Mapping the Spatial Distribution of Criteria Air Pollutants in Peninsular Malaysia Using Geographical Information System (GIS)

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CAQM (
) is designed to collect or measure data continuously during the monitoring period. CAQM typically include measurement instrumentation (for both pollutant gases and meteorological parameters); support instrumentation (support gases, calibration equipment); instrument shelters (temperature controlled enclosures); and data acquisition system (to collect and store data) (DOE, 2008). www.intechopen.com DOE Malaysia publishes the air quality status to public using Air Pollutant Index (API) system on its website. The API system of Malaysia closely follows the Pollutant Standard Index (PSI) system of the United States.
An API system normally includes the major air pollutants which could cause potential harm to human health should they reach unsafe levels. The air pollutants included in Malaysia's API are O 3 , CO, NO 2 , SO 2 and PM 10 .
The Table 1 show the category corresponds to a different level of health concern. The five levels of health concern and what they mean are (DOE, 1997):  "Good" API is 0 -50. Air quality is considered satisfactory, and air pollution poses little or no risk.  "Moderate" API is 51 -100. Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people.  "Unhealthy" API is 101 -200. People with lung disease, older adults and children are at a greater risk from exposure to ozone, whereas persons with heart and lung disease, older adults and children are at greater risk from the presence of particles in the air. Everyone may begin to experience some adverse health effects, and members of the sensitive groups may experience more serious effects.  "Very Unhealthy" API is 201 -300. This would trigger a health alert signifying that everyone may experience more serious health effects.  "Hazardous" API greater than 300. This would trigger a health warning of emergency conditions. The entire population is more likely to be affected.

Trend/status pollutions and air pollutants sources in Malaysia
An annual Environmental Quality Report (EQR) had been published in compliance with the Section 3(1)(i) of the Environmental Quality Act 1974. The EQR reported that air quality, noise monitoring, river water quality, groundwater quality, marine and island marine water quality, and pollution sources inventor in Malaysia. The highest number of unhealthy air quality status days was recorded in Shah Alam (41 days) for the state of Selangor (Figure 4). Figure 4 also showed that, from year 2001 until 2009, Shah Alam has the highest number of unhealthy air quality than other in the states of Selangor. The air quality of the northern and southern region of west coast of Peninsular Malaysia was between good to moderate most of the time. Then, east coast of Peninsular Malaysia the air quality remained good most of the time and occasionally moderate. Last, Sabah, Labuan and Sarawak were generally good and moderate.   , 2009b). There was an increase in emission load for CO, NO 2 and SO 2 compared to 2008. Figure 6 (a-d) showed the emission by source for SO 2 , PM 10 , NO 2 , and CO respectively. The results reveal that, power stations contributed the highest SO 2 and NO 2 emission load, 47% and 57% respectively. On the other hand, PM 10 and CO the highest contributor were industries (49%) and motor vehicles (95%) respectively.

Spatial air pollutants mapping in Malaysia
Presenting of the air pollutant concentration or API to public always is a challenge for the DOE. Air pollutants data from the monitoring station present in numerical or literal form www.intechopen.com are wearisome and cannot well present a large surrounding area. Meanwhile, actual situation of air pollution cannot be figured out to public. Moreover, the air pollutants data present in the numerical form have lack of geographical information. Beside, the way to get more actual pollution status in a huge area, have more monitoring stations is a high cost of the solvent method. Increase the monitoring station will increase the persistence of the pollution information. However, building new monitoring station is very costly. According to Alam Sekitar Malaysia Sdn. Bhd. (ASMA), an air monitoring station is cost about one million and the station only can present the area with the radius of 15 km. In other word, bigger areas for a States require more monitoring station and the higher cost of the air quality monitoring in the States.
A picture can describe thousand words, air pollutants statuses describe in an image (spatial map) can be more easily been visualized. In this century, presenting the data in a compact and full of information ways are preferred. There are many researcher nowadays attempt to study the dispersion of air pollutants with an image. In this study, a GIS-based approach of spatio-temporal analysis is attempted to use for presenting the air pollutions situation in an area.
Geographic information system (GIS) is an integrated assembly of computer hardware, software, geographic data and personnel designed to efficiently acquire, store, manipulate, retrieve, analyze, display and report all forms of geographically referenced information geared towards a particular set of purposes (Burrough, 1986;Kapetsky & Travaglia, 1995, as cited in Nath et al., 2000. The power of a GIS within the framework of spatio-temporal analysis depends on its ability to manage a wide range of data formats, which are represented by digital map layers extended by attributes with various observations, measurements and preprocessed data (Matějíček et al., 2006). The statistical data of the GIS can include area, perimeter and other quantitative estimates, including reports of variance and comparison among images (Nath et al., 2000). GIS is useful to produce the interpolated maps for visualization, and for raster GIS maps algebraic functions can calculate and visualize the spatial differences between the maps (Zhang & McGrath, 2003).
Nowadays, the applications of the GIS become wider. The increased use of GIS creates an apparently insatiable demand for new, high resolution visual information and spatial databases (Pundt & Brinkkötter-Runde, 2000). GIS able to do spatio-temporal analysis due to its ability to manage a wide range of data formats, which are represented by digital map layers extended by attributes with various observations, measurements and preprocessed data (Matějíček et al., 2006). Various technique of interpolating that GIS allow user to interpolate the variation of air pollutants. Inverse Distance Weighted (IDW), example, estimate of unknown value via a known value with the decrease of value through the increase of the distance as a simple interpolation method for air pollutants.

Inverse distance weighted of the PM 10 spatial mapping federal territory Kuala Lumpur
Federal Territory Kuala Lumpur has the total areas of 243 km 2 with the population of 1,655,100 people in 2009. In year 2009, Kuala Lumpur has the highest population density in Malaysia with 6,811 people per km 2 (Department of Statistics, 2009). Federal Territory Kuala Lumpur has a rapid transformation and its wider urban region during the last decade of the twentieth century demands greater critical scrutiny than it has so far attracted (Bunnell et al., 2002). Kuala Lumpur is the social and economic driving force of a nation eager to better itself, a fact reflected in the growing number of designer bars and restaurants in the city, and in the booming manufacturing industries surrounding it (Ledesma et al., 2006). Figure 7 shows the average temperature and the rainfall in area of Kuala Lumpur, temperatures have not much different and humidity is high all year around. Fig. 7. Graph average daily temperature and rainfall (Source: Ledesma et al., 2006).
Air quality of a small area with a high population is the most concern issue for the government. It cannot be ignoring to study the dispersion of the pollutants. In year 1997, Kuala Lumpur and surrounding areas had been shrouded by haze with a pall of noxious fumes, smelling of ash and coal, caused by the fires in the forests to clear land during dry weather at neighbor country Indonesia's Sumatra Island (msnbc.com, 2010). In year 2005, the highest number, 67 days, of unhealthy day were recorded in Kuala Lumpur (DOE, 2006).
In section 2.1 and 2.2, IDW is the method used to interpolate the dispersion of PM 10 concentrations in Kuala Lumpur. Changing of the study of pollutant with the air monitoring station had to change to more presentable of dispersion image form. The dispersion of the PM 10 concentration in Kuala Lumpur using the interpolation of IDW is the first attempted to have spatial air pollutants mapping in Malaysia. Some more, API in Malaysia always show by the concentration of PM 10 (DOE, 2009a), which mean that PM 10 is the parameter most contribute to the air pollution in Malaysia. So that, PM 10 was chose as study air pollutant. PM 10 concentration data which collected from DOE are the main data were used. There are three air monitoring stations located at the Federal Territory Kuala Lumpur. CAQM which build at Kuala Lumpur are station CA0012, station CA0016 and station CA0054 (Figure 8). Station CA0012 operated since December 1996 and ceased operation on February 2004. Whereas, Station CA0016 and CA0054 operated since December 1996 and February 2004 respectively until today both still well monitoring the air quality in Federal Territory Kuala Lumpur. However, to make the interpolation more persistent, all the PM 10 data which the CAQM located in States of Selangor were obtained to do the interpolation of the dispersion in Kuala Lumpur. Figure 9 show the CAQM station location on Selangor map.   IDW interpolations for the dispersion PM 10 concentration in Kuala Lumpur was computed by the GIS software validate with the in-situ monitoring (section 2.2.1). Then, the result will be further discussed in section 2.2. GIS software integrated collection of computer software and data used to view and manage information about geographic places, analyze spatial relationships, and model spatial processes. GIS provides a framework for gathering and organizing spatial data and related information so that it can be displayed and analyzed.

Decision making mapping
IDW method interpolates the pollutants concentration to a spatial air pollutants mapping. Spatial air pollutants mapping clearly show the dispersion of pollutant in a study area and leave a visual tool to the decision maker or public. Referring to the dispersion of PM 10 concentrations in Kuala Lumpur, it is more easily been visualized the air pollution status in Kuala Lumpur. www.intechopen.com PM 10 concentration at the CA0054 obtained is high and the Cheras is the nearest to the CA0054. Then, areas of Ampang and Sungai Besi have the estimated PM 10 concentrations about 66 -70 µg/m 3 as well as areas of Damansara and Sentul having 62 -66 µg/m 3 of estimation PM 10 concentrations. The areas of Sungai Besi, Ampang, Damansara and Sentul respectively far from the CAQM areas, so that, the interpolation PM 10 concentration decreasing across the areas.
One of the great powers of the GIS is the analysis of the values for every raster. Users can analysis the value of mean, maximum or minimum for a set of spatial air pollutants mapping. Figure

Validation of IDW interpolation
IDW interpolations for the dispersion PM 10 concentration in Kuala Lumpur have to validate with the in-situ monitoring. Due to the differences device are using to detect the PM 10 concentration, one relationship have to make between the data of both devices. The DOE PM 10 concentration data is the main reference in this study. The compatible in-situ PM 10 concentrations, after that, are used to validate the interpolation of the dispersion.
Equation 1, the regression between CAQM data and Casella Microdust Pro (PM 10 detection device) data (Figure 17)   Equation 2, the regression between interpolated and in-situ monitoring PM 10 concentrations, formed ( Figure 19). The relationship between interpolated and in-situ monitoring PM 10 concentrations is double reciprocal with the correlation coefficient 0.289: 1 0.042 0.020 y x  ( 2) where, y is the in-situ monitoring PM 10 concentrations or the actual PM 10 concentration; then, x is the interpolated or Spatio-temporal PM 10 concentrations. Fig. 19. Regression between interpolated and in-situ monitoring PM 10 concentrations.
A model is considered validate if the calculated and measured values do not differ by more than approximately a factor of 2 (Pratt et al., 2004;Weber, 1982). Table 3 show a part of the interpolated (Spatio-temporal) and in-situ monitoring (Microdust) PM 10 concentrations and have not differ by more than a factor of 2, thus, the IDW interpolation method can be used to describe the PM 10 dispersion in Kuala Lumpur. Similar to other ASEAN countries, the air quality in Malaysia is reported as the API. API system of Malaysia closely follows the PSI system of the United States. Four of the index's pollutant components (i.e., CO, O 3 , NO 2 , and SO 2 ) are reported in ppmv on the other hand PM 10 is reported in µg/m 3 . An individual score is assigned to the level of each pollutant and the final API is the highest of those 5 scores. To reflex the status of the air quality and its effects on human health, the ranges of index values is categorized as follows: good ( The different between the interpolated and in-situ monitoring PM 10 concentration at the selected point have not differ by more than a factor of 2, thus, the IDW interpolation method can be used to describe the PM 10 dispersion in Kuala Lumpur. Double reciprocal relationship formed between in-situ monitoring data and estimation IDW data with the correlation coefficient 0.289. Therefore, the IDW interpolation method is suitable for determining the air pollution status in areas which are not covered by the monitoring stations.