Mohd Asrul Jamalani

Bio: Mohd Asrul Jamalani is an academic researcher from Universiti Putra Malaysia. The author has contributed to research in topics: Haze & Emission inventory. The author has an hindex of 3, co-authored 6 publications receiving 27 citations.

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

Abstract: 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.

An overview of transboundary haze studies: The underlying causes and regional disputes on Southeast Asia Region

Abstract: 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.

Pm10 emission inventory of industrial and road transport vehicles in klang valley, peninsular malaysia

Abstract: Rapid development in industrial and road transportation sector in developing countries has contributing the environmental issue. Determining the estimated PM 10 emission in Klang Valley, Malaysia is based on the best available resources. Emission of PM 10 from both sources was estimated particularly from numbers of industries (industrial area and emission factor) and the usage of motor vehicles (traffic volume, vehicle kilometer travel and emission factor). The PM 10 emission from both industrial and road transportation sector were 88.59 tonne PM 10 /year and 32.36 tonne PM 10 /year respectively. Thus, the total estimated PM 10 emission was 120.95 tonne PM 10 /year. Therefore, the PM 10 emission from both sources in Klang Valley can be estimated based on the best available resources due to limitation of actual PM 10 emission from both sector.

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

Abstract: 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.

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

Abstract: 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.

Mitigation and Adaptation Strategies for Global Change

Abstract: ▶ Addresses a wide range of timely environment, economic and energy topics ▶ A forum to review, analyze and stimulate the development, testing and implementation of mitigation and adaptation strategies at regional, national and global scales ▶ Contributes to real-time policy analysis and development as national and international policies and agreements are discussed and promulgated ▶ 94% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again

Valutazione delle risorse forestali a livello globale

Abstract: The results of the Forest Resources Assessment 2000 carried out by FAO are synthetically presented and discussed. The world forest coverage is estimated equal to 38.6 million km 2 . The comparison of the estimates from the period 1990-2000 with those from the period 1980-1990 points out a certain general deceleration of the rate of net deforestation, that currently involves around 9 million hectares every year. However, the annua1 loss of tropical forests is still very large, while temperate and borea1 forests are in expansion. Overall, FRA2000 produced a relevant effort to compensate the existing technical, institutional and financial constraints and shortcomings for monitoring the world forest resources. The need to increase the quality and the frequency of forest surveys, both at national and international levels, s t a stands as a major issue to cope with.

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration

Abstract: Nowadays air quality is the main issue in urban areas that have been affecting human health, the environment, and the ecosystem. So, governmental authorities, environmental and health agencies usually need the prediction of daily air pollutants. This prediction is often based on statistical relations between various conditions and air pollution. This study aims to compare the performance of Multiple Linear Regression (MLR) and Multi-layer perceptron (MLP) for predicting SO2 concentration in the air of the Tehran. Different parameters namely meteorological parameters, urban traffic data, urban green space information, and time parameters were chosen for the prediction of SO2 daily concentration. Considering result, the correlation coefficient (R2), and root means square error (RMSE) of the MLR model are 0.708, and 6.025, respectively while these values for the MLP equal 0.9 and 0.42. According to the result of sensitivity analysis, the value of the one-day time delay, park indicator, season/year, and the total area parks were the main factors influencing SO2 concentration. MLP model suggested in this research could be applied to support, analysis, and improve predicting air pollution and air quality management. This study shows the importance of modeling and application of ANN in presenting management strategies to reduce urban pollution.

Impact of Extreme Drought Climate on Water Security in North Borneo: Case Study of Sabah

Abstract: For countries in Southeast Asia that mainly rely on surface water as their water resource, changes in weather patterns and hydrological systems due to climate change will cause severely decreased water resource availability. Warm weather triggers more water use and exacerbates the extraction of water resources, which will change the operation patterns of water usage and increase demand, resulting in water scarcity. The occurrence of prolonged drought upsets the balance between water supply and demand, significantly increasing the vulnerability of regions to damaging impacts. The objectives of this study are to identify trends and determine the impacts of extreme drought events on water levels for the major important water dams in the northern part of Borneo, and to assess the risk of water insecurity for the dams. In this context, remote sensing images are used to determine the degree of risk of water insecurity in the regions. Statistical methods are used in the analysis of daily water levels and rainfall data. The findings show that water levels in dams on the North and Northeast Coasts of Borneo are greatly affected by the extreme drought climate caused by the Northeast Monsoon, with mild to the high risk recorded in terms of water insecurity, with only two of the water dams being water-secure. This study shows how climate change has affected water availability throughout the regions.

Development of Multiple Linear Regression for Particulate Matter (PM10) Forecasting during Episodic Transboundary Haze Event in Malaysia

Abstract: Malaysia has been facing transboundary haze events every year in which the air contains particulate matter, particularly PM10, which affects human health and the environment. Therefore, it is crucial to develop a PM10 forecasting model for early information and warning alerts to the responsible parties in order for them to mitigate and plan precautionary measures during such events. Therefore, this study aimed to develop and compare the best-fitted model for PM10 prediction from the first hour until the next three hours during transboundary haze events. The air pollution data acquired from the Malaysian Department of Environment spanned from the years 2005 until 2014 (excluding years 2007–2009), which included particulate matter (PM10), ozone (O3), nitrogen oxide (NO), nitrogen dioxide (NO), carbon monoxide (CO), sulfur dioxide (SO2), wind speed (WS), ambient temperature (T), and relative humidity (RH) on an hourly basis. Three different stepwise Multiple Linear Regression (MLR) models for predicting the PM10 concentration were then developed based on three different prediction hours, namely t+1, t+2, and t+3. The PM10, t+1 model was the best MLR model to predict PM10 during transboundary haze events compared to PM10,.t+2 and PM10,t+3 models, having the lowest percentage of total error (28%) and the highest accuracy of 46%. A better prediction and explanation of PM10 concentration will help the authorities in getting early information for preserving the air quality, especially during transboundary haze episodes.