Air pollution, water pollution, and robots: Is technology the panacea

Interpretable prediction, classification and regulation of water quality: A case study of Poyang Lake, China

Effective identification and regulation of water quality impact factors is essential for water resource management and environmental protection. However, the complex coupling of water quality systems poses a significant challenge to this task. This study proposes coherent model for water quality prediction, classification and regulation based on interpretable machine learning. The decomposition-reconstruction module is used to transform non-stationary water quality series into stationary series while effectively reducing the feature dimensions. Spatiotemporal multi-source data is introduced by using the Maximum Information Coefficient (MIC) for feature selection. The Temporal Convolutional Network (TCN) is used to extract the temporal features of different variables, followed by the introduction of External Attention mechanism (EA) to construct the relationship between these features. Finally, the target water quality sequence is simulated using Gated Recurrent Unit (GRU). The proposed model was applied to Poyang Lake in China to predict six water quality indicators: ammonia nitrogen (NH3-N), dissolved oxygen (DO), pH, total nitrogen (TN), total phosphorus (TP), water temperature (WT). The water quality was then classified based on the prediction results using the XGBoost algorithm. The findings indicate that the proposed model's Nash-Sutcliff Efficiency (NSE) value ranges from 0.88 to 0.99, surpassing that of the benchmark model, and demonstrates strong interval prediction performance. The results highlight the superior performance of the XGBoost algorithm (with an accuracy of 0.89) in addressing water quality classification issues, particularly in cases of category imbalance. Subsequently, interpretability analysis using the SHapley Additive exPlanation (SHAP) method revealed that the model is capable of learning relationships between different variables and there exists a possibility of learning the physical laws. Ultimately, this study proposes a water quality regulation mechanism that improves TN and DO levels by stepwise changing the magnitude of water temperature, which significantly improves in the case of data limitations. In conclusion, this study presents an overall framework for integrating water quality prediction, classification and improvement for the first time, forming a complete set of water quality early warning and improvement management strategies. This framework provides new ideas and ways for lake water quality management.

Green workspace and urban health: exploring the impacts of industrial robotics in pollution emissions and public health

Introduction

This study addresses a critical gap in understanding how technological advancements, specifically industrial robots, influence urban pollution emissions and public health. The rapid evolution of technology and changing working conditions significantly affect these areas, yet research has not extensively explored this domain.

Methods

Utilizing 2018 China Labor-force Dynamic Survey (CLDS) dataset, this study examines the impact of industrial robots on public health. An analytical framework is employed to assess the correlation between the adoption of eco-friendly industrial robots and improvements in worker health, attributed to the reduction of pollution emissions.

Results

The findings reveal that the adoption of industrial robots significantly enhance both public physical and mental health. This study also identifies potential demographic heterogeneity in the effects of industrial robots. The benefits are more pronounced among non-insured manual female workers who are older, have lower education levels, and hold rural hukou. These benefits are closely linked to improvements in the quality of the production environment and reductions in pollution emissions at both macro and micro levels.

Discussion

The study underscores the significant potential of industrial robots to positively impact urban health, advocating for strategies that promote the development of safer, greener environments.

Biosorption of Remazol Brilliant Blue R textile dye using Clostridium beijerinckii by biorefinery approach

The current study proposes RBBR biosorption by Clostridium beijerinckii DSMZ 6422 biomass remaining after biobutanol production from pumpkin peel (PP) by a zero-waste approach. Efficient biobutanol production was achieved by investigating initial PP concentrations (5-20% without or with enzymatic hydrolysis) and fermentation time. According to this, the highest concentrations of biobutanol and total ABE were obtained as 4.87 g/L and 8.13 g/L in the presence of 10% PP without enzymatic hydrolysis at 96 h. Furthermore, based on the zero-waste approach, C. beijerinckii DSMZ 6422 biomass obtained after biofuel production was used as a biosorbent for the removal of RBBR dye. Response surface methodology (RSM), commonly utilized for the experimental design, was used to specify the optimized biosorption conditions of RBBR, including initial dye concentration (50-200 mg/L), initial pH (2-6), biosorbent concentration (1-3 g/L), and contact time (0-240 min). The highest biosorption under optimized conditions with RSM was 98% in the presence of 194.36 mg/L RBBR and 2.65 g/L biosorbent at pH 2 and 15 min. This is the first report in the literature about the biosorption of RBBR dye by anaerobic C. beijerinckii biomass after the biobutanol production process. This study also shows the efficient usage of agricultural and microbial wastes in different areas based on zero-waste applications.

Path to autonomous soil sampling and analysis by ground-based robots

Good site characterization is essential for the selection of remediation alternatives for impacted soils. The value of site characterization is critically dependent on the quality and quantity of the data collected. Current methods for characterizing impacted soils rely on expensive manual sample collection and off-site analysis. However, recent advances in terrestrial robotics and artificial intelligence offer a potentially revolutionary set of tools and methods that will help to autonomously explore natural environments, select sample locations with the highest value of information, extract samples, and analyze the data in real-time without exposing humans to potentially hazardous conditions. A fundamental challenge to realizing this potential is determining how to design an autonomous system for a given investigation with many, and often conflicting design criteria. This work presents a novel design methodology to navigate these criteria. Specifically, this methodology breaks the system into four components - sensing, sampling, mobility, and autonomy - and connects design variables to the investigation objectives and constraints. These connections are established for each component through a survey of existing technology, discussion of key technical challenges, and highlighting conditions where generality can promote multi-application deployment. An illustrative example of this design process is presented for the development and deployment of a robotic platform characterizing salt-impacted oil & gas reserve pits. After calibration, the relationship between the in situ robot chloride measurements and laboratory-based chloride measurements had a good linear relationship (R2-value = 0.861) and statistical significance (p-value = 0.003).

Inherited Structure Properties of Larch Arabinogalactan Affected via the TEMPO/NaBr/NaOCl Oxidative System

Arabinogalactan (AG), extracted from larch wood, is a β-1,3-galactan backbone and β-1,6-galactan side chains with attached α-1-arabinofuranosyl and β-1-arabinopyranosyl residues. Although the structural characteristics of arabinogalactan II type have already been studied, its functionalization using 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO) oxidation remains a promising avenue. In this study, the oxidation of AG, a neutral polysaccharide, was carried out using the TEMPO/NaBr/NaOCl system, resulting in polyuronides with improved functional properties. The oxidation of AG was controlled by analyzing portions of the reaction mixture using spectrophotometric and titration methods. To determine the effect of the TEMPO/NaBr/NaOCl system, air-dried samples of native and oxidized AG were studied by Fourier-transform infrared (FTIR) and nuclear magnetic resonance (NMR) spectroscopy, as well as by gel permeation chromatography. Compounds that model free (1,1-diphenyl-2-picrylhydrazyl (DPPH)) and hydroxyl radicals (iron(II) sulfate, hydrogen peroxide, and salicylic acid) were used to study the antioxidant properties. It was found that, in oxidized forms of AG, the content of carboxyl groups increases by 0.61 mmol compared to native AG. The transformation of oxidized AG into the H+ form using a strong acid cation exchanger leads to an increase in the number of active carboxyl groups to 0.76 mmol. Using FTIR spectroscopy, characteristic absorption bands (1742, 1639, and 1403 cm-1) were established, indicating the occurrence of oxidative processes with a subsequent reduction in the carboxyl group. The functionality of AG was also confirmed by gel permeation chromatography (GPC), which is reflected in an increase in molecular weights (up to 15,700 g/mol). A study of the antioxidant properties of the oxidized and protonated forms of AG show that the obtained antioxidant activity (AOA) values are generally characteristic of polyuronic acids. Therefore, the TEMPO oxidation of AG and other neutral polysaccharides can be considered a promising approach for obtaining compounds with the necessary controlled characteristics.

Advancements in application of modified biochar as a green and low-cost adsorbent for wastewater remediation from organic dyes

Synthetic organic dyes, which are resistant to biodegradation, pose a notable health risk, potentially leading to cancer and respiratory infections. Researchers have addressed this concern by exploring physicochemical methods to remove organic dyes from wastewater. A particularly promising solution involves modified biochar adsorbents, which demonstrate high efficiency in organic dye removal. Biochar, a charcoal-like material derived from biomass pyrolysis, offers advantages such as low cost, eco-friendliness, high efficiency and reusability. Beyond its role in sustainable soil remediation, biochar proves effective in removing organic dyes from wastewater after undergoing physical or chemical modification. Acid-base activation or metal-heteroatom impregnation enhances biochar's adsorption capacity. This comprehensive review examines the attributes of biochar, common methods for production and modification, and the impacts of raw materials, pyrolysis temperature, heating rate and residence time. It further elucidates the biochar adsorption mechanism in the removal of organic dyes, assessing factors influencing efficiency, including biochar feedstock, solution pH, adsorption temperature, particle size, initial dye concentration, biochar dosage and reaction time. It explores challenges, opportunities, reusability and regeneration methods of biochar in treating organic dye wastewater. It also discusses recent advances in organic dye removal using adsorption-based biochar. The review ultimately advocates for enhancing biochar's adsorption performance through post-modification.

Single and combined phytoextraction of lead and cadmium on submerged plants Potamogeton pusillus L.: removal, bioaccumulation pattern, and phytotoxicity

Under the present investigation, the submerged plant Potamogeton pusillus has been tested for the removal of lead (Pb) and cadmium (Cd). P. pusillus removal efficiency and accumulation capacity were examined in separated Pb and Cd solutions, at 0.5, 1.0, and 2 mg L-1, and in solutions where both metals were present at the same concentration (0.5, 1.0, and 2 mg L-1), under laboratory conditions for 3, 7, and 10 days. Also, we examined the removal efficiency and accumulation capacity when a set of plants were exposed to 0.5 mg L-1 of Pb (or Cd) and increasing concentrations (0.5, 1, and 2 mg L-1) of Cd (or Pb) for 10 days. The effect of Cd and Pb was assessed by measuring changes in the chlorophylls, carotenoids, and malondialdehyde contents. Results showed that P. pusillus could accumulate Cd and Pb from individual solutions. Roots and leaves accumulated the highest amount of Cd and Pb followed by the stems. Some phytotoxic effects were observed, especially at individual Cd exposures, but these effects were not observed in the two-metal system. The removal and accumulation of Pb by P. pusillus were significantly enhanced in the presence of Cd under certain conditions, presenting a good alternative for the removal of these metals from polluted aquifers. To the extent of our knowledge, this is the first report on both enhanced phytoextraction of Pb in the presence of Cd and bioaccumulation of these heavy metals by P. pusillus.

2D MXenes and their composites; design, synthesis, and environmental sensing applications

Novel 2D layered MXene materials were first reported in 2011 at Drexel University. MXenes are widely used in multidisciplinary applications due to their anomalous electrical conductivity, high surface area, and chemical, mechanical, and physical properties. This review summarises MXene synthesis and applications in environmental sensing. The first section describes different methods for MXene synthesis, including fluorinated and non-fluorinated methods. MXene's layered structure, surface terminal groups, and the space between layers significantly impact its properties. Different methods to separate different MXene layers are also discussed using various intercalation reagents and commercially synthesized MXene without compromising the environment. This review also explains the effect of MXene's surface functionalization on its characteristics. The second section of the review describes gas and pesticide sensing applications of Mxenes and its composites. Its good conductivity, surface functionalization with negatively charged groups, intrinsic chemical nature, and good mechanical stability make it a prominent material for room temperature sensing of environmental samples, such as polar and nonpolar gases, volatile organic compounds, and pesticides. This review will enhance the young scientists' knowledge of MXene-based materials and stimulate their diversity and hybrid conformation in environmental sensing applications.

Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild

The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting processes and the diverse forms of waste. Deep learning (DL) models have made remarkable strides in automating domestic waste recognition and sorting. However, the application of DL models to recognize the waste derived from construction, renovation, and demolition (CRD) activities remains limited due to the context-specific studies conducted in previous research. This paper aims to realistically capture the complexity of waste streams in the CRD context. The study encompasses collecting and annotating CRD waste images in real-world, uncontrolled environments. It then evaluates the performance of state-of-the-art DL models for automatically recognizing CRD waste in-the-wild. Several pre-trained networks are utilized to perform effectual feature extraction and transfer learning during DL model training. The results demonstrated that DL models, whether integrated with larger or lightweight backbone networks can recognize the composition of CRD waste streams in-the-wild which is useful for automated waste sorting. The outcome of the study emphasized the applicability of DL models in recognizing and sorting solid waste across various industrial domains, thereby contributing to resource recovery and encouraging environmental management efforts.

Electrochemical nitrate reduction over bimetallic Pd-Sn nanocatalysts with tunable selectivity toward benign nitrogen

Nitrate is recognized as a highly impactful water contaminant among various pollutants in water. To address the ever-growing demand for water purification, this work investigates the bimetallic palladium (Pd) and tin (Sn) catalysts, which are electrochemically deposited on stainless steel mesh support (Pd-Sn/SS) for the selective conversion of harmful nitrate (NO3-) into benign nitrogen (N2) gas. Results indicate that the bimetallic composition in Pd-Sn/SS electrodes substantially influenced the reaction route for nitrate reduction as well as the performance of nitrate transformation and nitrogen selectivity. It is found that the electrode prepared from Pd:Sn = 1:1 (mole ratio) demonstrates an outstanding nitrate conversion of 95%, nitrogen selectivity of 88%, and nitrogen yield of 82%, which outperform many reported values in the literature. The electrochemically synthesized bimetallic electrode proposed herein enables a new insight for promoting the reactivity and selectivity of nitrate reduction in water.

Multifunctional carboxymethyl cellulose/graphene oxide/polyaniline hybrid thin film for adsorptive removal of Cu(II) and oxytetracycline antibiotic from wastewater

The antibiotics and metal ions in the contaminated water bodies must be removed using appropriate methods for sustainable development. In this study, the multifunctional carboxymethyl cellulose/graphene oxide/polyaniline (CMC/GO/PANI) hybrid thin film was synthesized and utilized for adsorptive scavenging of (Cu(II) and oxytetracycline (OTC) from wastewater. The prepared thin films' morphology, chemical compositions, functionality, and surface charge were analyzed by well-known physicochemical techniques. The adsorption process of the selected model pollutants was examined as a function of reaction time, Cu(II), and OTC solution pH, concentrations, and temperatures. Results showed that CMC/GO/PANI hybrid thin film had higher Cu(II) and OTC adsorption than CMC, GO/CMC, and PANI/CMP thin films due to the multifunctional synergetic effect. The adsorption kinetic data were fitted to the pseudo-second-order model. Redlich-Peterson isotherm model well interpreted Cu(II) and OTC scavenging equilibrium data. Energetically, the adsorption was spontaneous and endothermic for both pollutants. The multifunctional CMC/GO/PANI thin film was recycled and reused seven times during adsorption-desorption cycles. The study outcomes demonstrated that CMC/GO/PANI thin film could be reused multiple times for large-scale wastewater purification.

Biogenic polymer nanoparticles to remove hydrophobic organic contaminants from water

Soil and water pollution is of significant concern worldwide because of the consequences of environmental degradation and harmful effects on human health. Water bodies are very much polluted by various organic and inorganic pollutants by different human activities, including industrial wastes. Environmental pollution remains high because of urbanization-induced industrial developments and human lifestyle. It accumulates pollutants in the environment including plants and living organisms. Even mothers' milk is poisoned because of the uncontrolled, widespread increase in pollution. The discharge levels of organic hydrophobic contaminants in the water and soil are increasing rapidly. This severe pollution must be remediated to upgrade the environment and ensure the safety of human beings. It is vital to eradicate soil and water pollution to guarantee sufficient food and water. Different techniques available to remove the pollutants vary according to the type of pollutants. Hydrophobic contaminants are more dangerous than heavy metals and other pollutants; they cannot be easily removed, requiring special care. Hydrophobic organoxenobiotics released in the environment pose severe contamination in soil and water. Therefore, developing efficient and cost-effective processes is necessary to remove hydrophobic contaminants from soil and water. With nanoparticle-mediated remediation techniques, the green-synthesized nanoparticles exhibit improved performance. This review consolidates reports on the remediation techniques of hydrophobic contaminants, focusing on green-synthesized remediation agents. The very limited works on green synthesis of polymeric nanoparticles, particularly polyurethane-based materials for organic contaminants removal demand more attention in this area. PRACTITIONER POINTS: Consolidated the effects of hydrophobic organic and plastic contaminants on environment degradation. Summarized the advantages of green synthesized polymer nanoparticles for efficient removal of hydrophobic contaminants. Discussed the different sources of pollution and remediation techniques referring 112 research works.

Do industrial robots reduce carbon intensity? The role of natural resource rents and corruption control

Although the research on the impact of robotics on carbon emissions is increasing, there are still relatively few studies on the impact of robots on carbon intensity from the perspective of natural resources and corruption. In order to fill in the research gaps, panel data from 66 countries between 1993 and 2018 are collected, and linear and nonlinear panel regression approaches are developed. Natural resource rent and corruption control are used as threshold variables, robot penetration is used as explanatory variables, and carbon emission intensity is the explained variable. The results of the linear model show that robot penetration is negatively correlated with carbon emission intensity, which means that robot penetration reduces carbon emission intensity. The results of the nonlinear model show that when natural resource rents and corruption control are used as thresholds, the relationship between robot penetration and carbon emission intensity presents a U shape and an inverted U shape, respectively. Specifically, the threshold for natural resource rents is 4.7%. When the natural resource rent is lower than this threshold, the robot penetration rate reduces the carbon emission intensity, but when the natural resource rent is higher than this threshold, the robot penetration rate increases the carbon emission intensity. The threshold value of corruption control is -0.4349. When the corruption control is lower than this threshold, the robot penetration rate increases the carbon emission intensity. If the corruption control is higher than this threshold, the robot reduces the carbon emission intensity. Finally, policy recommendations for better use of robotics to reduce carbon emission intensity are put forward from the perspective of natural resource rent and corruption control.

Graphitic carbon nitride metal-free photocatalyst for the simultaneous removal of emerging pharmaceutical pollutants in wastewater

The presence of pharmaceutical pollutants in water has emerged as a significant public health concern due to their potential adverse impacts, including the development of antibiotic resistance. Consequently, advanced oxidation processes based on photocatalysis have garnered considerable attention for treating pharmaceutical contaminants in wastewater. In this study, graphitic carbon nitride (g-CN), a metal-free photocatalyst, was synthesized by the polymerization of melamine and assessed as a potential candidate for the photodegradation of acetaminophen (AP) and carbamazepine (CZ) in wastewater. Under alkaline conditions, g-CN demonstrated high removal efficiencies of 98.6% and 89.5% for AP and CZ, respectively. The relationships between degradation efficiency and catalyst dosage, initial pharmaceutical concentration, and photodegradation kinetics were investigated. Increasing the catalyst dose facilitated the removal of antibiotic contaminants, with an optimum catalyst dose of 0.1 g, achieving a photodegradation efficiency of 90.2% and 82.7% for AP and CZ, respectively. The synthesized photocatalyst removed over 98% of AP (1 mg/L) within 120 min, with a rate constant of 0.0321 min-1, 2.14 times faster than that of CZ. Quenching experiments revealed that g-CN was active under solar light and generated highly reactive oxidants such as hydroxyl (•OH) and superoxide (•O2-). The reuse test confirmed the good stability of g-CN for treating pharmaceuticals during three repeated cycles. Finally, the photodegradation mechanism and environmental impacts were discussed. This study presents a promising approach for treating and mitigating pharmaceutical contaminants in wastewater.

The impact of artificial intelligence on pollution emission intensity-evidence from China

Artificial intelligence (AI) is a crucial component of sustainable economic development and an indicator of the next wave of technological progress. This study examines the effects and mechanisms of AI on the intensity of pollution emissions, using China as an example. Theoretical analysis demonstrates that the scale expansion effect and the technological innovation effect of AI can reduce the intensity of pollution emissions. In the meantime, AI can have a positive structural influence on reducing the intensity of pollution emissions through the upgrading of industrial structures. Therefore, we use panel data for 30 Chinese provinces from 2006 to 2019 to test the effect of AI on pollution emission intensity using a fixed effects model, employ explanatory variable substitution, endogenous analysis, regression after tailing, and eliminate related policy interference for robustness analysis. The results indicate that AI can significantly decrease the intensity of pollution emissions, with a 6.63% reduction for every 10% increase in AI utilization. We use the mediating effect model to conclude that AI can reduce the intensity of pollution emissions via the rationalization of industrial structure and advanced industrial structure, with the rationalization of industrial structure being the main mechanism. The examination of heterogeneity revealed that the implementation of AI in technology-intensive industries is an effective method for reducing the intensity of pollution emissions and that the positive impact of AI on the intensity of pollution emissions is more pronounced in the western region.

AI explainability framework for environmental management research

Deep learning networks powered by AI are essential predictive tools relying on image data availability and processing hardware advancements. However, little attention has been paid to explainable AI (XAI) in application fields, including environmental management. This study develops an explainability framework with a triadic structure to focus on input, AI model and output. The framework provides three main contributions. (1) A context-based augmentation of input data to maximize generalizability and minimize overfitting. (2) A direct monitoring of AI model layers and parameters to use leaner (lighter) networks suitable for edge device deployment, (3) An output explanation procedure focusing on interpretability and robustness of predictive decisions by AI networks. These contributions significantly advance state of the art in XAI for environmental management research, offering implications for improved understanding and utilization of AI networks in this field.

Preparation and Characterization of di- and Tricarboxylic Acids-Modified Arabinogalactan Plasticized Composite Films

To ensure the high quality of water, it is necessary to remove toxic pollutants. At present, purification of water is implemented using various sorbents. The efficient sorption materials are modified polysaccharides. In this study, we report on a new environmentally friendly method for modifying larch hemicellulose-arabinogalactan (AG)-with polybasic carboxylic acids (citric, succinic, oxalic, and adipic) to obtain composite materials. The synthesized AG derivatives have been explored by a complex of physicochemical methods, including gel permeation chromatography (GPC), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), X-ray diffractometry (XRD), scanning electron microscopy (SEM), and sorption capacity investigations. It is shown that the heat treatment results in the formation of additional inter- and intramolecular bonds between carboxylic acids and polysaccharide molecules. The formation of ester bonds has been confirmed by the appearance of absorption bands in the IR spectra in the range of 1750-1690 cm^-1. It has been found, using the TGA study, that the most thermally stable (up to 190 °C) sample is arabinogalactan oxalate obtained under heat treatment. The SEM study of the synthesized AG films has shown that the modified samples have the homogeneous film surface ensured by cross-linking. It has been established, when studying the sorption properties of the AG derivatives, that AG succinate (82.52%) obtained by lyophilization has the highest sorption capacity, due to the developed mesoporous surface, which, in turn, makes the synthesized films promising eco-friendly materials for use as drug carriers, sorbents, and water treatment agents.