Chemical Characteristics and Identification of PM10 Sources in Two Lima Districts, Peru

Ilizarbe-Gonzáles, Gina Mishel; Rojas-Quincho, Jhojan Pool; Torres, Rita Cabello; Ugarte-Alvan, Carlos Alfredo; Quispe, Patricia Reynoso; Valdiviezo-Gonzáles, Lorgio

doi:10.15446/dyna.v87n215.83688

Cited by 8 publications

(7 citation statements)

References 26 publications

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“…The Ramiro Prialé highway that crosses HCH and is the most used to access the central road connects the center and the east of the Peruvian territory, turning it into high traffic congestion. Moreover, 2,462,321 vehicles were circulating in Lima 64 in 2017, and according to the National Institute of Statistics and Informatics (INEI), the vehicle fleet in Peru grew by 4.4% between 2017 and 2018 65 . The aforementioned explains the influence of high traffic vehicles in critical pollution episodes in HCH, which according to what is referred by Srishti et al 66 , the traffic caused from vehicles contributes to about 21% of of the pollution.…”

Section: Resultsmentioning

confidence: 99%

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Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

Cordova

Portocarrero

Salas

et al. 2021

Sci Rep

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The prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city’s economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Peru, using artificial neural networks. The conventional feedforward backpropagation known as Multilayer Perceptron (MLP) and the Recurrent Artificial Neural network known as Long Short-Term Memory networks (LSTM) were implemented for the hourly prediction of $$\hbox {PM}_{10}$$ PM 10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The models were validated using two schemes: The Hold-Out and the Blocked-Nested Cross-Validation (BNCV). The simulation results show that periods of moderate $$\hbox {PM}_{10}$$ PM 10 concentration are predicted with high precision. Whereas, for periods of high contamination, the performance of both models, the MLP and LSTM, were diminished. On the other hand, the prediction performance improved slightly when the models were trained and validated with the BNCV scheme. The simulation results showed that the models obtained a good performance for the CDM, CRB, and SMP monitoring stations, characterized by a moderate to low level of contamination. However, the results show the difficulty of predicting this contaminant in those stations that present critical contamination episodes, such as ATE and HCH. In conclusion, the LSTM recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better predictability performance for this type of environmental data.

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Section: Resultsmentioning

confidence: 99%

“…The aforementioned explains the influence of high traffic vehicles in critical pollution episodes in HCH, which according to what is referred by Srishti et al 66 , the traffic caused from vehicles contributes to about 21% of of the pollution. In addition, it is associated with the wear of tires and brakes 64 .…”

Section: Resultsmentioning

confidence: 99%

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

Cordova

Portocarrero

Salas

et al. 2021

Sci Rep

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show abstract

“…The Ramiro Prialé highway, that crosses HCH and that is the most used to access the central road, connects the center and the east of the Peruvian territory, turning it into a place of high traffic congestion. Moreover, 2,462,321 vehicles were circulating in Lima 53 in 2017, and according to the National Institute of Statistics and Informatics (INEI), the vehicle fleet in Peru grew by 4.4% between 2017 and 2018 54 . The aforementioned explains the influence of high traffic vehicles in critical pollution episodes in HCH, which according to what is referred by Srishti et al 55 , the traffic caused from vehicles contributes to about 21% of PM 10 of the pollution.…”

Section: Critical Episodes Of Pm 10 Contamination At the Hch Stationmentioning

confidence: 99%

“…The aforementioned explains the influence of high traffic vehicles in critical pollution episodes in HCH, which according to what is referred by Srishti et al 55 , the traffic caused from vehicles contributes to about 21% of PM 10 of the pollution. In addition, it is associated with the wear of tires and brakes 53 .…”

Section: Critical Episodes Of Pm 10 Contamination At the Hch Stationmentioning

confidence: 99%

Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

Cordova

Portocarrero

Salas

et al. 2021

Preprint

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The prediction of air pollution is of great importance in highly populated areas because it has a direct impact on both the management of the city's economic activity and the health of its inhabitants. In this work, the spatio-temporal behavior of air quality in Metropolitan Lima was evaluated and predicted using the recurrent artificial neural network known as Long-Short Term Memory networks (LSTM). The LSTM was implemented for the hourly prediction of PM10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The model was evaluated under two validation schemes: the hold-out (HO) and the blocked-nested cross-validation (BNCV). The simulation results show that periods of low PM10 concentration are predicted with high precision. Whereas, for periods of high contamination, the LSTM network with BNCV has better predictability performance. In conclusion, recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better performance to forecast this type of environmental data, and can also be extrapolated to other pollutants.

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“…A fin de prevenir a futuro daños al medioambiente y a la salud mediante la regulación de la contaminación por material particulado, se fija la normativa nacional en la Resolución 2254 del 1 de noviembre de 2017 (Ministerio de Ambiente y Desarrollo Sostenible, 2017), que especifica la clasificación para estos contaminantes mediante rangos y colores, a fin establecer qué tan dañinas para la salud pueden llegar a ser las emisiones de estas partículas en una población que está expuesta a ellas diariamente. Estos parámetros se establecen con el objetivo de regular las industrias, el flujo de vehículos automotores, la emisión de hidrocarburos y las fuentes antropogénicas que contribuyen en mayor medida a la generación de estos contaminantes (Ilizarbe-Gonzáles et al, 2020). La tabla 1 presenta la clasificación de los contaminantes según dicha normativa.…”

Section: Introductionunclassified

Generación de mapas de contaminación por material particulado mediante sistemas embebidos: caso de estudio en Cúcuta (Colombia)

Pedraza

Sarmiento

Vergel

et al. 2021

RLCT

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En la actualidad, la contaminación ambiental es un problema de alto impacto en tanto a múltiples contaminantes, y principalmente el material particulado, pueden llegar a ser dañinos para la salud e incluso mortales; debido a ello, los principales entes gubernamentales han establecido estrategias para su monitoreo y control mediante estaciones fijas. Por este motivo, se desarrolla la caracterización del material particulado en el barrio Trigal del Norte de la ciudad de Cúcuta (Colombia), mediante un sistema móvil adaptable a múltiples estructuras, que incorpora una interfaz gráfica conectada a la red para el bosquejo de mapas de intensidad de contaminantes. El método propuesto tiene un alcance exploratorio/descriptivo, con un enfoque cualitativo y cuantitativo, y es aplicado mediante un diseño experimental que se basa en la identificación y selección de tecnologías, el diseño y ensamble del circuito electrónico y el desarrollo del software de integración tecnológica. Como resultado, se obtuvo un sistema compacto que presenta mapas de intensidad y gráficas de comportamiento, observándose que de los datos obtenidos del proceso de medición se evidencian niveles de contaminación por material particulado, PM2.5 y PM10, con valores de 19.95 μg/m3 y 21.61 μg/m3 , respectivamente, que cumplen con los niveles máximos permisibles según la Resolución 2254 del Ministerio de Ambiente y Desarrollo Sostenible de Colombia.

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Chemical Characteristics and Identification of PM10 Sources in Two Lima Districts, Peru

Cited by 8 publications

References 26 publications

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

Generación de mapas de contaminación por material particulado mediante sistemas embebidos: caso de estudio en Cúcuta (Colombia)

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