This study aimed to identify some optimum adsorption conditions for the use of low-cost adsorbent, seaweed (Ascophyllum nodosum), sawdust and reed plant (Phragmites australis) root, in the treatment of metal contaminated wastewater for the removal of cadmium, chromium and lead. The effect of pH on the absorption capacity of each of these biosorbents was found to be significant and dependent on the metal being removed. Post-adsorption FTIR analysis showed significant binding activities at the nitro NO groups site in all biosorbents, especially for lead. Competitive metal binding was found to have possibly affected the adsorption capacity for chromium by A. nodosum more than it affected sawdust and P. australis root. Adsorption is believed to take place mainly by ion exchange particularly at low pH values. P. australis root exhibited the highest adsorption for chromium at pH 2, cadmium at pH 10 and lead at pH 7. A. nodosum seaweed species demonstrated the highest adsorption capacity of the three biosorbents used in the study, for cadmium at pH 7 and for lead at pH 2. Sawdust proved to be an efficient biosorbent for lead removal only at pH 7 and 10. No significant effect of temperature on adsorption capacity was observed, particularly for cadmium and lead removal.

Five sites were selected representing the studied sites of the coastal water in Kelantan, Malaysia during 1996 until May 2012. These selected sites are the popular beaches in Kelantan which are Sri Tujuh Beach (STB) located in Tumpat region, Cahaya Bulan Beach (PCB) and Sabak Beach (SB) in Kota Bharu region, Irama Beach (PI) in Bachok region and Bisikan Bayu Beach (PBB) in Pasir Putih region. In other to identify the quality of coastal water in this five popular coasts along Kelantan, study of heavy metals contamination in coastal water has been done. Evaluation of heavy metal contamination in Kelantan coastal water has doing by combine the data from Department of Environment (DOE) Malaysia and the data was got from this research. There are four types of heavy metal concentration has evaluated in this research; Cadmium (Cd), Cupper (Cu), Lead (Pb) and Chromium (Cr). From this evaluation, we can see the fluctuation of concentration the four type heavy metal (Cd, Cu, Pb, Cr) from 1996 until May 2012. Most of the year, the concentration of heavy metal is between the range of Malaysia marine quality standard except for Lead. Lead was proven to be the highest concentration pollutant in the five beaches in certain time and also exceed the Malaysia marine standard.

A long term monitoring data of the dynamics changes on tree growth in Ulu Sat Forest Reserve, Kelantan was analysed. The growth plot was established in 1997 with status of poor forest. Permanent growth plot area was established to monitor the forest stands. Trees in the growth plot were measured every two years with various parameters include girth increment and tree height. Within sixteen years of observation, mean annual increment of trees in Ulu Sat Forest Reserve was at 0.33 cm/year. In general, mean DBH and tree height in this area was at 31.0 cm and 12.9 m respectively. Forest stands with DBH between 20-30 cm are majorly found in the area with 73 trees/ha. The non-dipterocarp species showed a significant decrease in terms of individual tree number when the diameter of the tree increases. Tree volume of non-dipterocarp species recorded at Ulu Sat Forest Reserve is 40 percent higher than dipterocarp species. Meanwhile, total basal area and tree volume contributed by dipterocarp species was 12.1 m2/ha and 227.0 m3/ha, respectively. Based on the enumeration and data analysis, this long term forest monitoring study is imperative to ensure each forest encompasses with adequate quantity of trees for future production, healthy in condition and could support the sustainability of forest ecosystem.

Flooding is one of the major natural hazards in the UK. Accurate flood estimation at ungauged catchment is an important component to understand and mitigate flood hazards, but still a difficult issue. This study therefore attempts to explore and improve an index flood estimation model, known as the FEH-QMED model, popular in the UK. It was developed under the assumption that the index flood of QMED, i.e., the median of the set of annual maximum (AMAX) flood data, standing for a flooding level of 2-year return period, can be explained by catchment descriptors. In this study, two fundamentals are empirically explored, including assessing reliability of the nonlinear functional impacts of the catchment descriptors on the logarithmic transformation of QMED, specified by the FEH-QMED model, and the potential to improve the model for more accurate index flood estimation, based on the flooding data of 586 gauged stations across the UK. Through a spatial additive regression analysis, we empirically find that the nonlinear impacts of the catchment descriptors in an updated FEH-QMED model appear reliable. However, spatial correlation tests including Moran's I and Lagrange multiplier tests show that strong spatial dependence exists in the residuals of, but was not fully taken into account by, the QMED type models. We have therefore empirically established new spatial index flood estimation models by proposing spatial autoregressive models to model the impacts of the neighboring sites. Cross-validation assessments demonstrate that the suggested spatial error-based index flood model outperforms the updated FEH-QMED model with a significant improvement, which is robust in the sense of different error measures, say by a reduction of 13.8% of the mean squared error of prediction, for the UK index flood estimation. Keywords Index flood estimation • Flood catchment descriptors • Flood Estimation Handbook (FEH)-QMED Model • Nonlinear effect of covariates • Spatial neighboring effect • Spatial error model