Improved Machine Learning Models by Data Processing for Predicting Life-Cycle Environmental Impacts of Chemicals

Prediction of PFAS bioaccumulation in different plant tissues with machine learning models based on molecular fingerprints

Due to the wastewater irrigation or biosolid application, per- and polyfluoroalkyl substances (PFASs) have been widely detected in agriculture soil and hence crops or vegetables. Consumption of contaminated crops and vegetables is considered as an important route of human exposure to PFASs. Machine learning (ML) models have been developed to predict PFAS uptake by plants with majority focus on roots. However, ML models for predicting accumulation of PFASs in above ground edible tissues have yet to be investigated. In this study, 811 data points covering 22 PFASs represented by molecular fingerprints and 5 plant categories (namely the root class, leaf class, cereals, legumes, and fruits) were used for model development. The Extreme Gradient Boosting (XGB) model demonstrated the most favorable performance to predict the bioaccumulation factors (BAFs) in all the 4 plant tissues (namely root, leaf, stem, and fruit) achieving coefficients of determination R2 as 0.82-0.93. Feature importance analysis showed that the top influential factors for BAFs varied among different plant tissues, indicating that model developed for root concentration prediction may not be feasible for above ground parts. The XGB model's performance was further demonstrated by comparing with data from pot experiments measuring BAFs of 12 PFASs in lettuce. The correlation between predicted and measured results was favorable for BAFs in both lettuce roots and leaves with R2 values of 0.76 and 0.81. This study developed a robust approach to comprehensively understand the uptake of PFASs in both plant roots and above ground parts, offering key insights into PFAS risk assessment and food safety.

Mapping global distributions of clay-size minerals via soil properties and machine learning techniques

Clay-size mineral is a vital ingredient of soil that influences various environment behaviors. It is crucial to establish a global distribution map of clay-size minerals to improve the recognition of environment variations. However, there is a huge gap of lacking some mineral contents in poorly accessible remote areas. In this work, machine learning (ML) approaches were conducted to predict the mineral contents and analyze their global abundance changes through the relationship between soil properties and mineral distributions. The average content of kaolinite, illite, smectite, vermiculite, chlorite, and feldspar were predicated to be 28.69 %, 22.30 %, 12.42 %, 5.43 %, 5.03 %, and 1.44 % respectively. Model interpretation showed that topsoil bulk density and drainage class were the most significant factors for predicting all six minerals. It could be seen from the feature importance analysis that bulk density notably reflected the distribution of 2:1 layered minerals more than that of 1:1 mineral. High drainage favored secondary minerals development, while low drainage was more benefited for primary minerals. Moreover, the content variation of different minerals aligned with the distribution of corresponding soil properties, which affirmed the accuracy of established models. This study proposed a new approach to predict mineral contents through soil properties, which filled a necessary step of understanding the geochemical cycles of soil-related processes.

Experimental insights and modeling innovations: Deciphering Fe(VI) oxidation in imidazole ionic liquids through QSAR and RFR

In this investigation, we conducted a detailed analysis of the oxidation of 16 imidazole ionic liquid variants by Fe(VI) under uniform experimental setups, thereby securing a dataset of second-order reaction rate constants (kobs). This methodology ensures superior data consistency and comparability over traditional methods that amalgamate disparate data from varied studies. Utilizing 16 chemical structural parameters obtained via Density Functional Theory (DFT) as descriptors, we developed a Quantitative Structure Activity Relationship (QSAR) model. Through rigorous correlation analysis, Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Applicability Domain (AD) evaluation, we identified a pronounced negative correlation between the molecular orbital gap energy (Egap) and kobs. MLR analysis further underscored Egap as a pivotal predictive variable, with its lower values indicating heightened oxidative reactivity towards Fe(VI) in the ionic liquids, leading the QSAR model to achieve a predictive accuracy of 0.95. Furthermore, we integrated an advanced machine learning approach - Random Forest Regression (RFR), which adeptly highlighted the critical factors influencing the oxidation efficiency of imidazole ionic liquids by Fe(VI) through elaborate decision trees, feature importance assessment, Recursive Feature Elimination (RFE), and cross-validation strategies. The RFR model demonstrated a remarkable predictive performance of 0.98. Both QSAR and RFR models pinpointed Egap as a key descriptor significantly affecting oxidation efficiency, with the RFR model presenting lower root mean square errors, establishing it as a more reliable predictive tool. The application of the RFR model in this study significantly improved the model's stability and the intuitive display of key influencing factors, introducing promising advanced analytical tools to the field of environmental chemistry.

Exploring pollutant joint effects in disease through interpretable machine learning

Identifying the impact of pollutants on diseases is crucial. However, assessing the health risks posed by the interplay of multiple pollutants is challenging. This study introduces the concept of Pollutants Outcome Disease, integrating multidisciplinary knowledge and employing explainable artificial intelligence (AI) to explore the joint effects of industrial pollutants on diseases. Using lung cancer as a representative case study, an extreme gradient boosting predictive model that integrates meteorological, socio-economic, pollutants, and lung cancer statistical data is developed. The joint effects of industrial pollutants on lung cancer are identified and analyzed by employing the SHAP (Shapley Additive exPlanations) interpretable machine learning technique. Results reveal substantial spatial heterogeneity in emissions from CPG and ILC, highlighting pronounced nonlinear relationships among variables. The model yielded strong predictions (an R of 0.954, an RMSE of 4283, and an R2 of 0.911) and emphasized the impact of pollutant emission amounts on lung cancer responses. Diverse joint effects patterns were observed, varying in terms of patterns, regions (frequency), and the extent of antagonistic and synergistic effects among pollutants. The study provides a new perspective for exploring the joint effects of pollutants on diseases and demonstrates the potential of AI technology to assist scientific discovery.

Enhanced Deep-Learning Model for Carbon Footprints of Chemicals

Millions of chemicals have been designed; however, their product carbon footprints (PCFs) are largely unknown, leaving questions about their sustainability. This general lack of PCF data is because the data needed for comprehensive environmental analyses are typically not available in the early molecular design stages. Several predictive tools have been developed to estimate the PCF of chemicals, which are applicable to only a narrow range of common chemicals and have limited predictive ability. Here, we propose FineChem 2, which is based on a novel transformer framework and first-hand industry data, for accurately predicting the PCF of chemicals. Compared to previous tools, FineChem 2 demonstrates significantly better predictive power, and its applicability domains are improved by ∼75% on a diverse set of chemicals on the global market, including the high-production-volume chemicals identified by regulators, daily chemicals, and chemical additives in food and plastics. In addition, through better interpretability from the attention mechanism, FineChem 2 may successfully identify PCF-intensive substructures and critical raw materials of chemicals, providing insights into the design of more sustainable molecules and processes. Therefore, we highlight FineChem 2 for estimating the PCF of chemicals, contributing to advancements in the sustainable transition of the global chemical industry.

End-to-end machine-learning for high-gravity ammonia stripping: Bridging the gap between scientific research and user-friendly applications

The removal and recovery of ammonia from wastewater are critical processes for achieving global environmental sustainability and promoting circular economic development. High-gravity technology is an advanced solution to achieve ammonia stripping from wastewater. This study used machine-learning (ML) techniques to provide more comprehensive insights on various influencing factors, including the operating parameters, wastewater characteristics, and design parameters of rotating packed beds. Bayesian auto-optimization combined with a boosting algorithm effectively overcame the challenges of modeling complex datasets with small sample sizes, multidimensional data, missing values, and skewed distributions. Accurate ML based predictive models for the ammonia removal efficiency (η) and mass transfer coefficient (KLa) were developed, the performance on the training set was R2 = 0.98 and R2 = 0.89, and on the testing set was R2 = 0.98 and R2 = 0.82. The developed model revealed that the stripping stage and gas-liquid ratio were the most influential features for predicting η, whereas the liquid flow and high-gravity factor were the most important features for predicting KLa. The well-trained model was then deployed in an online software application that could provide both predictive and auto-update functions for operators and managers, ensuring that practitioners could use the model. The end-to-end machine-learning approach used in this study-that is, covering data collection, model development, and application-could improve the availability of research results, providing valuable references for the further advancement of technology in the field of environmental.

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.

Networking Chemicals Flows: Efficiency-Value-Environment Functionalized Symbiosis Algorithms and Application

Maximizing the network of chemical symbiosis can enhance economic benefits and reduce life cycle environmental impacts, which are pivotal for achieving sustainability in the chemical industry. This study designs two innovative symbiosis algorithms, the Longest Path Algorithm and the Maximum Symbiosis Algorithm, specifically for chemical industrial clusters (CICs). The algorithms are applied to a prototypical CIC encompassing 569 distinct raw materials and yielding 435 unique products alongside 55 byproducts. First, the study provides an exhaustive overview of the assorted chemicals and their intrinsic traits, flow patterns, and conventional relationships within the cluster. On that basis, the former algorithm reveals that the longest path constitutes 5 enterprises, embodying the entire disperse dyestuff industry chain; the latter algorithm identifies 218 pairs of symbiotic relationships, leading to an additional 0.91 million tonnes of symbiotic chemicals. These interrelations also yield substantial cost savings of 1.25 billion CNY (0.17 billion US dollar) and enhance life cycle benefits by 0.62 to 11.87 times compared to the present status. The efficacious application of these algorithms to the cluster reaffirms their capacity to meet the designated objectives. This study introduces a fresh interdisciplinary standpoint to optimize chemical manufacturing processes and contributes essential theoretical underpinning for implementing pollution and carbon reduction strategies in similar CICs.

Emerging applications of biochar: A review on techno-environmental-economic aspects

Biomass fast pyrolysis produces bio-oil and biochar achieving circular economy. This review explored the emerging applications of biochar. Biochar possesses the unique properties for removing emerging contaminants and for mine remediation, owing to its negative charge surface, high specific surface area, large pore size distribution and surface functional groups. Additionally, biochar could adsorb impurities such as CO2, moisture, and H2S to upgrade the biogas. Customizing pyrolysis treatments, optimizing the feedstock and pyrolysis operating conditions enhance biochar production and improve its surface properties for the emerging applications. Life cycle assessment and techno-economic assessment indicated the benefits of replacing conventional activated carbon with biochar.

Contribution of molecular structures and quantum chemistry technique to root concentration factor: An innovative application of interpretable machine learning

Root concentration factor (RCF) is a significant parameter to characterize uptake and accumulation of hazardous organic contaminants (HOCs) by plant roots. However, complex interactions among chemicals, plant roots and soil make it challenging to identify underlying mechanisms of uptake and accumulation of HOCs. Here, nine machine learning techniques were applied to investigate major factors controlling RCF based on variable combinations of molecular descriptors (MD), MACCS fingerprints, quantum chemistry descriptors (QCD) and three physicochemical properties related to chemical-soil-plant system. Compared to models with variables including MACCS fingerprints or solitary physicochemical properties, the XGBoost-6 model developed by the variable combination of MD, QCD and three physicochemical properties achieved the most remarkable performance, with R2 of 0.977. Model interpretation achieved by permutation variable importance and partial dependence plots revealed the vital importance of HOCs lipophilicity, lipid content of plant roots, soil organic matter content, the overall deformability and the molecular dispersive ability of HOCs for regulating RCF. The integration of MD and QCD with physicochemical properties could improve our knowledge of underlying mechanisms regarding HOCs accumulation in plant roots from innovative structural perspectives. Multiple variables combination-oriented performance improvement of model can be extended to other parameters prediction in environmental risk assessment field.