Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction.

Throughout the years, wildfires have negatively impacted ecological systems and urban areas. Hence, reinforcing territorial risk management strategies against wildfires is essential. In this study, we built an early alert system (EAS) with two different Machine Learning (ML) techniques to calculate the meteorological conditions of two Colombian areas: (i) A 3D convolutional neural net capable of learning from satellite data and (ii) a convolutional network to bias-correct the Weather Research and Forecasting (WRF) model output. The results were used to quantify the daily Fire Weather Index and were coupled with the outcomes from a land cover analysis conducted through a Naïve-Bayes classifier to estimate the probability of wildfire occurrence. These results, combined with an assessment of global vulnerability in both locations, allow the construction of daily risk maps in both areas. On the other hand, a set of short-term preventive and corrective measures were suggested to public authorities to implement, after an early alert prediction of a possible future wildfire. Finally, Soil Management Practices are proposed to tackle the medium- and long-term causes of wildfire development, with the aim of reducing vulnerability and promoting soil protection. In conclusion, this paper creates an EAS for wildfires, based on novel ML techniques and risk maps.

De novo assembly and functional annotation of blood transcriptome of loggerhead turtle, and in silico characterization of peroxiredoxins and thioredoxins.

The aim of this study was to generate and analyze the atlas of the loggerhead turtle blood transcriptome by RNA-seq, as well as identify and characterize thioredoxin (Tnxs) and peroxiredoxin (Prdxs) antioxidant enzymes of the greatest interest in the control of peroxide levels and other biological functions. The transcriptome of loggerhead turtle was sequenced using the Illumina Hiseq 2000 platform and de novo assembly was performed using the Trinity pipeline. The assembly comprised 515,597 contigs with an N50 of 2,631 bp. Contigs were analyzed with CD-Hit obtaining 374,545 unigenes, of which 165,676 had ORFs encoding putative proteins longer than 100 amino acids. A total of 52,147 (31.5%) of these transcripts had significant homology matches in at least one of the five databases used. From the enrichment of GO terms, 180 proteins with antioxidant activity were identified, among these 28 Prdxs and 50 putative Tnxs. The putative proteins of loggerhead turtles encoded by the genes Prdx1, Prdx3, Prdx5, Prdx6, Txn and Txnip were predicted and characterized in silico. When comparing Prdxs and Txns of loggerhead turtle with homologous human proteins, they showed 18 (9%), 52 (18%) 94 (43%), 36 (16%), 35 (33%) and 74 (19%) amino acid mutations respectively. However, they showed high conservation in active sites and structural motifs (98%), with few specific modifications. Of these, Prdx1, Prdx3, Prdx5, Prdx6, Txn and Txnip presented 0, 25, 18, three, six and two deleterious changes. This study provides a high quality blood transcriptome and functional annotation of loggerhead sea turtles.

Validation of PM10 and PM2.5 early alert in Bogotá, Colombia, through the modeling software WRF-CHEM.

Air quality data from Bogotá, Colombia, show high levels of particulate matter (PM), which often generate respiratory problems to the population and a high economic cost to the government. Since 2016, air quality in the city of Bogotá has been measured through the Bogota Air Quality Index (IBOCA) which works as an indicator of environmental risk due to air pollution. However, available technological tools in Bogotá are not enough to generate early alerts due to PM10 and PM2.5. Currently, alerts are only announced once the measured PM values exceed a certain standard (e.g., 37 µ g/m3), but not with enough anticipation to efficiently protect the population. It is necessary to develop an early air quality alert in Bogotá, in order to provide information that improves risk management protocols in the capital district. The purpose of this investigation is to validate the corrective alert presented on the 14th and 15th of February of 2019, through the WRF-Chem model under different weather conditions, using three different setups of the model to simulate PM10 and PM2.5 concentrations during two different climatic seasons and different resolutions. The results of this article generate a validation of two configurations of the model that can be used for the Environmental Secretary of the District (SDA) forecasts in Bogotá, Colombia, in order to contribute to the prediction of pollution events produced by PM10 and PM2.5 as a tool for an early alert system (EAS) at least 24 h in advance.

Emerging pollutants in the urban water cycle in Latin America: A review of the current literature.

Emerging pollutants (EP) are increasingly studied and characterized worldwide to improve the understanding of their environmental and toxicological impacts and their occurrence and behaviors in different environmental systems. Latin America has been subject to both environmental and toxicological impacts due to EP. To better understand these impacts, studies concerning pollutants have increased for the last ten years. The current study presents a critical review on the occurrence of different emerging pollutants in various components of the urban water cycle (UWC) in Latin America. The review is based on studies performed in 11 different countries between 1999 and 2018. The countries where the higher number of investigations were conducted are Brazil (53%) and Mexico (15%). The EP most often studied within the literature are pharmaceuticals, followed by personal care products. The most common EP reported were 17ß-estradiol, bisphenol A and estrone; The UWC component with the greatest number of measurements in the reported studies were effluents from wastewater treatment plants.

Metals and metalloid in eight fish species consumed by citizens of Bogota D.C., Colombia, and potential risk to humans.

The risk imposed upon society by consumption of foods contaminated with metals and metalloids is an environmental problem attributed to the increasing number of mining extraction activities currently underway in Colombia. The aim of the current study was to determine concentrations of mercury (Hg), lead (Pb), cadmium (Cd), and a metalloid arsenic (As) found in the species of most consumed fish species by citizens of Bogota D.C. (Colombia), and the consequent potential risk to human health was also calculated. Muscle samples of 8 fish species were obtained from 203 individuals collected through 2014. The highest metal concentrations detected were as follows: Pb in Oncorhynchus sp. (0.0595 mg/kg), Cd and Hg in Pimelodus sp. (0.0072 and 0.0579 mg/kg, respectively), and As in Scomberomorus sp (0.0471 mg/kg). Further, the levels of metal accumulation from consumption of fish were calculated utilizing the metal pollution index (MPI), with elevated values noted in Pseudoplatystoma sp (0.06 mg/kg), followed by Scomberomorus sp. and Centropomus sp. (0.05 and 0.04 mg/kg, respectively). The multiple species exposure index (Em.j) denotes the level of exposure associated with consumption of various contaminated fish species, and this level occurred in decreasing order as follows: As > Pb > Cd > Hg. The multiple chemical exposure index (Ej.m), which accounts for exposure to multiple metals, identified Prochilodus sp. as the species displaying the highest level of exposure per consumption (8 × 10(-6) mg/kg-d). The target hazard quotient (THQ) for human health indicated high levels for Hg and Cd in Prochilodus sp. (0.026 and 0.005, respectively), Pb in Oncorhynchus sp (0.025), and As in Pseudoplatistoma sp. and Centropomus sp. (0.023). Data emphasize the need for adequate nationwide public policies that promote assessment of exposure levels and potential adverse health risks associated with dietary consumption of different fish species in Colombia.