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Single particle-inductively coupled plasma-time of flight-mass spectrometers (SP-ICP-TOF-MS) generates large datasets of the multi-elemental composition of nanoparticles. However, extracting useful information from such datasets is challenging. Hierarchical clustering (HC) has been successfully applied to extract elemental fingerprints from multi-element nanoparticle data obtained by SP-ICP-TOF-MS. However, many other clustering approaches can be applied to analyze SP-ICP-TOF-MS data that have not yet been evaluated. This study fills this knowledge gap by comparing the performance of three clustering approaches: HC, spectral clustering, and t-distributed Stochastic Neighbor Embedding coupled with Density-Based Spatial Clustering of Applications with Noise (tSNE-DBSCAN) for analyzing SP-ICP-TOF-MS data. The performance of these clustering techniques was evaluated by comparing the size of the extracted clusters and the similarity of the elemental composition of nanoparticles within each cluster. Hierarchical clustering often failed to achieve an optimal clustering solution for SP-ICP-TOF-MS data because HC is sensitive to the presence of outliers. Spectral clustering and tSNE-DBSCAN extracted clusters that were not identified by HC. This is because spectral clustering, a method developed based on graph theory, reveals the global and local structure in the data. tSNE reduces and maps the data into a lower-dimensional space, enabling clustering algorithms such as DBSCAN to identify subclusters with subtle differences in their elemental composition. However, tSNE-DBSCAN can lead to unsatisfactory clustering solutions because tuning the perplexity hyperparameter of tSNE is a difficult and a time-consuming task, and the relative distance between datapoints is not maintained. Although the three clustering approaches successfully extract useful information from SP-ICP-TOF-MS data, spectral clustering outperforms HC and tSNE-DBSCAN by generating clusters of a large number of nanoparticles with similar elemental compositions.

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Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of geo-physical and hydroclimate data. The representation of coupled socio-environmental factors has often been ignored in vulnerability assessment. This study develops an integrated socio-environmental Coastal Vulnerability Index (CVI), which simultaneously combines information from five vulnerability groups: biophysical, hydroclimate, socio-economic, ecological, and shoreline. Using the Multi-Criteria Decision Making (MCDM) approach, two CVI (CVI-50 and CVI-90) have been developed based on average and extreme conditions of the factors. Each CVI is then compared to a data-driven CVI, which is formed based on Probabilistic Principal Component Analysis (PPCA). Both MCDM and PPCA have been tied into geospatial analysis to assess the natural hazard vulnerability of six coastal counties in South Carolina. Despite traditional MCDM-based vulnerability assessments, where the final index is estimated based on subjective weighting methods or equal weights, this study employs an entropy weighting technique to reduce the individuals' biases in weight assignment. Considering the multivariate nature of the coastal vulnerability, the validation results show both CVI-90 and PPCA preserve the vulnerability results from biophysical and socio-economic factors reasonably, while the CVI-50 methods underestimate the biophysical vulnerability of coastal hazards. Sensitivity analysis of CVIs shows that Charleston County is more sensitive to socio-economic factors, whereas in Horry County the physical factors contribute to a higher degree of vulnerability. Findings from this study suggest that the PPCA technique facilitates the high-dimensional vulnerability assessment, while the MCDM approach accounts more for decision-makers' opinions.

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Titanium dioxide (TiO2) engineered particles are widely used in the urban environment as pigments in paints, and as active ingredients in photocatalytic coatings. Consequently, studies are necessary to quantify TiO2 engineered particle concentrations and their temporal variability in surface waters to gain better understanding about their abundance and environmental fate in order to minimize their potential environmental impacts. The objective of this study was to determine the temporal variability in the concentration of TiO2 engineered particles in the Broad River, Columbia, South Carolina, United States during dry and wet weather conditions and to examine the relationship between flow discharge, water quality indicators, and the concentration of TiO2 engineered particles. TiO2 engineered particle concentration in the Broad River water was determined by mass balance calculation using bulk titanium concentration and the increase in Ti/Nb ratio above the natural background ratio. The relative abundance of single metal and multi-metal Ti-bearing particles was determined by single particle-inductively coupled plasma-time of flight-mass spectrometer (SP-ICP-TOF-MS). Additionally, the elemental ratios of Ti/Nb, Ti/Al, and Ti/Fe within multi-metal Ti-bearing particles were determined at the single particle level. Discharge, bulk elemental concentrations (e.g., Ti, Al, Fe, and Nb), bulk elemental ratios (e.g., Ti/Al, Ti/Fe, and Ti/Nb), TiO2 engineered particle concentration, and turbidity displayed the same trend of rise and fall following storm events. Linear relationships were established between turbidity and TiO2 engineered particle concentrations in the Broad River for different flow regimes. However, no correlation was observed between TiO2 engineered particle concentrations and flow discharge, dissolved oxygen, pH, or ionic strength. The established correlations between turbidity and TiO2 engineered particle concentrations are important as they can be used to translate the continuously monitored turbidity to TiO2 concentrations.

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Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43% in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages.

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Detection and quantification of engineered nanomaterials in environmental systems require precise knowledge of the elemental composition, association, and ratios in homologous natural nanomaterials (NNMs). Here, we characterized soil NNMs at the single particle level using single particle-inductively coupled plasma-time of flight-mass spectrometer (SP-ICP-TOF-MS) in order to identify the elemental purity, composition, associations, and ratios within NNMs. Elements naturally present as a major constituent in NNMs such as Ti, and Fe occurred predominantly as pure/single metals, whereas elements naturally present at trace levels in NNMs occurred predominantly as impure/multi-metal NNMs such as V, Nb, Pr, Nd, Sm, Eu, Gd, Tb, Er, Dy, Yb, Lu, Hf, Ta, Pb, Th, and U. Other elements occurred as a mixture of single metal and multi-metal NNMs such as Al, Si, Cr, Mn, Ni, Cu, Zn, Ba, La, Ce, W, and Bi. Thus, elemental purity can be used to differentiate ENMs vs. NNMs only for those elements that occur at trace level in NNMs. We also classified multi-metal NNM into clusters of similar elemental composition and determined their mean elemental composition. Six major clusters accounted for more than 95% of the detected multi-metal NNMs including Al-, Fe-, Ti-, Si-, Ce-, and Zr-rich particles' clusters. The elemental composition of these multi-metal NNM clusters is consistent with naturally occurring minerals. Titanium occurred as a major element (>70% of the total metal mass in NNMs) in Ti-rich cluster and as a minor (<25% of the total metal mass in NNMs) element in likely clay, titanomagnetite, and aluminum oxide phases. Two rare earth element (REE) clusters were identified, characteristic of light REEs and heavy REEs. The findings of this study provide a methodology and baseline information on the elemental composition, associations, and ratios of NNMs, which can be used to differentiate NNMs vs. ENMs in environmental systems.

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Understanding the complexity and feedbacks among food, energy, and water (FEW) systems is key to making informed decisions about sustainable development. This paper presents qualitative representation and quantitative system dynamics simulation of the water resources system in the Qazvin Plain, Iran, taking into account the energy intensity of water supply and interconnected water use sectors (e.g., urban, industrial, and agricultural). Qazvin Plain faces water resources challenges that are common to arid/semi-arid areas, including frequent droughts, declining surface water and groundwater, and increased urban and agricultural water demand. A system dynamics model is developed using historical data (2006-2016) to investigate the effects of anticipated dynamics of integrated water and energy sectors in the next two decades. The results of policy scenarios (2020-2039) demonstrate that the continuation of the existing management policies will cause severe damage to the water and energy sectors, pushing the system towards water resources limits to growth. An annual groundwater table decline of nearly 1 m is anticipated, indicating significant overshoot of the plain's natural recharge capacity, which may lead to the depletion of recoverable groundwater in the plain within the next three decades. The groundwater table decline will cause energy consumption of water supply to increase by about 32% (i.e., 380 GWh) to maintain irrigated agriculture. It is critical to implement a combination of water demand and supply management policies (e.g., net agricultural water savings and recycling treated wastewater) to delay the problem of water limits to growth in the region.

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The San Francisco Estuary (California, USA) had abundant pelagic fish in the late 1960s, but has few pelagic fish today. A primary cause for this decline in fish is thought to be a trophic cascade, triggered by declining phytoplankton. Here, we describe the changes in pelagic community structure of the San Francisco Estuary. Then, we examine whether changes in hydrodynamics due to freshwater exports, which increased exponentially beginning in 1967, in addition to the 1986 invasion by the clam Potamocorbula amurensis, explain the phytoplankton loss. Hydrodynamic variables were reconstructed back to 1956 using statistical models fit to, and cross-validated against, output from a hydrodynamic model. Then, we regressed mean summer/fall chlorophyll a-the season with the largest phytoplankton decline-against the reconstructed hydrodynamic variables and the presence/absence of P. amurensis for 1969-2014. The regression model, which explained 78% of the interannual variation in chlorophyll a, was then used to quantify the influence of P. amurensis and exports on chlorophyll a. Based on monitoring data, chlorophyll a declined 22-fold from 1969-2014, zooplankton declined 32-fold from 1972-2014, and pelagic fish declined 92-fold from 1968-2014. Averaged over 1990-2014, the chlorophyll a model ascribed an 88% decline in chlorophyll a to P. amurensis, a 74% decline to exports (at minimum), and a 97% decline to the combined influence of P. amurensis and exports (at minimum). Thus, the decline in pelagic productivity in the San Francisco Estuary has occurred largely due to the combined impacts of the P. amurensis invasion and increased freshwater exports.

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