The world's tropical rainforests are decreasing at an alarming rate as they are converted to agricultural land, pasture, and plantations. Decreasing tropical forests affect global warming. As a result, afforestation progams have been suggested to mitigate this problem. The objective of this study was to determine the carbon and phosphorus accumulation of a rehabilitated forest of different ages. The size of the study area was 47.5 ha. Soil samples were collected from the 0-, 6-, 12-, and 17-year-old rehabilitated forest. Twenty samples were taken randomly with a soil auger at depths of 0-20 and 20-40 cm. The procedures outlined in the Materials and Methods section were used to analyze the soil samples for pH, total C, organic matter, total P, C/P ratio, yield of humic acid (HA), and cation exchange capacity (CEC). The soil pH decreased significantly with increasing age of forest rehabilitation regardless of depth. Age did not affect CEC of the rehabilitated forest. Soil organic matter (SOM), total C, and total P contents increased with age. However, C/P ratio decreased with time at 0-20 cm. Accumulation of HA with time and soil depth was not consistent. The rehabilitated forest has shown signs of being a C and P sink.
A variety of trace metals were measured in the egg contents of three clutches of Chelonia mydas collected from Kuala Terengganu state in Peninsular Malaysia. We quantified Mn, Cu, Zn, Se (essential trace metals) and As (anthropogenic pollutant) at several developmental stages obtained by incubating eggs at two different temperatures (27 °C and 31 °C). The incubation temperatures were chosen because they produce predominantly male or predominantly female hatchlings, respectively. The eggs were removed from the sand and washed before being placed in incubators, to ensure that the only possible source of the detected metals was maternal transfer. Other metals: Mo, Co, Ni, Cd, Sn, Sb, Hg, Tl and Pb (all non-essential metals) were detected at concentrations below the lower limit of quantitation (LLOQ). Trace metal concentrations, particularly [Zn], increased during development, other metals (Cu, As, Se and Cr) accumulated to a lesser degree than zinc but no significant differences were observed between the incubation temperatures at any stage of incubation. To date, only a few studies on trace metals in turtle embryos and hatchlings have been reported; this study will provide basic knowledge on the accumulation of trace metals during development at two different incubation temperatures.
This study investigates adsorption-desorption and the leaching potential of glyphosate and aminomethylphosphonic acid (AMPA) in control and amended-addition of cow dung or rice husk ash-acidic Malaysian soil with high oxide mineral content. The addition of cow dung or rice husk ash increased the adsorptive removal of AMPA. The isotherm data of glyphosate and AMPA best fitted the Freundlich model. The constant Kf for glyphosate was high in the control soil (544.873 mg g-1) followed by soil with cow dung (482.451 mg g-1) then soil with rice husk ash (418.539 mg g-1). However, for AMPA, soil with cow dung was high (166.636 mg g-1) followed by soil with rice husk ash (137.570 mg g-1) then the control soil (48.446 mg g-1). The 1/n values for both glyphosate and AMPA adsorptions were
In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo's derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).
Clean air is critical component for health and survival of human and wildlife, as atmospheric pollution is associated with a number of significant diseases including cancer. However, due to rapid industrialization and population growth, activities such as transportation, household, agricultural, and industrial processes contribute to air pollution. As a result, air pollution has become a significant problem in many cities, especially in emerging countries like India. To maintain ambient air quality, regular monitoring and forecasting of air pollution is necessary. For that purpose, machine learning has emerged as a promising technique for predicting the Air Quality Index (AQI) compared to conventional methods. Here we apply the AQI to the city of Visakhapatnam, Andhra Pradesh, India, focusing on 12 contaminants and 10 meteorological parameters from July 2017 to September 2022. For this purpose, we employed several machine learning models, including LightGBM, Random Forest, Catboost, Adaboost, and XGBoost. The results show that the Catboost model outperformed other models with an R2 correlation coefficient of 0.9998, a mean absolute error (MAE) of 0.60, a mean square error (MSE) of 0.58, and a root mean square error (RMSE) of 0.76. The Adaboost model had the least effective prediction with an R2 correlation coefficient of 0.9753. In summary, machine learning is a promising technique for predicting AQI with Catboost being the best-performing model for AQI prediction. Moreover, by leveraging historical data and machine learning algorithms enables accurate predictions of future urban air quality levels on a global scale.
The occurrence of n-alkanoic acids, amides, and nitriles in samples of aerosol particulate matter from Kuala Lumpur and Santiago suggests that emissions from cooking and biomass burning are the primary sources of these organic markers in the atmosphere. It is proposed that fatty acids react with ammonia during biomass burning or combustion to produce amides and nitriles, which can be applied as useful biomarker tracers. To test this hypothesis, nonadecanoic acid and hexadecanamide were used as reactants in hydrous pyrolysis experiments. These experiments produced amides and nitriles and indicated that ammonia is an essential agent in their formation. Thus amides and nitriles are of utility as indicators for input from combustion and biomass burning in the ambient atmosphere.
An advanced image processing approach integrated with communication technologies and a camera for waste bin level detection has been presented. The proposed system is developed to address environmental concerns associated with waste bins and the variety of waste being disposed in them. A gray level aura matrix (GLAM) approach is proposed to extract the bin image texture. GLAM parameters, such as neighboring systems, are investigated to determine their optimal values. To evaluate the performance of the system, the extracted image is trained and tested using multi-layer perceptions (MLPs) and K-nearest neighbor (KNN) classifiers. The results have shown that the accuracy of bin level classification reach acceptable performance levels for class and grade classification with rates of 98.98% and 90.19% using the MLP classifier and 96.91% and 89.14% using the KNN classifier, respectively. The results demonstrated that the system performance is robust and can be applied to a variety of waste and waste bin level detection under various conditions.
In this project, several surrogate surfaces designed to directly measure Hg dry deposition were investigated. Static water surrogate surfaces (SWSS) containing deionized (DI), acidified water, or salt solutions, and a knife-edge surrogate surface (KSS) using quartz fiber filters (QFF), KCl-coated QFF and gold-coated QFF were evaluated as a means to directly measure mercury (Hg) dry deposition. The SWSS was hypothesized to collect deposited elemental mercury (Hg⁰), reactive gaseous/oxidized mercury (RGM), and mercury associated with particulate matter (Hg(p)) while the QFF, KCl-coated QFF, and gold-coated QFF on the KSS were hypothesized to collect Hg(p), RGM+Hg(p), and Hg⁰+RGM+Hg(p), respectively. The Hg flux measured by the DI water was significantly smaller than that captured by the acidified water, probably because Hg⁰ was oxidized to Hg²+ which stabilized the deposited Hg and decreased mass transfer resistance. Acidified BrCl, which efficiently oxidizes Hg⁰, captured significantly more Hg than other solutions. However, of all collection media, gold-coated QFFs captured 6 to 100 times greater Hg mass than the other surfaces, probably because there is no surface resistance for Hg⁰ deposition to gold surfaces. In addition, the Hg⁰ concentration is usually 100-1000 times higher than RGM and Hg(p). For all other media, co-located samples were not significantly different, and the combination of daytime plus nighttime results were comparable to 24-h samples, implying that Hg⁰, RGM and Hg(p) were not released after they deposited nor did the surfaces reach equilibrium with the atmosphere. Based on measured Hg ambient air concentrations and fluxes, dry deposition velocities of RGM and Hg⁰ to DI water and other surfaces were 5.6±5.4 and 0.005-0.68 cm s⁻¹ in this study, respectively. These results suggest surrogate surfaces can be used to measure Hg dry deposition; however, extrapolating the results to natural surface can be challenging.
The spatial and temporal distributions of trace metals in dissolved forms mainly result from anthropogenic and lithogenic contributions. Surface water samples (∼0.5 m) were collected monthly at respective stations from Setiu Wetland. In this study, the behaviour of trace metals in the dissolved phases along the water column from sampling sites in the Setiu Wetland, Malaysia was investigated. In addition, dissolved organic carbon (DOC) and physical parameters such as salinity, temperature, pH and dissolved oxygen (DO) of the surface water were measured in order to evaluate the relationship between trace metals fractionation with different water quality parameters. Size fractionation study of dissolved trace metals using ultrafiltration technique were also carried out and analysed using inductively coupled plasma mass spectrometry (ICP-MS). Correlation of trace metals with other measured parameters was made to furthermore understand the dynamics of trace metals and its fractionated components in this area. The concentration of dissolved trace metals was in the range of 0.001-0.16 μg/L for Cd, 0.12-2.81 μg/L for Cu, 0.01-1.84 μg/L for Pb, 3-17 μg/L for Fe and 1-34 μg/L for Zn, suggesting the input of anthropogenic sources for trace metals such as municipal, industrial, agricultural and domestic discharge. The periodic monitoring and evaluation of trace metals in wetlands and protected tropical areas is highly recommended.
Illuminance level in the softcopy image viewing room is a very important factor to optimize productivity in radiological diagnosis. In today's radiological environment, the illuminance measurements are normally done during the quality control procedure and performed annually. Although the room is equipped with dimmer switches, radiologists are not able to decide the level of illuminance according to the standards. The aim of this study is to develop a simple real-time illuminance detector system to assist the radiologists in deciding an adequate illuminance level during radiological image viewing. The system indicates illuminance in a very simple visual form by using light emitting diodes. By employing the device in the viewing room, illuminance level can be monitored and adjusted effectively.
Heavy metals pollution has become a great threat to the world. Since instrumental methods are expensive and need skilled technician, a simple and fast method is needed to determine the presence of heavy metals in the environment. In this study, an inhibitive enzyme assay for heavy metals has been developed using crude proteases from Coriandrum sativum. In this assay, casein was used as a substrate and Coomassie dye was used to denote the completion of casein hydrolysis. In the absence of inhibitors, casein was hydrolysed and the solution became brown, while in the presence of metal ions such as Hg²⁺ and Zn²⁺, the hydrolysis of casein was inhibited and the solution remained blue. Both Hg²⁺ and Zn²⁺ exhibited one-phase binding curve with IC₅₀ values of 3.217 mg/L and 0.727 mg/L, respectively. The limits of detection (LOD) and limits of quantitation (LOQ) for Hg were 0.241 and 0.802 mg/L, respectively, while the LOD and LOQ for Zn were 0.228 and 0.761 mg/L, respectively. The enzyme exhibited broad pH ranges for activity. The crude proteases extracted from Coriandrum sativum showed good potential for the development of a rapid, sensitive, and economic inhibitive assay for the biomonitoring of Hg²⁺ and Zn²⁺ in the aquatic environments.
In this study, geophysics, geochemistry, and geostatistical techniques were integrated to assess seawater intrusion in Kapas Island due to its geological complexity and multiple contamination sources. Five resistivity profiles were measured using an electric resistivity technique. The results reveal very low resistivity <1 Ωm, suggesting either marine clay deposit or seawater intrusion or both along the majority of the resistivity images. As a result, geochemistry was further employed to verify the resistivity evidence. The Chadha and Stiff diagrams classify the island groundwater into Ca-HCO3, Ca-Na-HCO3, Na-HCO3, and Na-Cl water types, with Ca-HCO3 as the dominant. The Mg(2+)/Mg(2+)+Ca(2+), HCO3 (-)/anion, Cl(-)/HCO3 (-), Na(+)/Cl(-), and SO4 (2-)/Cl(-) ratios show that some sampling sites are affected by seawater intrusion; these sampling sites fall within the same areas that show low-resistivity values. The resulting ratios and resistivity values were then used in the geographical information system (GIS) environment to create the geostatistical map of individual indicators. These maps were then overlaid to create the final map showing seawater-affected areas. The final map successfully delineates the area that is actually undergoing seawater intrusion. The proposed technique is not area specific, and hence, it can work in any place with similar completed characteristics or under the influence of multiple contaminants so as to distinguish the area that is truly affected by any targeted pollutants from the rest. This information would provide managers and policy makers with the knowledge of the current situation and will serve as a guide and standard in water research for sustainable management plan.
This study investigates hydrocarbon pollution in the Ahoada community of the Niger Delta region of Nigeria. The study uses a geographic information system (GIS) for mapping oil spill hotspots in the region. The resistivity method was used to delineate the extent of hydrocarbon pollution to a depth of 19.7 m in the Ahoada area of the region. Three categories of soil samples, impacted soil (IMS), remediated soil (RS), and control soil (CS), were collected and analyzed for the presence of BTEX, PAH, TPH, TOC, and TOG. The concentrations of the samples from the IMS and RS were compared to that of the CS to determine the extent of pollution. The GIS mapping shows that the most polluted areas in the Niger Delta Region are Rivers, Bayelsa, and Delta states. Results of the geophysical images revealed contaminants' presence to depths beyond 20 m at some locations in the study area. The highest depth of contaminant travel was at Ukperede. Soil samples' analysis showed that the range of concentrations of TPH in IMS at Oshie was 17.27-58.36 mg/kg; RS was 11.73-50.78 mg/kg which were higher than the concentrations of 0.68 mg/kg in the CS. PAHs are more prevalent in Ukperede, ranging from 54.56 to 77.54 mg/kg. BTEX concentrations ranged from 0.02 to 0.38 mg/kg for IMP and 0.01-2.7 mg/kg for RS against a CS value of 0.01 mg/kg. The study revealed that there are characteristically high resistivity values in the samples which were corroborated by the findings from the resistivity survey. TOC was found to be higher in the IMS and RS than in the CS, demonstrating that a significant quantity of the hydrocarbon has undergone appreciable decomposition.
In this paper, numerous studies on groundwater in Malaysia were reviewed with the aim of evaluating past trends and the current status for discerning the sustainability of the water resources in the country. It was found that most of the previous groundwater studies (44 %) focused on the islands and mostly concentrated on qualitative assessment with more emphasis being placed on seawater intrusion studies. This was then followed by inland-based studies, with Selangor state leading the studies which reflected the current water challenges facing the state. From a methodological perspective, geophysics, graphical methods, and statistical analysis are the dominant techniques (38, 25, and 25 %) respectively. The geophysical methods especially the 2D resistivity method cut across many subjects such as seawater intrusion studies, quantitative assessment, and hydraulic parameters estimation. The statistical techniques used include multivariate statistical analysis techniques and ANOVA among others, most of which are quality related studies using major ions, in situ parameters, and heavy metals. Conversely, numerical techniques like MODFLOW were somewhat less admired which is likely due to their complexity in nature and high data demand. This work will facilitate researchers in identifying the specific areas which need improvement and focus, while, at the same time, provide policymakers and managers with an executive summary and knowledge of the current situation in groundwater studies and where more work needs to be done for sustainable development.
In this study, we assess the intense pollution episode of June 2013, in Riau province, Indonesia from land clearing. We relied on satellite retrievals of aerosols and Carbon monoxide (CO) due to lack of ground measurements. We used both the yearly and daily data for aerosol optical depth (AOD), fine mode fraction (FMF), aerosol absorption optical depth (AAOD) and UV aerosol index (UVAI) for characterizing variations. We found significant enhancement in aerosols and CO during the pollution episode. Compared to mean (2008-2012) June AOD of 0.40, FMF-0.39, AAOD-0.45, UVAI-1.77 and CO of 200 ppbv, June 2013 values reached 0.8, 0.573, 0.672, 1.77 and 978 ppbv respectively. Correlations of fire counts with AAOD and UVAI were stronger compared to AOD and FMF. Results from a trajectory model suggested transport of air masses from Indonesia towards Malaysia, Singapore and southern Thailand. Our results highlight satellite-based mapping and monitoring of pollution episodes in Southeast Asia.
Anionic surfactants are one of the pollutants derived from particulate matter (PM) and adversely affect the health of living organisms. In this study, the compositions of surfactants extracted from PM and vehicle soot collected in an urban area were investigated. A high-volume air sampler was used to collect PM sample at urban area based on coarse (> 1.5 µm) and fine (
A study of the impacts of anthropogenic activities on the distribution and biodiversity of benthic macroinvertebrates and water quality of the Langat River (Peninsular Malaysia) was conducted. Four pristine stations from the upstream and 4 stations at the downstream receiving anthropogenic impacts were selected along the river. For 4 consecutive months (March-June 1999), based on the Malaysian DOE (Malaysia Environmental Quality Report 2000, Department of Environment, Ministry of Science, Technology and Environment Malaysia. Maskha Sdn. Bhd. Kuala Lumpur, 86pp; Malaysia Environmental Quality Report 2001, Department of Environment, Ministry of Science, Technology and the Environment Malaysia) water quality index classes, the upstream stations recorded significantly (P<0.05) higher Biological Monitoring Working Party scores and better water quality indices than those of the downstream. The total number of macrobenthic taxa and their overall richness indices and diversity indices were significantly (P<0.05) higher at the upstream stations (54 taxa) than at the downstream stations (5 taxa). The upstream of the Langat River was dominated by Ephemeroptera and chironomid dipterans while other orders found in small quantities included Trichoptera, Diptera, Plecoptera, Odonata, Ephemeraptera, Coleoptera, and Gastropoda. On the other hand, the downstream of the river was mainly inhabited by the resistant Oligochaeta worms Limnodrilus spp. and Branchiodrilus sp. and Hirudinea in small numbers. The relationships between the physicochemical and the macrobenthic data were investigated by Pearson correlation analysis and multiple stepwise regression analysis. These statistical analyses showed that the richness and diversity indices were generally influenced by the total suspended solids and the conductivity of the river water. This study also highlighted the impacts of anthropogenic land-based activities such as urban runoff on the distribution and species diversity of macrobenthic invertebrates in the downstream of the Langat River. The data obtained in this study supported the use of the bioindicator concept for Malaysian rivers. Some sensitive (Trichopteran caddisflies and Ephemeraptera) and resistant species (Oligochaeta such as Limnodrilus spp.) are identified as potential bioindicators of clean and polluted river ecosystems, respectively, for Malaysian rivers.
Polycyclic aromatic hydrocarbons (PAHs) and linear alkylbenzenes (LABs) were used as anthropogenic markers of organic chemical pollution of sediments in the Selangor River, Peninsular Malaysia. This study was conducted on sediment samples from the beginning of the estuary to the upstream river during dry and rainy seasons. The concentrations of ƩPAHs and ƩLABs ranged from 203 to 964 and from 23 to 113 ng g(-1) dry weight (dw), respectively. In particular, the Selangor River was found to have higher sedimentary levels of PAHs and LABs during the wet season than in the dry season, which was primarily associated with the intensity of domestic wastewater discharge and high amounts of urban runoff washing the pollutants from the surrounding area. The concentrations of the toxic contaminants were determined according to the Sediment Quality Guidelines (SQGs). The PAH levels in the Selangor River did not exceed the SQGs, for example, the effects range low (ERL) value, indicating that they cannot exert adverse biological effects.
This study integrates the application of Dempster-Shafer-driven evidential belief function (DS-EBF) methodology with remote sensing and geographic information system techniques to analyze surface and subsurface data sets for the spatial prediction of groundwater potential in Perak Province, Malaysia. The study used additional data obtained from the records of the groundwater yield rate of approximately 28 bore well locations. The processed surface and subsurface data produced sets of groundwater potential conditioning factors (GPCFs) from which multiple surface hydrologic and subsurface hydrogeologic parameter thematic maps were generated. The bore well location inventories were partitioned randomly into a ratio of 70% (19 wells) for model training to 30% (9 wells) for model testing. Application results of the DS-EBF relationship model algorithms of the surface- and subsurface-based GPCF thematic maps and the bore well locations produced two groundwater potential prediction (GPP) maps based on surface hydrologic and subsurface hydrogeologic characteristics which established that more than 60% of the study area falling within the moderate-high groundwater potential zones and less than 35% falling within the low potential zones. The estimated uncertainty values within the range of 0 to 17% for the predicted potential zones were quantified using the uncertainty algorithm of the model. The validation results of the GPP maps using relative operating characteristic curve method yielded 80 and 68% success rates and 89 and 53% prediction rates for the subsurface hydrogeologic factor (SUHF)- and surface hydrologic factor (SHF)-based GPP maps, respectively. The study results revealed that the SUHF-based GPP map accurately delineated groundwater potential zones better than the SHF-based GPP map. However, significant information on the low degree of uncertainty of the predicted potential zones established the suitability of the two GPP maps for future development of groundwater resources in the area. The overall results proved the efficacy of the data mining model and the geospatial technology in groundwater potential mapping.
The growing interest in the environmental occurrence of veterinary and human pharmaceuticals is essentially due to their possible health implications to humans and ecosystem. This study assesses the occurrence of human pharmaceuticals in a Malaysian tropical aquatic environment taking a chemometric approach using cluster analysis, discriminant analysis and principal component analysis. Water samples were collected from seven sampling stations along the heavily populated Langat River basin on the west coast of peninsular Malaysia and its main tributaries. Water samples were extracted using solid-phase extraction and analyzed using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for 18 pharmaceuticals and one metabolite, which cover a range of six therapeutic classes widely consumed in Malaysia. Cluster analysis was applied to group both pharmaceutical pollutants and sampling stations. Cluster analysis successfully clustered sampling stations and pollutants into three major clusters. Discriminant analysis was applied to identify those pollutants which had a significant impact in the definition of clusters. Finally, principal component analysis using a three-component model determined the constitution and data variance explained by each of the three main principal components.