Statistical Model Research Articles

A human-in-the-loop ensemble fusion framework for road crash prediction: coping with imbalanced heterogeneous data from the driver-vehicle-environment system

ABSTRACT Road accidents are an inevitable aspect of daily life, and predicting crashes is crucial for minimizing disruptions and advancing intelligent transportation technologies. This study aims to design an ensemble fusion decision system using various base classifiers and a meta-classifier to improve crash prediction efficiency within the driver-vehicle-environment system. We adopted a data-driven strategy to analyze four categories of features—driver demographics, vehicle telemetry, driver inputs, and environmental conditions—collected from a driving simulator. Optimized modeling strategies using AdaBoost, XGBoost, GBM, LightGBM, and CatBoost were implemented. Moreover, statistical logit models were also used to assess the likelihood of crashes and the correlations among key variables. Furthermore, three resampling strategies, SMOTE-TL, SMOTE-ENN, and ADASYN, were employed to address class imbalance. The best performance was achieved with GBM, XGBoost, and AdaBoost as base classifiers, SMOTE-TL for balancing, and CatBoost as the meta-classifier, with 89.78% precision, 95.69% recall, and 92.64% F1-score.

Time-variations of wave energy and forecasting power availability at a site in Fiji using time-series, regression and ANN techniques

ABSTRACT Recently, there has been a shift in the global energy landscape to move to reliable, clean, and eco-friendly renewable energy sources to address global issues such as climate change and greenhouse gas emissions. One such energy source is wave energy; researchers attempt to develop models that can accurately forecast the availability of wave energy as an alternative energy source. In this paper, an Artificial Neural Network (ANN) model along with statistical models such as time series models, and regression models are proposed for forecasting wave energy at a site in Fiji using the wave height and wave period as the independent variables. The performance of the proposed models developed is compared using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the goodness-of-fit ( R 2 ) value. The proposed model is then further benchmarked with the naïve model. The empirical results reveal that the proposed ANN model outclassed all the other models and was more efficient and accurate in forecasting wave energy than the regression and time series models. By accurate wave modelling and by incorporating impedance matching, maximum power generation can be achieved.

Joint trajectory inference for single-cell genomics using deep learning with a mixture prior

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE's efficacy in integrative analyses of multiomic datasets with continuous cell population structures.

ASSESSMENT OF THE IMPACT OF WELL GRID DENSITY ON OIL RECOVERY UNDER CONDITIONS OF UNCERTAINTY

The density of the well grid is one of the most important parameters that has a significant impact on the rate and amount of oil reserves extraction. As practice shows, existing approaches to the reliable assessment and identification of the degree of influence of this indicator on oil recovery do not allow us to adequately form an idea of the dynamics of reservoir processes and their role in making managerial decisions in conditions of uncertainty. Taking into account the decrease in the number of hydrodynamic and geophysical studies conducted in relation to the total number of wells in operation at fields that are being developed for a long time or just being put into development, the issue of predicting the oil recovery coefficient according to the list of geological and field parameters remains open and requires an urgent solution, including those related to the use of unique complex algorithms. The purpose of the work is to determine the relationship between the density of the well grid and the final coefficient of oil recovery in the development of fields in natural conditions using computer modeling systems. The research methodology consists of the use of existing scientific and methodological approaches to the processing of geological and commercial information in conditions of different densities in combination with the use of various advanced systems for identifying hidden patterns. Geological and statistical models have been defined for the oil deposits of the Famensk tier of the South Tatar arch of the Volga-Ural oil and gas province, allowing for the forecasting procedure for groups of objects identified during differentiation, subject to the dynamic density of geological and field information and various technical and economic indicators of field development. A comprehensive interpretation of the nature and degree of influence of geological and technological parameters on the amount of oil reserves production has been carried out. The need for preliminary differentiation of deposits based on identification with the subsequent use of the presented geological and statistical models to reduce the risks of making erroneous decisions during activities aimed at optimizing the development of analog facilities and rational extraction of residual oil reserves is reliably substantiated.

Genetics Navigator: protocol for a mixed methods randomized controlled trial evaluating a digital platform to deliver genomic services in Canadian pediatric and adult populations

IntroductionGenetic testing is used across medical disciplines leading to unprecedented demand for genetic services. This has resulted in excessive waitlists and unsustainable pressure on the standard model of genetic healthcare....

Combined associations of obesity and physical activity with pain, fatigue, stiffness, and anxiety in adults with spondyloarthropathies: UK Biobank study

Abstract Objective Inflammatory spondyloarthropathies (ISpAs) are associated with pain, fatigue, stiffness, and anxiety. The National Institute for Care and Excellence (NICE) and the European Alliance of Associations for Rheumatology (EULAR) provide limited lifestyle guidance for managing symptoms with ISpAs. We investigated the combined associations of obesity and physical activity with symptom severity in ISpAs. Methods The relationship between BMI, physical activity, and symptom severity (spinal and general pain, fatigue, anxiety, mobility) was examined in people with ISpAs (n = 1,577). BMI categories were normal weight (18·5-24·9 kg/m2), overweight (25·0-29·9 kg/m2), and obese (≥30 kg/m2). Physical activity was assessed via the International Physical Activity Questionnaire (low<600 metabolic equivalent of task (MET)-min/week, moderate≥600METs, high≥3000METs). Statistical models adjusted for confounders, including medication, estimated the likelihood (odds ratios; OR) of higher symptom severity across BMI and physical activity categories. Results Overweight and obesity, compared to normal weight, were linked to higher severity of all symptoms, with stronger associations for obesity (OR ≥ 2·34, P < 0·001) than overweight (OR ≥ 1.37, P ≤ 0·032). Moderate activity, compared to low, was associated with lower severity of all symptoms (OR ≤ 0·77, P ≤ 0·032). High activity, compared to low, was associated with lower severity of fatigue, anxiety, and mobility issues (OR ≤ 0·74, P ≤ 0·029), but associations with spinal and general pain were not significant (OR ≤ 0·80, P ≥ 0·056). No BMI-by-physical activity combinations were detected, indicating physical activity benefits all BMI groups to a similar extent. Conclusion NICE and EULAR guidance for ISpAs should emphasise maintaining a normal weight. Moderate physical activity is optimal for reducing symptom severity and should be promoted in lifestyle guidance.

Revolutionize cold chain: an AI/ML driven approach to overcome capacity shortages

This research investigates how Artificial Intelligence (AI) and Machine Learning (ML) forecasting methodologies can be leveraged for cold chain capacity planning, specifically utilising Prophet and Seasonal Autoregressive Integrated Moving Average parametrised through grid search. In collaboration with Americold, the world's second-largest refrigerated logistic service provider, the study explores the challenges and opportunities in applying AI/ML techniques to complex operations covering 385 customers and a capacity of 73,296 pallet positions. We train and test several AI/ML and traditional statistical models using extensive data for every customer over 3.5 years. Based on the results, MAPE of 5.28% was achieved on the whole site level, and SARIMA outperformed ML models in most cases. Next, we show that developing and applying a Customer Segmentation Matrix has enabled more accurate forecasting and planning across various customer segments, addressing the issue of forecasting inaccuracies. This approach effectively improves forecasting inaccuracies, underscoring the significance of tailoring AI/ML models for demand forecasting within the cold-chain industry. Ultimately, this research presents an AI-driven approach that transcends mere forecasting, offering a practical pathway to manage capacity in light of the constraints.

Poverty‐Armed Conflict Nexus: Can Multidimensional Poverty Data Forecast Intrastate Armed Conflicts?

Poverty is widely acknowledged as a significant factor in the outbreak of armed conflicts, particularly fueling armed conflict within national borders. There is a compelling argument positing that poverty is a primary catalyst for intrastate armed conflicts; reciprocally, these conflicts exacerbate poverty. This article introduces a statistical model to forecast the likelihood of armed conflict within a country by scrutinizing the intricate relationship between intrastate armed conflicts and various facets of poverty. Poverty, arising from factors such as gender inequality and limited access to education and public services, profoundly affects social cohesion. Armed conflicts, a significant cause of poverty, result in migration, economic devastation, and adverse effects on social unity, particularly affecting disadvantaged and marginal groups. Forecasting and receiving early warnings for intrastate armed conflicts are crucial for international policymakers to take precautionary measures. Anticipating and proactively addressing potential conflicts can mitigate adverse consequences and prevent escalation. Hence, forecasting intrastate armed conflicts is vital, prompting policymakers to prioritize the development of effective strategies to mitigate their impact. While not guaranteeing absolute certainty in forecasting future armed conflicts, the model shows a high degree of accuracy in assessing security risks related to intrastate conflicts. It utilizes a machine‐learning algorithm and annually published fragility data to forecast future intrastate armed conflicts. Despite the widespread use of machine‐learning algorithms in engineering, their application in social sciences still needs to be improved. This article introduces an innovative approach to examining the correlation between various dimensions of poverty and armed conflict using machine‐learning algorithms.

Feline respiratory disease complex: insights into the role of viral and bacterial co-infections

Feline respiratory disease complex (FRDC) is a highly prevalent syndrome in cats that often result in fatal outcomes. FRDC etiology is complex, and often, multiple viral and bacterial pathogens are simultaneously associated with disease causation. There is limited information about the role of co-infections in pathogenesis and the current prevalence of pathogens in North America. We aimed to conduct a study using technical advances in molecular diagnosis and statistical modeling analysis to elucidate the occurrence of pathogens and how co-infections affect disease severity. We attained information from three diagnostic laboratories in North America regarding the occurrence of Bordetella bronchiseptica, Chlamydia felis, Mycoplasma, Felid alphaherpesvirus 1 (FeHV-1), feline calicivirus (FCV), and influenza A, along with age, seasonality, sex, and clinical signs. We also evaluated the role of co-infections in disease severity. These pathogens were also investigated in clinically normal cats (control). The most detected pathogens were Mycoplasma, FCV, and FeHV-1. Most pathogens were detected in the control group, highlighting the challenge of interpreting positive testing results. Co-infections of Mycoplasma and FCV, as well as Mycoplasma and FeHV-1, were important predictors of disease severity. Age, sex, and season had a minor impact on pathogen occurrence. This study provides new insights into FRDC and underlines the relevance of diagnostic panels to screen for a range of pathogens, providing knowledge for timely diagnosis and therapeutic interventions.

Teacher Cultivation of Classroom Statistical Modeling Practice: A Case Study

This report characterizes forms of dialogic support that a sixth-grade teacher generated during whole-class and small-group conversations to help students develop a practice of statistical modeling. During four weeks of instruction, students constructed and revised models to account for variability and uncertainty across a variety of random processes, many of which they experienced first-hand. Data sources for the research included field notes and video recordings of classroom conversations involving the teacher. Analyses identified and characterized teacher assistance during classroom episodes in which the teacher fostered the emergence and development of fundamental aspects of statistical modeling practice. These aspects included a) establishing mappings between models of chance and the situations or processes being modeled, b) imagining outcomes as constituents of a long-run random process, c) evaluating model fit, and d) making model-based inferences. Throughout instruction, the teacher systematically helped students coordinate these distinctive aspects of modeling practice. The findings suggest how teacher practices can promote the sustained development of statistical reasoning as a dialogue between data and model, and more generally, how teacher assistance attuned to student thinking about aspects of a disciplinary practice can foster a student-centered phenomenology of that practice.

Comparative study of multiple statistical damage mechanics models for rock behaviors under high temperature

Under the influence of external factors such as high temperature, chemical corrosion, and loading-unloading cycles, micro-cracks and pores may develop within rock structures. Continuum damage mechanics (CDM) characterizes these micro-defects arising within rocks, investigating the physico-mechanical behaviors of rocks in damaged states. This study, grounded in the theoretical framework of CDM and utilizing the Mohr-Coulomb failure criterion, integrates different probability distributions and damage coupling methods to develop three statistical damage models. The investigation primarily explores the effects of strength criteria, probability functions, and damage coupling mechanisms on the statistical damage models. In addressing the complex interplay between rock micro-defects and external stressors such as high temperatures and chemical corrosion, this study leverages the principles of CDM within the Mohr-Coulomb criterion framework. It advances the field by developing three innovative statistical damage models, enriched by diverse probability distributions and damage coupling methods. The research delineates the substantial impact of strength criteria, probability functions, and damage coupling mechanisms on the behavior of these models. Notably, it achieves a synthesis of theoretical constructs and experimental validation, demonstrating the models' capacity to reflect the nuanced behaviors of damaged rock accurately. Furthermore, this investigation contributes a methodological blueprint for statistical damage model construction, underscoring the critical selection of strength criteria and probability distributions. These efforts fortify the theoretical underpinnings necessary for the enhanced engineering application of statistics-driven damage analysis.

Analyzing Research on Air Quality Modeling in the Indian Setting: A Thorough Overview

India, being a developing nation, need efficient strategies to mitigate air pollution in order to prevent premature deaths of numerous individuals. Air quality models not only give data on the concentrations of air pollution, but also offer valuable knowledge about its sources. Prior research on air quality modelling conducted in India at the local and regional levels. This present study aims to evaluate the comprehensive grasp of the existing gaps and to explore potential future possibilities. The meticulously recorded studies conducted in various regions of India during the previous decade, employing systematic searches on various databases like Google Scholar and Google. The majority of air quality research mostly centers on megacities, disregarding the smaller cities that also require substantial attention in the future. There were very few research that were primarily focused on the central area of India, even though the majority of modelling studies were conducted in that region. Upon reviewing both local and regional numerical models, it became evident that there is a requirement for improved emission inputs. Furthermore, the statistical models have shown that it is important to meticulously choose key indicators in order to achieve precise source identification. Regardless of the emission inventory and models employed, Delhi consistently has significant underestimation of particulate matter concentrations, exacerbating its severe air pollution problems. The primary contributors to particulate matter in India are dust and emissions from transportation.

Association of SNP-SNP interactions of surfactant protein genes with severity of respiratory syncytial virus infection in children.

The severity of respiratory syncytial virus (RSV) may be linked to host genetic susceptibility. Surfactant protein (SP) genetic variants have been associated with RSV severity, but the impact of SNP-SNP (single nucleotide polymorphism) interactions remains unexplored. Therefore, we employed a novel statistical model to investigate the association of SNP-SNP interactions of SFTP genes with RSV severity in two and three-interaction models. We analyzed available genotype and clinical data from prospectively enrolled 405 children diagnosed with RSV, categorizing them into moderate or severe RSV groups. Using Wang's statistical model, we studied significant associations of SNP-SNP interactions with RSV severity in a case-control design. We observed, 1) association of three interactions with increased risk of severe RSV in a two-SNP model. One intragenic interaction was between SNPs of SFTPA2, and the other two were intergenic, involving SNPs of hydrophilic and hydrophobic SPs alone. 2) association of 22 interactions with RSV severity in a three-SNP model. Among these, 20 were unique, with 12 and 10 interactions associated with increased or decreased risk of RSV severity, respectively, and included at least one SNP of either SFTPA1 or SFTPA2. All interactions were intergenic, except one among SNPs of SFTPA1. The remaining interactions were either among SNPs of hydrophilic SPs alone (n=8) or among SNPs of both hydrophilic or hydrophobic SPs (n=11). Our findings indicate that SNPs of all SFTPs may contribute to genetic susceptibility to RSV severity. However, the predominant involvement of SFTPA1 and/or SFTPA2 SNPs in these interactions underscores their significance in RSV severity.

Transitive inference in a clinical childhood sample with a focus on autism spectrum disorder.

Transitive inference (TI) has a long history in the study of human development. There have, however, few pediatric studies that report clinical diagnoses have tested trial-and-error TI learning, in which participants infer item relations, rather than evaluate them explicitly from verbal descriptions. Children aged 8-10 underwent a battery of clinical assessments and received a range of diagnoses, potentially including autism spectrum disorder (ASD), attention-deficit hyperactive disorder (ADHD), anxiety disorders (AD), specific learning disorders (SLD), and/or communication disorders (CD). Participants also performed a trial-and-error learning task that tested for TI. Response accuracy and reaction time were assessed using a statistical model that controlled for diagnostic comorbidity at the group level. Participants in all diagnostic categories showed evidence of TI. However, a model comparison analysis suggested that those diagnosed with ASD succeeded in a qualitatively different way, responding more slowly to each choice and improving faster across trials than their non-ASD counterparts. Additionally, TI performance was not associated with IQ. Overall, our data suggest that superficially similar performance levels between ASD and non-ASD participants may have resulted from a difference in the speed-accuracy tradeoff made by each group. Our work provides a preliminary profile of the impact of various clinical diagnoses on TI performance in young children. Of these, an ASD diagnosis resulted in the largest difference in task strategy.

Synthesis estimators for transportability with positivity violations by a continuous covariate

Abstract Studies intended to estimate the effect of a treatment, like randomized trials, may not be sampled from the desired target population. To correct for this discrepancy, estimates can be transported to the target population. Methods for transporting between populations are often premised on a positivity assumption, such that all relevant covariate patterns in one population are also present in the other. However, eligibility criteria, particularly in the case of trials, can result in violations of positivity when transporting to external populations. To address nonpositivity, a synthesis of statistical and mathematical models can be considered. This approach integrates multiple data sources (e.g. trials, observational, pharmacokinetic studies) to estimate treatment effects, leveraging mathematical models to handle positivity violations. This approach was previously demonstrated for positivity violations by a single binary covariate. Here, we extend the synthesis approach for positivity violations with a continuous covariate. For estimation, two novel augmented inverse probability weighting estimators are proposed. Both estimators are contrasted with other common approaches for addressing nonpositivity. Empirical performance is compared via Monte Carlo simulation. Finally, the competing approaches are illustrated with an example in the context of two-drug vs. one-drug antiretroviral therapy on CD4 T cell counts among women with HIV.

Full-dimensional coupled-channel statistical approach to atom-triatom systems and applications to H/D + O3 reaction.

The statistical quantum model (SQM), which assumes that the reactivity is controlled by entrance/exit channel quantum capture probabilities, is well suited for chemical reactions with a long-lived intermediate complex. In this work, a time-independent coupled-channel implementation of the SQM approach is developed for atom-triatom systems in full dimensionality. As SQM treats the capture dynamics quantum mechanically, it is capable of handling quantum effects such as tunneling. A detailed study of the H/D + O3 capture dynamics was performed by applying the newly developed SQM method on an accurate global potential energy surface. Agreement with previous ring polymer molecular dynamics (RPMD) results on the same potential energy surface is excellent except for very low temperatures. The SQM results are also in reasonably good agreement with available experimental rate coefficients. The strong H/D kinetic isotope effect underscores the dominant role of quantum tunneling under an entrance channel barrier at low temperatures.

A robust time scale for space applications using the student's t-distribution

Abstract In this article, the principles of robust estimation are applied to the standard basic time scale equation to obtain a new method of assigning weights to clocks. Specifically, the Student's t-distribution is introduced as a new statistical model for an ensemble of clocks that are experiencing phase jumps, frequency jumps or anomalies in their measurement links. The proposed robust time scale is designed to mitigate the effects of these anomalies without necessarily identifying them, but through applying a method of robust estimation for the parameters of a Student's t-distribution. The proposed time scale algorithm using the Student's t-distribution (ATST) is shown to achieve comparable robustness to phase jumps, frequency jumps, and anomalies in the measurements with respect to the AT1 oracle time scale. The AT1 oracle is a special realization of the AT1 time scale which corrects all anomalies by having prior knowledge of their occurrences. The similar performance of ATST and AT1 oracle suggests that the ATST algorithm is efficient for obtaining robustness with no prior knowledge or detection of the occurrences of anomalies.

Effect of Indo-Mediterranean diet versus calorie-restricted diet in children with non-alcoholic fatty liver disease: A pilot randomized control trial.

Dietary interventions and increased physical activity are the cornerstones for management of the paediatric non-alcoholic fatty liver disease (NAFLD). Though, no specific diet has been proven superior, Indo-Mediterranean diet (IMD) has shown promise in adult literature. Thus, we aimed to compare the effect of IMD and a standard calorie-restricted diet (CRD) in Indian overweight children and adolescents with biopsy-proven NAFLD. Thirty-nine consecutive biopsy-proven NAFLD children between the ages of 8 and 18 years were randomized into either IMD or CRD for 180 days, and various parameters were evaluated at baseline and then after 180 days (NCT05073588). A total of 34 subjects (18 in IMD and 16 in CRD group) completed the study. There was a significantly higher decrease in controlled attenuation parameter (CAP) values (as a marker of hepatic steatosis; on transient elastography) (95% CI: 4.2-73.4, p = 0.042), weight (95% CI: 0.75-5.5, p = 0.046) and body mass index (BMI) (95% CI: 0.21-2.05, p = 0.014) (but not in Pediatric NAFLD Fibrosis Index or PNFI; as a marker of hepatic fibrosis) in IMD group compared to the CRD group. Liver stiffness measurement, serum cholesterol and low-density lipoprotein levels and HOMA-IR decreased only in the IMD group (p < 0.001). Our statistical model showed that delta-Weight was the only independent variable associated with delta-CAP. Both IMD and CRD can improve the various anthropometric, clinical, imaging and biochemical parameters but IMD was superior to CRD in terms of reducing CAP values and weight/BMI over 180 days in overweight/obese NAFLD children.

Accelerating development in wood‐plastic composites: Presenting key material properties and demonstrating statistical methods

AbstractIn a time when global initiatives to lower CO2 emissions are accelerating the shift towards bio‐based products, various efforts are being made to develop wood plastic composites (WPCs) and similar bio‐composites using a variety of raw materials. This paper first showcases benchmark material properties serving as standards and models the composition, for polypropylene‐based WPCs integrating wood flour and maleic anhydride‐modified polypropylene. Subsequently, by applying statistical modeling techniques, we demonstrate an adaptive experimental design that efficiently and rapidly adjusts formulations. Beginning with an empirical dataset (24 samples), we established high‐performing models that articulate the individual contributions of the constituents on ten vital properties of WPCs. Our adaptive experimental design, leveraging nonlinear partial least squares regression and the Bayesian optimization, successfully refined formulations to enhance bending and impact strengths. Notably, we proposed optimal conditions starting from just eight samples with minimal iterations. This study shows that statistical techniques can quickly optimize WPC formulations, making the overall development process faster. By addressing the multiple conflicting properties, these techniques can greatly reduce the time and effort needed for development.Highlights Applied several regression methods to analyze ten properties of PP‐based WPCs. Showcased distinct impacts of the formulations of WF and MAPP on WPCs. Implemented the adaptive experimental design for formulation optimization. Enhanced bending and impact strengths in the formulations of WPCs. Advanced sustainable material development with data‐driven approach.

Modeling 2,4-dichlorophenoxyacetic acid adsorption on candle bush pod-derived activated carbon: Insights from advanced statistical physics models

The widespread usage of 2,4-Dichlorophenoxyacetic acid (2,4-D) as an herbicide has led to alarming levels of environmental pollution, presenting severe risks to ecosystems and human health. This study aimed to synthesize a new adsorbent, activated carbon from candle bush pods (CBAC) via low-temperature phosphoric acid activation and investigate its ability for adsorptive elimination of 2,4-D. The setup of a new adsorption system requires the experimental determination of adsorption isotherms and their thorough modeling, which is achieved through advanced statistical physics models (ASPMs). The characterization of CBAC revealed a porous morphology with a remarkable specific surface area (415.31 m2/g). XRD revealed graphitic carbon structures, while XPS detected phosphate groups, graphitic structures, and oxygen-containing functional groups. Double layer with single energy (DLSE) model – one of the ASPMs revealed both non-parallel and parallel orientation of 2,4-D molecules on CBAC, with saturation adsorption capacity values increasing with temperature (up to 252.35 mg/g) at pH 2. The adsorption was physisorption (ΔE = 12.62–16.26 kJ/mol) and spontaneous and endothermic. Hence, the findings herein demonstrate the potential of CBAC as a sustainable and effective adsorbent for mitigating environmental pollution caused by 2,4-D.