scholarly journals Comparative Assessment of Co2 Emission Factors Estimated Using Stack Flue Gas Measurement Data for Two Types of Brick Kiln: A Case Study in Phu Tho Province, Vietnam

2015 ◽  
Vol 2 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Duc Luong Nguyen ◽  
Thanh Trung Nguyen ◽  
Duy Thai Nguyen ◽  
Duy Dong Nguyen
Keyword(s):  
Flue Gas ◽  
Co2 Emission ◽  
Measurement Data ◽  
Emission Factors ◽  
Comparative Assessment ◽  
Brick Kiln ◽  
Gas Measurement

ANALISIS KINERJA ASURANSI SYARIAH MENGGUNAKAN PENDEKATAN MASLAHAH SCORECARD (Studi Kasus Pada PT. Asuransi Takaful Umum)

2020 ◽  
Vol 1 (1) ◽  
pp. 108-125
Author(s):  
Jaharuddin Jahar ◽  
Melia Rostiana ◽  
R Melda Maesarach
Keyword(s):  
Measurement Data ◽  
Secondary Data ◽  
Process Performance ◽  
Measuring Process ◽  
Case Study Design ◽  
Quantitative And Qualitative Research ◽  
A Value ◽  
Takaful Insurance ◽  
Collection Methods

The purpose of this study was to decide the elements of performance at PT. General Takaful Insurance, to find out how to measure performance using the scorecard approach that is by measuring process performance and results performance, and interpreting in the form of conclusions. In this study, researchers tested apply maslahah at PT. General Takaful Insurance with a case study design. This research is a type of quantitative and qualitative research because it uses measurement data through formulas and if interpretative qualitative, and the data used are primary and secondary data. Data collection methods used are observation, interviews and documentation. The results showed that PT. General Takaful Insurance received a value of the performance benefit process of 0.7 which indicates that the company simply applied benefits in terms of process performance. And behave the benefit of PT. General Takaful Insurance got a value of 0.89 which shows that the company is quite good in providing benefits to stakeholders and shareholders. Keyword: Performance, Insurance, Scorecard Maslahah

scholarly journals Quantification of Non-Exhaust Particulate Matter Traffic Emissions and the Impact of COVID-19 Lockdown at London Marylebone Road

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 190
Author(s):  
William Hicks ◽  
Sean Beevers ◽  
Anja H. Tremper ◽  
Gregor Stewart ◽  
Max Priestman ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Measurement Data ◽  
Emission Factors ◽  
Meteorological Conditions ◽  
Traffic Emissions ◽  
Atmospheric Particulate Matter ◽  
Exhaust Emission ◽  
Brake Wear ◽  
The Impact ◽  
Exhaust Particulate

This research quantifies current sources of non-exhaust particulate matter traffic emissions in London using simultaneous, highly time-resolved, atmospheric particulate matter mass and chemical composition measurements. The measurement campaign ran at Marylebone Road (roadside) and Honor Oak Park (background) urban monitoring sites over a 12-month period between 1 September 2019 and 31 August 2020. The measurement data were used to determine the traffic increment (roadside–background) and covered a range of meteorological conditions, seasons, and driving styles, as well as the influence of the COVID-19 “lockdown” on non-exhaust concentrations. Non-exhaust particulate matter (PM)10 concentrations were calculated using chemical tracer scaling factors for brake wear (barium), tyre wear (zinc), and resuspension (silicon) and as average vehicle fleet non-exhaust emission factors, using a CO2 “dilution approach”. The effect of lockdown, which saw a 32% reduction in traffic volume and a 15% increase in average speed on Marylebone Road, resulted in lower PM10 and PM2.5 traffic increments and brake wear concentrations but similar tyre and resuspension concentrations, confirming that factors that determine non-exhaust emissions are complex. Brake wear was found to be the highest average non-exhaust emission source. In addition, results indicate that non-exhaust emission factors were dependent upon speed and road surface wetness conditions. Further statistical analysis incorporating a wider variability in vehicle mix, speeds, and meteorological conditions, as well as advanced source apportionment of the PM measurement data, were undertaken to enhance our understanding of these important vehicle sources.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

scholarly journals Spatio-Temporal Characteristics and Variation Pattern of the Atmospheric Particulate Matter Concentration: A Case Study of the Beijing–Tianjin–Hebei Region, China

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 120
Author(s):  
Haoran Zhai ◽  
Jiaqi Yao ◽  
Guanghui Wang ◽  
Xinming Tang
Keyword(s):  
Particulate Matter ◽  
Measurement Data ◽  
Atmospheric Particulate Matter ◽  
Temporal Characteristics ◽  
Double Peaks ◽  
Spatio Temporal ◽  
Pm2.5 And Pm10 ◽  
Air Quality Monitoring Stations ◽  
Matter Concentration

Based on measurement data from air quality monitoring stations, the spatio-temporal characteristics of the concentrations of particles with aerodynamic equivalent diameters smaller than 2.5 and 10 μm (PM2.5 and PM10, respectively) in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2018 were analysed at yearly, seasonal, monthly, daily and hourly scales. The results indicated that (1) from 2015 to 2018, the annual average values of PM2.5 and PM10 concentrations and the PM2.5/PM10 ratio in the study area decreased each year; (2) the particulate matter (PM) concentration in winter was significantly higher than that in summer, and the PM2.5/PM10 ratio was highest in winter and lowest in spring; (3) the PM2.5 and PM10 concentrations exhibited a pattern of double peaks and valleys throughout the day, reaching peak values at night and in the morning and valleys in the morning and afternoon; and (4) with the use of an improved sine function to simulate the change trend of the monthly mean PM concentration, the fitting R2 values for PM2.5 and PM10 in the whole study area were 0.74 and 0.58, respectively. Moreover, the high-value duration was shorter, the low-value duration was longer, and the concentration decrease rate was slower than the increase rate.

scholarly journals Geo-Spatial Distribution and Comparative Assessment of Brick Kiln Industry: Appraisal on Kulpi Block in Diamond Harbour Sub-Division, South 24 Parganas, West Bengal

2021 ◽  
Vol 9 (9) ◽  
pp. 1028-1036
Author(s):  
Shiladitya Purakayastha
Keyword(s):  
Building Materials ◽  
West Bengal ◽  
Comparative Assessment ◽  
Female Worker ◽  
Comparison Analysis ◽  
Brick Kiln ◽  
Brick Kilns ◽  
Block Based ◽  
Unorganized Sector ◽  
Settlement Growth

Abstract: Brick is one of the most important building materials and the demand of it is continuously rising for high increasing of population and the demand for settlement growth. Brick kilns in India are considered by traditional types of manufacturing and established as a significant industry in the unorganized sector. Percentage of female worker is more than male and in most of the cases total family be involved. Indian brick industry is the second biggest in the world after the China which provides livelihood. Among 9 Blocks of Diamond Harbour Sub-Division, Kulpi is the largest block based on number of brick kiln industry. Total brick kiln of the Sub division is 101. But Kulpi has 44 Brick Kilns (equal to 43.46%) covering an area of 60,000 Bigha or 80.3 Sq. Kms acquiring 25.83 % area of the Block itself. Author has attempted to observe the geo-spatial scenario and analysis of brick kiln industry of Kulpi block. Keywords: Locational Status, Brick Kiln Industry, Distribution, Comparison, Analysis, Kulpi Block

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