Biochar is a carbonized material obtained from the pyrolysis of biomass produced in a limiting environment of zero or very low oxygen. Its interest lies in its versatility for different applications in the water treatment, soil pollution, mitigation of greenhouse gases, etc. The synergy of this product with other amendments such as compost has been studied for different applications in the soil, including environmental remediation, crop yield, etc. The aim of the research is to identify the relevant aspects in the scientific literature of biochar, compost and soil through a bibliometric analysis for which 753 articles were selected from the Scopus database, having as keywords “biochar”, “compost” and “soil”. This research used R software, specifically the package Bibliometrix, to analyze descriptive analysis, author sources, document metrics, citation, co-citation analysis, co-occurrence network, co-word analysis, and collaboration analysis. Results showed that Zhang Z is the author with the greatest number of documents, and with a higher H index. Science of The Total Environment, Bioresource Technology, and Agronomy are the 3 topmost relevant sources. The keywords according to bond strength and most frequent use were biochar (538 occurrences), composting (349 occurrences), compost (436 occurrences), charcoal (295 occurrences), soil (255 occurrences). China is the country with the most collaboration. It is hoped that the bibliometric review will help to identify current research trends and provide information on the application of biochar and compost in the soil.

The co-treatment of acidic water (AW) and urban wastewater (UWW) is a technique that allows mitigating the negative impact of AW on natural aquatic environments, which represents one of the major environmental problems globally. The aim of this research was to determine the optimal conditions through the response surface methodology (RSM) with a central composite design (CCD) for the co-treatment of AW from the Quiulacocha lagoon in Pasco and UWW from a municipality in Lima, Peru, having as factors the molar ratio of total iron (FeT) and total phosphorus (PT), time (min) and stirring speed (rpm). Data processing was performed using the Design-Expert 11 software, and analysis of variance (ANOVA) with a confidence interval of 95% (α = 0.05) was used. The statistical models obtained showed high determination coefficients (R²), higher than 92% for pH, conductivity and FeT removal. While the removal of COD, PT and obtained a value of R²>0.80, as well as evidenced compliance with the level of significance P value > 0.05. The optimal conditions determined by the statistical model were given at a FeT /PT molar ratio (33:1), a stirring time of 5 min and a speed of 255 rpm. In these conditions, the COD was reduced by 71.78%, FeT by 99.48%, and PT by 84.29% with a residual concentration of 1.3 mg/L; the pH obtained a value of 5.7 and the turbidity 56 NTU. Better efficiency of the co-treatment for the reduction of pollutants in the AW of the Quiulacocha lagoon is evidenced, applying an experimental design to optimize the operating conditions, taking into account that the molar ratio is a significant factor and that optimizing it would allow the co-treatment to be replicated. Co-treatment is a sustainable and promising alternative for the treatment of AW and UWW, since it does not require the use of chemical agents to treat water. However, post-treatments would still be required to comply with certain regulations or to reuse the treated water on a larger scale.

This research reports a bibliometric analysis using the bibliometrix package with information from the SCOPUS database. A descriptive analysis was performed to provide an overview of the countries, institutions, authors, journals and keywords of 80 manuscripts published between 1997 and 2022 in the field of study of emergy and its application in wastewater treatment. The results showed 2 sources with the highest h and g impact index (Ecological Engineering and Journal of Cleaner Production) with a significant growth since 2009. The most cited keywords were "emergy" and "wastewater", and the countries with the most important scientific networks were China, Japan, Saudi Arabia, USA, Brazil, Colombia and Italy. In addition, it shows that collaborative groups among the authors from China and USA are the ones that consolidate the main research network, as well as the largest scientific production with the most cited articles in the field of study of emergy in wastewater treatment. Therefore, the application of the emergy method in wastewater treatment requires extensive research in different types of treatments. This type of analysis would allow the development of a new approach in the application of wastewater treatment in terms of sustainability and the introduction of Latin American countries in this emerging field.

La nueva enfermedad del coronavirus (COVID-19) generada por el virus SARS-CoV-2 se originó en China y el primer caso reportado fue en la ciudad de Wuhan, en diciembre del 2019. El virus comenzó a propagarse en otras regiones de China y al resto del mundo. El 30 de enero del 2020, la Organización Mundial de la Salud (OMS) declaró el brote del COVID-19 como emergencia internacional en salud pública. En Perú, el primer caso positivo de COVID-19 fue registrado el 6 de marzo del 2020 en la región Lima, y se declaró el estado de emergencia el 16 de marzo del 2020. A nivel mundial se han realizado diferentes investigaciones de variables ambientales asociadas a la propagación del COVID-19 así como variables sociales; sin embargo, la mayoría de estas han sido analizadas de forma individual, por lo que es necesario realizar un análisis que integre a dichas variables bajo ciertos criterios metodológicos. Es así que el objetivo de este texto es analizar las variables ambientales (columna troposférica de NO2, flujo vertical de aire, porcentaje de residuos sólidos dispuestos en botaderos y porcentaje de la población sin ningún mecanismo de eliminación de excreta) y sociales (niveles de pobreza monetaria, porcentaje del número de hospitales por población y población vulnerable) que intervienen directa e indirectamente a la propagación del virus SARS-CoV-2. Para ello se utilizaron técnicas de percepción remota, sistemas de información geográfica (SIG) integrados bajo el enfoque estadístico-determinístico multiparamétrico planteado por Saaty, determinando así qué regiones del Perú presentan mayor susceptibilidad, vulnerabilidad y riesgo de propagación del virus SARS-CoV-2. Los datos fueron recopilados a partir de instituciones mundiales y nacionales; para la columna troposférica de NO2 los datos fueron obtenidos del satélite Sentinel-5P; el flujo vertical del aire se determinó a partir de la información recolectada del laboratorio de ciencias físicas del NOAA (National Oceanic and Atmospheric Administration); la información concerniente a la población sin ningún mecanismo de excreta fue extraída del Instituto Nacional de Estadística e Informática (INEI) y el porcentaje de residuos sólidos dispuestos en botaderos se obtuvo del Ministerio del Ambiente (MINAM), mientras que los datos de las variables sociales fueron todos obtenidos a partir del INEI. Los resultados muestran que la prevalencia de altos valores de NO2 troposférico y valores cercanos a 0 Pa/s del flujo de aire vertical presentaron una relación directa con el número de casos positivos por COVID-19, además, se encontró que el 68% de las regiones se encuentran en un nivel de riesgo de propagación del virus SARS-CoV-2 “alto” y “muy alto”, y la mayoría de estas se encuentran en el norte y centro del Perú (Callao, Tumbes, Piura, Loreto, Lambayeque, Huancavelica, Amazonas, Cajamarca, Ucayali y Huánuco, entre otras), por lo que se debería tener especial cuidado con las medidas tomadas posaislamiento social a fin de evitar un rebrote y colapso de los sistemas de salud. En la presente investigación se concluye que las políticas públicas de gestión de la calidad de aire, gestión integral de residuos sólidos y servicios de saneamiento, deben mejorar a corto plazo a fin de disminuir el riesgo de propagación del virus SARS-CoV-2. Este estudio puede replicarse a una mayor escala, incluyendo más variables.