Month: March 2024
An exploratory analysis of COVID bot vs human disinformation dissemination stemming from the Disinformation Dozen on Telegram
Abstract
The COVID-19 pandemic of 2021 led to a worldwide health crisis that was accompanied by an infodemic. A group of 12 social media personalities, dubbed the “Disinformation Dozen”, were identified as key in spreading disinformation regarding the COVID-19 virus, treatments, and vaccines. This study focuses on the spread of disinformation propagated by this group on Telegram, a mobile messaging and social media platform. After segregating users into three groups—the Disinformation Dozen, bots, and humans, we perform an investigation with a dataset of Telegram messages from January to June 2023, comparatively analyzing temporal, topical, and network features. We observe that the Disinformation Dozen are highly involved in the initial dissemination of disinformation but are not the main drivers of the propagation of disinformation. Bot users are extremely active in conversation threads, while human users are active propagators of information, disseminating posts between Telegram channels through the forwarding mechanism.
Mapping the urban and rural planning response paths to pandemics of infectious diseases
Abstract
Modern urban and rural planning has a long history in terms of epidemics. However, contemporary urban and rural planning lacks a comprehensive response map for the prevention and control of infectious diseases. As the process of comprehensive policies making for social issues, a bridge between urban and rural development and the prevention and control of infectious diseases must be established. This research sorts out the academic literature that studies the relationship and logic between epidemic infectious diseases and urban and rural physical environments. This research constructed links between issues of urban and rural planning and the means of prevention and control of epidemic infectious diseases, and drew them to form a comprehensive map which illustrates the logic paths between 12 issues of urban and rural planning and intricate factors of epidemic infectious diseases. The atlas provided in this study shows that planners could have a simplified, without missing key points, way to make, examine, and evaluate planning strategies from limited perspectives such as pathogen exposure opportunities, pathogen resistance, hosts, travel, and trade, when addressing pandemic issues.
Prediction of groundwater level using GMDH artificial neural network based on climate change scenarios
Abstract
One of the main challenges regarding the prediction of groundwater resource changes is the climate change phenomenon and its impacts on quantitative variations of such resources. Groundwater resources are treated as one of the main strategic resources of any region. Given the climate change phenomenon and its impacts on hydrological parameters, it is necessary to evaluate and predict future changes to achieve an appropriate plan to maintain and preserve water resources. In this regard, the present study is put forward by utilizing the Statistical Down-Scaling Model (SDSM) to forecast the main climate variables (i.e., temperature and precipitation) based on new Rcp scenarios for greenhouse gas emissions within a period from 2020 to 2060. The results obtained from the prediction of climate parameters indicate different values in each emission scenario, so the limit, minimum and maximum values occur in the Rcp8.5, Rcp2.6 and Rcp4.5 scenarios, respectively. Also, a model is developed by utilizing the GMDH artificial neural network technique. The developed model predicts the average groundwater level based on the climate variables in such a way that by implementing the climate parameters forecasted by the SDSM model, the groundwater level within a time period from 2020 to 2060 is predicted. The results obtained from the verification and validation of the model imply its proper performance and reasonable accuracy in predicating groundwater level based on the climate variables. The findings derived from the present paper indicate that compared to the years prior to the prediction period, the groundwater level of the Sahneh Plain has dramatically dropped so that based on the Rcp scenarios, the groundwater level values are in their lowest state within the period from 2046 to 2056. The findings of this paper can be used by managers and decision makers as a layout for evaluating climate change effects in the Sahneh Plain.
Statistical downscaling of GCMs wind speed data for trend analysis of future scenarios: a case study in the Lombardy region
Abstract
Near-surface wind speed is a key climatic variable, affecting many sectors, such as energy production, air pollution, and natural hazard. Lombardy region of Italy is among the European areas with lowest average wind speed, leading generally to low air quality and wind energy potential. However, it is also one of the most affected area by tornadoes in Italy. Here we investigate possible changes in wind circulation as due to prospective global warming. We analysed wind speed WS under future scenarios (SSP1-2.6 and SSP5-8.5) from six Global Climate Models (GCMs) until 2100, tuned against observed WS data. We employed a statistical downscaling method, namely Stochastic Time Random Cascade (STRC) to correct locally GCMs outputs. Three statistical tests, i.e. Linear Regression, Mann Kendall, Moving Window Average, were carried out to analyse future trends of: annual WS averages, 95th quantile (as an indicator of large WS), and the number of days of calm wind per year (NWC). The proposed STRC algorithm can successfully adjust the mean, standard deviation, and autocorrelation structure of the GCM outputs. No strong trends are found for the future. The chosen variables would all display non-stationarity, and the 95th percentile display a positive trend for most of the stations. Concerning NWC, notable discrepancies among GCMs are seen. The STRC algorithm can be used to successfully adjust GCMs outputs to reflect locally observed data and to then generate credible long-term scenarios for WSs as a tool for decision-making.
Quantifying the spatiotemporal patterns and environmental impacts of surface coal mining in the Xilingol Steppe, Inner Mongolia
Abstract
Surface coal mining is one of the most environmentally destructive human disturbances. This study provides the first comprehensive assessment of the speed and scale of coal mining and its major environmental impacts in the Xilingol Steppe, Inner Mongolia from 1990 to 2015, using remote sensing data and landscape metrics. Our results show that during this period the number of surface coal mining areas (SCMAs) increased from 40 to 504 (about 13 times), while the size of SCMAs increased from 3.21 km2 to 283.62 km2 (greater than 88 times). The rapid expansion of SCMAs greatly fragmented the steppe landscape, consumed huge amounts of water, damaged rivers and wetlands, and substantially reduced grassland productivity. We estimated that the amount of water consumed by coal mining increased from 2.35 million m3 in 1990 to 242.61 million m3 in 2015 (more than 103 times), negatively affecting all six major rivers and most wetlands in the region. About 222 km2 of steppes were eradicated, resulting in a grassland production loss of 7.17×1010 g C. Our findings indicate that surface coal mining has transformed the steppe landscape and devastated its ecosystem function and services, posing a major threat to the environment of the region. Future studies need to focus on more in-depth integrative assessments of environmental, economic, and social impacts of surface coal mining to seek sustainability solutions for the region.
Investigating the research trends on the determinants of Environmental degradation: A bibliometric analysis
Abstract
Despite a surge in research on mitigating environmental degradation and creating environmental awareness in recent past, researcher and other policmakers are still of the view that environmental destruction is on the rise, thereby calling into question the viability of these works to address the problem. This study, therefore, seeks to conduct a trend analysis to examine the knowledge gaps in studies related to understanding the determining factors of environmental degradation using a bibliometric analysis. For this purpose, the study reviewed existing papers using keyword search engines in the Dimensions database to retrieve published scientific works from 2000 to 2023. The study revealed that the main focus of environmental degradation has evolved over time, shifting from the sole focus of ecological footprint in between 2000 and 2005 to more such as carbon footprint, greenhouse gases, carbon sequestration, renewable energy, and input–output analysis 2013 to 2023. Also, the last decade has seen a drastic rise in scholarly works on environmental degradation. The result also revealed that the emerging theme in relation to environmental degradation is associated with indoor air pollution. It is therefore imperative for researchers to shift their focus to indoor air pollution as this may have a more direct and devastating impact on not only the environment but also human health.
Investigating the research trends on the determinants of Environmental degradation: A bibliometric analysis
Abstract
Despite a surge in research on mitigating environmental degradation and creating environmental awareness in recent past, researcher and other policmakers are still of the view that environmental destruction is on the rise, thereby calling into question the viability of these works to address the problem. This study, therefore, seeks to conduct a trend analysis to examine the knowledge gaps in studies related to understanding the determining factors of environmental degradation using a bibliometric analysis. For this purpose, the study reviewed existing papers using keyword search engines in the Dimensions database to retrieve published scientific works from 2000 to 2023. The study revealed that the main focus of environmental degradation has evolved over time, shifting from the sole focus of ecological footprint in between 2000 and 2005 to more such as carbon footprint, greenhouse gases, carbon sequestration, renewable energy, and input–output analysis 2013 to 2023. Also, the last decade has seen a drastic rise in scholarly works on environmental degradation. The result also revealed that the emerging theme in relation to environmental degradation is associated with indoor air pollution. It is therefore imperative for researchers to shift their focus to indoor air pollution as this may have a more direct and devastating impact on not only the environment but also human health.