Systematic approach for the prediction of ground-level air pollution (around an industrial port) using an artificial neural network

Mahad S. Baawain, Aisha S. Al-Serihi

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

The prediction of air pollution levels is critical to enable proper precautions to be taken before and during certain events. In this paper a rigorous method of preparing air quality data is proposed to achieve more accurate air pollution prediction models based on an artificial neural network (ANN). The models consider the prediction of daily concentrations of various ground-level air pollutants, namely CO, PM10, NO, NO2, NOx, SO2, H2S, and O3, which were measured by an ambient air quality monitoring station in Ghadafan village, located 700 m downwind of the emissions of Sohar Industrial Port on the Al-Batinah coast of Oman. The training of the models is based on the multi-layer perceptron (MLP) method with the Back-Propagation (BP) algorithm. The results show very good agreement between the actual and predicted concentrations, as the values of the coefficient of multiple determinations (R2) for all ANN models exceeded 0.70. The results also show the importance of temperature in the daily variations of O3, SO2, and NOx, whilst the wind speed and wind direction play significant roles in the daily variations of NO, CO, NO2, and H2S. PM10 concentrations are influenced by almost all the measured meteorological parameters.

Original languageEnglish
Pages (from-to)124-134
Number of pages11
JournalAerosol and Air Quality Research
Volume14
Issue number1
DOIs
Publication statusPublished - Feb 2014

Fingerprint

Air pollution
artificial neural network
atmospheric pollution
Neural networks
Carbon Monoxide
prediction
Air quality
diurnal variation
Air Pollutants
Backpropagation algorithms
back propagation
Multilayer neural networks
ambient air
wind direction
Coastal zones
air quality
village
wind velocity
Monitoring
coast

Keywords

  • Air pollution
  • Artificial neural network
  • Industrial port
  • Oman

ASJC Scopus subject areas

  • Environmental Chemistry
  • Pollution

Cite this

@article{4ef1dfa200cb4d65a9bf25bceed33fa5,
title = "Systematic approach for the prediction of ground-level air pollution (around an industrial port) using an artificial neural network",
abstract = "The prediction of air pollution levels is critical to enable proper precautions to be taken before and during certain events. In this paper a rigorous method of preparing air quality data is proposed to achieve more accurate air pollution prediction models based on an artificial neural network (ANN). The models consider the prediction of daily concentrations of various ground-level air pollutants, namely CO, PM10, NO, NO2, NOx, SO2, H2S, and O3, which were measured by an ambient air quality monitoring station in Ghadafan village, located 700 m downwind of the emissions of Sohar Industrial Port on the Al-Batinah coast of Oman. The training of the models is based on the multi-layer perceptron (MLP) method with the Back-Propagation (BP) algorithm. The results show very good agreement between the actual and predicted concentrations, as the values of the coefficient of multiple determinations (R2) for all ANN models exceeded 0.70. The results also show the importance of temperature in the daily variations of O3, SO2, and NOx, whilst the wind speed and wind direction play significant roles in the daily variations of NO, CO, NO2, and H2S. PM10 concentrations are influenced by almost all the measured meteorological parameters.",
keywords = "Air pollution, Artificial neural network, Industrial port, Oman",
author = "Baawain, {Mahad S.} and Al-Serihi, {Aisha S.}",
year = "2014",
month = "2",
doi = "10.4209/aaqr.2013.06.0191",
language = "English",
volume = "14",
pages = "124--134",
journal = "Aerosol and Air Quality Research",
issn = "1680-8584",
publisher = "AAGR Aerosol and Air Quality Research",
number = "1",

}

TY - JOUR

T1 - Systematic approach for the prediction of ground-level air pollution (around an industrial port) using an artificial neural network

AU - Baawain, Mahad S.

AU - Al-Serihi, Aisha S.

PY - 2014/2

Y1 - 2014/2

N2 - The prediction of air pollution levels is critical to enable proper precautions to be taken before and during certain events. In this paper a rigorous method of preparing air quality data is proposed to achieve more accurate air pollution prediction models based on an artificial neural network (ANN). The models consider the prediction of daily concentrations of various ground-level air pollutants, namely CO, PM10, NO, NO2, NOx, SO2, H2S, and O3, which were measured by an ambient air quality monitoring station in Ghadafan village, located 700 m downwind of the emissions of Sohar Industrial Port on the Al-Batinah coast of Oman. The training of the models is based on the multi-layer perceptron (MLP) method with the Back-Propagation (BP) algorithm. The results show very good agreement between the actual and predicted concentrations, as the values of the coefficient of multiple determinations (R2) for all ANN models exceeded 0.70. The results also show the importance of temperature in the daily variations of O3, SO2, and NOx, whilst the wind speed and wind direction play significant roles in the daily variations of NO, CO, NO2, and H2S. PM10 concentrations are influenced by almost all the measured meteorological parameters.

AB - The prediction of air pollution levels is critical to enable proper precautions to be taken before and during certain events. In this paper a rigorous method of preparing air quality data is proposed to achieve more accurate air pollution prediction models based on an artificial neural network (ANN). The models consider the prediction of daily concentrations of various ground-level air pollutants, namely CO, PM10, NO, NO2, NOx, SO2, H2S, and O3, which were measured by an ambient air quality monitoring station in Ghadafan village, located 700 m downwind of the emissions of Sohar Industrial Port on the Al-Batinah coast of Oman. The training of the models is based on the multi-layer perceptron (MLP) method with the Back-Propagation (BP) algorithm. The results show very good agreement between the actual and predicted concentrations, as the values of the coefficient of multiple determinations (R2) for all ANN models exceeded 0.70. The results also show the importance of temperature in the daily variations of O3, SO2, and NOx, whilst the wind speed and wind direction play significant roles in the daily variations of NO, CO, NO2, and H2S. PM10 concentrations are influenced by almost all the measured meteorological parameters.

KW - Air pollution

KW - Artificial neural network

KW - Industrial port

KW - Oman

UR - http://www.scopus.com/inward/record.url?scp=84893071698&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893071698&partnerID=8YFLogxK

U2 - 10.4209/aaqr.2013.06.0191

DO - 10.4209/aaqr.2013.06.0191

M3 - Article

VL - 14

SP - 124

EP - 134

JO - Aerosol and Air Quality Research

JF - Aerosol and Air Quality Research

SN - 1680-8584

IS - 1

ER -