Estimates Of Total Phosphorus Research Articles

Temperature Is a Key Factor Affecting Total Phosphorus and Total Nitrogen Concentrations in Northeastern Lakes Based on Sentinel-2 Images and Machine Learning Methods

Nitrogen and phosphorus are limiting nutrients in freshwater ecosystems, and the remote estimation of total phosphorus (TP) and total nitrogen (TN) in eutrophic waters is of great significance. This study utilized machine learning algorithms based on Sentinel-2 satellite imagery for remote estimation of TP and TN concentrations in Lake Xingkai, Chagan and Songhua. Results indicate that random forest (RF) and XGBoost regression algorithms perform better. The performance of the GBDT algorithm was slightly lower than that of the RF and XGBoost regression algorithms, the BP algorithm had overfitting, and the SVR algorithm had poor fitting performance. Results showed that the TN concentration inversion model based on the RF algorithm had the highest accuracy (R2 = 0.98, RMSE = 0.09, MAPE = 19.74%). The Extreme Gradient Boosting (XGB) model also performed well, though slightly less accurately than RF (R2 = 0.97, RMSE = 0.14, MAPE = 20.67%). For TP concentration, the XGB model’s performance (R2 = 0.82, RMSE = 0.08, MAPE = 24.89%) was comparable to that of the RF model (R2 = 0.82, RMSE = 0.07, MAPE = 29.55%). The RF algorithm was applied to all cloud-free Sentinel-2 satellite images of these typical lakes in northeastern China during the non-glacial period from 2017 to 2023, generating spatiotemporal distribution maps of TP and TN concentrations. Between 2017 and 2023, TP concentrations in Lake Xingkai, Chagan and Songhua showed increasing, decreasing, and initially decreasing then increasing patterns, respectively. A positive correlation between temperature and TP concentration was observed, as higher temperatures enhance biological activity. In contrast, a negative correlation was found with TN concentration, as higher temperatures promote phytoplankton growth and reproduction. This study not only offers a new method for monitoring eutrophication in lakes but also provides valuable support for sustainable water resource management and ecological protection goals.

A novel total phosphorus concentration retrieval method based on two-line classification in lakes and reservoirs across China

Phosphorus is widely recognized as a nutrient that restricts growth and is the primary contributor to eutrophication in 80 % of water bodies. Consequently, the Chinese government has consistently prioritized monitoring and controlling total phosphorus (TP) levels. The remote estimation of TP in lakes and reservoirs at a national scale is a challenging task due to TP being a non-optically active parameter. Currently, there is a lack of developed TP inversion models specifically designed for lakes and reservoirs in China. For solving this problem, a novel two-line classification method drawn on scatter plots based on the natural logarithm of TP (Ln(TP)) and B33/B9 was proposed and used to classify 1211 measured samples obtained from field cruises in 105 lakes and reservoirs across China from 2012 to 2022 into three categories, Class 1, Class 2, and Class 3. Results demonstrate that the proposed classification method has the ability to enhance the correlation between Ln(TP) and 43 basic potential single band and band combinations. Specifically, the correlation range improved from (−0.31,0.15) to (−0.77,0.24) in Class 1, (−0.81, 0.36) in Class 2, and (−0.74, 0.52) in Class 3. Additionally, the classification method also improved the correlation range between Ln(TP) and 820 band ratios, from (−0.32, 0.32) to (−0.83, 0.82) in Class 1, (−0.86, 0.86) in Class 2, and (−0.86, 0.86) in Class 3. These datasets were subsequently utilized as input for eXtreme Gradient Boosting (XGBoost) models. Finally, well performing XGBoost models in Class 1 (R2 = 0.76, RMSE = 0.3, MAPE = 12 %), Class 2 (R2 = 0.84, RMSE = 0.49, MAPE = 38 %), and Class 3 (R2 = 0.74, RMSE = 0.46, MAPE = 14 %) were used to map TP of 563 large lakes and reservoirs (≥20 km2) across China using MODIS images from 2005, 2010, 2015, and 2020. This study presents a novel approach for estimating non-optically active parameters through remote sensing on a national scale.

TP Concentration Inversion and Pollution Sources in Nanyi Lake Based on Landsat 8 Data and InVEST Model

Phosphorus is a limiting nutrient in freshwater ecosystems. Therefore, it is of great significance to use remote sensing technology to estimate the Total phosphorus (TP) concentration in the lake body and identify the contribution of TP inflow load in the surrounding area of the lake body. In this study, two main frameworks (empirical method and machine learning algorithm) for TP estimation are proposed and applied to the development of the Nanyi Lake algorithm. Based on the remote sensing data and ground monitoring data, the results obtained by the two main algorithms are compared to explore whether the machine learning algorithm has better performance than the empirical method in the TP inversion prediction of Nanyi Lake. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was used to simulate the TP inflow load in the Nanyi Lake Basin and determine the key pollution source areas. The results show that the accuracy of the machine learning algorithm is higher than that of the empirical method and has better performance. Among the four machine learning algorithms—support vector machines (SVR), artificial neural network (BP), extreme gradient boosting algorithm (XGBoost) and random forest regression (RF)—the TP concentration inversion model established by the XGBoost algorithm is more accurate and has strong spatiotemporal heterogeneity. The simulation results in the southern and northeastern parts of the Nanyi Lake Basin contribute the most to the pollution load of the lake area, and the simulation results can provide direction for the effective prevention and control of Nanyi Lake, help to further effectively identify the key source areas of TP pollution in the water body of Nanyi Lake, and provide a meaningful scientific reference for water quality monitoring and management, to comprehensively improve the water quality of Nanyi Lake.

Development of remote sensing algorithm for total phosphorus concentration in eutrophic lakes: Conventional or machine learning?

Phosphorus is a limiting nutrient in freshwater ecosystems. Therefore, the estimation of total phosphorus (TP) concentration in eutrophic water using remote sensing technology is of great significance for lake environmental management. However, there is no TP remote sensing model for lake groups, and thus far, specific models have been used for specific lakes. To address this issue, this study proposes a framework for TP estimation. First, three algorithm development frameworks were compared and applied to the development of an algorithm for Lake Taihu, which has complex water environment characteristics and is a representative of eutrophic lakes. An Extremely Gradient Boosting (BST) machine learning framework was proposed for developing the Taihu TP algorithm. The machine learning algorithm could mine the relationship between FAI and TP in Lake Taihu, where the optical properties of the water body are dominated by phytoplankton. The algorithm exhibited robust performance with an R2 value of 0.6 (RMSE = 0.07 mg/L, MRE = 43.33%). Then, a general TP algorithm (R2 = 0.64, RMSE = 0.06 mg/L, MRE = 34.13%) was developed using the proposed framework and tested in seven other lakes using synchronous image data. The algorithm accuracy was found to be affected by aquatic vegetation and enclosure aquaculture. Third, compared with field investigations in other studies on Lake Taihu, the Taihu TP algorithm showed good performance for long-term TP estimation. Therefore, the machine learning framework developed in this study has application potential in large-scale spatio-temporal TP estimation in eutrophic lakes.

Performance of deep learning in mapping water quality of Lake Simcoe with long-term Landsat archive

Remote sensing provides full-coverage and dynamic water quality monitoring with high efficiency and low consumption. Deep learning (DL) has been progressively used in water quality retrieval because it efficiently captures the potential relationship between target variables and imagery. In this study, the multimodal deep learning (MDL) models were developed and rigorously validated using atmospherically corrected Landsat remote sensing reflectance data and synchronous water quality measurements for estimating long-term Chlorophyll-a (Chl-a), total phosphorus (TP), and total nitrogen (TN) in Lake Simcoe, Canada. Since TP and TN are non-optically active, their retrievals were based on the fact that they are closely related to the optically active constituents (OACs) such as Chl-a. We trained the MDL models with one in-situ measured data set (for Chl-a, N = 315, for TP and TN, N = 303), validated the models with two independent data sets (N = 147), and compared the model performances with several DL, machine learning, and empirical algorithms. The results indicated that the MDL models adequately estimated Chl-a (mean absolute error (MAE) = 32.57%, Bias = 10.61%), TP (MAE = 42.58%, Bias = −2.82%), and TN (MAE = 35.05%, Bias = 13.66%), and outperformed several other candidate algorithms, namely the progressively decreasing deep neural network (DNN), a DNN with trainable parameters similar to MDL but without splitting input features, the eXtreme Gradient Boosting, the support vector regression, the NASA Ocean Color two-band and three-band ratio algorithms, and another empirical algorithm of Landsat data in clear lakes. Using the MDL models, we reconstructed the historical spatiotemporal patterns of Chl-a, TP, and TN in Lake Simcoe since 1984, and investigated the effects of two water quality improvement programs. In addition, the physical mechanism and interpretability of the MDL models were explored by quantifying the contribution of each feature to the model outputs. The framework proposed in this study provides a practical method for long-term Chl-a, TP, and TN estimation at the regional scale.

Resource-Efficient Characterisation of Pit Latrine Sludge for Use in Agriculture

Resource recovery through reuse of by-products of the sanitation chain presents a great potential towards ensuring universal access to safely managed sanitation. Many developing countries are faced with uncertainty over public and environmental health concerns associated with use of faecal sludge in agriculture. Due to resource constraints, limited data exists on the characteristics of faecal sludge to inform proper and safe use in agriculture. Despite predictive characterisation being demonstrated to be a resource-efficient approach to generate data in other fields, its application in agricultural resource recovery from faecal sludge is lacking in the literature. This paper explored predictive modelling as a less resource-intensive approach for characterisation of nutrients and pathogens in faecal sludge. Specifically, it investigated the extent to which gravimetric parameters could predict nutrients and pathogens in pit latrine sludge from informal settlements in the cities of Malawi. The study explored predictive models to estimate total ammoniacal nitrogen (TAN), total phosphorus (TP), E.coli and helminth eggs from gravimetric parameters (total solids [TS] and total volatile solids [TVS]) in pit latrine sludge. The models developed in the study allow substantially reliable estimation of TAN (R2pred = 75.4%) and TP (R2pred = 78.2%); they also provide moderately reliable predictions for E.coli (R2pred = 69.1%) and helminth eggs (R2pred = 74.3%) from total solids. Since total solids are easy and inexpensive to measure, the models present an option that can reduce resource requirement for characterisation of pit latrine sludge for informed decision-making when using pit latrine sludge in agriculture. In the absence of data on faecal sludge characteristics at the national level, the models provide a starting point for estimation of pathogens and nutrients in sludge for agricultural use. However, stepwise refinement of the models needs to be done through their validation for different types of sludge and inclusion of spatially available demographic, technical and environmental (SPA-DET) data.

Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS

The total phosphorus (TP) concentration is a key water quality parameter for water monitoring and a major indicator of the state of eutrophication in inland lakes. Using remote-sensing to estimate TP concentration is useful, as it provides a synoptic view of the entire water region; however, the weak optical characteristics of TP lead to difficulty in accurately estimating TP concentration. The differences in water characteristics and components between lakes mean that most TP estimation methods are not applicable to all lakes. An artificial neural network (ANN) model was created to represent the correlation between TP concentration and the spectral bands of Moderate Resolution Imaging Spectroradiometer (MODIS) images in different research areas. We investigated the causal inference under the potential outcome framework to analyze the sensitivity of each band with regard to the TP concentration of different lakes for the research of water characteristics. Our results show that the accuracy of the ANN-based TP concentration estimation, with R2 > 0.73, root mean squared error (RMSE) < 0.037 mg/L in Lake Okeechobee and R2 > 0.73, RMSE < 4.1 μg/L in Lake Erie, respectively, is much higher than traditional empirical methods, e.g., linear regression. We found that the sensitive bands of TP concentration in Lake Erie are blue bands, whereas the sensitive bands in Lake Okeechobee are green bands. Various TP concentration maps were drawn to indicate the distribution of TP concentration and its tendency to change. The maps show that the distribution of TP concentration closely corresponds to the shore land-use, and a high TP concentration corresponds to the latest algal blooms breakout. Our proposed approach shows good potential for the remote-sensing estimation of TP concentration for inland lakes. Identifying the sensitive bands not only help characterize the lakes, but will also help the researchers to further observe the TP concentration of specific lakes in an efficient way.

Detecting Spatiotemporal Features of Phosphorus Concentrations Using MODIS Images: A Case Study of Hongze Lake, China

Spatiotemporal tendency of total phosphorus (TP) concentrations in Hongze Lake were estimated using multiple Moderate - Resolution Imaging Spectroradiometer/Aqua images (MODIS) from 2003 to 2017. Considering the non-significant spectral features of phosphorus, this study was designed to primarily link TP with sensitive water quality parameters. Suspended particulate matter (SPM) was identified because SPM contents are sensitive to TP in Hongze Lake and also have notable response wavelengths including red, blue, and green bands. Therefore, a TP estimation model was developed based on the band integration methods suitable for lake SPM. The results showed that: 1) the exponential model of 645 nm and 555 nm is the most suitable model for TP estimation in Hongze Lake, in which the R2 value for calculation and validation can both reach 0.7; 2) the best TP assessment accuracy always occurred when the SPM values were of a moderate level. The threshold values can be approximately designated as 40 ~ 70 mg/L; 3) the TP concentrations of the entire lake have apparently increased since 2012, and achieved a peak in 2014 because of substantial dredging activities; and 4) the center of the lake showed the highest TP concentrations, while the two bays in the northern and western lake showed the lowest TP concentra-tions. These spatial variations can be attributed to the integration of several factors including meteorological phenomena, land use change, and human disturbance such as dredging activities. This study further indicates that the Rayleigh-corrected reflectance of red band and green band derived from MODIS can be proposed as the suitable indicators for TP monitoring, especially in SPM-dominated lakes. In addition, more ideal modeling effect can be expected when lake SPM contents kept in a medium level (eg. 40 ~ 70 mg/L within Hongze Lake).

Remote Sensing Estimation of Lake Total Phosphorus Concentration Based on MODIS: A Case Study of Lake Hongze

Phosphorus (P) is an important substance for the growth of phytoplankton and an efficient index to assess the water quality. However, estimation of the TP concentration in waters by remote sensing must be associated with optical substances such as the chlorophyll-a (Chla) and the suspended particulate matter (SPM). Based on the good correlation between the suspended inorganic matter (SPIM) and P in Lake Hongze, we used the direct and indirect derivation methods to develop algorithms for the total phosphorus (TP) estimation with the MODIS/Aqua data. Results demonstrate that the direct derivation algorithm based on 645 nm and 1240 nm of the MODIS/Aqua performs a satisfied accuracy (R2 = 0.75, RMSE = 0.029mg/L, MRE = 39% for the training dataset, R2 = 0.68, RMSE = 0.033mg/L, MRE = 47% for the validate dataset), which is better than that of the indirect derivation algorithm. The 645 nm and 1240 nm of MODIS are the main characteristic band of the SPM, so that algorithm can effectively reflect the P variations in Lake Hongze. Additionally, the ratio of the TP to the SPM is positively correlated with the accuracy of the algorithm as well. The proportion of the SPIM in the SPM has a complex effect on the accuracy of the algorithm. When the SPIM accounts for 78%, the algorithm achieves the highest accuracy. Furthermore, the performance of this direct derivation algorithm was examined in two inland lakes in China (Lake Nanyi and Lake Chaohu), it derived the expected P distribution in Lake Nanyi whereas the algorithm failed in Lake Chaohu. Different water properties influence significantly the accuracy of this direct derivation algorithm, while the TP, Chla, and suspended particular inorganic matter (SPOM) of Lake Chaohu are much higher than those of the other two lakes, thus it is difficult to estimate the TP concentration by a simple band combination in Lake Chaohu. Although the algorithm depends on the dataset used in the development, it usually presents a good estimation for those waters where the SPIM dominated, especially when the SPIM accounts for 60% to 80% of the SPM. This research proposed a direct derivation algorithm for the TP estimation for the turbid lake and will provide a theoretical and practical reference for extending the optical remote sensing application and the TP empirical algorithm of Lake Hongze’s help for the local government management water quality.

Influence of sampling frequency and load calculation methods on quantification of annual river nutrient and suspended solids loads.

Better management of water quality in streams, rivers and lakes requires precise and accurate estimates of different contaminant loads. We assessed four sampling frequencies (2days, weekly, fortnightly and monthly) and five load calculation methods (global mean (GM), rating curve (RC), ratio estimator (RE), flow-stratified (FS) and flow-weighted (FW)) to quantify loads of nitrate-nitrogen (NO3--N), soluble inorganic nitrogen (SIN), total nitrogen (TN), dissolved reactive phosphorus (DRP), total phosphorus (TP) and total suspended solids (TSS), in the Manawatu River, New Zealand. The estimated annual river loads were compared to the reference 'true' loads, calculated using daily measurements of flow and water quality from May 2010 to April 2011, to quantify bias (i.e. accuracy) and root mean square error 'RMSE' (i.e. accuracy and precision). The GM method resulted into relatively higher RMSE values and a consistent negative bias (i.e. underestimation) in estimates of annual river loads across all sampling frequencies. The RC method resulted in the lowest RMSE for TN, TP and TSS at monthly sampling frequency. Yet, RC highly overestimated the loads for parameters that showed dilution effect such as NO3--N and SIN. The FW and RE methods gave similar results, and there was no essential improvement in using RE over FW. In general, FW and RE performed better than FS in terms of bias, but FS performed slightly better than FW and RE in terms of RMSE for most of the water quality parameters (DRP, TP, TN and TSS) using a monthly sampling frequency. We found no significant decrease in RMSE values for estimates of NO3-N, SIN, TN and DRP loads when the sampling frequency was increased from monthly to fortnightly. The bias and RMSE values in estimates of TP and TSS loads (estimated by FW, RE and FS), however, showed a significant decrease in the case of weekly or 2-day sampling. This suggests potential for a higher sampling frequency during flow peaks for more precise and accurate estimates of annual river loads for TP and TSS, in the study river and other similar conditions.

Establishing realistic management objectives for urban lakes using paleolimnological techniques: an example from Halifax Region (Nova Scotia, Canada)

To determine pre-disturbance limnological conditions, evaluate the impact of environmental stressors (surface water acidification, nutrient inputs, climate change, and winter deicing salt), and set realistic recovery targets for lake management strategies, a rapid assessment paleolimnological approach was used to determine the amount (and likely causes) of environmental changes over the past ∼100–150 years in 51 urban lakes from Halifax, Nova Scotia (Canada). Diatom assemblages from lake sediment cores were used with “top” (recently deposited, surface) samples being matched to measured limnological conditions, and “bottom” (generally from >15 cm deep) samples used to infer pre-disturbance limnological conditions such as pH, total phosphorus (TP), specific conductance, and shifts due to changing climatic conditions. Environmental change was assessed by calculating the metric of change in species composition between present-day and pre-disturbance diatom assemblages, and inferences from quantitative estimates of diatom-inferred pH, specific conductance, and TP. All 51 study lakes have experienced floristic changes in diatom species composition since pre-disturbance times, but different environmental stressors were implicated: 8 of the 51 lakes underwent significant (i.e., >2 times the root mean-squared error [2X RMSE] of the inference model) decreases in diatom-inferred pH; 8 lakes had significant increases in diatom-inferred TP; and 19 otherwise relatively pristine lakes had increases in planktonic taxa consistent with observations linked to changes in lake seasonality and limnological changes most closely linked to climate warming in Nova Scotia and other regions. The remaining 16 lakes did not have large and consistent changes in diatom flora or changes in diatom-inferred TP or pH >2X RMSE of the prediction of the models. Lake-specific factors were related to these inferences, and the lakes that acidified were mainly the currently most acidic sites, whereas those that experienced issues related to eutrophication were generally among the most alkaline sites. Of our 51 lakes, 22 (including some experiencing pH, TP, or showing floristic changes linked to climate changes) had increases in measured conductivity (1980–2002) and, correspondingly, increased relative abundances of halophilic diatom taxa. These lakes, often with catchments containing high surface areas of impervious surfaces, are examples of a trend of increasing salinity in northeastern North American lakes likely related to winter application of deicing (road) salt. The application of this paleolimnological approach enabled us to identify which lakes have undergone significant changes in diatom assemblages, as well as which environmental stressor(s) were most probable. This information can help lake managers develop more targeted and effective management strategies.

Relative importance of phosphorus, fish biomass, and watershed land use as drivers of phytoplankton abundance in shallow lakes

Phytoplankton abundance in shallow lakes is potentially influenced by ambient phosphorus concentrations, nutrient loading accentuated by human activities in lake watersheds, and abundance of planktivorous and benthivorous fish. However, few studies have simultaneously assessed the relative importance of these factors influencing phytoplankton abundance over large spatial scales. We assessed relative influences of watershed characteristics, total phosphorus concentrations, and fish biomass on phytoplankton abundance in 70 shallow lakes in western Minnesota (USA) during summer 2005 and 2006. Our independent variables included total phosphorus (TP), benthivore biomass, planktivore biomass, summed planktivore and benthivore biomass (summed fish), areal extent of agriculture in the watershed, region (prairie versus parkland lakes), and year. Predictive models containing from one to three independent variables were compared using an information theoretic approach. The most parsimonious model consisted of TP and summed fish, and had over 10,000-fold greater support compared to models using just TP or summed fish, or models comprised of other variables. We also found no evidence that relative importance of predictor variables differed between regions or years, and parameter estimates of TP and summed fish were temporally and spatially consistent. TP and summed fish were only weakly correlated, and the model using both variables was a large improvement over using either variable alone. This indicates these two variables can independently increase phytoplankton abundance, which emphasizes the importance of managing both nutrients and fish when trying to control phytoplankton abundance in shallow lakes.

Hyperspectral Remote Sensing of Total Phosphorus (TP) in Three Central Indiana Water Supply Reservoirs

The connection between nutrient input and algal blooms for inland water productivity is well known but not the spatial pattern of water nutrient loading and algae concentration. Remote sensing provides an effective tool to monitor nutrient abundances via the association with algae concentration. Twenty-one field campaigns have been conducted with samples collected under a diverse range of algal bloom conditions for three central Indiana drinking water bodies, e.g., Eagle Creek Reservoir (ECR), Geist Reservoir (GR), and Morse Reservoir (MR) in 2005, 2006, and 2008, which are strongly influenced anthropogenic activities. Total phosphorus (TP) was estimated through hyperspectral remote sensing due to its close association with chlorophyll a (Chl-a), total suspended matter, Secchi disk transparency (SDT), and turbidity. Correlation analysis was performed to determine sensitive spectral variables for TP, Chl-a, and SDT. A hybrid model combining genetic algorithms and partial least square (GA-PLS) was established for remote estimation of TP, Chl-a, and SDT with selected sensitive spectral variables. The result indicates that TP has close association with diagnostic spectral variables with R 2 ranging from 0.55 to 0.72. However, GA-PLS has better performance with an average R 2 of 0.87 for aggregated dataset. GA-PLS was applied to the airborne imaging data (AISA) to map spatial distribution of TP, Chl-a, and SDT for MR and GR. The eutrophic status was evaluated with Carlson trophic state index using TP, Chl-a, and SDT maps derived from AISA images. Mapping results indicated that most MR belongs to mesotrophic (48.6%) and eutrophic (32.7%), while the situation was more severe for GR with 57.8% belongs to eutrophic class, and more than 40% to hypereutrophic class due to the high turbidity resulting from dredging practices.

Ecology and distribution of diatoms in Biscayne Bay, Florida (USA): Implications for bioassessment and paleoenvironmental studies

The spatial and temporal distribution of planktonic, sediment-associated and epiphytic diatoms among 58 sites in Biscayne Bay, Florida was examined in order to identify diatom taxa indicative of different salinity and water quality conditions, geographic locations and habitat types. Assessments were made in contrasting wet and dry seasons in order to develop robust assessment models for salinity and water quality for this region. We found that diatom assemblages differed between nearshore and offshore locations, especially during the wet season when salinity and nutrient gradients were steepest. In the dry season, habitat structure was primary determinant of diatom assemblage composition. Among a suite of physicochemical variables, water depth and sediment total phosphorus (STP) were most strongly associated with diatom assemblage composition in the dry season, while salinity and water total phosphorus (TP) were more important in the wet season. We used indicator species analysis (ISA) to identify taxa that were most abundant and frequent at nearshore and offshore locations, in planktonic, epiphytic and benthic habitats and in contrasting salinity and water quality regimes. Because surface water concentrations of salts, total phosphorus, nitrogen (TN) and organic carbon (TOC) are partly controlled by water management in this region, diatom-based models were produced to infer these variables in modern and retrospective assessments of management-driven changes. Weighted averaging (WA) and weighted averaging partial least squares (WA-PLS) regressions produced reliable estimates of salinity, TP, TN and TOC from diatoms ( r 2 = 0.92, 0.77, 0.77 and 0.71, respectively). Because of their sensitivity to salinity, nutrient and TOC concentrations diatom assemblages should be useful in developing protective nutrient criteria for estuaries and coastal waters of Florida.

Quantifying species indicator values for trophic diatom indices: a comparison of approaches

This study compares two approaches for constructing diatom-based indices for monitoring river eutrophication. The first approach is based on weighted averaging of species indicator values with the underlying assumption that species have symmetrical unimodal distributions along the nutrient gradient, and their distributions are sufficiently described by a single indicator value per species. The second approach uses multiple indicator values for individual taxa and is based on the possibility that species have complex asymmetrical response curves. Multiple indicator values represent relative probabilities that a species would be found within certain ranges of nutrient concentration. We used 155 benthic diatom samples collected from rivers in the Northern Piedmont ecoregion (Northeastern U.S.A.) to construct two datasets: one used for developing models and indices, and another for testing them. To characterize the shape of species response curves we analyzed changes in the relative abundance of 118 diatom taxa common in this dataset along the total phosphorus (TP) gradient by fitting parametric and non-parametric regression models. We found that only 34 diatoms had symmetrical unimodal response to TP. Among several indices that use a single indicator value for each species, the best was the weighted averaging partial least square (WA-PLS) inference model. The correlation coefficient between observed and inferred TP in the test dataset was 0.67. The best index that employed multiple indicator values for each species had approximately the same predictive power as the WA-PLS based index, but in addition, this index provided a sample-specific measure of uncertainty for the TP estimation.

A rapid and simple technique for digestion and determination of total phosphorus in animal manures and herbage

This study describes a simple, innovative, acidified persulfate method of autoclave assisted digestion coupled with molybdenum blue spectrophotometric analysis for determination of total phosphorus (TP) in animal manures and herbage. Reagent addition was optimized to give maximum estimations of TP from four manure types of unknown phosphorus (P) content and a certified herbage material of known P content. This method was then compared with the established procedure, an aqua-regia digest followed by Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) determination. The persulfate method gave TP estimations of 18.95 mg g−1 for cow manure, 18.90 mg g−1 for broiler litter and 22.82 mg g−1 for pig slurry. These were significantly greater (P<0.01) than the values of 16.10 mg g−1, 17.48 mg g−1 and 19.16 mg g−1 for cow manure broiler litter and pig slurry respectively, determined by the aqua regia/ICP-OES method. No significant difference in TP estimation was found for layer manure or the certified herbage material between methods. The persulfate digestion was simpler, faster, used less hazardous reagents and did not require the continuous attention of the operator as recommended for the aqua-regia method. This work highlights the need for the implementation of a reliable standard method for TP estimation in manures and the need for certified reference manure materials against which laboratories can gauge the reliability of their procedures.

Internal HBV-96 Variables and Phosphorus Transport Processes

The high data requirements of total phosphorus (TP) transport prediction tools limit their application. Recently, research has therefore started in Sweden to use a conceptual hydrological model, HBV-96, as a base for TP estimation. A first step is, however, to understand the relationship between model variables and natural processes. A review of particulate and dissolved material transport enabled the most important TP transport processes to be found for small Swedish catchments. A probabilistic flow categorisation technique based on the internal HBV-96 variables is suggested. Days with a high probability of saturated overland flow are identified with an overland flow index based on the water content of the upper response box and rainfall/melt rates. Further categorisation into intermediate and base flow is done on the basis of water contents in the lower and upper response boxes of the model. The flow categorisation technique was tested in a catchment with TP and suspended sediment records. The results show significantly different concentration distributions and median values between the flow categories. The HBV-96 internal variables, in general, are more highly correlated to concentration than is observed river runoff and it may thus be beneficial to apply the model to assess TP transport compared to using observed runoff directly. Model parameter interdependence is highlighted as a shortfall in the general use of HBV-96 for flow categorisation.

Relationships Between Total Phosphorus Concentrations, Sampling Frequency, and Wind Velocity in a Shallow, Polymictic Lake

ABSTRACT The effect of sampling frequency and wind velocity on estimates of total phosphorus (TP) concentrations were evaluated in Lake Okeechobee, Florida, a shallow, polymictic lake. It was hypothesized that estimates of TP would be affected by sampling frequency because the lake's P-rich bottom sediments are frequently resuspended during wind events. However, differences in estimates of annual mean TP were small (< 5 %) when daily, weekly, bi-monthly, and monthly sampling frequencies were compared. In contrast, estimates of monthly mean TP often varied by 20 to 30 percent among sampling frequencies and differences in monthly maximum and minimum TP values commonly varied by 50 to 250 μg·L−1. Bottom sediments were most likely to be resuspended during the windy season (November-April) when calculated threshold wind velocities were exceeded more than 40 percent of the time. On a monthly basis, wind velocity explained as much as 70 percent of the daily variability in TP during the windy season. In comparison, threshold velocities were exceeded less frequently during the calm season (May – October) and wind velocity only accounted for 5 to 25 percent of the daily variability in TP. The effects of wind-driven mixing events on the chemical and physical attributes of shallow lakes and the influence of sampling frequency on estimates of these attributes should be considered when designing a monitoring program to evaluate short and long-term trends in water quality.

The validation of diatom‐phosphorus transfer functions: an example from Mondsee, Austria

SUMMARY1. A diatom‐phosphorus weighted averaging (WA) transfer function, derived from a training set of currently oligotrophic to mesotrophic European Alpine lakes, was applied to a high‐resolution sediment core with annual laminae from Mondsee, an Austrian pre‐alpine lake, in order to reconstruct the eurrophication history of the lake.2. The water chemistry records of total phosphorus (TP) available for Mondsee were compared with the diatom‐inferred TP from the model for the period 1975–93. The trend in TP values as inferred by the model paralleled the monitored trend in TP values closely, with matching peaks in 1979/80, a decrease in values from the early 1980s, a second smaller peak in 1986/7, and a further reduction in concentrations in the last 6 years.3. However, there was a clear mismatch between the actual timing of the major TP peak, with the water chemistry records reporting its occurrence in 1979, and the diatom model indicating a small peak in 1980 and the highest concentrations in 1982. This can be attributed to the uncertainty of the sediment chronology for this section of the core, and possibly to the inconsistency between the core resolution and the resolution of the diatom model.4. In terms of the actual concentrations of TP inferred by the model, they compared reasonably well with the measured data, although the model tends to underestimate for the lower core section owing largely to poor diatom assemblage analogues. In the upper part of the core, the diatom‐inferred TP values were in extremely close agreement with the monitored chemical data.5. This validation study indicates that diatom‐phosphorus transfer functions are robust and are able reliably to infer past‐TP concentrations from fossil diatom assemblages in sediment cores. Despite the natural intra‐ and interannual variability in diatom assemblages and epilimnetic water chemistry, the technique can provide accurate estimates of TP with an annual resolution. The model can be applied to selected sites with suitable sediment records to reconstruct lake TP histories, thus providing a pragmatic management tool for addressing lake eutrophication problems.

Optimal sampling period for predicting phosphores and chlorophyll concentrations in north-temperate lakes

Because total phosphorus and chlorophyll a concentrations vary seasonally within north-temperate lakes, estimates of lake trophic status are usually based on several to many sampling visits. However, because such effort is not always logistically possible, the empirical relationships developed between these trophic status indicators may depend upon seasonal patterns of sampling. Clearly, it is desirable to know if some periods of the year are more likely to produce representative estimates of total phosphorus and chlorophyll a, or, alternatively, if sampling in other periods should be restricted or even avoided. Data from north-temperate lakes are used to examine the degree to which single monthly samples for total phosphorus and chlorophyll a represent seasonal means. Analyses indicate that the least precise and least accurate estimates of the means occur when single-visit synoptic surveys are conducted during April. Not until August to September do single sampling visits provide data that approximate those represented by the means garnered through more frequent sampling.