Concentration Level Of Ozone Biology Essay

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The level of ozone concentration in Malaysia was explored using spatial analysis. It was used to map the distribution of ozone throughout Malaysia. It provides the visuals to identify the period with high ozone concentration in the study area. The levels of ozone in the selected sites were then investigated using descriptive statistics and time series plot. The descriptive statisticsare used to describe the basic features of the data sets in a study such as min, max, and mean. The time series plot in the other hand isused to display the time variation of ozone concentration and to identify the period with high ozone concentration.

In Figure 4.1, the hourly maximum ozone concentration in Peninsular Malaysia was higher compared to Sabah and Sarawak for all years. According to Malaysian Ambient Air Quality Guidelines (MAAQG), the guideline for 1-hour averaging time of ozone was 100ppb. The maximum ozone concentration in Peninsular Malaysia ranged between 50 and 180 ppb for all years except for 2008 and 2009. In 2008, highest value of hourly maximum ozone concentration was 130 ppb and in 2009 was 120 ppb. However, these values were much higher than the MAAQG's guideline for the hourly ozone concentration.

In Sabah and Sarawak, the range of hourly maximum ozone concentration was between 20 and 90 ppb for 2000, 2001, 2002, and 2003. In 2004 and 2005, Sabah and Sarawak illustrated lesser maximum ozone concentration from the previous four years, ranging between 20 and 80 ppb. However, the maximum ozone concentration in Sabah and Sarawak increased in 2006 and 2007 (20-100ppb). In 2008, Sabah and Sarawak recorded lower maximum ozone concentrations within 20 to 70 ppb of ozone. The highest maximum ozone concentration recorded in Sabah and Sarawak occurred in 2009, ranging between 20 and 110 ppb.

In summary, the maximum ozone concentrations in Peninsular Malaysia were higher for all years compared to Sabah and Sarawak. It was mostly due to land use activities in Peninsular Malaysia and Sabah and Sarawak. In Peninsular Malaysia, the industrial and development activities were higher than Sabah and Sarawak (Habibullah et al., 2012). As industrial and development activities lead to higher pollutants emission, higher concentrations of ozone were expected to form in the atmosphere (Young, 2001).

Despite high ozone concentration in most areas in Peninsular Malaysia, there is one area where the ozone concentration is relatively lower, which is located in Jerantut, Pahang. The range for maximum ozone concentration was between 50 and 70 ppb for all years except for 2004 (90 ppb). However, the maximum ozone concentrations from 2000 to 2009 were not exceeding the MAAQG's guideline (100ppb).

The ozone concentration in Jerantut was not only lower than other areas, but also did not exceed the Malaysian guideline. In Malaysia, the monitoring station in Jerantut is a background station established by the Malaysian Department of Environment. Based on this monitoring station, natural forest, soil dust and a low number of motor vehicles was expected to contribute to the higher air quality status (Azmi et al., 2010).

4.1.2 The Descriptive Statistics

Figure 4.2 illustrates the box plot and descriptive statistics for ozone concentration at Bakar Arang, Ipoh, Kajang, Seberang Jaya, Shah Alam, Nilai, and Jerantut.

4.1.2.1 Box Plots

The box plots for ozone concentration (ppb) at all sites (Figure 4.2) demonstrated that the line connecting the maximum value to the box is longer than the line connecting the minimum value of the box. It indicates that the data are positively skewed for every year at all study areas. Positive skewness exists when the long tails of the data sets are on the positive side of the peak and is commonly known as "skewed to the right".

Bakar Arang Ipoh

Year

Min

Max

Mean

Year

Min

Max

Mean

2004

1

109

22.6

2004

1

114

17.3

2005

1

105

24.2

2005

1

100

17.7

2006

1

91

20.9

2006

1

112

18.7

2007

1

97

20.7

2007

1

101

16.4

2008

1

108

26.3

2008

1

110

16.7

2009

1

90

22.4

2009

1

103

16.0

Kajang Seberang Jaya

Year

Min

Max

Mean

Year

Min

Max

Mean

2004

1

159

20.3

2004

1

85

12.9

2005

1

157

21.1

2005

1

92

16.1

2006

1

161

21.6

2006

1

97

16.7

2007

1

127

18.5

2007

1

98

16.7

2008

1

127

17.5

2008

1

99

16.2

2009

1

143

18.2

2009

1

95

14.3

Shah Alam Nilai

Year

Min

Max

Mean

Year

Min

Max

Mean

2004

1

172

23.3

2004

1

138

18.9

2005

1

171

21.8

2005

1

119

19.0

2006

1

152

22.1

2006

1

113

18.6

2007

1

163

21.0

2007

1

117

16.7

2008

1

149

21.6

2008

1

112

13.7

2009

1

145

22.5

2009

1

101

15.7

Jerantut

Year

Min

Max

Mean

2004

1

90

11.7

2005

1

68

13.8

2006

1

58

12.8

2007

1

71

11.8

2008

1

57

12.0

2009

1

60

11.9

Figure 4.2 Box plot and descriptive statistic of ozone concentration (ppb) for all sites

According to the MAAGQ, the guideline for 1-h averaging time of ozone is 100 ppb, and the annual ozone guideline is 60 ppb for 8-h averaging time. The ozone concentration for Bakar Arang in 2004 was ranging between 1 ppb and 109 ppb, for 2005 between 1 ppb and 105 ppb, 2006 between 1 ppb and 91 ppb, 2007 between 1 ppb and 97 ppb, 2008 between 1 ppb and 108, and 2009 between 1 ppb and 90 ppb. The ozone concentration recorded in Bakar Arang for 2004, 2005, and 2008 showed that the maximum values had exceeded the MAAQG. The highest maximum value of ozone concentration was observed in 2004 with 109 ppb. This was due to the unfavorable weather conditions of hot and dry, which led to formation of ozone (DoE, 2004).

The hourly mean ozone concentrations for Ipoh in 2004 to 2009 were 17ppb, 18ppb, 9ppb, 16ppb, 17ppb, and 16ppb, respectively. These values were lower than the annual ozone guideline value (60ppb). However, the maximum hourly ozone concentration for all years exceeded the guideline for 1-h averaging time (100ppb).

The maximum hourly ozone concentration in Kajang for all years exceeded the MAAQG, which was similar with Ipoh. The values were 159 ppb, 157 ppb, 161 ppb, 127 ppb, 127 ppb, and 143 ppb for 2004 to 2009, respectively. However, the maximum hourly ozone concentrations recorded in Kajang were higher than the ozone monitored in Ipoh. However, the hourly mean ozone concentrations in Kajang in 2004 to 2009 did not exceed the annual ozone guideline.

In Seberang Jaya, ozone concentration from 2004 to 2009 were ranging between 1 ppb and 85 ppb, between 1 ppb and 92 ppb, between 1 ppb and 97 ppb, between 1 ppb and 98 ppb, between 1 ppb and 99, and between 1 ppb and 95 ppb, respectively. The ozone concentration in Seberang Jaya was below the MAAQG for all years.

The maximum hourly of ozone concentration in Shah Alam, unlike in Seberang Jaya, exceeded the MAAQG by 72 ppb, 71 ppb, 52 ppb, 63 ppb, 49 ppb, and 45 ppb from 2004 to 2009, respectively. These values were much higher than the guideline. However, despite the high maximum hourly ozone concentration, the hourly mean ozone concentrations for all years were below the guideline.

In Nilai, the hourly mean ozone concentrations for 2004 to 2009 were 19 ppb, 19 ppb, 19 ppb, 17 ppb, 14 ppb, and 16 ppb, respectively. These values were lower than the annual ozone guideline value (60ppb). However, the maximum hourly ozone concentration for all years exceeded the guideline for 1-h averaging time (100ppb).

Jerantut, which is used as a background station established by the Malaysian Department of Environment recorded the lowest range of ozone concentration compared to all selected sites. The concentrations were ranging between 1 ppb and 90 ppb, 1 ppb to 68 ppb, 1 ppb to 58 ppb, 1 ppb to 71ppb, 1 ppb to 57, and 1 ppb to 60 ppb, for 2004 to 2009 respectively. None of the maximum ozone concentration in all observation years exceeded the guideline.

4.1.3 The Time Series Plot

The time series plots of maximum daily average ozone concentration for six years for every selected site were charted. Figure 4.3 to Figure 4.9 show the six years time series plot for all seven sites. The reference line showing the Malaysian Ambient Air Quality Guideline (MAAQG) for 1 hour averaging time (100 ppb) was drawn to show which year and site exceeded the guideline.

Figure 4.3 illustrates the time series plot for Bakar Arang in 2004, 2005, 2006, 2007, 2008, and 2009. The highest daily average ozone concentration recorded in 2004 and 2005 occurred in June with the concentration value of 109 ppb and 105, respectively. In 2006 and 2007, the highest daily average ozone concentration occurred in May with the value of concentration of 91 ppb and 97 ppb, respectively. The highest daily average concentration of ozone for 2007 was recorded in August with the value of concentration of ppb while April 2009 demonstrated the highest value of the daily average ozone concentration with 90 ppb.

X

X is missing records

Figure 4.3 Time series plot of ozone concentration for BakarArang for 2004 to 2009

In all the years of analysis, 2004 recorded the highest value of daily average ozone concentration with 109 ppb, which occurred in June. Among the twelve months in 2004, 4 readings exceeded the ozone guideline, which occurred in late May (102 ppb and 106 ppb), June (109 ppb), and September (101 ppb). According to the Malaysia Meteorological Services (MMS), the South-west monsoon occurs from the latter half of May or early June until the end of September. During this period, Bakar Arang experienced higher temperature and less rainfall compared to the other months. This phenomenon results in increased ozone formation in the atmosphere. According to Ghazali et al., (2010), the formation of ozone is heavily influenced by sunlight and temperature.

The time series plot for Ipoh is shown in Figure 4.4. Ipoh has 15 readings, which exceeded the MAAQG, where most of the values were recorded in 2006. All the values of the highest daily average ozone concentration in 2006 were monitored during October, in which the highest value among all was 112 ppb. In 2005, there was no value of highest daily average ozone concentration that exceeded the MAAQG.

X

X is missing records

Figure 4.4 Time series plot of ozone concentration for Ipoh for 2004 to 2009

Figure 4.5 illustrates the time series plot for Kajang in 2004, to 2009. In 2004, there were 42 readings exceeded MAAQS with the maximum value of highest daily average ozone concentration of 159 ppb, occurred in May. In 2005, there were 35 readings, which exceeded the guideline. The maximum highest daily average ozone concentration in 2005 was recorded on April (157 ppb). In 2006, the total readings of highest daily average ozone concentration that exceeded the MAAQS decreased to 29 readings. However, the value of daily average ozone concentration was higher than 2005, which was recorded in March (161 ppb). In 2007, 2008, and 2009, the total reading of highest daily average ozone concentration that exceeded the MAAQS significantly decreased to 24 readings, 11 readings, and 14 readings, respectively. The maximum value of highest daily average ozone concentration which occurred in 2007 was on December with 127 ppb, while in 2008 and 2009 were recorded in April with 127 ppb and 143 ppb, respectively.

Figure 4.5 Time series plot of ozone concentration for Kajang for 2004 to 2009

The time series plot for Seberang Jaya is presented in Figure 4.6. In 2004 to 2009, Seberang Jaya recorded none of the highest daily average ozone concentration that exceeded the MAAQS. The maximum value of highest daily average ozone concentration in Seberang Jaya occurred in 2008 with 99 ppb on February followed by 2007 with 98 ppb on July.

Figure 4.7 illustrated the time series plot for Shah Alam. In 2004, the highest ozone concentration was 172 ppb recorded in May. However, most of the highest value of ozone concentration, which exceeded the MAAQS, was found in April with 41 readings. In 2005, the highest ozone concentration was recorded in February (171 ppb), and March for both 2006 (152 ppb) and 2007 (163 ppb). In 2008 and 2009, the highest record of daily average ozone concentration was in August (149 ppb) and June (145 ppb), respectively. High level of ozone concentration in Shah Alam was due to the intensive industrial areas there (DoE, 2005-2010). Additionally, there is one major highway, which is the KESAS highway that is located near the monitoring station in Shah Alam. Thus, emissions from vehicles are expected to be the main contributor of ozone formation and lead to the high concentration of ozone in this study area.

X

X

X is missing records

Figure 4.6 Time series plot of ozone concentration Seberang Jaya 2004 to 2009

Figure 4.7 Time series plot of ozone concentration for Shah Alam2004 to 2009

The time series plot for Nilai is illustrated in Figure 4.8. Nilai have 23readings, which exceeds the MAAQG from 2004 to 2009. The highest ozone concentration in Nilai was found in July 2004 at 142 ppb, followed by March 2005 with ozone concentration value was 119 ppb. The major source of ozone in Nilai was from vehicles emission. Additionally, industrial activities and open burning contributed to the high concentration of ozone in Nilai. Nilai is located on a hilly terrain on the eastern side, which can reduce the dispersion of emission and lead to the accumulation of pollutants (DoE, 2004).

Figure 4.9 illustrates the time series plot for Jerantut in 2004, 2005, 2006, 2007, 2008, and 2009. The highest daily average ozone concentration recorded in 2004 occurred during August with the value of ozone concentration of 90 ppb. Overall, there were no readings of ozone concentration in Jerantut that exceeded the MAAQG. In Malaysia, the monitoring station in Jerantut is a background station established by the Malaysian Department of Environment. Here, the natural forest, soil dust and a low number of motor vehicles are expected to contribute to air quality status (Azmi et al., 2010).

X

X is missing records

Figure 4.8 Time series plot of ozone concentration for Nilai 2004 to 2009

X

X is missing records

Figure 4.9 Time series plot of ozone concentration for Jerantut 2004 to 2009

The time series plot for total daylight hours for ozone concentration for six years in all sites was illustrated in Figure 4.10.According to the DoA, Malaysia, there are two seasons for paddy planting in Malaysia; the main season, which usually occurs on July to September, and the off-season which occurs between March and May (DoA, 2010). Therefore, these two paddy-planting seasons were highlighted in Figure 4.10. It seems that total ozone concentration during the main season is lower than the off-season.

Main season

Off-season

Figure 4.10 Time series plot of total daylight hours for ozone concentration from 2004 to 2009

Figure 4.10 demonstrated the time series plot for total daylight hours for ozone concentration for all study areas. In the analysis of AOTX indexes, the total daylight hours of ozone concentration was used in determining the effects of ozone on crops. Therefore, the time series plot (Figure 4.10) was plotted to identify the series of ozone fluctuation for Bakar Arang, Ipoh, Kajang, Nilai, Seberang Jaya, Shah Alam, and Jerantut in all months for six years.

Referring to Figure 4.10, Shah Alam recorded the highest total ozone concentration during daylight hours in all six years, followed by Kajang, while Jerantut recorded the least of total ozone concentration from 2004 to 2009.

The fluctuation of total daylight hours of ozone concentration shows almost similar patterns at all seven sites indicating a typical pattern for high ozone concentration during off-seasons and low concentration during main seasons (Figure 4.10).The off-season of paddy yield is usually during the South-west monsoon. During this season, the area is drier with higher temperature and experiences less rainfall, as compared to other months. This phenomenon results in increased ozone formation in the atmosphere.

According to Ghazali et al., (2010), the formation of ozone is heavily influenced by sunlight and temperature. The main season of paddy plantation is during the northeast monsoon. This season provides heavy rainfall with high humidity. Rainfall cleans the atmosphere, thus, removes pollutants such as nitrogen dioxide, the main precursor of ozone. During the rainy season, the presence of UVB will lessen, the temperature will fall, and humidity will increase (Lal et al., 2000).

4.2 THRESHOLD VALUE OF AOTX INDEXES

The accumulated ozone concentration over a threshold of 0 ppb, 5 ppb, 10 ppb, 15 ppb, 20 ppb, 25 ppb, 30 ppb, 40 ppb, and 50 ppb (AOT0, AOT5, AOT10, AOT15, AOT20, AOT25, AOT30, AOT40, and AOT50) from 2004 to 2009 for Bakar Arang, Ipoh, Kajang, Nilai, Seberang Jaya, Shah Alam, and Jerantut was calculated. In Figure 4.10, the total daylight hours of ozone concentration in Shah Alam demonstrated the higher ozone concentration compared to other sites. Therefore, the threshold value for Shah Alam was displayed. For other sites, the threshold values are attached in the Appendix A.

The AOTX indexes' thresholds are based on cumulative value of ozone concentration during daylight hours over three- month's period. Therefore, Figure 4.11 presented the threshold value of ozone for nine different AOT indexes, which are AOT0, AOT5, AOT10, AOT15, AOT20, AOT25, AOT30, AOT40, and AOT50 in Shah Alam for 2004 to 2009.

Figure 4.11 demonstrated that AOT50 gave the lowest value of the accumulated exposure of the ozone compared to the others. The value of AOT50 from January to May for 2004 to 2009, recorded as the highest accumulated exposure of ozone in each particular year while July-November period for 2004, 2005, 2006, 2007, 2008, and 2009 showed lower value of AOT. This was due to the lower ozone concentration value in those particular months.

According to the meteorological condition, the rainfall pattern in the study area was much affected by early morning "Sumatras" from May to August, which provides heavy rainfall (Malaysian Meteorological Department, 2012). Besides, the maximum rainfall in all study areas starts from October to November every year. During wet season, colder temperatures results in more polar stratospheric cloud and lead to lower ozone level in the atmosphere (Abdul-Wahab et al., 2005). This explains the lower AOTX value on most of the July - November period for 2004, 2005, 2006, 2007, 2008, and 2009.The values were 2377 ppb, 2001 ppb, 3614 ppb, 2471 ppb, 2892 ppb, and 2033 ppb, respectively.

During the rainy season, temperature will decrease and some of pollutants will be removed from atmosphere. Besides, ozone formation is strongly correlated with temperature. Temperature affects ozone in a complex way, including the rates of chemical and photochemical reaction. Thus, the temperature dependence of ozone was considered seasonally variable (Sillman and Samson, 1995).

During periods where the area has less rainfall, humidity will decreased and temperature will increased. According to the Malaysian Meteorological Department (2012), minimum rainfall occurs in the study area is in January - February every year. This phenomenon results in the augmentation of ozone formation in the atmosphere. Hence, the AOTX value in the study area will increase. This explained the higher AOTX value, especially AOT50 in the study area on January to May (Figure 4.11).

According to Ghazali et al., (2010), the formation of ozone is heavily influenced by sunlight and temperature. Additionally, 'high-ozone episode' can be generally attributed to a buildup of air pollutants associated with stagnating meteorological conditions brought on by slow-moving high pressure systems (Luo et al., 2000). The threshold values presented in Figure 4.11 were important to understand the range of threshold values in Malaysia, which will then be used to determine the critical level of the AOTX index. As each AOTX index has its own range of threshold values and the critical limit, they have to be determined separately.

Figure 4.11: Ozone concentration's threshold for AOTX indexes in Shah Alam from 2004 to 2009.

From Figure 4.11, the AOT40 index threshold value range varied between 2000ppb and 9000ppb. According to the European benchmark, a threshold value which is above 3000ppb within a 3 months period is highly probable to result in a minimum of 5% crop reduction. On the other hand, the range of ozone threshold of is 40 ppb (AOT40) in Malaysia, hence suggesting that crop reduction in Malaysia is immensely affected by the ozone concentration of approximately 5 to 15% due to the high concentration of accumulated ozone exposure (up to 9000ppb).

4.3 ESTIMATION OF PADDY REDUCTION USING AOTX INDEXES

The analysis on the effect of AOTX indexes and paddy reduction was carried out for both, main and off-seasons from 2004 to 2009 for Bakar Arang, Ipoh, Kajang, Nilai, Seberang Jaya, Shah Alam, and Jerantut. As Bakar Arang and Seberang Jaya represents a sub urban area, Shah Alam and Kajang represents an urban area, and Ipoh and Nilai represents an industrial area the illustration of relationship between AOTX indexes and paddy reduction in Bakar Arang, Shah Alam, and Ipoh for two seasons (main and off-seasons), from 2004 to 2009was displayed. For other sites, the relationship between AOTX indexes and paddy reduction are attached in Appendix B.

Figure 4.12 to Figure 4.14 illustrates the relationship between AOTX indexes and paddy reduction in Bakar Arang, Shah Alam, and Ipoh for main and off-season, from 2004 to 2009, respectively.

2009

2008

2007

2006

2005

2004

Figure 4.12: (a): Main Season, (b): Off-season: Relationship between AOTX and paddy reduction in Bakar Arang for 2004 to 2009

2009

2008

2007

2006

2005

2004

Figure 4.13: (a): Main Season, (b): Off-season: Relationship between AOTX and paddy reduction in Shah Alam for 2004 to 2009

2009

2008

2007

2006

2005

2004

Figure 4.14: (a): Main Season, (b): Off-season: Relationship between AOTX and paddy reduction in Ipoh for 2004 to 2009

From Figure 4.12 to Figure 4.14, the relationship between AOTX indexes and paddy reduction showed similar pattern from 2004 to 2009, in Bakar Arang, Shah Alam and Ipoh, where the AOT50 indexes estimated the highest reduction of paddy due to ozone concentration. Even though the AOT40 index was recommended as the index to estimate the reduction of crops in Europe, the results (Figure 4.12 to Figure 4.14) shows that none of the AOT40 index estimated the highest paddy reduction in Malaysia.

Apart from AOT50demonstrating the highest paddy reduction as compared to others, the trend of paddy reduction in all sites (Figure 4.12 to Figure 4.14) illustrates the same trend, where the estimated paddy reductions during the off-season were higher compared to the main season. Moreover, AOT0 gave the lowest paddy reduction while AOT50 showed the highest reduction in paddy compared to the other indexes in both seasons.

From Figure 4.12, the highest percentage of estimated paddy reduction during main season in Bakar Arang was 18%, which was recorded in 2008. In the same year, highest percentage of paddy reduction was estimated during the off-season for paddy plantation with 21%. In Shah Alam, the highest percentage of estimated paddy reduction for both seasons was 17.5%, depicted by the AOT50 during the off-season in 2008. However, the highest percentage of estimated paddy reduction for main season was illustrated in 2006 with 13.8% (Figure 4.13). Figure 4.14 illustrates that the highest percentage of estimated paddy reduction in Ipoh during main season was in 2005 with 18.4%. However, the highest percentage of paddy reduction during off-season was recorded in 2008 with 21.5%.

From Figure 4.12 to Figure 4.14, the highest percentage of estimated paddy reduction was found in Ipoh, ranging between 10 to 22%, followed by Bakar Arang (8-21%), and Shah Alam (8-18%). This result demonstrates that high ozone concentration in an industrial are can cause detrimental effect on paddy by reducing the production. Besides, even in urban and suburban areas can also experienced high ozone concentration level and caused paddy reduction.

The dissimilarity of the percentage of paddy reduction among those years between the seasons was due to the differences in the threshold value of ozone for the indexes. Each indexes for all years for both season recorded the different value of the threshold, which was used to estimate the reduction. Therefore, the threshold value is so important in estimating the reduction of paddy in Malaysia. Overall, the estimated percentage of paddy reduction in Malaysia was proportionate with the threshold value of ozone.

All the monitoring record analysis apparently indicates that the highest reduction of paddy occurred during the off-season. During this season, the area is drier with higher temperature and less rainfall, as compared to other months. This phenomenon results in increased ozone formation in the atmosphere. Besides, ambient air becomes unstable and rises due to the heating of the earth surface by solar radiation. According to Naja and Lal (2002) and Ghazali et al, (2010), favorable conditions for photochemical ozone production are high temperature, high intensity of solar radiation, and sufficiently high concentrations of nitrogen oxides.

On the other hands, the main season of paddy plantation is during the northeast monsoon. Throughout the monsoon period, the sky is generally cloudy and provides heavy rainfall with high humidity. This condition leads to decreasing of the solar insulation thus reducing the photochemical processes (Reddy et al., 2010).

Although this phenomenon will reduce the rate of ozone transformation, higher humidity and mild temperatures can increase stomatal conductance and thus the ozone uptake by the leaves of plants, which results in yield loss (Wang et al., 2005). This scenario can clearly be observed from the results illustrated in Figure 4.12 to Figure 4.14 (a), where paddy reduction had occurred during the main season although the percentage reduction was less than in the off-season [Figure 4.12 to Figure 4.14 (b)].

4.3.1 Estimation of Paddy Reduction in the Malaysia using AOTX Indexes

The analysis of the effect of AOTX indexes and paddy reduction was then repeated using ozone concentration in all six sites except Jerantut for both, main and off-seasons from 2004 to 2009. The analysis on Jerantut sites was analyzed later to compare the effect of AOTX indexes and paddy reduction in all sites and the background sites. This analysis was conducted to determine the effects of AOTX indexes and paddy reduction in Malaysia by averaging the ozone concentration in all six sites. This analysis was carried out to identify the national effect of AOTX indexes and paddy reduction in Malaysia. Figure 4.15 demonstrates the relationship between AOTX indexes and paddy reduction in Malaysia from 2004 to 2009.

C:\PERSONAL\PhD. USM\MY PAPER\atmospheric environment\figure paper edited\paper 4.1.png

Figure 4.15(a): Main Season, (b): Off-season: Relationship between AOTX and paddy reduction in Malaysia

In Figure 4.15, there are two synchronized patterns between the AOTX indexes and paddy reduction in the Malaysian climate. During both seasons, AOT0 gave the lowest paddy reduction while AOT50 showed the highest reduction in paddy compared to the other indexes. The highest percentage for paddy reduction for both seasons was 12.11%, depicted by the AOT50 during the off-season while the highest percentage of paddy reduction in main season was 9.10%.

4.3.2 Estimation of Paddy Reduction in the Background Station using AOTX Indexes

The analysis of the effect of AOTX indexes and paddy reduction was then repeated using the 'control' ozone concentration where the value was obtained from a very low ozone site. In Malaysia, the monitoring station in Jerantut, Pahang is a background station established by the Malaysian Department of Environment. In this monitoring station, natural forest, soil dust and a low number of motor vehicles are expected to contribute to air quality status (Azmi et al., 2010). Therefore, this station was selected as the 'control' station in order to identify the possible paddy reduction in a very low ozone site. Figure 4.15 demonstrates the relationship between AOTX indexes and paddy reduction in background station, Jerantut.

F:\BACKUP\PERSONAL(13122011)\PhD. USM\MY PAPER\atmospheric environment\jerantut new reduction.png

Figure 4.16(a): Main Season, (b): Off-season: Relationship between AOTX and paddy reduction in Jerantut, Malaysia

Figure 4.16 illustrates paddy reduction at the control station against AOTX indexes. This analysis was carried out to identify possible paddy reduction under a low ozone concentration. The result showed that reductions in Jerantut were much lower than other sites in Malaysia although the paddy reduction pattern was analogous with Figure 4.12, Figure 4.13, Figure 4.14, and Figure 4.15. The highest paddy reduction in Jerantut recorded by AOT50 during the off -season was 3.5% (Figure 4.16b). This percentage was smaller than the reduction of paddy during the off -season in the study areas (12.11%).

The result demonstrates that paddy reduction is highly related to ozone concentration in Malaysia. According to Musselman et al., (1994), plant response is closely related to ozone exposure, where higher concentrations of ozone cause more injury and loss of productivity in vegetation. In a low ozone site (Figure 4.16), the percentage of paddy reduction was lower compared to the area with higher ozone concentration (Figure 4.12 to Figure 4.15). Therefore, both of these figures verified that the paddy reduction was likely to occur in the area with higher ozone exposure.

4.4LINEAR REGRESSION MODELS FOR AOTX INDEXES AND PADDY REDUCTION

The relationship between the AOTX indexes and the paddy reduction was analyzed using the linear regression analysis. Table 4.1 illustrates the R2 value for the relationship between these two parameters in all study areas.

Table 4.1 (a): Bakar Arang, Kedah, (b): Ipoh, Perak, (c): Kajang, Selangor (d): Nilai, Negeri Sembilan, (e): Seberang Jaya, Pulau Pinang, (f): Shah Alam, Selangor: Relationship between paddy reduction and AOTX indexes

Sites

AOTX Indexes

(x)

Coefficient of determination (R2)

Equation

(y = paddy reduction )

a) BakarArang,

AOT0

0.7695

y = 13080+ 0.7866x

Kedah

AOT5

0.7302

y = 11816+1.0313x

AOT10

0.8596

y = 11148+1.1993x

AOT15

0.8933

y = 10049+1.5258x

AOT20

0.9200

y = 8816.7+2.0056x

AOT25

0.9313

y = 8302.8+2.6285x

AOT30

0.9284

y = 8349.5+3.4511x

AOT40

0.9062

y = 10131+5.9638x

AOT50

* 0.9426

y = 11827+11.561x

b) Ipoh,

AOT0

0.6905

y = - 42.962+0.4907x

Perak

AOT5

0.6948

y = 230.44+0.5855x

AOT10

0.7012

y = 371.9+0.7097x

AOT15

0.7078

y = 544.64+0.8712x

AOT20

0.7126

y = 712.88+1.0905x

AOT25

0.7147

y = 869.34+1.3992x

AOT30

*0.7148

y = 988+1.8477x

AOT40

0.7109

y = 1133.5+3.5694x

AOT50

0.7036

y = 1270.5+7.9314x

c) Kajang,

AOT0

0.2963

y = 5657.1 +0.104x

Selangor

AOT5

0.3344

y = 5808.1 +0.1138x

AOT10

0.3721

y = 5943.5 +0.1246x

AOT15

0.415

y = 6015.1 +0.1398x

AOT20

0.4729

y = 6014.7 +0.1617x

AOT25

0.5453

y = 5920.4 +0.1942x

AOT30

0.6261

y = 5687.3 +0.2446x

AOT40

0.7761

y = 4782.5 +0.4497x

AOT50

*0.8494

y = 3465.4 +0.9551x

d) Nilai,

AOT0

0.4434

y = 25.508 +0.012x

Negeri Sembilan

AOT5

0.454

y = 44.711 +0.0137x

AOT10

0.4532

y = 72.479+0.0152x

AOT15

0.4462

y = 103.52+0.0168x

AOT20

0.4443

y = 130.61 +0.0189x

AOT25

0.4514

y = 150.57 +0.0219x

AOT30

0.4822

y = 156.69 +0.0271x

AOT40

*0.5333

y = 145.06 +0.0469x

AOT50

0.5228

y = 109.98 +0.1051x

e) Seberang Jaya,

AOT0

0.0139

y = 893.92 +0.0079x

Pulau Pinang

AOT5

0.0497

y = 779.93 +0.0159x

AOT10

0.0917

y = 732.4 +0.0232x

AOT15

0.1418

y = 707.28 +0.0319x

AOT20

0.1895

y = 695.13 +0.0432x

AOT25

0.2316

y = 685.94 +0.0594x

AOT30

0.2869

y = 657.94 +0.0871x

AOT40

0.3996

y = 564.33 +0.221x

AOT50

*0.4555

y = 429.5 +0.6713x

f) Shah Alam,

AOT0

0.1349

y = 5591.6 +0.1009x

Selangor

AOT5

0.1317

y = 6014.7 +0.1057x

AOT10

0.128

y = 6409+0.1107x

AOT15

0.1308

y = 6728.3 +0.1192x

AOT20

0.1383

y = 6994.3 +0.132x

AOT25

0.1493

y = 7233+0.1503x

AOT30

0.1574

y = 7521.1 +0.1715x

AOT40

0.1882

y = 7955.3 +0.2464x

AOT50

*0.2295

y = 8211.4+0.4043x

*The highest R2 value for each site

Table 4.1 depicts the R2 values for the best correlation for the study areas between paddy reduction and the different AOTX indexes. Almost all areas illustrated that the AOT50 index gave the highest R2 values compared to other indexes, except for Perak (AOT30) and Negeri Sembilan (AOT40). The R2value for Bakar Arang was 0.9426; Kajang 0.8494; Seberang Jaya 0.4555, and Shah Alam 0.2295.

Fuhrer et al., (1997) had compared several AOTX indexes such as AOT30, AOT40, and AOT60 in assessing the effect of different AOTX indexes on crops in European countries. He found that the AOT40 index provided a good fit relationship between the index and the crop response. However, as responses of crops to ozone are highly related to climate, the different indexes make an important contribution to the observed change in yield and should not be ignored (Fuhrer et al., 2007; Legge et al., 1996).

The AOTX index was based on the exposure of ozone concentration on crops. Therefore, different ozone exposures between regions of the world can lead to different crop responses. As AOT40 was developed according to European data and scenarios, a similar response between crops and the AOT40 in Europe can be robustly assumed to be different in other regions if different variables are presented; it might be a higher or lower AOTX index. Thus, the result in the Table 4.1 clearly proves that the response of crops to the AOTX index in tropical regions was different from the European regions.

4.5 PERFORMANCE INDICATOR ANALYSIS

Performance indicator is used to evaluate between the observed and predicted data that represents the global fit arrangement. In this study, the performance indicator analysis was carried out to identify the best model of AOTX indexes in predicting the paddy reduction in Malaysia. In order to indicate the performance of the models and to test the best model, performance indicator analysis was carried out. Five performance indicators were used in this study; normalized absolute error (NAE), prediction accuracy (PA), coefficient of determination (R2), root mean square error (RMSE), and index of agreement (IA).

Additionally, in order to select the best AOTX model in estimating the crops reduction in Malaysia, performance indicator was used to test the relationship between the AOTX index and the percentage of crop reduction. Table 4.2 to Table 4.7 shows the performance indicator for all AOTX indexes in Bakar Arang, Ipoh, Kajang, Nilai, Seberang Jaya, and Shah Alam, respectively. Bold fonts indicate the best prediction for the AOTX model. For a good model, NAE and RMSE should approach zero while PA, R2, and IA should closer to 1. The best AOTX model will be used as the index for estimating the crops reduction in Malaysia.

The best prediction model for Bakar Arang was AOT0 with IA 0.709, NAE 0.280, and RMSE 8867.1 and AOT50 with R2 0.655 and PA 0.971; Ipoh was AOT15 with IA 0.886, NAE 0.150, and RMSE 3305.154 and AOT30 with R2 0.496 and PA 0.846; Kajang was AOT40 with IA 0.716 and RMSE 224.7624, AOT30 NAE 0.237, and AOT50 with R2 0.5899 and PA 0.922; Nilai was AOT50 with IA 0.508, NAE 0.771, and RMSE 904.58 and AOT40 with R2 0.3703 and PA 0.730; Seberang Jaya was AOT50 with IA 0.751, NAE 0.227, RMSE 296.346, R2 0.316 and PA 0.675; Shah Alam was AOT40 with IA 0.564, NAE 0.189, and RMSE 2611.163 and AOT50 with R2 0.159 and PA 0.479.

Table 4.2 Performance Indicators of AOTX model for Bakar Arang

Site

AOTX

R2

PA

IA

NAE

RMSE

Bakar

AOT0

0.5344

0.8772

0.709384

0.2805

8867.0989

Arang

AOT5

0.5071

0.8545

0.527940

0.5789

14313.8628

AOT10

0.5969

0.9271

0.474045

0.8423

16287.0185

AOT15

0.6204

0.9452

0.397366

1.2703

19162.3041

AOT20

0.6389

0.9592

0.331412

1.867

21544.4428

AOT25

0.6467

0.9650

0.266431

2.707

23683.5593

AOT30

0.6447

0.9636

0.207747

3.9170

25545.2367

AOT40

0.6293

0.9519

0.117081

8.4554

28308.2968

AOT50

0.6546

0.9709

0.068150

19.7588

29510.6882

- Bold font indicates the best model

- Best performance indicator for values closest to 0 for NAE and RMSE and closest to 1 for PA, R2, and IA.

Table 4.3 Performance Indicators of AOTX model for Ipoh

Site

AOTX

R2

PA

IA

NAE

RMSE

Ipoh

AOT0

0.4795

0.8309

0.472

0.5108

16572.9252

AOT5

0.4825

0.8335

0.5759

0.4048

11180.3086

AOT10

0.4870

0.8374

0.7203

0.2715

6635.4487

AOT15

0.4915

0.8413

0.8864

0.1495

3305.1542

AOT20

0.4949

0.8442

0.8596

0.1825

3656.4821

AOT25

0.4963

0.8454

0.7076

0.3258

6537.6590

AOT30

0.4964

0.8455

0.4693

0.9707

9608.8529

AOT40

0.4937

0.8432

0.4998

0.7384

15408.5738

AOT50

0.4886

0.8388

0.4640

0.8823

20860.1150

- Bold font indicates the best model

- Best performance indicator for values closest to 0 for NAE and RMSE and closest to 1 for PA, R2, and IA.

Table 4.4 Performance Indicators of AOTX model for Kajang

Site

AOTX

R2

PA

IA

NAE

RMSE

Kajang

AOT0

0.2057

0.5443

0.2171

0.7268

26836.8409

AOT5

0.2322

0.5783

0.2553

0.6821

21545.9346

AOT10

0.2584

0.6100

0.2983

0.6295

16982.3877

AOT15

0.2882

0.6442

0.3512

0.5628

12854.3585

AOT20

0.3284

0.6877

0.4191

0.4762

9140.8992

AOT25

0.3787

0.7384

0.5088

0.3617

5901.6700

AOT30

0.4348

0.7913

0.6143

0.2367

3291.8079

AOT40

0.5390

0.8810

0.7156

0.2769

2224.7624

AOT50

0.5899

0.9216

0.5246

1.1401

3707.3886

- Bold font indicates the best model

- Best performance indicator for values closest to 0 for NAE and RMSE and closest to 1 for PA, R2, and IA.

Table 4.5 Performance Indicators of AOTX model for Nilai

Site

AOTX

R2

PA

IA

NAE

RMSE

Nilai

AOT0

0.3080

0.6659

0.2414

0.9870

28132.8829

AOT5

0.3153

0.6738

0.2570

0.9842

23063.3615

AOT10

0.3147

0.6732

0.2746

0.9805

18747.7740

AOT15

0.3098

0.6679

0.2944

0.9755

14877.3111

AOT20

0.3086

0.6666

0.3167

0.9684

11444.3367

AOT25

0.3135

0.6719

0.3429

0.9584

8450.5598

AOT30

0.3349

0.6944

0.3741

0.9437

5945.5670

AOT40

0.3703

0.7303

0.4343

0.8909

2555.9885

AOT50

0.3631

0.7231

0.5082

0.7705

904.5800

- Bold font indicates the best model

- Best performance indicator for values closest to 0 for NAE and RMSE and closest to 1 for PA, R2, and IA.

Table 4.6 Performance Indicators of AOTX model for Seberang Jaya

Sites

AOTX

R2

PA

IA

NAE

RMSE

Seberang

AOT0

0.009662

0.117957

0.158243

0.956465

26421.974716

Jaya

AOT5

0.034498

0.222885

0.178295

0.945825

21264.636519

AOT10

0.063715

0.302903

0.200575

0.932278

16968.989463

AOT15

0.098433

0.376488

0.225156

0.913569

13160.226802

AOT20

0.131591

0.435305

0.247790

0.886905

9839.474768

AOT25

0.160847

0.481269

0.271243

0.847249

6988.533321

AOT30

0.199232

0.535625

0.300491

0.786547

4635.453499

AOT40

0.277513

0.632153

0.419245

0.530897

1426.864444

AOT50

0.316309

0.674896

0.751122

0.226546

296.345824

- Bold font indicates the best model

- Best performance indicator for values closest to 0 for NAE and RMSE and closest to 1 for PA, R2, and IA.

Table 4.7 Performance Indicators of AOTX model for Shah Alam

Sites

AOTX

R2

PA

IA

NAE

RMSE

Shah

AOT0

0.0937

0.3672

0.1673

0.7572

32862.1559

Alam

AOT5

0.0914

0.3628

0.1912

0.7197

27369.5265

AOT10

0.0889

0.3578

0.2161

0.6775

22695.7821

AOT15

0.09085

0.3617

0.2487

0.6255

18332.3776

AOT20

0.09606

0.3719

0.2919

0.5595

14244.3726

AOT25

0.1037

0.3864

0.3512

0.4742

10446.0982

AOT30

0.1093

0.3968

0.4336

0.3621

7015.5166

AOT40

0.1307

0.4338

0.5640

0.1886

2611.1629

AOT50

0.1594

0.4791

0.4692

0.5816

5041.5689

- Bold font indicates the best model

- Best performance indicator for values closest to 0 for NAE and RMSE and closest to 1 for PA, R2, and IA.

As compared to the R2 from the regression analysis, R2 value in the performance indicator is lower for all sites. The difference is due to the difference in number of analysis data. In the performance indicator analysis, the missing values for both predictor and predicted variables will be omitted. In linear regression, however, only missing values in predictor variables will be omitted. This led to the differences in number of analysis data and different R2 value.

Although there were indicators that demonstrated that other AOTX indexes are better fit compared to AOT50, previous research had determined the best AOTX model by using coefficient of determination value. Thus, this study followed the literature review and determined the best AOTX model in Malaysia the using coefficient of determination value. Therefore, this study strongly suggests that AOT50 index demonstrated a better correlation between reductions of crops due to ozone exposure, as compared to other AOTX indexes in Malaysia.

4.6AOTX MODEL FOR MALAYSIAN CLIMATE

Results from all seven sites were used in analyzing the Malaysian scenario for crops reduction due to ozone concentration using AOTX indexes. The AOTX models were built up to compare the percentage of crops reduction and validate that the AOTX indexes can respond differently in Europe and in Malaysia, with significant findings if the accumulated ozone concentration was above 3000ppb.h in a 3-month period. As European guidelines state that 5% reduction in yield for agricultural crops will be expected to occur if the accumulation of ozone concentration within that duration is above 3000ppb.h (Ishii et al., 2007; LRTAP Convention,2004), we used the same critical limit as the European benchmark in order to differentiate the dissimilarity of responses between the European and Malaysian regions.

Figure 4.17 illustrates the estimated percentage of crop (paddy) reduction for each AOTX index in the Malaysian climate.

Figure 4.17: Estimated Crop (Paddy) Reduction for each AOTX Index under Malaysian Climatic conditions for 3000ppb.h ozone exposure over three months

Figure 4.15 showed that the AOT40 index gave the minimum percentage in crops reduction of 6%, when accumulated ozone concentration was above 3000ppb.h in three months of the growing season. These results however were different from the European benchmark, which predicts a 5% crop reduction if the accumulated ozone concentration over three months is above 3000ppb.h (Ishii et al., 2007; LRTAP Convention, 2004).

Based on the regression analysis and the performance indicator, AOT50 index fits best with the Malaysian climate compared to the other indexes (based on R2 value). However, the use of 3000 ppb.h for the critical level for AOT50in three months of the growing season seems to be high. Besides, the value of 3000 ppb.h as the critical limit is used for AOT40 index. Thus, a new critical level for the AOT50 index for Malaysian climate needs to be identified.

Therefore, an analysis was carried out to find the best critical level for AOT50 index for Malaysia by estimating the possible crop reduction for the 5% index. Figure 4.16 presents the critical level for the AOT50 index if the estimated crop reduction is 5%.

Figure 4.16: Estimated Critical Level of Ozone Exposure over three months for 5% of Crop (Paddy) Reduction for AOT50 in Malaysia

Figure 4.16 demonstrates that the critical level of ozone exposure for AOT50 was approximately 1400ppb.h over a three-month period, if the estimated crop reduction is 5%. The critical level based on the AOTX indexes play an important role in evaluating the ozone impact on crops (Fuhrer et al., 1997). Thus, the new critical level for AOT50 in Malaysia (±1400ppb.h) was more reasonable to use in estimating reduction of crops compared to the European critical level (3000ppb.h).

The dissimilarity in percentage of crop reduction between the tropical and the European region was due to the different climatic conditions, which can influence the response of crops to ozone concentration (Racherla and Adams, 2007). In Malaysia, the daylight hours are on average 12 hours (7 am to 7 pm) throughout the year, which increases crops' exposure to ozone. UVB from sunlight are always having positive correlation with ozone concentration. Thus, the longer daylight hours in any region will lead to higher ozone concentration if the sources of ozone are available. Higher ozone concentration can encourage the crop's stoma to close and cause a lower rate of photosynthesis (Fagnano, 2009). This scenario can lead to increased crop damage and reduced yields (Pleijel, 2000).Thus, crop reduction in tropical regions will be much greater than in Europe due to the longer ozone exposure.

Besides, Malaysia experiences uniform temperature, high humidity and copious rainfall throughout the year. Most of the studies reported that temperature was found to be one of the strongest meteorological predictor of ozone. This is because high air temperatures are indicators of anti cyclonic conditions with associated clear skies and light winds, which can foster ozone production and accumulation (Sohn et al., 2012). According to Sillman and Samson (1995), temperature effects ozone in a complex way, including the rates of chemical and photochemical reactions. Thus, the temperature dependence of ozone was considered regionally and seasonally variable.

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