Camilo Menares
University of Chile
10 Papers
3 Citations
Camilo Menares is an academic researcher from University of Chile. The author has contributed to research in topics: Environmental science & Troposphere. The author has an hindex of 4, co-authored 7 publications.
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Papers
Evaluation of anthropogenic air pollutant emission inventories for South America at national and city scale
Nicolás Huneeus,Hugo Denier van der Gon,Paula Castesana,Camilo Menares,Claire Granier,Claire Granier,Louise Granier,Marcelo Félix Alonso,Maria de Fátima Andrade,Laura Dawidowski,Laura Gallardo,Darío Gómez,Zbigniew Klimont,Greet Janssens-Maenhout,Mauricio Osses,S. Enrique Puliafito,Néstor Y. Rojas,Odón Román Sanchez Ccoyllo,Sebastián Tolvett,Rita Yuri Ynoue +19 more
TL;DR: In this paper, the authors examined the emission estimates of air pollutants from various global inventories for five SA countries, namely Argentina, Brazil, Chile, Colombia and Peru, in particular comparing local city-scale inventories of a major city in each country.
84
PM2.5 forecasting in Coyhaique, the most polluted city in the Americas
TL;DR: In this article, the authors developed a neural network model and a linear model aimed to forecast the maximum of the 24-hour moving average of PM2.5 one day in advance.
37
Forecasting PM2.5 levels in Santiago de Chile using deep learning neural networks
TL;DR: Deep learning techniques provide significant improvements compared to a traditional multi-layer neural networks, particularly the LSTM model configured with a 7-day memory window (synoptic scale of pollution patterns) can capture critical pollution events at sites with both primary and secondary air pollution problems.
36
Increasing trends (2001–2018) in photochemical activity and secondary aerosols in Santiago, Chile
TL;DR: Despite the decline in partially (PM10) and fully (PM2.5) inhalable particles observed in recent decades, Santiago in Chile shows high levels of particle and ozone pollution as mentioned in this paper.
23
Pm 2.5 forecasting in the most polluted city in south america
Patricio Perez,Camilo Menares,Camilo Ramirez +2 more
- 19 Jun 2018
TL;DR: In this paper, a neural network model that uses previous values of PM2.5, meteorological information and previous concentrations of NO2 and CO as input, which is trained with 2014 and 2015 data, is able to forecast 91% of these exceedances.