Organic pollution continues to be an important worldwide obstacle for tackling health and environmental concerns that require ongoing and prompt response. To identify the LAB content levels as molecular indicators for sewage pollution, surface sediments had obtained from the South region of Malaysia. The origins of the LABs were identified using gas chromatography-mass spectrometry (GC-MS). ANOVA and a Pearson correlation coefficient at p
Most components of petroleum oily sludge (POS) are toxic, mutagenic and cancer-causing. Often bioremediation using microorganisms is hindered by the toxicity of POS. Under this circumstance, phytoremediation is the main option as it can overcome the toxicity of POS. Cajanus cajan a legume plant, was evaluated as a phyto-remediating agent for petroleum oily sludge-spiked soil. Culture dependent and independent methods were used to determine the rhizosphere microorganisms' composition. Degradation rates were estimated gravimetrically. The population of total heterotrophic bacteria (THRB) was significantly higher in the uncontaminated soil compared to the contaminated rhizosphere soil with C. cajan, but the population of hydrocarbon-utilizing bacteria (HUB) was higher in the contaminated rhizosphere soil. The results show that for 1 to 3% oily sludge concentrations, an increase in microbial counts for all treatments from day 0 to 90 d was observed with the contaminated rhizosphere CR showing the highest significant increase (p
The main objective of this cross-sectional comparative study is to observe the relationship between traffic-related air pollutants, particularly particulate matter (PM) of total suspended particulate (TSP) and PM of size 10 µm (PM10), and vehicle traffic in Amman, Jordan. Two study areas were chosen randomly as a high-polluted area (HPA) and low-polluted area (LPA). The findings indicate that TSP and PM10 were still significantly correlated with traffic count even after controlling for confounding factors (temperature, relative humidity, and wind speed): TSP, r = 0.726, P < .001; PM10, r = 0.719, P < .001). There was a significant positive relationship between traffic count and PM level: TSP, P < .001; PM10, P < .001. Moreover, there was a significant negative relationship between temperature and PM10 level (P = .018). Traffic volume contributed greatly to high concentrations of TSP and PM10 in areas with high traffic count, in addition to the effect of temperature.
The impact of floating net cages culturing the seabass, Lates calcarifer, on planktonic processes and water chemistry in two heavily used mangrove estuaries in Malaysia was examined. Concentrations of dissolved inorganic and particulate nutrients were usually greater in cage vs. adjacent (approximately 100 m) non-cage waters, although most variability in water-column chemistry related to water depth and tides. There were few consistent differences in plankton abundance, production or respiration between cage and non-cage sites. Rates of primary production were low compared with rates of pelagic mineralization reflecting high suspended loads coupled with large inputs of organic matter from mangrove forests, fishing villages, fish cages, pig farms and other industries within the catchment. Our preliminary sampling did not reveal any large-scale eutrophication due to the cages. A crude estimate of the contribution of fish cage inputs to the estuaries shows that fish cages contribute only approximately 2% of C but greater percentages of N (32-36%) and P (83-99%) to these waters relative to phytoplankton and mangrove inputs. Isolating and detecting impacts of cage culture in such heavily used waterways--a situation typical of most mangrove estuaries in Southeast Asia--are constrained by a background of large, highly variable fluxes of organic material derived from extensive mangrove forests and other human activities.
This study aims to determine the concentration of sterols used as biomarkers in the surface microlayer (SML) in estuarine areas of the Selangor River, Malaysia. Samples were collected during different seasons through the use of a rotation drum. The analysis of sterols was performed using gas chromatography equipped with a flame ionisation detector (GC-FID). The results showed that the concentrations of total sterols in the SML ranged from 107.06 to 505.55 ng L(-1). The total sterol concentration was found to be higher in the wet season. Cholesterol was found to be the most abundant sterols component in the SML. The diagnostic ratios of sterols show the influence of natural sources and waste on the contribution of sterols in the SML. Further analysis, using principal component analysis (PCA), showed distinct inputs of sterols derived from human activity (40.58%), terrigenous and plant inputs (22.59%) as well as phytoplankton and marine inputs (17.35%).
This study aims to determine the levels of methylene blue active substances (MBAS) and ethyl violet active substances (EVAS) as anionic surfactants and of disulphine blue active substances (DBAS) as cationic surfactants in the surface microlayer (SML) around an estuarine area using colorimetric methods. The results show that the concentrations of surfactants around the estuarine area were dominated by anionic surfactants (MBAS and EVAS) with average concentrations of 0.39 and 0.51 μmol L⁻¹, respectively. There were significant between-station differences in surfactant concentrations (p<0.05) with higher concentrations found at the stations near the sea. The concentration of surfactants was higher during the rainy season than the dry season due to the influence of runoff water. Further investigation using total organic carbon (TOC) and total organic nitrogen (TON) shows that there is a significant correlation (p<0.05) between both anionic and cationic surfactants and the TON concentration.
Groundwater is a primary natural water source in the absence of surface water bodies. Groundwater in urban environments experiences unprecedented stress from urban growth, population increase, and industrial activities. This study assessed groundwater quality in terms of arsenic and heavy metal contamination in three industrial areas (Shahdara, Jhilmil, and Patparganj), Delhi, India. The water quality was assessed over a 3-year time interval (i.e., 2015 and 2018). The groundwater constituents investigated were As, Fe, Cr, Cd, Ni, Zn, Mn, Cu, and Pb. Metal index and heavy metal pollution indexes were estimated to assess groundwater pollution. The health risk was evaluated in terms of non-carcinogenic and carcinogenic risk assessment. Patparganj industrial area saw increment in concentration for Cu 0.23 mg/L (2015)-0.85 mg/L (2018), Zn 0.51 mg/L (2015)-7.2 mg/L (2018), Fe 0.32 mg/L (2015)-0.9 mg/L (2018), Cr 0.21 mg/L (2015)-0.26 mg/L (2018), Mn 0.14 mg/L (2015)-0.25 mg/L (2018), Ni 0.04 mg/L (2015)-0.34 mg/L (2018), and As 0.01 mg/L (2015)-0.18 mg/L (2018). Cd and Pb concentrations were observed to decrease by 40-90 % and 85-99% for all the three industrial areas. Metal index and heavy metal index values were found to be >1 for all locations. The risk quotient value > 1 was observed for all locations in the year 2015 but was found to increase further to a range of RQ 10-62 in the year 2018, inferring increased non-carcinogenic risk to consumers. The carcinogenic risk was significant with respect to Fe (0.2-0.7), Zn (0.001-0.007), and As (0.002-0.003) for all locations in the year 2015. This study concludes that groundwater in the three industrial areas is highly polluted and is not fit for human consumption. Further studies are required to explore possible control measures and develop methods to mitigate groundwater pollution, sustainable management, and optimized use to conserve it for future generations.
Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study's analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants' sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study's CBPF technique and time series signature analysis' outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.
An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.
Microplastics (MPs) have become a prevalent environmental concern, exerting detrimental effects on marine and terrestrial ecosystems, as well as human health. Addressing this urgent issue necessitates the implementation of coordinated waste management policies and strategies. In this study, we present a comprehensive review focusing on key results and the underlying mechanisms associated with microplastics. We examine their sources and pathways, elucidate their ecological and human health impacts, and evaluate the current state of waste management policies. By drawing upon recent research and pertinent case studies, we propose a range of practical solutions, encompassing enhanced recycling and waste reduction measures, product redesign, and innovative technological interventions. Moreover, we emphasize the imperative for collaboration and cooperation across sectors and jurisdictions to effectively tackle this pressing environmental challenge. The findings of this study contribute to the broader understanding of microplastics and provide valuable insights for policymakers, researchers, and stakeholders alike.
Concentrations of Cd, Cu, Pb, Zn, Ni and Fe were determined in the surface sediments to investigate the distributions, concentrations and the pollution status of heavy metals in Dumai coastal waters. Sediment samples from 23 stations, representing 5 different site groups of eastern, central and western Dumai and southern and northern Rupat Island, were collected in May 2005. The results showed that heavy metal concentrations (in microg/g dry weight; Fe in %) were 0.88 (0.46-1.89); 6.08 (1.61-13.84); 32.34 (14.63-84.90); 53.89 (31.49-87.11); 11.48 (7.26-19.97) and 3.01 (2.10-3.92) for Cd, Cu, Pb, Zn, Ni and Fe, respectively. Generally, metal concentrations in the coastal sediments near Dumai city center (eastern and central Dumai) which have more anthropogenic activities were higher than those at other stations. Average concentration of Cd in the eastern Dumai was slightly higher than effective range low (ERL) but still below effective range medium (ERM) value established by Long et al. (Environmental Management 19(1):81-97, 1995; Environmental Toxicology Chemistry 17(4):714-727, 1997). All other metals were still below ERL and ERM. Calculated enrichment factor (EF), especially for Cd and Pb, and the Pollution load index (PLI) value in the eastern Dumai were also higher than other sites. Cd showed higher EF when compared to other metals. Geo-accumulation indices (I(geo)) in most of the stations (all site groups) were categorized as class 1 (unpolluted to moderately polluted environment) and only Cd in Cargo Port was in class 2 (moderately polluted). Heavy metal concentrations found in the present study were comparable to other regions of the world and based on the calculated indices it can be classified as unpolluted to moderately polluted coastal environment.
Miri city has a dynamic coastal environment, mainly influenced by intensive sedimentation from the Baram River and excessive trace metal loading by the Miri River, which are significant environmental concerns. As the mobility, bioavailability, and toxicity of the trace metals in the sediments are largely controlled by their particulate speciation, the modified BCR sequential extraction protocol was applied to determine the particulate speciation of trace metals in the coastal sediments of Miri, to unravel the seasonal geochemical processes responsible for known observations, and to identify possible sources of these trace metals. The granulometric analysis results showed that littoral currents aided by the monsoonal winds have influenced the grain size distribution of the sediments, enabling us to divide the study area into north-east and south-west segments where the geochemical composition are distinct. The Cu (>84%) and Zn (82%) concentrations are predominantly associated with the exchangeable fraction, which is readily bioavailable. Pb and Cd are dominant in non-residual fractions and other metals viz., Fe, Mn, Co, Ni, and Cr are dominant in the residual fraction. Using Pearson's correlation and factor analysis, the major mechanisms controlling the chemistry of the sediments are identified as association of Cu and Zn with fine fraction sediments, sulphide oxidation in the SW segment of the study area, atmospheric fallout of Pb and Cd in the river basins, precipitation of dissolved Fe and Mn supplied from the rivers and remobilization of Mn from the coastal sediments. Based on various pollution indices, it is inferred that the coastal sediments of NW Borneo are contaminated with Cu and Zn, and are largely bioavailable, which can be a threat to the local aquatic organisms, coral reefs, and coastal mangroves.
Indonesia is the second-largest contributor of microplastics (MPs) pollution in the marine ecosystem. Most MPs pollution-related studies in Indonesia focus on seawater, sediment, with less information found on the commercially important fish species used for human consumption. Skipjack Tuna (Euthynnus affinis) is one of the major exporting fishery commodities from Indonesia. This exploratory study aimed to determine MPs presence in the digestive tract of Skipjack Tuna from the Southern Coast of Java, Indonesia. The fish samples were collected from five different fish traditional auction market along the Southern Coast of Java, Indonesia, namely Pangandaran, Pamayang Sari, Ciletuh, Santolo, and Palabuhan Ratu. The gastrointestinal tract of Skipjack tuna was pretreated using alkaline destruction and filtered. The presence of MPs in the treated samples was visually identified using an optical microscope, while Polybrominated diphenyl ethers (PBDEs) contaminants were analyzed using Gas Chromatography-Mass Spectrometry (GC-MS). A total of 19 suspected MPs particles were found in the form of filament (84%), angular (11%), and round (5%). This result would provide a better indication of the MPs contamination in marine life species in the Southern Coast of Java, Indonesia, as useful information for marine environmental monitoring program in the future.
Background: Level of comfort in working environment can contribute to increase level of health, emotion during working, level of safety, quality and productivity of work. A study of physical factors (heat, noise and lighting) is important to determine the level of comfort during working. This study was carried out to study those physical factors upon comfort level during working among Casting Shop workers in a car manufacturing factory.
Methods: Instruments for the physical monitoring including Questemp°36 Thermal Environment Monitor, Sound Level Meter and Lux Meter were used at seven measured areas. The information about the level of comfort during working was collected using questionnaires among 65 respondents by random sampling method.
Results: Measured data showed there were four measured areas which Wet Bulb Globe Temperature indoor (WBGTi) value are above the standard limit recommended by ACGIH, three measured areas recorded noise level above the standard limit recommended by Factories and Machineries (Noise Exposure) 1989, while there was no measured area recorded lighting reading below the standard limit recommended by MS ISO 8995:2005. Result from questionnaire found that the majority of the workers did not feel comfortable towards the heat and noise level in their workplace while most of the respondents felt comfortable towards lighting level in their workplace. Mean of WBGTi reading and lighting reading have a significant difference (p
The variation in the concentration of outdoor air pollutants during the COVID-19 lockdown was studied in Jakarta, Indonesia. The term lockdown was replaced by large-scale social restrictions (PSBB) in Indonesia by more flexible regulations to save the economy. Data on five air pollutants, namely, PM10, SO2, CO, O3, and NO2, from five monitoring stations located in five regions in Jakarta (West, East, Central, North, and South Jakarta) were utilized. We analyzed the changes in the concentrations of outdoor air pollutants before lockdown from January 1 to April 9, 2020, and during lockdown from April 10 to June 4, 2020. Overall, the CO concentration (39.9%) demonstrated the most significant reduction during lockdown, followed by NO2 (7.5%) and then SO2 (5.7%). However, we unexpectedly found that during lockdown, the PM10 concentration in Jakarta increased by 10.9% due to the southwest monsoon during the seasonal change in Jakarta. Among the five cities in Jakarta, East and Central Jakarta experienced the maximum improvement in their air quality, whereas North Jakarta had the least air quality improvement. To the best of our knowledge, this research is the first to study the effect of lockdown on outdoor air quality improvement in Indonesia using ground-level measurement data. The findings of the study provide additional strategies to the regulatory bodies for the reduction of temporal air pollutants in Jakarta, Indonesia, by restricting people mobility as a supplementary initiative.
Members of the catadromous eel live in various fresh, brackish and marine habitats. Therefore, these eels can accumulate organic pollutants and are a suitable bioindicator species for determining the levels of organic contaminants within different water bodies. The ecological risk for organochlorine compounds (OCs) in Anguilla japonica with various migration patterns, such as freshwater, estuarine and marine residences, was examined to understand the specific accumulation patterns. The concentrations of HCB, ∑HCHs, ∑CHLs and ∑DDTs in the silver stage (maturing) eel were significantly higher than those in the yellow stage (immature) eel, in accordance with the higher lipid contents in the former versus the latter. The OC accumulations were clearly different among migratory types in the eel. The ecological risk of OCs increased as the freshwater residence period in the eel lengthened. The migratory histories and the lipid contents directly affected the OC accumulation in the catadromous eel species.
The bioaccumulation of organochlorines (OCs) in the muscle tissue of sea-run (anadromous) and freshwater-resident (fluvial) white-spotted charr (Salvelinus leucomaenis) was determined to assess the ecological risk related to intraspecies variations in diadromous fish life history as they migrate between sea and freshwater. Generally, there were significant correlations between the accumulation of OCs such as DDTs, HCB, HCHs and CHLs. In addition, various biological characteristics, such as total length (TL), body weight (BW) and age, and number of downstream migration (NDM) were correlated. A positive correlation occurred between the lipid content and the OC concentrations. Close linear relationships were found between TL, BW and NDM and the lipid content. Although they are both the same species, the OCs concentrations in the anadromous fish were significantly higher than those in the fluvial individuals. These results suggest that anadromous S. leucomaenis have a higher ecological risk for OCs exposure than the fluvial fish.
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.
17α-ethynylestradiol (EE2) is a synthetic hormone, which is a derivative of the natural hormone, estradiol (E2). EE2 is an orally bio-active estrogen, and is one of the most commonly used medications for humans as well as livestock and aquaculture activity. EE2 has become a widespread problem in the environment due to its high resistance to the process of degradation and its tendency to (i) absorb organic matter, (ii) accumulate in sediment and (iii) concentrate in biota. Numerous studies have reported the ability of EE2 to alter sex determination, delay sexual maturity, and decrease the secondary sexual characteristics of exposed organisms even at a low concentration (ng/L) by mimicking its natural analogue, 17β-estradiol (E2). Thus, the aim of this review is to provide an overview of the science regarding EE2, the concentration levels in the environment (water, sediment and biota) and summarize the effects of this compound on exposed biota at various concentrations, stage life, sex, and species. The challenges in respect of EE2 include the extension of the limited database on the EE2 pollution profile in the environment, its fate and transport mechanism, as well as the exposure level of EE2 for better prediction and definition revision of EE2 toxicity end points, notably for the purpose of environmental risk assessment.
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).