Total Suspended Solids Data Research Articles

Analisis daya tampung beban pencemaran sungai bedadung Jember menggunakan software wasp

The distribution of pollution sources from domestic, agricultural, and industrial activities has the potential to reduce the water quality of the Bedadung River, thereby negatively impacting its self-purification. The aim of this research is to model the parameters, specifically the values of Biochemical Oxygen Demand (BOD) and Total Suspended Solid (TSS), and to determine the river's capacity to carry the pollution load using the Water Quality Analysis Simulation Program (WASP). The input parameters for this study include BOD, TSS, and water discharge data collected at 21 monitoring points along the Bedadung River, with the river water sampling technique employing the grab sampling method. The output validation of the modeling is assessed using the Root Mean Square Error (RSME) equation, and the research results indicate that the water quality modeling with WASP software effectively reflects the dynamics of BOD and TSS parameters in the Bedadung River, as supported by an average output validation value of 9%. The pollution load capacity values for the Bedadung River in the Patrang - Ajung segment are found to be 361.48 kg/day for BOD and 5368.31 kg/day for TSS. The positive values suggest that the Bedadung River still has the capacity to naturally degrade organic pollutants.

Calibration and validation of hillslope runoff and soil loss outputs from the Water Erosion Prediction Project model in Minnesota agricultural watersheds

AbstractThere is growing interest in studying the impact of alternative agricultural management practices on runoff and soil loss under future climate change scenarios. In order to address this interest, it is important to demonstrate that runoff and soil loss can be accurately simulated under existing climates based on comparisons between modeled and experimental results. This study calibrates and validates the Water Erosion Prediction Project (WEPP) model to quantify the accuracy of predicting growing season runoff and soil erosion in agricultural hillslopes based on comparisons with experimental data from five Minnesota hydrologic unit code 12 watersheds. In order to accurately predict runoff and soil erosion in each watershed, the baseline effective hydraulic conductivity (Kbe), interrill and rill erodibility (EIR and ER), and monthly precipitation standard deviations (Pstdev) were calibrated in WEPP using observed runoff and total suspended solids data from five Minnesota Discovery Farms field sites. Before calibration, Nash–Sutcliffe model efficiency (NSE) and percent bias (PBIAS) values for predicted versus measured monthly average total runoff (Ravg‐T), runoff ratios (RRT), and total soil loss were generally not in acceptable ranges. After calibration, the NSE values showed very good fits between measured and predicted monthly Ravg‐T (0.64–0.98), RRT (0.66–0.93), and soil loss (0.58–0.80). PBIAS values were also within acceptable ranges for Ravg‐T and RRT (±25%) and soil loss (±55%), except for RRT at site BE1. NSE and PBIAS values during validation were within acceptable ranges, except for RRT at site BE1. These findings suggest that the WEPP hillslopes calibrated in this study are sufficiently robust to accurately predict monthly runoff and soil erosion in Minnesota agricultural fields during the growing season.

Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty

Two fundamental problems have inhibited progress in the simulation of river water quality under climate (and other) uncertainty: 1) insufficient data, and 2) the inability of existing models to account for the complexity of factors (e.g., hydro-climatic, basin characteristics, land use features) affecting river water quality. To address these concerns this study presents a technique for augmenting limited ground-based observations of water quality variables with remote-sensed surface reflectance data by leveraging a machine learning model capable of accommodating the multidimensionality of water quality influences. Total Suspended Solids (TSS) can serve as a surrogate for chemical and biological pollutants of concern in surface water bodies. Historically, TSS data collection in the United States has been limited to the location of water treatment plants where state or federal agencies conduct regularly-scheduled water sampling. Mathematical models relating riverine TSS concentration to the explanatory factors have therefore been limited and the relationships between climate extremes and water contamination events have not been effectively diagnosed. This paper presents a method to identify these issues by utilizing a Long Short-Term Memory Network (LSTM) model trained on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite reflectance data, which is calibrated to TSS data collected by the Ohio River Valley Water Sanitation Commission (ORSANCO). The methodology developed enables a thorough empirical analysis and data-driven algorithms able to account for spatial variability within the watershed and provide effective water quality prediction under uncertainty.

Machine learning models applied to TSS estimation in a reservoir using multispectral sensor onboard to RPA

Water quality monitoring is fundamental for the maintenance and conservation of water resources. However, the conventional monitoring method, which uses point sampling, does not adequately represent the spatial variability of its parameters, besides being costly. As a result, remote sensing techniques with the use of orbital sensors have been applied to map some water quality parameters, mainly the Optically Active Components (OACs), such as the Total Suspended Solids (TSS), Chlorophyll-a (Chl-a), and Colored Dissolved Organic Matter (CDOM). However, the monitoring of small reservoirs with orbital sensors always presents limitations, such as spectral and spatial problems or time resolution issues, combined with the fact that the sensors are subject to atmospheric disturbances and clouds. Seeking to overcome these issues, the present work aimed to develop a system for monitoring the concentration of Total Suspended Solids (TSS) in reservoirs, based on multispectral sensors onboard a Remotely Piloted Aircraft (RPA). A MicaSense RedEdge multispectral sensor was used, which has five bands in the blue, green, red-edge, and infrared (NIR) spectrum bands. Four campaigns were carried out in two water reservoirs within the Atlantic Forest biome at different seasons of the year. The data were submitted to five machine learning algorithms: the Random Forest (RF), Support Vector Machine Radial Sigma (SVM-RS), Enhanced Adaptive Regression Through Hinges (EARTH), Multiple Linear Regression (MLR), and Cubist algorithms. The results demonstrated that the SVM-RS model was the best adapted to the data patterns (r2 = 0.87), while the RF, EARTH, MLR, and Cubist models presented limitations that hinder their use in the estimation of TSS in reservoirs. This study demonstrated that the use of a multispectral sensor onboard an RPA has high accuracy in estimating TSS in small reservoirs and that machine learning algorithms can generalize the TSS data well.

INTERSEASONAL VARIABILITY IN THE ANALYSIS OF TOTAL SUSPENDED SOLIDS(TSS) IN SURABAYA COASTAL WATERS USING LANDSAT-8 SATELLITE DATA

The spatial and temporal capabilities of remote sensing data are very effective for monitoring the value of total suspended solids(TSS) in water using optical sensors. In this study,TSS observations were conductedin the westseason, transition season 1, east season, and transition season 2 in 2018 and 2019. Landsat 8 image data wereused,extracted into TSS values using a semi-analytic model developed in the Mahakam Delta, East Kalimantan, Indonesia. The TSS data obtained were then analysed for distribution patterns in each season. The sample points were randomly scattered throughout the study area. The TSS distribution pattern in the west season showeda high concentration spread over the coastal area to theoff sea, while the pattern in the east season only showeda high concentration inthecoastal areas. Transitional seasons1 and 2 showed different patterns of TSS distribution in 2018 and 2019, with more varied values. The distribution of TSS is strongly influenced by the season. Observation of each cluster resultedin the conclusion thathuman activity and the rainfall rate can affect the concentration of TSS.

Dynamics Of Total Suspended Solid (TSS) Around Coral Reef Beach Damas, Trenggalek

Total suspended solids (TSS) are sediment material that floats in the water column that moves without touching the water bed, influenced by input from land, river flow, and water oceanography factors. TSS is closely related to sedimentation, which can harm aquatic ecosystems, especially coral reefs. This research was conducted using TSS data as well as flow data for each season (May, June, October 2019, February and March 2020) to know the distribution of suspended solids concentration, knowing the dynamics of changes in TSS concentrations on Damas Beach by season, and to determine the relationship TSS with the growth of coral reefs at Damas Beach. TSS levels in Damas waters are quite high, which can be indicated by the turbid waters. TSS levels in March 2020 showed dominating results with a range of 37.8–72.6 mg/L and an increase in the range of 72.7-145 mg/L. The highest distribution of TSS on Damas Beach is found in the Rainy season.

Aplication of Landsat 8 Oli for the total suspended solid (TSS) mapping in the Tri An reservoir, Dong Nai province

Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies. Total suspended solid (TSS) is an important water quality parameter and a surrogate for the water clarity. It can be used as the indicator of sediment in the reservoir, which usually consists of silt, fine sand and microorganisms. This study aimed to utilize the remote sensing technology, in particular Landsat 8 Operational Land Imager (Landsat 8 OLI), to determine the amount of TSS concentration as well as the spatial distribution of TSS concentration in the surface water of the Tri An reservoir. The relationship between field TSS data collected in March, 2020 and the reflectance values of the the Landsat 8 Oli images was investigated. Results showed that there was a strong linear relationshiop between TSS concentration and the reflectance of the red and near infrared reflectance bands from the Landsat 8 Oli (r ranged from 0.58–0.93), in which the ratio of the red band produced the best correlation with the TSS (r = 0.93, with a standard error of 0.6–1.39 mg/L). Based on the linear regression equation, the TSS concentration calculated from the red reflectance values was used for mapping the spatial distribution of TSS in the surface water of the Tri An reservoir. Our results confirmed the accuracy and potential of using the single band from Landsat 8 OLI for mapping the spatial distribution of TSS in the Tri An reservoir.

Water quality sampling methods may bias evaluations of watershed management practices

Accurate measurement of the discharge of total suspended solids (TSS) from watersheds is complicated by the extreme temporal variability of suspended solid concentrations during periods of high stream flow. Consequently, TSS loads estimated from data collected at different temporal frequencies may differ in accuracy and precision. Moreover, there remains a need for optimal sampling methods which yield the highest possible accuracy for the least effort. We investigated the effect of sampling frequency on estimations of TSS loads and retention within a restored stream in Maryland, USA. We found that coarser temporal sampling methods can lead to erroneous conclusions of restoration efficacy with poor accuracy and precision in estimates of sediment retention. Additionally, we synthesized 28 years of continuous turbidity-based TSS data from Europe and North America to assess the effect of sampling frequency. Our synthesis suggests that flow-paced composite sampling may be the most accurate and precise sampling method. This method is also economical, requiring analysis of only one sample per week. Thus, the flow-paced method may be a potential solution to economize and standardize water quality monitoring.

Erosion and Sediment Transport Modelling to Inform Payment for Ecosystem Services Schemes

Erosion and the impacts of the redistribution of sediment are a serious threat to the quality of water resources. They cause losses and/or additional expenses in many areas, such as water treatment, biodiversity or fisheries. The implementation of catchment management measures, aimed at preventing the transfer of sediment to rivers, can be a cost-beneficial way to address the problem. In order to select the measures and appropriate locations for erosion control, the spatially distributed soil erosion and sediment delivery model WaTEM-SEDEM was used. The model was calibrated against total suspended solids data at the outlet of the Wey catchment, South-east England, yielding satisfactory results. Different scenarios of catchment management were modelled to reduce the amount of sediment transported to the river. Scenarios introducing 24 retention ponds, 50-m-wide buffer strips and cover crops in areas with the largest erosion and sediment delivery were tested. The largest decrease in both sediment production and sediment export was obtained using cover crops, with reductions of 13.4% and 14.1%, respectively. A cost-benefit analysis considering multiple ecosystem services (e.g. control of erosion rates, attenuation of mass flow, pest control, wildlife and its outputs) identified the cover crops as the most cost-beneficial measure and a possible funding scheme based on payments for ecosystem services was developed as a way to enable its implementation.

Siltation load contribution of nickel laterite mining on the coastal water quality of Hinadkaban Bay, Surigao Provinces, Philippines

Satellite images of Hinadkaban Bay, in Surigao Provinces, Philippines have indicated quite extensive siltation. Apparently, hilltop mining operations and other anthropogenic activities have resulted to deposition of suspended solids into the stretch of Hinadkaban Bay. In this regard, a multidisciplinary team was dispensed to undertake study in line with determining the environmental conditions along Hinadkaban Bay. Coastal marine water samples and surface water samples were obtained in two separate occasions (March and October 2012) via grab sampling. It was found that soil erosion in the study area was exacerbated by mining operations, road constructions and other anthropogenic activities, with heavy siltation often observed in areas near causeways, stockyards or siltation ponds of mine companies. Mean total suspended solids data of 5.87 mg/L was obtained for samples collected during the dry season while as high as 1000 mg/L TSS was recorded during heavy runoff. Meanwhile, average concentration of total nickel in water samples ranged from 0.050 μg/mL during dry season to 0.25 μg/mL during wet season. On the other hand mean total chromium concentration ranged from 0.060 μg/mL (dry season) to 0.30 μg/mL (wet season). Results of this study shall contribute to the effort of protecting surface water resources and of optimizing their beneficial uses.

Physical waters suitability for floating net cages cultivation mapping using landsat 8 oli and worldview-2 imageries in part of Hurun Bay, Lampung Province, Indonesia

Marine aquaculture has become an important contribution in sustainable world food provision. Sumatra, Indonesia contribute significantly on marine aqua production through intensive floating net cages development which has been long become a source of marine products in domestic and international markets. However, less research conducted on the water quality suitability in this region due to its vast areas and costly water analysis. This research aims: 1) to identify the appropriate spatial resolution between Landsat 8 OLI and Worldview-2 imageries to obtain the chlorophyll-α parameter and total suspended solid data; and 2) to perform spatial analysis to determine the suitable area for floating net cages using remote sensing and GIS for decreasing the analysis cost. The research area is located in Hurun Bay, the southwestern part of Lampung Bay. Biophysical factors considered for the suitability of floating net cages are: water clarity, temperature, salinity, pH, water depth, total suspended solid, and chlorophyll-α, which were obtained from remote sensing imagery and field measurement data. Each parameter value is described based on the level of suitability for a floating net cage. The final map is determined using a weighted overlay approach on all parameters. Biophysical factor data measured were sampled using systematic sampling method. The result shows that Landsat 8 OLI imagery is more appropriately applied in the research area compared to Worldview-2 imagery according to the analyses of correlation, regression, and accuracy assessment. Most of the floating net cages (28.8%) in the research area were classified as conditionally suitable meaning that the research area is less potential for cultivation and thus requires special treatment to improve its capability. The used of remote sensing and GIS approach perform good synoptic overview on the research area and provide multi-temporal data for better analysis on different seasons effectively. The floating net cages cultivation suitability mapping delivered in this research provides powerful data for coastal water planning decision makers.

The effects of suspended sediment on coral reef fish assemblages and feeding guilds of north-west Australia

This study was funded by the Chevron-operated Wheatstone Project's State Environmental Offsets Program and administered by the Department of Biodiversity, Conservation and Attractions. The Wheatstone Project is a joint venture between Australian subsidiaries of Chevron, Kuwait Foreign Petroleum Exploration Company (KUFPEC), Woodside Petroleum Limited and Kyushu Electric Power Company, together with PE Wheatstone Pty Ltd (part owned by TEPCO). Funding support was also received from the Gorgon Barrow Island Net Conservation Benefits Fund, which is administered by the Department of Biodiversity, Conservation and Attractions. Thank you to Helen Chedzey and Mark Broomhall from the Remote Sensing and Satellite Research Group at Curtin University for generating and providing the remotely sensed total suspended solids data for this study.

Lebegtetett hordalékmérési módszerek összehasonlító vizsgálata balatoni részvízgyűjtőkön

The present study investigated the quantity of total suspended solids (TSS) in three small catchments and compared the data to turbidity measurements. The TSS data were based on filtration, drying and weight measurements, while the turbidity measurements were retrieved using a handheld device with a turbidity sensor. Water was collected daily at the catchment outlets from November 1, 2016 to May 31, 2017, representing the winter and spring seasons. The lowest quantity of TSS was detected at the catchment outlet of the Esztergályi Stream; however, there were two lakes close to the monitoring point where soil particles may have settled, possibly explaining the low TSS values. The Csorsza and Tetves streams had similar TSS values during winter, but in the spring samples the TSS values were approximately three times higher in the Csorsza Stream than in the Tetves Stream. The relationship between water discharge and TSS values was also investigated for the Tetves Stream, but no significant correlations were observed between them. The results suggested that the labour-intensive TSS measurements (e.g. filtration, soil weight measurements) could be replaced to a good approximation using the handheld device. The spatial heterogeneity within and between the catchments influences the amount of suspended sediment and hence the measurement accuracy. Therefore, the use of the handheld device should also be complemented with other methods, such as the filtration used in the present study, to attain more precise values.

Total Suspended Solid (TSS) Mapping of Wadaslintang Reservoir Using Landsat 8 OLI

Lakes, water reservoirs, and ponds are sources of fresh water that support the life of all living beings as well as human's social and economic activities. A common problem on reservoirs in Indonesia is the siltation due to the discharge of river materials into the reservoir. This process of sedimentation declines the function of the reservoir. Total Suspended Solid (TSS) can be used as the indicator of sediment in the reservoir, which usually consists of silt, fine sand and microorganisms. The high concentration of TSS in the water column can be used as the indication that the process of sedimentation in the reservoir is also high. This study aims to 1) map the spatial distribution of TSS and 2) estimate the TSS contained in Wadaslintang Reservoir using Landsat 8 OLI. The algorithm of TSS for Wadaslintang Reservoir was created based on the empirical relationship between field TSS data and reflectance values. Based on this research, it was found that the ratio of red and green band produced the best correlation with the TSS (r = 0.533). The resultant regression function of the regression analysis between ratio of red and green band and field TSS data was used to model the spatial distribution of the TSS on Wadaslintang Reservoir with standard error of 10.8 mg/l. The average TSS in the reservoir is estimated to be 22.61 mg/l.

COMPARISON OF C2WP BOUREAL LAKES PROCESSOR AND REGIONAL WATER PROCESSOR ALGORITHM ON EXTRACTING ESTIMATED TOTAL SUSPENDED SOLID DATA OF LAKE SENTANI

Lake Sentani is one of fifteen Indonesian National Priority Lakes. In the Jurnal Biologi Papua, Surbakti (2011) said that Lake Sentani is an euthropic lake. MEdium Resolution Imaging Spectrometer (MERIS) is one of a few sensors at Envisat Satellite which is used to monitor water-quality conditions. At BEAM VISAT, MERIS image can be processed using 3 algorithms to extract water-quality. Those algorithms are Case-2 Water Processor (C2WP) Euthropic Lakes, Case-2 Water Processor (C2WP) Boureal Lakes, and Regional Water Processor Water Processor (WP). In this research, we compared the performance of C2WP Boureal and Regional WP on extracting Total Suspended Solid (TSS) data. Extracting TSS data using C2WP Euthropic were used as data validation because Lake Sentani was an euthropic lake.

Using hyperspectral data to quantify water-quality parameters in the Wabash River and its tributaries, Indiana

A hand-held spectrometer was used to collect above-water spectral measurements for measuring optically active water-quality characteristics of the Wabash River and its tributaries in Indiana. Water sampling was undertaken concurrent with spectral measurements to estimate concentrations of chlorophyll (chl) and total suspended solids (TSS). A method for removing sky and Sun glint from field spectra for turbid inland waters was developed and tested. Empirical models were then developed using the corrected field spectra and in situ chl and TSS data. A subset of the field measurements was used for model development and the rest for model validation. Spectral characteristics indicative of waters dominated by different inherent optical properties (IOPs) were identified and used as the basis of selecting bands for empirical model development. It was found that the ratio of the reflectance peak at the red edge (704 nm) with the local minimum caused by chl absorption at 677 nm was a strong predictor of chl concentrations (coefficient of determination (R2) = 0.95). The reflectance peak at 704 nm was also a good predictor for TSS estimation (R2 = 0.75). In addition, we also found that reflectance within the near-infrared (NIR) wavelengths (700–890 nm) all showed a strong correlation (0.85–0.91) with TSS concentrations and generated robust models. Results suggest that hyperspectral information provided by field spectrometer can be used to distinguish and quantify water-quality parameters under complex IOP conditions.

Estimating wide range Total Suspended Solids concentrations from MODIS 250-m imageries: An improved method

The objective of this study was to evaluate the performance of moderate resolution imaging spectroradiometer (MODIS) 250m based TSS (Total Suspended Solids) retrieval model for developing the model of wide-range TSS concentrations. Using field spectral and TSS data (5.8–577.2mg/l) collected from estuary and coast of China, we calibrated (N=40) and validated (N=20) the 6 TSS retrieval models in forms of single band (B1 or B2), difference of the two bands, sediment index, band ratio and log-ratio by the least-squares technique. Results showed that the quadratic model of log-ratio is of the best accuracy (Calibration: R2=0.752, N=40. Validation: TSS>31mg/l, RMSE=37.9mg/l, N=12; TSS⩽31mg/l, RMSE=3.25mg/l, N=8). We also found that the spectral log-ratio values increased with the increasing of TSS when TSS<31mg/l, but decreased with increasing TSS when TSS>31mg/l. The findings of quadratic curve vertex (log-ratio: ∼1.58) in the proposed TSS model indicated that the spectral log-ratio may be sensitive to TSS of both low and high concentrations. We further applied the preferred model to retrieve TSS concentration from the MODIS 250-m images in turbid estuarial and clear coastal waters, and obtained a good mapping accuracy from the result (TSS>31mg/l: RMSE=38.6mg/l, MRE=24.7%, N=17; TSS⩽31mg/l: RMSE=2.1mg/l, MRE=19.5%, N=11). The vertex-based methodology of TSS model developed in the study is applicable in mapping TSS concentrations that are of a wide range variation in estuarine and coastal water bodies based on MODIS 250-m image, and as well to maximize its application potential due to high imaging frequency (two times during daytime) and appropriate space resolution of MODIS images. Therefore, the method can be used to detect the high temporal variability of TSS during tidal cycle. As such, the application of MODIS-water quality detection technology can be extended to coastal sediment movement.

Development of reference conditions for suspended solids in streams

Our aim was to develop protective physical and biological thresholds that identify the upper limit of the least disturbed (reference) condition for total suspended solids (TSS). The study focuses on streams in agricultural regions across Canada from which we collected contemporary TSS and benthic invertebrate data, and compiled long-term TSS data sets. Reference conditions for TSS were analyzed by five approaches previously applied in Canada, Australia and New Zealand, and the USA. In addition, ecological reference conditions for TSS were developed using regression-tree analysis to determine change-points along TSS gradients for three invertebrate metrics (percentage Ephemeroptera-Plecoptera-Trichoptera, total richness, and modified family biotic index). Increased agricultural land-cover was linked to higher TSS in most agricultural regions which, in turn, was associated with degraded environmental conditions and a reduction in the relative abundance of pollution-intolerant taxa. The five physical approaches produced a narrow range of regional TSS reference condition values (2.7 to 6.2 mg L–1). Ecological reference conditions for TSS ranged from 3.5 to 11.1 mg L–1 across Canada. We conclude that long-term TSS databases may serve to generate interim sediment criteria because ecological reference conditions were similar to those calculated from physical data alone. These interim sediment criteria may be appropriate targets for use in evaluation of restoration programs designed to improve environmental quality of aquatic ecosystems (e.g., beneficial or best management practice applications).

A satellite view of riverine turbidity plumes on the NE-E Brazilian coastal zone

Turbidity plumes of São Francisco, Caravelas, Doce, and Paraiba do Sul river systems, located along the NE/E Brazilian coast, are analyzed for their dispersal patterns of Total Suspended Solids (TSS) concentration using Landsat images and a logarithmic algorithm proposed by Tassan (1987) to convert satellite reflectance values to TSS. The TSS results obtained were compared to in situ collected TSS data. The analysis of the satellite image data set revealed that each river system exhibits a distinct turbidity plume dispersal pattern. The behavior, dimension and degree of turbidity of the São Francisco River plume have been greatly altered by the construction of a cascade of hydroelectric dam reservoirs in its hydrological basin. The plume has lost its typical unimodal seasonal pattern of material dispersion and its turbidity has decreased due to the regulation of river flow by the dams and TSS retainance by the reservoirs. In contrast, the Doce and Paraíba do Sul river plumes are still subject to seasonal pulsations and show more turbid conditions than the SF plume, as dams are less numerous, set in the middle river sections and the natural river flow has been maintained. The Caravelas Coastal System river plume is restricted to near shore shallow waters dominated by resuspension processes. During austral spring and summer when NE-E winds prevail, all plumes generally disperse southward. Short-term northward reversals may occur in winter with the passage of atmospheric cold fronts. The São Francisco and Doce river plumes tend to disperse obliquely to the coast and transport materials further offshore, while the Caravelas and Paraíba do Sul plumes tend to disperse mainly parallel to the coast, enhancing TSS retention nearshore.

Modelling the fate of organic micropollutants in stormwater ponds

Urban water managers need to estimate the potential removal of organic micropollutants (MP) in stormwater treatment systems to support MP pollution control strategies. This study documents how the potential removal of organic MP in stormwater treatment systems can be quantified by using multimedia models. The fate of four different MP in a stormwater retention pond was simulated by applying two steady-state multimedia fate models (EPI Suite and SimpleBox) commonly applied in chemical risk assessment and a dynamic multimedia fate model (Stormwater Treatment Unit Model for Micro Pollutants — STUMP). The four simulated organic stormwater MP (iodopropynyl butylcarbamate — IPBC, benzene, glyphosate and pyrene) were selected according to their different urban sources and environmental fate. This ensures that the results can be extended to other relevant stormwater pollutants. All three models use substance inherent properties to calculate MP fate but differ in their ability to represent the small physical scale and high temporal variability of stormwater treatment systems. Therefore the three models generate different results. A Global Sensitivity Analysis (GSA) highlighted that settling/resuspension of particulate matter was the most sensitive process for the dynamic model. The uncertainty of the estimated MP fluxes can be reduced by calibrating the dynamic model against total suspended solids data. This reduction in uncertainty was more significant for the substances with strong tendency to sorb, i.e. glyphosate and pyrene and less significant for substances with a smaller tendency to sorb, i.e. IPBC and benzene. The results provide support to the elaboration of MP pollution control strategies by limiting the need for extensive and complex monitoring campaigns targeting the wide range of specific organic MP found in stormwater runoff.