An inverse modelling approach to estimate the hygric parameters of clay-based masonry during a Moisture Buffer Value test

Methane (CH4) is the second most important anthropogenic greenhouse gas (GHG) with respect to radiative forcing. Since pre-industrial times, the globally averaged CH4 concentration in the atmosphere has risen by a factor of 2.5. A large fraction of global anthropogenic CH4 emissions originates from localized point sources, e.g. coal mine ventilation shafts. International treaties foresee GHG emission reductions, entailing independent monitoring and verification support capacities. Considering the spatially widespread distribution of point sources, remote sensing approaches are favourable, in order to enable rapid survey of larger areas. In this respect, active remote sensing by airborne lidar is promising, such as provided by the integrated-path differential-absorption lidar CHARM-F operated by DLR. Installed onboard the German research aircraft HALO, CHARM-F serves as a demonstrator for future satellite missions, e.g. MERLIN. CHARM-F simultaneously measures weighted vertical column mixing ratios of CO2 and CH4 below the aircraft. In spring 2018, during the CoMet field campaign, measurements were taken in the Upper Silesian Coal Basin (USCB) in Poland. The USCB is considered to be a European hotspot of CH4 emissions, covering an area of approximately 50 km &#215; 50 km. Due to the high number of coal mines and density of ventilation shafts in the USCB, individual CH4 exhaust plumes can overlap. This makes simple approaches to determine the emission rates of single shafts, i.e. the cross-sectional flux method, difficult. Therefore, we use an inverse modelling approach to obtain an estimate of the individual emission rates. Specifically, we employ the Weather Research and Forecast Model (WRF) coupled to the CarbonTracker Data Assimilation Shell (CTDAS), an Ensemble Kalman Filter. CTDAS-WRF propagates an ensemble realization of the a priori CH4 emissions forward in space and time, samples the simulated CH4 concentrations along the measurement&#8217;s flight path, and scales the a priori emission rates to optimally fit the measured values, while remaining tied to the prior. Hereby, we obtain a regularized a posteriori best emission estimate for the individual ventilation shafts. Here, we report on the results of this inverse modelling approach, including individual and aggregated emission estimates, their uncertainties, and to which extent the data are able to constrain individual emitters independently.

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Moisture Buffering of Multilayer Internal Wall Assemblies at the Micro Scale: Experimental Study and Numerical Modelling

Applied Sciences ◽

10.3390/app9163438 ◽

2019 ◽

Vol 9 (16) ◽

pp. 3438 ◽

Cited By ~ 1

Author(s):

Dobrosława Kaczorek

Keyword(s):

Penetration Depth ◽

Buffering Capacity ◽

Internal Wall ◽

Simplified Method ◽

Buffer Value ◽

Series Of Experiments ◽

Overall Performance ◽

Wall Assembly ◽

Moisture Buffering ◽

Moisture Buffer Value

In this paper, a series of experiments assessing the moisture buffer value (MBV) of four internal wall assembly samples made from hygroscopic materials was performed. A modified Nordtest protocol was used. Moisture buffer values of all the investigated wall assemblies, with varying moisture loads in the range of 50% to 80%, showed a moderate moisture buffer value (MBV: 0.5–1.0 (g·m−2·%RH−1)). The results showed that in a wall assembly where the MBV of the whole assembly is lower than the MBV of the outer layers, the moisture-buffering capacity of the inner layer is untapped. Outer layers affect inner layers by changing their moisture-buffering capacity, which in turn changes the overall performance of the whole assembly. In addition, it was observed that if the penetration depth value of the outer layer is greater than its thickness, vapour reaches into the deeper layer and wall assemblies made of layers with materials characterized by a lower value of penetration depth reach steady state more slowly. The WUFI Pro tool was used to compare the simulated and experimental results. Despite the discrepancies between these results, it offers a simplified method, helping designers make decisions about which materials to choose to improve the moisture-buffering effect.

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