A Cyclostationary Neural Network Model for the Prediction

of the NO2 Concentration

Monica Bianchini, Ernesto Di Iorio, Marco Maggini,Chiara Mocenni, Augusto Pucci

Dipartimento di Ingegneria dell’InformazioneVia Roma 56, 53100 Siena (ITALY)

Air Pollution ProblemAir Pollution Problem

Nitrogen oxide (NONitrogen oxide (NOxx = NO + NO = NO + NO22) emissions are ) emissions are

among the most important factors affecting the among the most important factors affecting the air quality in urban areasair quality in urban areas

Traffic is the main problem on a local urban Traffic is the main problem on a local urban scalescale

Modeling efforts to predict and control the NOModeling efforts to predict and control the NOx x

concentrationsconcentrations

Development of tools for pollution managementDevelopment of tools for pollution management

Project GoalsProject Goals

Build an efficient Build an efficient neural based model for the prediction of the NO2 concentration

First prediction approximation for an early warning

Independence from exogenous data

Modeling the NO2 time series only based on the past concentrations of NO and NO2

The Cyclostationary NeuralNetwork Model

Correlation of past NO and current NO2 (daily periodicity)NO2(t) follows a cyclostationary dynamics (period T = 24)CNN model composed by 24 MLP blocks one for each random variable of the cyclostationary process

where T = 24 and fk with k = (t mod T) + 1, represents the k–th approximation function realized by the k–th MLP block

Model ArchitectureModel Architecture

Experimental SetupExperimental Setup

Data gathered by ARPA Lombardia (northern Italy)ARPA supplies a real–time air pollution monitoring system composed by mobile and fixed stationsDataset made up by NO and NO2 concentrations detected hourly by the station number 649 (Brescia–Broletto)Performance measures: mean absolute error Performance measures: mean absolute error and mean square errorand mean square error

Experimental results – err 2 monthsExperimental results – err 2 months

Experimental results – err 12 monthsExperimental results – err 12 months

Experimental results – mse 2 monthsExperimental results – mse 2 months

Future WorksFuture Works

CNN hardware implementation on NOCNN hardware implementation on NOxx

sensorssensors

Management of multiple data from Management of multiple data from different sensorsdifferent sensors

Testing on other urban area datasetsTesting on other urban area datasets

Testing on wider datasetsTesting on wider datasets