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.
The integration of communication technologies such as radio frequency identification (RFID), global positioning system (GPS), general packet radio system (GPRS), and geographic information system (GIS) with a camera are constructed for solid waste monitoring system. The aim is to improve the way of responding to customer's inquiry and emergency cases and estimate the solid waste amount without any involvement of the truck driver. The proposed system consists of RFID tag mounted on the bin, RFID reader as in truck, GPRS/GSM as web server, and GIS as map server, database server, and control server. The tracking devices mounted in the trucks collect location information in real time via the GPS. This information is transferred continuously through GPRS to a central database. The users are able to view the current location of each truck in the collection stage via a web-based application and thereby manage the fleet. The trucks positions and trash bin information are displayed on a digital map, which is made available by a map server. Thus, the solid waste of the bin and the truck are being monitored using the developed system.
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.
In the last few years, air within homes have been indicates by various and emerging body as more serious polluted than those outdoor. Prevalence of respiratory inflammation among school children aged 8 and 10 years old attending national primary schools in urban and rural area were conducted in Klang Valley. Two population studies drawn from the questionnaires were used to investigate the association between indoor particulate matter (PM2.5 & PM10) in a home environment and respiratory implication through the understanding of biological responses. Approximately 430 healthy school children of Standard 2 and Standard 5 were selected. Indication of respiratory symptoms using adaptation questionnaire from American Thoracic Society (1978). Sputum sample collection taken for biological analysis. IL-6 then was analyse by using ELISA techniques. Indoor PM2.5 and PM10 were measured using Dust Trak Aerosol Monitor. The mean concentration of PM2.5 (45.38 µg/m3) and PM10 (80.07 µg/m3) in urban home environment is significantly higher compared to those in rural residential area (p=0.001). Similar trend also shows by the prevalence of respiratory symptom. Association were found with PM2.5 and PM10 with the level of IL-6 among school children. A greater exposure to PM2.5 and PM10 are associated with higher expression of IL-6 level suggesting that the concentration of indoor particulate in urban density area significantly influence the health of children.
This study emphasizes on near surface observation of chemically active trace gases such as nitrogen dioxide (NO2) over Islamabad on a regular basis. Absorption spectroscopy using backscattered extraterrestrial light source technique was used to retrieve NO2 differential slant column densities (dSCDs). Mini multi-axis-differential optical absorption spectroscopy (MAX-DOAS) instrument was used to perform ground-based measurements at Institute of Environmental Sciences and Engineering (IESE), National University of Sciences and Technology (NUST) Islamabad, Pakistan. Tropospheric vertical column densities (VCDs) of NO2 were derived from measured dSCDs by using geometric air mass factor approach. A case study was conducted to identify the impact of different materials (glass, tinted glass, and acrylic sheet of various thicknesses used to cover the instrument) on the retrieval of dSCDs. Acrylic sheet of thickness 5 mm was found most viable option for casing material as it exhibited negligible impact in the visible wavelength range. Tropospheric NO2 VCD derived from ground-based mini MAX-DOAS measurements exceeded two times the Pak-NEQS levels and showed a reasonable comparison (r (2) = 0.65, r = 0.81) with satellite observations (root mean square bias of 39 %) over Islamabad, Pakistan.
Slope failure has become a major concern in Malaysia due to the rapid development and urbanisation in the country. It poses severe threats to any highway construction industry, residential areas, natural resources and tourism activities. The extent of damages that resulted from this catastrophe can be lessened if a long-term early warning system to predict landslide prone areas is implemented. Thus, this study aims to characterise the relationship between Oxisols properties and soil colour variables to be manipulated as key indicators to forecast shallow slope failure. The concentration of each soil property in slope soil was evaluated from two different localities that consist of 120 soil samples from stable and unstable slopes located along the North-South Highway (PLUS) and East-West Highway (LPT). Analysis of variance established highly significant difference (P
Lead contamination in topsoil of the mining and smelting area of Mitrovica, Kosovo, was investigated for total concentrations and chemical fractions by sequential extraction analysis, mineralogical fractions by X-ray diffraction (XRD) and scanning electron microscopy with energy-dispersive X-ray spectrometer (SEM-EDX). The study revealed that all samples contained Pb exceeding USEPA standard of 400 mg kg-1. The highest total concentration of Pb (125,000 mg kg-1) was the soil from the former smelter. Sequential extraction results showed that the predominant form of Pb was associated with Fe-Mn oxide-bound fraction which ranged from 45.37 to 71.61% of total concentrations, while carbonate and silicate Pb-binding fractions were dominant when physical measurements (XRD and SEM-EDX) were applied. Application of Pb isotope ratios (206Pb/207Pb and 208Pb/206Pb), measured by inductively coupled plasma mass spectrometry, identified that Pb contamination is originated from similar anthropogenic source. The results reflected that the Pb contamination in the soil of this area is serious. In order to provide proper approaches on remediation and prevention of health impacts to the people in this area, a continuous monitoring and health risk assessment are recommended.
Results are presented of a demonstration of real-time fish blast location in Sabah, Malaysia using a networked hydroacoustic array based on the ShotSpotter gunshot location system. A total of six acoustic sensors - some fixed and others mobile - were deployed at ranges from 1 to 9 km to detect signals from controlled test blasts. This allowed the blast locations to be determined to within 60 m accuracy, and for the calculated locations to be displayed on a map on designated internet-connected computers within 10 s. A smaller three-sensor system was then installed near Semporna in Eastern Sabah that determined the locations of uncontrolled blasts set off by local fishermen. The success of these demonstrations shows that existing technology can be used to protect reefs and permit more effective management of blast fishing activity through improved detection and enforcement measures and enhanced community engagement.
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.
Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM 2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM 2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM 2.5 concentrations in the smart cities of Malaysia by comparing supervised ML techniques, which helps to mitigate its adverse effects. Methods In this paper, ML models for forecasting PM 2.5 concentrations were investigated on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data cleaning and a normalization process. Next, it was reduced into an informative dataset with location and time factors in the feature extraction process. The dataset was fed into three supervised ML classifiers, which include random forest (RF), artificial neural network (ANN) and long short-term memory (LSTM). Finally, their output was evaluated using the confusion matrix and compared to identify the best model for the accurate prediction of PM 2.5. Results Overall, the experimental result shows an accuracy of 97.7% was obtained by the RF model in comparison with the accuracy of ANN (61.14%) and LSTM (61.77%) in predicting PM 2.5. Discussion RF performed well when compared with ANN and LSTM for the given data with minimum features. RF was able to reach good accuracy as the model learns from the random samples by using decision tree with the maximum vote on the predictions.
Although coastal water marine algae have been popularly used by others as indicators of heavy metal pollution, data within the Bay of Bengal for the estuarine Cox's Bazar region and Saint Martin's Island has remained scarce. Using marine algae, the study herein forms an effort in biomonitoring of metal contamination in the aforementioned Bangladesh areas. A total of 10 seaweed species were collected, including edible varieties, analyzed for metal levels through the use of the technique of EDXRF. From greatest to least, measured mean metal concentrations in descending order have been found to be K > Fe > Zr > Br > Sr > Zn > Mn > Rb > Cu > As > Pb > Cr > Co. Potential toxic heavy metals such as Pb, As, and Cr appear at lower concentration values compared to that found for essential mineral elements. However, the presence of Pb in Sargassum oligocystum species has been observed to exceed the maximum international guidance level. Given that some of the algae species are cultivated for human consumption, the non-carcinogenic and carcinogenic indices were calculated, shown to be slightly lower than the maxima recommended by the international organizations. Overall, the present results are consistent with literature data suggesting that heavy metal macroalgae biomonitoring may be species-specific. To the best of our knowledge, this study represents the first comprehensive macroalgae biomonitoring study of metal contamination from the coastal waters of Cox's Bazar and beyond.
A facile dispersive-micro-solid phase extraction (D-μ-SPE) method coupled with HPLC for the analysis of selected non-steroidal anti-inflammatory drugs (NSAIDs) in water samples was developed using a newly prepared magnetic sporopollenin-cyanopropyltriethoxysilane (MS-CNPrTEOS) sorbent. Sporopollenin homogenous microparticles of Lycopodium clavatum spores possessed accessible functional groups that facilitated surface modification. Simple modification was performed by functionalization with 3-cyanopropyltriethoxysilane (CNPrTEOS) and magnetite was introduced onto the biopolymer to simplify the extraction process. MS-CNPrTEOS was identified by infrared spectrometrywhile the morphology and the magnetic property were confirmed by scanning electron microscopy (SEM) and vibrating sample magnetometer (VSM), respectively. To maximize the extraction performance of ketoprofen, ibuprofen, diclofenac and mefenamic acid using the proposed MS-CNPrTEOS, important D-μ-SPE parameters were comprehensively optimized. The optimum extraction conditions were sorbent amount, 40 mg; extraction time, 5 min; desorption time; 5 min; sample volume, 15 mL; sample pH 2.0; and salt addition, 2.5% (w/v). The feasibility of the developed method was evaluated using spiked tap water, lake water, river water and waste water samples. Results showed that ketoprofen and ibuprofen were linear in the range of 1.0-1000 μg L-1whilst diclofenac and mefenamic acid were linear in the range 0.8-500 μg L-1. The results also showed good detection limits for the studied NSAIDs in the range of 0.21-0.51 μg L-1and good recoveries for spiked water samples in the range of 85.1-106.4%. The MS-CNPrTEOS proved a promising dispersive sorbent and applicable to facile and rapid assay of NSAIDs in water samples.
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.
Steroid estrogens such as 17β-Estradiol (E2) and 17α-Ethynylestradiol (EE2) are highly potent estrogens that widely detected in environmental samples. Mathematical modelling such as concentration addition (CA) and estradiol equivalent concentration (EEQ) models are usually associated with measuring techniques to assess risk, predict the mixture response and evaluate the estrogenic activity of mixture. Wastewater has played a crucial role because wastewater treatment plant (WWTP) is the major sources of estrogenic activity in aquatic environment. The aims of this is to determine E2 and EE2 concentrations in six WWTPs effluent, to predict the estrogenic activity of the WWTPs effluent using CA and EEQ models where lastly the effectiveness of two models is evaluated. Results showed that all the six WWTPs effluent had relative high E2 concentration (35.1-85.2 ng/L) compared to EE2 (0.02-1.0 ng/L). The estrogenic activity predicted by CA model was similar among the six WWTPs (105.4 ng/L), due to the similarity of individual dose potency ratio calculated by respective WWTPs. The predicted total EEQ was ranged from 35.1 EEQ-ng/L to 85.3 EEQ-ng/L, explained by high E2 concentration in WWTPs effluent and E2 EEF value that standardized to 1.0 μg/L. The CA model is more effective than EEQ model in estrogenic activity prediction because EEQ model used less data and causes disassociation from the predicted behavior. Although both models predicted relative high estrogenic activity in WWTPs effluent, dilution effects in receiving river may lower the estrogenic response to aquatic inhabitants.
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
Four methods were developed for the analysis of fluroxypyr in soil samples from oil palm plantations. The first method involved the extraction of the herbicide with 0.05 M NaOH in methanol followed by purification using acid base partition. The concentrated material was subjected to derivatization and then cleaning process using a florisil column and finally analyzed by gas chromatography (GC) equipped with electron capture detector (ECD). By this method, the recovery of fluroxypyr from the spiked soil ranged from 70 to 104% with the minimum detection limit at 5 microg/kg. The second method involved solid liquid extraction of fluroxypyr using a horizontal shaker followed by quantification using high performance liquid chromatography (HPLC) equipped with UV detector. The recovery of fluroxypyr using this method, ranged from 80 to 120% when the soil was spiked with fluroxypyr at 0.1-0.2 microg/g soil. In the third method, the recovery of fluroxypyr was determined by solid liquid extraction using an ultrasonic bath. The recovery of fluroxypyr at spiking levels of 4-50 microg/L ranged from 88 to 98% with relative standard deviations of 3.0-5.8% with a minimum detection limit of 4 microg/kg. In the fourth method, fluroxypyr was extracted using the solid liquid extraction method followed by the cleaning up step with OASIS HLB (polyvinyl dibenzene). The recovery of fluroxypyr was between 91 and 95% with relative standard deviations of 4.2-6.2%, respectively. The limit of detection in method 4 was further improved to 1 pg/kg. When the weight of soil used was increased 4 fold, the recovery of fluroxypyr at spiking level of 1-50 microg/kg ranged from 82-107% with relative standard deviations of 0.5-4.7%.
Microfluidics-based lab-on-chip (LOC) systems are an active research area that is revolutionising high-throughput sequencing for the fast, sensitive and accurate detection of a variety of pathogens. LOCs also serve as portable diagnostic tools. The devices provide optimum control of nanolitre volumes of fluids and integrate various bioassay operations that allow the devices to rapidly sense pathogenic threat agents for environmental monitoring. LOC systems, such as microfluidic biochips, offer advantages compared to conventional identification procedures that are tedious, expensive and time consuming. This paper aims to provide a broad overview of the need for devices that are easy to operate, sensitive, fast, portable and sufficiently reliable to be used as complementary tools for the control of pathogenic agents that damage the environment.
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.
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.
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.