Displaying publications 21 - 40 of 289 in total

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  1. Madadi R, Mohamadi S, Rastegari M, Karbassi A, Rakib MRJ, Khandaker MU, et al.
    Sci Rep, 2022 Nov 17;12(1):19736.
    PMID: 36396803 DOI: 10.1038/s41598-022-21242-z
    Rapid industrialization and urbanization have resulted in environmental pollution and unsustainable development of cities. The concentration of 12 potentially toxic metal(loid)s in windowsill dust samples (n = 50) were investigated from different functional areas of Qom city with the highest level of urbanization in Iran. Spatial analyses (ArcGIS 10.3) and multivariate statistics including Principal Component Analysis and Spearman correlation (using STATISTICA-V.12) were adopted to scrutinize the possible sources of pollution. The windowsill dust was very highly enriched with Sb (50 mg/kg) and Pb (1686 mg/kg). Modified degree of contamination (mCd) and the pollution load indices (PLIzone) indicate that windowsill dust in all functional areas was polluted in the order of industrial > commercial > residential > green space. Arsenic, Cd, Mo, Pb, Sb, Cu, and Zn were sourced from a mixture of traffic and industrial activities, while Mn in the dust mainly stemmed from mining activities. Non-carcinogenic health risk (HI) showed chronic exposure of Pb for children in the industrial zone (HI = 1.73). The estimations suggest the possible carcinogenic risk of As, Pb, and Cr in the dust. The findings of this study reveal poor environmental management of the city. Emergency plans should be developed to minimize the health risks of dust to residents.
    Matched MeSH terms: Environmental Monitoring/methods
  2. Rendana M, Idris WMR, Rahim SA
    Environ Monit Assess, 2022 Dec 17;195(1):205.
    PMID: 36527450 DOI: 10.1007/s10661-022-10833-y
    Mining activities in the Chini Lake catchment area have been extensive for several years, contributing to acid mine drainage (AMD) events with high concentrations of iron (Fe) and other heavy metals impacting the surface water. However, during the restriction period due to the COVID-19 outbreak, anthropogenic activities have been suspended, which clearly shows a good opportunity for a better environment. Therefore, we aimed to analyze the variation of AMD-associated water pollution in three main zones of the Chini Lake catchment area using Sentinel-2 data for the periods pre-movement control order (MCO), during MCO, and post-MCO from 2019 to 2021. These three zones were chosen due to their proximity to mining areas: zone 1 in the northeastern part, zone 2 in the southeastern part, and zone 3 in the southern part of the Chini Lake area. The acid mine water index (AMWI) was a specific index used to estimate acid mine water. The AMWI values from Sentinel-2 images exhibited that the mean AMWI values in all zones during the MCO period decreased by 14% compared with the pre-MCO period. The spatiotemporal analysis found that the highest polluted zones were recorded in zone 1, followed by zone 3 and zone 2. As compared with during the MCO period, the maximum percentage of increment during post-MCO in all zones was up to 25%. The loosened restriction policy has resulted in more AMD flowing into surface water and increased pollution in Chini Lake. As a whole, our outputs revealed that Sentinel-2 data had a major potential for assessing the AMD-associated pollution of water.
    Matched MeSH terms: Environmental Monitoring/methods
  3. 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
  4. Chen HL, Selvam SB, Ting KN, Gibbins CN
    Environ Monit Assess, 2023 Jan 18;195(2):307.
    PMID: 36652034 DOI: 10.1007/s10661-022-10856-5
    Recent increase in awareness of the extent of microplastic contamination in marine and freshwater systems has heightened concerns over the ecological and human health risks of this ubiquitous material. Assessing risks posed by microplastic in freshwater systems requires sampling to establish contamination levels, but standard sampling protocols have yet to be established. An important question is whether sampling and assessment should focus on microplastic concentrations in the water or the amount deposited on the bed. On three dates, five replicated water and bed sediment samples were collected from each of the eight sites along the upper reach of the Semenyih River, Malaysia. Microplastics were found in all 160 samples, with mean concentrations of 3.12 ± 2.49 particles/L in river water and 6027.39 ± 16,585.87 particles/m2 deposited on the surface of riverbed sediments. Fibres were the dominant type of microplastic in all samples, but fragments made up a greater proportion of the material on the bed than in the water. Within-site variability in microplastic abundance was high for both water and bed sediments, and very often greater than between-site variability. Patterns suggest that microplastic accumulation on the bed is spatially variable, and single samples are therefore inadequate for assessing bed contamination levels at a site. Sites with the highest mean concentrations in samples of water were not those with the highest concentrations on the bed, indicating that monitoring based only on water samples may not provide a good picture of either relative or absolute bed contamination levels, nor the risks posed to benthic organisms.
    Matched MeSH terms: Environmental Monitoring/methods
  5. Tao H, Al-Hilali AA, Ahmed AM, Mussa ZH, Falah MW, Abed SA, et al.
    Chemosphere, 2023 Mar;317:137914.
    PMID: 36682637 DOI: 10.1016/j.chemosphere.2023.137914
    Heavy metals (HMs) are a vital elements for investigating the pollutant level of sediments and water bodies. The Murray-Darling river basin area located in Australia is experiencing severe damage to increased crop productivity, loss of soil fertility, and pollution levels within the vicinity of the river system. This basin is the most effective primary production area in Australia where agricultural productivity is increased the gross domastic product in the entire mainland. In this study, HMs contaminations are examined for eight study sites selected for the Murray-Darling river basin where the inverse Distance Weighting interpolation method is used to identify the distribution of HMs. To pursue this, four different pollution indices namely the Geo-accumulation index (Igeo), Contamination factor (CF), Pollution load index (PLI), single-factor pollution index (SPLI), and the heavy metal pollution index (HPI) are computed. Following this, the Pearson correlation matrix is used to identify the relationships among the two HM parameters. The results indicate that the conductivity and N (%) are relatively high in respect to using Igeo and PLI indexes for study sites 4, 6, and 7 with 2.93, 3.20, and 1.38, respectively. The average HPI is 216.9071 that also indicates higher level pollution in the Murray-Darling river basin and the highest HPI value is noted in sample site 1 (353.5817). The study also shows that the levels of Co, P, Conductivity, Al, and Mn are mostly affected by HMs and that these indices indicate the maximum HM pollution level in the Murray-Darling river basin. Finally, the results show that the high HM contamination level appears to influence human health and local environmental conditions.
    Matched MeSH terms: Environmental Monitoring/methods
  6. Li Q, Zhang K, Li R, Yang L, Yi Y, Liu Z, et al.
    Sci Total Environ, 2023 May 10;872:162071.
    PMID: 36775179 DOI: 10.1016/j.scitotenv.2023.162071
    Biomass burning (BB) has significant impacts on air quality and climate change, especially during harvest seasons. In previous studies, levoglucosan was frequently used for the calculation of BB contribution to PM2.5, however, the degradation of levoglucosan (Lev) could lead to large uncertainties. To quantify the influence of the degradation of Lev on the contribution of BB to PM2.5, PM2.5-bound biomass burning-derived markers were measured in Changzhou from November 2020 to March 2021 using the thermal desorption aerosol gas chromatography-mass spectrometry (TAG-GC/MS) system. Temporal variations of three anhydro-sugar BB tracers (e.g., levoglucosan, mannosan (Man), and galactosan (Gal)) were obtained. During the sampling period, the degradation level of air mass (x) was 0.13, indicating that ~87 % of levoglucosan had degraded before sampling in Changzhou. Without considering the degradation of levoglucosan in the atmosphere, the contribution of BB to OC were 7.8 %, 10.2 %, and 9.3 % in the clean period, BB period, and whole period, respectively, which were 2.4-2.6 times lower than those (20.8 %-25.9 %) considered levoglucosan degradation. This illustrated that the relative contribution of BB to OC could be underestimated (~14.9 %) without considering degradation of levoglucosan. Compared to the traditional method (i.e., only using K+ as BB tracer), organic tracers (Lev, Man, Gal) were put into the Positive Matrix Factorization (PMF) model in this study. With the addition of BB organic tracers and replaced K+ with K+BB (the water-soluble potassium produced by biomass burning), the overall contribution of BB to PM2.5 was enhanced by 3.2 % after accounting for levoglucosan degradation based on the PMF analysis. This study provides useful information to better understand the effect of biomass burning on the air quality in the Yangtze River Delta region.
    Matched MeSH terms: Environmental Monitoring/methods
  7. Sugumaran D, Blake WH, Millward GE, Yusop Z, Mohd Yusoff AR, Mohamad NA, et al.
    Environ Sci Pollut Res Int, 2023 Jun;30(28):71881-71896.
    PMID: 35411514 DOI: 10.1007/s11356-022-19904-6
    Pristine tropical river systems are coming under increasing pressure from the development of economic resources such as forestry and mining for valuable elements. The Lebir catchment, north eastern Malaysia, is now under development as a result of unregulated tree felling and mining for essential and rare metals. Two sediment cores, one in the upstream reaches and the other from the downstream reaches, were taken from flood prone area of the Lebir River, Malaysia, and analysed for their elemental composition by XRF, specifically Al, Si, Fe, Ca, K, Mg, Mn, V, Cu, Ni, Pb, Cr, Zn, As, Th and U. Activities of fallout radionuclides, 137Cs and 210Pb were also determined to from a geochronological context. The elemental concentrations in the soils were assessed in terms of their enrichment factor and Si, Ca, K, Mg, Mn, V, Cu, Ni and Zn were found not to be enriched, whereas As, Th and U had elevated enrichment factors. The Th and U were particularly enriched in the downstream core indicating inputs from a tributary that drains a catchment with known deposits of Th and possibly U. The results suggest that the growth in economic development is fostering the transport of contaminants by the major rivers which, in turn, is contaminating the riverine floodplains. This points to the need for a more integrated and holistic approach to river basin management to maintain the environmental quality of these fragile aquatic systems.
    Matched MeSH terms: Environmental Monitoring/methods
  8. Schepaschenko D, Chave J, Phillips OL, Lewis SL, Davies SJ, Réjou-Méchain M, et al.
    Sci Data, 2019 10 10;6(1):198.
    PMID: 31601817 DOI: 10.1038/s41597-019-0196-1
    Forest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.
    Matched MeSH terms: Environmental Monitoring/methods
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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*
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
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