Diatomaceous deposits in the Miocene-Pliocene Pisco For- mation contain abundant whales preserved in pristine condition (bones articulated or at least closely associated), in some cases in- cluding preserved baleen. The well-preserved whales indicate rapid burial. The 346 whales within ;1.5 km2 of surveyed surface were not buried as an event, but were distributed uninterrupted through an 80-m-thick sedimentary section. The diatomaceous sediment lacks repeating primary laminations, but instead is mostly massive, with irregular laminations and speckles. There is no evidence for bioturbation by invertebrates in the whale-bearing sediment. Cur- rent depositional models do not account for the volume of diato- maceous sediments or the taphonomic features of the whales. These taphonomic and sedimentary features suggest that rapid burial due to high diatom accumulation, in part by lateral advection into pro- tected, shallow embayments, is responsible for the superb preser- vation of these whales, leading to a higher upper limit on phyto- plankton accumulation rates than previously documented.

Plastic pollution is one of the most serious environmental problems of this century because most plastics are single-use, and once their useful life is over, they become pollutants, since their decomposition takes approximately 100–400 years. The objective of this research is to evaluate the efficacy of low-density polyethylene (LDPE) biodegradation by G. mellonella in the district of Pangoa, Junín, Peru. For the development of the study, the G. mellonella was conditioned in three groups of beekeeping residues (beeswax, balanced diet, and wheat bran); after the conditioning stage, the biodegradation treatment was developed, which consisted of placing the G. mellonella in terrariums with the LDPE, the treatments were carried out at three different times (24, 36, and 48 h). To evaluate the efficacy of biodegradation, two analyses were taken into account: the Raman analysis of the low-density polyethylene samples and the weight reduction of the treated LDPE. The results of the Raman analysis indicated that the best treatment was the one applied with G. mellonella conditioned with beeswax, obtaining a wavelength intensity of 0.45 μ.a., while the weight reduction of the LDPE indicated that the best results were given at 36 h and conditioned with beeswax with a reduction of 236.3 mg. In conclusion, the use of G. mellonella for the biodegradation of low-density polyethylene is effective when it is conditioned with beeswax and the treatment is carried out at 36 h.

PM_2.5 pollution is increasing, causing health problems. The objective of this study was to model the behavior of PM_2.5AQI (air quality index) using machine learning (ML) predictive models of linear regression, lasso, ridge, and elastic net. A total of 16,543 records from the Huachac, Junin area in Peru were used with regressors of humidity in % and temperature in °C. The focus of this study is PM_2.5AQI and environmental variables. Methods: Exploratory data analysis (EDA) and machine learning predictive models were applied. Results: PM_2.5AQI has high values in winter and spring, with averages of 52.6 and 36.9, respectively, and low values in summer, with a maximum value in September (spring) and a minimum in February (summer). The use of regression models produced precise metrics to choose the best model for the prediction of PM_2.5AQI. Comparison with other research highlights the robustness of the chosen ML models, underlining the potential of ML in PM_2.5AQI. Conclusions: The predictive model found was α = 0.1111111 and a Lambda value λ = 0.150025, represented by PM_2.5AQI = 83.0846522 − 10.302222000 (Humidity) − 0.1268124 (Temperature). The model has an adjusted R² of 0.1483206 and an RMSE of 25.36203, and it allows decision making in the care of the environment.