Artificial intelligence models to predict acute phytotoxicity in petroleum contaminated soils

Synthetic biology and artificial intelligence in crop improvement

Synthetic biology plays a pivotal role in improving crop traits and increasing bioproduction through the use of engineering principles that purposefully modify plants through "design, build, test, and learn" cycles, ultimately resulting in improved bioproduction based on an input genetic circuit (DNA, RNA, and proteins). Crop synthetic biology is a new tool that uses circular principles to redesign and create innovative biological components, devices, and systems to enhance yields, nutrient absorption, resilience, and nutritional quality. In the digital age, artificial intelligence (AI) has demonstrated great strengths in design and learning. The application of AI has become an irreversible trend, with particularly remarkable potential for use in crop breeding. However, there has not yet been a systematic review of AI-driven synthetic biology pathways for plant engineering. In this review, we explore the fundamental engineering principles used in crop synthetic biology and their applications for crop improvement. We discuss approaches to genetic circuit design, including gene editing, synthetic nucleic acid and protein technologies, multi-omics analysis, genomic selection, directed protein engineering, and AI. We then outline strategies for the development of crops with higher photosynthetic efficiency, reshaped plant architecture, modified metabolic pathways, and improved environmental adaptability and nutrient absorption; the establishment of trait networks; and the construction of crop factories. We propose the development of SMART (self-monitoring, adapted, and responsive technology) crops through AI-empowered synthetic biotechnology. Finally, we address challenges associated with the development of synthetic biology and offer potential solutions for crop improvement.

Bioremediation of petroleum-contaminated soil based on both toxicity risk control and hydrocarbon removal-progress and prospect

Petroleum contamination remains a worldwide issue requiring cost-effective bioremediation techniques. However, establishing a universal bioremediation strategy for all types of oil-polluted sites is challenging. This difficulty arises from the heterogeneity of soil textures, the complexity of oil products, and the variations in local climate and environment across different oil-contaminated regions. Several factors can impede bioremediation efficacy: (i) differences in bioavailability and biodegradability between aliphatic and aromatic fractions of crude oil; (ii) inconsistencies between hydrocarbon removal efficiency and toxicity attenuation during remediation; (iii) varying adverse effect of aliphatic and aromatic fractions on soil microorganisms. This review examines the ecotoxicity risk of petroleum contamination to soil fauna and flora. It also discusses three primary bioremediation strategies: biostimulation with nutrients, bioaugmentation with petroleum degraders, and phytoremediation with plants. Based on current research and state-of-the-art challenges, we highlighted future research scopes should focus on (i) exploring the ecotoxicity differentiation of aliphatic and aromatic fractions of crude oil, (ii) establishing unified risk factors and indicators for evaluating oil pollution toxicity, (iii) determining the fate and transformation of aliphatic and aromatic fractions of crude oil using advanced analytical techniques, and (iv) developing combined bioremediation techniques that improve petroleum removal and ecotoxicity attenuation.

Understanding the phytotoxic effects of organic contaminants on rice through predictive modeling with molecular descriptors: A data-driven analysis

The widespread introduction of organic compounds into environments poses significant risks to ecosystems. Assessing the adverse effects of organic contaminants on crops is crucial for ensuring food safety. However, laboratory research is often time-consuming and costly, and machine learning (ML) methods can offer a viable solution to address these challenges. This study aimed at developing a ML model that incorporates chemical descriptors to predict the phytotoxicity of organic contaminants on rice. A dataset was compiled by gathering published experimental data on the phytotoxicity of 60 organic compounds, with a focus on morphological inhibition, photosynthesis perturbation, and oxidative stress. Four ML models (RF, SVM, GBM, ANN) were developed using chemical molecular descriptors (CMD) and the Molecular ACCess System (MACCS) keys. RF-MACCS model demonstrated the highest fitness, achieving an R2 value of 0.79 and an RMSE of 0.14. Feature importance analysis highlighted nAtom, HBA, logKow, and TPSA as the most influential CMDs in our model. Additionally, substructures containing oxygen atoms, carbonyl group and carbon chains with nitrogen and oxygen atoms were identified as significant factors associated with phytotoxicity. This data-driven study could aid in predicting the phytotoxicity of organic contaminants on crops and evaluating the potential risks of emerging contaminants in agroecosystems.

Cadmium accumulation in paddy soils affected by geological weathering and mining: Spatial distribution patterns, bioaccumulation prediction, and safe land usage

The abnormal enrichment of cadmium (Cd) in soil caused by rock weathering and mining activities is an issue in southern China. Although the soil Cd content in these regions is extremely high, the bioavailability of Cd in the soils differs significantly. The carbonate area (CBA) and tin-mining area (TIA) in Hezhou City were investigated to determine the primary features of soil Cd mobility in these regions and improve environmental management. Lateral and vertical spatial distributions revealed different accumulation and migration mechanisms of soil Cd in the CBA and TIA. Further analyses revealed that mining activities and geological weathering resulted in different soil geochemical parameters, thus yielding significantly lower levels of Cd in rice grains in the CBA than in the TIA. The random forest (RF) model predicted the bioaccumulation factor (BAF) (R2 = 0.69) better than the support vector machine (SVM) model (R2 = 0.68). Subsequently, a novel land management scheme was proposed based on soil Cd and the prediction of Cd in rice to optimize the spatial resources of agricultural land and ensure the safety of rice for consumption. This study provides a novel approach for land management in Cd-contaminated areas.

Advanced bioremediation by an amalgamation of nanotechnology and modern artificial intelligence for efficient restoration of crude petroleum oil-contaminated sites: a prospective study

Crude petroleum oil spillage is becoming a global concern for environmental pollution and poses a severe threat to flora and fauna. Bioremediation is considered a clean, eco-friendly, and cost-effective process to achieve success among the several technologies adopted to mitigate fossil fuel pollution. However, due to the hydrophobic and recalcitrant nature of the oily components, they are not readily bioavailable to the biological components for the remediation process. In the last decade, nanoparticle-based restoration of oil-contaminated, owing to several attractive properties, has gained significant momentum. Thus, intertwining nano- and bioremediation can lead to a suitable technology termed 'nanobioremediation' expected to nullify bioremediation's drawbacks. Furthermore, artificial intelligence (AI), an advanced and sophisticated technique that utilizes digital brains or software to perform different tasks, may radically transfer the bioremediation process to develop an efficient, faster, robust, and more accurate method for rehabilitating oil-contaminated systems. The present review outlines the critical issues associated with the conventional bioremediation process. It analyses the significance of the nanobioremediation process in combination with AI to overcome such drawbacks of a traditional approach for efficiently remedying crude petroleum oil-contaminated sites.

A review on control and abatement of soil pollution by heavy metals: Emphasis on artificial intelligence in recovery of contaminated soil

"Save Soil Save Earth" is not just a catchphrase; it is a necessity to protect soil ecosystem from the unwanted and unregulated level of xenobiotic contamination. Numerous challenges such as type, lifespan, nature of pollutants and high cost of treatment has been associated with the treatment or remediation of contaminated soil, whether it be either on-site or off-site. Due to the food chain, the health of non-target soil species as well as human health were impacted by soil contaminants, both organic and inorganic. In this review, the use of microbial omics approaches and artificial intelligence or machine learning has been comprehensively explored with recent advancements in order to identify the sources, characterize, quantify, and mitigate soil pollutants from the environment for increased sustainability. This will generate novel insights into methods for soil remediation that will reduce the time and expense of soil treatment.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma—optical emission spectroscopy (ICP-OES)—was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 ± 37.9 mg/kg), Copper (Cu) (6.76 ± 12.54 mg/kg), Lead (Pb) (6.34 ± 14.55 mg/kg), and Zinc (Zn) (23.44 ± 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51–91% and 80–99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making.

Express-phytotest for choosing conditions and following process of soil remediation

Phyto- and bioremediation are perspective methods for soil recultivation. In spite of resistance of plant-hyperaccumulators and degrading microorganisms to some contaminants, there are soil toxicity limits for their growth and activity. Therefore, simple and express methods are needed to estimate the soil phytotoxicity. This article is devoted to description of an express-phytotest evaluated by germination rate of white clover (Trifolium repens) (Ph_CG) for estimating phytotoxicity of contaminated soils. This phytotest was developed on the example of grey forest soil contaminated with diesel fuel or copper(II) and approbated during our long-year experiments on adsorptive bioremediation of petroleum-contaminated soils. The sensitivity of the phytotest values Ph_CG to these contaminants is much higher compared to those phytotests evaluated by germination of larger seeds: cress (Lepidium sativum), and wheat (Triticum vulgare). A significant increase of Ph_CG in those soils by 10% was already recorded at 50-100 mg of available Cu²⁺ kg^-1 and 1-5 g total petroleum hydrocarbons kg^-1, depending on the hydrocarbon composition. The sensitivity of the standard phytotests evaluated by root length of wheat seedlings or by plant (T. vulgare or T. repens) biomass is higher than that of Ph_CG determination. However, bio- and phytoremediation are mostly applied for heavily contaminated soils. Therefore, use of the simple and cheap express phytotest for choosing optimal conditions of the soil remediation and following the process is quite justified. Besides, measuring an additional parameter-root length of the white clover seedlings may significantly increase the sensitivity of the express phytotest for lower contaminated soils.

Rapid diagnosis of heavy metal pollution in lake sediments based on environmental magnetism and machine learning

Environmental magnetism in combination with machine learning can be used to monitor heavy metal pollution in sediments. Magnetic parameters and heavy metal concentrations of sediments from Chaohu Lake (China) were analyzed. The magnetic measurements, high- and low-temperature curves, and hysteresis loops showed the primary magnetic minerals were ferrimagnetic minerals in sediments. For most metals, their concentrations were highest during the wet season and lowest during the medium-water period. Cd, Hg, and Zn were moderately enriched and Cd and Hg posed a considerable ecological risk. A redundancy analysis indicated a relationship between physicochemical indexes and magnetic parameters and heavy metal concentrations. An artificial neural network (ANN) and support vector machine (SVM) were used to construct six models to predict the heavy metal concentrations and ecological risk index. The inclusion of both the physicochemical indexes and magnetic parameters as input factors in the models were significantly ameliorated the simulation accuracy for the majority of heavy metals. The training and test R, for Be, Fe, Pb, Zn, As, Cu, and Cr were > 0.8. The SVM showed better performance and hence it has potential for the efficient and economical long-term tracking and monitoring of heavy metal pollution in lake sediments.

An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions

The development of computer aid models for heavy metals (HMs) simulation has been remarkably advanced over the past two decades. Several machine learning (ML) models have been developed for modeling HMs over the past two decades with outstanding progress. Although there have been a noticeable number of diverse ML models investigations, it is essential to have an informative vision on the progression of those computer aid models. In the current short review covering the simulation of heavy metals in contaminated soil, water bodies and removal from aqueous solution, numerous aspects on the methodological and conceptual HMs modeling are reviewed and discussed in detail. For instance, the limitation of the classical analytical methods, types of heavy metal dataset, necessity for new versions of ML models exploration, HM input parameters selection, ML models internal parameters tuning, performance metrics selection and the types of the modelled HM. The current review provides few outlooks in understanding the underlying od the ML models application for HM simulation. Tackling these modeling aspects is significantly essential for ML developers and environmental scientists to obtain creditability and scientific consistency in the domain of environmental science. Based on the discussed modeling aspects, it was concluded several future research directions, which will promote environmental scientists for better understanding of the underlying HMs simulation.