The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant relationship status over particulate matter (PM10) in eastern region. Eight monitoring studies were used, involving 14 input parameters as independent variables including meteorological factors. In order to investigate the efficiency of ANN and MLR performance, two different weather circumstances were selected; haze and non-haze. The performance evaluation was characterized into two steps. Firstly, two models were developed based on ANN and MLR which denoted as full model, with all parameters (14 variables) were used as the input. SA was used as additional feature to rank the most contributed parameter to PM10 variations in both situations. Next, the model development was evaluated based on selected model, where only significant variables were selected as input. Three mathematical indices were introduced (R2, RMSE and SSE) to compare on both techniques. From the findings, ANN performed better in full and selected model, with both models were completely showed a significant result during hazy and non-hazy. On top of that, UVb and carbon monoxide were both variables that mutually predicted by ANN and MLR during hazy and non-hazy days, respectively. The precise predictions were required in helping any related agency to emphasize on pollutant that essentially contributed to PM10 variations, especially during haze period.

Air pollution is now ranked as the ninth worst scenario globally and is expected to be the most serious global issue by the year 2050. The objective of this study is to get information regarding transboundary haze phenomenon blanketing the Southeast Asia that has been happened for decades ago. Various techniques such as qualitative and quantitative techniques have been applied to get the informative input detailed out by previous researchers. The finding shows that that the smoky haze occurred in the dry season, which at this point, the activities of cleaning and ground maintenance being carried out by Indonesian farmers. Indonesia is one of the countries drastically affected by deforestation process where their forest loss is 2% yr-1 which is equal to 1.9 million ha each year. The establishment of ASEAN in 2002 would be a turning point in addressing on more reliance on prevention and cooperation than establishing a liability regime or adopting legal instruments to protect the environment. However, the reflection of so-called ‘ASEAN Way', which preferred on non-interference in other states has inhibited the reliance on strong regional efforts in executing a more effective action in order to address and combat the transboundary haze pollution in Southeast Asia.

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 (PM 10 ), to determine the characteristic of each location and to classify hierarchical of the location in relation to their impact on PM 10 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 PM 10 which were determined as one of the pollutant that deteriorated the air quality. Cluster analysis (CA) has classified the PM 10 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 PM 10 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 PM 10 . Keywords: particulate matter, Spearman correlation test, principal component analysis, cluster analysis AbstrakProses pembandaran di Lembah Klang, Semenanjung Malaysia sedekad lalu telah mendorong kepada risiko pencemaran atmosfera yang memberi impak negatif kepada alam sekitar. Kajian ini dilakukan bertujuan untuk mengenalpasti hubungkait antara ruang dan tempoh bagi partikel terampai (PM 10 ), menentukan ciri -ciri setiap lokasi dan menentukan pengkelasan hirarki lokasi berhubungan dengan impak kepekatan PM 10 di Lembah Klang. Ujian korelasi Spearman menunjukkan hubungkait yang kuat antara semua lokasi (> 0.7; p < 0.001) dan hubungan

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