Impact of NO2 on Ground-Level Ozone and Prediction using Neural Network Model

Review Article

Austin Environ Sci. 2016; 1(1): 1001.

Impact of NO2 on Ground-Level Ozone and Prediction using Neural Network Model

Punithavathy IK¹, Vijayalakshmi S²* and Jeyakumar JS³

Department of Physics, TBML College, India

*Corresponding author: Vijayalakshmi S, Department of Physics, TBML College, Porayar, Tamilnadu, India

Received: February 03, 2016; Accepted: June 01, 2016; Published: June 06, 2016

Abstract

Surface ozone or Ground level ozone has a vital role in the radiative and chemical processes in the atmosphere and poses as a potential problem to all developing countries. Measurements of surface ozone and NO2, one of the precursor for ground level ozone production has been carried out for the first time in karaikal (10.9327 °N and 79.8319 °E), a coastal region along the South eastern India for a period from October 2013 to September 2014. The results obtained in this study show a clear dependence of ozone on NO2 levels. The results further shows a positive correlation between O3 and NO2 (r2=0.3073) on a monthly scale. A neural network model has been developed for short term prediction of Ground level ozone. The model can predict the mean surface ozone levels based on the parameters like Nitrogen-di-oxide, temperature and wind speed. The model exhibits a good correlation between the actual and predicted data points.

Keywords: Ground level ozone concentration; NO2; Neural network; Short term prediction

Introduction

Surface or ground level ozone (O3) plays a very vital role in atmospheric oxidation processes and hence subsequently in air quality and its increase enhances the greenhouse effect in the free troposphere [1]. O3 is regarded as the third most powerful green house gas in the atmosphere after CO2 and CH4 with a radiative forcing of +0.35 Wm-2. Each molecule of O3 added in the atmosphere proves to be about 1200-2000 times more powerful in global warming than an addition of CO2 molecule [2]. O3 is also a major component of photochemical smog and is a well known hazardous element for human health. It is has been well proven that human exposure to ozone leads to respiratory problems [3]. Ground level ozone is not directly emitted into the atmosphere. It occurs as a result of photochemical reactions between oxides of nitrogen and volatile organic compounds in the presence of sunlight [4]. Nitrogen Oxides (NOx) and Volatile Organic Compounds (VOCs), well known precursors of ground level ozone have various anthropogenic and biogenic sources and exhibit non-linear effects on ozone production.

Several studies have been conducted aiming to develop tools and methods capable to achieve a short-term forecast of ozone levels [5,6]. The analysis often aims on investigating whether or not a threshold condition is exceeded. However, this means of analysis can often be exploited by environmental and medical authorities in issuing public warnings.

The most common method widely used for developing prediction models is to correlate meteorological and pollution data with the concentration of a certain pollutant. In this aspect, it has been shown that neural network technique can be employed for short term prediction.

Neural network techniques have recently become the focus of much attention as they can handle the complex and non-linear problems much better than the conventional statistical techniques. It is a simple mathematical input-output model which learns the relationship (linear or non-linear) between the input and output during the training period. Neural network model brings out the maximum information available within the data during the training period and reflects these in the independent period.

In this paper, an attempt has been made to study the impact of NO2 on Ground level ozone at Karaikal, a part of the union territory of Puducherry, a coastal region located along the south eastern side of the Indian Peninsula. Ground level ozone measurements have been carried out for the first time in this study region.

Measurement site and methodology

Karaikal (10.9327 °N, 79.8319 °E), is situated in the eastern coast of India (Figure 1).