Y. Jeffrey Yang

Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: techniques and experimental results.

Accurate detection and identification of natural or intentional contamination events in a drinking water pipe is critical to drinking water supply security and health risk management. To use conventional water quality sensors for the purpose, we have explored a real-time event adaptive detection, identification and warning (READiw) methodology and examined it using pilot-scale pipe flow experiments of 11 chemical and biological contaminants each at three concentration levels. The tested contaminants include pesticide and herbicides (aldicarb, glyphosate and dicamba), alkaloids (nicotine and colchicine), E. coli in terrific broth, biological growth media (nutrient broth, terrific broth, tryptic soy broth), and inorganic chemical compounds (mercuric chloride and potassium ferricyanide). First, through adaptive transformation of the sensor outputs, contaminant signals were enhanced and background noise was reduced in time-series plots leading to detection and identification of all simulated contamination events. The improved sensor detection threshold was 0.1% of the background for pH and oxidation-reduction potential (ORP), 0.9% for free chlorine, 1.6% for total chlorine, and 0.9% for chloride. Second, the relative changes calculated from adaptively transformed residual chlorine measurements were quantitatively related to contaminant-chlorine reactivity in drinking water. We have shown that based on these kinetic and chemical differences, the tested contaminants were distinguishable in forensic discrimination diagrams made of adaptively transformed sensor measurements.

Optimal expansion of a drinking water infrastructure system with respect to carbon footprint, cost-effectiveness and water demand.

Urban water infrastructure expansion requires careful long-term planning to reduce the risk from climate change during periods of both economic boom and recession. As part of the adaptation management strategies, capacity expansion in concert with other management alternatives responding to the population dynamics, ecological conservation, and water management policies should be systematically examined to balance the water supply and demand temporally and spatially with different scales. To mitigate the climate change impact, this practical implementation often requires a multiobjective decision analysis that introduces economic efficiencies and carbon-footprint matrices simultaneously. The optimal expansion strategies for a typical water infrastructure system in South Florida demonstrate the essence of the new philosophy. Within our case study, the multiobjective modeling framework uniquely features an integrated evaluation of transboundary surface and groundwater resources and quantitatively assesses the interdependencies among drinking water supply, wastewater reuse, and irrigation water permit transfer as the management options expand throughout varying dimensions. With the aid of a multistage planning methodology over the partitioned time horizon, such a systems analysis has resulted in a full-scale screening and sequencing of multiple competing objectives across a suite of management strategies. These strategies that prioritize 20 options provide a possible expansion schedule over the next 20 years that improve water infrastructure resilience and at low life-cycle costs. The proposed method is transformative to other applications of similar water infrastructure systems elsewhere in the world.

Integrated data fusion and mining techniques for monitoring total organic carbon concentrations in a lake

Monitoring water quality on a near-real-time basis to address water resource management and public health concerns in coupled natural systems and the built environment is by no means an easy task. Total organic carbon (TOC) in surface waters is a known precursor of disinfection by-products in drinking water treatment such as total trihalomethanes (TTHMs), which are a suspected carcinogen and have been related to birth defects if water treatment plants cannot remove them. In this paper, an early warning system using integrated data fusion and mining (IDFM) techniques was proposed to estimate spatiotemporal distributions of TOC on a daily basis for monitoring water quality in a lake that serves as the source of a drinking water treatment plant. Landsat satellite images have high spatial resolution, but such application suffers from a long overpass interval of 16 days. On the other hand, coarse-resolution sensors with frequent revisit times, such as MODIS, are incapable of providing detailed water quality information because of low spatial resolution. This issue can be resolved by using data or sensor fusion techniques, such as IDFM, in which the high-spatial-resolution Landsat and the high-temporal-resolution MODIS images are fused and analysed by a suite of regression models to optimally produce synthetic images with both high spatial and temporal resolution. Analysis of the results using four statistical indices confirmed that the genetic programming model can accurately estimate the spatial and temporal variations of TOC concentrations in a small lake. The model entails a slight bias towards overestimating TOC, and it requires cloud-free input data for the lake. The IDFM efforts lead to the reconstruction of the spatiotemporal TOC distributions in a lake in support of healthy drinking water treatment.

Spatiotemporal pattern validation of chlorophyll-a concentrations in Lake Okeechobee, Florida, using a comparative MODIS image mining approach

A comparative analysis was conducted using three types of data-mining models produced from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra Surface Reflectance 1-day or 8-day composite images to estimate chlorophyll-a (chl-a) concentrations in Lake Okeechobee, Florida. To understand the pros and cons of these three models, a genetic programming (GP) model was compared to an artificial neural network (ANN) model and multiple linear regression (MLR) model with respect to two different data sets related to model formulation. The first data set included the MODIS Terra bands from 1 to 7; the second data set extended the first data set by adding environmental parameters such as Secchi disc depth (SDD), total suspended solids (TSS), wind speed, water level, rainfall and air temperature collected around the lake in 2003 and 2004. The GP algorithm, which has an advantage in machine learning allowing us to select the appropriate input parameters that significantly impact the prediction accuracy, outperformed the other two models based on four statistical indices. Specifically, the GP modelling outputs revealed interesting determinations of chl-a concentrations for MODIS bands 3, 5, 6 and 7, corresponding to wavelengths 459–479, 1230–1250, 1628–1652 and 2105–2155 nm, respectively. The number of training data points is limited; therefore, the inclusion of additional environmental variables cannot improve the prediction accuracy of the GP-derived chl-a concentrations.

Comparative Sensor Fusion Between Hyperspectral and Multispectral Satellite Sensors for Monitoring Microcystin Distribution in Lake Erie

Urban growth and agricultural production have caused an influx of nutrients into Lake Erie, leading to eutrophication in the water body. These conditions result in the formation of algal blooms, some of which are toxic due to the presence of Microcystis (a cyanobacteria), which produces the hepatotoxin microcystin. The hepatotoxin microcystin threatens human health and the ecosystem, and it is a concern for water treatment plants using the lake water as a tap water source. This study demonstrates the prototype of a near real-time early warning system using integrated data fusion and mining (IDFM) techniques with the aid of both hyperspectral (MERIS) and multispectral (MODIS and Landsat) satellite sensors to determine spatiotemporal microcystin concentrations in Lake Erie. In the proposed IDFM, the MODIS images with high temporal resolution are fused with the MERIS and Landsat images with higher spatial resolution to create synthetic images on a daily basis. The spatiotemporal distributions of microcystin within western Lake Erie were then reconstructed using the band data from the fused products with machine learning or data mining techniques such as genetic programming (GP) models. The performance of the data mining models derived using fused hyperspectral and fused multispectral sensor data are quantified using four statistical indices. These data mining models were further compared with traditional two-band models in terms of microcystin prediction accuracy. This study confirmed that GP models outperformed traditional two-band models, and additional spectral reflectance data offered by hyperspectral sensors produces a noticeable increase in the prediction accuracy especially in the range of low microcystin concentrations.

Comparative data mining analysis for information retrieval of MODIS images: monitoring lake turbidity changes at Lake Okeechobee, Florida

In the remote sensing field, a frequently recurring question is: Which computational intelligence or data mining algorithms are most suitable for the retrieval of essential information given that most natural systems exhibit very high non-linearity. Among potential candidates might be empirical regression, neural network model, support vector machine, genetic algorithm/genetic programming, analytical equation, etc. This paper compares three types of data mining techniques, including multiple non-linear regression, artificial neural networks, and genetic programming, for estimating multi-temporal turbidity changes following hurricane events at Lake Okeechobee, Florida. This retrospective analysis aims to identify how the major hurricanes impacted the water quality management in 2003-2004. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day composite imageries were used to retrieve the spatial patterns of turbidity distributions for comparison against the visual patterns discernible in the in-situ observations. By evaluating four statistical parameters, the genetic programming model was finally selected as the most suitable data mining tool for classification in which the MODIS band 1 image and wind speed were recognized as the major determinants by the model. The multi-temporal turbidity maps generated before and after the major hurricane events in 2003-2004 showed that turbidity levels were substantially higher after hurricane episodes. The spatial patterns of turbidity confirm that sediment-laden water travels to the shore where it reduces the intensity of the light necessary to submerged plants for photosynthesis. This reduction results in substantial loss of biomass during the post-hurricane period.

Experimental testing and modeling analysis of solute mixing at water distribution pipe junctions.

Flow dynamics at a pipe junction controls particle trajectories, solute mixing and concentrations in downstream pipes. The effect can lead to different outcomes of water quality modeling and, hence, drinking water management in a distribution network. Here we have investigated solute mixing behavior in pipe junctions of five hydraulic types, for which flow distribution factors and analytical equations for network modeling are proposed. First, based on experiments, the degree of mixing at a cross is found to be a function of flow momentum ratio that defines a junction flow distribution pattern and the degree of departure from complete mixing. Corresponding analytical solutions are also validated using computational-fluid-dynamics (CFD) simulations. Second, the analytical mixing model is further extended to double-Tee junctions. Correspondingly the flow distribution factor is modified to account for hydraulic departure from a cross configuration. For a double-Tee(A) junction, CFD simulations show that the solute mixing depends on flow momentum ratio and connection pipe length, whereas the mixing at double-Tee(B) is well represented by two independent single-Tee junctions with a potential water stagnation zone in between. Notably, double-Tee junctions differ significantly from a cross in solute mixing and transport. However, it is noted that these pipe connections are widely, but incorrectly, simplified as cross junctions of assumed complete solute mixing in network skeletonization and water quality modeling. For the studied pipe junction types, analytical solutions are proposed to characterize the incomplete mixing and hence may allow better water quality simulation in a distribution network.

Evaluation of Climate Change Impact on Drinking Water Treatment Plant Operation

AbstractThis paper describes a technique for evaluating the impact of climate change on drinking water treatment operations and for applying engineering principles to minimize those impacts. The U.S. Environmental Protection Agency (USEPA) Water Treatment Plant model was modified, validated, and applied to a case study based on the Greater Cincinnati Water Works’ Richard Miller treatment plant to provide quantitative measures of these impacts. Multivariate Monte Carlo experiments were executed to simulate and track performance of the Miller plant subject to nine jointly distributed source water quality parameters under both current and potential future hydrologic conditions. Results from the case study indicate a risk that finished water may exceed critical total organic carbon (TOC) levels, leading to potential violations of disinfection by-product regulations under plausible future scenarios. The future risk, however, can be managed with operational adjustments at the water treatment plant, such as incr...

Developing the remote sensing-based early warning system for monitoring TSS concentrations in Lake Mead

Adjustment of the water treatment process to changes in water quality is a focus area for engineers and managers of water treatment plants. The desired and preferred capability depends on timely and quantitative knowledge of water quality monitoring in terms of total suspended solids (TSS) concentrations. This paper presents the development of a suite of nowcasting and forecasting methods by using high-resolution remote-sensing-based monitoring techniques on a daily basis. First, the integrated data fusion and mining (IDFM) technique was applied to develop a near real-time monitoring system for daily nowcasting of the TSS concentrations. Then a nonlinear autoregressive neural network with external input (NARXNET) model was selected and applied for forecasting analysis of the changes in TSS concentrations over time on a rolling basis onward using the IDFM technique. The implementation of such an integrated forecasting and nowcasting approach was assessed by a case study at Lake Mead hosting the water intake for Las Vegas, Nevada, in the water-stressed western U.S. Long-term monthly averaged results showed no simultaneous impact from forest fire events on accelerating the rise of TSS concentration. However, the results showed a probable impact of a decade of drought on increasing TSS concentration in the Colorado River Arm and Overton Arm. Results of the forecasting model highlight the reservoir water level as a significant parameter in predicting TSS in Lake Mead. In addition, the R-squared value of 0.98 and the root mean square error of 0.5 between the observed and predicted TSS values demonstrates the reliability and application potential of this remote sensing-based early warning system in terms of TSS projections at a drinking water intake.

Exploring the effects of population growth on future land use change in the Las Vegas Wash watershed: an integrated approach of geospatial modeling and analytics

The Las Vegas Valley metropolitan area is one of the fastest growing areas in the southwestern United States. The rapid urbanization has presented many environmental challenges. For instance, as population growth and urbanization continue, the supply of sufficient clean water will become a concern. In addition, the area is also experiencing the longest drought in history, and the volume of water storage in Lake Mead, the main fresh water supply for the entire region, has been reduced greatly. The water quality in the main stem of the Las Vegas Wash (LVW) and Lake Mead may also be significantly affected. In order to develop effective sustainable management plans, the very first step is to predict the plausible future urbanization and land use patterns. This paper presents an approach to predict the future land use pattern at the LVW watershed using a Markov cellular automata model. The multi-criteria evaluation was used to couple population density as a variable depicting the driving force of urbanization in the model. Moreover, landscape metrics were used to analyze land use changes in order to better understand the dynamics of urban development in the LVW watershed. The predicted future land use maps for the years 2030 and 2050 show substantial urban development in the area, much of which are located in areas sensitive to source water protections. The results of the analysis provide valuable information for local planners and policy makers, assisting their efforts in constructing alternative sustainable urban development schemes and environmental management strategies.