Development and Application of Suspect and Nontarget Screening to Characterize Organic Micropollutants in Aquatic Environments of New York State

Organic micropollutants (OMPs) have presented a global challenge to water resources management due to concerns over their adverse impacts on aquatic biota and human health at low exposure concentrations (e.g., at ng/L to μg/L levels in aquatic systems). OMPs encompass an extensive array of synthetic organic compounds (e.g., pharmaceuticals, pesticides, personal care products, household chemicals, industrial additives) and their transformation products. My research has been centered around establishing analytical methods based on liquid chromatography-high-resolution mass spectrometry (LC-HRMS), with a focus on the development and application of suspect and nontarget screening workflows for the identification and prioritization of OMPs in inland lakes, streams, and urban wastewater in New York State. In Chapter 1, I collaborated with volunteers from the Citizens Statewide Lake Assessment Program and scientists at the Upstate Freshwater Institute to conduct the first statewide investigation of OMP occurrence in New York inland lakes. Through this project, I developed a suspect screening method based on LC-HRMS to identify and quantify 65 OMPs in 314 lake water samples collected by volunteers from 111 lakes, ponds, and reservoirs across the state. I also performed partial least squares regression and multiple linear regression analyses to prioritize total dissolved nitrogen, specific conductance, and a wastewater-derived fluorescent organic matter component as the best combination of explanatory predictors for the inter-lake variability in OMP occurrence patterns. I further applied the exposure-activity ratio approach to estimate the potential for biological effects associated with OMPs. My work demonstrated that engaging an established network of citizen volunteers offers a viable approach to increasing the spatiotemporal coverage of OMP monitoring while raising public awareness of their prevalence. In Chapter 2, I collaborated with Drs. Christa Kelleher and Rebecca Schewe to investigate the occurrence patterns of OMPs in streams draining mixed-use watersheds in central New York. I combined the use of polar organic chemical integrative samplers (POCIS) with suspect screening and nontarget screening based on LC-HRMS to identify and quantify 133 OMPs in samples collected from 20 stream sites over two sampling seasons. I also performed hierarchical clustering to establish the co-occurrence profiles of OMPs in connection with watershed attributes indicative of anthropogenic influences. I further evaluated the feasibility of deploying POCIS for estimating daily average loads of OMPs and their potential for biological effects in streams via screening-level risk assessments. My work supported the prospect of combining passive sampling with high-resolution accurate mass screening for the multi-watershed characterization of OMP contamination status in streams. In Chapter 3, I collaborated with colleagues from the School of Public Health to pursue one of the earliest wastewater-based epidemiology studies on population-level substance use during the COVID-19 pandemic. I developed and validated an online solid-phase extraction method for sample preconcentration before LC-HRMS analyses to achieve rapid screening of health and lifestyle-related substances in urban wastewater. I applied this method to quantify the levels of 26 pharmaceuticals and lifestyle chemicals in wastewater influent samples collected from six sewersheds in central New York over a period spanning the rising and falling of COVID-19 prevalence. I back-calculated the population-level consumption rates of antidepressants, antiepileptics, antihistamines, antihypertensives, and central nervous system stimulants and further identified their co-variation with disparities in household income, marital status, and/or age of the contributing populations as well as the detection frequency of SARS-CoV-2 RNA in wastewater and the COVID-19 test positivity within the sewersheds. My work highlighted the utility of high-throughput wastewater analysis for assessing substance use patterns during a public health crisis such as COVID-19

Occurrence and Mass Flows of Organic Micropollutants in the Onondaga Lake – Three Rivers System

Organic micropollutants (OMPs) are synthetic and naturally occurring organic compounds that may pose long-term ecotoxicological risks to the aquatic life occur at low levels. This work seeks to characterize the spatiotemporal occurrence and mass flows of OMPs in the Onondaga Lake-Three Rivers system in central New York. In collaboration with the Upstate Freshwater Institute, multiple batches of water samples were collected from the lake-river system between June and October 2017 and analyzed for OMPs using a suspect screening workflow developed on liquid chromatography-high resolution mass spectrometry. To date, a total of 52, 31, and 37 OMPs were identified and quantified in Onondaga Lake, its four major tributaries, and the Three Rivers, respectively. Lamotrigine, estradiol, benzotriazole, methyl benzotriazole, sucralose, and atrazine were measured in every sample, suggesting their ubiquitous presence in the lake-river system. Over the study period, the horizontal concentration profiles of OMPs in Onondaga Lake showed relatively consistent patterns, but the vertical distribution of OMPs in the lake was influenced by thermal stratification and wastewater discharge from a regional WWTP serving the Syracuse metropolitan area. Specifically, OMPs derived from point source wastewater discharge exhibited peak concentrations in the thermocline in July 2017, but such phenomenon disappeared in October 2017, likely due to changes in lake stratification. OMPs were generally detected at lower levels in the lake tributaries and the Three Rivers, suggesting diffuse inputs from agricultural activities or irregular wastewater discharge. Further calculations of the OMP mass flow revealed that the WWTP might account for up to 67-86% of the OMP mass flow entering the lake, which is in line with its high percentage of wastewater inflow. Onondaga Lake itself contributed 12-24% of the OMP mass flow entering the Three Rivers, confirming its role as a regionally important source of OMPs

A family of Barzilai-Borwein steplengths from the viewpoint of scaled total least squares

The Barzilai-Borwein (BB) steplengths play great roles in practical gradient methods for solving unconstrained optimization problems. Motivated by the observation that the two well-known BB steplengths correspond to the ordinary and the data least squares, respectively, we present a family of BB steplengths from the viewpoint of scaled total least squares. Numerical experiments demonstrate that a high performance can be received by a carefully-selected BB steplength in the new family.Comment: 13 pages, 2figure

Lagrangian-based methods in convex optimization: prediction-correction frameworks with non-ergodic convergence rates

Lagrangian-based methods are classical methods for solving convex optimization problems with equality constraints. We present novel prediction-correction frameworks for such methods and their variants, which can achieve $O(1/k)$ non-ergodic convergence rates for general convex optimization and $O(1/k^2)$ non-ergodic convergence rates under the assumption that the objective function is strongly convex or gradient Lipschitz continuous. We give two approaches ($updating~multiplier~once$ $or~twice$) to design algorithms satisfying the presented prediction-correction frameworks. As applications, we establish non-ergodic convergence rates for some well-known Lagrangian-based methods (esp., the ADMM type methods and the multi-block ADMM type methods)

A faster prediction-correction framework for solving convex optimization problems

He and Yuan's prediction-correction framework [SIAM J. Numer. Anal. 50: 700-709, 2012] is able to provide convergent algorithms for solving convex optimization problems at a rate of $O(1/t)$ in both ergodic and pointwise senses. This paper presents a faster prediction-correction framework at a rate of $O(1/t)$ in the non-ergodic sense and $O(1/t^2)$ in the pointwise sense, {\it without any additional assumptions}. Interestingly, it provides a faster algorithm for solving {\it multi-block} separable convex optimization problems with linear equality or inequality constraints

Nano-Porous Light-Emitting Silicon Chip as a Potential Biosensor Platform

Nano-porous silicon (PS) offers a potential platform for biosensors with benefits both in terms of light emission and the large functional surface area. A light emitting PS chip with a stable and functional surface was fabricated in our laboratory. When protein was deposited on it, the light emission was reduced in proportion to the protein concentration. Based on this property, we developed a rudimentary demonstration of a label-free sensor to detect bovine serum albumin (BSA). A serial concentration of BSA was applied to the light chip and the reduction in light emission was measured. The reduction of the light intensity was linearly related to the concentration of the BSA at concentrations below 10-5 M. The detection limit was 8×10-9 M

Organization Structure, Operation Mode and Management Mechanism in Confucius Institute of Barcelona

本研究以运营方式独特的西班牙巴塞罗那 孔子学院为例，聚焦孔院建制、运营模式和机 制，从五个方面深入剖析其成功的经验和面临 的问题以及解决方案。本文涉及孔院制度建设、 教学、文化学术活动、与政府的关系和融入大 学等问题，旨在总结其成功经验，供其他孔院， 尤其是西语国家孔院借鉴，最终以期对汉语和 中国文化传播有所裨益。 Focusing on the organizational system, operation mode and mechanism of Confucius Institute of Barcelona, this paper explores the successful experience, current problems and solutions of Confucius Institute of Barcelona. This study involves the issues of administration, Chinese language teaching, cultural activity, relations and integration with the local government, aiming at summarizing the successful experience of Confucius Institute of Barcelona in order to provide reference for the construction of Confucius Institutes, especially the ones in Spanish-speaking countries and further spreading the Chinese language and culture globall

CDSD: Chinese Dysarthria Speech Database

We present the Chinese Dysarthria Speech Database (CDSD) as a valuable resource for dysarthria research. This database comprises speech data from 24 participants with dysarthria. Among these participants, one recorded an additional 10 hours of speech data, while each recorded one hour, resulting in 34 hours of speech material. To accommodate participants with varying cognitive levels, our text pool primarily consists of content from the AISHELL-1 dataset and speeches by primary and secondary school students. When participants read these texts, they must use a mobile device or the ZOOM F8n multi-track field recorder to record their speeches. In this paper, we elucidate the data collection and annotation processes and present an approach for establishing a baseline for dysarthric speech recognition. Furthermore, we conducted a speaker-dependent dysarthric speech recognition experiment using an additional 10 hours of speech data from one of our participants. Our research findings indicate that, through extensive data-driven model training, fine-tuning limited quantities of specific individual data yields commendable results in speaker-dependent dysarthric speech recognition. However, we observe significant variations in recognition results among different dysarthric speakers. These insights provide valuable reference points for speaker-dependent dysarthric speech recognition.Comment: 9 pages, 3 figure