Spatial patterns reveal negative density dependence and habitat associations in tropical trees

Understanding how plant species coexist in tropical rainforests is one of the biggest challenges in community ecology. One prominent hypothesis suggests that rare species are at an advantage because trees have lower survival in areas of high conspecific density due to increased attack by natural enemies, a process known as negative density dependence (NDD). A consensus is emerging that NDD is important for plant-species coexistence in tropical forests. Most evidence comes from short-term studies, but testing the prediction that NDD decreases the spatial aggregation of tree populations provides a long-term perspective. While spatial distributions have provided only weak evidence for NDD so far, the opposing effects of environmental heterogeneity might have confounded previous analyses. Here we use a novel statistical technique to control for environmental heterogeneity while testing whether spatial aggregation decreases with tree size in four tropical forests. We provide evidence for NDD in 22% of the 139 tree species analyzed and show that environmental heterogeneity can obscure the spatial signal of NDD. Environmental heterogeneity contributed to aggregation in 84% of species. We conclude that both biotic interactions and environmental heterogeneity play crucial roles in shaping tree dynamics in tropical forests.     

Carbon nanotubes from waste cooking palm oil as adsorbent materials for the adsorption of heavy metal ions

Environmental Science and Pollution Research - In this work, waste cooking palm oil (WCPO)-based carbon nanotubes (CNTs) with encapsulated iron (Fe) nanoparticles have been successfully produced...     

Investigating Malaysian stakeholders’ perceptions of the government’s aim to replace conventional plastic bags with biodegradable and compostable bioplastic bags

Journal of Material Cycles and Waste Management - A transition from petroleum-based plastic bags to biodegradable and compostable bioplastic bags will affect stakeholders across the value chain of...     

Synthesis of magnetite-based nanocomposites for effective removal of brilliant green dye from wastewater

Environmental Science and Pollution Research - The present study aims at evaluating the batch scale potential of cotton shell powder (CSP), Moringa oleifera leaves (ML), and magnetite-assisted...     

Coffee,its roasted form,and their residues cause birth failure and shorten lifespan in dengue vectors

Environmental Science and Pollution Research - In dengue mosquitoes, successful embryonic development and long lifespan are key determinants for the persistence of both virus and vector. Therefore,...     

Modeling of nitrate concentration in groundwater using artificial intelligence approach—a case study of Gaza coastal aquifer

Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making     

Monsoonal differences and probability distribution of PM10 concentration

There are many factors that influence PM₁₀ concentration in the atmosphere. This paper will look at the PM₁₀ concentration in relation with the wet season (north east monsoon) and dry season (south west monsoon) in Seberang Perai, Malaysia from the year 2000 to 2004. It is expected that PM₁₀ will reach the peak during south west monsoon as the weather during this season becomes dry and this study has proved that the highest PM₁₀ concentrations in 2000 to 2004 were recorded in this monsoon. Two probability distributions using Weibull and lognormal were used to model the PM₁₀ concentration. The best model used for prediction was selected based on performance indicators. Lognormal distribution represents the data better than Weibull distribution model for 2000, 2001, and 2002. However, for 2003 and 2004, Weibull distribution represents better than the lognormal distribution. The proposed distributions were successfully used for estimation of exceedences and predicting the return periods of the sequence year.     

Biodegradation of 4-aminobenzenesulfonate by Ralstonia sp. PBA and Hydrogenophaga sp. PBC isolated from textile wastewater treatment plant

A co-culture consisting of Hydrogenophaga sp. PBC and Ralstonia sp. PBA, isolated from textile wastewater treatment plant could tolerate up to 100 mM 4-aminobenzenesulfonate (4-ABS) and utilize it as sole carbon, nitrogen and sulfur source under aerobic condition. The biodegradation of 4-ABS resulted in the release of nitrogen and sulfur in the form of ammonium and sulfate respectively. Ninety-eight percent removal of chemical oxygen demand attributed to 20 mM of 4-ABS in cell-free supernatant could be achieved after 118 h. Effective biodegradation of 4-ABS occurred at pH ranging from 6 to 8. During batch culture with 4-ABS as sole carbon and nitrogen source, the ratio of strain PBA to PBC was dynamic and a critical concentration of strain PBA has to be reached in order to enable effective biodegradation of 4-ABS. Haldane inhibition model was used to fit the degradation rate at different initial concentrations and the parameters μ_max, K_s and K_i were determined to be 0.13 h⁻¹, 1.3 mM and 42 mM respectively. HPLC analyses revealed traced accumulation of 4-sulfocatechol and at least four unidentified metabolites during biodegradation. This is the first study to report on the characterization of 4-ABS-degrading bacterial consortium that was isolated from textile wastewater treatment plant.