In this study, the quality of sediment and surface water in two natural wetlands, Paca and Tragadero, in the central region of Peru was evaluated using pollution indices, including the geoaccumulation index, pollutant load index, modified pollution degree, potential ecological risk index, and site rank index, for four heavy metals. Principal component analysis was used to identify potential metal contaminant sources. The determination of Fe, Zn, Pb, and As was performed by flame atomic absorption spectrophotometry. The average concentrations of metals in the sediments of both lagoons decreased in the order Fe > Zn > Pb > As. The analysis of the contamination indices determined that As and Pb are the elements that contribute the most to environmental degradation in both wetlands. There is a strong correlation between the values of potential ecological risk and the modified degree of contamination, revealing that the Paca wetland has a moderate degree of contamination and potential ecological risk, while Tragadero presents a high degree of contamination and considerable potential ecological risk. The application of the site rank index showed that more than 50% of the sampling sites have between high and severe contamination. The principal component analysis presented 79.2% of the total variance. Finally, the results of this study are essential in order to carry out preventive actions for environmental protection in these lake ecosystems of great importance for many activities, such as bird watching.

Surface sediment quality of potential rivers in Peru was evaluated using indicators of contamination, accumulation and ecological risk of heavy metals and As. Surface sediment samples were collected at 54 sampling sites in the Tishgo and Chia rivers during 2018. The determination of Cu, Pb, Zn...

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

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.

Environmental risk assessment (ERA) uses toxicity estimates, such as x% lethal effect concentrations (LC_x), to protect ecosystems from chemical harm. At Tier 1, these estimates are typically derived from static dose-response (DR) models. Current Tier-1 assessments only consider final survival measurements, ignoring time-variable exposures and discarding temporal dynamics. This can result in an underestimation of ecological risk in complex, real-world environments. The General Unified Threshold model of Survival (GUTS) offers a mechanistic alternative, integrating toxicokinetic and toxicodynamic processes in order to model survival probability continuously over time. Although GUTS has been formally endorsed by the European Food Safety Authority (EFSA) for Tier-2 regulatory assessments of plant protection products, its potential at Tier-1 remains largely unexplored.Here, we demonstrate that the reduced GUTS framework (GUTS-RED) is applicable at Tier-1 and consistently outperforms classical DR approaches. Across 64 datasets spanning 46 species and 15 chemicals, GUTS-RED-derived LC_x estimates were broadly consistent with DR-derived values, yet were markedly more precise, yielding twice more LC_50 estimates with a smaller coefficient of variationt. Beyond this improved precision, GUTS-RED offers three capabilities that are unavailable with DR models: (i) the prediction of LC_{(x, t)} estimates at any effect level x and time point t which are not part of the initial experimental design; (ii) the derivation of incipient toxicity thresholds representing theoretically safe long-term concentrations; and (iii) the direct incorporation of time-variable exposure profiles, eliminating the need for subjective summary metrics such as arithmetic means or time-weighted averages. When applied to a dataset exhibiting gradual chemical decay, GUTS-RED revealed data quality issues that DR models had concealed behind wide credible intervals, thereby transforming an apparent modelling limitation into a diagnostic strength.Supported by freely available, user-friendly tools, we argue that GUTS-RED is ready for routine Tier 1 implementation. Our results strongly support advancing GUTS-RED as a unified modelling standard across regulatory tiers and chemical classes, paving the way for risk assessments that are more precise, time-aware and scientifically rigorous.