Displaying publications 121 - 140 of 959 in total

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  1. Fulltext Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam
    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
  2. Abundance and characteristics of microplastic in some commercial species from the Persian Gulf, Iran
    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
  3. Seasonal influence on physicochemical properties of the sediments from Bay of Bengal coast with statistical approach
    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
  4. Fulltext Assessing the ecological risk of polycyclic aromatic hydrocarbons in sediments at Langkawi Island, Malaysia
    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*
  5. Fulltext Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm
    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
  6. Fulltext Temperature vegetation dryness index (TVDI) for drought monitoring in the Guangdong Province from 2000 to 2019
    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
  7. Exploring the microplastic pollution: Unveiling origins and varieties in coastal sediments and waters of the Bushehr Province, Persian Gulf, Iran
    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
  8. Fulltext Factors influencing occupational exposure to pyrethroids and glyphosate: An analysis of urinary biomarkers in Malaysia, Uganda and the United Kingdom
    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
  9. Fulltext Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters
    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
  10. Fulltext Research Priorities of Applying Low-Cost PM2.5 Sensors in Southeast Asian Countries
    Lung SC, Thi Hien T, Cambaliza MOL, Hlaing OMT, Oanh NTK, Latif MT, et al.
    Int J Environ Res Public Health, 2022 Jan 28;19(3).
    PMID: 35162543 DOI: 10.3390/ijerph19031522
    The low-cost and easy-to-use nature of rapidly developed PM2.5 sensors provide an opportunity to bring breakthroughs in PM2.5 research to resource-limited countries in Southeast Asia (SEA). This review provides an evaluation of the currently available literature and identifies research priorities in applying low-cost sensors (LCS) in PM2.5 environmental and health research in SEA. The research priority is an outcome of a series of participatory workshops under the umbrella of the International Global Atmospheric Chemistry Project-Monsoon Asia and Oceania Networking Group (IGAC-MANGO). A literature review and research prioritization are conducted with a transdisciplinary perspective of providing useful scientific evidence in assisting authorities in formulating targeted strategies to reduce severe PM2.5 pollution and health risks in this region. The PM2.5 research gaps that could be filled by LCS application are identified in five categories: source evaluation, especially for the distinctive sources in the SEA countries; hot spot investigation; peak exposure assessment; exposure-health evaluation on acute health impacts; and short-term standards. The affordability of LCS, methodology transferability, international collaboration, and stakeholder engagement are keys to success in such transdisciplinary PM2.5 research. Unique contributions to the international science community and challenges with LCS application in PM2.5 research in SEA are also discussed.
    Matched MeSH terms: Environmental Monitoring/methods
  11. Fulltext Estimating biodiversity impacts without field surveys: A case study in northern Borneo
    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*
  12. Pollution characteristics and risk assessment of organophosphate esters in mollusks along the coast of South China
    Wu H, Yu M, Huang J, Zhang Q, Yao R, Liu H, et al.
    Mar Pollut Bull, 2025 Jan;210:117317.
    PMID: 39579595 DOI: 10.1016/j.marpolbul.2024.117317
    Organophosphate esters (OPEs) are emerging pollutants and used extensively in industrial production as alternative to the traditional flame retardants. This study investigated the contamination characteristics and health risks of OPEs in 104 mollusks from 15 cities along the coastal region of South China. Σ8OPEs ranged from 48.2 to 1937 ng/g dw, with a mean value of 295 ng/g dw. TDCIPP, TCPP, and TCEP were the dominant OPEs. Different spatial distributions were observed, with higher concentrations in Guangdong Province. A statistically positive but non-significant linear correlation was found between the trophic level of mollusk and OPEs concentration. The trophic magnification factors were >1, suggesting that OPEs have the potential to biomagnify in mollusks. OPEs in mollusks pose low non-carcinogenic and carcinogenic risks to consumers. This study provides an important basis for managing the safety risks associated with OPEs in mollusks.
    Matched MeSH terms: Environmental Monitoring*
  13. Spatial distribution of heavy metal in sands and sediments of Parki Beach, Chattogram, Bangladesh
    Rusdi MS, Karim MR, Hossain S, Chowdhury MDA, Nazim-Ud-Doulah, Rahman MS, et al.
    Environ Monit Assess, 2024 Nov 30;196(12):1275.
    PMID: 39614922 DOI: 10.1007/s10661-024-13399-z
    To assess the sources, levels, spatial distributions and exposure to human health, the concentration of heavy metals Pb, Cu, Mn, Zn, and Fe in the sand/sediment of the Parki Beach area of Anowara, Chattogram, Bangladesh are determined using Atomic Absorption Spectroscopy (AAS) for the first time. A total of 40 surface and subsurface sand and sediment samples were collected from 20 different sampling points along the 15 km long Parki Beach area, Bangladesh. Average concentrations of Pb, Cu, Mn, Zn and Fe in surface samples are 14.60, 10.10, 283, 407 and 25,256 mg/kg respectively and 9.95, 4.20, 193, 156.6 and 24,404 mg/kg for sub-surface samples, respectively, which shows that the values are higher in surface samples than those in sub-surface samples. According to the Consensus-Based Sediment Quality Guidelines (CBSQG), the northern part of the beach becomes moderately polluted by Mn and Fe, and a smaller area of the southern part is highly polluted by Zn. The average Contamination Factor (CF) of Zn was greater than 1(CF > 1), while the CF of other metals was less than 1(CF 
    Matched MeSH terms: Environmental Monitoring*
  14. Water impact analysis due to coal-electricity generation using the life cycle assessment method: a case study in Malaysia
    Mardi NH, Ean LW, Malek MA, Chua KH, Ahmed AN
    Water Sci Technol, 2025 Jan;91(2):219-234.
    PMID: 39882924 DOI: 10.2166/wst.2024.402
    Coal power plants adversely impact air pollution, but they also pose a risk to our water sources. Discharge wastewater from power plants may degrade the quality of nearby water bodies. This study evaluates the potential water-related environmental impacts of electricity generation at an ultra-supercritical coal power plant in Malaysia using the life cycle assessment method. The inventory data were gathered from a Malaysian power plant, and supporting data were taken from the relevant literature. Utilizing the ReCiPe 2016 impact assessment method, this study analyses the mid-point impact categories of freshwater eutrophication (FEP), marine eutrophication (MEP), freshwater ecotoxicity (FETP), and marine ecotoxicity (METP). The results indicate that METP is the leading risk, with an average impact of 1.94 × 10-2 kg 1,4-DCB per kWh electricity generated, followed by FETP (1.40 × 10-2 kg 1,4-DCB), FEP (4.66 × 10-4 kg P eq), and MEP (2.95 × 10-5 kg N eq). About 95% of the mid-point impact is due to the extraction and processing of hard coal. These findings underscore a critical aspect of environmental management at the supply chain level. Furthermore, mitigating direct emissions from power generation could reduce the mid-point impact, as demonstrated by comparisons with previous research.
    Matched MeSH terms: Environmental Monitoring/methods
  15. Fulltext Occurrence of endocrine disruptors in Malaysia's water systems: A scoping review
    Khairul Hasni NA, Anual ZF, Rashid SA, Syed Abu Thahir S, Veloo Y, Fang KS, et al.
    Environ Pollut, 2023 May 01;324:121095.
    PMID: 36682614 DOI: 10.1016/j.envpol.2023.121095
    Contamination of water systems with endocrine disrupting chemicals (EDCs) is becoming a major public health concern due to their toxicity and ubiquity. The intrusion of EDCs into water sources and drinking water has been associated with various adverse health effects on humans. However, there is no comprehensive overview of the occurrence of EDCs in Malaysia's water systems. This report aims to describe the occurrence of EDCs and their locations. Literature search was conducted electronically in two databases (PubMed and Scopus). A total of 41 peer-reviewed articles published between January 2000 and May 2021 were selected. Most of the articles dealt with pharmaceuticals (16), followed by pesticides (7), hormones (7), mixed compounds (7), and plasticisers (4). Most studies (40/41) were conducted in Peninsular Malaysia, with 60.9% in the central region and almost half (48.8%) in the Selangor State. Only one study was conducted in the northern region and East Malaysia. The Langat River, the Klang River, and the Selangor River were among the most frequently studied EDC-contaminated surface waters, while the Pahang River and the Skudai River had the highest concentrations of some of the listed compounds. Most of the risk assessments resulted in a hazard quotient (HQ) and a risk quotient (RQ)  1 in the Selangor River. An RQ > 1 for combined pharmaceuticals was found in Putrajaya tap water. Overall, this work provides a comprehensive overview of the occurrence of EDCs in Malaysia's water systems. The findings from this review can be used to mitigate risks and strengthen legislation and policies for safer drinking water.
    Matched MeSH terms: Environmental Monitoring/methods
  16. The older, the better: a comprehensive survey of soil organic carbon under commercial oil palm plantations
    Golicz K, Cheak SC, Jacobs S, Große-Stoltenberg A, Safaei M, Bellingrath-Kimura S, et al.
    Environ Monit Assess, 2024 Dec 21;197(1):86.
    PMID: 39708179 DOI: 10.1007/s10661-024-13540-y
    Soil conditions of croplands are a frequent topic of scientific research. In contrast, less is known about large-scale commercial plantations of perennial crops such as oil palm. Oil palm is a globally important tropical commodity crop which contributes to both food and energy security due to its exceptional productivity. However, oil palm crops are associated with short lifecycles and high nutrient demands, which may disproportionately affect soil health. With the goal of exploring baseline soil properties in commercial oil palm plantations, we evaluated data from two large-scale soil surveys carried out in 2014/2015 and 2018/2019 across more than 400 fields located throughout Peninsular Malaysia. We examined variation in field-measured soil quality indicators with a focus on soil organic carbon content at three depths (0-15 cm, 15-30 cm, 30-45 cm) and investigated links with spatial covariates, including plantation age. We found SOC contents to be low (1.6-2%) across the sampled locations with limited correlation with spatial predictors employed in soil organic carbon modelling. Furthermore, we found that immature and young mature plantations, which consisted of fields that were re-planted as part of a 20-year-long oil palm rotation, were characterised by significantly lower soil organic carbon content than the mature plantations. This suggests that management practices should target younger oil palm plantations for soil organic conservation measures to increase the overall baseline SOC content, which will subsequently accumulate over the plantation's lifespan. We further provide recommendations for future soil sampling efforts, which could increase the robustness of collected data and facilitate their use for soil monitoring through modelling approaches involving, for example, digital soil mapping.
    Matched MeSH terms: Environmental Monitoring*
  17. Characterizing persistent organic pollutants in seawater at a multifunctional international harbor influenced by industrial riverbank activities
    Wardiani FE, Dong CD, Chen CW, Liu TK, Hsu ZP, Lam SS, et al.
    Mar Pollut Bull, 2024 Dec;209(Pt B):117213.
    PMID: 39489051 DOI: 10.1016/j.marpolbul.2024.117213
    The objective of this study is to comprehensively characterize persistent organic pollutants (POPs) in seawater at Kaohsiung Harbor, focusing on their concentrations, partitioning behaviors, and profiles in both particle and liquid phases. We analyzed 100 L seawater for each sample, finding total dioxin-like toxicity (PCDD/Fs + PCBs + PBDD/Fs) ranging from 0.00936 to 0.167 pg WHO-TEQ/L, with PCDD/Fs accounting for 68 % of total toxicity. POPs predominantly appeared in the particle phase, observed in over 80 % of samples, except for PCBs. The observed correlations between particulate matter (PM) and chlorinated POPs at sites receiving river effluents suggest shared pollution sources. The liquid partition of PCDD/Fs, PCBs, and PBDEs in the seawater shows an inverse relationship with log Kow and a direct proportionality with solubility, particularly above 0.1 μg/L. Furthermore, PBDEs in seawater can transform into PBDD/Fs upon UV light exposure, highlighting another potential pathway for the persistence and spread of these harmful contaminants in the environment. These findings emphasize the need for field-based investigations to assess PBDF formation in aquatic environments and underscore the importance of stronger mitigation strategies, including better wastewater treatment and stricter discharge regulations to reduce POPs in marine ecosystems.
    Matched MeSH terms: Environmental Monitoring*
  18. Evaluation of industrial air pollution contribution by type of industry in Pasir Gudang using AERMOD
    Tan AZL, Ho WS, Hassim MH, Abdullah F, Lim LY
    Environ Monit Assess, 2025 Mar 12;197(4):385.
    PMID: 40072627 DOI: 10.1007/s10661-025-13797-x
    In industrialized areas, air pollution is a recurring problem, especially in areas with high manufacturing and energy-intensive businesses. The challenge lies in the tension between industrial growth and environmental protection, as these sectors significantly contribute to pollution, resource depletion, and climate change. The objectives of the study were (1) to assess the contribution of each industrial group to the air quality in and around the Pasir Gudang industrial area, Malaysia, and (2) to evaluate the Air Pollution Index (API). Industrial emission sources were grouped into 10 specific groups, namely (1) biomass energy plants, (2) ceramic and mineral, (3) chemical, (4) electrical, (5) food and beverage, (6) metal industries, (7) paint, (8) rubber product, (9) waste recovery, and (10) wood respectively. For this, hourly, daily, and annual modelling of pollutant gases and particulate matter (SO2, NO2, and PM10) for 3 years (2019 to 2021) were conducted using the AERMOD dispersion model. The maximum hourly, daily, and annual average ground level concentrations of SO2 and PM10 were mainly contributed by Wood industry group emissions, while NO2 was dominated by metal industry group emissions. It was observed that the average hourly and daily NO2 levels exceeded Malaysia's standard limits. The API during the study period was 146.97, contributed by NO2, which indicates unhealthy conditions for sensitive groups. Therefore, NO2 pollutant control measures from the Metal industry group should be prioritized in this industrial area.
    Matched MeSH terms: Environmental Monitoring*
  19. Selecting a probability distribution for extreme rainfall series in Malaysia
    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
  20. Forest fires cause pollution crisis in Asia
    Swinbanks D
    Nature, 1997 Sep 25;389(6649):321.
    PMID: 9311764
    Matched MeSH terms: Environmental Monitoring
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