Complex Environmental Systems Research Articles

Combining multiple data sources to identify actor involvement in environmental governance: Wildfire in the American West

In this paper, we developed a novel data collection and analysis approach that uses manual and automated processing of large datasets and combines them to offset the limitations of past research and practice when it comes to identifying actors in complex environmental governance systems. Our empirical context is wildfire governance in Colorado. Wildfire governance represents a pressing challenge for interested actors in the western United States. We characterize Colorado’s wildfire governance system by using big data triangulation techniques to identify key actors and their topical domains to reveal meaningful subgroups. Our results highlight the importance of understanding the pitfalls of each source of data and the potential benefits of combining them to gain a more complete understanding of the system. The true potential of multi-source analysis becomes apparent when one pays special attention to the degree of overlap that exists between sources. Each source reveals a unique makeup of actors (both in terms of individuals and organizational types) and different forms of engagement in the system. In addition to providing different sets of actors, we found evidence of different emergent topics in each data source.

Imaging of CO2 and Dissolved Inorganic Carbon via Electrochemical Acidification-Optode Tandem.

Dissolved inorganic carbon (DIC) is a key component of the global carbon cycle and plays a critical role in ocean acidification and proliferation of phototrophs. Its quantification at a high spatial resolution is essential for understanding various biogeochemical processes. We present an analytical method for 2D chemical imaging of DIC by combining a conventional CO2 optode with localized electrochemical acidification from a polyaniline (PANI)-coated stainless-steel mesh electrode. Initially, the optode response is governed by local concentrations of free CO2 in the sample, corresponding to the established carbonate equilibrium at the (unmodified) sample pH. Upon applying a mild potential-based polarization to the PANI mesh, protons are released into the sample, shifting the carbonate equilibrium toward CO2 conversion (>99%), which corresponds to the sample DIC. It is herein demonstrated that the CO2 optode-PANI tandem enables the mapping of free CO2 (before PANI activation) and DIC (after PANI activation) in complex samples, providing high 2D spatial resolution (approx. 400 μm). The significance of this method was proven by inspecting the carbonate chemistry of complex environmental systems, including the freshwater plant Vallisneria spiralis and lime-amended waterlogged soil. This work is expected to pave the way for new analytical strategies that combine chemical imaging with electrochemical actuators, aiming to enhance classical sensing approaches via in situ (and reagentless) sample treatment. Such tools may provide a better understanding of environmentally relevant pH-dependent analytes related to the carbon, nitrogen, and sulfur cycles.

Lane Reconstruction for Self-Driving Vehicles on Dynamic Road Networks

Lane detection is a crucial task that involves identifying lane lines and proper markings on road structures. The field of lane detection has gained significant attention due to the increasing use of autonomous vehicles and ADAS systems, as they require robust lane detection systems to navigate safely in complex environments. While previous studies have focused on lane detection in various scenarios, lane line reconstruction for dynamic road networks has been largely overlooked. To address this gap, we propose a novel approach to reconstruct lane lines for dynamic road networks using roadblocks, specifically red and white striped markers, as cues. To evaluate the effectiveness of our proposed approach, we first built and labeled our own dataset by extracting 1854 frames from recorded videos. We trained a Faster-RCNN V2 model on 80% of the dataset to detect the roadblocks and used the OpenCV DNN module, along with a range of techniques, such as ROI selection, color selection, region masking, and polynomial and linear fittings for lane detection and reconstruction. Our approach successfully reconstructed the left, middle, and right lane lines, achieving an overall accuracy of 68%, a recall of 73%, and a precision score of 73% on an unseen set of 50 images. Our study contributes to the field by proposing a novel approach for reconstructing lane lines in dynamic road networks using roadblocks as markers. This approach has the potential to enhance the accuracy and robustness of lane detection systems in complex environments. Furthermore, our work addresses a gap in the existing literature and provides insights for future research in this area.

Stability and change in young children's linguistic experience during the COVID-19 pandemic: insight from a citizen-science sample in the United States.

Early in the COVID-19 pandemic, extensive lockdowns interrupted daily routines, including childcare. We asked whether these interruptions, and the inevitable changes in the people with whom children spent their waking hours, caused changes in the languages that children heard. We retrospectively queried parents of young children (0-4 years) in the US about childcare arrangements and exposure to English and non-English languages at four timepoints from February 2020 to September 2021. Despite discontinuity in childcare arrangements, we found that children's exposure to English versus other languages remained relatively stable. We also identified demographic variables (child age at pandemic onset, parental proficiency in a non-English language) that consistently predicted exposure to non-English languages. Thus, multilingually-exposed children in this population did not appear to significantly gain or lose the opportunity to hear non-English languages overall. These results provide insight into the experiences of this unique cohort and inform our understanding of how language development can be shaped by complex environmental systems.

The Energy Efficiency Multi-Robot System and Disinfection Service Robot Development in Large-Scale Complex Environment.

In recent years, multi-robot control systems and service robots equipped with graphical computing have been introduced in various application scenarios. However, the long-term operation of VSLAM calculation leads to reduced energy efficiency of the robot, and accidental localization failure still persists in large-scale fields with dynamic crowds and obstacles. This study proposes an EnergyWise multi-robot system based on ROS that actively determines the activation of VSLAM using real-time fused localization poses by an innovative energy-saving selector algorithm. The service robot is equipped with multiple sensors and utilizes the novel 2-level EKF method and incorporates the UWB global localization mechanism to adapt to complex environments. During the COVID-19 pandemic, three disinfection service robots were deployed to disinfect a large, open, and complex experimental site for 10 days. The results demonstrated that the proposed EnergyWise multi-robot control system successfully achieved a 54% reduction in computing energy consumption during long-term operations while maintaining a localization accuracy of 3 cm.

Molecular Insights into the Interaction Mechanism Underlying the Aggregation of Humic Acid and Its Adsorption on Clay Minerals.

Humic acid (HA) is ubiquitous in both terrestrial and aquatic environments, and understanding the molecular interaction mechanisms underlying its aggregation and adsorption is of vital significance. However, the intermolecular interactions of HA-HA and HA-clay mineral systems in complex aqueous environments remain elusive. Herein, the interactions of HA with various model surfaces (i.e., HA, mica, and talc) were quantitatively measured in aqueous media at the nanoscale using an atomic force microscope. The HA-HA interaction was found to be purely repulsive during surface approach, consistent with free energy calculation; during retraction, pH-dependent adhesion was observed due to the protonation/deprotonation of HA that influences the formation of hydrogen bonds. Different from the mica case, hydrophobic interaction was detected for the HA-talc system at pH 5.8, contributing to the stronger HA-talc adhesion, as also evidenced by adsorption results. Notably, HA-mica adhesion strongly depended on the loading force and contact time, most likely because of the short-range and time-dependent interfacial hydrogen bonding interaction under confinement, as compared to the dominant hydrophobic interaction for the HA-talc case. This study provides quantitative insights into the fundamental molecular interaction mechanisms underlying the aggregation of HA and its adsorption on clay minerals of varying hydrophobicity in environmental processes.

Exploring Sign System Design for a Medical Facility: A Virtual Environment Study on Wayfinding Behaviors

The National Taiwan University Hospital (NTUH) is a significant institution in modern medicine in Taiwan. Its West Campus, the oldest medical center in the country, has been recognized as a municipal historic site due to its architectural and historical importance. However, visitors have faced navigation difficulties for a long time. To address this issue, a study was conducted to find navigation solutions within the constraints of architectural structure, hardware, and busy crowds. Blender and Unity were used to recreate the environment, and interactive virtual wayfinding experiments were conducted with 64 participants divided into two groups. Each group completed 12 tasks in two scenarios, and their task performance, wayfinding behavior, and questionnaire responses were collected to evaluate the overall environment. The pretest helped identify problems in the existing signage system and weak areas, leading to redesigning of a new signage system. The main objective of the posttest was to evaluate the new design’s effectiveness. Our research contributes to future signage system layout design references, enhancing readability and information coherence while recommending locations within medical facilities. We set specific design standards to facilitate wayfinding signage systems in complex environments.

Selectivity Quantification with the Fluorescent Quantitative Model-Assisted Semi-Selective Probe (Carbon Dots): Accurate Determination of Chlortetracycline in Aqueous Environments with Interference.

Probes such as carbon dots (C-dots) have extensive and important applications in the quantitative analysis of complex biological and environmental systems. However, the development of probes is often hindered by incomplete selectivity, i.e., a probe that responds to one substance is also prone to respond to coexisting structurally similar substances. Therefore, the above dilemma often leads to be developed as semi-selective probes, so that the development of probes is abandoned halfway. This work shows how a semi-selective probe can enhance selectivity by combining a proper multivariate calibration model. Primarily, we developed a semi-selective fluorescent probe that responded to tetracyclines (TCs) with discarded tobacco leaves. Then, we introduced the multivariate quantitative fluorescence model (QFM) to enhance its selectivity and solve the problem of fluorescence spectral shift. For the determination of chlortetracycline (CTC) with this semi-selective C-dots probe in mineral and lake water samples and compared to the traditional quantitative model, the introduced QFM resulted in an average relative predictive error (ARPE) in mineral water spiked samples decreased from 57.1 to 5.6%, which reduced the ARPE in the lake water spiked samples from 18.1 to 4.7%. The above results show that the QFM-assisted semi-selective probe C-dots strategy (QFMC-dots) can enhance selectivity, and QFMC-dots achieved high-selective and accurate determination of CTC in interfering mineral and lake water samples, with the limit of detection and limit of quantitation of 0.55 and 1.66 μM, respectively. The proposed strategy of enhancing selectivity by introducing a proper multivariate calibration model can reduce the difficulty and increase success rate of developing probes, which can be expected to provide an interesting alternative for the development of probes, especially when encountering semi-selective problems.

Performance Analysis of Distributed Compressed Air Energy Storage under Different Air Storage Chamber Models

To address the challenges brought by geographical, climate, and user dispersion in regional microgrids, villages in northwest China for example, a distributed compressed air energy storage combined heat and power model considering the thermodynamic characteristics of air storage devices is proposed. Firstly, the architecture of distributed compressed air energy storage system based on regional microgrids is constructed. Then, considering the thermodynamic characteristics of the air storage device under constant volume and insulation, a cogeneration model of distributed compressed air energy storage is established. The model includes compression subsystem, air/heat storage subsystem, and expansion subsystem. Finally, the results of combined heat and power supply of distributed compressed air energy storage system are discussed by case study simulation in different air storage chamber models. The results show that constant volume insulation as the air storage device is the best choice, which improve the system efficiency by up to 25.6%. The research results provide a theoretical basis and decision-making reference for the application of distributed compressed air energy storage system in complex environment.

Low Carbon Operation Strategy for Microgrids with Distributed Compressed Air Energy Storage

To improve the operation economy of the microgrid in a complex environment, a low-carbon operation strategy of microgrid with distributed compressed air energy storage is proposed. Firstly, considering the complex environment of regional microgrids, the coupling structure of distributed compressed air energy storage systems and microgrids is proposed. The new energy output and compressed air energy storage system model are established. On this basis, considering the consumption of new energy, a low-carbon operation strategy of the microgrid is constructed to improve the consumption level of new energy in the microgrid. Finally, the feasibility and competitiveness of the proposed low-carbon operation scheme are verified by simulation. The results show that the operation strategy of a low-carbon microgrid with distributed compressed air energy storage can reduce the operation cost by 57.3 %, and the new energy consumption rate can reach 97.2 %. The research results provide a theoretical basis and decision-making reference for applying distributed compressed air energy storage systems in complex environments.

Deep learning based spatio‐temporal hand gesture recognition system in complex environment

AbstractGesture recognition nowadays has grabbed the attention of researchers as they represent human behaviour in multiple practical ways. Amongst a variety of gestures available, hand gestures play an essential role in the field of human‐computer interaction when recognised efficiently in complex and dynamic environments. In this paper, we propose a dynamic hand gesture recognition system to recognise hand gestures appearing in different indoor and outdoor environments. Hand detection and tracking uses a two‐level system resulting in the formation of gesture trajectory in challenging conditions in which existing detection and tracking algorithms could not do so. A set of 45 features is provided as input to the various classification techniques. The redundancy problem has been reduced by selecting a set of optimum features using the analysis of variance method, which ranks the list of features. An incremental feature selection technique calculates recognition accuracy by selecting features according to rankings. This system provides an accuracy of 96.32% when used with machine learning and 97.5% when used with deep learning techniques. Recognition accuracy is calculated for various environments, including an extra hand, multiple persons in the video frame, and outdoor environment. All machine‐learning classifiers are combined using classifier combination to calculate the accuracy according to the majority‐voting rule. Based on the experimental results, it has been observed that deep learning provides better results compared to machine learning.

A comparison of numerical approaches for statistical inference with stochastic models

Due to our limited knowledge about complex environmental systems, our predictions of their behavior under different scenarios or decision alternatives are subject to considerable uncertainty. As this uncertainty can often be relevant for societal decisions, the consideration, quantification and communication of it is very important. Due to internal stochasticity, often poorly known influence factors, and only partly known mechanisms, in many cases, a stochastic model is needed to get an adequate description of uncertainty. As this implies the need to infer constant parameters, as well as the time-course of stochastic model states, a very high-dimensional inference problem for model calibration has to be solved. This is very challenging from a methodological and a numerical perspective. To illustrate aspects of this problem and show options to successfully tackle it, we compare three numerical approaches: Hamiltonian Monte Carlo, Particle Markov Chain Monte Carlo, and Conditional Ornstein-Uhlenbeck Sampling. As a case study, we select the analysis of hydrological data with a stochastic hydrological model. We conclude that the performance of the investigated techniques is comparable for the analyzed system, and that also generality and practical considerations may be taken into account to guide the choice of which technique is more appropriate for a particular application.

A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA

Abstract. Geoscientific models are simplified representations of complex earth and environmental systems (EESs). Compared with physics-based numerical models, data-driven modeling has gained popularity due mainly to data proliferation in EESs and the ability to perform prediction without requiring explicit mathematical representation of complex biophysical processes. However, because of the black-box nature of data-driven models, their performance cannot be guaranteed. To address this issue, we developed a generalizable framework for improving the efficiency and effectiveness of model training and the reduction of model overfitting. This framework consists of two parts: hyperparameter selection based on Sobol global sensitivity analysis and hyperparameter tuning using a Bayesian optimization approach. We demonstrated the framework efficacy through a case study of daily edge-of-field (EOF) runoff predictions by a tree-based data-driven model using the extreme gradient boosting (XGBoost) algorithm in the Maumee domain, USA. This framework contributes towards improving the performance of a variety of data-driven models and can thus help promote their applications in EESs.

Study on learning algorithm of transfer reinforcement for multi-agent formation control

Considering the obstacle avoidance and collision avoidance for multi-agent cooperative formation in multi-obstacle environment, a formation control algorithm based on transfer learning and reinforcement learning is proposed. Firstly, in the source task learning stage, the large storage space required by Q-table solution is avoided by using the value function approximation method, which effectively reduces the storage space requirement and improves the solving speed of the algorithm. Secondly, in the learning phase of the target task, Gaussian clustering algorithm was used to classify the source tasks. According to the distance between the clustering center and the target task, the optimal source task class was selected for target task learning, which effectively avoided the negative transfer phenomenon, and improved the generalization ability and convergence speed of reinforcement learning algorithm. Finally, the simulation results show that this method can effectively form and maintain formation configuration of multi-agent system in complex environment with obstacles, and realize obstacle avoidance and collision avoidance at the same time.

2D defect-engineered Ag-doped γ-Fe2O3/BiVO4: The effect of noble metal doping and oxygen vacancies on exciton-triggering photocatalysis production of singlet oxygen

The selectivity of singlet oxygen (1O2) holds promising applications in complex environmental systems due to its ability to preferentially oxidize target pollutants. Usually, 1O2 in photocatalytic systems is generated via the electron transfer pathway and •O2− plays an important role as an intermediate, while the exciton-based energy transfer pathway for 1O2 generation has been less studied. Here, a 2D Ag-γ-Fe2O3/BiVO4 with oxygen vacancies was designed which was capable of generating 1O2 by an exciton-based energy transfer-dominated approach, as strongly demonstrated by the results of steady-state fluorescence spectroscopy and phosphorescence spectroscopy. In the Z-type heterojunction photocatalyst system, Ag acted as an electron mediator to promote not only the generation of free carriers but also the generation of singlet excitons, while the appropriate concentration of oxygen vacancies further promotes the exciton-triggering photocatalysis production of 1O2. The Ag-γ-Fe2O3/BiVO4 could degrade 99.4% of sulfadiazine within 90 min, and 1O2 played an important role in the degradation of sulfadiazine, as shown by EPR and active species capture experiments. Ecotoxicity predictions indicated that the main byproducts of sulfadiazine degradation by Ag-γ-Fe2O3/BiVO4 were low in toxicity. The prepared photocatalysts provide a new idea for obtaining 1O2 and designing photocatalysts with selectivity.

3D Viologen-based covalent organic framework for selective and efficient adsorption of ReO4−/TcO4−

Due to the long half-life and high environmental mobility, the selective and efficient capture of TcO4− from nuclear effluents is very important, but it is still very challenging. Herein, we design and synthesize a novel three-dimensional (3D) cationic covalent organic framework (TFAM-BDNP) via Zincke reaction for selective capture of TcO4−/ReO4−. TFAM-BDNP exhibits high adsorption capacity (998 mg g−1) and extremely fast exchange kinetic (60 s) for ReO4− (the non-radioactive alternative to TcO4−), attributing to the open 3D hydrophobic channels, abundant active sites, and high chemical stability. More importantly, TFAM-BDNP shows good adsorption performance for ReO4− in the presence of significant excess competing anions with a wide pH value range of 2 to 12. Under complex simulated Hanford flow sample, TFAM-BDNP has outstanding removal efficiency of ReO4−. The adsorption mechanism of ReO4− is mainly caused by anion exchange process. This study provides a novel adsorbent for efficient capture of TcO4−/ReO4− in complex environmental systems and exploits an effective strategy for broadening the types of 3D COFs.

Research on Real-Time Robust Optimization of Perishable Supply-Chain Systems Based on Digital Twins.

Aiming at the real-time robust optimization problem of perishable supply-chain systems in complex environments, a real-time robust optimization scheme based on supply-chain digital twins is proposed. Firstly, based on the quantitative logical relationship between production and sales of single-chain series supply-chain system products, the state space equation of the supply-chain system with logical characteristics, structural characteristics, and quantitative characteristics was constructed, and twin data were introduced to construct the digital twins of supply chains based on the state-space equation. Secondly, the perishable supply-chain system in complex environments was regarded as an uncertain closed-loop system from the perspective of the state space equation, and then a robust H∞ controller design strategy was proposed, and the supply-chain digital twins was used to update and correct the relevant parameters of the supply-chain system in real-time, to implement the real-time robust optimization based on the supply-chain digital twins. Finally, the simulation experiment was carried out with a cake supply-chain production as an example. The experimental results show that the real-time updating of relevant parameters through the digital twins can help enterprise managers to formulate reasonable management plans, effectively avoid the shortage problem of enterprises in the cake supply-chain system, and reduce the maximum inventory movement standard deviation of each link by 12.65%, 6.50%, and 14.87%, and the maximum production movement standard deviation by 70.21%, 56.84%, and 45.19%.

UV-Resonance Raman Spectra of Systems in Complex Environments: A Multiscale Modeling Applied to Doxorubicin Intercalated into DNA.

UV-Resonance Raman (RR) spectroscopy is a valuable tool to study the binding of drugs to biomolecular receptors. The extraction of information at the molecular level from experimental RR spectra is made much easier and more complete thanks to the use of computational approaches, specifically tuned to deal with the complexity of the supramolecular system. In this paper, we propose a protocol to simulate RR spectra of complex systems at different levels of sophistication, by exploiting a quantum mechanics/molecular mechanics (QM/MM) approach. The approach is challenged to investigate RR spectra of a widely used chemotherapy drug, doxorubicin (DOX) intercalated into a DNA double strand. The computed results show good agreement with experimental data, thus confirming the reliability of the computational protocol.

Heavy metal bioremediation using microbially induced carbonate precipitation: Key factors and enhancement strategies.

With the development of economy, heavy metal (HM) contamination has become an issue of global concern, seriously threating animal and human health. Looking for appropriate methods that decrease their bioavailability in the environment is crucial. Microbially induced carbonate precipitation (MICP) has been proposed as a promising bioremediation method to immobilize contaminating metals in a sustainable, eco-friendly, and energy saving manner. However, its performance is always affected by many factors in practical application, both intrinsic and external. This paper mainly introduced ureolytic bacteria-induced carbonate precipitation and its implements in HM bioremediation. The mechanism of HM immobilization and in-situ application strategies (that is, biostimulation and bioaugmentation) of MICP are briefly discussed. The bacterial strains, culture media, as well as HMs characteristics, pH and temperature, etc. are all critical factors that control the success of MICP in HM bioremediation. The survivability and tolerance of ureolytic bacteria under harsh conditions, especially in HM contaminated areas, have been a bottleneck for an effective application of MICP in bioremediation. The effective strategies for enhancing tolerance of bacteria to HMs and improving the MICP performance were categorized to provide an in-depth overview of various biotechnological approaches. Finally, the technical barriers and future outlook are discussed. This review may provide insights into controlling MICP treatment technique for further field applications, in order to enable better control and performance in the complex and ever-changing environmental systems.

Building blocks of polycentric governance

AbstractSuccess or failure of a polycentric system is a function of complex political and social processes, such as coordination between actors and venues to solve specialized policy problems. Yet there is currently no accepted method for isolating distinct processes of coordination, nor to understand how their variance affects polycentric governance performance. We develop and test a building‐blocks approach that uses different patterns or “motifs” for measuring and comparing coordination longitudinally on Australia's Great Barrier Reef. Our approach confirms that polycentric governance comprises an evolving substrate of interdependent venues and actors over time. However, while issue specialization and actor participation can be improved through the mobilization of venues, such a strategy can also fragment overall polycentric capacity to resolve conflict and adapt to new problems. A building‐blocks approach advances understanding and practice of polycentric governance by enabling sharper diagnosis of internal dynamics in complex environmental governance systems.