Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emissi...
Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emission sources. We use X-ray fluorescence spectroscopy to characterize the elemental composition of PM samples collected from 27 globally distributed sites in the Surface PARTiculate mAtter Network (SPARTAN) over 2019-2023. Consistent protocols are applied to collect all samples and analyze them at one central laboratory, which facilitates comparison across different sites. Multiple quality assurance measures are performed, including applying reference materials that resemble typical PM samples, acceptance testing, and routine quality control. Method detection limits and uncertainties are estimated. Concentrations of dust and trace element oxides (TEO) are determined from the elemental dataset. In addition to sites in arid regions, a moderately high mean dust concentration (6 μg/m) in PM is also found in Dhaka (Bangladesh) along with a high average TEO level (6 μg/m). High carcinogenic risk (>1 cancer case per 100000 adults) from airborne arsenic is observed in Dhaka (Bangladesh), Kanpur (India), and Hanoi (Vietnam). Industries of informal lead-acid battery and e-waste recycling as well as coal-fired brick kilns likely contribute to the elevated trace element concentrations found in Dhaka.
作者：
Abhishek KumarR. DhanuskodiR. KaliappanK. NandakumarAbhishek Kumar teaches design philosophies at Anant National University
Ahmedabad. He earned his Ph.D in Management from Pondicherry University. He is an Economics graduate from Calcutta University and MBA from BIM Trichy. He has published more than 20 articles in reputed international journals has authored two books written articles and columns for newspapers and is quoted on issues related to leadership and marketing by various media platforms. His research work comprises construction of brand personality scale for media aesthetics and phenomenological design. His recent publications are on philosophy of a photograph hermeneutic reality of product and on philosophy of intimate spaces. R. Dhanuskodi has nearly 40 years of R&D experience at BHEL
India in technical areas applicable for thermal power plants. He is a life member of The Institution of Engineers (India) and The Combustion Institute. He has won two BHEL’s Excel awards under the best author category for technical papers. He has visited France Netherlands and Germany under Indo-Europe Clean Coal Development Program. He has guided 42 UG PG and PhD project works. He holds 11 patents 40 copyrights and 2 design registrations. He has presented papers in 20 conferences and published in 10 national and international journals. R. Kaliappan completed his bachelors in electrical and electronics engineering and Masters in Computer Science. He has 36 years of research experience in different fields of power generation and power plant subsystems such as heat transfer studies on boiler circulation
efficiency improvements of boiler subsystems product improvements/ enhancements and setting up test facilities for research studies. He has published a number of technical papers on MHD power generation and heat transfer studies in various national and international journals. He has more than 25 patents and copyrights on products development related to power boilers. He has won BHEL’S gold medal for product development for Smart Wall Blowing system. K. Nandakumar
This study explores the spatial-temporal patterns of particulate matter (PM) in Taiwan. Probability map of PM and daily patterns are discussed in this study. Data mining provides more detailed spatial-temporal informa...
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This study explores the spatial-temporal patterns of particulate matter (PM) in Taiwan. Probability map of PM and daily patterns are discussed in this study. Data mining provides more detailed spatial-temporal information for PM variations and trends. The proposed model will show that data mining provides a relatively high goodness of fit and sufficient space-time explanatory power, particularly air pollution frequency and affect areas. In the proposed model, a method using Dynamic Time Warping is proposed to analyse temporal similarity between stations. The proposed model can eliminate global effect on a single station through the performance of multiple stations. The proposed model will further be used for prediction of PM2.5. The prediction results will discuss the spatial-temporal relations between stations. This study will investigate the distribution of PM and its cyclicality.
In laparoscopic surgery, the surgeon must operate with a limited field of view and reduced depth perception. This makes spatial understanding of critical structures difficult, such as an endophytic tumour in a partial...
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In laparoscopic surgery, the surgeon must operate with a limited field of view and reduced depth perception. This makes spatial understanding of critical structures difficult, such as an endophytic tumour in a partial nephrectomy. Such tumours yield a high complication rate of 47%, and excising them increases the risk of cutting into the kidney's collecting system. To overcome these challenges, an augmented reality guidance system is proposed. Using intra-operative ultrasound, a single navigation aid, and surgical instrument tracking, four augmentations of guidance information are provided during tumour excision. Qualitative and quantitative system benefits are measured in simulated robot-assisted partial nephrectomies. Robot-to-camera calibration achieved a total registration error of 1.0 ± 0.4 mm while the total system error is 2.5 ± 0.5 mm. The system significantly reduced healthy tissue excised from an average (±standard deviation) of 30.6 ± 5.5 to 17.5 ± 2.4 cm3 (p
An approach for robustness analysis of non-dominated solutions to a multi-objective optimization model of an energy management system aggregator (EMSA) in face of uncertainty is presented. The EMSA is an intermediary ...
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ISBN:
(纸本)9781509042418
An approach for robustness analysis of non-dominated solutions to a multi-objective optimization model of an energy management system aggregator (EMSA) in face of uncertainty is presented. The EMSA is an intermediary entity between households and the System Operator (SO), capable of contributing to balance load and supply, and therefore coping with the intermittency of renewable energy sources (RES) and facilitating a load follows supply strategy in a Smart Grid environment. Household clusters provide load flexibility to satisfy system services requested by the SO, involving decreasing or increasing load in specific time slots. The EMSA multi-objective optimization model considers the maximization of profits and the minimization of the imbalance between the amounts of load flexibility provided by the end-user clusters to satisfy SO requests, taking into account revenues from the SO and payments to the clusters. A hybrid evolutionary approach combining Genetic Algorithms (GA) with Differential Evolution (DE) has been designed to deal with this model, and its behaviour subject to different scenarios of uncertainty is evaluated. The robustness analysis of non-dominated solutions produced by the hybrid evolutionary approach is based on the degree of robustness concept, taking into account the changes in the performance of the objective functions when small perturbations of the model nominal coefficients occur.
In this paper we present a controller that achieves global input-to-state stabilty for a linear system of arbitrary relative degree, subjected to matched and unmatched disturbances. This controller combines the proper...
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This paper proposes a new application based on fuzzy logic to search the optimal Step Size of the Normalized Least Mean Square (NLMS) algorithm for beamforming systems. The searching of the step size depends on the fu...
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ISBN:
(纸本)9781479984992
This paper proposes a new application based on fuzzy logic to search the optimal Step Size of the Normalized Least Mean Square (NLMS) algorithm for beamforming systems. The searching of the step size depends on the fuzzy inference results of an estimation of the final value of the cost function (J_E) instead of using its instantaneous value. The update of the step-size is performed outside of the adaptive algorithm and given it feedback by the fuzzy inference system;therefore the step-size is still fixed for the NLMS algorithm but variable for the complete searching scheme. Simulation experimental results show that a useful approximation of the optimal step-size can be obtained for different conditions of signal-to-noise plus interference ratios (SINR) and the minimization of the mean square error for the adaptive beamforming algorithm is also achieved.
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