Modelling biocide and herbicide concentrations in catchments of the Rhine basin

Abstract

Abstract. Impairment of water quality by organic micropollutants such as pesticides, pharmaceuticals or household 10 chemicals is a problem in many catchments worldwide. These chemicals originate from different urban and agricultural usages and are transferred to surface waters from point or diffuse sources by a number of transport pathways. The quantification of this form of pollution in streams is challenging and especially demanding for diffuse pollution due to the high spatio-temporal concentration dynamics, which requires large sampling and analytical efforts to obtain representative data on the actual water quality. 15 Models can also be used to predict information to which degree streams are affected by these pollutants. However, spatially distributed modeling of water quality is challenging for a number of reasons. Key issues are the lack of such models that incorporate both urban and agricultural sources of organic micropollutants, the large number of parameters to be estimated for many available water quality models, and the difficulty to transfer parameter estimates from calibration sites to areas 20 where predictions are needed.To overcome these difficulties, we used the parsimonious iWaQa model that simulates herbicide transport from agricultural fields and diffuse biocide losses from urban areas (mainly façades and roof materials) and tested its predictive capabilities in the Rhine River basin. The model only requires between one and eight global model parameters per compound that need to 25 be calibrated. Most of the data requirements relate to spatially distributed land use and comprehensive time series of precipitation, air temperature and spatial data on discharge.The model was calibrated with data sets from three different small catchments (0.5 -24.6 km 2 ) for three agricultural herbicides (isoproturon, S-metolachlor, terbuthylazine) and two urban biocides (carbendazim, diuron). Subsequently, it was 30 validated for different years on 12 catchments of much larger size (31 -160'000 km 2 ) without any modification. For most compound-catchment combinations, the model predictions revealed a satisfactory correlation (median r 2 : 0.5) with the observations and the peak concentrations mostly predicted within a factor of two to four (median: 2.1 fold difference for herbicides and 3.2 for biocides respectively). The seasonality of the peak concentration was also well simulated, the predictions of the actual timing of peak concentrations however, was generally poor. 35Limited spatio-temporal data, first on the use of the selected pesticides and second on their concentrations in the river network, restrict the possibilities to scrutinise model performance. Nevertheless, the results strongly suggest that input data and model structure are major sources of predictive uncertainty. The latter is for example seen in background concentrations that are systematically overestimated in certain regions, which is most probably linked to the modelled coupling of 40 background concentrations to land use intensity.H...

Relating Degradation of Pharmaceutical Active Ingredients in a Stream Network to Degradation in Water‐Sediment Simulation Tests

“…For conservative APIs we found that the emissions calculated from consumption data from Moser et al () and the consumer excretion rates plus WWTP removal rates from Singer et al () nicely matched the observed flux data of Ruff et al () for almost each measurement point. This fact makes the same likely for the degrading compounds, thereby indirectly supporting our statements on the extent and place of degradation.…”

“…The stream network is built up from reaches, and basic physical properties were assigned based on the CCM2 river and catchment database (EU JRC, http://ccm.jrc.ec.europa.eu/). The model is calibrated for seven active pharmaceutical ingredients (APIs) using emission data from the CrossWater project (Ingold et al, ; Moser et al, ), estimated excretion and wastewater treatment plant (WWTP) removal data from Singer et al (), and pharmaceutical flux measurements by Ruff et al (). Model results are analyzed both in terms of parameter values and spatial distribution.…”

“…Strahler stream order, drainage area (km 2 ), channel slope rounded to integer percent, reach length, and ID of the downstream neighbor reach were given for each reach. Due to the very coarse resolution of channel slope data, slope values were averaged across neighboring stream reaches weighted by drainage area (so that smaller steep streams cannot bias the mean slope of major rivers). A table with consumption amounts for the selected APIs for each reach from the CrossWater project (Moser et al, ). A table with the observed weekly mean flux of the selected APIs for 16 sites from the Rhine and major incoming triburaties by Ruff et al (; Figure ).…”

Passive samplers in sewers and rivers with highly fluctuating micropollutant concentrations – Better than we thought

“…For sewer overflows, modelled mecoprop concentration patterns [25] were used to calculate MPS.mod for each of 216,052 modelled events (10-minute resolution). Similarly, 2,199 modelled river concentrations interpolated to hourly resolution were used to estimate the ratio of CPS.mod/Ctrue for six contaminants for which modelled data was available: diuron and atrazine at 10min resolution for 7 months, atrazine at 20min resolution for 5 years [26,27], and isoproturon, metolachlor, and terbutylazine at 1h resolution in 12 locations for 1 year [28]. These modelled concentration patterns are potentially more representative for contaminants such as diuron and metolachlor that originate from surface runoff.…”

Métodos para monitoramento e estimativa de cargas poluidoras difusas em bacias hidrográficas.

“…Nitrogênio e fósforo possuem maior relevância pra estudos em áreas rurais com potencial impacto sobre mananciais (LANG et al, 2013;ZHU, 2015;FERREIRA et al, 2018;MALAGÓ et al, 2019;WANG et al, 2019), mas também têm parcela significativa em cargas originárias de bacias urbanizadas. Claramente, substâncias tóxicas presentes em defensivos agrícolas também estão vinculadas a área rurais destinadas a agricultura (MOSCHET et al, 2014;MOSER et al, 2018). Alguns trabalhos realizaram monitoramento de vazão pelo método velocidadeárea, mas não especificaram como se dava a obtenção do nível d'água (CALTRANS, 2005;ZHANG et al, 2012;ZHU et al, 2015 (LEE et al, 2002;CALTRANS, 2005;BRITES & GASTALDINI, 2007;ZHANG et al, 2012ZHANG et al, , 2015LANG et al, 2013;ZHU et al, 2015;YUAN et al, 2017;MARTINS, 2017;de SOUZA et al, 2017;RADULESCU et al, 2019 (SOORTHORNNONDA et al, 2008;CALTRANS, 2005;HE et al, 2010;LEE et al, 2011;BAI & LI, 2013;KIM et al, 2014;RIGHETTO et al, 2017;FERREIRA et al, 2018;CHOLET et al, 2018;YAZDI et al, 2019).…”