Monitoring of priority pollutants in dynamic stormwater discharges from urban areas

Abstract

The European Water Framework Directive (WFD) from 2000 has put focus on the chemical status of surface waters by the specified Environmental Quality Standard (EQSs) and the requirements for monitoring of surface water quality throughout Europe. When considering the water quality of urban stormwater runoff it is evident that surface waters receiving large amount of urban stormwater runoff will be at risk of failing to meet the EQSs. Therefore stormwater treatment is crucial. However, as stormwater quality varies orders of magnitude between sites, stormwater monitoring is important in order to design the right treatment level to protect surface waters. Stormwater runoff is very dynamic both quality and quantity wise. In order to optimize the sampling of such phenomena, advanced sampling equipment is required. Such equipment is expensive, and furthermore, it is time consuming to conduct the sampling campaigns. Therefore this PhD project aimed at improving monitoring programs for priority pollutants in stormwater runoff. By comparing results from a literature study and a screening campaign to the EQSs, it was found that heavy metals (especially Cu and Zn), polyaromatic hydrocarbons (PAHs), Di(2-ethylhexyl)-phthalate (DEHP) and pesticides were the main pollutants of general concern in stormwater runoff and of concern at the studied catchment (glyphosate was found to be the most relevant pesticide in a Copenhagen setting). These priority pollutants are therefore relevant to monitor in stormwater discharges. Sorption of pollutants to particulate matter and dissolved organic carbon is important for both the toxicity of the pollutants and for removal in stormwater treatment systems. Furthermore sorption is important for sampling using the most common types of passive samplers, which are based on uptake of analytes by diffusion, since they only sample the freely dissolved and labile fraction of analytes. Passive dosing was therefore developed during this PhD project as an easy, fast and precise method for partition measurements of hydrophobic organic compounds (HOC) in aqueous samples such as stormwater runoff. The principle of the method is that the freely dissolved concentration of the HOC is controlled by partitioning from a pre-loaded polymer and the total concentration in the sample at equilibrium is measured. Partition measurements in stormwater runoff samples revealed a partition ratio log KTSS for fluoranthene of 4.59, and free fractions in stormwater runoff of 0.04-0.5. The partition ratio can be used in modeling of stormwater treatment systems. The passive dosing method can be used for surface water monitoring to relate freely dissolved concentrations to total concentrations. For stormwater monitoring, diffusion based passive samplers are not appropriate to use. The reason is that the sampler measures time-weighted concentrations over periods of weeks to months with no regard to whether it rains or not. Therefore a flow-through passive sampler, SorbiCell, was tested. It consists of a cartridge containing a sorbent and was installed directly in the stormwater drainage ditch letting the momentum from the water velocity force water through the sampler. This novel installation method ensures sampling mainly during runoff events and dependant on the velocity of the runoff. Even though a filter prevented large particles from entering the sampler, it revealed concentrations comparable to volume proportional total concentrations measured in stormwater runoff and modeled using a dynamic stormwater quality model. There are still many questions and assumptions when using this installation method. However it has potential for monitoring the load of priority pollutants to surface waters from the large amounts of stormwater discharge points often contributing to the deterioration of the water quality. When evaluating the pollutant level at specific sites based on measurements, an interpretation of the system is always involved. This interpretation can be formulated in stormwater quality models. Event mean concentrations (EMCs) are often found to follow a lognormal distribution. However more complicated models including dynamics of accumulation in the catchment and influence of rain characteristics on the runoff concentrations can also be used. The advantage of using models for monitoring purposes is that information about the system beyond the time interval of sampling can be obtained based on knowledge of processes and observed patterns. It was found here that model prediction bounds for annual average concentrations obtained by a dynamic stormwater quality model were narrower than uncertainty on the mean when assuming lognormal distribution of EMCs. Furthermore, the use of passive sampler measurements in combination with volume proportional measurements for calibration reduced the model prediction bounds on annual average concentrations more than simply increasing the number of volume proportional samples. This work demonstrated how models and passive samplers can be used for monitoring purposes.