Complete Routing Research Articles

Ship path planning based on improved multi-scale A* algorithm of collision risk function

To improve the safety of ship navigation in complex sea areas and reduce planning time while achieving optimal path planning. The paper proposes an improved A* algorithm that incorporates ship collision risk assessment. The paper utilizes multi-scale raster maps to divide the sea chart in the context of complex sea areas, and combines the Line-of-sight (LOS) algorithm to solve the zigzag paths that may appear in this planning context. Moreover, in order to improve the efficiency of optimal path planning in the context of complex sea areas while ensuring path safety, the paper proposes a collision risk function to optimize the determination of the cost of A* algorithm nodes, thereby enhancing the heuristic function of the A* algorithm. The improved A* algorithm can consider both path length and collision risk to plan the optimal path and to enhance the overall quality of the planning results. To verify the advantages of the improved algorithm proposed in the paper, the Zhoushan Islands sea area with complex environment is selected as the planning background for simulation study. The results show that the improved algorithm with the introduction of the collision risk function reduces the path planning time, the number of expanded nodes, and the path length by 30%, 11%, and 5.8%, respectively, compared with the original algorithm, which can effectively reduce the computational burden of the algorithm. This study provides a relatively complete and scientific route planning strategy for the study of the safe navigation of smart ships in complex sea areas.

Comprehensive Study on Optimizing Inland Waterway Vessel Routes Using AIS Data

Inland waterway transport is an important mode of transportation for many countries and regions. Route planning optimization can reduce navigation time, avoid traffic congestion, and improve transportation efficiency. In actual operations, many vessels determine their navigation routes based on the experience of their shipowners. When the captain fails to obtain accurate information, experience-based routes may pose significant navigation risks and may not consider the overall economic efficiency. This study proposes a comprehensive method for optimizing inland waterway vessel routes using automatic identification system (AIS) data, considering the geographical characteristics of inland waterways and navigation constraints. First, AIS data from vessels in inland waters are collected, and the multi-objective Peak Douglas–Peucker (MPDP) algorithm is applied to compress the trajectory data. Compared to the traditional DP algorithm, the MPDP algorithm reduces the average compression rate by 5.27%, decreases length loss by 0.04%, optimizes Euclidean distance by 50.16%, and improves the mean deviations in heading and speed by 23.53% and 10.86%, respectively. Next, the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm is used to perform cluster analysis on the compressed route points. Compared to the traditional DBSCAN algorithm, the OPTICS algorithm identifies more clusters that are both detailed and hierarchically structured, including some critical waypoints that DBSCAN may overlook. Based on the clustering results, the A* algorithm is used to determine the connectivity between clusters. Finally, the nondominated sorting genetic algorithm II is used to select suitable route points within the connected clusters, optimizing objectives, including path length and route congestion, to form an optimized complete route. Experiments using vessel data from the waters near Shuangshan Island indicate that, when compared to three classic original routes, the proposed method achieves path length optimizations of 4.28%, 1.67%, and 0.24%, respectively, and reduces congestion by 24.15%. These improvements significantly enhance the planning efficiency of inland waterway vessel routes. These findings provide a scientific basis and technical support for inland waterway transport.

Pencarian Rute Terpendek dari Kampung Duri Kosambi Menuju Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika Jakarta Timur Menggunakan Algoritma A* (A-Star)

Cipta Karya Informatika Computer Science College (STIKOM CKI) is one of the educational institutions that focuses on computer education. Marked by the issuance of a permit on September 5, 2002, in the form of a Decree of the Minister of National Education No.: 204 / D / O / 2002 which legally has two departments of Informatics Engineering and Information Systems that have implemented a competency-based curriculum. The campus is on Jalan Radin Inten II Duren Sawit, East Jakarta. One of the important educational institutions for the local community and students who live around it. However, accessibility to the campus can be a challenge for individuals who come from the Kampung Duri Kosambi area, right in Duri Kosambi Village, Cengkareng District, West Jakarta. To determine the closest route, it has now been widely applied in various navigation applications such as the Global Positioning System, Google Maps, A * Star is one of the optimal route search algorithms. Optimal means that the route produced is the best and most complete route, the algorithm can achieve the expected goal. In addition, the purpose of this study is to provide information about the location of the STIKOM CKI campus for the nearest route so that it can help users to choose the fastest route to get to the location and at the same time provide information on the nearest route based on the results of the A*Star algorithm calculations applied in this study.

Map‐matching for cycling travel data in urban area

AbstractTo promote urban sustainability, many cities are adopting bicycle‐friendly policies, leveraging GPS trajectories as a vital data source. However, the inherent errors in GPS data necessitate a critical preprocessing step known as map‐matching. Due to GPS device malfunction, road network ambiguity for cyclists, and inaccuracies in publicly accessible streetmaps, existing map‐matching methods face challenges in accurately selecting the best‐mapped route. In urban settings, these challenges are exacerbated by high buildings, which tend to attenuate GPS accuracy, and by the increased complexity of the road network. To resolve this issue, this work introduces a map‐matching method tailored for cycling travel data in urban areas. The approach introduces two main innovations: a reliable classification of road availability for cyclists, with a particular focus on the main road network, and an extended multi‐objective map‐matching scoring system. This system integrates penalty, geometric, topology, and temporal scores to optimize the selection of mapped road segments, collectively forming a complete route. Rotterdam, the second‐largest city in the Netherlands, is selected as the case study city, and real‐world data is used for method implementation and evaluation. Hundred trajectories were manually labelled to assess the model performance and its sensitivity to parameter settings, GPS sampling interval, and travel time. The method is able to unveil variations in cyclist travel behavior, providing municipalities with insights to optimize cycling infrastructure and improve traffic management, such as by identifying high‐traffic areas for targeted infrastructure upgrades and optimizing traffic light settings based on cyclist waiting times.

Production of bio-based adipic acid using a combination of engineered Pseudomonas putida strains

Adipic acid is an industrially important dicarboxylic acid and an essential precursor in the synthesis of Nylon-6,6. Due to the environmental impact associated with the current synthetic methods for Nylon-6,6 production, greener alternatives based on renewable and bio-based feedstocks are needed. In the present study, the potential of two Pseudomonas putida strains engineered for muconic acid accumulation was used to produce adipic acid from the lignin-derived aromatic compound guaiacol. This was achieved by expressing genes enabling the conversion of guaiacol to catechol in the first strain, and the conversion of muconic acid to adipic acid via expression of the Bacillus coagulans enoate reductase gene in the second strain. Through process engineering, a 2-step production was designed in which 2.93 mM bio-adipic acid was produced, with a yield of 0.57 mol/mol. This is the first demonstration that process and strain engineering can be combined to obtain a complete bio-based route for adipic acid production from depolymerized lignin compounds using the robust P. putida chassis.

Demand study of freight transportation via railway of four commodity groups: A case study on Costa Rica's Pacific Coast Route

Having railway mobility as an option among transportation modes is of prime importance for the freight logistics of a country and the overall performance of its commerce sector. This work presents a case study in Costa Rica, where freight transportation via railway was formerly available during the last century but has not been in full service since 1995. Specifically, this paper assesses the link connecting the capital city of San Jose to the port of Caldera on the Pacific Ocean coast, henceforth referred to as the “Pacific Coast Route”. Although government institutions and industries alike have stressed the importance of its reactivation in recent years, few efforts have been made to specifically study this route with a level of detail that enables them to understand the composition of freight markets.This study presents a novel demand estimation differentiated by four types of commodities developed to calculate the potential amount of goods that could flow through this corridor, measured in monetary terms and as volumes. Trends in the expected share were analyzed to identify variables that may improve the attractiveness of the railway service and the participation of clients along the route from the country's main economic activities which includes the following: agriculture & livestock, manufacturing, minerals, and forestry.The four-step transportation model was chosen for its suitability towards this case study; where available data sets were limited, the information was aggregated into yearly values, and the network was defined with few transportation zones. Activity-based models were not feasible in this case due to the lack of information on complete routes and volume flows. The model evaluated a first scenario given the present design of the route and a second scenario involving improvements in the alignment and the addition of a new segment.Regarding the opportunities of moving goods according to their type, in terms of volume, minerals (48 %) and agricultural (31 %) have the largest shares of participation in this region, followed by manufacturing (20 %) and forestry products (1 %). However, the manufacturing sector stands out in financial terms, accounting for 71 % of the total value of goods mobilized in the study area.It was found that the demand is most sensible for changes in the service fare, while the benefits of shorter travel times and a larger attraction area in scenario 2 were not significant. A service fee of $USD 2.35/ton in average is recommended to reach a 30 % modal shift towards the railway (following the European Commission's goals to reduce CO2 emissions from freight transportation in 2030).Finally, recommendations for public policies are provided to the entities in the country in the sectors of Agriculture, Health, Energy and Environment, Transportation, and Public Services. The suggestions aim to develop the new railway project with a holistic perspective, considering the inclusion of less profitable economic activities of the surrounding communities that require connectivity, compliance with environmental commitments, and design for resilience while aiming for a financially feasible business model.

Research on Quantitative Analysis Methods for the Spatial Characteristics of Traditional Villages Based on Three-Dimensional Point Cloud Data: A Case Study of Liukeng Village, Jiangxi, China

Traditional villages are important carriers of cultural heritage, and the quantitative study of their spatial characteristics is an important approach to their preservation. However, the rapid extraction, statistics, and estimation of the rich spatial characteristic indicators in these villages have become bottlenecks in traditional village research. This paper employs UAV (unmanned aerial vehicle) and handheld laser scanners to acquire three-dimensional point cloud data and construct a spatial feature three-dimensional calculation workflow of “field data collection—data processing—data analysis and application”, which enables the rapid acquisition, processing, and analysis of three-dimensional village data. Typical case studies are conducted in Liukeng Village, China, focusing on the quantification of village spatial characteristics at three levels: topography, streets and alleys, and individual buildings, as well as comparative studies of multiple villages across different regions. The quantification of three-dimensional data reveals the regularity of village spatial characteristics and uncovers the spatial wisdom embedded in the site selection and spatial structure of traditional villages. This paper establishes a complete technical route for the quantitative analysis of villages, deepens public understanding of the diverse value of traditional villages, and provides technical support for research and practice in related fields.

The total biosynthesis route of rosmarinic acid in Sarcandra glabra based on transcriptome sequencing

Sarcandra glabra is a widely distributed and valuable plant in food and daily chemical industries, and is also a common-used medicinal plant for treating inflammatory diseases and tumors. Rosmarinic acid (RA) with significant pharmacological activity is an abundant and important constituent in S. glabra, however, little information about key enzymes involving the biosynthesis of RA in S. glabra is available and the underlying biosynthesis mechanisms of RA in S. glabra remain undeciphered. Therefore, in this study, by full-length transcriptome sequencing analyses of S. glabra, we screened the RA biosynthesis candidate genes based on sequence similarity and conducted enzymatic function characterization in vitro and in vivo. As a result, a complete set of 7 kinds of enzymes (SgPALs, SgC4H, Sg4CL, SgTATs, SgHPPRs, SgRAS and SgC3H) involving the biosynthesis route of RA from phenylalanine and tyrosine, were identified and fully characterized. This research systematically revealed the complete biosynthesis route of RA in S. glabra, which helps us better understand the process of RA synthesis and accumulation, especially the substrate promiscuities of SgRAS and SgC3H provide the molecular biological basis for the efficient biosynthesis of specific and abundant RA in S. glabra. The 7 kinds of key enzymes revealed in this study can be utilized as tool enzymes for production of RA by synthetic biology methods.

Navigation Based on Hybrid Decentralized and Centralized Training and Execution Strategy for Multiple Mobile Robots Reinforcement Learning

In addressing the complex challenges of path planning in multi-robot systems, this paper proposes a novel Hybrid Decentralized and Centralized Training and Execution (DCTE) Strategy, aimed at optimizing computational efficiency and system performance. The strategy solves the prevalent issues of collision and coordination through a tiered optimization process. The DCTE strategy commences with an initial decentralized path planning step based on Deep Q-Network (DQN), where each robot independently formulates its path. This is followed by a centralized collision detection the analysis of which serves to identify potential intersections or collision risks. Paths confirmed as non-intersecting are used for execution, while those in collision areas prompt a dynamic re-planning step using DQN. Robots treat each other as dynamic obstacles to circumnavigate, ensuring continuous operation without disruptions. The final step involves linking the newly optimized paths with the original safe paths to form a complete and secure execution route. This paper demonstrates how this structured strategy not only mitigates collision risks but also significantly improves the computational efficiency of multi-robot systems. The reinforcement learning time was significantly shorter, with the DCTE strategy requiring only 3 min and 36 s compared to 5 min and 33 s in the comparison results of the simulation section. The improvement underscores the advantages of the proposed method in enhancing the effectiveness and efficiency of multi-robot systems.

From Synthesis to Functionality: Tailored Ionic Liquid-Based Electrospun Fibers with Superior Antimicrobial Properties.

Herein, we report an efficient and facile strategy for the preparation of imidazolium-based ionic liquid (IL) monomers ([CnVIm][Br], n = 2, 4, 6, 8, 10, and 12) and their corresponding polymeric ionic liquids (PILs) with potent antimicrobial activities against Gram-negative and Gram-positive bacteria and fungi. The electrospinning technique was utilized to tailor the polymers with the highest antimicrobial potency into porous membranes that can be easily implemented into diverse systems and extend their practical bactericidal application. The antimicrobial mechanism of obtained ILs, polymers, and nanomaterials is considered concerning the bearing chain length, polymerization process, and applied processing technique that provides a unique fibrous structure. The structure composition was selected due to the well-established inherent amphiphilicity that 1-alkylimidazolium ILs possess, coupled with proven antimicrobial, antiseptic, and antifungal behavior. The customizable nature of ILs and PILs complemented with electrospinning is exploited for the development of innovative antimicrobial performances born from the intrinsic polymer itself, offering solutions to the increasing challenge of bacterial resistance. This study opens up new prospects toward designer membranes providing a complete route in their designing and revolutionizing the approach of fabricating multi-functional systems with tunable physicochemical, surface properties, and interesting morphology.

Technology-Driven Intergenerational Physical Activity Intervention: An Instrumental Case Study

Intergenerational contact offers an under-explored strategic approach to challenging age stereotypes and positively influencing health behaviors in older adults and children. It is postulated that through the application of the constructs of Contact Theory, an effective platform for change could be established. Using an instrumental case study, the experiential effects of the intergenerational contact phenomenon were pragmatically explored from the perspective of a single familial dyad (61-year-old woman; 9-year-old boy). Semi-structured interviews were conducted with each participant, both during and following engagement with a remote, 12-week, technology-driven physical activity intervention focused on using daily step counts (acquired via any activity of their choice, using physical activity trackers) to collaboratively complete virtual walk routes during the COVID-19 pandemic. Through reflexive thematic analysis and interpretation of the data in line with the study’s aim and propositions, four core themes were identified: Reciprocal Encounter; Opportunity for Reflection and Re-evaluation; Platform for Change; and COVID-19. In addition to the perceived positive effects on targeted health outcomes (physical activity, sedentary behavior, health-related quality of life), facilitating intergenerational contact through virtual methods could also, through opportunities to nurture and build relationships, challenge age stereotypes.

Optimal scheduling and trading in joint electricity and carbon markets

The collaborative development of the electricity and carbon markets can reduce transaction costs, stimulate energy conservation and emission reductions, and accelerate the social transition to low carbon. In this paper, we present a comprehensive review of the current state and policy challenges of electricity-carbon trading and present an in-depth analysis of key research directions and technical details of electricity-carbon collaboration in the context of large-scale access to renewable energy. Then, by constructing a multi-agent behavioral decision-making model, we refine the decision-making mechanism of electricity-carbon synergy, simulated market clearing outcomes under different mechanisms, and proposed policies and incentive mechanisms to promote electricity-carbon trading. Furthermore, leveraging the massive data in energy systems, we design a carbon measurement method and carbon emission flow tracking method for low-carbon energy companies, achieving real-time carbon accounting for the power system. We establish a collaborative simulation model of the electricity and carbon markets, enabling an accurate prediction of the day-ahead carbon emission intensity. This study provides a new research framework and complete technical route for electricity-carbon coordination, which can further promote the low-carbon transformation of the energy system.

Highly scalable and robust ribbon-like coordination polymer as green catalyst for Hantzsch condensation in synthesis of DHPs and bioactive drug molecule

Ribbon-like coordination polymers (CP) represents a highly unexplored innovative class of metal-coordination network. Herein, we have developed a highly scalable and chemically robust (pH = 3–10 stable) ribbon-like CP [{Cu(Pim)(L)(H2O)·H2O}]n(1) following a complete environment-friendly green synthesis route. Considering the presence of surface flanked labile coordinated water molecules and their appealing correlation with one-dimensional structural characteristics, such sort of ribbon-like CP was explored for the first time as excellent heterogeneous surface catalyst for largely unexplored three-component Hantzsch condensation for synthesis of different classes of dihydropyridine (DHP). Moreover, 1 is employed to synthesize bio-responsive drug ‘Ethidine’ (possessing high anti-oxidant and anticarcinogenic properties) characterized with Single Crystal X-ray Diffraction (SCXRD) analysis. Several DHP-based products are also analysed through in-depth SCXRD analysis. This report inaugurates the usage of a Cu(II) based ribbon-like CPs as heterogeneous surface catalyst following environmentally benign manner for synthesis of bioactive DHPs and Drugs.

Two-Stage Probe-Based Search Optimization Algorithm for the Traveling Salesman Problems

As a classical combinatorial optimization problem, the traveling salesman problem (TSP) has been extensively investigated in the fields of Artificial Intelligence and Operations Research. Due to being NP-complete, it is still rather challenging to solve both effectively and efficiently. Because of its high theoretical significance and wide practical applications, great effort has been undertaken to solve it from the point of view of intelligent search. In this paper, we propose a two-stage probe-based search optimization algorithm for solving both symmetric and asymmetric TSPs through the stages of route development and a self-escape mechanism. Specifically, in the first stage, a reasonable proportion threshold filter of potential basis probes or partial routes is set up at each step during the complete route development process. In this way, the poor basis probes with longer routes are filtered out automatically. Moreover, four local augmentation operators are further employed to improve these potential basis probes at each step. In the second stage, a self-escape mechanism or operation is further implemented on the obtained complete routes to prevent the probe-based search from being trapped in a locally optimal solution. The experimental results on a collection of benchmark TSP datasets demonstrate that our proposed algorithm is more effective than other state-of-the-art optimization algorithms. In fact, it achieves the best-known TSP benchmark solutions in many datasets, while, in certain cases, it even generates solutions that are better than the best-known TSP benchmark solutions.

Unveiling the Initiation Route of Coronal Mass Ejections through Their Slow Rise Phase

Understanding the early evolution of coronal mass ejections (CMEs), in particular their initiation, is the key to forecasting solar eruptions and induced disastrous space weather. Although many initiation mechanisms have been proposed, a full understanding of CME initiation, which is identified as a slow rise of CME progenitors in kinematics before impulsive acceleration, remains elusive. Here, with a state-of-the-art thermal magnetohydrodynamics simulation, we determine a complete CME initiation route in which multiple mainstream mechanisms occur in sequence yet are tightly coupled. The slow rise is first triggered and driven by the developing hyperbolic flux tube (HFT) reconnection. Subsequently, the slow rise continues as driven by the coupling of the HFT reconnection and the early development of torus instability. The end of the slow rise, i.e., the onset of the impulsive acceleration, is induced by the start of the fast magnetic reconnection coupled with the torus instability. These results unveil that CME initiation is a complicated process involving multiple physical mechanisms, thus being hardly resolved by a single initiation mechanism.

A hybrid genetic search and dynamic programming-based split algorithm for the multi-trip time-dependent vehicle routing problem

We design a hybrid algorithm for the multi-trip time-dependent vehicle routing problem (MT-TD-VRP). One of its components is the Time-Dependent SPlit Algorithm (TD-SPA), which is a dynamic programming-based algorithm specifically designed to handle both the multi-trip per vehicle and the time-dependent aspects of the problem. The hybrid algorithm combines the proposed TD-SPA, designed to efficiently split a giant tour into complete vehicle routes, with a genetic algorithm for generating these tours. We introduce a monotone queue optimization (MQO) technique to accelerate the TD-SPA. The effectiveness of MQO is evaluated by comparing computation times between the original split algorithm for the capacitated vehicle routing problem (CVRP) and its MQO-enhanced counterpart. Extensive numerical experiments with a real-world dataset from a Singapore food and beverage company are conducted to assess our algorithm’s performance on various MT-TD-VRP instances. The results indicate that our algorithm surpasses the performance of the commercial solver Gurobi, with an average improvement of 25.13% on the best solutions found within a prescribed duration. Our numerical simulations further reveal the algorithm’s ability to efficiently solve both the capacitated vehicle routing problem (CVRP) and the multi-trip vehicle routing problem (MTVRP), consistently producing competitive solutions. Moreover, to highlight the importance of incorporating the time-dependent (TD) factor into our model and algorithm, we demonstrate a notable enhancement in performance–averaging at 7.47% for the best solutions under TD conditions for an MTVRP dataset.

High-performance of Mg2+ additive for addressing NH3 escape in inorganic ammonia carbon capture

The urgency to reduce the energy consumption of organic amine carbon capture, while the inorganic ammonia carbon capture (IACC) and co-production of ammonium fertilizer eliminates the desorption energy consumption. Inhabiting NH3 escape at the end of IACC process requires a large amount of water consumption, and there is a lack of corresponding process parameters and technical routes. In this paper, Mg2+, one of major components in desulfurization wastewater, was used as an NH3 inhibitor. It’s found that the addition of MgCl2 can increase the total NH3 absorption rate by 13.02 % and reduce the total NH3 escape rate by 87.02 %. Furthermore, the effects of six process parameters in the forward and reverse NH3 mass transfer experiments could be distinctly quantified by the normalization method to determine the differentiation between priority and others. It’s concluded that NH3 can enter the first solvent shell of Mg2+ to replace some H2O based on the first-principles calculations, which is the main cause for the decrease of NH3 diffusion coefficient. Finally, simulation with Aspen Plus was applied to construct the complete process route, and the Arrhenius formula for the Mg(OH)2 nucleation was used to refine the reaction kinetic parameters of the simulation. The heat and water balance of IACC could achieve within this system, and the excellent NH3 inhibition effect of added Mg2+ was verified, which could effectively keep from the NH3 escape within 3 ppm.

Research on Power Service Route Planning Scheme Based on SDN Architecture and Reinforcement Learning Algorithm

The power communication network carries various power services to ensure the safe operation of the power network, among which, the relay protection service is the most important service. Reasonable planning of the service route can improve the effectiveness and reliability of data transmission in the power communication network, thereby ensuring the reliable operation of the power grid. This paper constructs a route planning architecture for the power communication network based on a software-defined network. On this basis, parameters such as the power service and network-carrying service status are defined. With the goal of minimizing network risk variance and considering link bandwidth utilization and overload constraints of relay protection services, a service route allocation problem has been raised. To solve this problem, a power service route planning scheme based on a reinforcement learning algorithm is proposed. This algorithm uses the state–action–reward–state–action (SARSA) algorithm to complete service route planning. The simulation results show that using the route planning scheme proposed in this paper can avoid the overload of relay protection services, reduce network risk variance, and effectively balance network risk.

Milling or grinding for manufacturing of an Alloy 718 gas turbine component? – A comparison of surface integrity and productivity

Milling is traditionally the most used machining method when manufacturing complex gas turbine components. In particular those made from nickel-based superalloys. However, for larger free form surfaces, grinding may be an efficient alternative that could be used throughout the complete manufacturing route, from roughing to finishing. Hence, in this work the two processing methods has been compared in regard to surface integrity and productivity.Machining tests have been performed on case plates of heat-treated Alloy 718 using best practise setting for roughing and finishing with grinding and milling. The surface integrity of the work pieces was evaluated regarding surface topography, residual stresses, and deformation. This comparison showed that the main advantage with grinding is the ability to switch between roughing and finishing by just altering the depth of cut. Further, grinding offers lower surface roughness, compressive residual stresses, and significantly lower degree of deformation.From a productivity perspective, deep grinding may offer high material removal rates and ability to machine several work pieces in the same setup. However, grinding is limited to simpler free form geometries and may result in minor surface damages and abrasive surface residue. For selection of machining strategy, advantages and drawbacks shown in this work need to be considered for the application at hand in respect to productivity, surface integrity and requirements on fatigue life.

Harmonizing pesticides environmental quality standards: A fate-pathway perspective

Regulatory agencies worldwide set pesticide environmental quality standards, which are proposed independently in each dependent environmental media rather than across the complete fate route. Thus, lacking the fate-pathway perspective in defining pesticide environmental quality standards might cause undesirable pesticide residue from the upper compartment (e.g., soil) to the lower compartment (e.g., water). This study aimed to harmonize the self-consistency of pesticide environmental quality standards across environmental media via the fate-pathway analysis. The introduced qualitative and quantitative rules defined environmental quality standards of pesticides in six major environmental scenarios in the soil and water system based on related regulatory objectives. Fate factors simulated via USEtox were used to create a preliminary quantitative link between theoretical maximum legal masses of pesticides across environmental compartments. Using chlorpyrifos and 2,4-D as examples, their standard values were comparatively assessed in selected environmental media in China and the United States. According to the investigative findings, missing the respective environmental quality standards of pesticides in the agricultural soil could significantly influence the implementation of those in freshwater. Taking a fate-pathway perspective, the self-consistency test highlighted that defining pesticide environmental quality standards for freshwater was the most challenging task, as the freshwater compartment typically comprises multiple lower environmental compartments with diverse regulatory objectives. Overall, this theoretical study has the potential to illuminate the harmonization of pesticide environmental quality standards throughout the entire environmental fate pathway, ultimately leading to improved regulatory efficiency and communication. Future research should focus on risk-based model implementation, regulatory response evaluation, and legal limit interpretation to better integrate environmental pesticide management under a variety of regulatory goals.