Gas-diffusion-based passive sampler for ammonia monitoring in marine waters
Graphical abstract
Introduction
Ammonia nitrogen is a biologically important nutrient found naturally at low concentrations in aquatic environments. However, inorganic nitrogen runoff as a result of anthropogenic activity is increasing the nutrient loading of estuarine and coastal waters [1]. Ammonia is therefore monitored in these ecosystems as an indicator of water quality [2]. Effective monitoring of this analyte is not without its challenges. Most water quality monitoring programs involve periodic ‘spot’ sampling (i.e. the collection of discrete water samples). The overall analytical process involves sample collection, preservation, storage, and analysis, which can be laborious, time consuming, and expensive, without the guarantee that episodic pollution events will be detected, resulting in a poor understanding of nutrient sources and fluxes.
Integrative and passive sampling techniques are an efficient alternative to ‘spot’ sampling as they allow for the collection and accumulation of the analyte from the aquatic environment to be performed in situ and over extended periods of time [3]. Linear uptake passive samplers, such as Chemcatcher®, polar organic chemical integrative samplers (POCIS), semipermeable membrane devices (SPMD), and diffusion gradients in thin-film (DGT) samplers have been described in the literature for the monitoring of a wide range of analytes of environmental interest [4], [5], and allow for the time-weighted average concentration (CTWA) of an analyte to be determined for the period of deployment. To the best of our knowledge, only two passive sampling devices have been reported for the determination of CTWA of ammonium (NH4+) in environmental waters, namely DGT with a Microlite cation-exchange resin [6] and polymer inclusion membrane (PIM)-based passive sampler (PS) [7]. However, both devices were applied to low salinity freshwater matrices only. Neither of these samplers is suitable for monitoring ammonia in marine waters (viz. estuarine and coastal waters), because of interference from alkali and alkaline earth metals in the ion-exchange process, which are found in concentrations several orders of magnitude higher than the ammonia concentration. The aim of the present work is thus to develop a PS which can be applied to the monitoring of total ammonia nitrogen in marine waters, and for this purpose a gas-diffusion membrane (GDM) was used as the selective barrier between the sampled medium and the receiving solution.
Section snippets
Reagents and solutions
Analytical grade chemicals were used without further purification for the preparation of synthetic seawater and for the PS receiving solutions. Deionized water (Synergy 185, Millipore, France, resistivity ≥18 MΩ cm) was used throughout this study.
A stock solution of 55.56 mM NH4+ was prepared by dissolving NH4Cl (BDH Chemicals, 99.8%), previously oven dried at 105 °C overnight, in 250.0 mL of deionized water.
The un-buffered source solution (2 L) used in the GDM study contained 2 g L−1 NaCl (Merck
Gas-diffusion-based passive sampler
A schematic of the PS, developed in this study, is shown in Fig. 1b. The sampler contains an acidic receiving solution (10 mL), which is separated from the source solution by a porous and hydrophobic GDM. The ammonia accumulation mechanism involves ammonia evaporating from the source solution into the pores of hydrophobic GDM and then diffusing to the membrane/acidic receiving solution interface to readily react with hydrogen ions and forming the ammonium ion. Molecular ammonia is effectively
Conclusions
A novel PS based on gas-diffusion separation of ammonia was developed and applied as a proof of concept for the monitoring of this analyte in marine and estuarine waters. The performance of the PS was tested under laboratory conditions using seawater under stable and controlled pH, temperature and salinity conditions, and in the field at an estuarine site. The relative error between the concentration of ammonia nitrogen measured, and the experimentally determined time weighted average
Acknowledgements
This article is dedicated to Professor Purnendu (Sandy) Dasgupta, the recipient of the 2017 Talanta Medal.
The authors would like to thank the Australian Research Council (ARC) and Melbourne Water Corporation for funding this research in the framework of the ARC Linkage Scheme (Grant LP160100687). We also acknowledge Mr. Edward Nagul for assisting with the collection of the SEM images, and Mr. Marcus Hammarstedt for creating Fig. 1b.
L. O’Connor Šraj is grateful to the University of Melbourne for
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