Displaying publications 81 - 100 of 881 in total

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  1. Irnidayanti Y, Soegianto A, Brabo AH, Abdilla FM, Indriyasari KN, Rahmatin NM, et al.
    Environ Monit Assess, 2023 Jun 26;195(7):884.
    PMID: 37358711 DOI: 10.1007/s10661-023-11535-9
    The Jakarta Bay is the estuary for thirteen rivers that flow through densely populated and industrialized upstream regions. This condition has the potential to pollute the Jakarta Bay with microplastics that are transported from the upstream river. Meanwhile, people, particularly fishermen, continue to use Jakarta Bay for fishing and aquaculture. This study examined microplastics (MP) abundance in the whole tissues of green mussels (Perna viridis) grown in Jakarta Bay, Indonesia, and their health risks. MP was identified in all 120 green mussels, with fiber > film > fragment being the most common kinds. The abundance of fiber was 19 items/g of tissue, whereas the abundances of fragments and film were 14.5 items/g and 15 item/g, respectively. Fourier transform infrared spectroscopy tests on MP from the tissues of green mussels showed that there were 12 different types of MP polymers. The estimated amount of MP that humans consume each year varied from 29,120 MP items/year to 218,400 MP items/year for different age groups. Based on the total mean number of MP found in the tissues of green mussels and the amount of shellfish consumed per person in Indonesia, it was estimated that people ate 775,180 MP through shellfish each year.
    Matched MeSH terms: Environmental Monitoring/methods
  2. Moharir KN, Pande CB, Gautam VK, Singh SK, Rane NL
    Environ Res, 2023 Jul 01;228:115832.
    PMID: 37054834 DOI: 10.1016/j.envres.2023.115832
    The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater management, it is crucial to monitoring and planning based on geology, slope, relief, land use, geomorphology, and the types of the basaltic aquifer in the drought-groundwater deficit area. Moreover, the majority of farmers in the area are heavily dependent on groundwater for their crops. Therefore, delineation of groundwater potential zones (GPZ) is essential, which is defined based on various thematic layers, including geology, geomorphology, slope, aspect, drainage density, lineament density, topographic wetness index (TWI), topographic ruggedness index (TRI), and land use/land cover (LULC). The processing and analysis of this information were carried out using Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) methods. The validity of the results was trained and tested using Receiver Operating Characteristic (ROC) curves, which showed training and testing accuracies of 0.713 and 0.701, respectively. The GPZ map was classified into five classes such as very high, high, moderate, low, and very low. The study revealed that approximately 45% of the area falls under the moderate GPZ, while only 30% of the region is classified as having a high GPZ. The area receives high rainfall but has very high surface runoff due to no proper developed soil and lack of water conservation structures. Every summer season show a declined groundwater level. In this context, results of study area are useful to maintain the groundwater under climate change and summer season. The GPZ map plays an important role in implementing artificial recharge structures (ARS), such as percolation ponds, tube wells, bore wells, cement nala bunds (CNBs), continuous contour trenching (CCTs), and others for development of ground level. This study is significant for developing sustainable groundwater management policies in semi-arid regions, that are experiencing climate change. Proper groundwater potential mapping and watershed development policies can help mitigate the effects of drought, climate change, and water scarcity, while preserving the ecosystem in the Limestone, Shales, and Sandstone compact rock region. The results of this study are essential for farmers, regional planners, policy-makers, climate change experts, and local governments, enabling them to understand the groundwater development possibilities in the study area.
    Matched MeSH terms: Environmental Monitoring/methods
  3. Hossain S, Ahmad Shukri ZN, Waiho K, Ibrahim YS, Minhaz TM, Kamaruzzan AS, et al.
    Environ Pollut, 2023 Jul 15;329:121697.
    PMID: 37088255 DOI: 10.1016/j.envpol.2023.121697
    Microplastics (MPs) occurrence in farmed aquatic organisms has already been the prime priority of researchers due to the food security concerns for human consumption. A number of commercially important aquaculture systems have already been investigated for MPs pollution but the mud crab (Scylla sp.) aquaculture system has not been investigated yet even though it is a highly demanded commercial species globally. This study reported the MPs pollution in the mud crab (Scylla sp.) aquaculture system for the first time. Three different stations of the selected aquafarm were sampled for water and sediment samples and MPs particles in the samples were isolated by the gravimetric analysis (0.9% w/v NaCl solution). MP abundance was visualized under a microscope along with their size, shape, and color. A subset of the isolated MPs was analyzed by scanning electron microscope (SEM), and Fourier transform infrared spectroscopy (FTIR) for the surface and chemical characterization respectively. The average MPs concentration was 47.5 ± 11.875 particles/g in sediment and 127.92 ± 14.99 particles/100 L in the water sample. Fibrous-shaped (72.17%) and transparent-colored (59.37%) MPs were dominant in all the collected samples. However, smaller MPs (>0.05-0.5 mm) were more common in the water samples (47.69%) and the larger (>1-5 mm) MPs were in the sediment samples (47.83%). SEM analysis found cracks and roughness on the surface of the MPs and nylon, polyethylene, polypropylene, and polystyrene MPs were identified by FTIR analysis. PLI value showed hazard level I in water and level II in sediment. The existence of deleterious MPs particles in the mud crab aquaculture system was well evident. The other commercial mud crab aquafarms must therefore be thoroughly investigated in order to include farmed mud crabs as an environmentally vulnerable food security concern.
    Matched MeSH terms: Environmental Monitoring/methods
  4. Ravindiran G, Hayder G, Kanagarathinam K, Alagumalai A, Sonne C
    Chemosphere, 2023 Oct;338:139518.
    PMID: 37454985 DOI: 10.1016/j.chemosphere.2023.139518
    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.
    Matched MeSH terms: Environmental Monitoring/methods
  5. Gholizadeh M, Shadi A, Abadi A, Nemati M, Senapathi V, Karthikeyan S
    J Environ Manage, 2023 Oct 15;344:118386.
    PMID: 37352628 DOI: 10.1016/j.jenvman.2023.118386
    Global production of plastics has increased dramatically in recent decades and is considered a major threat to marine life and human health due to their stability, persistence, and potential to move through food chains. The study was conducted to detect, identify and quantify microplastics (MP) in the gastrointestinal tract (GI) of some commercial fish species in the North Persian Gulf in Bushehr Province: Psettodes erumei, Sphyraena jello, Sillago sihama, Metapenaeus affinis and Portunus segnis. A total of 216 plastic particles were collected from 102 individuals (72.68% of all sampled individuals; MP prevalence of 85.1% for M. affinis, 80% for P. segnis, 70% for P.erumei, 60.3% for S.sihama, 45.2% for S.jello). The average number of microplastics per organism was 2.26 ± 0.38 MP/ind (considering only species that ingested plastic, n = 102) and 1.51 ± 0.40 pieces/ind (considering all species studied, n = 140). Microfibers accounted for 58.49% of the total microplastics, followed by fragments (33.02%) and pellets (8.49%). The most common color of microplastic was black (52.83%), followed by blue (22.64%) and transparent (15.09%). The length of microplastic ranged from 100 to 5000 μm with an average of 854 ± 312 μm. Microplastics were significantly (p 
    Matched MeSH terms: Environmental Monitoring/methods
  6. Pandion K, Dowlath MJH, Arunachalam KD, Abd-Elkader OH, Yadav KK, Nazir N, et al.
    Environ Res, 2023 Oct 15;235:116611.
    PMID: 37437863 DOI: 10.1016/j.envres.2023.116611
    The current study aims to investigate the influence of seasonal changes on the pollution loads of the sediment of a coastal area in terms of its physicochemical features. The research will focus on analyzing the nutrients, organic carbon and particle size of the sediment samples collected from 12 different sampling stations in 3 different seasons along the coastal area. Additionally, the study discusses about the impact of anthropogenic activities such as agriculture and urbanization and natural activities such as monsoon on the sediment quality of the coastal area. The nutrient changes in the sediment were found to be: pH (7.96-9.45), EC (2.89-5.23 dS/m), nitrogen (23.98-57.23 mg/kg), phosphorus (7.75-11.36 mg/kg), potassium (217-398 mg/kg), overall organic carbon (0.35-0.99%), and sediment proportions (8.91-9.3%). Several statistical methods were used to investigate changes in sediment quality. According to the three-way ANOVA test, the mean value of the sediments differs significantly with each season. It correlates significantly with principal factor analysis and cluster analysis across seasons, implying contamination from both natural and man-made sources. This study will contribute to developing effective management strategies for the protection and restoration of degraded coastal ecosystem.
    Matched MeSH terms: Environmental Monitoring/methods
  7. Nasher E, Heng LY, Zakaria Z, Surif S
    ScientificWorldJournal, 2013;2013:858309.
    PMID: 24163633 DOI: 10.1155/2013/858309
    Tourism-related activities such as the heavy use of boats for transportation are a significant source of petroleum hydrocarbons that may harm the ecosystem of Langkawi Island. The contamination and toxicity levels of polycyclic aromatic hydrocarbon (PAH) in the sediments of Langkawi were evaluated using sediment quality guidelines (SQGs) and toxic equivalent factors. Ten samples were collected from jetties and fish farms around the island in December 2010. A gas chromatography/flame ionization detector (GC/FID) was used to analyse the 18 PAHs. The concentration of total PAHs was found to range from 869 ± 00 to 1637 ± 20 ng g⁻¹ with a mean concentration of 1167.00 ± 24 ng g⁻¹, lower than the SQG effects range-low (3442 ng g⁻¹). The results indicated that PAHs may not cause acute biological damage. Diagnostic ratios and principal component analysis suggested that the PAHs were likely to originate from pyrogenic and petrogenic sources. The toxic equivalent concentrations of the PAHs ranged from 76.3 to 177 ng TEQ/g d.w., which is lower compared to similar studies. The results of mean effects range-median quotient of the PAHs were lower than 0.1, which indicate an 11% probability of toxicity effect. Hence, the sampling sites were determined to be the low-priority sites.
    Matched MeSH terms: Environmental Monitoring/methods*
  8. Masood A, Hameed MM, Srivastava A, Pham QB, Ahmad K, Razali SFM, et al.
    Sci Rep, 2023 Nov 29;13(1):21057.
    PMID: 38030733 DOI: 10.1038/s41598-023-47492-z
    Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.
    Matched MeSH terms: Environmental Monitoring/methods
  9. Chen A, Jiang J, Luo Y, Zhang G, Hu B, Wang X, et al.
    PeerJ, 2023;11:e16337.
    PMID: 38130929 DOI: 10.7717/peerj.16337
    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 
    Matched MeSH terms: Environmental Monitoring/methods
  10. Gholizadeh M, Shadi A, Abadi A, Nemati M, Senapathi V, Karthikeyan S, et al.
    Mar Pollut Bull, 2024 Jan;198:115939.
    PMID: 38128339 DOI: 10.1016/j.marpolbul.2023.115939
    In this study, microplastic (MP) pollution in the coastal sediments and tidal waters of Bushehr province in the Persian Gulf was comprehensively investigated. The sampling stations were selected based on their proximity to various human activities in January and February 2022, such as tourism, fishing, urban development and industry. The results showed that the abundance of MP associated with different human activities varied. The highest concentrations were observed near the petrochemical industry in Asaluyeh, followed by the densely populated Bushehr and the fishing port of Dayyer. Other areas such as Ganaveh, Deylam and Mand also showed varying levels of MP contamination. The average MP concentration was 1.67 × 104 particles/km2 in surface water and 1346.67 ± 601.69 particles/kg in dry sediment. Fiber particles were in the majority in both sediment and water samples, mainly black. The sediment samples had a size range of 100-500 μm (41.34 %), while the water samples were between 500 and 1000 μm (33.44 %). The main polymers found were polyethylene (PE) and polypropylene (PP). This assessment highlights the widespread problem of microplastic pollution in the coastal and intertidal zones of Bushehr province in the Persian Gulf.
    Matched MeSH terms: Environmental Monitoring/methods
  11. Mueller W, Jones K, Fuhrimann S, Ahmad ZNBS, Sams C, Harding AH, et al.
    Environ Res, 2024 Feb 01;242:117651.
    PMID: 37996007 DOI: 10.1016/j.envres.2023.117651
    BACKGROUND: Long-term exposure to pesticides is often assessed using semi-quantitative models. To improve these models, a better understanding of how occupational factors determine exposure (e.g., as estimated by biomonitoring) would be valuable.

    METHODS: Urine samples were collected from pesticide applicators in Malaysia, Uganda, and the UK during mixing/application days (and also during non-application days in Uganda). Samples were collected pre- and post-activity on the same day and analysed for biomarkers of active ingredients (AIs), including synthetic pyrethroids (via the metabolite 3-phenoxybenzoic acid [3-PBA]) and glyphosate, as well as creatinine. We performed multilevel Tobit regression models for each study to assess the relationship between exposure modifying factors (e.g., mixing/application of AI, duration of activity, personal protective equipment [PPE]) and urinary biomarkers of exposure.

    RESULTS: From the Malaysia, Uganda, and UK studies, 81, 84, and 106 study participants provided 162, 384 and 212 urine samples, respectively. Pyrethroid use on the sampling day was most common in Malaysia (n = 38; 47%), and glyphosate use was most prevalent in the UK (n = 93; 88%). Median pre- and post-activity 3-PBA concentrations were similar, with higher median concentrations post-compared to pre-activity for glyphosate samples in the UK (1.7 to 0.5 μg/L) and Uganda (7.6 to 0.8 μg/L) (glyphosate was not used in the Malaysia study). There was evidence from individual studies that higher urinary biomarker concentrations were associated with mixing/application of the AI on the day of urine sampling, longer duration of mixing/application, lower PPE protection, and less education/literacy, but no factor was consistently associated with exposure across biomarkers in the three studies.

    CONCLUSIONS: Our results suggest a need for AI-specific interpretation of exposure modifying factors as the relevance of exposure routes, levels of detection, and farming systems/practices may be very context and AI-specific.

    Matched MeSH terms: Environmental Monitoring/methods
  12. Tao H, Jawad AH, Shather AH, Al-Khafaji Z, Rashid TA, Ali M, et al.
    Environ Int, 2023 May;175:107931.
    PMID: 37119651 DOI: 10.1016/j.envint.2023.107931
    This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.
    Matched MeSH terms: Environmental Monitoring/methods
  13. Kek HY, Tan H, Othman MHD, Nyakuma BB, Ho WS, Sheng DDCV, et al.
    Environ Res, 2024 Mar 15;245:118055.
    PMID: 38154562 DOI: 10.1016/j.envres.2023.118055
    Airborne Microplastics (MPs), an emerging environmental issue, have gained recent attention due to their newfound presence in indoor environments. Utilizing the Web of Science database for literature collection, the paper presents a comprehensive review of airborne MPs including emission sources, assessment methods, exposure risks, and mitigation strategies. This review delves into the diverse sources and mechanisms influencing indoor airborne MP pollution, underscoring the complex interplay between human activities, ventilation systems, and the characteristics of indoor environments. Major sources include the abrasion of synthetic textiles and the deterioration of flooring materials, with factors like carpeting, airflow, and ventilation significantly impacting MP levels. Human activities, such as increased movement in indoor spaces and the intensive use of plastic-based personal protective equipment (PPE) post-pandemic, notably elevate indoor MP concentrations. The potential health impacts of airborne MPs are increasingly concerning, with evidence suggesting their role in respiratory, immune, and nervous system diseases. Despite this, there is a scarcity of information on MPs in diverse indoor environments and the inhalation risks associated with the frequent use of PPE. This review also stresses the importance of developing effective strategies to reduce MP emissions, such as employing HEPA-filtered vacuums, minimizing the use of synthetic textiles, and enhancing indoor ventilation. Several future research directions were proposed, including detailed temporal analyses of indoor MP levels, interactions of MP with other atmospheric pollutants, the transport dynamics of inhalable MPs (≤10 μm), and comprehensive human exposure risk assessments.
    Matched MeSH terms: Environmental Monitoring/methods
  14. Junaid M, Sultan M, Liu S, Hamid N, Yue Q, Pei DS, et al.
    Sci Total Environ, 2024 Mar 20;917:170535.
    PMID: 38307287 DOI: 10.1016/j.scitotenv.2024.170535
    Owing to a wide range of advantages, such as stability, non-invasiveness, and ease of sampling, hair has been used progressively for comprehensive biomonitoring of organic pollutants for the last three decades. This has led to the development of new analytical and multi-class analysis methods for the assessment of a broad range of organic pollutants in various population groups, ranging from small-scale studies to advanced studies with a large number of participants based on different exposure settings. This meta-analysis summarizes the existing literature on the assessment of organic pollutants in hair in terms of residue levels, the correlation of hair residue levels with those of other biological matrices and socio-demographic factors, the reliability of hair versus other biomatrices for exposure assessment, the use of segmental hair analysis for chronic exposure evaluation and the effect of external contamination on hair residue levels. Significantly high concentrations of organic pollutants such as pesticides, flame retardants, polychlorinated biphenyls and polycyclic aromatic hydrocarbon were reported in human hair samples from different regions and under different exposure settings. Similarly, high concentrations of pesticides (from agricultural activities), flame retardants (E-waste dismantling activities), dioxins and furans were observed in various occupational settings. Moreover, significant correlations (p 
    Matched MeSH terms: Environmental Monitoring/methods
  15. Alkhadher SAA, Sidek LM, Zakaria MP, A Al-Garadi M, Suratman S
    Environ Geochem Health, 2024 Mar 15;46(4):140.
    PMID: 38488953 DOI: 10.1007/s10653-024-01916-5
    Organic pollution continues to be an important worldwide obstacle for tackling health and environmental concerns that require ongoing and prompt response. To identify the LAB content levels as molecular indicators for sewage pollution, surface sediments had obtained from the South region of Malaysia. The origins of the LABs were identified using gas chromatography-mass spectrometry (GC-MS). ANOVA and a Pearson correlation coefficient at p 
    Matched MeSH terms: Environmental Monitoring/methods
  16. Kitzes J, Shirley R
    Ambio, 2016 Feb;45(1):110-9.
    PMID: 26169084 DOI: 10.1007/s13280-015-0683-3
    In many regions of the world, biodiversity surveys are not routinely conducted prior to activities that lead to land conversion, such as development projects. Here we use top-down methods based on global range maps and bottom-up methods based on macroecological scaling laws to illuminate the otherwise hidden biodiversity impacts of three large hydroelectric dams in the state of Sarawak in northern Borneo. Our retrospective impact assessment finds that the three reservoirs inundate habitat for 331 species of birds (3 million individuals) and 164 species of mammals (110 million individuals). A minimum of 2100 species of trees (900 million individuals) and 17 700 species of arthropods (34 billion individuals) are estimated to be affected by the dams. No extinctions of bird, mammal, or tree species are expected due to habitat loss following reservoir inundation, while 4-7 arthropod species extinctions are predicted. These assessment methods are applicable to any data-limited system undergoing land-use change.
    Matched MeSH terms: Environmental Monitoring/methods*
  17. Zalina MD, Desa MN, Nguyen VT, Kassim AH
    Water Sci Technol, 2002;45(2):63-8.
    PMID: 11890166
    This paper discusses the comparative assessment of eight candidate distributions in providing accurate and reliable maximum rainfall estimates for Malaysia. The models considered were the Gamma, Generalised Normal, Generalised Pareto, Generalised Extreme Value, Gumbel, Log Pearson Type III, Pearson Type III and Wakeby. Annual maximum rainfall series for one-hour resolution from a network of seventeen automatic gauging stations located throughout Peninsular Malaysia were selected for this study. The length of rainfall records varies from twenty-three to twenty-eight years. Model parameters were estimated using the L-moment method. The quantitative assessment of the descriptive ability of each model was based on the Probability Plot Correlation Coefficient test combined with root mean squared error, relative root mean squared error and maximum absolute deviation. Bootstrap resampling was employed to investigate the extrapolative ability of each distribution. On the basis of these comparisons, it can be concluded that the GEV distribution is the most appropriate distribution for describing the annual maximum rainfall series in Malaysia.
    Matched MeSH terms: Environmental Monitoring
  18. Swinbanks D
    Nature, 1997 Sep 25;389(6649):321.
    PMID: 9311764
    Matched MeSH terms: Environmental Monitoring
  19. Li X, Hussain SA, Sobri S, Md Said MS
    Chemosphere, 2021 May;271:129502.
    PMID: 33465622 DOI: 10.1016/j.chemosphere.2020.129502
    Most developing countries in the world face the common challenges of reducing air pollution and advancing the process of sustainable development, especially in China. Air pollution research is a complex system and one of the main methods is through numerical simulation. The air quality model is an important technical method, it allows researchers to better analyze air pollutants in different regions. In addition, the SCB is a high-humidity and foggy area, and the concentration of atmospheric pollutants is always high. However, research on this region, one of the four most polluted regions in China, is still lacking. Reviewing the application of air quality models in the SCB air pollution has not been reported thoroughly. To fill these gaps, this review provides a comprehensive narration about i) The status of air pollution in SCB; ii) The application of air quality models in SCB; iii) The problems and application prospects of air quality models in the research of air pollution. This paper may provide a theoretical reference for the prevention and control of air pollution in the SCB and other heavily polluted areas in China and give some1inspirations for air pollution forecast in other countries with complex terrain.
    Matched MeSH terms: Environmental Monitoring
  20. Behera DP, Kolandhasamy P, Sigamani S, Devi LP, Ibrahim YS
    Mar Pollut Bull, 2021 Apr;165:112100.
    PMID: 33581571 DOI: 10.1016/j.marpolbul.2021.112100
    Marine debris is a global issue with adverse impacts on marine organisms, ecological processes, aesthetics, and economies of nations. Several studies have been conducted to quantify the plastic debris along Indian beaches. This baseline study describes the results of a survey conducted on the types of plastic litters and their quantification during January to March 2020 along Mandvi beach in Gujarat. A quadrate having 10 × 10 m size was used for sampling the plastic litter on the shoreline. A total of 10 quadrates along the shore was considered for quantification of the plastic materials based on their density, color, and weight. The plastic material observed includes gutkha pouches, food wrappers, and fragments, along with plastic straws, cutleries, and fragments of various dimensions and thickness. The major contributing factors for the debris abundance in Mandvi beach are land-based sources and recreational activities. The results suggest that similar long-term projects covering extensive areas should be undertaken for accurate quantification of available debris and their impacts on coastal habitats of Gujarat.
    Matched MeSH terms: Environmental Monitoring
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