The Bernam River is one of the most important rivers in Malaysia in that it provides water for industries and agriculture located along its banks. The present study was conducted to assess the level of contamination of heavy metals (Cd, Ni, Cr, Sn, and Fe) in surface sediments in the Bernam River. Nine surface sediment samples were collected from the lower, middle, and upper courses of the river. The results indicated that the concentrations of the metals decreased in the order of Sn > Cr > Ni > Fe > Cd (56.35, 14.90, 5.3, 4.6, and 0.62 μg/g(1) dry weight). Bernam River sediments have moderate to severe enrichment for Sn, moderate for Cd, and no enrichment for Cr, Ni, and Fe. The contamination factor (CF) results demonstrated that Cd and Sn are responsible for the high contamination. The pollution load index (PLI), for all the sampling sites, suggests that the sampling stations were generally unpolluted with the exception of the Bagan Tepi Sungai, Sabak Bernam, and Tanjom Malim stations. Multivariate techniques including Pearson's correlation and hierarchical cluster analysis were used to apportion the various sources of the metals. The results suggested that the sediment samples collected from the upper course of the river had lower metal concentrations, while sediments in the middle and lower courses of the river had higher metal concentrations. Therefore, our results can be useful as a baseline data for government bodies to adopt corrective measure on the issues related to heavy metal pollution in the Bernam River in the future.
The present study investigates the concentration of Pb, Cd, Ni, Zn, and Cu in the paddy field soils collected from Tumpat, Kelantan. Soil samples were treated with sequential extraction to distinguish the anthropogenic and lithogenic origin of Pb, Cd, Ni, Zn, and Cu. ELFE and oxidizable-organic fractions were detected as the lowest accumulation of Pb, Cd, Ni, Zn, and Cu. Therefore, all the heavy metals examined were concentrated, particularly in resistant fraction, indicating that those heavy metals occurred and accumulated in an unavailable form. The utilization of agrochemical fertilizers and pesticides might not elevate the levels of heavy metals in the paddy field soils. In comparison, the enrichment factor and geoaccumulation index for Pb, Cd, Ni, Zn, and Cu suggest that these heavy metals have the potential to cause environmental risk, although they present abundance in resistant fraction. Therefore, a complete study should be conducted based on the paddy cycle, which in turn could provide a clear picture of heavy metals distribution in the paddy field soils.
Forest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.
Drought monitoring is crucial for assessing and mitigating the impacts of water scarcity on various sectors and ecosystems. Although traditional drought monitoring relies on soil moisture data, remote sensing technology has have significantly augmented the capabilities for drought monitoring. This study aims to evaluate the accuracy and applicability of two temperature vegetation drought indices (TVDI), TVDINDVI and TVDIEVI, constructed using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) vegetation indices for drought monitoring. Using Guangdong Province as a case, enhanced versions of these indices, developed through Savitzky-Golay filtering and terrain correction were employed. Additionally, Pearson correlation analysis and F-tests were utilized to determine the suitability of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) in correlation with TVDINDVI and TVDIEVI. The results show that TVDINDVI had more meteorological stations passing both significance test levels (P
Nitrate-nitrogen leaching from agricultural areas is a major cause for groundwater pollution. Polluted groundwater with high levels of nitrate is hazardous and cause adverse health effects. Human consumption of water with elevated levels of NO3-N has been linked to the infant disorder methemoglobinemia and also to non-Hodgkin's disease lymphoma in adults. This research aims to study the temporal patterns and source apportionment of nitrate-nitrogen leaching in a paddy soil at Ladang Merdeka Ismail Mulong in Kelantan, Malaysia. The complex data matrix (128 x 16) of nitrate-nitrogen parameters was subjected to multivariate analysis mainly Principal Component Analysis (PCA) and Discriminant Analysis (DA). PCA extracted four principal components from this data set which explained 86.4% of the total variance. The most important contributors were soil physical properties confirmed using Alyuda Forecaster software (R2 = 0.98). Discriminant analysis was used to evaluate the temporal variation in soil nitrate-nitrogen on leaching process. Discriminant analysis gave four parameters (hydraulic head, evapotranspiration, rainfall and temperature) contributing more than 98% correct assignments in temporal analysis. DA allowed reduction in dimensionality of the large data set which defines the four operating parameters most efficient and economical to be monitored for temporal variations. This knowledge is important so as to protect the precious groundwater from contamination with nitrate.
In this work, the DRASTIC and GALDIT models were employed to determine the groundwater vulnerability to contamination from anthropogenic activities and seawater intrusion in Kapas Island. In addition, the work also utilized sensitivity analysis to evaluate the influence of each individual parameter used in developing the final models. Based on these effects and variation indices of the said parameters, new effective weights were determined and were used to create modified DRASTIC and GALDIT models. The final DRASTIC model classified the island into five vulnerability classes: no risk (110-140), low (140-160), moderate (160-180), high (180-200), and very high (>200), covering 4, 26, 59, 4, and 7 % of the island, respectively. Likewise, for seawater intrusion, the modified GALDIT model delineates the island into four vulnerability classes: very low (<90), low (90-110), moderate (110-130), and high (>130) covering 39, 33, 18, and 9 % of the island, respectively. Both models show that the areas that are likely to be affected by anthropogenic pollution and seawater intrusion are within the alluvial deposit at the western part of the island. Pearson correlation was used to verify the reliability of the two models in predicting their respective contaminants. The correlation matrix showed a good relationship between DRASTIC model and nitrate (r = 0.58). In a similar development, the correlation also reveals a very strong negative relationship between GALDIT model and seawater contaminant indicator (resistivity Ωm) values (r = -0.86) suggesting that the model predicts more than 86 % of seawater intrusion. In order to facilitate management strategy, suitable areas for artificial recharge were identified through modeling. The result suggested some areas within the alluvial deposit at the western part of the island as suitable for artificial recharge. This work can serve as a guide for a full vulnerability assessment to anthropogenic pollution and seawater intrusion in small islands and will help policy maker and manager with understanding needed to ensure sustainability of the island's aquifer.
Metisa plana (Walker) is a leaf defoliating pest that is able to cause staggering economical losses to oil palm cultivation. Considering the economic devastation that the pest could bring, an early warning system to predict its outbreak is crucial. The state of art of satellite technologies are now able to derive environmental factors such as relative humidity (RH) that may influence pest population's fluctuations in rapid, harmless, and cost-effective manners. This study examined the relationship between the presence of Metisa plana at different time lags and remote sensing (RS) derived RH by using statistical and machine learning approaches. Metisa plana census data of cumulated larvae instar 1, 2, 3, and 4 were collected biweekly in 2014 and 2015 in an oil palm plantation in Muadzam Shah, Pahang, Malaysia. Relative humidity values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6) and paired with the respective census data. Pearson's correlation was carried out to analyse the relationship between Metisa plana and RH at different time lags. Regression analyses and artificial neural network (ANN) were also conducted to develop the best prediction model of Metisa plana's outbreak. The results showed relatively high correlations, positively or negatively, between the presences of Metisa plana with RH ranging from 0.46 to 0.99. ANN was found to be superior to regression models with the adjusted coefficient of determination (R2) between the actual and predicted Metisa plana values ranging from 0.06 to 0.57 versus 0.00 to 0.05. The analysis on the best time lags illustrated that the multiple time lags were more influential on the Metisa plana population than the individual time lags. The best Metisa plana prediction model was derived from T1, T2 and T3 multiple time lags modelled using the ANN algorithm with R2 value of 0.57, errors below 1.14 and accuracies above 93%. Based on the result of this study, the elucidation of Metisa plana's landscape ecology was possible with the utilization of RH as the predictor variable in consideration of the time lag effects of RH on the pest's population.
The objective of the study is to examine the impact of environmental indicators and air pollution on "health" and "wealth" for the low-income countries. The study used a number of promising variables including arable land, fossil fuel energy consumption, population density, and carbon dioxide emissions that simultaneously affect the health (i.e., health expenditures per capita) and wealth (i.e., GDP per capita) of the low-income countries. The general representation for low-income countries has shown by aggregate data that consist of 39 observations from the period of 1975-2013. The study decomposes the data set from different econometric tests for managing robust inferences. The study uses temporal forecasting for the health and wealth model by a vector error correction model (VECM) and an innovation accounting technique. The results show that environment and air pollution is the menace for low-income countries' health and wealth. Among environmental indicators, arable land has the largest variance to affect health and wealth for the next 10-year period, while air pollution exerts the least contribution to change health and wealth of low-income countries. These results indicate the prevalence of war situation, where environment and air pollution become visible like "gun" and "bullet" for low-income countries. There are required sound and effective macroeconomic policies to combat with the environmental evils that affect the health and wealth of the low-income countries.
This study aims to determine the concentration of sterols used as biomarkers in the surface microlayer (SML) in estuarine areas of the Selangor River, Malaysia. Samples were collected during different seasons through the use of a rotation drum. The analysis of sterols was performed using gas chromatography equipped with a flame ionisation detector (GC-FID). The results showed that the concentrations of total sterols in the SML ranged from 107.06 to 505.55 ng L(-1). The total sterol concentration was found to be higher in the wet season. Cholesterol was found to be the most abundant sterols component in the SML. The diagnostic ratios of sterols show the influence of natural sources and waste on the contribution of sterols in the SML. Further analysis, using principal component analysis (PCA), showed distinct inputs of sterols derived from human activity (40.58%), terrigenous and plant inputs (22.59%) as well as phytoplankton and marine inputs (17.35%).
The spatial distributions of Na, Mg, K, Ca, Cr, Fe, Ni, Cu, Zn, As, Se and Pb in Hemibagrus sp. from Selangor River and a reference site were determined with inductively coupled plasma-mass spectrometer, in comparison to the levels in their surrounding water body and sediments. The results demonstrated significant differences in elemental accumulation pattern in different fish tissues originated from both sites. The variations observed were mainly subjected to their metabolic activities, and also the influence of the surrounding medium. In general, the liver tends to accumulate higher concentration of metals followed by the gills, and muscle tissues. The data also indicate associations between the concentrations of metal contaminants measured in the fish and the levels observed at the sites. The concentrations of hazardous metals As, Se and Pb in all the studied tissues reflect the influence of anthropogenic inputs. This suggests the potential utility of widely available Hemibagrus sp. as a valuable bioindicator of metal pollution in environmental monitoring and assessment.
Evaluation of health risks due to heavy metals exposure via drinking water from ex-mining ponds in Klang Valley and Melaka has been conducted. Measurements of As, Cd, Pb, Mn, Fe, Na, Mg, Ca, and dissolved oxygen, pH, electrical conductivity, total dissolved solid, ammoniacal nitrogen, total suspended solid, biological oxygen demand were collected from 12 ex-mining ponds and 9 non-ex-mining lakes. Exploratory analysis identified As, Cd, and Pb as the most representative water quality parameters in the studied areas. The metal exposures were simulated using Monte Carlo methods and the associated health risks were estimated at 95th and 99th percentile. The results revealed that As was the major risk factor which might have originated from the previous mining activity. For Klang Valley, adults that ingested water from those ponds are at both non-carcinogenic and carcinogenic risks, while children are vulnerable to non-carcinogenic risk; for Melaka, only children are vulnerable to As complications. However, dermal exposure showed no potential health consequences on both adult and children groups.
The Guerouaou aquifer investigation spanning 280 km2 in Ain Zohra yields promising outcomes, instilling optimism for regional water quality. These analyses were applied to 45 sampling instances from 43 wells, enabling a comprehensive water quality assessment. Groundwater conductivity ranged from medium to high, peaking at 18360 ms/cm2. The conductivity reveals insights about the groundwater's mineralization. Key physiochemical parameters fell within desirable thresholds, bolstering the positive perspective. HCO3- levels spanned 82-420 mg/L, while chloride content ranged from 38 to 5316 mg/L, benefiting water quality. NO3- ions, vital for gauging pollution, ranged from 0 to 260 mg/L, indicating favorable results. Cation concentrations exhibited encouraging variations: Ca2+- 24 to 647 mg/L, Mg2+- 12 to 440 mg/L, Na+- 18 to 2722 mg/L, K+- 1.75 to 28.65 mg/L. These collectively favor water quality. Halite breakdown dominated mineralization, as evidenced by the prevalence of Na-Cl-Na-SO4 facies. Water resource management and local communities need effective management and mitigation strategies to prevent saltwater intrusion.
The over-exploitation of mineral resources has led to increasingly serious dust pollution in mines, resulting in a series of negative impacts on the environment, mine workers (occupational health) and nearby residents (public health). For the environment, mine dust pollution is considered a major threat on surface vegetation, landscapes, weather conditions and air quality, leading to serious environmental damage such as vegetation reduction and air pollution; for occupational health, mine dust from the mining process is also regarded as a major threat to mine workers' health, leading to occupational diseases such as pneumoconiosis and silicosis; for public health, the pollutants contained in mine dust may pollute surrounding rivers, farmlands and crops, which poses a serious risk to the domestic water and food security of nearby residents who are also susceptible to respiratory diseases from exposure to mine dust. Therefore, the second section of this paper combines literature research, statistical studies, and meta analysis to introduce the public mainly to the severity of mine dust pollution and its hazards to the environment, mine workers (occupational health), and residents (public health), as well as to present an outlook on the management of mine dust pollution. At the same time, in order to propose a method for monitoring mine dust pollution on a regional scale, based on the Dense Dark Vegetation (DDV) algorithm, the third section of this paper analysed the aerosol optical depth (AOD) change in Dexing City of China using the data of 2010, 2014, 2018 and 2021 from the NASA MCD19A2 Dataset to explore the mine dust pollution situation and the progress of pollution treatment in Dexing City from 2010 to 2021. As a discussion article, this paper aims to review the environmental and health risks caused by mine dust pollution, to remind the public to take mine dust pollution seriously, and to propose the use of remote sensing technologies to monitor mine dust pollution, providing suggestions for local governments as well as mines on mine dust monitoring measures.
In order to expound on the present situation and potential risk of road dust heavy metals in major cities, a total of 114 literatures mainly over the past two decades, involving more than 5000 sampling sites in 61 cities of 21 countries, were screened through the collection and analysis of research papers. The concentration, sources, distribution, health risk, sample collection, and analytical methods of heavy metal research on road dust in cities around the world are summarized. The results show that Cd, Zn, and Cu in many urban road dusts in the world are higher than the grade II of the Chinese maximum allowable concentration of potentially toxic elements in the soil. Geo-accumulation index values show that Pb > Cd > Zn > Cu had the highest contamination levels. Hazard index assessment indicates Pb and Cr had the highest potential health risk, especially for children in which ingestion was found as the main exposure pathway. Moreover, through comparative analysis, it is found that some pollutants are higher in developed and industrialized cities and transport (53%) followed by industrial emissions (35%) provide the major contributions to the sources of heavy metals.
The characteristics of urban stormwater pollution in the tropics are still poorly understood. This issue is crucial to the tropical environment because its rainfall and runoff generation processes are so different from temperate regions. In this regard, a stormwater monitoring program was carried out at three urban catchments (e.g. residential, commercial and industrial) in the southern part of Peninsular Malaysia. A total of 51 storm events were collected at these three catchments. Samples were analyzed for total suspended solids, 5-day biochemical oxygen demand, chemical oxygen demand (COD), oil and grease, nitrate nitrogen, nitrite nitrogen, ammonia nitrogen (NH3-N), soluble reactive phosphorus and total phosphorus. Principal component analysis (PCA) and hierarchical cluster analysis were used to interpret the stormwater quality data for pattern recognition and identification of possible sources. The most likely sources of stormwater pollutants at the residential catchment were from surface soil and leachate of fertilizer from domestic lawns and gardens, whereas the most likely sources for the commercial catchment were from discharges of food waste and washing detergent. In the industrial catchment, the major sources of pollutants were discharges from workshops and factories. The PCA factors further revealed that COD and NH3-N were the major pollutants influencing the runoff quality in all three catchments.
Several organisms have been used as indicators, bio-monitoring agents or test organisms in ecotoxicological studies. A close relative of the well established Japanese medaka, the Java medaka (Oryzias javanicus), has the potential to be a test organism. The fish is native to the estuaries of the Malaysian Peninsula, Thailand, Indonesia and Singapore. In this study, newly fertilised eggs were exposed to different concentrations of Cd and Hg. Observations were done on the development of the embryos. Exposure to low levels of Cd and Hg (0.01-0.05 ppm) resulted in several developmental disorders that led to death. Exposure to ≥1.0 ppm Cd resulted in immediate developmental arrest. The embryos of Java medaka showed tolerance to a certain extent when exposed to ≥1.0 ppm Hg compared to Cd. Based on the sensitivity of the embryos, Java medaka is a suitable test organism for ecotoxicology in the tropical region.
Microfluidics-based lab-on-chip (LOC) systems are an active research area that is revolutionising high-throughput sequencing for the fast, sensitive and accurate detection of a variety of pathogens. LOCs also serve as portable diagnostic tools. The devices provide optimum control of nanolitre volumes of fluids and integrate various bioassay operations that allow the devices to rapidly sense pathogenic threat agents for environmental monitoring. LOC systems, such as microfluidic biochips, offer advantages compared to conventional identification procedures that are tedious, expensive and time consuming. This paper aims to provide a broad overview of the need for devices that are easy to operate, sensitive, fast, portable and sufficiently reliable to be used as complementary tools for the control of pathogenic agents that damage the environment.
To manage and conserve biodiversity, one must know what is being lost, where, and why, as well as which remedies are likely to be most effective. Metabarcoding technology can characterise the species compositions of mass samples of eukaryotes or of environmental DNA. Here, we validate metabarcoding by testing it against three high-quality standard data sets that were collected in Malaysia (tropical), China (subtropical) and the United Kingdom (temperate) and that comprised 55,813 arthropod and bird specimens identified to species level with the expenditure of 2,505 person-hours of taxonomic expertise. The metabarcode and standard data sets exhibit statistically correlated alpha- and beta-diversities, and the two data sets produce similar policy conclusions for two conservation applications: restoration ecology and systematic conservation planning. Compared with standard biodiversity data sets, metabarcoded samples are taxonomically more comprehensive, many times quicker to produce, less reliant on taxonomic expertise and auditable by third parties, which is essential for dispute resolution.
Strong global demand for tropical timber and agricultural products has driven large-scale logging and subsequent conversion of tropical forests. Given that the majority of tropical landscapes have been or will likely be logged, the protection of biodiversity within tropical forests thus depends on whether species can persist in these economically exploited lands, and if species cannot persist, whether we can protect enough primary forest from logging and conversion. However, our knowledge of the impact of logging and conversion on biodiversity is limited to a few taxa, often sampled in different locations with complex land-use histories, hampering attempts to plan cost-effective conservation strategies and to draw conclusions across taxa. Spanning a land-use gradient of primary forest, once- and twice-logged forests, and oil palm plantations, we used traditional sampling and DNA metabarcoding to compile an extensive data set in Sabah, Malaysian Borneo for nine vertebrate and invertebrate taxa to quantify the biological impacts of logging and oil palm, develop cost-effective methods of protecting biodiversity, and examine whether there is congruence in response among taxa. Logged forests retained high species richness, including, on average, 70% of species found in primary forest. In contrast, conversion to oil palm dramatically reduces species richness, with significantly fewer primary-forest species than found on logged forest transects for seven taxa. Using a systematic conservation planning analysis, we show that efficient protection of primary-forest species is achieved with land portfolios that include a large proportion of logged-forest plots. Protecting logged forests is thus a cost-effective method of protecting an ecologically and taxonomically diverse range of species, particularly when conservation budgets are limited. Six indicator groups (birds, leaf-litter ants, beetles, aerial hymenopterans, flies, and true bugs) proved to be consistently good predictors of the response of the other taxa to logging and oil palm. Our results confidently establish the high conservation value of logged forests and the low value of oil palm. Cross-taxon congruence in responses to disturbance also suggests that the practice of focusing on key indicator taxa yields important information of general biodiversity in studies of logging and oil palm.
Five homologs (C10-C14) of linear alkylbenzene sulfonate (LAS) were quantitated in surface water collected in the Langat and Selangor River basins using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A geographic information system (GIS) was used to spatially analyze the occurrence of LAS in both river basins, and the LAS contamination associated with the population was elucidated by spatial analysis at a sub-basin level. The LAS concentrations in the dissolved phase (<0.45 μm) and 4 fractions separated by particle size (<0.1 μm, 0.1-1 μm, 1-11 μm and >11 μm) were analyzed to elucidate the environmental fate of LAS in the study area. The environmental risks of the observed LAS concentration were assessed based on predicted no effect concentration (PNEC) normalized by a quantitative structure-activity relationship model. The LAS contamination mainly occurred from a few populated sub-basins, and it was correlated with the population density and ammonia nitrogen. The dissolved phase was less than 20% in high contamination sites (>1000 μg/L), whereas it was more than 60% in less contaminated sites (<100 μg/L). The environmental fate of LAS in the study area was primarily subject to the adsorption to suspended solids rather than biodegradation because the LAS homologs, particularly in longer alkyl chain lengths, were considerably absorbed to the large size fraction (>11 μm) that settled in a few hours. The observed LAS concentrations exceeded the normalized PNEC at 3 sites, and environmental risk areas and susceptible areas to the LAS contamination were spatially identified based on their catchment areas.