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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.
between the metrology and air quality in terms of
Introduction and Objective air quality index and develop a forecasting
mechanism using artificial neural network for the
Air pollutants at ground level can be harmful to developed AQI.
human health if their concentrations exceed
certain acceptable levels. As pollutants A scheme utilizing neural network simulation is
accumulate in or near large metropolitan areas, developed in the course of this work for
this typically exposes people to unhealthy prediction of AQI for a location in the city of
pollutant concentrations In light of the health Kanpur (longitude 88° 22' E and Latitude 26° 26'
effects of air pollutants, environmental agencies N), India. The neural network model is trained
(e.g. USEPA, 1998) have been using air quality and tested using the air quality and meteorology
index (AQI) for public information (about air data. Two neural network models were
quality) and data interpretation. AQI is defined developed one for developing a meteorology -air
as an overall scheme that transforms the quality linkage and second for forecasting the
weighted values of individual air pollution meteorology three days in advance. Sampling
related parameters (e.g. SO2, CO, visibility, etc.) location at Agricultural University (AU), Kanpur
into a single number or set of numbers. An AQI was selected for this study as concurrent data of
is developed in Indian context (Sharma et al air quality (from National Ambient Air quality
(2001)) which include three pollutants (sulphur Monitoring Programme) and meteorology were
dioxide (SO2), nitrogen dioxide (NO2) and available for last several years at this site, which
Suspended Particulate Matter (SPM)). were essential for this study.
Segmented linear functions are used for relating
the actual air pollution concentrations (of each
pollutant) to a normalized number. The
Meteorology-Air Quality Model
categories of index system are: Development
0-100; Good, 101-200; Moderate, 201-300; Air pollution system has three components:
Poor, 301-400; Very poor, 401-500; Severe emission source, transport medium (atmosphere)
and receptor. Pollution reaching a receptor
Ideally, the developed AQI should have accurate depends not only on the emitted quantity but also
forecasting capability. Any advance information on the atmospheric dynamics. The impact on
about AQI can forewarn the public of unhealthy receptor can be estimated by developing source-
air and encourage people to voluntarily reduce receptor linkages through atmosphere.
emissions-producing activities and avoid
exposure to polluted environment. The purpose A modeling system (source-receptor linkages) is
of this paper is to establish a mathematical model necessary to predict impact on the receptor and
translate the impact into an air quality index.
1
Steps in Model Formulation monsoon and post monsoon). In other words, for
a given season and meteorological data, the
The impact on receptor at any point is a function model should predict concentration of the
of source strength, and meteorological pollutants. For this purpose, a neural network
parameters like wind speed, wind direction, model (presented below) was developed.
temperature, relative humidity, stability, rainfall
etc. Thus, the AQI forecasting modelling Artificial Neural Network (ANN) Model for Air
completion is a three-step process. Quality Modelling
(i) Air Quality Models: Establish the model that For construction of neural network model, the air
can predict the air quality for various pollutants pollution system is looked upon as a system that
in terms of meteorology under varying sets of meteorological inputs (e.g.
weather conditions) will respond by producing
(ii) Meteorology Forecasting Models: Forecast different sets of output. Such a model
meteorology for coming next days and combined presupposes no prior knowledge about the
the forecasted meteorology to forecast air quality structure of relationship that exists between input
using the model developed in step (i) above and output variables (e.g. pollutant
concentrations).
(iii) AQI Forecasting: Estimate and forecast the
air quality index based on forecasted air quality Neural network comprises a number of
levels in step (ii) above. interconnected entities, similar in many ways to
biological neurons. The choice of the
architecture of the network depends on the task
Air Quality Models
to be performed. For modelling physical system
such as air pollution system, a feed-forward layer
Since air quality data were available on three
is normally employed (Wasserman, 1989). It
pollutants, SPM, SO2, and NO2, these pollutants
consists of layers of input neurons, and one and
are selected for model prediction and index
more hidden layers. For this study, a software
formulation.
"Neuro Genetic Optimizer" (NGO, version 2.6)
is used. The modelling results are shown in
Four-year air quality data for the site Figure 1. The other details of model are given
(Agriculture University, Kanpur) for the years below.
1997 to 2000 were collected from Central
Pollution Control Board, Delhi. Similarly,
ANN model - Steps Followed
meteorological data for the same time period and
at the same location were also collected. 1. The air quality data were categorized into
Meteorological data included temperature, wind four seasons as explained earlier.
speed and direction, relative humidity and 2. The meteorology data were regressed with
rainfall. air quality data. Only average wind speed and
average temperature of the day showed
Initial data analysis showed significant seasonal significant correlation with air quality. Hence,
variability in air pollutant concentrations. average temperature and wind speed of day was
Monsoon period was the cleanest and winter only taken as input layer. Output layer consisted
months were the most polluted. Therefore, of concentration of SO2, NO2, and SPM
meteorological and air quality data were concentration.
segregated as per the following seasons: 3. Four models one for each season were
developed for each pollutant. For this purpose,
Winter: December, January, and February, data set of each season for four years was
march randomly divided into training and testing record
Summer: April, may, June (50% for each).
Monsoon: July, August, September and 4. Input data was normalized between -1 and 1
Post monsoon: October, November and output was subsequently de-normalized
In all, twelve models need to be developed for 5. Weights were randomly initialized between
predicting air quality concentrations for each set +0.3 to -0.3.
of meteorological parameters. The twelve 6. Momentum constant was selected in the
models include three models (for SO2, NO2 and range 0.1 to 0.3
SPM) for four seasons (winter, summer,
2
7. Multiple hidden layers were used; their end, two neural networks were developed one for
number was selected using genetic algorithms. forecasting mean temperature and another for
8. Selection of hidden layer and number of wind speed. It may be stated that meteorology is
hidden neurons was decided using genetic a very complicated phenomenon and explaining
algorithms it on basis of simple mathematical models is very
9. Limit on the number of hidden neurons was difficult and one has to resort to ANN. The
set as eight. following steps were undertaken to develop two
10. The minimum number of passes for each neural networks-based forecasting model one
network was 20 and maximum was 50. each temperature and for wind speed.
11. Learning rate constants were selected in the
range 0.1 to 0.4. Collection of data : four years of
MONSOON MODEL FOR SO2
MONSOON MODEL FOR NO2 R = 0.7814
R = 0.7613 25
35
PREDICTED SO2
20
30
PREDICTED NO2
25 15
20
10
15
10 5
5 0
0 0.0 5.0 10.0 15.0 20.0 25.0
0.0 5.0 10.0 15.0 20.0 25.0 30.0 ACTUAL SO2
ACTUAL NO2
meteorological data for various parameters
MONSOON MODEL FOR SPM
R = 0.4262
350
300
PREDICTED SPM
250
200
150
100
50
0
0.0 50.0 100.0 150.0 200.0 250.0 300.0
ACTUAL SPM
Figure 1 Neural Network Modelling of NO2, SO2 like temperature, wind speed and direction,
and SPM relative humidity, rainfall etc were collected,
Preparation of data: for advance forecasting
Figure 1 shows model performance and values of
for three days, input data for each set of
coefficient of correlation (R). The R-values were
output was prepared. A value of previous
found significant at 1% level of significance in a
four days was found adequate to forecast
statistical sense and model was accepted for
temperature and wind speed for next three
further analysis. It can be concluded that the
days. Therefore, the data were arranged in a
developed neural network model establishes a
set of four; total 1100 such sets were made
reasonable relationship between meteorological
and fed to the ANN model, and
inputs and air concentrations.
Other steps were identical to those presented
in the section on air quality modelling.
Meteorology Forecasting Model
Based on the above exercise two models were
As identified that for predicting air concentration developed. Model for temperature showed much
only average temperature and wind speed of the higher accuracy in prediction than wind speed
day were adequate for predicting air (Figure 2)
concentrations. It was decided that attempt
should be made to forecast AQI for next three The temperature forecasting results are excellent,
days to provide enough warning period to people and it demonstrates the high predictive capacity
likely to be exposed from air pollution. To this of ANN. Both for temperature and wind speed,
3
the value of R was found significant at 1% of like good, moderate, poor, severe etc. The model
level of significance, and model was accepted for strength must be tested on predictability of AQI
further analysis. in the right band rather than in terms of absolute
values.
AQI Forecasting: Results and
Discussion Figure 4 compares the model-computed AQI
band vis-a-vis observed AQI bands. Considering
the AQI bands, it can be stated that the
Having established two models one fore
performance of the model improves and it can be
predicting pollutant concentration based on wind
used for public information as an advance
speed and temperature and other model for
warning system. Results of other seasons are not
forecasting wind speed and temperature, the
shown here. It may be mentioned that for
values of model-computed AQI can then be
estimated for all days of air quality monitoring.
Figure 3 compares the AQI values based on WIND SPEED FORECASTING DAY 1
actual measurements vis-à-vis model computed
AQI. R2 = 0.569
60
PREDICT ED VALUES
50
TEMPERATURE FORECAST FIRST DAY 40
2
R = 0.9447 30
50 20
PREDICED VAL UES
40 10
30 0
0 5 10 15 20 25
20
ACYUAL VALUES
10
0 summer and winter the model has performed
0.0 10.0 20.0 30.0 40.0
ACTUAL VALUES ACTUAL Vs PREDICTED AQI FOR MONSOON
R = 0.5731
180
160
Figure 2 Neural Modelling
PREDICTED AQI
140
results for temperature and wind speed 120
forecasting 100
80
60
The model does not show very high correlation
40
between computed and observed AQI (Figure 3). 20
The probable reason for such a behavior can be 0
attributed high background concentrations and 0 50 100 150
low values of pollutant concentration that may be ACTUAL AQI
fluctuating from one day to another and model is
not capable of picking these subtle changes. better than monsoon season (Aggarwal, 2001).
However, the aim of this study is to develop a
method, which could predict AQI into a band
Figure 3 Comparison of AQI predicted and Actual
for Monsoon.
4
100%
90%
Percent Category
80%
70%
60% Poor
50% Moderate
40% Good
30%
20%
10%
0%
Observed Forecasted
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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