Meteorology-based
Forecasting of Air Quality Index Using Neural Network
Mukesh Sharma,* Sachin
Aggarwal*, Purnendu Bose* and Ashok Deshpande+
*Department of Civil
Engineering, Indian Institute of Technology
Kanpur, 208016, India
+Distinguished
Professor SIES-Indian Institute of Environment Management,
Navi Mumbai, 400 076 India
Abstract:
Air Quality Index (AQI), a system for transforming air pollution levels into a
single number, aims at providing information about air quality in simple terms
to general public. Any advance information
about AQI can forewarn the public of unhealthy air and encourage people to
voluntarily reduce emissions-producing activities and avoid exposures to
polluted environment. Two mathematical models (i) meteorology-based air quality
level predictions and (ii) meteorology forecasting, have been developed (based
on four year data) using neural network to forecast AQI for following three
days. The AQI forecasting model was concluded as being satisfactory and useful
for information dissemination to general public.
Introduction and Objective
Air
pollutants at ground level can be harmful to human health if their
concentrations exceed certain acceptable levels. As pollutants accumulate in or
near large metropolitan areas, this typically exposes people to unhealthy
pollutant concentrations In light of the health effects of air pollutants,
environmental agencies (e.g. USEPA, 1998) have been using air quality index
(AQI) for public information (about air quality) and data interpretation. AQI
is defined as an overall scheme that transforms the weighted values of
individual air pollution related parameters (e.g. SO2, CO,
visibility, etc.) into a single number or set of numbers. An AQI is developed
in Indian context (Sharma et al (2001)) which include three pollutants (sulphur
dioxide (SO2), nitrogen dioxide (NO2) and Suspended
Particulate Matter (SPM)). Segmented linear functions are used for relating the
actual air pollution concentrations (of each pollutant) to a normalized number.
The categories of index system are:
0-100; Good, 101-200; Moderate, 201-300; Poor,
301-400; Very poor, 401-500; Severe
Ideally,
the developed AQI should have accurate forecasting capability. Any advance
information about AQI can forewarn the public of unhealthy air and encourage
people to voluntarily reduce emissions-producing activities and avoid exposure
to polluted environment. The purpose of this paper is to establish a
mathematical model between the metrology and air quality in terms of air
quality index and develop a forecasting mechanism using artificial neural
network for the developed AQI.
A scheme utilizing neural network
simulation is developed in the course of this work for prediction of AQI for a
location in the city of Kanpur (longitude
88° 22' E and Latitude 26° 26' N), India. The neural network model is
trained and tested using the air quality and meteorology data. Two neural
network models were developed one for developing a meteorology -air quality
linkage and second for forecasting the meteorology three days in advance.
Sampling location at Agricultural University (AU), Kanpur was selected for this study as concurrent data of air quality
(from National Ambient Air quality Monitoring Programme) and meteorology were
available for last several years at this site, which were essential for this
study.
Meteorology-Air Quality
Model Development
Air pollution system has three components:
emission source, transport medium (atmosphere) and receptor. Pollution reaching
a receptor depends not only on the emitted quantity but also on the atmospheric
dynamics. The impact on receptor can be estimated by developing source-receptor
linkages through atmosphere.
A modeling system (source-receptor
linkages) is necessary to predict impact on the receptor and translate the
impact into an air quality index.
Steps in Model Formulation
The impact on receptor at any point is a
function of source strength, and meteorological parameters like wind speed,
wind direction, temperature, relative humidity, stability, rainfall etc. Thus,
the AQI forecasting modelling completion is a three-step process.
(i) Air Quality Models: Establish the model
that can predict the air quality for various pollutants in terms of meteorology
(ii) Meteorology Forecasting Models:
Forecast meteorology for coming next days and combined the forecasted
meteorology to forecast air quality using the model developed in step (i) above
(iii) AQI Forecasting: Estimate and
forecast the air quality index based on forecasted air quality levels in step
(ii) above.
Air Quality Models
Since air quality data were available on
three pollutants, SPM, SO2, and NO2, these pollutants are
selected for model prediction and index formulation.
Four-year air quality data for the site
(Agriculture University, Kanpur) for the years 1997 to 2000 were collected from
Central Pollution Control Board, Delhi. Similarly, meteorological data for the
same time period and at the same location were also collected. Meteorological
data included temperature, wind speed and direction, relative humidity and
rainfall.
Initial data analysis showed significant
seasonal variability in air pollutant concentrations. Monsoon period was the
cleanest and winter months were the most polluted. Therefore, meteorological
and air quality data were segregated as per the following seasons:
Winter: December, January, and
February, march
Summer: April, may, June
Monsoon: July, August, September
and
Post monsoon: October, November
In all, twelve models need to be developed
for predicting air quality concentrations for each set of meteorological
parameters. The twelve models include three models (for SO2, NO2 and SPM) for
four seasons (winter, summer, monsoon and post monsoon). In other words, for a
given season and meteorological data, the model should predict concentration of
the pollutants. For this purpose, a neural network model (presented below) was
developed.
Artificial Neural Network (ANN) Model for
Air Quality Modelling
For construction of neural network model,
the air pollution system is looked upon as a system that under varying sets of
meteorological inputs (e.g. weather conditions) will respond by producing
different sets of output. Such a model presupposes no prior knowledge about the
structure of relationship that exists between input and output variables (e.g.
pollutant concentrations).
Neural network comprises a number of
interconnected entities, similar in many ways to biological neurons. The choice
of the architecture of the network depends on the task to be performed. For
modelling physical system such as air pollution system, a feed-forward layer is
normally employed (Wasserman, 1989). It consists of layers of input neurons,
and one and more hidden layers. For this study, a software "Neuro Genetic
Optimizer" (NGO, version 2.6) is used. The modelling results are shown in
Figure 1. The other details of model are given below.
ANN model
- Steps Followed
1.
The air quality data were categorized into four seasons
as explained earlier.
2.
The meteorology data were regressed with air quality
data. Only average wind speed and average temperature of the day showed
significant correlation with air quality. Hence, average temperature and wind
speed of day was only taken as input layer. Output layer consisted of
concentration of SO2, NO2, and SPM concentration.
3.
Four models one for each season were developed for each
pollutant. For this purpose, data set of each season for four years was
randomly divided into training and testing record (50% for each).
4.
Input data was normalized between -1 and 1 and output
was subsequently de-normalized
5.
Weights were randomly initialized between +0.3 to -0.3.
6.
Momentum constant was selected in the range 0.1 to 0.3
7.
Multiple hidden layers were used; their number was
selected using genetic algorithms.
8.
Selection of hidden layer and number of hidden neurons
was decided using genetic algorithms
9.
Limit on the number of hidden neurons was set as eight.
10.
The minimum number of passes for each network was 20
and maximum was 50.
11.
 |
 |
Learning rate constants were selected in the range 0.1 to 0.4.
 |
Figure 1 Neural Network Modelling of NO2, SO2 and SPM
Figure 1 shows model performance and values
of coefficient of correlation (R). The R-values were found significant at 1%
level of significance in a statistical sense and model was accepted for further
analysis. It can be concluded that the
developed neural network model establishes a reasonable relationship between
meteorological inputs and air concentrations.
Meteorology Forecasting Model
As identified that for predicting air
concentration only average temperature and wind speed of the day were adequate
for predicting air concentrations. It was decided that attempt should be made
to forecast AQI for next three days to provide enough warning period to people
likely to be exposed from air pollution. To this end, two neural networks were
developed one for forecasting mean temperature and another for wind speed. It
may be stated that meteorology is a very complicated phenomenon and explaining
it on basis of simple mathematical models is very difficult and one has to
resort to ANN. The following steps were undertaken to develop two neural
networks-based forecasting model one each temperature and for wind speed.
·
Collection of data : four years of
meteorological data for various parameters like
temperature, wind speed and
direction, relative humidity, rainfall etc were collected,
·
Preparation of data: for advance forecasting for
three days, input data for each set of output was prepared. A value of previous
four days was found adequate to forecast temperature and wind speed for next
three days. Therefore, the data were arranged in a set of four; total 1100 such
sets were made and fed to the ANN model, and
·
Other steps were identical to those presented in
the section on air quality modelling.
Based on the above exercise two models were
developed. Model for temperature showed much higher accuracy in prediction than
wind speed (Figure 2)
The temperature forecasting results are
excellent, and it demonstrates the high predictive capacity of ANN. Both for
temperature and wind speed, the value of R was found significant at 1% of level
of significance, and model was accepted for further analysis.
AQI Forecasting: Results and
Discussion
 |
 |
||
Having established two models one fore predicting pollutant concentration based on wind speed and temperature and other model for forecasting wind speed and temperature, the values of model-computed AQI can then be estimated for all days of air quality monitoring. Figure 3 compares the AQI values based on actual measurements vis-à-vis model computed AQI.
 |
Figure 2 Neural Modelling results for temperature and wind speed forecasting
The model does not show very high
correlation between computed and observed AQI (Figure 3). The probable reason
for such a behavior can be attributed high background concentrations and low
values of pollutant concentration that may be fluctuating from one day to
another and model is not capable of picking these subtle changes. However, the
aim of this study is to develop a method, which could predict AQI into a band
like good, moderate, poor, severe etc. The model strength must be tested on
predictability of AQI in the right band rather than in terms of absolute
values.
Figure 4 compares the model-computed AQI
band vis-a-vis observed AQI bands. Considering the AQI bands, it can be stated
that the performance of the model improves and it can be used for public
information as an advance warning system. Results of other seasons are not
shown here. It may be mentioned that for summer and winter the model has
performed better than monsoon season (Aggarwal, 2001).
Figure 3 Comparison of AQI predicted and Actual
for
Monsoon.
 |
Figure 4. Comparison of
Observed and predicted AQI Bands For Monsoon season.
References
Aggarwal Sachin (2001). Application of Neural Network to Forecast Air
Quality Index. Thesis submitted in partial fulfillment of requirements for a
degree in Bachelor of Technology, April 2001.
NGO (version 2.6) (2000).
Neuro-Genetic optimizer URL www.bio-comp.com
Sharma M., Sengupta, B., Shukla,
B.P. and Maheshwari, M (2000). Air Quality Index for Data Interpretation and
Public Information. Presented in International Conference, Centre for Science
and Environment, New Delhi, June 6-8, 2000.
USEPA (1998). Federal Register
Vol.63 No. 236/Wednesday, December 9, 1998
Wassermann P.D. (1989). Neural
Computing theory and Practice. New York Van Nostrand Reinhold.
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