Environmental pollution from microplastics (MPs), an anthropogenically mediated menace to the biosphere, is a global concern. This research conducted a bibliometric analysis of MPs in soil and terrestrial ecosystems using R software and biblioshiny to analyze descriptive statistics, author contributions, documents, and conceptual structures. The Scopus database was selected, and various keywords optimized the search. An analysis of research trends divided the timeline into three periods: period I (2012–2018), period II (2019–2021), and period III (2022). Results showed Wang J. as the author with the highest h-index, while Science of the Total Environment had an h-index of 43. In period I, research focused on MPs and plastic pollution, raising questions about their impacts on terrestrial ecosystems. Research found that MPs negatively affected soil organisms by decreasing growth rates and causing weight loss. In period II, research diversified, exploring interactions between MPs and heavy metals, bioaccumulation, phytotoxicity, soil pollution, and gut microbiota. Research revealed that MPs could influence heavy metal uptake by plants and affect bacterial community structures in soil. In period III, research emphasized oxidative stress, a common ecotoxicity response in plants and animals. MPs induced oxidative stress, leading to reactive oxygen species (ROS) buildup. Polyethylene MPs were more toxic to plant seedlings than polystyrene and polypropylene, causing significant oxidative stress. Similarly, oxidative stress impacts on soil organisms were highlighted when exposed to MPs and pesticides. Research in this area is expected to continue growing, aiding in delineating distinct research paths, particularly focusing on oxidative stress, bioaccumulation, and heavy metals.

The global pursuit of sustainable development has intensified the need to integrate Circular Economy (CE), Sustainable Development Goals (SDGs), and Industry 4.0 (I4.0) as mutually reinforcing frameworks. This study explores the scientific evolution and interconnections among these pillars through a dual approach: (i) a scientometric analysis using CiteSpace, VOSviewer, and Bibliometrix in RStudio (2024.12.1+563), and (ii) a targeted mini-review of high-impact literature. A dataset of 478 Scopus-indexed articles (2016–2024) was analyzed, revealing CE and I4.0 as key technological and strategic enablers of the SDGs—particularly SDG 12 (Responsible Consumption and Production), SDG 9 (Industry, Innovation and Infrastructure), and SDG 13 (Climate Action). Moreover, the results underscore an increasing role of enabling digital technologies—such as IoT, blockchain, and big data—in shaping sustainable production systems. An important insight from this work is the growing relevance of policy frameworks as catalysts for implementing CE and I4.0 strategies, especially within national and international sustainability agendas. However, the low citation frequency of “policy” as a keyword indicates a gap in the literature that merits further exploration. Future research is encouraged to conduct in-depth bibliometric studies focused on sustainability-related policies, including regulations that operationalize CE and I4.0 to support SDG achievement. This study contributes a comprehensive overview of emerging research trends, identifies strategic knowledge gaps, and highlights the need for cohesive governance mechanisms to accelerate the digital–ecological transition.

The novel coronavirus disease (COVID-19) generated by the SARS-CoV-2 virus was originated in the city of Wuhan (China) in December 2019, the virus began to spread in other regions of China until it spread to the rest of the world. In this research, an analysis was made of environmental factors (tropospheric column of NO ₂ , vertical airflow, percentage of solid waste disposed of in open dumps and percentage of the population without any mechanism of faeces disposal) and social factors (levels of monetary poverty, percentage of the number of hospitals per population and vulnerable population) that could directly and indirectly affect the spread of SARS-CoV-2 virus in the regions of Peru. Remote sensing techniques, geographic information systems and an analysis under the multi-parametric statistical approach proposed by Saaty were used to determine which regions present greater susceptibility, vulnerability and risk of spreading the SARS-CoV-2 virus. The results show that the prevalence of high values of tropospheric NO ₂ and values close to 0 Pa/s of the vertical airflow were directly related to the number of positive cases by COVID-19. In addition, it was found that 68% of the regions of Peru are at a “high” and “very high” risk of spreading SARS-CoV-2 virus, and most of them are in northern and central Peru (Callao, Tumbes, Piura, Loreto, Lambayeque, Huancavelica, Amazonas, Cajamarca, Ucayali, Huanuco and among others), therefore, special care should be taken with the measures adopted after social isolation in order to avoid the resurgence and collapse of the of health systems. It concludes that public policies on air quality management, integrated solid waste management and sanitation services should be improved in order to reduce the risk of spreading the SARS-CoV-2 virus. This research can be replicated on a longer scale, including more variables. HIGHLITGHS: 68% of the regions are at a “high” and “very high” risk of spreading of SARS-CoV-2 Tropospheric NO2 concentration and number of cases of COVID-19 are related directly Cases of COVID-19 are linked to tropospheric NO2 and vertical airflow to 0 Pa/s Environmental and social factors are analyzed together in the regions of Peru

This study addresses the importance of conducting mass movement susceptibility mapping and hazard assessment using quantitative techniques, including machine learning, in the Northern Lima Commonwealth (NLC). A previous exploration of the topographic variables revealed a high correlation and multicollinearity among some of them, which led to dimensionality reduction through a principal component analysis (PCA). Six susceptibility models were generated using weights of evidence, logistic regression, multilayer perceptron, support vector machine, random forest, and naive Bayes methods to produce quantitative susceptibility maps and assess the hazard associated with two scenarios: the first being El Niño phenomenon and the second being an earthquake exceeding 8.8 Mw. The main findings indicate that machine learning models exhibit excellent predictive performance for the presence and absence of mass movement events, as all models surpassed an AUC value of >0.9, with the random forest model standing out. In terms of hazard levels, in the event of an El Niño phenomenon or an earthquake exceeding 8.8 Mw, approximately 40% and 35% respectively, of the NLC area would be exposed to the highest hazard levels. The importance of integrating methodologies in mass movement susceptibility models is also emphasized; these methodologies include the correlation analysis, multicollinearity assessment, dimensionality reduction of variables, and coupling statistical models with machine learning models to improve the predictive accuracy of machine learning models. The findings of this research are expected to serve as a supportive tool for land managers in formulating effective disaster prevention and risk reduction strategies.

This study addresses the importance of conducting mass movement susceptibility mapping and hazard assessment using quantitative techniques, including machine learning, in the Northern Lima Commonwealth (NLC). Previous exploration of the topographic variables revealed high correlation and multicollinearity among some of them, which led to dimensionality reduction through principal component analysis (PCA). Six susceptibility models were generated using weights of evidence, logistic regression, multilayer perceptron, support vector machine, random forest, and naive Bayes methods to produce quantitative susceptibility maps and assess the hazard associated with two scenarios: the first being an El Niño phenomenon and the second being an earthquake exceeding 8.8Mw. The main findings indicate that machine learning models exhibit excellent predictive performance for the presence and absence of mass movement events, as all models surpassed an AUC value of >0.9, with the random forest model standing out. In terms of hazard levels, in the event of an El Niño phenomenon or an earthquake exceeding 8.8Mw, approximately 40% of the NLC area would be exposed to the highest hazard levels. It also highlights the importance of integrating methodologies to reduce model uncertainty, such as comprehensive analysis of variables supported by high quality data and organized workflows. The findings of this research are expected to serve as a supportive tool for land managers in formulating effective disaster prevention and risk reduction strategies.