scholarly journals Optimization of an Urban Monitoring Network for Retrieving an Unknown Point Source Emission

2018 ◽  
Author(s):  
Hamza Kouichi ◽  
Pierre Ngae ◽  
Pramod Kumar ◽  
Amir-Ali Feiz ◽  
Nadir Bekka
Keyword(s):  
Point Source ◽  
Simulated Annealing Algorithm ◽  
Monitoring Network ◽  
Urban Setting ◽  
Cfd Modeling ◽  
Urban Region ◽  
Original Network ◽  
Continuous Point ◽  
Source Emission ◽  
Optimal Networks

Abstract. This study presents a methodology for the optimization of a monitoring network of sensors measuring the polluting substances in an urban environment with a view to estimate an unknown emission source. The methodology was presented by coupling the Simulated Annealing algorithm with the renormalization inversion technique and the Computational Fluid Dynamics (CFD) modeling approach. Performance of a network was analyzed by reconstructing the unknown continuous point emissions using the concentration measurements from the sensors in that optimized network. This approach was successfully applied and validated with 20 trials of the Mock Urban Setting Test (MUST) tracer field experiment in an urban-like environment. The optimal networks in the MUST urban region enabled to reduce the size of original network (40-sensors) to ~ 1/3rd (13-sensors) and to 1/4th (10-sensors). The 10 and 13 sensors optimal networks have estimated the averaged location errors of 19.20 m and 17.42 m, respectively, which are comparable to 14.62 m from the original 40-sensors network. In 80 % trials, emission rates with the 10 and 13 sensors networks were estimated within a factor of two which are also comparable to 75 % from the original network. This study presents the first application of the renormalization data-assimilation approach for the optimal network design to estimate a continuous point source emission in an urban-like environment.

scholarly journals An optimization for reducing the size of an existing urban-like monitoring network for retrieving an unknown point source emission

2019 ◽  
Vol 12 (8) ◽  
pp. 3687-3705 ◽  
Author(s):  
Hamza Kouichi ◽  
Pierre Ngae ◽  
Pramod Kumar ◽  
Amir-Ali Feiz ◽  
Nadir Bekka
Keyword(s):  
Monitoring Network ◽  
Urban Setting ◽  
Cfd Modeling ◽  
Urban Region ◽  
Emission Rates ◽  
Original Network ◽  
Continuous Point ◽  
Optimization Methodology ◽  
Must Experiment ◽  
Optimal Networks

Abstract. This study presents an optimization methodology for reducing the size of an existing monitoring network of the sensors measuring polluting substances in an urban-like environment in order to estimate an unknown emission source. The methodology is presented by coupling the simulated annealing (SA) algorithm with the renormalization inversion technique and the computational fluid dynamics (CFD) modeling approach. This study presents an application of the renormalization data-assimilation theory for optimally reducing the size of an existing monitoring network in an urban-like environment. The performance of the obtained reduced optimal sensor networks is analyzed by reconstructing the unknown continuous point emission using the concentration measurements from the sensors in that optimized network. This approach is successfully applied and validated with 20 trials of the Mock Urban Setting Test (MUST) tracer field experiment in an urban-like environment. The main results consist of reducing the size of a fixed network of 40 sensors deployed in the MUST experiment. The optimal networks in the MUST urban region are determined, which makes it possible to reduce the size of the original network (40 sensors) to ∼1/3 (13 sensors) and 1∕4 (10 sensors). Using measurements from the reduced optimal networks of 10 and 13 sensors, the averaged location errors are obtained as 19.20 and 17.42 m, respectively, which are comparable to the 14.62 m obtained from the original 40-sensor network. In 80 % of the trials with networks of 10 and 13 sensors, the emission rates are estimated within a factor of 2 of the actual release rates. These are also comparable to the performance of the original network, whereby in 75 % of the trials the releases were estimated within a factor of 2 of the actual emission rates.

scholarly journals MAMAP – a new spectrometer system for column-averaged methane and carbon dioxide observations from aircraft: retrieval algorithm and first inversions for point source emission rates

2011 ◽  
Vol 4 (9) ◽  
pp. 1735-1758 ◽  
Author(s):  
T. Krings ◽  
K. Gerilowski ◽  
M. Buchwitz ◽  
M. Reuter ◽  
A. Tretner ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Point Source ◽  
Integral Method ◽  
Power Plants ◽  
Retrieval Algorithm ◽  
Detailed Knowledge ◽  
Emission Rates ◽  
Gaussian Plume ◽  
Gaussian Integral ◽  
Source Emission

Abstract. MAMAP is an airborne passive remote sensing instrument designed to measure the dry columns of methane (CH4) and carbon dioxide (CO2). The MAMAP instrument comprises two optical grating spectrometers: the first observing in the short wave infrared band (SWIR) at 1590–1690 nm to measure CO2 and CH4 absorptions, and the second in the near infrared (NIR) at 757–768 nm to measure O2 absorptions for reference/normalisation purposes. MAMAP can be operated in both nadir and zenith geometry during the flight. Mounted on an aeroplane, MAMAP surveys areas on regional to local scales with a ground pixel resolution of approximately 29 m × 33 m for a typical aircraft altitude of 1250 m and a velocity of 200 km h−1. The retrieval precision of the measured column relative to background is typically ≲1% (1σ). MAMAP measurements are valuable to close the gap between satellite data, having global coverage but with a rather coarse resolution, on the one hand, and highly accurate in situ measurements with sparse coverage on the other hand. In July 2007, test flights were performed over two coal-fired power plants operated by Vattenfall Europe Generation AG: Jänschwalde (27.4 Mt CO2 yr−1) and Schwarze Pumpe (11.9 Mt CO2 yr−1), about 100 km southeast of Berlin, Germany. By using two different inversion approaches, one based on an optimal estimation scheme to fit Gaussian plume models from multiple sources to the data, and another using a simple Gaussian integral method, the emission rates can be determined and compared with emissions reported by Vattenfall Europe. An extensive error analysis for the retrieval's dry column results (XCO2 and XCH4) and for the two inversion methods has been performed. Both methods – the Gaussian plume model fit and the Gaussian integral method – are capable of deriving estimates for strong point source emission rates that are within ±10% of the reported values, given appropriate flight patterns and detailed knowledge of wind conditions.

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