@article{borah_giudice_aupperle_obenour_2025, title={Exploring internal phosphorus loads and management interventions through Bayesian reservoir modeling}, volume={384}, url={https://doi.org/10.1016/j.jenvman.2025.125538}, DOI={10.1016/j.jenvman.2025.125538}, abstractNote={Phosphorus is a limiting nutrient for eutrophication in many lakes and reservoirs across the world. While most management strategies aim at reducing external (watershed) phosphorus sources, internal phosphorus loading (IPL) often receives less attention, as it is challenging to measure or estimate through modeling. Here, we present a novel approach to characterize internal loading dynamics, leveraging multi-decadal monitoring data and a mass-balance model developed within a Bayesian inference framework. The model performs well (R<sup>2</sup> = 57 % and RMSE = 0.032 mg/L for total phosphorus) when applied to Jordan Lake, a segmented eutrophic reservoir in North Carolina, USA. Results highlight the dominant and increasing role of internal loading in summers (0.53 g/m<sup>2</sup>/month), contributing nearly twice the external phosphorus loading. We also explore long-term lake warming scenarios that intensify fluxes both into and out of the sediment layer, but have little effect on water-column phosphorus concentrations. Additionally, we investigate long-term phosphorus dynamics under different management interventions. Our simulations demonstrate the potential for IPL mitigation (e.g., capping, dredging) to accelerate and sustain water quality improvements, particularly when paired with external loading reductions. The modeling approach is transferable to similar waterbodies, especially where there is a need to characterize internal phosphorus fluxes based largely on water-column monitoring data. It also provides a computationally efficient framework for making long-term, probabilistic forecasts accounting for future climate, anthropogenic impacts, and the gradual accumulation (or depletion) of phosphorus in the sediment layer.}, journal={Journal of Environmental Management}, author={Borah, Smitom S. and Giudice, Dario Del and Aupperle, Matthew and Obenour, Daniel R.}, year={2025}, month={Apr} }
 @article{borah_nelson_duckworth_obenour_2025, title={Quantifying Summer Internal Phosphorus Loading in Large Lakes across the United States}, volume={5}, url={https://doi.org/10.1021/acs.est.4c13431}, DOI={10.1021/acs.est.4c13431}, abstractNote={Internal phosphorus loading (IPL) can be a significant phosphorus (P) source in freshwater systems, often causing water-quality improvement delays. Despite its importance, IPL estimates are missing for many freshwater systems due to several large-scale measuring and modeling challenges. In this study, we develop a modeling framework to estimate summer anoxic sediment release rates (SRRs) for P in 5899 large lakes and reservoirs (surface area > 1.0 km<sup>2</sup>; mixing depth < maximum depth) across the contiguous US (CONUS). Our framework combines random forest models for bottom-water temperature (BT) and surface-water total P (TP) with a mixed-effects regression model for SRR, and it includes uncertainty propagation across these models. Our results indicate that mean summer SRR ranges from 1 to 37 mg/m<sup>2</sup>/day across CONUS lakes, with 31% of waterbodies having SRR > 10 mg/m<sup>2</sup>/day. Areas of high SRR are generally associated with high predicted surface-water TP, which is particularly common in agricultural areas. Uncertainties in SRR predictions are largely attributable to the random forest-based inputs and predictive error in the SRR regression. In relatively dry summers, IPL is likely to be higher than external loading in 26% of watersheds. Overall, our results reveal where IPL can be a critical factor in watershed nutrient management.}, journal={Environmental Science & Technology}, author={Borah, Smitom S. and Nelson, Natalie G. and Duckworth, Owen W. and Obenour, Daniel R.}, year={2025}, month={May} }
 @article{dash_borah_kalamdhad_2020, title={Application of positive matrix factorization receptor model and elemental analysis for the assessment of sediment contamination and their source apportionment of Deepor Beel, Assam, India}, url={http://dx.doi.org/10.1016/j.ecolind.2020.106291}, DOI={10.1016/j.ecolind.2020.106291}, abstractNote={The present study is a first of its kind on the sediment contamination in Deepor Beel, which makes use of source apportionment receptor modelling technique {positive matrix factorization (PMF)} for determining and quantifying the sources' contribution to the pollution of the sediment column of Deepor Beel, Assam. Sediment samples were collected and analysed for seven different heavy metals from 23 sampling locations for a period from October 2017 to February 2019. Polling the entire dataset to a single matrix and carrying out multiple iterations revealed that four factors were optimum and thus, was applied for the simulation of the model. It was observed that the factors 1, 2, 3 and 4 corresponded to the soil parent material, leaching from the Boragaon landfill, discharge of agricultural and domestic wastes, and effluents from the industries and traffic emissions respectively. The sediment samples were further subjected to elemental analysis; X-ray powder diffraction (XRD) followed by Scanning electron microscope - Energy Dispersive X-Ray Spectroscopy (SEM – EDS), to determine the elemental composition and forms of heavy metals present in the sediment columns from various parts of the wetland. Sediment sample collected from the proximity of the landfill site was observed to be affected the most, probably due to leaching effects, especially during the monsoon. The central zone, however, was found to be devoid of any anthropogenic contaminations, while the sediment column near the industrial complex was found to be contaminated to a moderate extent. The study indicates the quantum of sediment contamination in the wetland and the causative parameters responsible, thus proving to be of immense help to the various governmental bodies in the planning and management of resources for sediment remediation of Deepor Beel.}, journal={Ecological Indicators}, author={Dash, Siddhant and Borah, Smitom Swapna and Kalamdhad, Ajay S.}, year={2020}, month={Mar} }
 @article{dash_borah_kalamdhad_2020, title={Heavy metal pollution and potential ecological risk assessment for surficial sediments of Deepor Beel, India}, url={http://dx.doi.org/10.1016/j.ecolind.2020.107265}, DOI={10.1016/j.ecolind.2020.107265}, journal={Ecological Indicators}, author={Dash, Siddhant and Borah, Smitom Swapna and Kalamdhad, Ajay S.}, year={2020}, month={Dec} }
 @article{dash_borah_kalamdhad_2019, title={A modified indexing approach for assessment of heavy metal contamination in Deepor Beel, India}, url={http://dx.doi.org/10.1016/j.ecolind.2019.105444}, DOI={10.1016/j.ecolind.2019.105444}, journal={Ecological Indicators}, author={Dash, Siddhant and Borah, Smitom Swapna and Kalamdhad, Ajay}, year={2019}, month={May} }
 @article{dash_borah_kalamdhad_2019, title={Study of the limnology of wetlands through a one-dimensional model for assessing the eutrophication levels induced by various pollution sources}, url={http://dx.doi.org/10.1016/j.ecolmodel.2019.108907}, DOI={10.1016/j.ecolmodel.2019.108907}, journal={Ecological Modelling}, author={Dash, Siddhant and Borah, Smitom Swapna and Kalamdhad, Ajay S.}, year={2019}, month={Dec} }
 @article{dash_borah_kalamdhad_2018, title={Monitoring and assessment of Deepor Beel water quality using multivariate statistical tools}, url={http://dx.doi.org/10.2166/wpt.2018.098}, DOI={10.2166/wpt.2018.098}, abstractNote={Abstract The aim of this study was application of multivariate statistical techniques – e.g., hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) – to analyse significant sources affecting water quality in Deepor Beel. Laboratory analyses for 20 water quality parameters were carried out on samples collected from 23 monitoring stations. HCA was used on the raw data, categorising the 23 sampling locations into three clusters, i.e., sites of relatively high (HP), moderate (MP) and low pollution (LP), based on water quality similarities at the sampling locations. The HCA results were then used to carry out PCA, yielding different principal components (PCs) and providing information about the respective sites' pollution factors/sources. The PCA for HP sites resulted in the identification of six PCs accounting for more than 84% of the total cumulative variance. Similarly, the PCA for LP and MP sites resulted in two and five PCs, respectively, each accounting for 100% of total cumulative variance. Finally, the raw dataset was subjected to DA. Four parameters, i.e., BOD5, COD, TSS and SO42− were shown to account for large spatial variations in the wetland's water quality and exert the most influence.}, journal={Water Practice & Technology}, author={Dash, Siddhant and Borah, Smitom Swapna and Kalamdhad, Ajay}, year={2018}, month={Dec} }