Post-digestion treatment is an important step during sample preparation to facilitate the removal of undigested materials for better detection of ingested microplastics. Sieving, density separation with zinc chloride solution (ZnCl2), and oil extraction protocol (OEP) have been introduced in separating microplastics from sediments. The clean-up methods are rarely highlighted in previous studies, especially in the separation of microplastics from marine biota. Thus, this study proposed and compared the suitability of three techniques, which can reduce the number of undigested particles from the digestate of GIT and gills. Our result has shown excellent removal of non-plastics materials and reduces the coloration of filter paper in all treated samples. Both sieving and density separation achieved optimum post-digestion efficiencies of >95% for both GIT and gill samples, which former showed no effect on polymer integrity. Additionally, high recovery rate was obtained for the larger size microplastics (>500 μm) with approximately 97.7% (GIT) and 95.7% (gill), respectively. Exposure to the ZnCl2 solution led to a significant loss of smaller size PET and changed the absorption spectrums of all tested polymers. Particle morphology determined by SEM revealed such exposure eroded the surface of PET fragments and elemental analysis has shown detectable peaks of zinc and chlorine appeared. Low microplastics recoveries were achieved through OPE and residue of oil was observed from the infrared spectrum of all tested polymer. The findings demonstrate sieving with size fractioning can provide exceptional removal of non-plastics materials from the digestate of GIT and gill samples.
This study aimed to determine the spatial distribution of PM2.5 and PM10 collected in four regions (North, Central, South and East Coast) of Peninsular Malaysia during the southwest monsoon. Concurrent measurements of PM2.5 and PM10 were performed using a high volume sampler (HVS) for 24 h (August to September 2018) collecting a total of 104 samples. All samples were then analysed for water soluble inorganic ions (WSII) using ion chromatography, trace metals using inductively coupled plasma-mass spectroscopy (ICP-MS) and polycyclic aromatic hydrocarbon (PAHs) using gas chromatography-mass spectroscopy (GC-MS). The results showed that the highest average PM2.5 concentration during the sampling campaign was in the North region (33.2 ± 5.3 μg m-3) while for PM10 the highest was in the Central region (38.6 ± 7.70 μg m-3). WSII recorded contributions of 22% for PM2.5 and 20% for PM10 mass, with SO42- the most abundant species with average concentrations of 1.83 ± 0.42 μg m-3 (PM2.5) and 2.19 ± 0.27 μg m-3 (PM10). Using a Positive Matrix Factorization (PMF) model, soil fertilizer (23%) was identified as the major source of PM2.5 while industrial activity (25%) was identified as the major source of PM10. Overall, the studied metals had hazard quotients (HQ) value of <1 indicating a very low risk of non-carcinogenic elements while the highest excess lifetime cancer risk (ELCR) was recorded for Cr VI in the South region with values of 8.4E-06 (PM2.5) and 6.6E-05 (PM10). The incremental lifetime cancer risk (ILCR) calculated from the PAH concentrations was within the acceptable range for all regions.
Total mercury (THg) and methylmercury (MeHg) concentrations were determined from sediment samples collected from thirty sampling stations in Port Klang, Malaysia. Three stations had THg concentrations exceeding the threshold effect level of the Florida Department of Environmental Protection and the Canadian interim sediment quality guidelines. THg and MeHg concentrations were found to be concentrated in the Lumut Strait where inputs from the two most urbanized rivers in the state converged (i.e. Klang River and Langat River). This suggests that Hg in the study area likely originated from the catchments of these rivers. MeHg made up 0.06-94.96% of the sediment's THg. There is significant positive correlation (p
This study evaluates the impact of anthropogenic activities on the Shahrood River using water physico-chemical variables and macroinvertebrates data sets obtained over a period of 12 months between February 2012 and February 2013 at 8 sampling sites. Biotic indices i.e. FBI and BMWP based on macroinvertebrates and physico-chemical indices (MPI, HPI and NSF-WQI) were employed to evaluate the water quality status in connection with natural- and human-induced pressures. Based on physico-chemical indices, water quality was categorized as low polluted level and it is suitable for drinking purposes. The water quality based on biotic indices was related to the anthropic activities; a clear deterioration of the water quality was observed from upstream to downstream sites. The water quality along the river changed from very good (class I; reference sites) to good (class II; midstream sites) and turned into moderate (class III) and poor (class IV) quality (downstream sites). These findings indicate that biotic indices are more powerful indicators in assessing water quality than physico-chemical indices. Allocapnia, Glossosoma and Hesperoperla were exclusively related to least disturbed sites, and Naididae, Orthocladiinae and Ecdyonurus were found in sites showing notable degradation. Our results recommended that the use of macroinvertebrates could be employed as a cost-effective tool for biomonitoring and controlling of polluted riverine ecosystems in the Middle East. Finally, the results from this study may be useful not only for developing countries, but also for any organization struggling to use macroinvertebrate based indices with restricted financial resources and knowledge.
Five homologs (C10-C14) of linear alkylbenzene sulfonate (LAS) were quantitated in surface water collected in the Langat and Selangor River basins using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A geographic information system (GIS) was used to spatially analyze the occurrence of LAS in both river basins, and the LAS contamination associated with the population was elucidated by spatial analysis at a sub-basin level. The LAS concentrations in the dissolved phase (<0.45 μm) and 4 fractions separated by particle size (<0.1 μm, 0.1-1 μm, 1-11 μm and >11 μm) were analyzed to elucidate the environmental fate of LAS in the study area. The environmental risks of the observed LAS concentration were assessed based on predicted no effect concentration (PNEC) normalized by a quantitative structure-activity relationship model. The LAS contamination mainly occurred from a few populated sub-basins, and it was correlated with the population density and ammonia nitrogen. The dissolved phase was less than 20% in high contamination sites (>1000 μg/L), whereas it was more than 60% in less contaminated sites (<100 μg/L). The environmental fate of LAS in the study area was primarily subject to the adsorption to suspended solids rather than biodegradation because the LAS homologs, particularly in longer alkyl chain lengths, were considerably absorbed to the large size fraction (>11 μm) that settled in a few hours. The observed LAS concentrations exceeded the normalized PNEC at 3 sites, and environmental risk areas and susceptible areas to the LAS contamination were spatially identified based on their catchment areas.
With rapid urbanization and large-scale industrial activities, modern human populations are being increasingly subjected to chronic environmental heavy metal exposures. Elemental uptake in tooth dentine is a bioindicator, the uptake occurring during the formation and mineralization processes, stored to large extent over periods of many years. The uptake includes essential elements, most typically geogenic dietary sources, as well as non-essential elements arising through environmental insults. In this study, with the help of the Dental Faculty of the University of Malaya, a total of 50 separate human teeth were collected from dental patients of various ethnicity, age, gender, occupation, dietary habit, residency, etc. Analysis was conducted using inductively coupled plasma-mass spectrometry (ICP-MS), most samples indicating the presence of the following trace elements, placed in order of concentration, from least to greatest: As, Mn, Ba, Cu, Cr, Pb, Zn, Hg, Sb, Al, Sr, Sn. The concentrations have been observed to increase with age. Among the ethnic groups, the teeth of ethnic Chinese showed marginally greater metal concentrations than those of the Indians and Malays, the teeth dentine of females generally showing greater concentrations than that of males. Greater concentrations of Hg, Cu and Sn were found in molars while Pb, Sr, Sb and Zn were present in greater concentrations in incisors. With the elevated concentration levels of heavy metals in tooth dentine reflecting pollution from industrial emissions and urbanization, it is evident that human tooth dentine can provide chronological information on exposure, representing a reliable bio-indicator of environmental pollution.
This is the first report on bioaccumulation of polycyclic aromatic hydrocarbons (PAHs) and their derivatives (oxygen, nitrogen, sulfur, hydroxyl, carbonyl and methyl-containing PAHs) in three edible marine fishes, namely Lutjanus argentimaculatus, Lethrinus microdon and Scomberomorus guttatus, from Kharg Island, Persian Gulf, Iran. The concentrations (ng g-1dw) of Σ39PAHs resulted significantly higher in fish liver than muscle, with the PAH composition pattern dominated by low molecular weight compounds (naphthalene, alkyl-naphthalenes and phenanthrene). The highest mean concentrations of ∑9 oxygenated and ∑15 hydroxylated PAHs (ng g-1dw) were found ound in L. microdon and L. argentimaculatus, respectively, while the lowest values in S. guttatus. Additionally, the highest mean concentrations of Σ5 carbonylic PAHs (ng g-1dw) were found in L. argentimaculatus, followed by L. microdon. The PAHs levels and distribution in fish liver and muscle were dependent on both the Kow of PAHs congeners and fish lipid contents. Overall, the present findings provide important baseline data for further research on the ecotoxicity of PAHs in aquatic organisms, and consequent implications for human health.
This study was conducted to assess the reliability of Nerita lineata as a bioindicator for metals in sediment and the factors influencing the accumulation of metals and methylmercury in its soft tissue. The two matrices were analyzed for Co, Cr, Cu, THg, MeHg, Mn, Ni, Pb, and Zn. The metal concentrations in N. lineata were comparable to previously reported results with the exception of Ni which was higher. Cu, Mn, and Pb in N. lineata were significantly (p
This study investigated chlorinated transformation products (TPs) and their parent micropollutants, aromatic pharmaceuticals and personal care products (PPCPs) in the urban water bodies of two metropolitan cities. Nine PPCPs and 16 TPs were quantitatively or semi-quantitatively determined using isotope dilution techniques and liquid chromatography-tandem mass spectrometry. TPs and most PPCPs were effectively removed by conventional wastewater treatments in a wastewater treatment plant (WWTP). Chlorinated parabens and all PPCPs (at concentrations below 1000 ng/L) were present in the waters receiving treated wastewater. By contrast, the waters receiving untreated wastewater contained higher levels of PPCPs (up to 9400 ng/L) and more species of chlorinated TPs including chlorinated parabens, triclosan, diclofenac, and bisphenol A. The very different chemical profiles between the water bodies of the two cities of similar geographical and climatic properties may be attributed to their respective uses of chemicals and policies of wastewater management. No apparent increase in the number of species or abundances of TPs was observed in either the chlorinated wastewater or the seawater rich in halogens. This is the first study to elucidate and compare the profiles of multiple TPs and their parent PPCPs in the water bodies of coastal cities from tropical islands. Our findings suggest that chlorinated derivatives of bisphenol A, diclofenac, triclosan, and parabens in the surface water originate from sources other than wastewater disinfection or marine chlorination. Although further studies are needed to identify the origins, conventional wastewater treatments may protect natural water bodies against contamination by those chlorinated substances.
Toxicity Identification Evaluation (TIE) is a useful method for the classification and identification of toxicants in a composite environment water sample. However, its extension to a larger sample size has been restrained owing to the limited throughput of toxicity bioassays. Here we reported the development of a high-throughput method of TIE Phase I. This newly developed method was assisted by the fluorescence-based cellular oxidation (CO) biosensor fabricated with roGFP2-expressing bacterial cells in 96-well microplate format. The assessment of four river water samples from Langat river basin by this new method demonstrated that the contaminant composition of the four samples can be classified into two distinct groups. The entire toxicity assay consisted of 2338 tests was completed within 12 h with a fluorescence microplate reader. Concurrently, the sample volume for each assay was reduced to 50 μL, which is 600 to 4700 times lesser to compare with conventional bioassays. These imply that the throughput of the CO biosensor-assisted TIE Phase I is now feasible for constructing a large-scale toxicity monitoring system, which would cover a whole watershed scale.
The aim of the present study was to investigate the potential sources of heavy metals in fine air particles (PM2.5) and benzene, toluene, ethylbenzene, and isomeric xylenes (BTEX) in gas phase indoor air. PM2.5 samples were collected using a low volume sampler. BTEX samples were collected using passive sampling onto sorbent tubes and analyzed using gas chromatography-mass spectrometry (GC-MS). For the lower and upper floors of the evaluated building, the concentrations of PM2.5 were 96.4 ± 2.70 μg/m3 and 80.2 ± 3.11 μg/m3, respectively. The compositions of heavy metals in PM2.5 were predominated by iron (Fe), zinc (Zn), and aluminum (Al) with concentration of 500 ± 50.07 ng/m3, 466 ± 77.38 ng/m3, and 422 ± 147.38 ng/m3. A principal component analysis (PCA) showed that the main sources of BTEX were originated from vehicle emissions and exacerbate because of temperature variations. Hazard quotient results for BTEX showed that the compounds were below acceptable limits and thus did not possess potential carcinogenic risks. However, a measured output of lifetime cancer probability revealed that benzene and ethylbenzene posed definite carcinogenic risks. Pollutants that originated from heavy traffic next to the sampling site contributed to the indoor pollution.
Mariculture fish contains a rich source of protein, but some species may bioaccumulate high levels of heavy metals, making them unsafe for consumption. This study aims to identify heavy metal concentration in Lates calcarifer (Barramudi), Lutjanus campechanus (Red snapper) and Lutjanus griseus (Grey snapper). Three species of mariculture fish, namely, L. calcarifer, L. campechanus and L. griseus were collected for analyses of heavy metals. The concentration of heavy metal (As, Cd, Cu, Cr, Fe, Pb, Mn, Ni, Se, and Zn) was determined using inductive coupled plasma mass spectrometry (ICP-MS). The distribution of heavy metals mean concentration in muscle is Zn > Fe > As > Se > Cr > Cu > Mn > Pb > Ni > Cd for L. calcarifer, Fe > Zn > Cr > As > Ni > Mn > Se > Cu > Pb > Cd for L. campechanus and Fe > Zn > Cr > Ni > Se > Cu > As > Mn > Pb > Cd for L. griseus. Among all of the species under investigation, the highest concentration of Fe was found in the muscle tissue of L. campechanus (19.985 ± 1.773 mg kg-1) and liver tissue of L. griseus (58.248 ± 8.736 mg kg-1). Meanwhile, L. calcarifer has the lowest concentration of Cd in both muscle (0.007 ± 0.004 mg kg-1) and liver tissue (0.027 ± 0.016 mg kg-1). The heavy metal concentration in muscle tissue is below the permissible limit guidelines stipulated by the Food & Agriculture Organization, 1983 and Malaysia Food Act, 1983. The concentration of heavy metals varies significantly among fish species and tissues. L. campechanus was found to have a higher ability to accumulate heavy metals as compared to the other two species (p
Evaluation of health risks due to heavy metals exposure via drinking water from ex-mining ponds in Klang Valley and Melaka has been conducted. Measurements of As, Cd, Pb, Mn, Fe, Na, Mg, Ca, and dissolved oxygen, pH, electrical conductivity, total dissolved solid, ammoniacal nitrogen, total suspended solid, biological oxygen demand were collected from 12 ex-mining ponds and 9 non-ex-mining lakes. Exploratory analysis identified As, Cd, and Pb as the most representative water quality parameters in the studied areas. The metal exposures were simulated using Monte Carlo methods and the associated health risks were estimated at 95th and 99th percentile. The results revealed that As was the major risk factor which might have originated from the previous mining activity. For Klang Valley, adults that ingested water from those ponds are at both non-carcinogenic and carcinogenic risks, while children are vulnerable to non-carcinogenic risk; for Melaka, only children are vulnerable to As complications. However, dermal exposure showed no potential health consequences on both adult and children groups.
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.
Synthetic plastics, which are lightweight, durable, elastic, mouldable, cheap, and hydrophobic, were originally invented for human convenience. However, their non-biodegradability and continuous accumulation at an alarming rate as well as subsequent conversion into micro/nano plastic scale structures via mechanical and physio-chemical degradation pose significant threats to living beings, organisms, and the environment. Various minuscule forms of plastics detected in water, soil, and air are making their passage into living cells. High temperature and ambient humidity increase the degradation potential of plastic polymers photo-catalytically under sunlight or UV-B radiations. Microplastics (MPs) of polyethylene terephthalate, polyethylene, polystyrene, polypropylene, and polyvinyl chloride have been detected in bottled water. These microplastics are entering into the food chain cycle, causing serious harm to all living organisms. MPs entering into the food chain are usually inert in nature, possessing different sizes and shapes. Once they enter a cell or tissue, it causes mechanical damage, induces inflammation, disturbs metabolism, and even lead to necrosis. Various generation routes, types, impacts, identification, and treatment of microplastics entering the water bodies and getting associated with various pollutants are discussed in this review. It emphasizes potential detection techniques like pyrolysis, gas chromatography-mass spectrometry (GC-MS), micro-Raman spectroscopy, and fourier transform infrared spectroscopy (FT IR) spectroscopy for microplastics from water samples.
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.
Polycyclic Aromatic Hydrocarbons (PAHs) profoundly impact public and environmental health. Gaining a comprehensive understanding of their intricate functions, exposure pathways, and potential health implications is imperative to implement remedial strategies and legislation effectively. This review seeks to explore PAH mobility, direct exposure pathways, and cutting-edge bioremediation technologies essential for combating the pervasive contamination of environments by PAHs, thereby expanding our foundational knowledge. PAHs, characterised by their toxicity and possession of two or more aromatic rings, exhibit diverse configurations. Their lipophilicity and remarkable persistence contribute to their widespread prevalence as hazardous environmental contaminants and byproducts. Primary sources of PAHs include contaminated food, water, and soil, which enter the human body through inhalation, ingestion, and dermal exposure. While short-term consequences encompass eye irritation, nausea, and vomiting, long-term exposure poses risks of kidney and liver damage, difficulty breathing, and asthma-like symptoms. Notably, cities with elevated PAH levels may witness exacerbation of bronchial asthma and chronic obstructive pulmonary disease (COPD). Bioremediation techniques utilising microorganisms emerge as a promising avenue to mitigate PAH-related health risks by facilitating the breakdown of these compounds in polluted environments. Furthermore, this review delves into the global concern of antimicrobial resistance associated with PAHs, highlighting its implications. The environmental effects and applications of genetically altered microbes in addressing this challenge warrant further exploration, emphasising the dynamic nature of ongoing research in this field.
Due to the rapid development of economy and society around the world, the most urban city is experiencing tropospheric ozone or commonly known as ground-level air pollutants. The concentration of air pollutants must be identified as an early precaution step by the local environmental or health agencies. This work aims to apply the artificial neural network (ANN) in estimating the ozone concentration forecast in Bangi. It consists of input variables such as temperature, relative humidity, concentration of nitrogen dioxide, time, UVA and UVB rays obtained from routine monitoring, and data recorded. Ten hidden layer is utilized to obtain the optimized ozone concentration, which is the output layer of the ANN framework. The finding showed that the meteorology condition and emission patterns play an important part in influencing the ozone concentration. However, a single network is sufficient enough to estimate the concentration despite any circumstances. Thus, it can be concluded that ANN is able to give reliable and satisfactory estimations of ozone concentration for the following day.
In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo's derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).
The progress of novel sorbents and their function in preconcentration techniques for determination of trace elements is a topic of great importance. This review discusses numerous analytical approaches including the preparation and practice of unique modification of solid-phase materials. The performance and main features of ion-imprinting polymers, carbon nanotubes, biosorbents, and nanoparticles are described, covering the period 2007-2012. The perspective and future developments in the use of these materials are illustrated.