Environmental Challenges最新文献

{"title":"Long-term monitoring, predicting and connection between built-up land and urban heat island patterns based on remote sensing data","authors":"","doi":"10.1016/j.envc.2024.101036","DOIUrl":"10.1016/j.envc.2024.101036","url":null,"abstract":"<div><div>The alterations observed in urbanized areas have given rise to urban climate change, contributing to the emergence of urban heat islands (UHIs). This study investigates changes and predicts the built-up land/UHIs in Rasht city from 1991 to 2031. Built-up lands were classified using the normalized built-up composite index (NBCI) and their prediction for 2031 was performed. Surface biophysical parameters were then derived for the prediction of land surface temperature (LST) for 2031 using multiple linear regression (MLR) and Markov chain-cellular automata (CA-Markov) modeling. Finally, alterations in both built-up land and UHI within the city were scrutinized across various geographical directions and temporal periods. The study's findings reveal commendable overall classification accuracy for NBCI (ranging from 87% to 91% across different years) and CA-Markov (89%) in 2021. The MLR analysis produced favorable results with a root mean square error of 1.33 K in predicting LST for 2021. The significant correlation (<em>R</em> = 0.89) between changes in built-up lands and UHI indicatesthat built-up land/UHI exhibit a notable degree of freedom and sprawl, resulting in a negative urban degree-of-goodness.These results demonstrate the direct effects of built-up lands on UHI changes. Therefore, by determining the appropriate pattern in the built-up lands, it is possible to control the pattern of UHI. These findings hold practical significance for urban planners, offering valuable insights to mitigate adverse impacts on the urban environment.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overcoming barriers to proactive plastic recycling toward a sustainable future","authors":"","doi":"10.1016/j.envc.2024.101040","DOIUrl":"10.1016/j.envc.2024.101040","url":null,"abstract":"<div><div>The plastics sector, accounting for a significant portion of global emissions, presents a challenge and an opportunity in achieving carbon neutrality. Despite Japan's commendable polyethylene terephthalate (PET) bottle recycling rates, most plastics are thermally recycled, creating environmental issues. This study proposes an evaluation framework to enhance recycling, aligned with end-user preferences and fostering a circular plastics economy. Employing a mixed-methods approach, this study conducts fieldwork including interviews with plastic recyclers and analysis of industry data. A weighted sum multicriteria analysis integrating end-user preferences, recycling effectiveness, and market dynamics is utilized. Systemic, process, and policy challenges were shown to hinder sustainable recycling practices, while varying willingness to pay, emission and cost reduction potentials, among acceptability and sectoral diversity informed priority plastic types for recycling. Multicriteria analysis showed that although PET is favored by end users, Polyoxymethylene (POM) emerges as a potential priority target for manufacturers and recyclers. Sensitivity analysis underscores the potential impact of establishing or enhancing willingness to pay (WTP) toward certain plastic types. Moreover, manufacturer and recycler evaluations suggest a broader willingness to recycle plastics than previously assumed. The proposed evaluation framework offers insights toward plastic recycling strategies. Policy interventions such as sustained subsidies for recyclers, market incentives leveraging WTP preferences, and technological advances, including chemical recycling and the broadening of plastic type recycling in line with user and manufacturer preferences, could all contribute to promoting sustainable plastic recycling practices.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of meta-heuristic hybrid models in estimating the average air temperature of Caspian sea coast of Iran","authors":"","doi":"10.1016/j.envc.2024.101039","DOIUrl":"10.1016/j.envc.2024.101039","url":null,"abstract":"<div><div>The rise of industrial societies leads to higher greenhouse gas emissions, profoundly affecting the climate in coastal regions. Consequently, air temperature readings from standard meteorological stations are key indicators of the Earth's environmental condition. Therefore, accurate estimation of daily temperature in each region is one of the important prerequisites for agricultural planning as well as water resources management and drought prevention, which can be done in different ways such as experimental, semi-experimental and intelligent models. In this research, WSVR, AIG-SVR, GWO-SVR and BAT-SVR hybrid models were investigated and evaluated in order to estimate the average daily air temperature on the shores of the Caspian Sea located in the north of Iran. For modeling, weather station data from Babolsar meteorological station located in Mazandaran province were used. During the water year from 2012 to 2022, daily parameters including relative humidity, maximum temperature, minimum temperature, wind speed, and evaporation were selected as network inputs, with the average daily air temperature as the network output. To assess and compare model performances, several criteria were employed including correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percentage bias. Comparative analysis revealed that the WSVR model surpassed other models, demonstrating the highest correlation coefficient (0.992), lowest RMSE (0.096), and lowest MAE (0.042). The highest Nash Sutcliffe criterion (0.996) and bias percentage (0.001) were prioritized in the validation stage.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a brand value measurement model with a corporate social responsibility perspective. A comparative analysis of consumer perception of energy providers in Spain and Colombia","authors":"","doi":"10.1016/j.envc.2024.101032","DOIUrl":"10.1016/j.envc.2024.101032","url":null,"abstract":"<div><div>Sustainable development of companies and their products involves the integration of multiple dimensions, including economic, ethical, social, and environmental considerations, all of which are increasingly important to consumers. The present study identified key variables that define brand value in the context of Corporate Social-Environmental Responsibility (CSR), comparing two companies in the energy sector in Spain and Colombia (Naturgy and Ecopetrol S.A.). Among the factors that contribute to brand success, this study evaluated three variables: Visibility, Loyalty and Experience as perceived by consumers. Data measuring brand value were collected through an online survey of 640 respondents in Spain and Colombia, assessing their response to company logos and corporate messaging regarding environmental sustainability of their operations. Structural equation models (SEM) were then used to measure brand value based on latent variables and compared to survey data. Our results show that company visibility had a positive impact on brand loyalty and consumer experience, which ultimately increases brand value. Conversely, an improved consumer experience can also enhance brand loyalty and visibility. Our findings represent a framework to quantify brand value within the energy sector that is based on integrating multiple indicators of brand equity (BE) using the analysis of company logos, while simultaneously considering CSR, greenwashing and other forms of company messaging. This integration is a significant departure from traditional approaches and offers a new and novel perspective when developing a comprehensive corporate model. Our study suggests that implicit and explicit allusion to corporate environmental-social responsibility has a profound influence on brand recognition and brand acceptance by customers, due to the positive responses it elicits. The current study's findings support incorporating references to CSR in sustainable branding in its broadest spectrum and definition and offer a procedure to quantify and measure brand value, which can help company managers make effective branding decisions.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global change drives potential niche contraction and range shift of globally threatened African vulture","authors":"","doi":"10.1016/j.envc.2024.101038","DOIUrl":"10.1016/j.envc.2024.101038","url":null,"abstract":"<div><div>Human-induced global change poses an increasingly severe threat to biodiversity, with species having limited population sizes being particularly vulnerable. Mapping and modeling the distribution ranges of such species, along with detecting potential range shifts and contractions at both local and regional scales, are essential for developing effective conservation plans. Ruppell's vulture <em>Gyps rueppelli</em>, an ecologically important bird species native to Africa, is experiencing a rapid decline in its range. The purpose of this study is to map and model potential regional spatio-temporal distribution of Ruppell's vulture in Africa, alongside detecting the possibility of the species' range shifts and contractions. A total of 804 rarefied localities were identified where the Ruppell's vulture was the dominant bird species. This study employed the Maximum Entropy (MaxEnt) algorithm to perform species distribution modeling for the Ruppell's vulture. The modeling considered current climate conditions (1970s-2000s) as a baseline, along with two future climate change scenarios (Shared Socioeconomic Pathways: SSPs 245 and 585) for two future time periods (2050s and 2070s). The model's performance was evaluated by optimizing settings and examining the Area under the Receiver Operating Characteristic Curve (AUC-ROC). Among the 13 bioclimatic and anthropogenic variables included in the model, four (isothermality, cropland expansion, anthropogenic biomes, and urban expansion (in order of importance)) emerged as the most influential drivers of Ruppell's vulture regional distribution. All considered Species distribution models (SDMs) achieved high predictive performance, with AUC-ROC values exceeding 0.9.The model predicted a total of approximately 19,453 ha of suitable habitat for Ruppell's vultures in Africa, with East Africa identified as the most prominent region under the current climate scenario. Isothermality (38.8%) was the primary factor influencing Ruppell's vulture distribution, followed by agricultural expansion (29.9%) and anthropogenic biomes (7.2%) in the face of global change. The results reveal considerable future habitat loss (up to 61%) for Ruppell's vultures in the study area, alongside an eastward range shift (longitudinal axis) by the 2050s under projected climate change scenarios. These imply that Ruppell's vultures face imminent population decline and range shift due to significant habitat loss and climate change. Hence, prioritizing the development and implementation of a coordinated conservation program that incorporates captive breeding and assisted migration is critical to save this vulture species in its native African range.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Carbon offsets compatible with the Paris Agreement to limit global warming: Call for a direct action","authors":"","doi":"10.1016/j.envc.2024.101034","DOIUrl":"10.1016/j.envc.2024.101034","url":null,"abstract":"<div><div>The societal commitment to combat climate change is reflected in the Paris Agreement with the primary focus to mitigate climate change by reducing or limiting greenhouse gas emissions. To facilitate the achievement of emission reduction targets, innovative carbon crediting and offsetting mechanisms have been developed. These mechanisms enable stakeholders to offset their emissions by using carbon offset credits if needed. These carbon offset methodologies can be classified into two main categories. The first category involves directly reducing greenhouse gas emissions from the environment through green and emission-capturing solutions, such as reforestation and carbon capture and storage. The second category focuses on achieving a relative reduction in carbon emissions by using or investing in technologies with lower carbon intensity compared to business-as-usual practices, such as renewable energy. The reduction achieved in this second category is assumed to be equivalent to not emitting the calculated amount of emissions. However, both categories generally do not address the emissions' sources directly. This study introduces a third approach by proposing the creation of a carbon offset market at the emissions' source, offering a novel way to directly tackle the origins of carbon emissions. This approach aims to prevent emissions from being released in the first place, directly addressing the source of emissions. It aligns with the precautionary principle, which advocates for proactive measures to prevent harm. This approach should not be confused with the non-consumption approach, which is a top-down strategy focused on reducing demand. Instead, it is a bottom-up approach that seeks to reduce the supply of emissions. This study developed a four-step methodology for implementing a carbon offset market at the source, starting with fixing fossil fuel extraction per producer, then fixing the profit margin per unit of extraction, then calculating the carbon content per unit of fossil fuel, and finally creating a carbon offset market at the source where one can offset their carbon footprint by paying an amount equivalent to the profit from fossil fuel extraction to the producer in exchange for a reduction in an equivalent amount of fossil fuel extraction. It also offers insights into emission reductions potential through this approach, along with cost calculations per unit of reduction based on historical records, literature data, and statistical databases. The main advantage of the proposed approach is its bottom-up focus on reducing the supply of emissions, which leads to tangible and quantifiable reductions in real time. This method eliminates potential loopholes in traditional methodologies, ensuring that the reductions are both immediate and verifiable.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Poultry slaughterhouse waste management through anaerobic digestion with varying proportions of chicken litter","authors":"","doi":"10.1016/j.envc.2024.101035","DOIUrl":"10.1016/j.envc.2024.101035","url":null,"abstract":"<div><div>Nepal's burgeoning poultry industry which is a key sector in its economy leads to a considerable generation of slaughterhouse waste (SHW), necessitating effective and sustainable disposal methods. This study explores anaerobic digestion as an optimal solution for poultry SHW management, aiming to produce energy-rich biogas while efficiently mitigating pollution in these facilities. Particularly, the feasibility and effectiveness of co-digestion with chicken litter (CL) were investigated to enhance biogas production and waste utilization. Four distinct runs of anaerobic digestion were performed, each utilizing varying substrate compositions with SHW to CL ratios of 1:0, 4:1, 1:1, and 1:4. Throughout the process, essential parameters, including total solids (TS), volatile solids (VS), biological oxygen demand (BOD₅), pH, temperature, biogas generation, were meticulously measured. The cumulative biogas production for each run was as follows: 14.87 liters and a biogas yield of 88.73 ml/gVS with a 17.39 % VS reduction for Run 1, 25.98 liters and a biogas yield of 147.21 ml/gVS with a 28.64 % VS reduction for Run 2, 78.32 liters and a biogas yield of 314.69 ml/gVS with a 54.32 % VS reduction for Run 3, and 89.195 liters and a biogas yield of 344.36 ml/gVS with a 63.05 % VS reduction for Run 4. Notably, as the proportion of CL increased in the mixture from 4:1 to 1:1, a considerable VS reduction was observed. Furthermore, when the ratio of SHW to CL reached 1:1 and 1:4, a significant BOD₅ reduction of 50 % and 63.13 % was achieved, respectively, surpassing previous runs. The results reveal that the addition of CL in an appropriate ratio effectively manages poultry SHW, with the optimum SHW:CL ratio for significant biogas yield lying between 1:1 and 1:4.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios","authors":"","doi":"10.1016/j.envc.2024.101029","DOIUrl":"10.1016/j.envc.2024.101029","url":null,"abstract":"<div><div>Flash floods are highly destructive, and their frequency and intensity are expected to escalate due to climatic changes. This study thus investigated flash flood susceptibility (FFS) by applying machine learning algorithms and climate projection to predict both present and future hazard scenarios in the southeastern hilly regions of Bangladesh. To predict FFS, we evaluated twelve flood-influencing variables: elevation (EL), slope (SL), aspect (AS), drainage density (DD), distance to stream (DS), topography roughness index (TRI), stream power index (SPI), topographic wetness index (TWI), soil permeability (SP), precipitation (PR), land use and land cover (LULC) and normalized difference vegetation index (NDVI). Earth observation data, field surveys, and past flood records were used to create a detailed flood inventory. Among the machine learning models tested, the random forest (RF) algorithm outperformed others, including support vector machine (SVC), logistic regression (LR), and extreme gradient boosting (XGBoost), and was subsequently used for flood susceptibility mapping based on future precipitation projections under two Sixth Coupled model intercomparison project (CMIP6) climate change scenarios: SSP1-2.6 and SSP5-8.5. Our findings indicated that the areas at high to very high risk of flooding are projected to increase significantly under both the SSP1-2.6 and SSP5-8.5 scenarios. Initially, around 38 % of the studied region had high to very high flood susceptibility, but this is expected to rise to 40–42 % over the projected time periods. These spatial delineations of flood-prone areas can provide guidance for developing effective mitigation and adaptation strategies to address the adverse impacts of flash flooding in the hilly river basins of Bangladesh.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing agricultural drought using remote sensing indices in the east bale zone, southeastern Ethiopian lowlands","authors":"","doi":"10.1016/j.envc.2024.101031","DOIUrl":"10.1016/j.envc.2024.101031","url":null,"abstract":"<div><div>The practical applications of satellite data in comprehending the changing environment must take in account quantitative awareness of the uncertainty across different satellite products. However, prior drought research efforts in Ethiopia paid less attention to evaluating satellite products although drought has led to agricultural failures in the lowlands of Ethiopia. This study aimed to evaluate the spatial-temporal distribution of agricultural drought in the lowlands of the Bale zone throughout the crop growing season (March to May) from 2012 to 2022. The dataset utilized in this study was chosen by assessing the performance of enhanced MODerate resolution Imaging Spectroradiometer (eMODIS) and enhanced Visible Infrared Imaging Radiometer Suite (eVIIRS) Normalized difference vegetation index (NDVI) in comparison to observed gridded rainfall, employing the simple linear regression model. The assessment of agricultural drought was conducted using the NDVI anomaly and the vegetation condition index (VCI). The eMODIS exhibited a coefficient of determination of 0.45 with a p-value of 0.02, whereas the eVIIRS had a coefficient of determination of 0.47 with a p-value of 0.01. Thus, eVIIRS NDVI was selected as the best dataset for evaluating agricultural drought in the research site. The findings indicated that in both 2012 and 2022, there were periods of agricultural drought. During these periods, severe to extreme drought conditions were seen in 7.6 % to 54.98 % of the study area, encompassing both lowland and midland regions. The biggest influence was detected in the northern, middle, and southern regions of the research area. Severe and moderate drought dominated study area as depicted by NDVI anomaly while extreme and severe as seen from VCI. Whereas the years 2014 and 2020 were the wettest. The study implies that eVIIRS NDVI might be an alternate approach to give empirical information that would aid stakeholders in limiting the consequences of agricultural drought on farming activities.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering the impact on environmental challenges through the predictors of m-tourism apps adoption: SEM-NCA approaches","authors":"","doi":"10.1016/j.envc.2024.101028","DOIUrl":"10.1016/j.envc.2024.101028","url":null,"abstract":"<div><div>This study investigates the factors influencing the adoption of m-tourism apps in Bangladesh and their impact on being free from environmental challenges. Utilizing Structural Equation Modeling (SEM) and Necessity Condition Analysis (NCA), the research explores how various predictors affect the Intention to Adopt Tourism Apps (IATA) and, subsequently, the outcome of being free from environmental challenges (FEC). SEM results reveal that Perceived Usefulness (PU), Cultural Exchange of Technology (CET), Social Amusement and Entertainment (SAE), and Tourists' Lifestyles (TL) significantly enhance IATA, with IATA having a strong positive impact on FEC. Conversely, Attitudes Towards Technology (ATT) and Government Supportive Roles (GSR) show limited influence on adoption. NCA identifies CET, IATA, SAE, and TL as critical predictors with medium to large effect sizes, particularly at higher thresholds, indicating their role as major bottlenecks. Government Supportive Roles and Tourist Technology Readiness are less influential. These findings offer valuable insights for developers and policymakers to focus on enhancing the perceived value, cultural exchange, and lifestyle compatibility of m-tourism apps to improve adoption rates and effectively address environmental challenges.</div></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}