showed the results obtained by expanding current volume of receiving phase by 1.5 times, and then the maximum ammonia detection concentration subsequently extended to corresponding 1.5 times to 810 mg L−1. Current detection range of CFAA could cover the ammonia concentrations in a variety of water environments, which is 5 times higher than the conductimetric flow-injection system developed by Jaroon,45 and 25 times higher than the potentiometric flow-injection method proposed by Anissa.46 More importantly, no dilution is required by CFAA, which absolutely surpass most current ammonia monitoring devices where tedious dilution processes are necessary for measuring high concentrations of ammonia-containing samples. Effect Factors and Calibration. Effect of Concentration of Boric Acid. The effects of four concentrations of boric acid (0.1 mol L−1, 0.3 mol L−1, 0.5 mol L−1, and 0.7 mol L−1) on CFAA performance were investigated. Their original conductivity values were listed in Table S2. Apparently, the higher concentration of boric acid presents stronger hydrolysis ability, producing more ions, so further bringing about higher conductivity value. Figure S5 showed a set of original σ−t curves of measuring different [NH3]s ranges from 0.102 to 8.16 mg L−1 when using these four boric acids as receiving solution. Excellent linear relationships were observed for all cases investigated. The identical RCI− [NH3]s linear relationships for all tested boric acids shown in Figure 4a implied that concentrations of boric acid had no effect on CFAA performance. Considering that different concentrations of boric acid may have different ammonia receiving capacity, a high concentration ammonia sample (i.e., 204 mg L−1) was used for investigating their receiving capacity, and the results were shown in Figure 4b. As expected, for such high ammonia concentration monitoring, the receiving capacity kept increasing with the increase of boric acid concentration from 0.1 to 0.5 mol L−1, but no further increase to 0.7 mol L−1 during the investigated period. As 0.7 mol L−1 boric acid is close to its saturation concentration at 25 °C, the 0.5 mol L−1 boric acid is selected for all the experiments in this study. Effect and Calibration of Flow Rate. In CFAA, flow rate would affect the residence time of sample solution, and further affect the rate of {NH3}aq converting into {NH3}g, so the effect of flow rate was investigated by continuously measuring 1884


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Figure 4. (a) RCI− [NH3]s obtained by detecting a series of ammonia concentrations ranges from 0.102 to 8.16 mg L−1. Data derived from Figure S5. (b) σ−t profiles of measuring ammonia sample with 204 mg L−1 when using four boric acids as receiving solution.

Figure 5. (a) K−flow rate plot derived from Figure S6. (b)K− temperature plot derived from Figure S7.

25 °C) based on the embedded temperature correction formula of the self-developed control software. The temperature correction of K was carried out by detecting different [NH3]s ranges from 1.02 to 20.4 mg L−1 under a series of temperatures between 11 to 40 °C, and the profiles of σ−t and corresponding RCI− [NH3]s curves determined by CFAA were shown in Figure S7. The K and temperature-dependent relationship can be determined by plotting K(T) against T (Figure 5b)

ammonia-containing samples in concentrations ranges from 1.02 to 20.4 mg L−1 under 8 different sampling flow rates. Figure S6 showed a set of σ−t and corresponding RCI− [NH3]s curves obtained under different flow rates. The CFAA constant and flow rate dependent relationship can be determined from the K−flow rate curve shown in Figure 5a. This means that the analyzer constant K of different flow rates can be readily obtained by simply substitute the corresponding flow rate into eq 4. y = −0.0148x 2 + 0.0280x + 0.0130

y = 0.0011x + 0.00024

(4)

(5)

Based on eq 5, the temperature effect can be automatically corrected by the self-developed sensor control software. It should be mentioned that the CFAA does not require ongoing calibration once it is calibrated by the above methods. Figure S8 illustrated a set of σ−t curves obtained by CFAA when detecting ammonia of 8.16 mg L−1 weekly. The determined [NH3]s from CFAA were almost identical to the known ammonia concentration, demonstrating the long-term stability of proposed CFAA, which is highly desirable for online monitoring. Field Deployment. For field applications, the CFAA was deployed at four functioning sections in wastewater treatment process, including suburban pump station, influent section, primary sedimentation tank, and effluent section. The flow rate of 0.75 mL min−1 was selected for all the field experiments and temperature was recorded in real-time for temperature correction. The real-time concentration of ammoniacal nitrogen (NH3−N) over the deployment period for each site was shown in Figure 6a. Grab samples were collected ca. every 30

As shown in Figure 5a, K kept increasing gradually with the increase of sampling flow rate, and tended to be stable after reaching 0.6 mL min−1. As for the reason, the faster flow rate could accelerate the process of {NH3}aq converting into {NH3}g, further leading to the increase of K. When the flow rate is sufficiently fast (i.e., >0.6 mL min−1), under current experimental condition, the rate of {NH3}aq converting into {NH3}g reaches the maximum and it is no longer influenced by the flow rate. Additionally, in a continuous flow system, the sufficiently fast flow rate can ensure that the analyte concentration in the system is consistent with that of actual monitoring environment.47 Therefore, the relatively fast flow rate of 0.75 mL min−1 is usually selected for all the experiments in this study. Effect and Calibration of Temperature. As the process of ammonia transportation is affected by temperature, for a given CFAA, K is temperature dependent, and then the effect of temperature on K was investigated. In this work, all the measured σ values were corrected to σ25 values (i.e., σ values at 1885


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features broad chemical compatibility with acids, bases, and solvents. It also hardly affords suitable environment for microorganisms/biofilm under a fast flow rate, so it has a low possibility to get a biofouling. In this study, the gas permeable membrane has been used continuously for 7 months without replacement. By contrast, the electrode membrane of ammonia selective electrode has to be replaced frequently because of its easy aging,49 which not only brings the high maintenance cost but also restricts its long-term application.

min for lab analysis for each site and Figure S9 showed the sample photos collected from these four sites.

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CONCLUSION A continuous, reliable, and robust conductimetric flow-through analyzer for ammonia monitoring in wastewater treatment process has been developed in this work. The detection principle has been experimentally validated and the wide detection range between 10.2 μg L−1 and 500 mg L−1 of ammonia concentration was obtained. This method is capable of long-term monitoring by periodic renewal of the receiving solution and with no need for ongoing calibration on the basis of the developed precalibration strategy. The successful application of CFAA in real-time ammoniacial nitrogen monitoring at different wastewater treatment stages without any pretreatment demonstrates it is a promising method for continuous ammonia control in harsh water environment.

Figure 6. (a) Measured [NH3−N]−t profiles at four different sites. (b) Plot of [NH3−N] measured by CFAA ([NH3−N]CFAA) against SFA ([NH3−N]SFA).

For all the monitoring sites, NH3−N concentrations presented consistent fluctuations during the deployment period. At suburban pump station, NH3−N concentrations fluctuated dramatically within a very wide range (i.e., 130 to 250 mg L−1) and the average NH3−N concentration was 179 mg L−1, because it was a suburban industrial pump station where sewage was mainly from nearby industries, such as fertilizer plants and dairy product processing factories, which potentially contained high levels of nitrogen compounds. When the sewage source from all kinds of pump stations entered the WWTP, at the influent section, the NH3−N concentration appeared to decrease to 50−60 mg L−1, averaging 56 mg L−1, due to the effective buffer effect of the different types of sewages. At the primary sedimentation tank, NH3−N concentration kept going down to 30−50 mg L−1, as some nitrogen was removed due to the removal of large suspended particles. Before the treated water was discharged into natural waters, NH3−N was further removed by several important functioning sections in the WWTP, such as the biological treatment tank where most NH3−N could be consumed by all kinds of microorganisms. Ultimately, the treated water could be legally released to natural waters when the NH3−N concentration in effluent was stabilized at/under 1 mg L−1 with average in 0.9 mg L−1, meeting the criteria to protect 95% speciation in water in Australia.48 Figure 6b demonstrated the NH 3 −N concentration determined by CFAA ([NH3−N]CFAA) against SFA ([NH3− N]SFA). The significant positive correlation suggested an excellent agreement between these two methods, indicating that the CFAA is reliable for real-time ammonia monitoring in wastewater treatment process. It is worth mentioning that sample pretreatment is not imperative for CFAA. This is far superior to other ammonia detection methods, such as spectrophotometric and titrimetric methods, which require filtration and dilution or preliminary distillation.24 These tedious pretreatment processes inevitably consume additional time and manpower, and are only suitable for laboratory analysis. Additionally, the gas permeable hydrophobic membrane adopted in this study is very durable because it bonds to a high density polyethylene support and

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssensors.9b00768. Typical system setup; σ−t profiles at different experimental conditions; plots of σ−t and corresponding RCI−[NH3]s; example photos of samples collected from four monitoring sites; comparison of characteristics of six potential receiving solutions and original conductivity values of different concentrations of boric acid (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: h.zhao@griffith.edu.au. Tel: +61-7-55528261. Fax: +61-7-55528067. *E-mail: s.zhang@griffith.edu.au. Tel: +61-7-55528155. Fax: +61-7-55528067. ORCID

Tianling Li: 0000-0002-8185-9698 Jianyin Huang: 0000-0003-1584-9341 Yonghong Wu: 0000-0002-2985-219X Huijun Zhao: 0000-0003-3794-4497 Shanqing Zhang: 0000-0001-5192-1844 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors gratefully acknowledge the financial supports from National Natural Science Foundation of China (No. 21806080), the Startup Foundation for Introducing Talent of NUIST (No. 2018r017), and the Natural Science Research Projects in Colleges and Universities of Jiangsu Province (No. 18KJB610015). The authors also acknowledge the financial support from Jiangsu Key Laboratory for Food Quality and 1886


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Safety-State Key Laboratory Cultivation Base, Ministry of Science and Technology.

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Online Conductimetric Flow-Through Analyzer Based on Membrane

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