Determining Temperature trends in the Granary Areas of Peninsular Malaysia using Mann-Kendall and Sen’s Slope Estimator

The spatiotemporal dynamics of temperature as well as rainfall have received greater attention from the scientific communities This study analysed temperature variability in the three granary areas of Peninsular Malaysia using descriptive statistics parametric least square regression and nonparametric Mann- Kendall and Sen s slope estimator The study identified significant warming trend in the annual mean maximum temperature in two of the study areas i e Subang Jaya and Kota Bharu Also significant warming trend was detected in the annual minimum temperature and significant increasing trend in some of the monthly maximum and minimum temperatures for all the three stations Also the result reveals spatial and temporal variation in both the maximum and minimum temperature at annual monthly and seasonal scales For the annual scale maximum temperature this study identified a warming trend for the two stations with about 0 014oC per year 1 4oC per 100 year

A status quo review of approach, method, and empirical studies on assessing the impacts of climate change variability on agriculture

It is no longer contentious that climate change has a serious impact on agriculture for the past decades with varying consequences across the globe. These consequences are beneficial to some areas while others the story is disastrous. Therefore, estimating the impact of climate change variability has been the onus of many academic and professional researchers using various methods and approaches such as the partial equilibrium models or the economy-wide models. This review paper therefore, highlights on the various methods and approaches use to estimate the impact of climate change variability on crop production and further reviewed past empirical studies with a view to, (a) understanding the merits and demerits of each method and approach; (b) understanding the regional spread of the past research directions and current knowledge across the globe including Malaysia. The paper concluded by drawing attention to the need in paradigm shift. Refocusing future research efforts toward integrated vulnerability assessment as it offers fuller appreciation of the roles of adaptation in facilitation, supporting, and invariably sustaining the communities affected by the climate change vulnerability as suggested by many scientific bodies including the Intergovernmental Panel on Climate Change (IPCC)

Identification of heavy metals in particulate matter PM10 of traffic area, Cheras, Malaysia

This study was initiated to determine the heavy metals in particulate matter (PM10) in three sampling sites with different traffic density: high (CH), medium (CM) and low density (CL) density traffic, PM10 Samples were collected from June to July 2014 during the southwest monsoon using a high volume sampler. The results showed that the PM10 mean levels were significantly higher in CH and CM (207.63 and 164.92 μg/m3) than the CL (90.09 μg/m3). The mean quantity of heavy metals in PM10 was in the order Ba>Zn>Pb>Mn > Cr > As. The highest level of these heavy metals was recorded at CH followed by CM and CL except the mean level of As, which was recorded as being higher compared to CH. Based on the correlation and enrichment analyses, the heavy metals could be divided into two source emissions in the study area – Crustal and non-crustal. Among the heavy metals, As, Pb, Zn and Ba were found to be highly enriched in the atmospheric particulate matter, Cr showed a high EF in low density. Therefore, they were visibly affected by non-crustal sources (vehicular emission sources). In contrast, the EF calculated for Cr was lower in high and medium density suggesting chiefly crustal origin sources. This clearly indicates that traffic density is the main source in the study areas

A GIS-based emission inventory at 1 KM -1KM spatial resolution for particular matter (PM10) in Klang Valley, Malaysia

Traffic has greatly contributed to the socio-economic development as well as its inherent environmental impacts. This study estimated the emission of PM10 from the exhaust and nonexhaust, particularly from the use different type of vehicles in Klang valley region. The total PM10 emission from the region was calculated based on US-EPA and the EEA methodologies. Arc GIS is one of the most suitable methods to estimate the total PM10 emission and split between different vehicle types as it is determined by the kilometer covered for each vehicle category. The inventory is further used for traffic account, activity data and a domain size of 50 km×50 km, with cell resolution of 1km × 1km to spatially disaggregate these emissions. The results show that nearly 54% of the PM10 emitted in the region emitted from cars. The results also revealed that nearly 61% of the PM emissions emitted from exhaust. Exhaust and Non-exhaust PM10 emissions are higher in the central part of the Klang Valley, an area with higher volume of vehicles

Assessing paddy rice yield sensitivity to temperature and rainfall variability in Peninsular Malaysia using DSSAT model

The study assessed the seasonal potential effect of temperature and rainfall variability on MR219 using Ceres rice model v4.6.1.0 of the DSSAT modelling system. The model simulated sensibly rice yield with RMSPE OF 8.9%, with D- Index for grain yield of 0.99. However, the simulated yield positively correlates with observed yield (r = 0.715; p < .05), while the coefficient of determination (r2 = 0.511). The model predicted changes in rice yield in all the three granary areas with varying degrees of gains and losses in the two seasons. The result from sensitivity analysis showed that during the main season +10C rise in the maximum temperature caused decrease in yield from -0.2 to -4.5% for MADA and KADA.A rise in maximum temperature up to +50C caused decrease in the yield ranging from -3.3 to -14.3 % for all the areas. Minimum temperature increase of +10C resulted in decrease in the yield ranging from -1.3 to -3.5%. During the off season, +10C increase in temperature caused decrease in yield from -0.5 to -2.3% for MADA and IADA. A rise in +30C maximum temperature caused decrease in the yieldranging from -2.5 to -7.5% for all the areas. While +10C rise in minimum temperature caused decrease in the yield from -3.1 to -6.6% for all the areas. Increase or decrease in the mean daily rainfall could be both beneficial as well as destructive depending on the season and location. The result showed that increase in mean daily rainfall of +1mm to +2mm decrease yield ranging from -4.0% to -51.5%. For MADA decrease in daily rainfall of 1mm to 2mm was shown to increase yield up to about 5.4%. In IADA, BLS during the main season decrease in the rainfall up to -7mm caused increased in yield from 6% to 7.2%. During the off season +1mm to +2mm increase in mean daily rainfall caused increased in yield ranging from 0.9% to 2.0%, but decrease in the mean daily rainfall caused yield to decreased ranging from -9.5% to -44.8%. For KADA, Kelantan during the main season increase or decrease in the rainfall decrease yield ranging from -4.4% to -22%. During off season, increase or decrease in the mean daily rainfall caused the yield to decrease ranging from -1.0% to -43.0%. Result from Analysis of variance revealed that under the likely changing condition, productivity in IADA will still likely be higher than in MADA, while KADA being the least will certainly continue to be more vulnerable to these changes than the other two granary areas

Estimation of aerosol dispersion & urban air quality evaluation over Malaysia using MODIS satellite

Natural and anthropogenic aerosols varied extremely within space and time and affect the global radiation balance, and influence climatic changes. The objectives of this paper are to evaluate and characterize the dispersion of aerosols in the tropical region of Peninsular Malaysia using MODerate Resolution Imaging Spectro-radiometer (MODIS) measurements. The MODIS sensors on board the Terra satellite which enables remote sensing of aerosols at high special resolution and daily global coverage of data. This paper demonstrates the capability of MODIS to show the distribution of aerosol optical thickness (AOT) over the study area. Spectral characterizers of AOT measured over Peninsular Malaysia for the period 2005 – 2007 are analyzed to understand the variability of the AOT in different seasons and location. The result showed low values on wet season and high values during the dry season for case some days. For the Correlations of the MODIS-AOT with the ground-based particulate matter indicates the spread of the aerosols all over Kuala Lumpur. The regression analyses of the MODIS-AOT and PM2.5 concentration is strongly correlated (correlation coefficient R = 0.75).From the findings of this study we illustrate the strong potentiality of satellite remote sensing in regional ambient air quality monitoring as an extension to ground measurements. With the continual advancement of remote sensing technology and global data assimilation systems, AOT measurements derived from satellite remote sensors may provide a cost effective approach as a supplementary source of information for determining ground-level particle concentration

CO2 Emission Inventory of on road vehicles in Selangor State Inpeninsular Malaysia

The transportation sector has greatly contributed to the socio-economic development with inherent environmental impacts. This study estimated the emission of CO2 from transportation sector, particularly from the use of passenger cars in Selangor Malaysia. The total CO2 emission from the region was calculated based on total fuel consumption (Kg) and Emission Factor of CO2 (gr/kg fuel). Lorries and cars were responsible for the highest CO2 emission and the emissions rate were directly related to the type and amount of fuel used and emission factor of fuel. High amount of CO2 emission was due to increase in vehicles on the road thereby increasing pollution on the environment. GIS is one of the most suitable methods to estimate the total CO2 emission and the split between different vehicle types as it determined by the kilometre covered for each vehicle category

Monthly analysis of PM10 in ambient air of Klang Valley, Malaysia

The urbanization in Klang Valley, Peninsular Malaysia over the last decades has induce the atmospheric pollution’s risk resulted to negative impact on the environment. The aims of this paper are to identify the spatial-temporal relationship of particulate matter (PM10), to determine the characteristic of each location and to classify hierarchical of the location in relation to their impact on PM10 concentration in Klang Valley. The Spearman correlation test indicate that there was strong significant relationship between all the locations (> 0.7; p < 0.001) and moderate relationship between Petaling Jaya-Kajang and Kajang-Shah Alam (< 0.7; p < 0.001). The principal component analysis (PCA) identifies all four locations have been affected by PM10 which were determined as one of the pollutant that deteriorated the air quality. Cluster analysis (CA) has classified the PM10 pattern into three (3) different classes; Class 1 (Klang), Class 2 (Petaling Jaya and Kajang) and Class 3 (Shah Alam) based on location. Further analysis of CA would be able to classify the PM10 classes into groups depending on their dissimilarities characteristic. Thus, possible period of extreme air quality degradation could be identified. Therefore, statistical and envirometric techniques have proved the impact of the various location on increasing concentration of PM10

Vulnerability of paddy farmers to climate change variability in Peninsular Malaysia

Climate changes greatly affect agricultural crop production and the associated farming community. The magnitude of the climatic stressor, the sensitivity and capacity of the affected communities to adapt with such stressors affect farmer vulnerability. This study assessed the vulnerability of paddy farmers to climate change variability in Peninsular Malaysia. The study employed an integrated vulnerability assessment approach using three component of vulnerability i.e. exposure, sensitivity and adaptive capacity. Trend analysis was conducted using Mann – Kendall to detect temperature and rainfall variability from 1981- 2014. DSSAT Ceres- rice model was used to predict rice yield for the study areas from 2016 until 2035 and determine the sensitivity of rice yield to temperature and rainfall changes. Household survey was conducted using multi- stage systematic random sampling on 450 sampled respondents to measure their adaptive capacity. Trend analysis shows that annual maximum temperature warming trend ranges from 0.0080C to 0.014oC per year, while annual minimum temperature warming trend ranges from 0.018oC to 0.063oC per year.Rainfall variability analysis revealed greater variability in terms of annual, monthly as well as seasonal patterns for all the areas under this study. The mean annual rainfall ranges from as low as 2016.7mm to as high as 2576.5mm for all the areas. The DSSAT model rice yield prediction result shows that Muda Agricultural Development Area (MADA) Kedah, will have highest increase in yield of 24.9% in 2029, with few years of decreasing yield by up to - 10.1% in 2035. In the Integrated Agricultural Development Area (IADA) Northwest Selangor, the model predicted decrease in yield of up to -7.1% by the year 2024. In the Kemubu Agricultural Development Area (KADA) Kelantan, the model predicted highest increase in the yield (14.0%) in the year 2028 and highest yield decline of - 13.3% by the year 2030. 22.9% of respondents were found to be less vulnerable, 32% were vulnerable and45.1% were highly vulnerable.Based on granaries, MADA has the highest vulnerability followed by KADA with IADA as the least vulnerable. Ordinal logistic regression revealed that 17 factors have significant influence on the vulnerability outcome of the respondents. Conclusively, the respondents in the study areas are vulnerable to the effects of climate change variability. Therefore, decision makers should tailor policies to address local specific conditions by placing climate change vulnerability issues within the broader developmental context