Most of our knowledge of the patterns and trends in nutrient concentrations and loads is based on data obtained from traditional monitoring techniques (i.e., grab samples collected every week or month) [1, 2]. While this form of monitoring coupled with modelling has provided crucial information, more representative monitoring methods are needed for water quality agencies to develop new effective response strategies, identify specific pollution sources and account for harmful events that are difficult to predict (e.g., floods) [2].
In recent years, on-site and in-situ continuous monitoring techniques have been developed to provide real time monitoring of aquatic environments and improve the estimation of nutrient loads. However, these techniques are not extensively used because they are costly to purchase, install and maintain [3]. Time integrated monitoring techniques, provide representative data from an extended period of time rather than a single point in time, such as grab sampling does. These monitoring techniques are robust, inexpensive and easy to use. New time integrated monitoring techniques have been recently developed to sample dissolved inorganic and particulate nutrients in freshwater ecosystems [4, 5].
The diffusive gradients in a thin film (DGT) technique passively accumulates dissolved inorganic nutrients (phosphate, ammonium and nitrate) from sample waters using a combination of diffusion and analyte specific binding components [4-6]. While, a modified version of a suspended sediment sampling technique, developed by Phillips et al. (2000), constantly draws sample water through the sampling device and collects particulate matter via induced sedimentation and filtration [7]. For both of these sampling techniques, the mass of the target nutrient accumulated within the sampling device can be used to back calculate a time-weighted concentration (i.e., the hourly average concentration in the sample waters for the time period of sampler deployment). This poster will present recent optimisation and field application data of these monitoring techniques.