Key Impact Factors Research Articles

Assessing the ecological response plant and soil to the seawalls in the Laizhou bay coastal wetland, China

Coastal wetlands are extremely vulnerable to both marine damage and human activities. In order to protect these wetlands, many artificial seawalls have been constructed. However, studies are required to understand how coastal wetlands will evolve under the influence of artificial seawalls. Therefore, to understand this succession process of plants and their adaptation to habitats divided by seawalls, two different habitats inside and outside the seawalls were selected in Laizhou Bay, China. The results showed that there were 5 plant species outside the seawalls that were lower than the 13 species inside. Additionally, the dominant plant species were varied between the two habitats, with mostly annual herbs observed outside the seawalls and perennial shrubs inside. Soil salinity was higher outside the seawalls, which was the key impact factor of soil nutrient differences. The distribution of annual and perennial species may be constrained by spatial differences in soil stoichiometry. Therefore, the plants in coastal wetlands vary significantly at a small scale in response to the disturbance of artificial seawalls. The differences in soil and plants between the two habitats divided by the artificial seawalls provide a new insight for evaluating the artificial coastal projects. The only way to reduce the effects of seawalls on natural coastal wetland vegetation and ecosystem functions is to restore connectivity of tidal flow inside and outside the seawalls.

Using a combination of δ13CDIC-DOC-difference in dissolved inorganic and organic carbon, δ2H, and δ18O to localize leachate leaks at landfill sites in China

The investigation of leachate leakage at numerous landfill sites is urgently needed. This study presents an exploration of environmental tracing methods using δ2H and δ13C-difference in dissolved carbon (δ13CDIC-DOC) to localize leachate leak points at landfill sites. δ2H, δ13CDIC, δ13CDOC, δ18O, and an array of physicochemical indices (e.g., total dissolved solids, temperature, and oxidation reduction potential) were monitored in both leachate and groundwater from different zones of a landfill site in China during the year of 2021–2023. Moreover, data for these parameters (i.e., the isotopic composition and physicochemical indices) from twelve published landfill cases were also collected, and these groundwater/leachate data points were located within 1 km away from the landfill boundary. Then statistical analyses, such as Pearson correlation analysis and redundancy analysis (RDA), were performed using both the detected and collected parameters at landfill sites. Consequently, the intensity of interaction between leachate and background groundwater was found to significantly control the isotopic fractionation features of hydrogen and carbon, and both the content of major contamination indicators (total dissolved solids, chemical oxygen demand, and ammoniacal nitrogen) and the oxidation reduction potential were the key impact factors. Accordingly, the water type used to indicate leachate leakage points was determined to be leachate that significantly interacted with the background groundwater or precipitation (LBGP). δ2H showed a perfect linear correlation (0.81 ≤ r2 < 1.0) with δ13CDIC-DOC in leachate under highly anaerobic landfill conditions, and the δ2H &δ13CDIC-DOC combinations in the LBGP were significantly different from those in the other water types. For groundwater with total dissolved solids lower than 1400 mg/L at landfill sites, a strong positive linear correlation (r = 0.83) was revealed between δ13CDIC and δ13CDOC. Based on these insights, δ2H versus δ13CDIC-DOC plots and RDA using δ2H and δ13CDIC-DOC as response variables were proposed to localize leak points at both lined landfills and leachate facilities. These findings further understanding of the isotopic fractionation features of hydrogen, carbon, and oxygen and provide novel environmental tracer methods for investigating leachate leak points at MSW landfill sites.

72‐2: The Research on FSR Optimization and Efficiency Improvement of Needle Placement for Mini LED Transfer Process

This paper introduces the Fast &amp; Slow mode of needle placement for Mini LED transfer from source wafer to target substrate and lead into the FSR illustration, the target and the key impact factors of FSR. Study how to improve the FSR and bonding efficiency.

Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without non-alcoholic fatty liver.

The prevalence of non-alcoholic fatty liver (NAFLD) has increased recently. Subjects with NAFLD are known to have higher chance for renal function impairment. Many past studies used traditional multiple linear regression (MLR) to identify risk factors for decreased estimated glomerular filtration rate (eGFR). However, medical research is increasingly relying on emerging machine learning (Mach-L) methods. The present study enrolled healthy women to identify factors affecting eGFR in subjects with and without NAFLD (NAFLD+, NAFLD-) and to rank their importance. To uses three different Mach-L methods to identify key impact factors for eGFR in healthy women with and without NAFLD. A total of 65535 healthy female study participants were enrolled from the Taiwan MJ cohort, accounting for 32 independent variables including demographic, biochemistry and lifestyle parameters (independent variables), while eGFR was used as the dependent variable. Aside from MLR, three Mach-L methods were applied, including stochastic gradient boosting, eXtreme gradient boosting and elastic net. Errors of estimation were used to define method accuracy, where smaller degree of error indicated better model performance. Income, albumin, eGFR, High density lipoprotein-Cholesterol, phosphorus, forced expiratory volume in one second (FEV1), and sleep time were all lower in the NAFLD+ group, while other factors were all significantly higher except for smoking area. Mach-L had lower estimation errors, thus outperforming MLR. In Model 1, age, uric acid (UA), FEV1, plasma calcium level (Ca), plasma albumin level (Alb) and T-bilirubin were the most important factors in the NAFLD+ group, as opposed to age, UA, FEV1, Alb, lactic dehydrogenase (LDH) and Ca for the NAFLD- group. Given the importance percentage was much higher than the 2nd important factor, we built Model 2 by removing age. The eGFR were lower in the NAFLD+ group compared to the NAFLD- group, with age being was the most important impact factor in both groups of healthy Chinese women, followed by LDH, UA, FEV1 and Alb. However, for the NAFLD- group, TSH and SBP were the 5th and 6th most important factors, as opposed to Ca and BF in the NAFLD+ group.

Wear diagnosis for rail profile data using a novel multidimensional scaling clustering method

AbstractThe diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The rail surface wear is the key impact factor when considering the health conditions of rails. This paper accomplishes contactless rail wear diagnosis by using multidimensional scaling based on a novel informational dissimilarity measure (IDM) to cluster intact and different worn rail profile data. The IDM uses weighted‐probability distribution of dispersion patterns to extract accurate time domain features from rail profile data, and the loss of information is minimized, which can greatly improve the accuracy for wear diagnosis. All the analyzing data for real experiments are collected by a laser scanner camera on an inspection car, where heavy‐haul railway rails with different types of surface wear are inspected. Experimental results with simulated and reality‐based data show that the proposed methods can identify worn profile data and discriminate different types of worn profiles more effectively when compared with existing methods. Thus, the proposed method offers a new thinking for the diagnosis of rail surface wear for heavy‐haul railways.

Historical development, impact mechanism and future trends of nitrogen footprint in Wuxi City, China

Human activities have changed the biogeochemical cycle of nitrogen, leading to a large amount of reactive nitrogen (Nr) into the environment, aggravating a series of environmental problems, affecting human and ecosystem health. Cities are the core areas driving nitrogen cycling in terrestrial ecosystems, however, there are numerous influencing factors and their contributions are unclear. The nitrogen footprint is an important index to understand the impact of human activities on the environment, however, the calculation of urban nitrogen footprint needs a simplified and accurate system method. Here we use a nitrogen footprint calculation model at the urban system level based on system nitrogen balance, and a multi-factor extended STIRPAT (stochastic impact by regression on population, affluence, and technology) model suitable for analyzing the impact mechanism of nitrogen footprint to estimate nitrogen footprint of Wuxi City during 1990–2050. We find that: (1) from 1990 to 2020, the total nitrogen footprint of Wuxi City was in an increasing trend, but the per capita nitrogen footprint was in a decreasing trend. The per capita nitrogen footprint of 22.36 kg capita−1 in 2020 was at a lower level globally. (2) Nr discharge from fossil fuel combustion and Haber-Bosch nitrogen fixation accounted for the main proportion of nitrogen footprint. (3) Dietary choice (Ad), GDP per capita (Ag), urbanization rate (Au), population (P), and fossil energy productivity (Te) were the key factors contributing to the increase of the nitrogen footprint, which resulted in an annual increase of 1.39 %. While nitrogen footprint productivity (Tn), nitrogen use efficiency in crop farming (Tc), and nitrogen use efficiency in animal breeding (Ta) were the key inhibit factors that inhibit the increase of nitrogen footprint, and these factors slow down the annual growth rate of nitrogen footprint by 0.39 %. (4) The continuous growth of nitrogen footprint in the baseline and population growth scenarios will bring more environmental problems and greater environmental governance pressure to Wuxi City, while the sustainable scenario that includes comprehensive means such as economic adaptation and technological improvement is more in line with the requirements of high-quality development in China. Several mitigation measures are then proposed by considering Wuxi's realities from both key impact factors and potential for nitrogen footprint reduction in different scenarios, which can provide valuable policy insights to other cities, especially lakeside cities to mitigate nitrogen footprint.

Quantitative assessment of the impact factors upon remediation performance of the PRB technique in conjunction with pumping/recharge

Installation of pumping/recharge wells adjacent to contaminant plume and permeable reactive barrier (PRB) can effectively improve PRB's performance by artificially altering groundwater flow field attracting more contaminants to pass through PRB within a shorten time. However, the impact factors affecting the performance of PRB in conjunction with pumping/recharge (abbreviated as PRB-PR technique) have not been quantitatively assessed. In this study, numerical approach was adopted and multiple scenarios were implemented based on the developed MODFLOW and MT3MDS models to quantitatively evaluate the influences of impact factors on PRB-PR's remediation performance over a hypothetical contaminated site. Simulation results indicated that: (1) PRB should be installed nearby the downstream boundary of contaminant plume; (2) PRB's contaminant removal efficiency (η), pumping/recharge rate (Q), and aquifer hydraulic conductivity (K) affect PRB-PR's performance significantly, and a linear relationship exists between contaminant removal rate (R) and the product of K and Q no matter η changes (a series of fitting equations representing the linear relationship between R and KQ under various η were yielded based on data fitting); and (3) PRB's hydraulic conductivity, pumping/recharge wells' locations, and pumping/recharge wells' quantity do not affect PRB-PR's performance seriously. The equations indicating PRB-PR's remediation performance and key impact factors can be utilized to calculate the minimum values of Q and η that can meet the requirements of remediation objectives of real contaminated sites, contributing to supervising the installation and operation of the PRB-PR technique accordingly.

Simultaneous methane mitigation and nitrogen removal by denitrifying anaerobic methane oxidation in lake sediments

Methane (CH4) is a potent greenhouse gas, with lake ecosystems significantly contributing to its global emissions. Denitrifying anaerobic methane oxidation (DAMO) process, mediated by NC10 bacteria and ANME-2d archaea, links global carbon and nitrogen cycles. However, their potential roles in mitigating methane emissions and removing nitrogen from lake ecosystems remain unclear. This study explored the spatial variations in activities of nitrite- and nitrate-DAMO and their functional microbes in Changdanghu Lake sediments (Jiangsu Province, China). The results showed that although the average abundance of ANME-2d archaea (5.0 × 106 copies g−1) was significantly higher than that of NC10 bacteria (2.1 × 106 copies g−1), the average potential rates of nitrite-DAMO (4.59 nmol 13CO2 g−1 d−1) and nitrate-DAMO (5.01 nmol 13CO2 g−1 d−1) showed no significant difference across all sampling sites. It is estimated that nitrite- and nitrate-DAMO consumed approximately 6.46 and 7.05 mg CH4 m−2 d−1, respectively, which accordingly achieved 15.07–24.95 mg m−2 d−1 nitrogen removal from the studied lake sediments. Statistical analyses found that nitrite- and nitrate-DAMO activities were both significantly related to sediment nitrate contents and ANME-2d archaeal abundance. In addition, NC10 bacterial and ANME-2d archaeal community compositions showed significant correlations with sediment organic carbon content and water depth. Overall, this study underscores the dual roles of nitrite- and nitrate-DAMO processes in CH4 mitigation and nitrogen elimination and their key environmental impact factors (sediment organic carbon and inorganic nitrogen contents, and water depth) in shallow lake, enhancing the understanding of carbon and nitrogen cycles in freshwater aquatic ecosystems.

Phosphorus Distribution within Aggregates in Long-Term Fertilized Black Soil: Regulatory Mechanisms of Soil Organic Matter and pH as Key Impact Factors

Understanding soil phosphorus (P) distribution and its key drivers is fundamental for sustainable P management. In this study, a 21-year fertilization experiment on black soil was carried out, setting up five fertilization treatments: unfertilized control (CK), nitrogen and potassium (NK), nitrogen, P and potassium (NPK), NPK plus straw (NPKS), and NPK plus manure (NPKM). The distribution and effecting factors of P pools within soil aggregates were investigated. Compared to CK, the NK and NPK treatments decreased calcium-associated P concentration in all aggregate fractions. Meanwhile, the NPK treatment significantly increased the organic P extracted from NaOH in unaggregated particles (&lt;0.053 mm). This was mainly due to the reduction in soil pH. The NPKS and NPKM treatments increased almost all P forms in aggregates, especially Ca-P. For the NPKM treatment, inorganic P extracted from resin, NaHCO3, and NaOH increased as aggregate size increased. This was mainly because straw or manure addition promoted soil organic carbon (SOC) storage in aggregates, creating more sorption sites via association with amorphous metallic minerals, and, thus, facilitating P accumulation. In conclusion, decreasing soil pH by chemical fertilizers is an effective strategy for mobilizing soil P, whereas increasing SOC by straw or manure facilitates P accumulation.

Spatial Distribution and the Key Impact Factors of Soil Selenium of Cultivated Land in Lianyuan City, China

Selenium (Se) is a micronutrient that has attracted significant attention, because the threshold for human health is low. During soil surveys in China, large areas of low-Se soil were found, and this condition may increase the probability of people suffering from Se deficiency. A multi-purpose regional geochemical survey conducted in the Lou Shao basin of Hunan Province found abundant Se-rich soils in Lianyuan City. However, as the primary grain-producing area in Hunan Province, the key factors affecting the spatial distribution of soil Se in the cultivated land of Lianyuan City remain to be elucidated. Therefore, based on the data of 5516 topsoil samples (0–20 cm) of cultivated land in Lianyuan City, we used geostatistics, correlation analysis, and a Geodetector to explore the effects of geological conditions (strata), soil types, soil properties, and topography on the distribution of Se in soil. The results showed that (1) in comparison to cultivated land in the Chinese mainland, Japan, Belgium, and Sweden, the cultivated land in Lianyuan City exhibits higher Se contents, with Se-sufficient and Se-rich areas accounting for 9.74% and 88.96% of the total area, respectively; (2) the distribution of high-Se soil was consistent with that in the Longtan Formation, Dalong Formation, and Daye Formation; (3) organic matter (OM) showed a positive correlation with Se, while both the elevation and slope were negatively correlated with Se; (4) stratum had the most significant effect on the spatial variation in soil Se, followed by OM. Lianyuan City is a typical Se-rich area, and the high level of Se in soil reduces the risk of local residents suffering with diseases caused by Se deficiency. The synergistic effect of stratum and OM is the key factor influencing Se enrichment in soils. Moreover, low-lying flat areas are more conducive to the accumulation of Se. This study will help farmers to identify suitable Se-rich cultivation areas in order to increase the Se content in crops, thereby providing a valuable basis for improvements in human health and the optimization of agricultural strategies.

Assessing environmental impact: Micro-energy network optimization in a Chinese industrial park

Micro-energy systems contribute significantly to environmental improvement by reducing dependence on power grids through the utilization of multiple renewable energy sources. This study quantified the environmental impact of a micro-energy network system in an industrial park through a life cycle assessment using the operation of the micro-energy network over a year as the functional unit and “cradle-to-gate” as the system boundary. Based on the baseline scenario, a natural gas generator set was added to replace central heating, and the light pipes were expanded to constitute the optimized scenario. The results showed that the key impact categories for both scenarios were global warming, fine particulate matter formation, human carcinogenic toxicity, and human non-carcinogenic toxicity. The overall environmental impact of the optimized scenario was reduced by 68% compared to the baseline scenario. A sensitivity analysis of the key factors showed that electricity from the power grid was the key impact factor in both scenarios, followed by central heating and natural gas. Therefore, to reduce the environmental impact of network systems, it is necessary to further optimize the grid power structure. The research approach can be used to optimize micro-energy networks and evaluate the environmental impact of different energy systems.

Numerical simulation and spray model development of liquid ammonia injection under diesel-engine conditions

Ammonia is regarded as a promising alternative fuel in internal combustion engine for its potential to reduce carbon emissions. Injection process of liquid ammonia will affect air-fuel mixture distribution and therefore combustion efficiency and NOx emissions. However, the experimental studies of liquid ammonia injection under engine conditions are limited, and the key impact factors of spray evolution are still unclear. This study aims to evaluate the applicability of numerical models for liquid ammonia spray, and numerically investigate spray characteristics under diesel-engine conditions (ambient temperature > 800K, ambient pressure > 2 MPa). Large eddy simulation coupled with Lagrangian particle tracking method were conducted, and the results show that the non equilibrium - Frössling phase change model coupled with Schiller-Naumann drag model can provide a good prediction of spray penetration. Liquid penetration increases with ambient pressure decreasing; vapor penetration increases with ambient density decreasing, but insensitive to ambient temperature. Temperature - mixture fraction distribution is sensitive to ambient temperature and superheat degree. Besides, a three-stage 0D model of spray tip penetration under diesel-engine conditions is developed firstly, which can reflect the key impact factors of ammonia spray evolution and provide theoretical support to optimize fuel injection strategy of liquid ammonia-fueled engines.

Machine Learning and Statistical Test–Based Culvert Condition Impact Factor Analysis

For managers of road infrastructure, culvert deterioration is a major concern since culvert failures can cause serious risks to the traveling public. The efficiency of the cost- and labor-intensive culvert inspection and maintenance process can be improved by properly identifying the key impact factors on culvert condition deterioration. Although the use of machine learning (ML) techniques to predict culvert conditions has been proven to be a promising tool for enhancing culvert management and enabling proactive scheduling of maintenance tasks, the information provided by the developed ML models has been given little attention for further use and analysis. By utilizing the predictor importance results of an evaluated decision tree (DT) culvert condition prediction model and the Mann–Whitney U test, this study provided insights to the identification of the key variables influencing culvert deterioration. According to the findings, five impact factors, including culvert span, pH, age, rise, and cover height, often have significant impact on the condition ratings of culverts made of various materials. In addition, such a statistical test-assisted factor identification process offered a way of identifying and enhancing the input variable selection for predictive ML model development.

ІNFORMATION SYSTEMS FOR MODELING LAND PRICES UNDER THE URBANIZATION PROCESS

The dynamic shifts associated with urban growth or decline significantly influence both municipal and national economic policies. Urban expansion often necessitates substantial land resources allocation, whereas in regions with limited land availability or geographical restrictions, a innovative approach to urban planning is mandated. Key land value factors have been determined and classified. Each direct and indirect land prices key economic impact factor had been summarized and used to build information system model. Among these factors financial and economic data combined with geo-spatial information are considered the main input data sources for the digital information systems solution. An eight steps algorithm was constructed to enable information system output for economical assessment of land plot objects. The presented equations for economical assessment of a single land plot object can be used to calculate important parameters for the information system. Going one step further, a pyramid-shaped diagram denotes four stages of general economical valuation for prospective land object in context of investment opportunities. The price of each individual land slot can be efficiently calculated using an information system that relies upon pre-processed data, price impact factors and general land slot information. The findings underscore the necessity for adopting a more comprehensive approach towards the modeling of land prices in urbanized territories. This approach should adequately reflect the current delineation of urban and suburban boundaries, incorporate data pertinent to urban zones, and consider the spatial distribution of most significant industrial sites and business districts within larger urban zone (metropolitan region).

Analyzing the Risk Factors of Hazardous Chemical Road Transportation Accidents Based on Grounded Theory and a Bayesian Network

With the increase in the production and use of hazardous chemicals in China, road transportation safety issues are becoming increasingly prominent. To study the causes of road transportation accidents involving hazardous chemicals, prevent accident occurrence, and realize the safety and sustainable development of the transportation of hazardous chemicals, we combined grounded theory (GT) and a Bayesian network (BN) model to quantify the causal relationship of the interactions among the influencing factors leading to hazardous chemical road transportation accidents that occurred in China in the period of 2017–2020. The influencing factors of these accidents were classified into 5 core categories, 12 main categories, and 28 categories through the GT method, and then a BN-based model was established for these collected accidents. The conditional probability and posterior probability of each influencing factor leading to an accident were determined through BN learning, and then the causal relationship of the interactions between the influencing factors was quantified. The results indicated that the probability of road transportation accidents involving hazardous chemicals considered in this study reaches 72.5% under the combined influence of various factors, and the most likely causal chain of an accident is that equipment failure during the hazardous chemical transportation process contributes to an Unsafe Tanker State, which in turn leads to an accident. The sensitivity analysis confirmed that the key impact factor of hazardous chemical road transportation accidents is equipment failure, followed by improper operation. Overall, this study presents a reference and a foundation for avoiding or reducing risks as much as possible during daily hazardous chemical road transportation operations and risk supervision, realizing safe, sustainable development.

Urban Resilience Evaluation and Optimization Research based on Rainfall Scenarios

The development process of urbanization has changed the natural factors such as topography, geomorphology, and bit difference, which increases the risk of urban storm flooding in case of extreme weather. The concept of urban resilience can guide urban planning and construction to resist rain and flood disasters and improve urban rain and flood safety resilience capacity. In order to effectively assess the level of urban rain and flood resilience, and effectively tap the key impact factors of resilience, this project takes Bengbu City as an example, and analyzes the urban infrastructure construction, economic and social development, urban ecological environment and hydrological situation of Bengbu City in recent years, constructs a comprehensive assessment model of urban rain and flood resilience, obtains the level of rain and flood safety resilience and key constraints in Bengbu City, and looks for optimization of the management of urban flooding, and find the effective implementation path to optimize the management of urban flooding.

Design impacts of citizen science. A comparative analysis of water monitoring projects

Citizen science is often promoted as having the capacity to enable change–from increasing data provision and knowledge product development, via behavioral change of citizens, to problem-solving. Likewise, researchers increasingly emphasize the role of project design in initiating these changes through citizen science. However, respective claims are mostly based on single case studies and reviews, calling for a systematic comparative approach to understanding the effects of project design on change. Based on a survey of 85 water-related citizen science projects from 27 countries, we analyze the comparative effects of literature-based design principles on project impacts. Factor analysis first reveals three key impact factors which are ‘Data output’, ‘Citizen outcome’, and ‘Impact chain’. Regression analysis then shows that these impact factors are significantly influenced by several design factors, amongst which motivational factors are most prominent. The analysis also shows that design factors are most important for ‘Impact chain’, followed by ‘Citizen outcome’, and ‘Data output’. While design factors only partly explain the overall project effects, the regression results are rather stable and significant when including other potential influencing factors like project responsibility and funding. In sum, the results provide an empirically substantiated and differentiated understanding of citizen science impacts and how these are influenced by project design.

Urban human settlements' resilience measurement and characteristics and their mechanism model in China.

This study introduces the principle of resilience into the study of human settlements. In this study, a comprehensive evaluation model of urban human settlements' resilience based on the provincial region of China was constructed using the Driver-Pressure-State-Impact-Response framework. The spatio-temporal evolution characteristics of urban human settlements' resilience was explored. The influencing factors were analysed by geographical detectors, and the driving mechanism was constructed. Results show that the following. (1) The resilience level of human settlements in China continued to increase, and the resilience level of each province and city changed significantly. The overall clustering effect showed a tendency to fluctuate and weaken. The distribution of cold spot areas became less and less, and the hot spots were moving from northeast China to southeast China. (2) Significant differences existed in the intensity of the impact of different indicators on the resilience system. The value of the impact factor showed an overall upward trend, and the number of key impact factors increased. (3) Improving the ability of scientific and technological innovation, accelerating the transformation and upgrading of the regional economy, increasing the training of talents and making financial inclination in scientific and technological development and industrial pollution control were all important ways for developing and maintaining the resilience of urban human settlements. This study not only introduces a new evaluation of urban human settlements from the perspective of resilience but also explores key impact indices and driving mechanisms, which provides new ideas for studying urban human settlements.

The dynamic impact of landscape urbanization on ecosystem service multifunctionality in the Chengdu–Chongqing urban agglomeration: A panel VAR approach

AbstractUnderstanding the influence mechanism of landscape urbanization on ecosystem services (ESs) will provide useful guides for ecosystem conservation and urban sustainable management. However, the models used in existing studies tended to evaluate the various impacts of landscape urbanization on ES in the same period and fail to reveal the dynamic response of ES to landscape urbanization and the contribution of each impact variable, which is not conducive to the long‐term planning and management of urban ecosystems. To fill the gap, based on the panel data of the 141 counties in the Chengdu–Chongqing urban agglomeration (CCUA) from 2000 to 2015, this study applied the panel vector autoregressive (PVAR) model to investigate the dynamic impact of different aspects (i.e., urban scale, density, shape, and compactness) of landscape urbanization on ES multifunctionality (ESM) and identified the key impact factors. The results showed that (1) the proportion of high‐level ESM decreased by 9.13% in 2000–2010 and then increased by 19.14% in 2010–2015; (2) the ESM of the CCUA existed significant spatial agglomeration, and the high‐level areas were concentrated in the southwestern mountains, while the low‐level areas were mainly located in the central plains and urban areas; (3) There was a strong non‐linear relationship between landscape urbanization and ESM, with variability in the intensity and direction of ESM's response to landscape urbanization shocks as time. Specifically, urban landscape scale and shape (i.e., percentage of landscape, largest patch index (LPI), and landscape shape index) were generally more influential on the ESM than urban landscape density and compactness (i.e., patch density and pacth cohesion index), with LPI making the greatest contribution. These findings will help to clarify the dynamic relationship between landscape urbanization and ESM, which could provide important guidance for ecosystem conservation and urban planning.

Multi-objective optimization for multimodal transportation routing problem with stochastic transportation time based on data-driven approaches

We study a multi-objective optimization model of a stochastic multimodal transportation network considering key impact factors such as transit cost, time, and transport mode schedule while minimizing total transportation cost and transportation time. In this study, we apply the Monte Carlo simulation to deal with the stochastic transportation time in the network and propose a data-driven approach that combines historical data and the dataset generated by the data mining algorithm to accelerate the search for the nondominated solution in the simulation. To validate the effectiveness of the proposed Data-Driven Multi-Objective Simulation Ant Colony (DD-MSAC) algorithm, we compare the optimum-seeking performance and the running time consumption of the Nondominated Sorting Genetic Algorithm-II (NSGA-II) and the Multi-Objective Simulation Ant Colony (MSAC) algorithm. Then, the MSAC algorithm is adopted as the benchmark for the comparison study on the solving performance of the proposed DD-MSAC algorithm. We conducted 30 times simulation run under different network scales in our numerical examples to show that the DD-MSAC algorithm can be equally effective as the non-data-driven MSAC algorithm in finding a nondominated solution as the average error does not exceed 5%. Meanwhile, we analyze the impact of different data-driven approaches, including data pool and support vector machine, on the solution quality and the running time. Finally, we use an example of China’s Belt Road Initiative to verify the effectiveness of the proposed algorithm.