Displaying publications 1 - 20 of 290 in total

  1. Palanichamy N, Haw SC, S S, Murugan R, Govindasamy K
    F1000Res, 2022;11:406.
    PMID: 36531254 DOI: 10.12688/f1000research.73166.1
    Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM 2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM 2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM 2.5 concentrations in the smart cities of Malaysia by comparing supervised ML techniques, which helps to mitigate its adverse effects. Methods In this paper, ML models for forecasting PM 2.5 concentrations were investigated on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data cleaning and a normalization process. Next, it was reduced into an informative dataset with location and time factors in the feature extraction process. The dataset was fed into three supervised ML classifiers, which include random forest (RF), artificial neural network (ANN) and long short-term memory (LSTM). Finally, their output was evaluated using the confusion matrix and compared to identify the best model for the accurate prediction of PM 2.5. Results Overall, the experimental result shows an accuracy of 97.7% was obtained by the RF model in comparison with the accuracy of ANN (61.14%) and LSTM (61.77%) in predicting PM 2.5. Discussion RF performed well when compared with ANN and LSTM for the given data with minimum features. RF was able to reach good accuracy as the model learns from the random samples by using decision tree with the maximum vote on the predictions.
    Matched MeSH terms: Environmental Monitoring/methods
  2. Menon V, Sharma S, Gupta S, Ghosal A, Nadda AK, Jose R, et al.
    Chemosphere, 2023 Mar;317:137848.
    PMID: 36642147 DOI: 10.1016/j.chemosphere.2023.137848
    Synthetic plastics, which are lightweight, durable, elastic, mouldable, cheap, and hydrophobic, were originally invented for human convenience. However, their non-biodegradability and continuous accumulation at an alarming rate as well as subsequent conversion into micro/nano plastic scale structures via mechanical and physio-chemical degradation pose significant threats to living beings, organisms, and the environment. Various minuscule forms of plastics detected in water, soil, and air are making their passage into living cells. High temperature and ambient humidity increase the degradation potential of plastic polymers photo-catalytically under sunlight or UV-B radiations. Microplastics (MPs) of polyethylene terephthalate, polyethylene, polystyrene, polypropylene, and polyvinyl chloride have been detected in bottled water. These microplastics are entering into the food chain cycle, causing serious harm to all living organisms. MPs entering into the food chain are usually inert in nature, possessing different sizes and shapes. Once they enter a cell or tissue, it causes mechanical damage, induces inflammation, disturbs metabolism, and even lead to necrosis. Various generation routes, types, impacts, identification, and treatment of microplastics entering the water bodies and getting associated with various pollutants are discussed in this review. It emphasizes potential detection techniques like pyrolysis, gas chromatography-mass spectrometry (GC-MS), micro-Raman spectroscopy, and fourier transform infrared spectroscopy (FT IR) spectroscopy for microplastics from water samples.
    Matched MeSH terms: Environmental Monitoring/methods
  3. Alkhadher SAA, Suratman S, Mohd Sallan MIB
    J Environ Manage, 2023 Nov 01;345:118464.
    PMID: 37454570 DOI: 10.1016/j.jenvman.2023.118464
    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.
    Matched MeSH terms: Environmental Monitoring/methods
  4. Chahban M, Akodad M, Skalli A, Gueddari H, El Yousfi Y, Ait Hmeid H, et al.
    Environ Res, 2024 Mar 01;244:117939.
    PMID: 38128604 DOI: 10.1016/j.envres.2023.117939
    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.
    Matched MeSH terms: Environmental Monitoring/methods
  5. Ahmad-Kamil EI, Ramli R, Jaaman SA, Bali J, Al-Obaidi JR
    ScientificWorldJournal, 2013;2013:892746.
    PMID: 24163635 DOI: 10.1155/2013/892746
    Seagrass is a valuable marine ecosystem engineer. However, seagrass population is declining worldwide. The lack of seagrass research in Malaysia raises questions about the status of seagrasses in the country. The seagrasses in Lawas, which is part of the coral-mangrove-seagrass complex, have never been studied in detail. In this study, we examine whether monthly changes of seagrass population in Lawas occurred. Data on estimates of seagrass percentage cover and water physicochemical parameters (pH, turbidity, salinity, temperature, and dissolved oxygen) were measured at 84 sampling stations established within the study area from June 2009 to May 2010. Meteorological data such as total rainfall, air temperature, and Southern Oscillation Index were also investigated. Our results showed that (i) the monthly changes of seagrass percentage cover are significant, (ii) the changes correlated significantly with turbidity measurements, and (iii) weather changes affected the seagrass populations. Our study indicates seagrass percentage increased during the El-Nino period. These results suggest that natural disturbances such as weather changes affect seagrass populations. Evaluation of land usage and measurements of other water physicochemical parameters (such as heavy metal, pesticides, and nutrients) should be considered to assess the health of seagrass ecosystem at the study area.
    Matched MeSH terms: Environmental Monitoring/methods*
  6. Wong YB, Gibbins C, Azhar B, Phan SS, Scholefield P, Azmi R, et al.
    Environ Monit Assess, 2023 Apr 17;195(5):577.
    PMID: 37062786 DOI: 10.1007/s10661-023-11113-z
    Oil palm agriculture has caused extensive land cover and land use changes that have adversely affected tropical landscapes and ecosystems. However, monitoring and assessment of oil palm plantation areas to support sustainable management is costly and labour-intensive. This study used an unmanned aerial vehicles (UAV) to map smallholder farms and applied multi-criteria analysis to data generated from orthomosaics, to provide a set of sustainability indicators for the farms. Images were acquired from a UAV, with structure from motion (SfM) photogrammetry then used to produce orthomosaics and digital elevation models of the farm areas. Some of the inherent problems using high spatial resolution imagery for land cover classification were overcome by using texture analysis and geographic object-based image analysis (OBIA). Six spatially explicit environmental metrics were developed using multi-criteria analysis and used to generate sustainability indicator layers from the UAV data. The SfM and OBIA approach provided an accurate, high-resolution (~5 cm) image-based reconstruction of smallholder farm landscapes, with an overall classification accuracy of 89%. The multi-criteria analysis highlighted areas with lower sustainability values, which should be considered targets for adoption of sustainable management practices. The results of this work suggest that UAVs are a cost-effective tool for sustainability assessments of oil palm plantations, but there remains the need to plan surveys and image processing workflows carefully. Future work can build on our proposed approach, including the use of additional and/or alternative indicators developed through consultation with the oil palm industry stakeholders, to support certification schemes such as the Roundtable on Sustainable Palm Oil (RSPO).
    Matched MeSH terms: Environmental Monitoring/methods
  7. Kurniawan R, Budi Alamsyah AR, Fudholi A, Purwanto A, Sumargo B, Gio PU, et al.
    Environ Pollut, 2023 Oct 01;334:122212.
    PMID: 37454714 DOI: 10.1016/j.envpol.2023.122212
    The high concentration of nitrogen dioxide (NO2) is to blame for West Java's poor Air Quality Index (AQI). So, this study aims to determine the influence of industrial activity as reflected by the value of its imports and exports, wind speed, and ozone (O3) on the high concentration of tropospheric NO2. The method used is the econometric Vector Error Correction Model (VECM) approach to capture the existence of a short-term and long-term relationship between tropospheric NO2 and its predictor variables. The data used in this study is in the form of monthly time series data for the 2018-2022 period sourced from satellite images (Sentinel-5P and ECMWF Climate Reanalysis) and publications of the Central Bureau of Statistics (BPS-Statistics Indonesia). The results explained that, in the short-term, tropospheric NO2 and O3 influence each other as they would in a photochemical reaction. In the long-term, exports from the industrial sector and wind speed have a significant effect on the concentration of tropospheric NO2. The short-term effect occurs directly in the first month after the shock, while the long-term effect occurs in the second month after the shock. Wind gusts originating from industrial areas cause air conditions to be even more alarming because tropospheric NO2 pollutants spread throughout the region in West Java. Based on the coefficient correlation result, the high number of pneumonia cases is one of the impacts caused by air pollution.
    Matched MeSH terms: Environmental Monitoring/methods
  8. Umar HA, Khanan MFA, Shiru MS, Ahmad A, Rahman MZA, Din AHM
    Environ Sci Pollut Res Int, 2023 Nov;30(55):116848-116859.
    PMID: 36633746 DOI: 10.1007/s11356-023-25144-z
    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.
    Matched MeSH terms: Environmental Monitoring/methods
  9. Kumar A, Singh UK, Pradhan B
    J Environ Manage, 2024 Feb;351:119943.
    PMID: 38169263 DOI: 10.1016/j.jenvman.2023.119943
    Acid mine drainage (AMD) is recognized as a major environmental challenge in the Western United States, particularly in Colorado, leading to extreme subsurface contamination issue. Given Colorado's arid climate and dependence on groundwater, an accurate assessment of AMD-induced contamination is deemed crucial. While in past, machine learning (ML)-based inversion algorithms were used to reconstruct ground electrical properties (GEP) such as relative dielectric permittivity (RDP) from ground penetrating radar (GPR) data for contamination assessment, their inherent non-linear nature can introduce significant uncertainty and non-uniqueness into the reconstructed models. This is a challenge that traditional ML methods are not explicitly designed to address. In this study, a probabilistic hybrid technique has been introduced that combines the DeepLabv3+ architecture-based deep convolutional neural network (DCNN) with an ensemble prediction-based Monte Carlo (MC) dropout method. Different MC dropout rates (1%, 5%, and 10%) were initially evaluated using 1D and 2D synthetic GPR data for accurate and reliable RDP model prediction. The optimal rate was chosen based on minimal prediction uncertainty and the closest alignment of the mean or median model with the true RDP model. Notably, with the optimal MC dropout rate, prediction accuracy of over 95% for the 1D and 2D cases was achieved. Motivated by these results, the hybrid technique was applied to field GPR data collected over an AMD-impacted wetland near Silverton, Colorado. The field results underscored the hybrid technique's ability to predict an accurate subsurface RDP distribution for estimating the spatial extent of AMD-induced contamination. Notably, this technique not only provides a precise assessment of subsurface contamination but also ensures consistent interpretations of subsurface condition by different environmentalists examining the same GPR data. In conclusion, the hybrid technique presents a promising avenue for future environmental studies in regions affected by AMD or other contaminants that alter the natural distribution of GEP.
    Matched MeSH terms: Environmental Monitoring/methods
  10. Huang L, Zhu Y, Liu H, Wang Y, Allen DT, Chel Gee Ooi M, et al.
    Environ Int, 2023 Jan;171:107710.
    PMID: 36566719 DOI: 10.1016/j.envint.2022.107710
    In recent years, ozone pollution in China has been shown to increase in frequency and persistence despite the concentrations of fine particulate matter (PM2.5) decreasing steadily. Open crop straw burning (OCSB) activities are extensive in China and emit large amounts of trace gases during a short period that could lead to elevated ozone concentrations. This study addresses the impacts of OCSB emissions on ground-level ozone concentration and the associated health impact in China. Total VOCs and NOx emissions from OCSB in 2018 were 798.8 Gg and 80.6 Gg, respectively, with high emissions in Northeast China (31.7%) and North China (23.7%). Based on simulations conducted for 2018, OCSB emissions are estimated to contribute up to 0.95 µg/m3 increase in annual averaged maximum daily 8-hour (MDA8) ozone and up to 1.35 µg/m3 for the ozone season average. The significant impact of OCSB emissions on ozone is mainly characterized by localized and episodic (e.g., daily) changes in ozone concentration, up to 20 µg/m3 in North China and Yangtze River Delta region and even more in Northeast China during the burning season. With the implementation of straw burning bans, VOCs and NOx emissions from OCSB dropped substantially by 46.9%, particularly over YRD (76%) and North China (60%). Consequently, reduced OCSB emissions result in an overall decrease in annual averaged MDA8 ozone, and reductions in monthly MDA8 ozone could be over 10 µg/m3 in North China. The number of avoided premature death due to reduced OCSB emissions (considering both PM2.5 and ozone) is estimated to be 6120 (95% Confidence Interval: 5320-6800), with most health benefits gained over east and central China. Our results illustrate the effectiveness of straw burning bans in reducing ozone concentrations at annual and national scales and the substantial ozone impacts from OCSB events at localized and episodic scales.
    Matched MeSH terms: Environmental Monitoring/methods
  11. Venkatraman G, Giribabu N, Mohan PS, Muttiah B, Govindarajan VK, Alagiri M, et al.
    Chemosphere, 2024 Mar;351:141227.
    PMID: 38253087 DOI: 10.1016/j.chemosphere.2024.141227
    Polycyclic Aromatic Hydrocarbons (PAHs) profoundly impact public and environmental health. Gaining a comprehensive understanding of their intricate functions, exposure pathways, and potential health implications is imperative to implement remedial strategies and legislation effectively. This review seeks to explore PAH mobility, direct exposure pathways, and cutting-edge bioremediation technologies essential for combating the pervasive contamination of environments by PAHs, thereby expanding our foundational knowledge. PAHs, characterised by their toxicity and possession of two or more aromatic rings, exhibit diverse configurations. Their lipophilicity and remarkable persistence contribute to their widespread prevalence as hazardous environmental contaminants and byproducts. Primary sources of PAHs include contaminated food, water, and soil, which enter the human body through inhalation, ingestion, and dermal exposure. While short-term consequences encompass eye irritation, nausea, and vomiting, long-term exposure poses risks of kidney and liver damage, difficulty breathing, and asthma-like symptoms. Notably, cities with elevated PAH levels may witness exacerbation of bronchial asthma and chronic obstructive pulmonary disease (COPD). Bioremediation techniques utilising microorganisms emerge as a promising avenue to mitigate PAH-related health risks by facilitating the breakdown of these compounds in polluted environments. Furthermore, this review delves into the global concern of antimicrobial resistance associated with PAHs, highlighting its implications. The environmental effects and applications of genetically altered microbes in addressing this challenge warrant further exploration, emphasising the dynamic nature of ongoing research in this field.
    Matched MeSH terms: Environmental Monitoring/methods
  12. Lee SC, Hashim R, Motamedi S, Song KI
    ScientificWorldJournal, 2014;2014:494020.
    PMID: 24955408 DOI: 10.1155/2014/494020
    Threats to beaches have accelerated the coastal destruction. In recent decades, geotextile tubes were used around the world to prevent coastal erosion, to encourage beach nourishment, and to assist mangrove rehabilitation. However, the applications of geotextile tube in sandy and muddy coasts have different concerns as the geological settings are different. Applications of geotextile tubes in sandy beaches were mainly to prevent coastline from further erosion and to nourish the beach. However, for the muddy coasts, mangrove rehabilitation and conservation were additional concerns in coastal management schemes. The mangrove forests are natural barriers which can be found on the muddy coasts of many tropical countries. In this paper, the viability of geotextile tubes in sandy and muddy beaches was analysed. The advantages and disadvantages of the utilization of geotextile tubes in coastal management were discussed based on the experiences from the tropical countries such as Mexico, Malaysia, and Thailand. From the case studies, impressive improvements in coastal restoration after installation of geotextile tubes were shown. Based on the discussion, several recommendations to improve the application of geotextile tubes were suggested in this paper.
    Matched MeSH terms: Environmental Monitoring/methods*
  13. Jaafar SA, Latif MT, Chian CW, Han WS, Wahid NB, Razak IS, et al.
    Mar Pollut Bull, 2014 Jul 15;84(1-2):35-43.
    PMID: 24930738 DOI: 10.1016/j.marpolbul.2014.05.047
    This study was conducted to determine the composition of surfactants in the sea-surface microlayer (SML) and atmospheric aerosol around the southern region of the Peninsular Malaysia. Surfactants in samples taken from the SML and atmospheric aerosol were determined using a colorimetric method, as either methylene blue active substances (MBAS) or disulphine blue active substances (DBAS). Principal component analysis with multiple linear regressions (PCA-MLR), using the anion and major element composition of the aerosol samples, was used to determine possible sources of surfactants in atmospheric aerosol. The results showed that the concentrations of surfactants in the SML and atmospheric aerosol were dominated by anionic surfactants and that surfactants in aerosol were not directly correlated (p>0.05) with surfactants in the SML. Further PCA-MLR from anion and major element concentrations showed that combustion of fossil fuel and sea spray were the major contributors to surfactants in aerosol in the study area.
    Matched MeSH terms: Environmental Monitoring/methods*
  14. Fulazzaky MA, Abdul Gany AH
    J Environ Manage, 2009 Jun;90(8):2387-92.
    PMID: 19346056 DOI: 10.1016/j.jenvman.2009.02.017
    Most developing countries, particularly Indonesia, will be facing problems of sludge pressure in the next decades due to the increase in practices of legal and illegal logging as well as land and water demands. Consequently, they will also be facing the challenges of soil erosion and sludge management due to increased quantities of sludge coming from several potential sources, such as activated sludge, chemical sludge, fecal sludge and solid wastes as well as erosion and sedimentation. Although the government of Indonesia has enacted laws and policies to speed up the implementation of the programs and activities related to sludge management, the detailed practice concepts in implementing the programs need to be identified. Discussion of role-sharing amongst the related government agencies, private institutions and other stakeholders is urgent for clarifying the participation of each party in the next years to come. This paper proposes a management approach and level of responsibilities in sludge management. Implementation of zero DeltaQ, zero DeltaS and zero DeltaP policies needs to be adopted by local and central governments. Application of sludge on the agricultural lands and other uses will promote sustainable development.
    Matched MeSH terms: Environmental Monitoring/methods*
  15. Jahed Armaghani D, Hajihassani M, Marto A, Shirani Faradonbeh R, Mohamad ET
    Environ Monit Assess, 2015 Nov;187(11):666.
    PMID: 26433903 DOI: 10.1007/s10661-015-4895-6
    Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.
    Matched MeSH terms: Environmental Monitoring/methods*
  16. Prabakaran K, Nagarajan R, Eswaramoorthi S, Anandkumar A, Franco FM
    Chemosphere, 2019 Mar;219:933-953.
    PMID: 30572242 DOI: 10.1016/j.chemosphere.2018.11.158
    The geochemistry and distribution of major, trace and rare earth elements (REE's) was studied in the surface sediments of the Lower Baram River during two seasons: the Monsoon (MON) and Post - monsoon (POM). The major geochemical processes controlling the distribution and mobility of major, trace and REE's in the Lower Baram River surface sediments was revealed through factor analysis. The risk assessment of major and trace element levels was studied at three specific levels; i.e. the enrichment level [Contamination Factor (Cf), with the geo-accumulation index (Igeo)], the availability level [metals bound to different fractions, risk assessment code (RAC)], and the biological toxicity level [effect range low (ERL) and effect range medium (ERM)]. The results of all the indices indicate that Cu is the element of concern in the Lower Baram River sediments. The geochemical fractionation of major and trace elements were studied through sequential extraction and the results indicated a higher concentration of Mn in the exchangeable fraction. The element of concern, Cu, was found to be highly associated in the organic bound (F4) fraction during both seasons and a change in the redox, possibly due to storms or dredging activities may stimulate the release of Cu into the overlying waters of the Lower Baram River.
    Matched MeSH terms: Environmental Monitoring/methods*
  17. Bong CH, Lau TL, Ab Ghani A, Chan NW
    Water Sci Technol, 2016 Oct;74(8):1876-1884.
    PMID: 27789888
    The understanding of how the sediment deposit thickness influences the incipient motion characteristic is still lacking in the literature. Hence, the current study aims to determine the effect of sediment deposition thickness on the critical velocity for incipient motion. An incipient motion experiment was conducted in a rigid boundary rectangular flume of 0.6 m width with varying sediment deposition thickness. Findings from the experiment revealed that the densimetric Froude number has a logarithmic relationship with both the thickness ratios ts/d and ts/y0 (ts: sediment deposit thickness; d: grain size; y0: normal flow depth). Multiple linear regression analysis was performed using the data from the current study to develop a new critical velocity equation by incorporating thickness ratios into the equation. The new equation can be used to predict critical velocity for incipient motion for both loose and rigid boundary conditions. The new critical velocity equation is an attempt toward unifying the equations for both rigid and loose boundary conditions.
    Matched MeSH terms: Environmental Monitoring/methods*
  18. Ajab H, Ali Khan AA, Nazir MS, Yaqub A, Abdullah MA
    Environ Res, 2019 09;176:108563.
    PMID: 31280029 DOI: 10.1016/j.envres.2019.108563
    Environmental monitoring is important to determine the extent of eco-system pollution and degradation so that effective remedial strategies can be formulated. In this study, an environmentally friendly and cost-effective sensor made up of novel carbon electrode modified with cellulose and hydroxyapatite was developed for the detection of trace lead ions in aqueous system and palm oil mill effluent. Zinc, cadmium, and copper with lead were simultaneously detected using this method. The electrode exhibited high tolerance towards twelve common metal ions and three model surface active substances - sodium dodecyl sulfate, Triton X-100, and cetyltrimethylammonium bromide. Under optimum conditions, the sensor detected lead ions in palm oil mill effluent in the concentration range of 10-50 μg/L with 0.11 ± 0.37 μg/L limit of detection and 0.37 ± 0.37 μg/L limit of quantification. The validation using tap water, blood serum and palm oil mill effluent samples and compared with Atomic Absorption Spectroscopy, suggested excellent sensitivity of the sensor to detect lead ions in simple and complex matrices. The cellulose produced based on "green" techniques from agro-lignocellulosic wastes, in combination with hydroxyapatite, were proven effective as components in the carbon electrode composite. It has great potential in both clinical and environmental use.
    Matched MeSH terms: Environmental Monitoring/methods*
  19. Aburas MM, Ho YM, Ramli MF, Ash'aari ZH
    Environ Monit Assess, 2018 Feb 20;190(3):156.
    PMID: 29464400 DOI: 10.1007/s10661-018-6522-9
    The identification of spatio-temporal patterns of the urban growth phenomenon has become one of the most significant challenges in monitoring and assessing current and future trends of the urban growth issue. Therefore, spatio-temporal and quantitative techniques should be used hand in hand for a deeper understanding of various aspects of urban growth. The main purpose of this study is to monitor and assess the significant patterns of urban growth in Seremban using a spatio-temporal built-up area analysis. The concentric circles approach was used to measure the compactness and dispersion of built-up area by employing Shannon's Entropy method. The spatial directions approach was also utilised to measure the sustainability and speed of development, while the gradient approach was used to measure urban dynamics by employing landscape matrices. The overall results confirm that urban growth in Seremban is dispersed, unbalanced and unsustainable with a rapid speed of regional development. The main contribution of using existing methods with other methods is to provide several spatial and statistical dimensions that can help researchers, decision makers and local authorities understand the trend of growth and its patterns in order to take the appropriate decisions for future urban planning. For example, Shannon's Entropy findings indicate a high value of dispersion between the years 1990 and 2000 and from 2010 to 2016 with a growth rate of approximately 94 and 14%, respectively. Therefore, these results can help and support decision makers to implement alternative urban forms such as the compactness form to achieve an urban form that is more suitable and sustainable. The results of this study confirm the importance of using spatio-temporal built-up area and quantitative analysis to protect the sustainability of land use, as well as to improve the urban planning system via the effective monitoring and assessment of urban growth trends and patterns.
    Matched MeSH terms: Environmental Monitoring/methods
  20. Arora S, Sawaran Singh NS, Singh D, Rakesh Shrivastava R, Mathur T, Tiwari K, et al.
    Comput Intell Neurosci, 2022;2022:9755422.
    PMID: 36531923 DOI: 10.1155/2022/9755422
    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).
    Matched MeSH terms: Environmental Monitoring/methods
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