Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process.

As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.

Advancing statistical treatment of photolocomotor behavioral response study data.

Fish photolocomotor behavioral response (PBR) studies have become increasingly prevalent in pharmacological and toxicological research to assess the environmental impact of various chemicals. There is a need for a standard, reliable statistical method to analyze PBR data. The most common method currently used, univariate analysis of variance (ANOVA), does not account for temporal dependence in observations and leads to incomplete or unreliable conclusions. Repeated measures ANOVA, another commonly used method, has drawbacks in its interpretability for PBR study data. Because each observation is collected continuously over time, we instead consider each observation to be a function and apply functional ANOVA (FANOVA) to PBR data. Using the functional approach not only accounts for temporal dependency but also retains the full structure of the data and allows for straightforward interpretation in any subregion of the domain. Unlike the traditional univariate and repeated measures ANOVA, the FANOVA that we propose is nonparametric, requiring minimal assumptions. We demonstrate the disadvantages of univariate and repeated measures ANOVA using simulated data and show how they are overcome by applying FANOVA. We then apply one-way FANOVA to zebrafish data from a PBR study and discuss how those results can be reproduced for future PBR studies.

A Holistic Evaluation of Multivariate Statistical Process Monitoring in a Biological and Membrane Treatment System.

Unsupervised process monitoring for fault detection and data cleaning is underdeveloped for municipal wastewater treatment plants (WWTPs) due to the complexity and volume of data produced by sensors, equipment, and control systems. The goal of this work is to extensively test and tune an unsupervised process monitoring method that can promptly identify faults in a full-scale decentralized WWTP prior to significant system changes. Adaptive dynamic principal component analysis (AD-PCA) is a dimension reduction method modified to address autocorrelation and nonstationarity in multivariate processes and is evaluated in this work for its ability to continuously detect drift, shift, and spike faults. For spike faults, univariate drift faults, and multivariate shift faults, implementing AD-PCA on data that are subset by treatment processes and operating states with significant differences in covariates and whose model parameters use week-long training windows, moderate cumulative variance, and a high threshold for detection was found to detect faults prior to existing operational thresholds. To improve the consistency with which the AD-PCA method detects out-of-control conditions in real time, additional work is needed to remove outliers prior to model fitting and to detect multivariate drift faults in which the covariates change slowly.

Evidence of multi-decadal behavior and ecosystem-level changes revealed by reconstructed lifetime stable isotope profiles of baleen whale earplugs.

Biological time series datasets provide an unparalleled opportunity to investigate regional and global changes in the marine environment. Baleen whales are long-lived sentinel species and an integral part of the marine ecosystem. Increasing anthropogenic terrestrial and marine activities alter ocean systems, and such alterations could change foraging and feeding behavior of baleen whales. In this study, we analyzed δ13C and δ15N of baleen whale earplugs from three different species (N = 6 earplugs, n = 337 laminae) to reconstruct the first continuous stable isotope profiles with a six-month resolution. Results of our study provide an unprecedented opportunity to assess behavioral as well as ecological changes. Abrupt shifts and temporal variability observed in δ13C and δ15N profiles could be indicative of behavior change such as shift in foraging location and/or trophic level in response to natural or anthropogenic disturbances. Additionally, five out of six individuals demonstrated long-term declining trends in δ13C profiles, which could suggest influence of emission of depleted 13CO2 from fossil fuel combustion referred to as the Suess effect. After adjusting the δ13C values of earplugs for the estimated Suess effect and re-evaluating δ13C profiles, significant decline in δ13C values as well as different rate of depletion suggest contribution of other sources that could impact δ13C values at the base of the food web.

Effects of Environmentally Relevant Concentration Exposure Profiles on Polar Organic Chemical Integrative Sampler (POCIS) Sampling Rates.

Polar organic chemical integrative sampler (POCIS) is a passive sampling device that offers many advantages over traditional discrete sampling methods, but quantitative time-weighted average (TWA) concentrations rely heavily on the robustness of sampling rates. The effects of changing chemical concentration exposures on POCIS sampling rates and its ability to operate in an integrative regime were investigated for 12 pesticides across a range of environmentally relevant concentrations. In five independent 21-day experiments, POCIS devices were exposed to these compounds at constant concentrations ranging from 3 to 60 µg/L and multiple pulsed concentrations with maximum peaks ranging from 5 to 150 µg/L (TWA concentrations = 3 to 92 µg/L). For the 21-day exposures to constant and pulsed concentrations, there were no significant differences in the POCIS sampling rates between corresponding TWA concentrations. Similarly, there was no significant effect on POCIS ability to operate in an integrative regime. However, loss of linearity was visible for some replicates when exposed to higher pulsed concentrations over an extended period. Modeling and Freundlich isotherms did not predict sorbent saturation, but the extraction and reconstitution protocol likely contributed to atrazine dissolution and subsequent underestimation of sorbed chemical mass when HLB adsorption exceeded 400 µg.

Suspect and non-target screening of acutely toxic Prymnesium parvum.

Harmful algal blooms (HABs) are increasing in frequency, magnitude, and duration around the world. Prymnesium parvum is a HAB species known to cause massive fish kills, but the toxin(s) it produces contributing to this acute toxicity to fish have not been confirmed. In the present study, a 2 × 2 factorial design was employed to examine influences of salinity (2.4 or 5 ppt) and nutrient limitation (f/2 or f/8) on P. parvum acute toxicity to fish and produced molecules. Acute toxicity (LC50) of these cultures, following a 48-h mortality assay, ranged from 10,213 to 96,816 cells mL-1. Non-targeted analysis was performed using liquid chromatography high-resolution mass spectrometry (LC-HRMS) to investigate compounds contributing to the differential toxicological responses. When P. parvum elicited toxicity to fish, suspect screening confirmed the presence of several prymnesins, and the peak area of PRM-A (3 Cl; prymnesin2aglycone) was significantly (p < 0.05) and positively related to acute toxicity. In addition, a non-targeted approach to highlighting peaks that differ between two chemical fingerprints was developed, termed a relative difference plot, and used to search for peaks co-varying with P. parvum induced acute toxicity to fish. Several peaks were highlighted along with the prymnesins identified through suspect screening when acute toxicity to fish was observed.

Copula-based semiparametric models for spatiotemporal data.

The joint analysis of spatial and temporal processes poses computational challenges due to the data's high dimensionality. Furthermore, such data are commonly non-Gaussian. In this paper, we introduce a copula-based spatiotemporal model for analyzing spatiotemporal data and propose a semiparametric estimator. The proposed algorithm is computationally simple, since it models the marginal distribution and the spatiotemporal dependence separately. Instead of assuming a parametric distribution, the proposed method models the marginal distributions nonparametrically and thus offers more flexibility. The method also provides a convenient way to construct both point and interval predictions at new times and locations, based on the estimated conditional quantiles. Through a simulation study and an analysis of wind speeds observed along the border between Oregon and Washington, we show that our method produces more accurate point and interval predictions for skewed data than those based on normality assumptions.

Data-driven performance analyses of wastewater treatment plants: A review.

Recent advancements in data-driven process control and performance analysis could provide the wastewater treatment industry with an opportunity to reduce costs and improve operations. However, big data in wastewater treatment plants (WWTP) is widely underutilized, due in part to a workforce that lacks background knowledge of data science required to fully analyze the unique characteristics of WWTP. Wastewater treatment processes exhibit nonlinear, nonstationary, autocorrelated, and co-correlated behavior that (i) is very difficult to model using first principals and (ii) must be considered when implementing data-driven methods. This review provides an overview of data-driven methods of achieving fault detection, variable prediction, and advanced control of WWTP. We present how big data has been used in the context of WWTP, and much of the discussion can also be applied to water treatment. Due to the assumptions inherent in different data-driven modeling approaches (e.g., control charts, statistical process control, model predictive control, neural networks, transfer functions, fuzzy logic), not all methods are appropriate for every goal or every dataset. Practical guidance is given for matching a desired goal with a particular methodology along with considerations regarding the assumed data structure. References for further reading are provided, and an overall analysis framework is presented.

Refining In Vitro Toxicity Models: Comparing Baseline Characteristics of Lung Cell Types.

There is an ever-evolving need in the field of in vitro toxicology to improve the quality of experimental design, ie, from ill-defined cell cultures to well-characterized cytotoxicological models. This evolution is especially important as environmental health scientists begin to rely more heavily on cell culture models in pulmonary toxicology studies. The research presented in this study analyzes the differences and similarities of cells derived from two different depths of the human lung with varying phenotypes. We compared cell cycle and antioxidant-related mRNA and protein concentrations of primary, transformed, and cancer-derived cell lines from the upper and lower airways. In all, six of the most commonly used cell lines reported in in vitro toxicology research papers were included in this study (ie, PTBE, BEAS-2B, A549, PSAE, Met-5A, and Calu-3). Comparison of cell characteristics was accomplished through molecular biology (q-PCR, ELISA, and flow cytometry) and microscopy (phase and fluorescence) techniques as well as cellular oxidative stress endpoint analyses. After comparing the responses of each cell type using statistical analyses, results confirmed significant differences in background levels of cell cycle regulators, inherent antioxidant capacity, pro-inflammatory status, and differential toxicological responses. The analyzed data improve our understanding of the cell characteristics, and in turn, aids in more accurate interpretation of toxicological results. Our conclusions suggest that in vitro toxicology studies should include a detailed cell characterization component in published papers.

Statistical Exposé of a Multiple-Compartment Anaerobic Reactor Treating Domestic Wastewater.

Mainstream anaerobic treatment of domestic wastewater is a promising energy-generating treatment strategy; however, such reactors operated in colder regions are not well characterized. Performance data from a pilot-scale, multiple-compartment anaerobic reactor taken over 786 days were subjected to comprehensive statistical analyses. Results suggest that chemical oxygen demand (COD) was a poor proxy for organics in anaerobic systems as oxygen demand from dissolved inorganic material, dissolved methane, and colloidal material influence dissolved and particulate COD measurements. Additionally, univariate and functional boxplots were useful in visualizing variability in contaminant concentrations and identifying statistical outliers. Further, significantly different dissolved organic removal and methane production was observed between operational years, suggesting that anaerobic reactor systems may not achieve steady-state performance within one year. Last, modeling multiple-compartment reactor systems will require data collected over at least two years to capture seasonal variations of the major anaerobic microbial functions occurring within each reactor compartment.

Using geochemical indicators to distinguish high biogeochemical activity in floodplain soils and sediments.

A better understanding of how microbial communities interact with their surroundings in physically and chemically heterogeneous subsurface environments will lead to improved quantification of biogeochemical reactions and associated nutrient cycling. This study develops a methodology to predict potential elevated rates of biogeochemical activity (microbial "hotspots") in subsurface environments by correlating microbial DNA and aspects of the community structure with the spatial distribution of geochemical indicators in subsurface sediments. Multiple linear regression models of simulated precipitation leachate, HCl and hydroxylamine extractable iron and manganese, total organic carbon (TOC), and microbial community structure were used to identify sample characteristics indicative of biogeochemical hotspots within fluvially-derived aquifer sediments and overlying soils. The method has been applied to (a) alluvial materials collected at a former uranium mill site near Rifle, Colorado and (b) relatively undisturbed floodplain deposits (soils and sediments) collected along the East River near Crested Butte, Colorado. At Rifle, 16 alluvial samples were taken from 8 sediment cores, and at the East River, 46 soil/sediment samples were collected across and perpendicular to 3 active meanders and an oxbow meander. Regression models using TOC and TOC combined with extractable iron and manganese results were determined to be the best fitting statistical models of microbial DNA (via 16S rRNA gene analysis). Fitting these models to observations in both contaminated and natural floodplain deposits, and their associated alluvial aquifers, demonstrates the broad applicability of the geochemical indicator based approach.

Variability of trace organic chemical concentrations in raw wastewater at three distinct sewershed scales.

The site-specific daily fluctuations and scale-dependent variability of influent water quality, particularly concentrations of trace organic chemicals (TOrCs), have not yet been well described. In this study, raw wastewater from three distinct sewershed scales was sampled including a centralized wastewater treatment facility in Boulder, Colorado (population ~125,000) and two decentralized wastewater catchments in Golden, Colorado (clustered system population 400, and septic system population 32). Each site was sampled hourly for 26 h and samples were subsequently analyzed in triplicate for 32 TOrCs using liquid chromatography with tandem mass spectrometry and stable isotope dilution. Detection frequency (DF) of the various TOrCs was positively correlated with sewershed size with the greatest DF of the targeted TOrCs at the Boulder site and with decreasing DF with decreasing sewershed size. Site-specific fluctuations were both scale and compound-specific. The 11 TOrCs detected greater than 75% of the time across all three sites were used to further investigate and quantify variability and to develop a statistical model to investigate the flow-dependence and time-dependence of TOrC variability. Sewershed scale was inversely correlated to variability with coefficients of variation ranging from 0.24 to 0.96, 0.39 to 2.22, and 0.32 to 3.93 for the Boulder, cluster, and septic sites, respectively. A significant linear relationship was observed between concentration and flow and concentration and the concentration at prior time points for most TOrCs at the Boulder site. This suggests less variable influent concentrations result from dispersion and mixing in the conveyance system and a larger number of discrete inputs. A notable exception was the chlorinated flame retardant TCPP, which is likely associated with a high concentration, low-flow industrial input. A significant linear relationship between flow and concentration and sequential time points was not common at the decentralized sites. Scientists and engineers developing decentralized treatment systems must consider a larger range of influent qualities, particularly with respect to TOrCs.