Prediction Of Pm10 Concentrations Using Logistic Regression Analysis: Case Study In Jerantut

Abstract

Particulate matter (PM ₁₀) can cause several serious negative health effects to humans when it is present in the environment. Thus, it is important for us to forecast its concentration levels in the environment so that we can reduce the risk of exposure towards particulate matter. Secondary data on the concentration of PM ₁₀, sulphur dioxide (SO ₂), nitrogen dioxide (NO ₂), ground level ozone (O ₃), carbon monoxide (CO) along with temperature and relative humidity at Jerantut monitoring stations between 2010 to 2012 obtained from Department of Environment. The main objective of this study is to describe the relationship between PM ₁₀ with other gases and weather conditions by using correlation. It also aims to determine the best prediction categories. Furthermore, this research aims to find a model for predicting the concentration of PM ₁₀ using logistic regression. PM ₁₀ and O ₃ at Jerantut monitoring station were found to have a strong positive correlation. The best logistic regression model was obtained at Jerantut station in 2010 with an R ² value of 0.565. The best prediction category for Jerantut monitoring stations was shown to be healthy with a correct percentage of more than 85% obtained from the analysis of the overall and annual results between 2010 to 2012.

Keywords: Air pollutionParticulate matterprediction model

Introduction

Air pollution basically refers to the contamination of the indoor or outdoor environment by any types of agent that modifies the natural characteristics of the atmosphere ( World Health Organization, 2014). Hanapi and Din ( 2012) have described that the cause of air pollution may come from many sources such as waste products, construction work, factory emissions and vehicles. Pollutants at ground level are caused by human activities and natural events. Acson International in their Healthy Air Booklet stated that the main sources of air pollution in Malaysia are industrial fuel burning, motor vehicles, domestic fuel burning, power stations as well as the burning of industrial and municipal waste ( Acson Malaysia Sales and Service Sdn Bhd, 2012). Malaysia, Air Pollution Index (API) is currently use as an indicator to measure the air quality ( Hanapi & Din, 2012). According to the Department of Environment Malaysia (2013), API is calculated based on five major types of air pollutants at air pollution monitoring stations belong to Department of Environment Malaysia. These include PM ₁₀, sulphur dioxide, nitrogen dioxide, ground level ozone and carbon monoxide. The indications of API value below 50 classified as good, 51-100 classified as moderate, 201-300 classified as unhealthy, more than 300 is classified as hazardous whereas an API value above 500 is classified as an emergency.

PM known as particulate matter or fine dust, it is a complex mixture of liquid droplets with extremely small particles. In addition, it is made up of several components including organic chemicals, acids, dust particles, soil and metals. PM with an aerodynamic diameter of less than 10μ m (PM ₁₀) is one of the major air pollutants in Malaysia and in most of cities in Southeast Asia ( Afroz, Hassan, & Ibrahim, 2003). The yearly average ambient concentration levels of PM ₁₀ between 1999 and 2013 in Malaysia were generally within the Malaysian Ambient Air Quality Guidelines (MAAQG) with a value of less than 50μ g/m ³ . The highest level of concentration was 50μ g/m ³ which was recorded in 2002 whereas the lowest level of concentration was 39μ g/m ³ which was recorded in 2010. The concentration of PM at a specific location depends on many factors such as local and regional particulate matter sources as well as geographical situation and meteorological conditions ( Titos, Lyamani, Pandilfi, Alastuey, & Alados-Arboledas, 2014). The main source of air pollutants, especially PM is traffic exhaust emissions ( Bycenkiene, Plauskaite, Dudoitis, & Ulevicius, 2014).

Department of Occupational Safety and Health Malaysia (2014) stated that PM ₁₀ can negatively affect human health if the API value exceeds 100. Environment Statistics Time Series Malaysia (2013) summarised that unhealthy events caused by transboundary heavy particulate matter were recorded between 2002 to 2013 with a maximum of 3 days recorded in 2005. By referring to heavy particulate matter pollution reported by the New Straits Times ( 2014), the standard operating procedure for schools to close is when the API exceeds 200 where the air quality is at a “very unhealthy” level. However, the number of days for the school to be closed depends on the duration of the high particulate event. This causes uncertainty to the public because they would not be able to know the duration of closure. It would make it difficult for them to plan or schedule outdoor social activities. In reduced the difficulties, the appropriate method of prediction.

Problem Statement

According to Pascal et al. ( 2014), exposure to PM ₁₀ has been consistently associated with serious health outcomes, resulting in an increase in mortality and hospital admissions predominantly related to cardiovascular and respiratory disease. There are many significant studies have linked PM ₁₀ to a series of significant health problems, including aggravated asthma, increase in respiratory symptoms like coughing and difficult breathing, chronic bronchitis, decreased lung function, and premature death. One of the unhealthy events in Malaysia is the presence of heavy particulate matter caused by uncontrolled forest fires originating from the Indonesian province of Sumatra during the burning season ( Norela, Saidah, & Mahmud, 2013). Forest fires are normally used for land preparation and forest clearance by people involved in farming. Unfortunately, this could develop into uncontrollable wildfires. This situation usually happens between June and November coinciding with drier weather conditions ( Salinas et al., 2013). Due to these issues, there are need to provide an early warning to those who may be effected. Short term prediction is quite relevant to provide the information about PM ₁₀ concentration.

Research Questions

Is logistic regression suitable for prediction of PM ₁₀ concentration?

Purpose of the Study

The main objective in this study is to describe the relationship between PM ₁₀ with other gases and weather conditions by using correlation. It also aims to determine the best prediction categories. Furthermore, this research aims to develop a model for predicting the concentration of PM ₁₀ using logistic regression.

Research Methods

From a set of variables that can be continuous, discrete, dichotomous or a mixture of these variables, we can use a method to predict a discrete outcome. This method is known as logistic regression. Logistic regression can be used to answer the same questions as discriminant analysis. However, the difference between logistic regression and discriminant analysis is that it has no assumption about the distribution of independent variables. The application of logistic analysis is predicting the success or failure of a new product, determining what category of a credit risk a person will fall into and predicting whether a firm will be successful or otherwise.

In statistical analysis, the main objectives of logistic regression are to correctly predict categories of outcome for individual cases as well as to establish a relationship between the outcome and the independent variables.

The main purpose of logistic regression in statistical analysis to correctly predict categories of outcome for individual cases. A model must create that includes useful and related independent variables in order accomplish this purpose. Beside that, logistic regression also purposely to measure the relationship between categorical dependent variable and independent variables.

Logistic regression does not require the assumption of normality. However, the sample size must be large enough, at least 100 observations and a ratio of 20 observations for each independent variable. For this distribution, a log transformation needed along to create link with a normal regression equation. The log transformation or known as logistic regression of p also called as logit ( p ) defined as:

(1) l o g i t p = log e ⁡ p 1 - p = ln ⁡ p 1 - p

Logit ( p ) is the log base e of the p . From Equation 1, value p must in range between 0 and 1, then logit ( p ) will scale from negative infinity and positive infinity. The graph of logit ( p ) symmetrical at p = 0.5 . From Equation ( 1 ), the logistic regression equation form:

(2) l o g i t p = ln ⁡ p 1 - p = α + β 1 X 1 + β 3 X 3 + … + β k X k # 2

Equation ( 2 ) show the logistic equation form behaviour of linear fit model. This model uses maximum likehood in criterion for find the best fit rather than least square deviation. The value of p can calculated by following formula:

(3) p = e α + β 1 X 1 + β 2 X 2 + … 1 + e α + β 1 X 1 + β 2 X 2 + … # 3

where p is the probability of the parameter of interest, which is the probability of the concentration of PM ₁₀, e is value of natural logarithm (approximate 2.178…), α is the value of constant coefficient and β is the coefficient for independent variables (temperature, relative humidity, NO ₂, SO ₂, O ₃ and CO. There are three possible outcomes of PM ₁₀ level for the logistic regression model which are healthy (Y=1), moderate (Y=2) and unhealthy (Y=3). These variables of PM ₁₀ are grouped according to the relationship between PM ₁₀ concentration and Air Pollution Index in Malaysia as shown in Table 1 .

60% of the training data was used to obtain the logistic regression model. Another 40% of the data was used for validation purposes. When the percentage correct prediction of the training data is the same or higher than the validation data, the model is considered as good and suitable to used for prediction.

Findings

Based on the descriptive statistics provided in Table 02 , the reading of PM10 concentration does not exceed the hourly Malaysia Ambient Air Quality Guidelines (MAAQG) which is 150 μ g/ m 3 . The highest average of PM ₁₀ concentrations recorded in 2011 ( 39.09 μ g/ m 3 ) while in 2010 ( 37.00 μ g/ m 3 ) and 2012 ( 37.49 μ g/ m 3 ) . These averages are lower than 50 μ g / m 3 (daily MAAQG), give indication the PM ₁₀ concentration in Jerantut area still meet the standard set by DoE. For the standard deviation and coefficient of variance, the lowest value show in 2011 rather than 2010 and 2012. The value of kurtosis (-.10) and skewness (0.77) in 2010 lowest from this three consecutive years state that the pattern of distribution of data in 2010 close to the normal distribution.

Conclusion

From the secondary data obtained from the DoE which was analysed via descriptive statistics and correlation, the result shows that the level of maximum concentration of PM ₁₀ at Jerantut station was under the limit based on the Malaysian Ambient Air Quality Guidelines (MAAQG) from 2010 to 2012. The correlation analysis between PM ₁₀ and other gases and meteorological parameters at Jerantut station showed a strong correlation value of 0.614 between PM ₁₀ and O ₃. The result of the logistic regression analysis had a classification percentage of more than 90% for training and validation data every year. Moreover, the best logistic regression at Jerantut station in 2010 was an R ² value of 0.565. The best prediction of percentage correct obtained was more than 85% which is considered healthy for the overall and yearly analysis.

Acknowledgments

Special thanks to Universiti Sains Malaysia for the funding with a Short-term Grant (PJJAUH/6315089).

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