Environmental Data Research Articles

Interpolation of Environmental Data Using Deep Learning and Model Inference

Interpolation of Environmental Data Using Deep Learning and Model Inference

Environmental conditions influencing the early colonization stage of Ludwigia hexapetala , an aquatic plant recently invasive in Italy

Freshwater ecosystems are among the most susceptible to biological invasions. The South American Ludwigia hexapetala is an aquatic plant that is becoming an increasing threat in many European waterbodies, recently including Italy. This study aimed to define the main parameters influencing the early colonization stage of L. hexapetala by overlapping the percentage cover of this species with environmental parameter data collected at 24 aquatic sites from six waterbodies in north-central Italy. At each site, chemical and physical characteristics of the water (temperature, pH, dissolved oxygen, conductivity, nitrates, phosphates, ammonia, depth, transparency), grain size of the substrate and level of anthropogenic disturbance were evaluated. The results showed that although L. hexapetala prefers shallow, warm, alkaline, moderately rich in ions and nutrients (especially phosphates) and oxygen-poor waters, it can grow in a wide range of environmental conditions. Moreover, as a typical invasive alien species, it spreads opportunistically in disturbed, unstable sites. Thus, L. hexapetala can invade freshwater habitats with different environmental conditions and subjected to anthropogenic disturbance. However, the results suggest that water depth may be a limiting factor in the early colonization stage of this species, which does not seem to be able to colonise waters deeper than 1 m in investigated sites, while it has been observed in significantly deeper waters in other European countries with a longer invasion history. Detecting the environmental parameters that most influence the growth of L. hexapetala becomes crucial both to identify the sites most at-risk of invasion in which to initiate timely monitoring actions for the species, and to be able to develop better management and control actions for this alien species in sites that have already been invaded.

Architectural technology and labour organisation at the late Neolithic Liangzhu City, Yangtze Delta region, China

Building the UNESCO World Cultural Heritage site of Liangzhu City and its hinterlands was an enormous undertaking that required an unprecedented level of architectural innovations, clever logistic planning, and sophisticated labour organisation. We draw on environmental, archaeological and experimental data on the preparation and construction of grass-wrapped clay blocks at the Liangzhu City and investigate the importance of technological innovations to understand architectural energetics and labour organisation behind the unprecedented urbanisation at Liangzhu and beyond in prehistoric Yangtze Delta region. We estimate that each clay block took only around 5 min to prepare by 2–4 workers. Considering digging and other tasks, a small group of 3–4 workers would have been the most efficient arrangement in the preparation of grass-wrapped clay blocks. We reproduced different types of clay blocks, classified their sizes, and identified the standard sized (45x15 × 15cm) clay blocks that match with archaeological finds. Our results also show that standard-sized clay blocks had a more optimal grass-earth ratio which increased the drainage efficiency of the built clay-blocked structures and their resistance to weathering in wet conditions. The different architectural technologies applied by the Liangzhu builders constituted the Liangzhu builders’ earth-building toolkit. To maximize the efficiency, labour was divided and organised according to different construction tasks. The clay-blocked structures and stone beddings were mostly built by smaller groups, who were responsible for the preparation, transportation and construction tasks. Mobilization and division of labour during these construction activities might not follow the classical top-down process. Instead, the need to apply different architectural technologies, each restrained by availability of resources, convenience of transportation, and/or intrinsic engineering properties, at construction sites in a predominantly aquatic environment, prompted a creative form of labour organisation. Our study therefore provides fresh insights into understanding labour organisation, social structure and power relations at the Liangzhu Civilisation.

Predictive mapping of organic carbon stocks in surficial sediments of the Canadian continental margin

Abstract. Quantification and mapping of surficial seabed sediment organic carbon have wide-scale relevance for marine ecology, geology and environmental resource management, with carbon densities and accumulation rates being a major indicator of geological history, ecological function and ecosystem service provisioning, including the potential to contribute to nature-based climate change mitigation. While global analyses can appear to provide a definitive understanding of the spatial distribution of sediment carbon, regional maps may be constructed at finer resolutions and can utilise targeted data syntheses and refined spatial data products and therefore have the potential to improve these estimates. Here, we report a national systematic review of data on organic carbon content in seabed sediments across Canada and combine this with a synthesis and unification of the best available data on sediment composition, seafloor morphology, hydrology, chemistry and geographic settings within a machine learning mapping framework. Predictive quantitative maps of mud content, dry bulk density, organic carbon content and organic carbon density were produced along with cell-specific estimates of their uncertainty at 200 m resolution across 4 489 235 km2 of the Canadian continental margin (92.6 % of the seafloor area above 2500 m) (https://doi.org/10.5683/SP3/ICHVVA, Epstein et al., 2024). Fine-scale variation in carbon stocks was identified across the Canadian continental margin, particularly in the Pacific Ocean and Atlantic Ocean regions. Overall, we estimate the standing stock of organic carbon in the top 30 cm of surficial seabed sediments across the Canadian shelf and slope to be 10.9 Gt (7.0–16.0 Gt). Increased empirical sediment data collection and higher precision in spatial environmental data layers could significantly reduce uncertainty and increase accuracy in these products over time.

Arctic phytoplankton microdiversity across the marginal ice zone: Subspecies vulnerability to sea-ice loss

Seasonal phytoplankton blooms are important Arctic phenomena, contributing to global primary production and biogeochemical cycling. The decline in sea-ice extent and thickness favors a longer open-water period with impacts on phytoplankton dynamics. Arctic net productivity is influenced by microalgae living associated with sea ice, with distinct species thought to be favored by ice-covered and ice-free waters. In this study, we investigated the phytoplankton community structure in Baffin Bay, a semi-enclosed sea where Arctic and North Atlantic water masses interact. We compared communities from the ice-free Atlantic-influenced eastern, the marginal ice zone, and the ice-covered Arctic-influenced western Baffin Bay. The community was characterized using 18S rRNA high-throughput amplicon sequencing and flow cytometry cell counting, and compared to environmental data collected during the Green Edge campaign. We sampled 16 stations grouped by sectors according to sea-ice cover. In the sectors associated with sea ice, phytoplankton formed a highly diverse community of smaller taxa, which contrasted with a low-diversity community in ice-free sectors, dominated by larger centric diatoms and Phaeocystis pouchetii adapted to high light/low nutrient conditions. Several phytoplankton species were flagged as indicators for the under-ice and marginal ice zone sectors, including ice-associated taxa such as the diatoms Melosira arctica and Pseudo-nitzschia seriata, but also subspecies representatives of the early-blooming alga Micromonas polaris and the cryptophyte Baffinella frigidus. The strong association of certain taxa with under-ice and marginal ice zone sectors, including Pterosperma sp., Chrysochromulina sp., Micromonas polaris, and B. frigidus, suggest that they might be indicators of diversity loss due to ongoing sea-ice changes in Baffin Bay. We report new intra-species variability of Micromonas polaris suggesting that seasonal specialists could wax and wane over the bloom and non-bloom periods, highlighting the need for detailed year-long studies and the importance of microdiversity when assessing the diversity and distribution of polar phytoplankton.

Environmentally associated colour divergence does not coincide with population structure across Lesser Antillean anoles

Abstract Species distributed across heterogeneous environments may undergo local adaptation, which can be limited by the homogenizing effects of gene flow. Lesser Antillean anoles exhibit dorsal colour variation associated with dramatic shifts in environment across small spatial scales, providing an excellent system for studying the maintenance of local adaptation despite ongoing gene flow. The 1995 Soufriere Hills eruption on Montserrat provides an opportunity to understand how natural disasters may influence the evolution of insular species. Thus, our study had two main objectives. First, we collected genetic, phenotypic, and environmental data to investigate whether environmentally associated dorsal coloration reflects underlying population structure in three species of Lesser Antillean anoles. Second, we examined the short-term evolutionary consequences of volcanism by comparing pre- and posteruption samples of Anolis lividus using multilocus sequence data. We found all three species exhibited genetic isolation-by-distance but not isolation-by-environment. Furthermore, phenotypic differences within each species were not predicted by genetic distances, suggesting gene flow across colour morphs. We confirmed the repopulation of A. lividus in the impacted area and found genetic diversity equivalent to pre-eruption sampling. Our findings contribute to our understanding of local adaptation in spatially small-scale systems and the impact of catastrophic natural disasters on population structure.

Portable Arduino-Based Multi-Sensor Device (SBEDAD): Measuring the Built Environment in Street Cycling Spaces

The built environment’s impact on human activities has been a hot issue in urban research. Compared to motorized spaces, the built environment of pedestrian and cycling street spaces dramatically influences people’s travel experience and travel mode choice. The streets’ built environment data play a vital role in urban design and management. However, the multi-source, heterogeneous, and massive data acquisition methods and tools for the built environment have become obstacles for urban design and management. To better realize the data acquisition and for deeper understanding of the urban built environment, this study develops a new portable, low-cost Arduino-based multi-sensor array integrated into a single portable unit for built environment measurements of street cycling spaces. The system consists of five sensors and an Arduino Mega board, aimed at measuring the characteristics of the street cycling space. It takes air quality, human sensation, road quality, and greenery as the detection objects. An integrated particulate matter laser sensor, a light intensity sensor, a temperature and humidity sensor, noise sensors, and an 8K panoramic camera are used for multi-source data acquisition in the street. The device has a mobile power supply display and a secure digital card to improve its portability. The study took Beijing as a sample case. A total of 127.97 G of video data and 4794 Kb of txt records were acquired in 36 working hours using the street built environment data acquisition device. The efficiency rose to 8474.21% compared to last year. As an alternative to conventional hardware used for this similar purpose, the device avoids the need to carry multiple types and models of sensing devices, making it possible to target multi-sensor data-based street built environment research. Second, the device’s power and storage capabilities make it portable, independent, and scalable, accelerating self-motivated development. Third, it dramatically reduces the cost. The device provides a methodological and technological basis for conceptualizing new research scenarios and potential applications.

Response mechanism of rodent burrow density to natural environmental factors in desert areas based on multisource data

Timely and accurate prediction and early warning of rodent infestation are highly important for the sustainable development of desert ecosystems. This study combines data from 1376 UAV images of actual rodent burrows with natural environmental factor data, aiming to reveal the mechanism through which these factors affect rodent burrow density in the desert areas. The relationships between rodent burrow density and the 9 plant communities and 34 plant species in this area were analyzed by the K-W test and Spearman rank correlation analysis, respectively. The Boruta algorithm and the boosted regression trees (BRT) model were used to determine the six main factors influencing rodent burrow density from a total of 32 factors and to determine the distribution pattern of the burrows. The results show that (1) the density of rodent burrows in the Junggar Basin desert area gradually decreases from south to north and from west to east. (2) There were significant differences in rodent burrow density across the different plant community types (P = 0.048**). A total of 50.0 % of the species significantly influenced rodent burrow density at at least one level. (3) Among the 17 factors with lag effects, nine had the most significant correlation with rodent burrow density in April. Only two natural environmental factors demonstrated lag effects of less than one month. (4) The soil clay content, elevation, Normalized Difference Salinity Index (NDSI), vegetation cover, precipitation, and soil bulk density were the most important influencing factors on rodent burrow density. Considering the pairwise interactions among factors, the collective impact of these factors on rodent burrow density is at least sevenfold greater than the independent effect of a single factor. This study provides a theoretical basis for obtaining a timely and accurate understanding of the spatial distribution of rodent burrows, mitigating the degradation trend of desert ecosystems, and ensuring healthy and sustainable development.

PESTI GUARD: FARMER-FRIENDLY PESTICIDE ROBOT

Agriculture is the primary source of revenue for India's population, which accounts for nearly 60% of the country's total. Farmers work in their fields to cultivate various crops based on the environment and resources available. Farmers must use large quantities of pesticides to increase food production in order to meet such high food demand for such a large population. Traditional manual pesticide spraying operations is full of direct exposure to the pesticide liquid work environment, great harm to human body and when this pesticide may come into contact with the farmer during spraying, which may trigger skin cancer and asthma illnesses. Increased pesticide spraying can impact consumer health as it enters the food chain. The widespread use of pesticides in agriculture presents significant health risks to both farmers and consumers. The pesticide spraying robot is equipped with state-of-the-art sensors to record real-time temperature and humidity levels, providing crucial environmental data for optimized pesticide application. Farmers can remotely control the robot using a user-friendly interface, instructing it to perform specific spraying tasks while maintaining a safe distance from potentially hazardous chemicals. To address this challenge, this paper proposes a Pesticide Spraying Robot designed to minimize human contact during operation while prioritizing human health through advanced environmental monitoring and remote-control capabilities.

An Inhomogeneous Weibull–Hawkes Process to Model Underdispersed Acoustic Cues

AbstractA Hawkes point process describes self-exciting behaviour where event arrivals are triggered by historic events. These models are increasingly becoming a popular choice in analysing event-type data. Like all other inhomogeneous Poisson point processes, the waiting time between events in a Hawkes process is derived from an exponential distribution with mean one. However, as with many ecological and environmental data, this is an unrealistic assumption. We, therefore, extend and generalise the Hawkes process to account for potential under- or overdispersion in the waiting times between events by assuming the Weibull distribution as the foundation of the waiting times. We apply this model to the acoustic cue production times of sperm whales and show that our Weibull–Hawkes model better captures the inherent underdispersion in the interarrival times of echolocation clicks emitted by these whales.

The application of artificial intelligence in Unmanned Underwater Vehicle communication systems

Artificial Intelligence (AI) has the potential to significantly enhance decision-making techniques in Unmanned Underwater Vehicle (UUV) communication systems by utilizing real-time sensor feedback and environmental data. To address the challenges posed by high data volume and error rates, we propose an advanced methodology known as the Variational Deep Network (VDN). This method integrates Variational Modal Decomposition (VMD) algorithms with the Deep Convolutional Neural Network (DCNN) to create a realizable AI decision-making system. In conjunction with the DCNN and VMD algorithms, our research focuses on accurately identifying received signals under data constraints. Considering the spectral efficiency and robustness, the Filter-Bank Multicarrier (FBMC) technology have been employed in this paper. Our results demonstrate that the VDN significantly reduces the volume of data needed for neural network training and effectively recognizes FBMC signals. These insights confirm the potential of VDN decision-making systems to drive advancements in UUV communication technologies.

Crop and Weed Segmentation and Fractal Dimension Estimation Using Small Training Data in Heterogeneous Data Environment

The segmentation of crops and weeds from camera-captured images is a demanding research area for advancing agricultural and smart farming systems. Previously, the segmentation of crops and weeds was conducted within a homogeneous data environment where training and testing data were from the same database. However, in the real-world application of advancing agricultural and smart farming systems, it is often the case of a heterogeneous data environment where a system trained with one database should be used for testing with a different database without additional training. This study pioneers the use of heterogeneous data for crop and weed segmentation, addressing the issue of degraded accuracy. Through adjusting the mean and standard deviation, we minimize the variability in pixel value and contrast, enhancing segmentation robustness. Unlike previous methods relying on extensive training data, our approach achieves real-world applicability with just one training sample for deep learning-based semantic segmentation. Moreover, we seamlessly integrated a method for estimating fractal dimensions into our system, incorporating it as an end-to-end task to provide important information on the distributional characteristics of crops and weeds. We evaluated our framework using the BoniRob dataset and the CWFID. When trained with the BoniRob dataset and tested with the CWFID, we obtained a mean intersection of union (mIoU) of 62% and an F1-score of 75.2%. Furthermore, when trained with the CWFID and tested with the BoniRob dataset, we obtained an mIoU of 63.7% and an F1-score of 74.3%. We confirmed that these values are higher than those obtained by state-of-the-art methods.

The analysis of ecological security and tourist satisfaction of ice-and-snow tourism under deep learning and the Internet of Things

This paper aims to propose a prediction method based on Deep Learning (DL) and Internet of Things (IoT) technology, focusing on the ecological security and tourist satisfaction of Ice-and-Snow Tourism (IST) to solve practical problems in this field. Accurate predictions of ecological security and tourist satisfaction in IST have been achieved by collecting and analyzing environment and tourist behavior data and combining with DL models, such as convolutional and recurrent neural networks. The experimental results show that the proposed method has significant advantages in performance indicators, such as accuracy, F1 score, Mean Squared Error (MSE), and correlation coefficient. Compared to other similar methods, the method proposed improves accuracy by 3.2%, F1 score by 0.03, MSE by 0.006, and correlation coefficient by 0.06. These results emphasize the important role of combining DL with IoT technology in predicting ecological security and tourist satisfaction in IST.

Analysis of the sustainability index for ecologically low-input integrated farming: A comprehensive assessment of environmental, economic, and social impact

A comprehensive sustainability index was designed in this study to evaluate the effectiveness and environmental impact of ecologically low-input integrated farming systems. By integrating diverse agricultural practices while minimizing reliance on external, non-renewable resources, this approach aims to enhance ecological harmony and sustainability. The index provides a multi-dimensional assessment, encompassing environmental, economic, and social aspects, to gauge the overall sustainability of these integrated farming practices. The procedure involved defining critical sustainability criteria across environmental, economic, and social dimensions, collecting relevant data on farming practices, and constructing a model to assess and score these practices. The index was validated through real-world applications and engaged with various stakeholders for insights and practical verification. Environmental data involves measuring soil nutrients (5 %), water usage (500 gallons/acre), and biodiversity (30 species/acre) to assess the ecological impact of farming practices. Economic data focuses on recording crop yields, input costs, and market prices to evaluate the financial viability of these practices. Social data is collected through surveys on labor practices, job satisfaction, and community impact, providing insights into the social implications and acceptability of the farming methods. The data revealed that the farming system aligned well with sustainability benchmarks, exhibiting no deviation from optimal levels in soil nitrogen content and water usage. Strong profitability and input cost efficiency were observed in the economic dimension, with a net income per acre of $400/acre and an input cost efficiency of 50 %. In the social aspect, although reasonably scored, specific labor metrics and regional/industry standards were lacking for a comprehensive evaluation. Overall, the farming system demonstrated commendable sustainability practices, with an environmental score of 7.5/10, an economic score of 8/10, and a social score of 7/10, suggesting potential for further improvements, particularly in addressing social sustainability factors.

Perspective: could Ethiopian potatoes contribute to environmental sustainability, the Ethiopian economy, and human health?

Environmental data are rapidly accruing on the unsustainability of diets based on animal products, such as dairy and meats. Shifting to alternative sources of protein is inevitable given an increase in the projected global population and protein demand. Left unchecked, a collision between food security and sustainability is imminent. Potatoes could be the strategic food and cash crop to harmonize food security and sustainability worldwide. Recently, there has been a growing interest in extracting proteins from the byproduct of the potato starch industry known as potato fruit juice. These proteins are garnering attention due to their nutritional value, characterized by a well-balanced amino acid profile, as well as their functional properties including emulsifying, foaming, and gelling capabilities. Moreover, these proteins are considered to be less allergenic than some other protein sources. Extracting potato protein, which is sourced as a byproduct, reduces food loss and waste, thereby eliminating pathogenic microorganisms from the environment and mitigating greenhouse gas emissions. Ethiopia is a major potato producer in East Africa. Potatoes help the country increase household income, ensure food security and revenue generation, and produce starch. However, Ethiopia’s potato starch industry has not yet begun protein extraction, despite the vital role of the proteins and the country’s huge cultivation potential. Furthermore, the global potato protein market is experiencing significant growth. This information urgently calls for innovative approaches to assess the impact of extracting protein from potatoes produced in Ethiopia. Therefore, this perspective article has two main objectives. First, to scan the extent of potato production in Ethiopia in relation to environmental sustainability and the economy. Second, to provide prospects on the impact of extracting protein isolate from potatoes produced in Ethiopia on environmental sustainability, Ethiopia’s economy, and human health.

Thermal Environment Analysis and Optimization for Large Space Buildings with Radiant Cooling Floors: A Case Study of Xianyang International Airport

Radiant cooling floors combined with ventilation systems have been widely applied in large space buildings. However, there has been a lack of research on system control strategies for their adaptation to weather changes. This study aimed to find control strategies for radiant cooling floors combined with displacement ventilation systems used in large space buildings in order to achieve energy conservation and environmental improvement. Supply air temperature and cooling surface temperature were determined to be the control variables. It was found that cooling capacity of the combined system and the comfort index, PMV (predicted mean vote), were linear in relation to the supply air temperature and cooling surface temperature. The linear equations regarding cooling capacity and PMV were established separately using environment data, and then the optimal region was determined. A case study on Terminal 3 of Xi’an Xianyang International Airport was conducted. The thermal environment was investigated through on-site measurements, questionnaires, and numerical simulations with CFD (computational fluid dynamics). It was found that supply air temperature and cooling surface temperature had a significant impact on PMV, and less impact on the cooling capacity. Therefore, it was determined that the supply air temperature should be altered first when the indoor temperature exceeds the upper limit, and then the cooling surface temperature should be changed if the indoor environment continues to overheat with the supply air temperature set to 18 °C. Thus, the supply air temperature was kept at 18 °C, and the floor surface temperature was set to be 22 °C on a high-temperature day. The average PMV was 0.87, and the cooling capacity of the combined system was 200 W/(m2·K), according to the CFD simulation. In addition, the surface heat transfer coefficient of the cooling floor was found to be 10.26 W/(m2·K). This research provides important references for the design and operational management of radiant cooling floors in large space buildings.

Clustering of Wind Speed Time Series as a Tool for Wind Farm Diagnosis

In several industrial fields, environmental and operational data are acquired with numerous purposes, potentially generating a huge quantity of data containing valuable information for management actions. This work proposes a methodology for clustering time series based on the K-medoids algorithm using a convex combination of different time series correlation metrics, the COMB distance. The multidimensional scaling procedure is used to enhance the visualization of the clustering results, and a matrix plot display is proposed as an efficient visualization tool to interpret the COMB distance components. This is a general-purpose methodology that is intended to ease time series interpretation; however, due to the relevance of the field, this study explores the clustering of time series judiciously collected from data of a wind farm located on a complex terrain. Using the COMB distance for wind speed time bands, clustering exposes operational similarities and dissimilarities among neighboring turbines which are influenced by the turbines’ relative positions and terrain features and regarding the direction of oncoming wind. In a significant number of cases, clustering does not coincide with the natural geographic grouping of the turbines. A novel representation of the contributing distances—the COMB distance matrix plot—provides a quick way to compare pairs of time bands (turbines) regarding various features.

Spatio-Temporal Changes of Arable Land and Their Impacts on Grain Output in the Yangtze River Economic Belt from 1980 to 2020

The “Yangtze River Economic Belt Development Strategy” is one of China’s three major national development strategies. Enhancing the protection and quality of arable land in the Yangtze River Economic Belt (YEB) is pivotal for fostering regional growth. In this study, land use data spanning the years 1980 to 2020 in the YEB were extracted from the national land use database maintained by the Resource and Environment Data Center of the Chinese Academy of Sciences. Employing Geographic Information System (GIS) spatial analysis techniques and arable land change metrics, the study delineated the spatiotemporal characteristics of arable land alterations across the YEB for the period. Additionally, using grain output data at the prefecture level from 2011 to 2020, the paper calculated provincial grain output to analyze the impact of arable land changes over the last four decades on grain output. The findings revealed that: (1) From 1980 to 2020, the total arable land area in the YEB decreased by approximately 41,775 square kilometers, with the most significant decrease occurring in the downstream region. (2) From 1980 to 1990, the primary factor contributing to the decrease in arable land area was the expansion of water bodies, while from 1990 to 2020, the principal reason for the reduction in arable land area was the expansion of construction land. (3) From 1980 to 2020, the decrease in arable land area resulted in a net reduction of approximately 25.12 million tons in total grain output, with the largest decline observed in the downstream regions and the smallest decline in the upstream regions. (4) Consistent with the trends in arable land area reduction, the main reason for the decline in grain output from 1980 to 1990 was the expansion of water bodies encroaching upon arable land, whereas from 2000 to 2010, the primary cause of arable land reduction was the expansion of construction land areas. In conclusion, the research suggested that over the past four decades, the primary driver behind the reduction in arable land within the YEB has been the expansion of construction land areas. Particularly noteworthy was the period from 2000 to 2010, during which the impact of arable land reduction on grain output was most pronounced. This period coincided with the rapid economic development and accelerated urbanization process within the YEB.

Comparison of linear and nonlinear modeling approaches to develop an abundance index based on voyage and market data for a data-limited fishery

IntroductionWorldwide coastal fish resources face severe threats from fisheries overexploitation. However, the evaluation of abundance trends in most coastal fisheries is constrained by limited data. This study took blackmouth croaker (Atrobucca nibe), a stock depleted by coastal trawl fishery in southwestern Taiwan, as an example to showcase the development of a relative abundance index from data-limited fishery (only landing data were available).MethodsThis study employed unique data sourcing from voyage data recorders (VDRs) to estimate fishing effort (in combination with landing data to estimate the catch per unit effort, CPUE) that demonstrated the potential application in global data-limited fisheries and assessed alternative approaches for predictors of fishery-targeting practices to condition effort for producing more accurate metrics of relative abundance. The nominal CPUE was standardized using three statistical models: generalized linear model, generalized additive model (GAM), and vector-autoregressive spatiotemporal models (VASTs) with two treatments of each of the four effects: environmental (sea temperature, salinity, density of mixing layer, seafloor temperature, and chlorophyll), vessel, spatial, and targeting effects. A total of 15 models were designed and compared for these effects, and their explanatory power (EP) was evaluated using cross-validation R2 and other metrics.Results and discussionResults indicated that the targeting effect exerted the most significant influence on standardization and was suggested to be addressed through the principal component analysis (PCA) approach. Both vessel and spatial effects demonstrated considerable influence, whereas the environmental effect exhibited a limited impact, possibly due to the small fishing area in this study. Regarding models’ EP, given the nonlinear nature of the PCA algorithm and environmental data, the study highlighted the superiority of the GAM over linear-based models. However, incorporating nonlinear features in VAST (M15) makes it the most effective model in terms of predictive power in this study. Concerning the stock status, despite variations in relative CPUE trends among major models, a general declining trend since 2015 signals the potential decline of the blackmouth stock and urges fishery managers to consider further design of management measures.

Airlines’ Corporate Growth and Environmental Conservation: Evidence from Global Carriers and Forwarders

This study explores how global airlines can achieve growth and environmental conservation using transport, financial performance, and carbon dioxide (CO₂) emissions data before, during, and after the COVID-19 pandemic from 2019 to 2022 with manually collected data for 38 leading international airlines. First, the regression analyses identify a clear range of turning points that airlines should consider in terms of Scopes 1 and 2 CO₂ emissions per employee and cargo ton-kilometers per employee (cargo ton-kilometers (CTK)/EMP) considering the environmental Kuznets Curve (EKC) hypothesis. Second, the deciding factors, including fundamental efforts by the airlines themselves, result from the interaction of three points, which have been encouraged and promoted in the airline industry in recent years. (1) Tighter emissions controls for air transport, (2) investors’ emphasis on environment, society, and governance (ESG), and (3) assessments and guidelines from ratings agencies and economic and environmental organizations. Third, increasing CTK/EMP to verified thresholds and taking an ESG-oriented approach can contribute to airlines’ combined achievement of growth and environmental conservation and related data will expand academic and policy-related research.