Artificial neural networks can be used for Ambrosia pollen emission parameterization in COSMO-ART

Bürki, Charlotte; Šikoparija, Branko; Thibaudon, M.; Oliver, G.; Magyar, Donát; Udvardy, Orsolya; Leelőssy, Ádám; Charpilloz, Christophe; Pauling, Andreas

doi:10.1016/j.atmosenv.2019.116969

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…cumulated meteorological variables). Predicted values of pollen data may be used in transport (source-based) models (Zink et al 2012;Prank et al 2013;Burki et al 2019). It is expected that source-based models would be improved when pollen forecasts based on 3-day lagged pollen monitoring 206 211 216 221 226 231 236 241 246 251 256 261 266 271 276 281Pollen grains / m 3 Fig.…”

Section: Discussionmentioning

confidence: 99%

The application of a neural network-based ragweed pollen forecast by the Ragweed Pollen Alarm System in the Pannonian biogeographical region

et al. 2019

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Ragweed Pollen Alarm System (R-PAS) has been running since 2014 to provide pollen information for countries in the Pannonian biogeographical region (PBR). The aim of this study was to develop forecast models of the representative aerobiological monitoring stations, identified by analysis based on a neural network computation. Monitoring stations with 7-day Hirst-type pollen trap having 10-year long validated data set of ragweed pollen were selected for the study from the PBR. Variables including forecasted meteorological data, pollen data of the previous days and nearby monitoring stations were used as input of the model. We used the multilayer perceptron model to forecast the pollen concentration. The multilayer perceptron (MLP) is a feedforward artificial neural network. MLP is a datadriven method to forecast the behaviour of complex systems. In our case, it has three layers, one of which is hidden. MLP utilizes a supervised learning technique called backpropagation for training to get better

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Section: Discussionmentioning

confidence: 99%

The application of a neural network-based ragweed pollen forecast by the Ragweed Pollen Alarm System in the Pannonian biogeographical region

et al. 2019

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Distribution of PM₁₀, PM_2.5, and NO₂ in the Cergy‐Pontoise urban area (France)

Lagmiri,

Dahech

2024

Meteorological Applications

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This study employed a network comprising 16 fixed “Ecosmart” sensors deployed in the Cergy‐Pontoise conurbation. Continuous measurements of PM10, PM2.5, and NO2, significant pollutants in the Paris region, were conducted from April 8 to June 6, 2022. The collected data were represented as statistically composite spatial matrices due to the heterogeneous urban landscape and the overlapping of multiple pollution sources. Temporal variations on a daily basis were influenced by both traffic and meteorological conditions. Daytime, characterized by denser traffic compared to nighttime, exhibited higher concentrations of PM10 and PM2.5. Conversely, NO2 concentration levels displayed two peaks associated with traffic volume, and relatively elevated nocturnal values compared with midday due to atmospheric vertical stability during the nighttime phase. The analysis of weather‐type impacts revealed that during unstable weather conditions, elevated particle concentrations stemmed from dust resuspension from the ground and long‐range transport. Maximum NO2 concentrations were observed during stable weather conditions, whereas minimum concentrations occurred during unstable weather.

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RETRACTED CHAPTER: Neural Network Configuration for Pollen Analysis

Viloria

Mercado

Lezama

2020

Advances in Intelligent Systems and Computing

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Palynology is a botanical discipline devoted to the study of pollen and spores [1], focusing mainly on the analysis of the external morphology that presents structural patterns different from those of the variations in the exine, which is the external wall of the pollen grains. The study and microscopic analysis of its symmetry, wall opening, contour, shape, size, etc., have a taxonomic value and allows distinguishing different taxa at different levels: family, genera, species. The study of pollen grains is a difficult task, in its different phases, from small microscopic samples. The analysis of these is an important source of information for many scientific and industrial applications, making palynology a valuable tool for various areas of knowledge [1]. In palynology, neural networks have been successfully applied for the classification of pollen grains. For this purpose, RPROP was selected as a neural network training algorithm for the classification of a previously reported dataset.

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Artificial neural networks can be used for Ambrosia pollen emission parameterization in COSMO-ART

Cited by 6 publications

References 32 publications

The application of a neural network-based ragweed pollen forecast by the Ragweed Pollen Alarm System in the Pannonian biogeographical region

The application of a neural network-based ragweed pollen forecast by the Ragweed Pollen Alarm System in the Pannonian biogeographical region

Distribution of PM₁₀, PM_2.5, and NO₂ in the Cergy‐Pontoise urban area (France)

RETRACTED CHAPTER: Neural Network Configuration for Pollen Analysis

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Artificial neural networks can be used for Ambrosia pollen emission parameterization in COSMO-ART

Cited by 6 publications

References 32 publications

The application of a neural network-based ragweed pollen forecast by the Ragweed Pollen Alarm System in the Pannonian biogeographical region

The application of a neural network-based ragweed pollen forecast by the Ragweed Pollen Alarm System in the Pannonian biogeographical region

Distribution of PM10, PM2.5, and NO2 in the Cergy‐Pontoise urban area (France)

RETRACTED CHAPTER: Neural Network Configuration for Pollen Analysis

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Distribution of PM₁₀, PM_2.5, and NO₂ in the Cergy‐Pontoise urban area (France)