Characterization and prediction of SO222 concentrations in the

Characterization and prediction of SO222 concentrations in the

REVISTA MEXICANA DE FÍSICA 48 SUPLEMENTO 3, 111–112
DICIEMBRE 2002
Characterization and prediction of SO2 concentrations in the atmosphere of
Santiago, Chile
P. Pérez
Departamento de Fı́sica, Universidad de Santiago de Chile
Casilla 307, Correo 2, Santiago, Chile
e-mail: [email protected]
Recibido el 18 de enero del 2001; aceptado el 18 de julio del 2001
Emissions of sulfur dioxide in the city of Santiago are due mainly to industries and construction. Analyzing the hourly averages of this
pollutant measured at a station located in Av. La Paz ( three kilometers north of downtown) we observe that most of the days (including
Saturdays and Sundays) maximum concentrations occur between 10 A.M. and noon. We have investigated the possibility to build a model
for SO2 forecasting based on values of concentrations of the pollutant on the previous day and forecasted values of temperature, relative
humidity and wind speed. We have compared the results obtained with three different models, using different combinations of input variables:
persistence, linear regression and a three layer neural network (which is equivalent t a non linear regression). We have found that that best
fit is obtained with a neural network that has SO2 concentrations every six hours on the previous day plus the forecasted meteorological
variables as input. Our findings can be used to do practical forecasts of SO2 the following years.
Keywords: Atmospheric physics, neuronal network.
Las emisiones de dióxido de azufre en Santiago son debidas principalmente a la industria y la construcción. Analizando las mediciones
horarias del perı́odo de invierno de 1995 de la estación de monitoreo ubicada en Av. La Paz observamos que todos los dı́as, incluyendo
sábados y domingos, las concentraciones de SO2 tienen un comportamiento regular, con un máximo entre las 10 A.M. y mediodı́a. Hemos
investigado la posibilidad de construir un modelo para predecir las concentraciones de SO2 basándonos en las concentraciones de este
contaminante medidas en el dı́a previo y valores pronosticados de temperatura, humedad relativa y velocidad del viento a la hora de interés.
Comparamos los resultados obtenidos con tres modelos para distintas combinaciones de variables de entrada: persistencia, regresión lineal y
red neuronal de tres capas (que equivale a una regresión no lineal). Hemos encontrado que el mejor ajuste se consigue con una red neuronal
que tiene como entradas las concentraciones del dı́a anterior medidas cada seis horas junto con las variables meteorológicas mencionadas
más arriba. Lo encontrado se podrı́a usar para hacer predicciones efectivas los años siguientes.
Descriptores: Fı́sica atmosférica, redes neuronales.
PACS: 07.05.Mh; 05.45.Tp; 92.60.Sz
Introduction
The main source of Sulfur dioxide (SO2 ) emissions is the
combustion of fossil fuels and it is among the most prevalent
air pollutants in industrialized areas. In particular, the combustion of fuels for heating and power generation is believed
to be responsible for most of the SO2 to which the population in urban regions is exposed. Given the topographical
and meteorological conditions in Santiago, Chile, dispersion
of pollutants is very slow, especially in winter. Exposure of
humans to high levels of SO2 has been related to increase
in hospital admission for chronic bronchitis and to low birth
weights. The World Health Organization has determined that
the safety limit for SO2 concentrations is 100 µg/m3 (33 parts
per billion) for 24 hour averages [1]. It seems then very useful to have at hand reliable methods to forecast sulfur dioxide
concentrations several hours in advance, in order to have the
opportunity to take emergency actions to decrease emissions
when conditions that favor high levels are foreseen. Classical statistical methods and neural network methods have been
used by several authors for short term prediction of gas and
particulate matter pollution. Existing models for SO2 forecasting in industrialized areas allow predictions of concentrations only 30 minutes in advance. In Ref. 2 have shown
that a three layer neural network may be a useful tool to predict PM2.5 concentrations in the atmosphere of downtown
Santiago, Chile several hours in advance when hourly concentrations of the previous day are used as input. Some improvement of the prediction is obtained by including values
of forecasted temperature, relative humidity and wind speed
as inputs. Perez and Trier [3] have used a neural network
to predict atmospheric NO2 concentrations near a street with
heavy traffic in Santiago, Chile. In this case inputs are NO
concentrations on previous hours plus forecasted temperature
at the time of the intended prediction. Results show a five
hour in advance prediction error of the order of 25. In this
article we report a study on the possibility to forecast hourly
averages of atmospheric SO2 concentrations based on data
obtained in a station located at a fixed point near downtown
Santiago. The selected station is one among the several stations that form the network designed to monitor air quality in
the city. The expected emissions in the neighborhood in the
station come from industries, heating and vehicle traffic.
The data
We consider data corresponding to hourly averages for the
period that goes from 05/18/95 to 09/30/95. SO2 concen-
112
P. PÉREZ
trations are in parts per billion (ppb) with an average value
of 12.2 and a standard deviation of 13.5. We can verify that
the 24 hour average of 33 ppb mentioned as a safety limit in
the introduction is exceeded several times during this period.
We also have data corresponding to temperature, relative humidity and wind speed measured at the same station with the
same frequency. Figure 1 shows the auto-correlation function
for the SO2 series. Memory effects are evident from the slow
decay of the curve. The peak at T=24 signals that daily variations of SO2 concentrations tend to be repeated. We have
calculated cross-correlations between the SO2 series and the
meteorological series obtaining 0.35 for temperature, zero for
wind speed and -0.45 for relative humidity.
We have compared predictions generated using persistence, linear regressions and feed forward neural networks
(which can be considered as a type of non linear regression).
Inputs for the regression models are past values of SO2 concentrations and data of temperature and relative humidity. In
order to compare the performance of the different models we
have run autotests with the 1995 data.
Results and discussion
Based in part on recent findings on forecasting of other pollutants [2, 3], we have investigated the performance of three
methods: persistence, linear regression and neural networks.
Persistence assumes that SO2 concentration at a given time
of the day will be equal to concentration on the previous day
at the same time. The linear regression is an optimized linear combination of past values of SO2 concentrations and
forecasted values of meteorological variables that generates
a forecasted value for the pollutant. The forecasting with the
neural network is basically a non linear mapping between
past values of SO2 concentrations and forecasted values of
meteorological variables (inputs) and the forecasted value of
the pollutant (output). The neural network we used is a feed
forward with three layers (details of the structure are similar
as those of Ref. [2] ). The measure of the performance of the
method is the fractional error between actual values and forecasted values: err =< |ytp − yta | > / < yta >, where ytp
is the forecasted value, yta is the actual value at time t and ¡¿
means average over all cases. We have investigated the performance of different combinations of input data for the three
models under study. We show the results for the most representative cases in Figure 2. In all cases the output is SO2 concentration at a given time on the following day. lr-s6-t-h is a
linear regression that has SO2 concentrations every six hours
on a given day plus actual values of temperature end relative
humidity at the time of the intended prediction on the following day. nn-s6-t-h is a three layer neural network with inputs
as in lr-s6-t-h and 5 units in the hidden layer. P is persistence
and lr-p-t-h is a linear regression that has actual SO2 concentration 24 hours before the intended prediction and values of
temperature and relative humidity as in the previous models
as input.
From these results we can conclude that in order to predict SO2 concentrations at a given day at a given time, we
must consider:
• Information about SO2 concentrations on the previous
day is important.
• Knowledge of actual meteorological conditions improves pollutant forecasting. This may be related to
the fact that entrance of SO2 from distant sources to
the area under study is facilitated when temperature increases because in this situation the altitude of the mixture layer gets higher.
F IGURE 1.
• Non linear effects are important when combining pollutant and meteorological information as input.
Acknowledgments
We would like to thank the support from Universidad de Santiago de Chile through project Dicyt-049631PJ.
F IGURE 2.
1. Environmental Health Criteria 8, (World Health Organization,Geneva, 1979).
2. P. Perez et al., Atmospheric Environment 34 (2000) 1189.
3. P. Perez et al., Atmospheric Environment (2000), (in press).
Rev. Mex. Fı́s. 48 S3 (2002) 111–112