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Review

Addressing the Uncertainties in the Environmental Analysis, Modeling, Source and Risk Assessment of Emerging Contaminants

by
Wenxing Zhao
1,2,
Bin Wang
2,3,* and
Gang Yu
4
1
School of Urban Construction, Changzhou University, Changzhou 213164, China
2
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
3
Research Institute for Environmental Innovation (Suzhou), Tsinghua University, Suzhou 215163, China
4
Advanced Interdisciplinary Institute of Environment and Ecology, Beijing Normal University, Zhuhai 519087, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(2), 215; https://doi.org/10.3390/w17020215 (registering DOI)
Submission received: 17 December 2024 / Revised: 10 January 2025 / Accepted: 13 January 2025 / Published: 14 January 2025
(This article belongs to the Section Water Resources Management, Policy and Governance)
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Abstract

:
Emerging contaminants (ECs) have become a growing source of worry for environmental researchers and stakeholders in recent decades. Compared with conventional pollutants, ECs can pose environmental risks even at a trace level. The analysis of ECs is typically significantly more challenging than that of conventional pollutants because of their trace amounts and diverse chemical structures. For sound environmental management, it is necessary to perform a comprehensive study of these pollutants. Global concern has increasingly grown over the occurrence, fate, environmental modeling, and risk assessment of such contaminants. Due to the dearth of knowledge in this area, various uncertainties inevitably exist in the investigation of ECs. Environmental problems cannot be precisely understood due to the ubiquitous uncertainties in environmental research. Uncertainties and their sources have been reviewed in this study, including spatial and temporal variability, uncertainty in sample collection and analysis, uncertainty in environmental modeling, uncertainty in risk assessment, and uncertainty in source characterization. Some suggestions to reduce uncertainties are summarized. An awareness of uncertainty is necessary for us to have a more accurate understanding and contribute to sound environmental decision-making and management. In addition, more work remains to be performed to reveal the uncertainties in the analysis and risk assessment of ECs.

1. Introduction

The uncertainty principle was first proposed by the German physicist Werner Heisenberg in 1927. As a fundamental scientific principle, it states that the position and momentum of particles cannot be determined precisely at the same time. Similarly, “uncertainty” is ubiquitous in the field of environmental research. In many situations, we cannot precisely understand environmental problems due to the ubiquitous uncertainties in environmental research. Unlike Heisenberg uncertainty at the subatomic level, uncertainty in the macroscopic environment is more complicated because various sources may contribute to environmental uncertainty.
With the development of society and the economy, more and more chemicals have been consumed, some of which are released into the environment and pose potential risks to the environment and human health. For aquatic environmental protection, the conventional pollution indicators (e.g., chemical oxygen demand (COD), biochemical oxygen demand (BOD), total phosphorous (TP), and total nitrogen (TN)) have been routinely monitored and controlled in most countries all over the world. With the development of environmental monitoring technologies and the demand for better environmental quality, emerging contaminants (ECs) [1,2], which are not target substances in routine environmental monitoring and regulations but have the potential to enter the environment and cause known or suspected adverse ecological or human health effects, have raised more and more concern among the public and scientists, and include pharmaceuticals and personal care products (PPCPs), endocrine-disrupting chemicals (EDCs), persistent organic pollutants (POPs), microplastics, and so on. The risk that ECs pose to human health and the environment is not yet fully understood and has received much attention in recent years.
More and more ECs at trace levels (several ng/L in natural water bodies) in the environment have been recognized with the rapid development of analytical techniques. Meanwhile, their occurrence, fate, environmental modeling, and risk assessment have aroused the widespread interest of environmental researchers. The occurrence and fate of some ECs have been studied in both surface water [3,4] and wastewater [5,6,7]. Most ECs cannot be completely removed by wastewater treatment plants (WWTPs), and, usually, there are no mandatory discharge standards for these contaminants in WWTP effluent. Environmental models are regarded as useful tools to fill the environmental data gap. Various environmental models have been developed, such as multimedia models [8,9,10], exposure models [11,12], and quantitative structure–activity relationship (QSAR) models [13,14]. When entering the ecosystem, such ECs might be environmentally persistent and bioactive, and their environmental hazards cause widespread concern [15,16].
However, ECs are not as well studied as conventional pollutants at present. In environmental monitoring, conventional pollution indicators (e.g., COD, BOD, TP, and TN) usually have national monitoring standards. In contrast, ECs are not target substances in routine environmental monitoring or regulations. More uncertainty issues inevitably exist in the research of ECs due to the information gap and the complexity of environmental issues [17,18,19,20]. In many situations, we cannot precisely understand their environmental pollution, fate, and risk due to ubiquitous uncertainties.
Both environmental variability and uncertainty are referred to as uncertainty in this study. Uncertainty has often been discussed and analyzed in the environmental modeling of input, structures, and output in earlier work [21,22,23]. However, environmental analysis and sampling have received little attention. Uncertainties, which are rarely recognized, could have the opposite effect. For instance, two multimedia models were used to reverse the classifications of 208 compounds based on their propensity for long-distance transport, which may have resulted in an error in the risk assessment of these organic substances [24]. Understanding the existence of these uncertainties is crucial for environmental researchers and managers. However, a lot of earlier research has not addressed the issue of uncertainty.
Thus, an overall understanding of the uncertainty of ECs is necessary for environmental monitoring, modeling, risk assessment, and chemical management. A convincing result cannot be obtained without considering all aspects of the uncertainties. Based on previous work, this study reviewed the sources of uncertainty in all aspects of research on ECs, including environmental sampling and analysis, modeling, risk assessment, and source characterization. The framework of this study is presented in Figure 1. As illustrated in Figure 1, potential issues may arise during the ecological system study of ECs. Particularly for the possible uncertainties, some analysis needs to be performed. In order to give stakeholders fresh ideas for solving the problem of ECs, it is more conducive to formulate environmental decision-making and environmental management when uncertainties are more thoroughly characterized. This review is mainly aimed at raising awareness among researchers and government managers of ECs, recognizing the issue of uncertainties, and minimizing uncertainties in research as much as possible.

2. Uncertainty in Environmental Sampling and Analysis of ECs

2.1. Spatial and Temporal Variability in the Occurrence of ECs

ECs are not frequently monitored, and there is rarely continuous monitoring in the same area, partly because their detection is costly and time-consuming. Another reason is that most non-conventional pollutant monitoring is not routine work conducted by government agencies but scientific research conducted by environmental researchers. Because novelty is most important for scientific research, monitoring is seldom repeatedly implemented for the same contaminants in the same region. Hence, the monitoring frequency of ECs is usually very low, which will certainly cause uncertainty in our understanding of their occurrence. In many cases, the environmental concentration ranges of ECs are very large [25,26,27]. An order of magnitude increase in PPCP concentration fluctuations was observed even in composite samples in the same wastewater treatment plant during four sampling days [27]. For example, the concentration of naproxen in the influent had a relative standard deviation (RSD) of 343%, with a range between 1.79 and 611 μg/L [28]. For paracetamol, its levels ranged from 5.53 to 292 μg/L with an RSD of 152% [28]. Many factors may affect the occurrence of PPCPs, such as rainfall, land use, and human activity. For example, PPCP levels are usually elevated in areas with >100 people per km2 and >8% of subwatershed area [29]. Therefore, spatial and temporal variability should be cautioned against in the study of ECs. Ignorance of such variability may cause uncertainty in sampling and environmental analysis.

2.2. Uncertainty in Environmental Sample Collection of ECs

Sample collection is an indispensable part of characterizing the occurrence, fate, and risk of ECs. The representativeness of environmental samples is the most important issue. Both site selections and sample modes (methods and frequency) can influence the sample’s representativeness. If the spatial and temporal variability are neglected, the samples will be unrepresentative, and uncertainty will be generated. A field investigation is necessary prior to site selection to ensure representativeness.
Sampling modes usually include grab sampling, composite sampling, and passive sampling. For water samples, the most widely used monitoring method is grab sampling. Randomness may be an inevitable problem in grab sampling to some extent. Subsequently, composite sampling and passive sampling have been developed to overcome the shortage of grab sampling. However, the concentration of ECs obtained by passive samplers is not affected by short-term fluctuations. Its sample can be regarded as reflecting the general pollution level during the sampling period. However, the pollutants diffuse into the sorbent layer of the sampler, and their concentrations are derived indirectly from the extraction of the sorbent layer, which may not be able to reflect the real environmental conditions because the environment varies all the time. Table 1 contains a list of some studies that yielded varying outcomes as a result of the various sampling methodologies employed. It has been determined that errors in the results may result from either active sampling or passive sampling.

2.3. Uncertainty in the Sample Analysis of ECs

Besides sampling uncertainty, uncertainty in the sample analysis is equally deserving of attention. As we know, many ECs in the environment always belong to the category of trace contaminants. Their concentrations are generally at a very low level (for example, ng/L for PPCPs in water). Thus, it is necessary to adopt an advanced and more sensitive monitoring methodology. In turn, if the method does not reach the detection limit, the trace contaminants may not be detected, even though they exist in the samples. This will bring uncertainty to our understanding of the occurrence of such pollutants. For example, brominated diphenyl ether (BDE) 85 was below the detection limit and undetected in both environmental samples and standard solutions, though it was found in a standard BDE solution sample and may have existed in samples [34].
Additionally, there are numerous pitfalls in both data analysis and sophisticated sample analysis [35,36,37,38]. For example, due to Deca BDE’s degradation under daylight, poor solubility, and high background concentrations, it is recommended to use UV filters and fluorescent lighting, check its solubility in the solvent, keep it clean, and note the temperature in its analysis [39]. At least two specific reaction monitoring transitions for each analyte is thought to be an effective method for identifying pesticides and antibiotics in water, while the use of only one might lead to some false positives [40,41]. Recently, liquid chromatography-mass spectrometry (LC-MS) or LC-MS/MS has become the most popular technology to analyze polar ECs, such as PPCPs. Matrix effects often exist in LC-MS or LC-MS/MS analysis [42,43,44]. As the source of uncertainty, matrix effects may lead to a misunderstanding of environmental issues without being taken into account in environmental analysis. Internal standard substances are considered to be able to solve this problem, especially stable-isotope-labeled (SIL) analogues. In an ideal case, the peak area ratio of an analyte standard to an internal standard should be constant despite any variations. However, the SIL internal standard may have the wrong response. The use of an SIL might not guarantee the constancy of the response in certain batches of urine [45]. A high degree of matrix effect has been observed in the analysis of larger samples, which resulted in a change in the SIL response ratio [45]. In another case, the deuterium isotope effect was found in the analysis of carvedilol enantiomers in human plasma, which caused a difference in the retention time between the analyte and the SIL internal standard [46]. Due to this difference, the peak area ratio of the analyte to the SIL internal standard was significantly changed, and the method accuracy was affected.

2.4. Interlaboratory Comparison

In certain situations, it is advised to use interlaboratory comparison analysis to verify the monitoring capability of participating laboratories in order to improve EC analysis and reduce method uncertainty. For instance, some standard analytical methods have been established for POPs, such as polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), according to the requirements of the Stockholm Convention on POPs. However, there are no unified standard detection methods for the vast majority of ECs yet. Due to the complexity of environmental samples and the limitations of the laboratories or operators, issues may arise and create ambiguity during the actual execution of a standard procedure. Interlaboratory comparison analyses can help us discover the potential uncertainty in the analytic capacity of laboratories.
For example, microplastics, as one category of ECs, often display some differences in their quantification in different studies. Based on the results of 12 experienced laboratories, the number of microplastics (<1 mm) was still underestimated by 20% even with the best practice of measurements [47]. According to the interlaboratory study of sewage drug biomarkers in 19 sewage treatment plants, the community drug consumption was estimated with a 26% uncertainty [48]. For illicit drugs, interlaboratory testing in 37 laboratories was developed to improve analytical procedures considering the sample conditions, spiking levels, and matrix types [49]. So, interlaboratory comparisons cannot eliminate uncertainty but can make research more accurate.

3. Uncertainty in Environmental Modeling of ECs

Because of the low frequency of environmental monitoring and costly analysis, it is difficult to obtain enough experimental data on ECs to fully characterize their occurrence, fate, and environmental risk. Environmental models are regarded as useful tools to support environmental research, decision-making, and policy analysis [21,50,51,52]. Combined with the investigated information, environmental analysis, and modeling, researchers can gain a full understanding of ECs and make good predictions. With the development of environmental models, their complexity has been enhanced, and their application has become more integrated. Social (e.g., populations, birth rates, education attainments, etc.), economic, and institutional aspects have been gradually integrated into modeling. The uncertainty will definitely increase as the complexity of models increases. Thus, environmental modeling without uncertainties taken into account may lower the scientificalness and accuracy of the research results.
Uncertainty typologies have displayed many differences in the literature, and there is no one generic type. In this section, uncertainty is discussed with regard to the input data (such as chemical properties and environmental parameters) and model structures in detail, as well as the model output. A detailed framework of this section is shown in Figure 2. Some assessment methods to analyze the uncertainty of the above factors are subsequently discussed.

3.1. Uncertainty of Model Input Data

Environmental models require a variety of physical and chemical properties and environmental parameters as their input data. Small changes in these data may result in significant differences in the model output.
Some cases of the variability of input parameters and the differences caused by their uncertainties are shown below. Octanol-water partition coefficients (Kow), as one of the most important input parameters, have been used in many environmental models. The Kow of dichlorodiphenyl trichloroethane (DDT) can vary by more than three orders of magnitude among different studies [53]. In another study, the Kow of DDT was found to have different values in different publications, and even the “recommended values” ranged over more than two orders of magnitude [54]. The range of the values of Kow and aqueous solubility (SW) [54,55] is summarized in Figure 3. As can be seen, the maximum range can be up to four orders of magnitude. Various Kow values were used to calculate the health-protective sediment quality objectives (SQOs) of polychorinated biphenyls (PCBs) [55], in which the SQOs differed by a factor of 5 based on the different Kow values. In sediment remediation, the cost could be quite uncertain due to the uncertainty of SQOs. To evaluate whether a chemical is POP-like or not, a multimedia model was proposed based on the overall persistence (Pov) and potential for long-range transport (LRTP) [56]. The partition coefficients are the main factors causing LRTP uncertainties. In addition, a different classification could be obtained by a different set of reference chemicals, despite the chemicals having similar properties.
A sensitivity analysis is typically used to determine the input parameter uncertainty. The Monte Carlo analysis and polynomial chaos approaches are two techniques that have been used to describe the uncertainty of input data. Using polynomial chaos methods, researchers have analyzed the uncertainties of the input data of a global-scale chemical transport model for polycyclic aromatic hydrocarbons (PAHs) [57]. The uncertainty in the atmospheric lifetime was more sensitive to the concentrations of PAHs [57]. The emissions and degradation constants were the most influential sources of uncertainty for the DDT concentrations based on a Monte Carlo analysis [53]. Similarly, an uncertainty analysis was implemented in our previous study to reflect the influence of input parameter variability on the model outputs in a fugacity model [58]. The environmental loads were considered to be the most important parameter influencing the model outputs, followed by the water percent, surface area, and water depth. As a numerical indicator of an organic chemical’s transportation or accumulation potential, arctic contamination potentials were investigated, as well as which environmental input parameters were sensitive to it [59]. The results indicated that the factors with the greatest influence on arctic contamination potentials were the sea ice cover, latitudinal temperature gradient, macro-diffusive atmospheric transport coefficients, and precipitation rate. One study showed that the POP distribution and fate were sensitive to climate change [60]. Different climate change scenarios and even a minor environmental change could lead to a noticeable variation in the POP concentration. In a scenario with moderate climate change, the environmental concentrations of PCBs and PCDFs may differ by two times compared to a scenario with a stable climate for the next fifty years.

3.2. Uncertainty in Model Structures

If the input parameters are the arms and legs of environmental models, the model structure is the main body. It is crucial to note the uncertainty in model structures. There are various environmental models, including QSAR models, pesticide fate models, exposure models, bioaccumulation models, multimedia models, and so on. The uncertainty for each category of models is explained in the following sections.
QSAR models have been widely used to predict the physical and chemical activities of chemicals based on their structures. Though QSAR models have made a huge contribution to filling the environmental data gap, uncertainty is inevitable in their development [61,62,63,64]. The uncertainties in QSAR models arise from input data preprocessing, model generation and validation, applicability domain characterization, and model interpretation [62,64,65], which are summarized in Table 2. For instance, when validating QSAR models, alternative calculation methods for the external validation coefficient should be adopted based on the size and distribution of the dataset [61]; otherwise, the external validation may contain errors.
Besides QSAR models, uncertainties also exist in other environmental models. The Pov and LRTP of chemicals have been estimated with nine multimedia models [24]. The model-to-model correlation coefficients of the Pov and LRTP are displayed in Figure 4. The minimum correlation coefficient was close to 0.2 and considerably lower than 1, indicating that uncertainty existed when using different models. The impact of sporadic rain events was taken into account in modeling to assess the long-term fate and transport of organic air pollutants [66]. Most of the time, rain is thought of as an ongoing phenomenon. According to Jolliet and Hauschild [66], this assumption may result in an underestimation of the atmospheric residence time and travel distance with an uncertainty of three orders of magnitude. Different descriptions of the gas-particle partitioning of semi-volatile organic compounds could result in a definitely different global fate [67]. An assumption of the steady-state conditions for the removal of chemicals was not acceptable and caused 25% of 336 chemicals’ potential dose to be underestimated [68].

3.3. Uncertainty in Model Output

Since it was essentially addressed in the model input and structures section, this study’s discussion of the uncertainty in model outputs is less thorough. Succinctly, a model uncertainty analysis based on a Monte Carlo simulation is usually conducted to characterize the output variations. For example, our previous study of PPCP pollution in Beijing based on a model uncertainty analysis showed that the calculated PPCP levels in water varied greatly and covered a range of three orders of magnitude [58]. Moreover, it is necessary to emphasize the propagation of uncertainty in environmental modeling [69]. The current knowledge of uncertainty is only at its beginnings [70]. More investigation is required to reveal the uncertainties of environmental modeling.

4. Uncertainty in Priority Screening and Environmental Risk Assessment of ECs

4.1. Uncertainty in Priority Screening of ECs

Based on the essential information obtained from the environmental monitoring and modeling of ECs, their risks to ecosystems and human health are attracting increasing attention. The priority screening of ECs is usually a preliminary step for their comprehensive environmental risk assessment (ERA). Based on the massive amount of data on these contaminants, researchers usually conduct a priority screening to obtain a rough result and then carry out a detailed risk assessment.
Due to data limitations, there are always various sources of uncertainty during the priority screening process. The assumption used to fill in the data gap may significantly alter the outcomes. For instance, in the priority screening of PPCPs in China, the emission of 55% of PPCPs was arbitrarily assumed to be 1000 metric tons per year [71]. Additionally, the toxic endpoints were constrained. Both of these aspects would cause significant uncertainty in the priority screening process. According to certain studies [72,73], the persistence, bioaccumulation, and toxicity of ECs were consistently the sources of uncertainty determining risk priority. The results of the priority screening usually did not agree very well with the different suggested methods. For example, hexachloroethane and 1,3,5-tribromobenzene were listed in the Canadian Domestic Substances List. But due to their low hazards and risk potential, they should be judged as low priorities [73]. Three criteria were used to determine the priority PBT/LRT chemicals [74]. However, the ignorance of two components, namely, the emission factor and the tendency factor to address the environmental fate, was the source of the ambiguity in this priority screening.
Like the uncertainty in modeling, input parameter uncertainty also has an influence on the results of priority screening. A simple general robustness parameter was defined, and the magnitude of the ranking uncertainty increased rapidly as this parameter value decreased [75]. And the ranking uncertainty became nearly constant when this parameter value exceeded 5. As an indicator to reflect the potential harm of chemicals released into the environment [76], the toxicity potential differed by several orders of magnitude [77]. For example, the toxicity potentials of atrazine and 2,3,7,8-TCDD have an uncertainty range of about 1.5 to 6 orders of magnitude.

4.2. Uncertainty in the Environmental Risk Assessment of ECs

The ecological hazards of certain ECs have drawn a lot of attention due to their frequent identification. As long as the monitoring technique is sensitive enough with a low detection limit, pollutants can theoretically be detected. Therefore, a substance’s detection does not necessarily indicate that it poses a risk to the environment or human health. Extensive environmental behavior data and sufficient toxicological data must be obtained for the effect assessment in order to assure the correctness of the ERA. In some instances, the absence of these data will result in uncertainty that will cause people to misunderstand the ecological risk. The sources of uncertainty in ERAs are mainly from exposure assessment and effect assessment, such as variability in exposure data, species effect data, the risk characterization model, and a lack of knowledge (Figure 5).
In ERAs, we need to obtain the exposure concentration for the exposure assessment and the predicted no-effect concentration (PNEC) for the risk characterization. Combined with the two values, the risk quotient value can finally be calculated [78]. Firstly, the exposure concentrations have temporal and spatial variability [79,80]. For exposure concentration predictions, model extrapolation is often required. However, the extrapolated values and the genuine values can diverge by several orders of magnitude [81]. For example, the lab exposure concentrations (~107–1012 particles/m3) for microplastics are much higher than the actual concentrations (0.004–9200 particles/m3) [82]. Secondly, PNECs for ECs are also limited. And in many cases, a PNEC value is calculated by dividing the no-observed-effect concentration (NOEC) of the most sensitive species by a safety factor because there is typically insufficient data to create a species sensitivity distribution [83]. In the absence of NOEC data, they can be replaced by other data, such as the half maximal effective concentration (EC50), half lethal concentration (LC50), minimal inhibitory concentrations, lowest observable effect concentrations, and other toxicity thresholds [84]. A tiered aquatic ERA of organochlorine pesticides and their mixtures was developed [85]. And the risk was calculated using data on the determined concentrations and their reported toxicity values. Chronic toxicities are recommended for PNEC levels. However, chronic toxicities tend to be far scarcer for ECs. In this situation, the acute-to-chronic ratio (ACR) is used to derive these chronic toxicities. ACRs vary with chemicals and species, and they may change with different mechanisms of action for various experimental organisms or under various environmental circumstances. Hence, this will undoubtedly make the obtained “surrogate NOEC” dubious. Some chemical properties are very important for environmental risk management [85,86,87,88]. For instance, bioaccumulative substances are identified as hydrophobic and fat-soluble chemicals, which are priorities for regulation. They are commonly regarded as having a high KOW (≥100,000). However, chemicals with a low KOW and high KOA have been found to be an unidentified class of potentially bioaccumulative substances [87].
Additionally, the majority of the present risk assessment focuses on individual contaminants. In the actual environmental system, however, many non-conventional contaminants coexist. Moreover, some special ECs, such microplastics, serve as vectors to adsorb other ECs, leading to complex combined pollution [81]. All these situations result in a combined effect as opposed to separate effects [89,90,91,92,93]. As a result, our understanding of environmental risks will be ambiguous if the combined effect is not taken into account.

5. Uncertainty in Source Characterization of ECs

It is worth mentioning that source characterization makes a significant contribution to the environmental management of ECs. An ideal source characterization is to directly investigate all the potential pollution sources in the field and then obtain the emission loads from all the sources. But this will cost massive amounts of manpower, materials, and financial resources. Some receptor models or statistical analysis tools based on their environmental occurrence are typically used as feasible alternatives [94,95,96].
Uncertainties could unavoidably result from this indirect source characterization. One of these uncertainties may arise from the application of these statistical analysis tools. A principal component analysis with multiple linear regression was used in the source characterization of PPCPs in the Beiyun River of Beijing [94]. Only factors with eigenvalues larger than 1 were employed in this statistical method to identify the possible sources. Therefore, the results of this source characterization may not accurately reflect all the sources of pollution. The source characterization of perfluoroalkyl substances in WWTPs was performed using a cluster analysis [97]. However, the limitation of this analysis was that the cluster would be generated even when the data were random. A chemical mass balance model was used for the source characterization of sediment PAHs in Lake Calumet [98]. The number of fitting species in this model was a source of uncertainty influencing the model outcome. The accuracy of the model outcome was reduced as the species number decreased. In addition, the propagation of uncertainty from fundamental data to a mathematical model during the whole source characterization is also worth a mention. In a recent investigation, more than 60% of the PPCP burden was identified from discharged untreated sewage [94]. However, this contribution percentage cannot be validated, which might be unprecise due to the existence of uncertainty in environmental monitoring and statistical analysis tools. Thus, more attention should be paid to the potential uncertainties in further source characterization research.

6. Suggestions and Conclusions

This work summarized various uncertainties in the environmental research of ECs, including the variability and uncertainties in sample analysis, environmental modeling, priority screening, risk assessment, and source characterization. It is clear that uncertainty is ubiquitous due to the lack of available information and some simplified assumptions. The uncertainties can be reduced with careful consideration but cannot be removed completely. According to the sources of uncertainties, some suggestions for reducing the uncertainty are summarized below.
(1) Environmental analysis
a. Spatial and temporal variability: realize the existence of great variability and do not rush to conclusions when performing comparisons between different studies; do not extrapolate the pollutant occurrence in a specific site to represent a whole area subjectively.
b. Sample collection: pay attention to the site selection and sample modes to ensure sampling representativeness; suggest the use of consecutive sampling instead of grab sampling; increase the sampling frequency in composite sampling; pay attention to the hydraulic retention time when sampling.
c. Sample analysis: realize that no detection may not represent no existence; understand the physical and chemical properties of pollutants to establish a more suitable experimental program; do not excessively depend on the current approach (e.g., SIL internal standards) to solve matrix effects absolutely; employ interlaboratory comparison if possible.
(2) Environmental modeling
a. Model input: select the input data values of the physical/chemical properties and environmental parameters cautiously (confirmed by different studies if possible).
b. Model structure: pay attention to the rationality of assumptions; select suitable statistical methods.
c. Model output: it is better to employ uncertainty propagation in modeling.
(3) Environmental risk assessment
a. Priority screening: make a reasonable assumption when data are limited.
b. Risk assessment: it is better to use chronic toxicities data instead of acute toxicities data; obtain experimental ACR values from previous studies if possible; select representative species for assessments; use unified and integrative assessment methods.
(4) Source characterization
Recommend the investigation of the direct pollution sources instead of the derivation from their occurrence; pay attention to the selection of statistical analysis tools; realize that the results from statistical analysis do not completely correspond to the facts.
Thus, in the research and management of ECs, the precaution of uncertainty should always be kept in mind, especially when data are rather limited. With the realization of uncertainties, it is necessary to make an effort to minimize them. The results will be more accurate if all aspects of the uncertainties are considered. However, more work should be conducted to reveal and reduce uncertainties in the analysis and assessment of ECs, and, hence, assist with their proper management.

Author Contributions

Conceptualization, W.Z.; methodology, W.Z.; investigation, W.Z.; resources, B.W. and G.Y.; data curation, W.Z.; writing—original draft preparation, W.Z.; writing—review and editing, B.W.; supervision, B.W. and G.Y.; project administration, B.W. and W.Z.; funding acquisition, B.W. and W.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the Major Project of National Natural Science Foundation of China (Grant No. 52091544) and Shuangchuang Doctoral Program of Jiangsu Province (Grant No. JSSCBS20230238).

Data Availability Statement

The data are contained within the article.

Acknowledgments

Thanks to Oliver J. Hao for his valuable comments in structuring the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Framework of uncertainty study of emerging contaminants.
Figure 1. Framework of uncertainty study of emerging contaminants.
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Figure 2. Framework of uncertainty in environmental modeling.
Figure 2. Framework of uncertainty in environmental modeling.
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Figure 3. The range of logSw and logKow for some chemicals [54,55].
Figure 3. The range of logSw and logKow for some chemicals [54,55].
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Figure 4. The correlation coefficients between different models for Pov and LRTP. For Pov, release into the air (A), water (B), and soil (C). For LRTP, release into the air (D).
Figure 4. The correlation coefficients between different models for Pov and LRTP. For Pov, release into the air (A), water (B), and soil (C). For LRTP, release into the air (D).
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Figure 5. The uncertainty existing in ERAs.
Figure 5. The uncertainty existing in ERAs.
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Table 1. The uncertainty in environmental sample collection.
Table 1. The uncertainty in environmental sample collection.
Source of UncertaintyResultsReference
Sampling frequenciesThe load of benzotriazole calculated at the highest temporal resolution (10 s) was 3022 mg. At intervals of 5 min and 15 min, the load became 2954 mg and 1949 mg, respectively.[30]
Composite sampling modesThe highest concentration of ranitidine was measured on day 4 in sampling mode B1 (flow-proportional, continuous), followed by the concentrations on day 1, 3, and 2. But a reverse concentration order was obtained with sampling mode B4 (one grab sampling).[31]
Flow variationsThe flow variations with different diurnal flow patterns reflected factors of about 2 or 10 between the night minimum and maximum dry weather flow. The pollutant load would be misestimated in time-proportional sampling when the flow rate varied.[32]
Passive samplingThe concentrations of hormone and β-blocker were overestimated because of the enhanced matrix effect of polar organic chemical integrative sampler.[33]
Table 2. The uncertainty factors in QSAR models.
Table 2. The uncertainty factors in QSAR models.
Major Parts in QSAR ModelsUncertainty Factors
Data preparation and preprocessingData size and variety
Errors and inappropriate operations in experiments
Some contraindications for QSAR models
Model generation and validation, applicability domain characterizationSelection of descriptors
Collinearity of variables
Robust statistical methods or “black boxes”
Over- and under-determined equations
Linearity assumption
Model quality and outliers
Starting geometries in 3D-QSAR models
3D-QSAR blindness
Selection of applicability domain characterization approaches
Model interpretationWrong understanding between correlations and causalities
Chance correlation
Multiple solutions
Extrapolation and interpolation
Without validation nor biased validation
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MDPI and ACS Style

Zhao, W.; Wang, B.; Yu, G. Addressing the Uncertainties in the Environmental Analysis, Modeling, Source and Risk Assessment of Emerging Contaminants. Water 2025, 17, 215. https://doi.org/10.3390/w17020215

AMA Style

Zhao W, Wang B, Yu G. Addressing the Uncertainties in the Environmental Analysis, Modeling, Source and Risk Assessment of Emerging Contaminants. Water. 2025; 17(2):215. https://doi.org/10.3390/w17020215

Chicago/Turabian Style

Zhao, Wenxing, Bin Wang, and Gang Yu. 2025. "Addressing the Uncertainties in the Environmental Analysis, Modeling, Source and Risk Assessment of Emerging Contaminants" Water 17, no. 2: 215. https://doi.org/10.3390/w17020215

APA Style

Zhao, W., Wang, B., & Yu, G. (2025). Addressing the Uncertainties in the Environmental Analysis, Modeling, Source and Risk Assessment of Emerging Contaminants. Water, 17(2), 215. https://doi.org/10.3390/w17020215

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