We report a case of a 59 year old man who developed venous air embolism (VAE) during an elective craniotomy for parasagittal meningioma resection. The surgery was done in the supine position with slightly elevated head position. VAE was provisionally diagnosed by sudden decreased in the end tidal carbon dioxide pressure from 34 to 18 mmHg, followed by marked hypotension and atrial fibrillation. Prompt central venous blood aspiration, aggressive resuscitation and inotropic support managed to stabilize the patient. Post operatively, he was admitted in neuro intensive care unit and made a good recovery without serious complications.
China's seaborne foreign oil supply through the Malacca Strait is facing security challenges due to territorial disputes, pirate attacks, and geopolitics. To overcome these challenges, China plans to import oil through one of the corridors of the Belt and Road Initiative (BRI)-the China-Pakistan Economic Corridor (CPEC). This study estimated and compared ship emissions and their externalities associated with seaborne oil supply from the top five oil suppliers to China through the existing shipping route via the Malacca Strait and proposed route via CEPC. Ship activity-based methodology is applied to estimate the emissions of air pollutants (CO2, NOx, SO2, PM10, and CO) during cruising, maneuvering, and hoteling periods. The results show that the total ship emissions of China's seaborne oil supply can be significantly reduced from 6.2 million tons to 2.1 million tons via the CPEC route. While external cost can be reduced up to 65.9% via the CPEC route.
In this era, most of us are suffering some level of respiratory problem. Respiratory system of our children is even more sensitive compare to adults. As our children spending an average of 8 hours in school, indoor air quality of the classroom become an important element. Many studies have shown that indoor air quality not only affecting the respiratory system of schoolchildren but their performance in academy as well.
Matched MeSH terms: Air Pollutants; Air Pollution, Indoor
This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers' drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.
A laboratory scale test rig to treat simulated flue gas using electron beam technology was installed at the Alurtron EB-Irradiation Center, MINT. The experiment test rig was proposed as a result of feasibility studies conducted jointly by IAEA, MINT and TNB Research in 1997. The test rig system consists of several components, among others, diesel generator sets, pipe ducts, spray cooler, ammonia dosage system, irradiation vessel, bag filter and gas analyzers. The installation was completed and commissioned in October 2001. Results from the commissioning test runs and subsequent experimental work showed that the efficiency of flue gas treatment is high. It was proven that electron beam technology might be applied in the treatment of air pollutants. This paper describes the design and work function of the individual major components as well as the full system function. Results from the initial experimental works are also presented.
The air pollution index (API) has been recognized as one of the important air quality indicators used to record the
correlation between air pollution and human health. The API information can help government agencies, policy makers
and individuals to prepare precautionary measures in order to eliminate the impact of air pollution episodes. This study
aimed to verify the monthly API trends at three different stations in Malaysia; industrial, residential and sub-urban areas.
The data collected between the year 2000 and 2009 was analyzed based on time series forecasting. Both classical and
modern methods namely seasonal autoregressive integrated moving average (SARIMA) and fuzzy time series (FTS) were
employed. The model developed was scrutinized by means of statistical performance of root mean square error (RMSE).
The results showed a good performance of SARIMA in two urban stations with 16% and 19.6% which was more satisfactory
compared to FTS; however, FTS performed better in suburban station with 25.9% which was more pleasing compared
to SARIMA methods. This result proved that classical method is compatible with the advanced forecasting techniques in
providing better forecasting accuracy. Both classical and modern methods have the ability to investigate and forecast
the API trends in which can be considered as an effective decision-making process in air quality policy.
Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forecasting model by integrating fuzzy time series to Markov chain and C-Means clustering techniques with an optimal number of clusters is presented. This hybridization contributes to generating effective lengths of intervals and thus, improving the model accuracy. The proposed model was verified and validated with real time series data sets, which are the benchmark data of actual trading of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and PM10 concentration data from Melaka, Malaysia. In addition, a comparison was made with some existing fuzzy time series models. Furthermore, the mean absolute percentage error, mean squared error and Theil's U statistic were calculated as evaluation criteria to illustrate the performance of the proposed model. The empirical analysis shows that the proposed model handles the time series data sets more efficiently and provides better overall forecasting results than existing FTS models. The results prove that the proposed model has greatly improved the prediction accuracy, for which it outperforms several fuzzy time series models. Therefore, it can be concluded that the proposed model is a better option for forecasting air pollution parameters and any kind of random parameters.
At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.
Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. One of the major effects of air pollution on health is hospitalizations associated with air pollution. Recently, the estimation and prediction of air pollution-based hospitalization is carried out using artificial intelligence (AI) and machine learning (ML) techniques, i.e., deep learning and long short-term memory (LSTM). However, there is ample room for improvement in the available applied methodologies to estimate and predict air pollution-based hospital admissions. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the artificial intelligence (AI) techniques. We propose the enhanced long short-term memory (ELSTM) model and provide a comparison with other AI techniques, i.e., LSTM, DL, and vector autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The utilized dataset contains the data from January 2006 to December 2016 for five study locations, i.e., Klang (KLN), Shah Alam (SA), Putrajaya (PUJ), Petaling Jaya (PJ), and Cheras, Kuala Lumpur (CKL). The dataset for Banting contains data from April 2010 to December 2016, and the data for Batu Muda, Kuala Lumpur, contains data from January 2009 to December 2016. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.
Numerous mitigation techniques have been incorporated to capture or remove SO2 with flue gas desulfurization (FGD) being the most common method. Regenerative FGD method is advantageous over other methods due to high desulfurization efficiency, sorbent regenerability, and reduction in waste handling. The capital costs of regenerative methods are higher than those of commonly used once-through methods simply due to the inclusion of sorbent regeneration while operational and management costs depend on the operating hours and fuel composition. Regenerable sorbents like ionic liquids, deep eutectic solvents, ammonium halide solutions, alkyl-aniline solutions, amino acid solutions, activated carbons, mesoporous silica, zeolite, and metal-organic frameworks have been reported to successfully achieve high SO2 removal. The presence of other gases in flue gas, e.g., O2, CO2, NOx, and water vapor, and the reaction temperature critically affect the sorption capacity and sorbent regenerability. To obtain optimal SO2 removal performance, other parameters such as pH, inlet SO2 concentration, and additives need to be adequately governed. Due to its high removal capacity, easy preparation, non-toxicity, and low regeneration temperature, the use of deep eutectic solvents is highly feasible for upscale utilization. Metal-organic frameworks demonstrated highest reported SO2 removal capacity; however, it is not yet applicable at industrial level due to its high price, weak stability, and robust formulation.
Concerns about chemical exposure in the electronics manufacturing industry have long been recognized, but data are lacking in Southeast Asia. We conducted a study in Batam, Indonesia, to evaluate chemical exposures in electronics facilities, using participatory research and biological monitoring approaches. A convenience sample of 36 workers (28 exposed, 8 controls) was recruited, and urine samples were collected before and after shifts. Five solvents (acetone, methyl ethyl ketone, toluene, benzene, and xylenes) were found in 46%-97% of samples, and seven metals (arsenic, cadmium, cobalt, tin, antimony, lead, and vanadium) were detected in 60%-100% of samples. Biological monitoring and participatory research appeared to be useful in assessing workers' exposure when workplace air monitoring is not feasible due to a lack of cooperation from the employer. Several logistical challenges need to be addressed in future biomonitoring studies of electronics workers in Asia in factories where employers are reluctant to track workers' exposure and health.
Human usage of non-renewable energy resources has caused many environmental issues, which include air pollution, global warming, and climate irregularities. To counter these issues, researchers have been seeking after alternative renewable energy sources and ways to manage energy more efficiently. This is where energy recovery technologies such as waste heat recovery (WHR) come into play. WHR is a form of waste to energy conversion. Waste heat can be captured and converted into usable energy instead of dumping it into the environment. In the more recent years, the WHR research field has gained great attention in the scientific community as well as in some energy-intensive industries. This article presents a bibliometric overview of the academic research on WHR over the span of 30 years from 1991 to 2020. A total of 5682 documents from Web of Science (WoS) have been retrieved and analyzed using various bibliometric methods, including performance analysis and network analysis. The analyses were performed on different actors in the field, i.e., funding agencies, journals, authors, organizations, and countries. In addition, several network mappings were done based on co-citation, co-authorship, and co-occurrences of keywords analyses. The research identified the most productive and influential actors in the field, established and emergent research topics, as well as the interrelations and collaboration patterns between different actors. The findings can be a robust roadmap for further research in this field.
Microplastic pollution has become a major global environmental issue, negatively impacting terrestrial and aquatic ecosystems as well as human health. Tackling this complex problem necessitates a multidisciplinary approach and collaboration among diverse stakeholders. Within this context, the Quintuple Helix framework, which highlights the involvement of academia, government, industry, civil society, and the environment, provides a comprehensive and inclusive perspective for formulating effective policies to manage atmospheric microplastics. This paper discusses each helix's roles, challenges, and opportunities and proposes strategies for collaboration and knowledge exchange among them. Furthermore, the paper highlights the importance of interdisciplinary research, innovative technologies, public awareness campaigns, regulatory frameworks, and corporate responsibility in achieving sustainable and resilient microplastic management policies. The Quintuple Helix approach can mitigate microplastics, safeguard ecosystems, and preserve planetary health by fostering collaboration and coordination among diverse stakeholders.
Indoor air quality (IAQ) has recently gained substantial traction as the airborne transmission of infectious respiratory disease becomes an increasing public health concern. Hospital indoor environments are complex ecosystems and strategies to improve hospital IAQ require greater appreciation of its potentially modifiable determinants, evidence of which are currently limited. This mini-review updates and integrates findings of previous literature to outline the current scientific evidence on the relationship between hospital IAQ and building design, building operation, and occupant-related factors. Emerging evidence has linked aspects of building design (dimensional, ventilation, and building envelope designs, construction and finishing materials, furnishing), building operation (ventilation operation and maintenance, hygiene maintenance, access control for hospital users), and occupants' characteristics (occupant activities, medical activities, adaptive behavior) to hospital IAQ. Despite the growing pool of IAQ literature, some important areas within hospitals (outpatient departments) and several key IAQ elements (dimensional aspects, room configurations, building materials, ventilation practices, adaptive behavior) remain understudied. Ventilation for hospitals continues to be challenging, as elevated levels of carbon monoxide, bioaerosols, and chemical compounds persist in indoor air despite having mechanical ventilation systems in place. To curb this public health issue, policy makers should champion implementing hospital IAQ surveillance system for all areas of the hospital building, applying interdisciplinary knowledge during the hospital design, construction and operation phase, and training of hospital staff with regards to operation, maintenance, and building control manipulation. Multipronged strategies targeting these important determinants are believed to be a viable strategy for the future control and improvement of hospital IAQ.
Wind is a renewable energy source. Overall, using wind to produce energy has fewer effects on the environment than many other energy sources. Wind and solar energy provide public health and environmental benefits to the social. Wind turbines may also reduce the amount of electricity generation from fossil fuels, which results in lower total air pollution and carbon dioxide emissions. In order to better optimize the effect of social energy economic management and facilitate the multiobjective decision making of coordinated development of energy and socioeconomic environment, a modeling and analysis method of economic benefits of wind power generation based on deep learning is proposed. In this paper, based on the principle of deep learning, the evaluation indicators of wind power economic benefits are excavated, a scientific and reasonable economic benefit evaluation system is constructed, a wind power economic benefit analysis model supported by public management is constructed, and the steps of wind power economic benefit analysis are simplified. It is concluded that the modeling and analysis method of wind power economic benefits based on deep learning has high practicability in the actual application process, which is convenient for the prediction and analysis of energy demand for social and economic development.
The processing of amang (one of a number of tin-tailing products) for its valuable minerals has associated with the radiological and environmental problems. The processing and stockpiling of amang and ilmenite in open-air spaces, subject as it is to environmental influences, gives rise to a potential for affecting residents in adjacent area. A case study was carried out in a residential area neighbouring a typical amang plant to investigate the radiological impact to the residents. The average Effective Dose rates, calculated based on the contributions of Effective Dose rates from inhaled suspended radioactive dust, radon-thoron and their progeny, and external gamma radiation, were determined for selected houses. Results show that the occupants of those houses received Effective Dose rate, which cannot be differentiated from background. The major contributor to the average Effective Dose rate came from external radiation sources. Inhaled radon and its progeny was the second major contributor.
Matched MeSH terms: Air Pollutants, Radioactive/adverse effects*; Air Pollutants, Radioactive/analysis; Air Pollution, Indoor/adverse effects; Air Pollution, Indoor/analysis
Exposure to air pollution has been linked to elevated blood pressure (BP) and hypertension, but most research has focused on short-term (hours, days, or months) exposures at relatively low concentrations. We examined the associations between long-term (3-year average) concentrations of outdoor PM2.5 and household air pollution (HAP) from cooking with solid fuels with BP and hypertension in the Prospective Urban and Rural Epidemiology (PURE) study. Outdoor PM2.5 exposures were estimated at year of enrollment for 137,809 adults aged 35-70 years from 640 urban and rural communities in 21 countries using satellite and ground-based methods. Primary use of solid fuel for cooking was used as an indicator of HAP exposure, with analyses restricted to rural participants (n = 43,313) in 27 study centers in 10 countries. BP was measured following a standardized procedure and associations with air pollution examined with mixed-effect regression models, after adjustment for a comprehensive set of potential confounding factors. Baseline outdoor PM2.5 exposure ranged from 3 to 97 μg/m3 across study communities and was associated with an increased odds ratio (OR) of 1.04 (95% CI: 1.01, 1.07) for hypertension, per 10 μg/m3 increase in concentration. This association demonstrated non-linearity and was strongest for the fourth (PM2.5 > 62 μg/m3) compared to the first (PM2.5
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/analysis*; Air Pollution, Indoor/analysis*
In this study, we assess the intense pollution episode of June 2013, in Riau province, Indonesia from land clearing. We relied on satellite retrievals of aerosols and Carbon monoxide (CO) due to lack of ground measurements. We used both the yearly and daily data for aerosol optical depth (AOD), fine mode fraction (FMF), aerosol absorption optical depth (AAOD) and UV aerosol index (UVAI) for characterizing variations. We found significant enhancement in aerosols and CO during the pollution episode. Compared to mean (2008-2012) June AOD of 0.40, FMF-0.39, AAOD-0.45, UVAI-1.77 and CO of 200 ppbv, June 2013 values reached 0.8, 0.573, 0.672, 1.77 and 978 ppbv respectively. Correlations of fire counts with AAOD and UVAI were stronger compared to AOD and FMF. Results from a trajectory model suggested transport of air masses from Indonesia towards Malaysia, Singapore and southern Thailand. Our results highlight satellite-based mapping and monitoring of pollution episodes in Southeast Asia.
Matched MeSH terms: Air Pollutants/analysis; Air Pollution/analysis; Air Pollution/statistics & numerical data*