Formation, Speciation, and Temporal Variability of DBPs in Drinking Water Distribution Systems in the Context of ASR Operations and Extended Storage Periods
Chemosphere（2024）
Department of Civil and Environmental Engineering
Abstract
As climate change induces changes in water quality and available water quantity of drinking water supply sources, the final product water quality changes in terms of trace organics including disinfection byproducts (DBPs) formed during water treatment. In this study, the seasonal variability and speciation of DBPs across nine sample sites within a drinking water distribution system serving ∼400k people over a one-year period was investigated considering the governing parameters of water quality and treatment/transport/storage of finished water. The system considered treats surface water from a river and practices aquifer storage and recovery to address seasons water availability changes. Eighty-eight (88) sample sets were collected and held for 6-months in the laboratory to simulate extended storage scenarios associated with ASR operations, and each was analyzed at 9 different timesteps for concentration and speciation of chlorinated DBPs. Samples from groundwater influenced sites exhibited significantly lower total organic carbon (TOC) compared to other sites from the river source, and also were observed to have the lowest DBP formation. Three sites exceeded the Maximum Contaminant Level (MCL) for four total trihalomethanes (THM4) within 30-60 days of storage. Chloroform was the predominant THM4 species, even in groundwater-influenced locations, whereas di- and tri-chloroacetic acid (DCA and TCA) were the most prevalent haloacetic acids (HAA5). Extended water age at one site, coupled with low initial chlorine concentrations exhibited higher initial THM4 concentrations and flat DBP formation curves. The study results provide new insights into DBP occurrence and fate in drinking water distribution systems which consider water storage such as in ASR.
MoreTranslated text
Key words
disinfection byproducts,aquifer storage and recovery,groundwater,surface water,total organic carbon
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined