Ranking Approach Research Articles

Location–allocation problem for green efficient two-stage vehicle-based logistics system: A type-2 neutrosophic multi-objective modeling approach

In recent decades, global climate change has become one of the most critical environmental issues, leading to increased environmental and social concerns about the sustainability of logistics networks. This study proposes a novel multi-objective, multi-facility location–allocation model for a two-stage solid logistics network that supports green practices. To account for uncertainty in real-world problems, some parameters of the model are represented as triangular type-2 neutrosophic numbers to deal with hesitant situations. The fuzzy model is then converted into a deterministic one using a new ranking approach. Three conflicting objective functions – minimizing total financial costs, maximizing customers’ satisfaction levels, and ensuring sustainable and effective conveyances – are simultaneously optimized using the ϵ-constraint approach. The proposed model and solution approach are tested with two numerical examples. A comparative study is also presented, comparing the proposed approach with other Pareto-based multi-objective approaches, such as the global criterion approach, multi-objective fuzzy programming, and weighted Chebychev programming. Finally, the paper concludes with some remarks.

Fully fuzzy DEA: a novel additive slacks-based measure model

AbstractThe subject of assessing the relative efficiency of Decision-Making Units (DMUs) has been explored extensively in the literature on Data Envelopment Analysis (DEA). While typical DEA models require exact and conclusive data, the observed values of the inputs and outputs in real-world problems may be inaccurate or ambiguous. This paper presents a novel fully fuzzy DEA (FFDEA) model based on the additive slacks-based measure (ASBM). FFDEA models imply that all inputs, outputs, and variables, are fuzzy. It is demonstrated how fuzzy ranking and fuzzy arithmetic approaches can be used to handle the trapezoidal fuzzy number assumptions. To solve the fuzzy ASBM model under variable returns-to-scale assumptions, we proposed two different methods, first method use of lexicographic method for solving multi-objective linear programming (MOLP) approach, which provides an efficiency measure and a fuzzy goal operating point for each DMU under assessment. In alternative method, we convert the fuzzy linear fractional programming problems to an equivalent MOLP problem. A case study is provided to illustrate the applicability of the proposed approach for selecting sustainable suppliers, we compare the results with other fuzzy DEA approaches, while shedding light on future research venues.

Development of real time ECG monitoring and unsupervised learning classification framework for cardiovascular diagnosis

In this work, a novel meta-heuristic-based feature ranking and classification approach is developed on the real-time ECG data. Initially, the data is captured using AD8232 biopotential sensor and transmitted to Amazon Web Service (AWS) Internet of Things (IoT) core through ESP8266 gateway via Message Queuing and Telemetry Transport (MQTT) protocol. An improved inter quartile range (IIQR) based filtering is applied on each feature measure to find outliers in the data. A probability estimator-based feature ranking method is used to find the key features. With unsupervised clustering the essential key segments are calculated and used as class labels to train the classifier. Finally, the prediction rate of the model on the segmented classes is improved with the use of ensemble learning. The real-time ECG data of three different individuals are used to test the unsupervised ensemble learning model (Support Vector Machine (SVM) + Linear Regression (LR) + ORF (Optimized Random Forest) + (K-Nearest Neighbor (KNN) + Gradient Boost (XGBoost)) and obtains an overall accuracy of 99.63% (Person 1), 99.75% (Person 2) and 99.78% (Person 3) which significantly outperforms the base ensemble classifiers such as, (LR + SVM + XGBoost) with 94.2% accuracy, (LR + KNN + XGBoost) with 95.8% accuracy and (Random Forest (RF) + KNN + XGBoost) with 95.67% accuracy.

Heart failure classifications via non-invasive pressure volume loops from echocardiography.

Left ventricular pressure-volume (LV-PV) loops provide comprehensive characterization of cardiovascular system in both health and disease, which are the essential element of the hemodynamic evaluation of heart failure (HF). This study attempts to achieve more detailed HF classifications by non-invasive LV-PV loops from echocardiography and analyzes contribution of parameters to HF classifications. Firstly, non-invasive PV loops are established by time-varying elastance model where LV volume curves were extracted from apical-four-chambers view of echocardiographic videos. Then, 16 parameters related to cardiac structure and functions are automatically acquired from PV loops. Next, we applied six machine learning (ML) methods to divide four categories. On this premise, we choose the best performing classifier among machine learning approaches for feature ranking. Finally, we compare the contributions of different parameters to HF classifications. By the experimental, the PV loops were successfully acquired in 1076 cases. When single left ventricular ejection fraction (LVEF) is used for HF classifications, the accuracy of the model is 91.67%. When added parameters extracted from ML-derived LV-PV loops, the classification accuracy is 96.57%, which improved by 5.1%. Especially, our parameters have a great improvement in the classification of non-HF controls and heart failure with preserved ejection fraction (HFpEF). We successfully presented the classification of HF by machine derived non-invasive LV-PV loops, which has the potential to improve the diagnosis and management of heart failure in clinic. Moreover, ventriculo-arterial (VA) coupling and ventricular efficiency were demonstrated important factors for ML-based HF classification model besides LVEF.

Personalized Prediction of Change in Fasting Blood Glucose Following Basal Insulin Adjustment in People With Type 2 Diabetes: A Proof-of-Concept Study.

For people with type 2 diabetes treated with basal insulin, suboptimal glycemic control due to clinical inertia is a common issue. Determining the optimal basal insulin dose can be difficult, as it varies between individuals. Thus, insulin titration can be slow and cautious which may lead to treatment fatigue and non-adherence. A model that predicts changes in fasting blood glucose (FBG) after adjusting basal insulin dose may lead to more optimal titration, reducing some of these challenges. To predict the change in FBG following adjustment of basal insulin in people with type 2 diabetes using a machine learning framework. A multiple linear regression model was developed based on 786 adults with type 2 diabetes. Data were divided into training (80%) and testing (20%) sets using a ranking approach. Forward feature selection and fivefold cross-validation were used to select features. Participants had a mean age of approximately 59 years, a mean duration of diabetes of 12 years, and a mean HbA1c at screening of 65 mmol/mol (8.1%). Chosen features were FBG at week 2, basal insulin dose adjustment from week 2 to 7, trial site, hemoglobin level, and alkaline phosphatase level. The model achieved a relative absolute error of 0.67, a Pearson correlation coefficient of 0.74, and a coefficient of determination of 0.55. A model using FBG, insulin doses, and blood samples can predict a five-week change in FBG after adjusting the basal insulin dose in people with type 2 diabetes. Implementation of such a model can potentially help optimize titration and improve glycemic control.

Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection.

Although it is clinically important, a reliable and economical solution to automatic seizure detection for patients at home is yet to be developed. Traditional algorithms rely on multi-channel EEG signals and features of canonical EEG power description. This study is aimed to propose an effective single-channel EEG seizure detection method centered on novel EEG power parameterization and channel selection algorithms.We employed the publicly available multi-channel CHB-MIT Scalp EEG database to gauge the effectiveness of our approach. We first adapted a power spectra parameterization algorithm to characterize the aperiodic and periodic components of the ictal and inter-ictal EEGs. We selected four features based on their statistical significance and interpretability, and developed a ranking approach to channel selection for each patient. We then tested the effectiveness of our approaches to channel and feature selection for automatic seizure detection using support vector machine (SVM) as the classifier. The performance of our algorithm was evaluated using five-fold cross-validation and compared to those methods of comparable complexity (using one or two channels of EEG), in terms of accuracy, specificity, sensitivity, precision and F1 score.Some channels of EEG signals show strikingly different distributions of PSD features between the ictal and inter-ictal states. Four features including the offset and exponent parameters for the aperiodic component and the first and second highest total power (TPW1 and TPW2) form the basis of channel selection and the input of SVM classifier. The selected channel is found to be patient-specific. Our approach has achieved a mean sensitivity of 95.6%, specificity of 99.2%, accuracy of 98.6%, precision of 95.5%, and F1 score of 95.5%. Compared with algorithms in previous studies that used one or two channels of EEG signals, ours outperforms in specificity and accuracy with comparable sensitivity.EEG power spectra parameterization to feature extraction and feature ranking-based channel selection are found to enable efficient and effective automatic seizure detection based on single-channel EEG signal.

Closed-loop multi-objective waste management through vehicle routing problem in neutrosophic hesitant fuzzy environment

Waste management contributes in various fields for global development. A multi-objective waste management (MOWM) problem is devised in an area that generates commercial, industrial and residential waste items. The target is to reduce the negative impact of waste items on social, economical and environmental sites by completing a closed-loop MOWM through a vehicle routing problem under time window restriction. This MOWM problem is optimizing the objectives including maximum profit, minimum carbon emission under carbon cap-and-trade policy, and minimum work load deviation to maintain the sustainability. Here, neutrosophic hesitant fuzzy (NHF) environment is preferred to overcome the hesitancy of MOWM problem. A new ranking approach is initiated for defuzzifying NHF data. The appropriateness of the formulated model is justified by evaluating two realistic applications. To derive the Pareto-optimal solution of the proposed MOWM problem, two fuzzy techniques, namely, neutrosophic linear programming and neutrosophic hesitant fuzzy programming, and one non-fuzzy technique global criterion method are utilized in NHF environment. The obtained Pareto-optimal solutions are compared by TOPSIS for determining the final Pareto-optimal solution and to select a better approach among the proposed three approaches. Comparison analysis, sensitivity analysis, managerial insights and conclusions with future research scopes are outlined at the end.

Integrative review of project portfolio management ranking criteria - understanding better the decision-making process

Project portfolio management (PPM) relies on rankings of projects, which can be challenging due to the many factors and people involved. Project prioritization is essential when a business works on multiple projects simultaneously (whether the projects are wholly independent or interdependent). Managing the projects already in the company’s portfolio and selecting new ones to add to the portfolio are among the essential tasks for many companies. The organization must prioritize the proper initiatives within its diversity to attain its goals and objectives. The ranking approach is a way to prioritize the projects by placing them in order from most important to least important by assigning each task a rating based on a set of criteria. These can be very diverse and depend on many factors, such as different financial and non-financial impacts). This study aims to provide a comprehensive assessment of the existing literature on studies in project portfolio management ranking, focusing on the ranking criteria employed and identifying the journals that published the papers we selected for this analysis. Before ranking, it is necessary to specify which criteria are most relevant to suit the organization’s goals. This research presents the main applicable criteria for ranking project portfolios and their sub-criteria to understand the potential of PPM better, leading to selecting more suitable and valuable projects for the organization. Based on this review, exploring the criteria used for the process, identifying the possibilities for their application, and gaining further knowledge of them enable better portfolio management for the company.

Ranking approach based on compression transformation and distance factor in Pythagorean fuzzy environment with its application in multi-attribute group decision-making

Ranking approach based on compression transformation and distance factor in Pythagorean fuzzy environment with its application in multi-attribute group decision-making

Two in One: A New Tool to Combine Two Rankings Based on the Voronoi Diagram

AbstractIn this paper, we propose a novel method for ranking items such as countries, individuals, or firms based on two indices. This approach is particularly useful when constructing a composite indicator that combines both dimensions is not feasible. The proposed ranking approach involves an iterative scheme where the Voronoi algorithm is applied in a two-dimensional space at each step. To provide empirical evidence that our approach works satisfactorily, we applied the Voronoi-based iterative scheme to rank 34 European countries based on two dimensions: the Human Development Index (HDI) and the Happiness Index (HI). The correlation coefficient between the rankings based on HDI and HI is lower than the correlation coefficients between the Voronoi-based ranking and HDI, as well as between the Voronoi-based ranking and HI. These results suggest that the new method is capable of better capturing the information from both original indices.

Sustainable Last-Mile Delivery Solution Evaluation in the Context of a Developing Country: A Novel OPA–Fuzzy MARCOS Approach

With the surge in e-commerce volumes during COVID-19, improving last-mile logistics is extremely challenging, specifically for developing economies, due to poor infrastructures, lack of stakeholders’ cooperation, and untapped resources. In the context of Vietnam, there are certain solutions that can bring more efficient and sustainable last-mile logistics. In this paper, to evaluate and rank these potentially sustainable last-mile solutions (LMSs), we propose a novel hybrid multiple attribute decision-making (MADM) model that combines the Ordinal Priority Approach (OPA) and fuzzy Measurement of Alternatives and Ranking according to the COmpromise Solution (fuzzy MARCOS). Twelve sustainability factors of technical, economic, social, and environmental aspects were determined through a literature review and experts’ opinions to employ the MADM approach. A case study evaluating five LMSs in Vietnam concerning their sustainable implementation is solved to exhibit the proposed framework’s applicability. From the OPA findings, “efficiency”, “costs of implementation and control”, “voice of customer”, “reliability”, and “flexibility” are the topmost criteria when considering a new LMS implementation in the context of Vietnam. Moreover, sensitivity analysis and comparative analysis were performed to test the robustness of the approach. The results illustrate that the applied methods reach consistent solution rankings, where LMS-03 (convenience store pickup), LMS-02 (parcel lockers), and LMS-01 (green vehicles) are the best solutions in Vietnam. The study holds novelty in evaluating last-mile initiatives for Vietnam by utilizing a unique approach in the form of two novel MADM techniques, thus providing significant insights for research and applications.

Determining the underlying role of corporate sustainability criteria in a ranking problem using UW-TOPSIS

AbstractESG criteria are becoming increasingly important for institutional and retail investors with a consequent growing demand of reliable and transparent ESG data to support their decisions. Several ESG rating agencies assess companies providing ratings and rankings. However, their rating methodologies are subject to some criticisms. One of the main weaknesses is the determination of the relative importance of the ESG criteria involved in the rating process. In this work, we propose the use of a MCDM rating and ranking approach with which the decision maker can rank firms based on their ESG global performance without the elicitation of aggregation weights. The approach, UW-TOPSIS, provides three outputs: the global ESG rating of the firms, a ranking based on the ratings and, for each alternative, a vector of weights describing the discriminatory power of the ESG criteria on the alternative, thus overcoming one of the criticisms to the methodologies of ESG rating agencies. However, UW-TOPSIS has a limitation as it does not provide a global vector of weights valid for all the alternatives in the ranking, expressing the overall role or contribution of each criteria to the componsition of the ranking. The acknowledge and analysis of this situation and the proposal of a solution, is the main objective of this paper.

Rating versus ranking in a Delphi survey: a randomized controlled trial

BackgroundThe Delphi technique has steeply grown in popularity in health research as a structured approach to group communication process. Rating and ranking are two different procedures commonly used to quantify participants’ opinions in Delphi surveys. We explored the influence of using a rating or ranking approach on item prioritization (main outcome), questionnaire completion time, and evaluation of task difficulty in a Delphi survey aimed at identifying priorities for the organization of primary cardiovascular care.MethodsA randomized controlled parallel group trial was embedded in a three-round online Delphi survey. After an “open” first round, primary care patients, trained patient partners, and primary care clinicians from seven primary care practices were allocated 1:1 to a rating or ranking assessment group for the remainder of the study by stratified permuted block randomization, with strata based on participants’ gender and status. Agreement on item prioritization between the experimental groups was measured by calculating Krippendorff’s alpha reliability coefficient on the aggregate rank order of items in each group after the final round. Self-reported ease or difficulty with the assessment task was measured with the Single Ease Question.ResultsThirty-six panelists (13 clinic patients, 7 patient partners, 16 clinicians; 60% females) were randomized to the rating (n = 18) or ranking (n = 18) group, with 30 (83%) completing all rounds. Both groups identified the same highest priorities from a set of 41 items, but significant discrepancies were found as early as the seventh top item. There was moderately strong agreement between the priority ordering of top items common to both groups (Krippendorff’s alpha = 0.811, 95% CI = 0.669–0.920). A 9-min mean difference to complete the third-round questionnaire in favor of the rating group failed to achieve statistical significance (p = 0.053). Ranking was perceived as more difficult (p < 0.001).ConclusionsA rating or ranking procedure led to modestly similar item prioritization in a Delphi survey, but ranking was more difficult. This study should be replicated with a larger number of participants and with variations in the ranking and rating procedures.Trial registrationNot applicable.

A ranking approach for robust portfolio decision analysis based on multilinear portfolio utility functions and incomplete preference information

Uncertainties are often present in typical portfolio decision analysis (PDA), and it is prudent to conduct a robust PDA to identify project portfolios that perform reasonably well across the entire range of uncertain parameter values. Previous research on robust PDA and applications has almost entirely relied on additive portfolio preference functions, with little research on robustness under multilinear portfolio utility functions (PUFs). A ranking approach for robust PDA is proposed based on multilinear PUFs, incomplete preference information, and the algorithm for accelerating stochastic multicriteria acceptability analysis (SMAA). First, the initial set of portfolios is screened through various constraints to produce a smaller set of desirable portfolios. Second, several types of incomplete preference information are thought to be elicited with regard to multilinear PUFs, including preference information on coefficients, portfolios, and uncertainties on preference elicitation techniques. Third, the SMAA decomposition algorithm is implemented as a ranking approach to perform robust PDA on the basis of multilinear PUFs, incomplete preference information, and potentially numerous portfolios in the set of desirable portfolios. Finally, the approach is applied to a case to validate its effectiveness, and comparisons among additive, multiplicative, and multilinear PUFs in terms of decision recommendations are performed.

A Fermatean fuzzy GLDS approach for ranking potential risk in the Fine-Kinney framework

The Fine-Kinney (F-K) model has been broadly employed for evaluating and ranking risk in various fields. The risk scoring information expression and priority ranking are two significant operations for its application. Numerous approaches have been extended to the two operations to improve the performance of conventional Fine-Kinney for risk analysis. Nevertheless, current literature on the F-K framework seldom considers the collective and individual risk attitudes in ranking potential hazards, especially with Fermatean fuzzy-based -risk scoring information. This paper generates a new ranking approach for risk prioritization in F-K to fulfill this gap by integrating the Fermatean fuzzy sets with the GLDS (gained and lost dominance score) method. First, the Fermatean fuzzy sets-based risk scale is introduced to acquire risk scores. Then, a new collective risk scoring matrix establishment approach based on Fermatean fuzzy Bonferroni mean (BM) operator is built for considering the interactive effects between experts. Next, an extended Fermatean fuzzy GLDS method with CRITIC (Criteria Importance Through Inter-criteria Correlation)is proposed to rank the potential hazards, in which the Fermatean fuzzy CRITIC method is adopted to determine the weights. Especially, this developed weighting method can depict the inter-correlation among risk parameters. Finally, this paper presents a risk evaluation case of professional hazards for construction operations to display the application and advantages of this improved hybrid risk ranking model in the F-K framework. The result reveals that the enhanced framework can effectively rank potential hazards with complex risk information.

Application of machine learning in algorithmic investment strategies on global stock markets

The research undertakes the subject of machine learning based algorithmic investment strategies. Several technical analysis indicators were employed as inputs to machine learning models such as Neural Networks, K Nearest Neighbor, Regression Trees, Random Forests, Naïve Bayes classifiers, Bayesian Generalized Linear Models, and Support Vector Machines. Models were used to generate trading signals on WIG20, DAX, S&P500, and selected CEE indices in the period between 2002-01–01 and 2023–03–31. Strategies were compared with each other and with the benchmark buy-and-hold strategy in terms of achieved levels of risk and return. Sensitivity analysis was used to assess the quality of the estimation on independent subsets. The findings of the study showed that algorithmic strategies outperformed passive strategies in terms of risk-adjusted returns and that for the analyzed indices, Linear Support Vector Machine and Bayesian Generalized Linear Model were the best-performing models. The Linear Support Vector Machine was chosen as the model that, on average, produced the best results using a more thorough rank approach based on the outcomes for all examined models and indices.

The Organophosphate Esters Used as Flame Retardants and Plasticizers Affect H295R Adrenal Cell Phenotypes and Functions.

Adverse effects associated with exposure to brominated flame retardants have led to regulations for their use and their replacement with organophosphate esters (OPEs). However, little is known about the impact of OPEs on the adrenal, a vital endocrine gland. Here, we used a high-content screening approach to elucidate the effects of OPEs on H295R human adrenal cell phenotypic endpoints and function. The effects of 2,2',4,4'-tetrabromodiphenyl ether (BDE-47), a legacy brominated flame retardant, on H295R cell cytotoxicity, oxidative stress, mitochondria, lysosomes, and lipid droplets were compared with those of 6 OPEs. Most OPEs reduced oxidative stress, increased the numbers of mitochondria, decreased lysosomes, and increased lipid droplets. Two potency ranking approaches, the lowest benchmark concentration/administered equivalent dose methods and Toxicological Prioritization Index analyses, revealed that the triaryl-OPEs (isopropylated triphenyl phosphate [IPPP], tris(methylphenyl) phosphate [TMPP], and triphenyl phosphate [TPHP]) and 1 nontriaryl OPE (tris(1,3-dichloro-2-propyl) phosphate [TDCIPP]) were more potent than BDE-47. The steroidogenic activity of adrenal cells in the presence or absence of forskolin, a steroidogenic stimulus, was determined after exposure to triaryl-OPEs. The basal production of cortisol and aldosterone was increased by IPPP but decreased by TPHP or TMPP exposure; the response to forskolin was not affected by these OPEs. All 3 triaryl OPEs altered the expression of rate-limiting enzymes involved in cholesterol and steroid biosynthesis; CYP11B1 and CYP11B2 were the most prominently affected targets. The OPE chemical-specific effects on cortisol and aldosterone production were best explained by alterations in STAR expression. Thus, the adrenal may be an important target for these endocrine-disrupting chemicals.

Is this news article still relevant? Ranking by contemporary relevance in archival search

Our civilization creates enormous volumes of digital data, a substantial fraction of which is preserved and made publicly available for present and future usage. Additionally, historical born-analog records are progressively being digitized and incorporated into digital document repositories. While professionals often have a clear idea of what they are looking for in document archives, average users are likely to have no precise search needs when accessing available archives (e.g., through their online interfaces). Thus, if the results are to be relevant and appealing to average people, they should include engaging and recognizable material. However, state-of-the-art document archival retrieval systems essentially use the same approaches as search engines for synchronic document collections. In this article, we develop unique ranking criteria for assessing the usefulness of archived contents based on their estimated relationship with current times, which we call contemporary relevance. Contemporary relevance may be utilized to enhance access to archival document collections, increasing the likelihood that users will discover interesting or valuable material. We next present an effective strategy for estimating contemporary relevance degrees of news articles by utilizing learning to rank approach based on a variety of diverse features, and we then successfully test it on the New York Times news collection. The incorporation of the contemporary relevance computation into archival retrieval systems should enable a new search style in which search results are meant to relate to the context of searchers’ times, and by this have the potential to engage the archive users. As a proof of concept, we develop and demonstrate a working prototype of a simplified ranking model that operates on the top of the Portuguese Web Archive portal (arquivo.pt).

BT-CKBQA: An efficient approach for Chinese knowledge base question answering

Knowledge Base Question Answering (KBQA), as an increasingly essential application, can provide accurate responses to user queries. ensuring that users obtain relevant information and make decisions promptly. The deep learning-based approaches have achieved satisfactory QA results by leveraging the neural network models. However, these approaches require numerous parameters, which increases the workload of tuning model parameters. To address this problem, we propose BT-CKBQA, a practical and highly efficient approach incorporating BM25 and Template-based predicate mapping for CKBQA. Besides, a concept lattice based approach is proposed for summarizing the knowledge base, which can largely improve the execution efficiency of QA with little loss of performance. Concretely, BT-CKBQA leverages the BM25 algorithm and custom dictionary to detect the subject of a question sentence. A template-based predicate generation approach is then proposed to generate candidate predicates. Finally, a ranking approach is provided with the joint consideration of character similarity and semantic similarity for predicate mapping. Extensive experiments are conducted over the NLPCC-ICCPOL 2016 and 2018 KBQA datasets, and the experimental results demonstrate the superiority of the proposed approach over the compared baselines. Particularly, the averaged F1-score result of BT-CKBQA for mention detection is up to 98.25%, which outperforms the best method currently available in the literature. For question answering, the proposed approach achieves superior results than most baselines with the F1-score value of 82.68%. Compared to state-of-the-art baselines, the execution efficiency and performance of QA per unit time can be improved with up to 56.39% and 44.06% gains, respectively. The experimental results for the diversification of questions indicate that the proposed approach performs better for diversified questions than domain-specific questions. The case study over a constructed COVID-19 knowledge base illustrates the effectiveness and practicability of BT-CKBQA.

Predicting Environmental Risks of Pharmaceuticals.

Environmental Risk Assessment (ERA) of pharmaceuticals relies on available measured environmental concentrations, but often such data are sparse. Predicted Environmental Concentrations (PECs), calculated from sales weights, are an attractive alternative, but often cover only prescription sales. We aimed to rank, by environmental risk in Norway, around 200 Active Pharmaceutical Ingredients (APIs) over 2016 to 2019, based on sales PECs. To assess the added value of wholesale and veterinary data we compared exposure and risk predictions with and without these additional sources. Finally, we aimed to characterise the persistence, mobility, and bioaccumulation of these APIs. We compared our PECs to available Norwegian measurements, then, using public Predicted No Effect Concentrations, we calculated Risk Quotients (RQs), and appended experimental and predicted persistence and bioaccumulation. Our approach overestimated environmental concentrations compared to measurements for 18 of 20 APIs with comparable predictions and measurements. 17 APIs had mean RQs > 1, indicating potential risk, while the mean RQ was 2.05 and median 0.001, driven by sex hormones, antibiotics, the antineoplastic abiraterone, and common painkillers. Some high-risk APIs were also potentially persistent or bioaccumulative (e.g. levonorgestrel (RQ = 220) and ciprofloxacin (RQ = 56), potentially persistent), raising the possibility of impacts beyond their RQs. Exposure and risk were also calculated with and without over-the-counter sales, showing prescriptions explained 70% of PEC magnitude. Likewise, human sales, compared to veterinary, explained 85%. Sales-predicted environmental concentrations provide an efficient option for ERA, designed to overestimate compared to analytical techniques, and potentially held back by limited data availability and an inability to quantify uncertainty, but nevertheless, an ideal initial approach for identification and ranking of risks.