Elsevier

Chemosphere

Volume 243, March 2020, 125367
Chemosphere

Development of an integral strategy for non-target and target analysis of site-specific potential contaminants in surface water: A case study of Dianshan Lake, China

https://doi.org/10.1016/j.chemosphere.2019.125367Get rights and content

Highlights

  • Provided abundant information for the identification of potential contaminants.

  • Rapid qualitative identification and quantitative analysis of high-risk compounds.

  • Simple and rapid screening and preliminary detection of site-specific contaminants.

Abstract

Surface water contains a large number of potential pollutants and their transformation products, which cannot be discovered by normal target analysis alone. To detect site-specific and unknown contaminants in the environment, we established an integral analytical strategy based on liquid chromatography-high resolution mass spectrometry (LC-HRMS) combined with data processing using specific software (Compound Discoverer 3.0). In this case study of Dianshan Lake, 95 potential contaminants were tentatively identified and ranked by the scoring system. Then, the 95 compounds were categorized into 4 subgroups: pesticides, drugs, plastic additives and surfactants. To determine the sources and distribution of those pollutants, 4 heat maps were developed based on the sum of peak areas of respective categories. In addition, 19 substances with high exposure risk among the 95 compounds tentatively identified were confirmed and quantified. In the present study, the analytical strategy with non-target screening followed by target analysis demonstrated that pesticides and plastic additives are the two dominant types of contaminants in Dianshan Lake. High accuracy and high-resolution data combined with integrated software provided abundant information for the identification of a wide range of potential contaminants in the environment. This approach can be a useful tool for the simple and rapid screening and tentative detection of site-specific contaminants.

Introduction

Over the past few years, the numbers and types of emerging and unregulated pollutants (Gogoi et al., 2018; Hug et al., 2014; Jiang et al., 2013; Wilkinson et al., 2017) detected in the environment have been increasing, including pharmaceuticals (Danner et al., 2019; Quesada et al., 2019; Vatovec et al., 2017), endocrine disrupting compounds (EDCs) (Vilela et al., 2018), surfactants (Becker et al., 2008), biocides, pesticides (Lan et al., 2019), personal care products (Montes-Grajales et al., 2017) and their transformation products (TPs) (Susanne, 2009). This situation brings new challenges to the work of environmental pollution monitoring and ecological risk assessment. Improvements in sample pretreatment and analytical methods are required to evaluate those novel chemicals in the environment comprehensively. Target analysis (Bletsou et al., 2015) (with specific targets and requirements for reference standards) is adopted in most environmental monitoring work by regulatory and enforcement agencies. Obviously, a complete target analysis cannot be performed for all compounds of potential environmental relevance because the purchase of hundreds or thousands of reference standards is time- and money-consuming and is not feasible. Thus, most present target analysis methods focus on a specific class of pollutants that are regarded as being responsible for the most significant share of human health and economic risk. Target analysis may ignore those site-specific and potential ecotoxicologically related compounds in the environment. In addition, those emerging chemicals can change into their TPs by biotic and abiotic transformation processes after release into the environment. Some unknown TPs may be more persistent and toxic than their precursors in the environment, but target analysis can only determine the concentration levels of these precursors.

In recent years, the evolution of high-resolution mass spectrometry (HRMS) coupled to gas or liquid chromatography (GC or LC, respectively) has opened a window to detect hundreds of contaminants without pre-selection of analytes. Suspect screening (Campos-Manas et al., 2019; Schymanski et al., 2014) with LC-HRMS usually begins with building a database of predicted compounds suspected of being present in environmental samples. Those suspected compounds can be selected via reports and literature surveys. The database contains the accurate mass and isotope information of those suspected chemicals for tentative identification. In contrast to matching mass spectral peaks purposefully in suspect screening, non-target screening involves the investigation of all remaining masses detected in the samples with no specific restrictions or a-priori information of target chemicals (Samanipour et al., 2019; Schymanski et al., 2014). Then, in some cases, reference standards are purchased to confirm the identification and quantify the compounds of interest. Therefore, suspect and non-target screening are more suitable for site-specific assessment of unknown pollution levels than target analysis.

Because extensive libraries of comparable electron ionization (EI) mass spectra are available, non-target screening is more often performed with GC-HRMS (Hernández et al., 2011; Jernberg et al., 2013). However, the range of application is limited because GC-HRMS requires the targets to be relatively non-polar, volatile and thermally stable. Instead, LC-HRMS allows the detection of a wider range of compounds, including those with strong polarity. Since the TPs of pollutants tend to be more hydrophilic than their parent compounds, LC-HRMS is more suitable for the analysis of environmental samples (surface water, wastewater and sediments). Furthermore, that the databases of LC-HRMS mass spectra are growing rapidly improves the chance for a correct and reliable identification of unknown spectra (e.g., the mzCloud database used in present study with over 8450 substances and 2,913,613 MS or MS/MS spectra). The development of the software with automated processes integrated also makes the work of confirmation of the masses screened become less time-consuming.

Currently, several comprehensive analytical strategies for suspect and non-target screening have been developed and applied for environmental sample analysis. Some novel micropollutants were detected based on these strategies in many cases (Campos-Manas et al., 2019; Hug et al., 2014; Schemeth et al., 2019; Susanne, 2009). However, the studies focus on generic non-target screening applied to environmental pollution assessment, and spatiotemporal distributions at a specific location are scarce.

Dianshan Lake is situated in the Yangtze Delta, which is one of the most developed regions in China. As the largest freshwater lake in Shanghai, Dianshan Lake is an important source of drinking water for Shanghai and surrounding areas. Meanwhile, Dianshan Lake serves as an aquaculture base and provides irrigation water for the large agricultural area. In recent decades, rapid economic growth and urban development have caused the lake to become polluted by wastewater and waste from industry, agriculture and households. Moreover, the development of a tourism industry around Dianshan Lake also burdens the ecological protection of Dianshan Lake. It is therefore urgent to understand the level and spatiotemporal distribution of pollutants in Dianshan Lake and to establish a long-term water quality monitoring plan. Therefore, the aims of this study was to use a screening and quantification strategy based on ultra-high performance liquid chromatography-quadrupole-orbitrap mass spectrometry and software Compound Discoverer 3.0 to perform non-target analysis of pollutants in water to prioritize pollutants of environmental relevance and combined with ecological hazard prediction tools that could be contributing to the pollution of the Dianshan lake.

Section snippets

Site description and sample collection

Dianshan Lake (120 53′-121 17′E and 30 59′-31 16′N) is located at the junction of Jiangsu Province, Zhejiang Province and Shanghai. As the largest freshwater lake in Shanghai, Dianshan Lake is characterized by large surface area (62 km2) and shallowness (mean depth = 2.1 m). Dianshan Lake has a complicated inflow and outflow system (a total of 59 inflow and outflow rivers), with the water retention time of approximately 29 days. Most of the runoff sources are from Taihu Lake via rivers such as

First prioritization and source implications

The results from software-aided non-target screening suggested 190,530 (113,954 in positive mode and 76,576 in negative) features with formulas. After manual inspections for peak shape, peak area and fragment patterns as well as exclusion for duplicate substances, 95 compounds were finally identified. (Filtering parameters on peak area are provided in Fig.S2). The prioritization procedures are provided in Table S2. These 95 compounds were divided into four categories: pesticides, drugs,

Conclusions

A wealth of information is available in HRMS (/MS) data, and this study demonstrates that such data can be used to perform fast non-target and target screening of potential pollutants at specific sites, as well as to study the pollution sources and spatial distributions of contaminants. In this study, an SPE-based clean up step through self-packed SPE cartridges was introduced to minimize the false negatives. Additionally, with optimization of pretreatment, both positive and negative analysis

Acknowledgements

This work was supported by National Key Research and Development Project of China (Grant No. 2018YFC1801601).

References (30)

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