Solutions to mitigate the impact of measurement noise on the air pollution source strength estimation in a multi-zone building

Indoor contaminants jeopardize people’s health and may even cause serious consequences under extreme conditions. Therefore, the prompt and accurate identification of indoor airborne contaminant characteristics is significant for indoor health and safety. In this paper, we used an inverse Markov chain model, combined with the regularization proposed in our previous research, to identify periodic source strength under steady airflow in a multi-zone building. The impact of different measurement noise (0.05%, 0.1%, 0.2%) on the inverse results was investigated. The results showed that the greater the noise, the greater the oscillation of the inverse result. Furthermore, we also investigated the effect of adjusting the calculation time step (5 s, 10 s, 20 s, 30 s) and adding digital filters (Sliding window filter and Butterworth low pass filter) on the inverse source release rate. The results showed that properly increasing the calculation time step can reduce the impact of measurement noise. The root mean square error (RMSE) of the inverse source strength with 0.1% noise decreased from 22.89 under a 5-s time step to 0.9793 under a 30-s time step. It was also found that adding digital filters could reduce the oscillation of the inverse source results, and the performance of the filters also depends on the calculation time steps.

[1]  Kai Zhang,et al.  Dynamical source term estimation in a multi-compartment building under time-varying airflow , 2019, Building and Environment.

[2]  Yu Zhang,et al.  Approach to identifying a sudden continuous emission pollutant source based on single sensor with noise , 2014 .

[3]  M. Sohn,et al.  Rapidly Locating and Characterizing Pollutant Releases in Buildings , 2000, Journal of the Air & Waste Management Association.

[4]  Kurt Manal,et al.  A general solution for the time delay introduced by a low-pass Butterworth digital filter: An application to musculoskeletal modeling. , 2007, Journal of biomechanics.

[5]  Hyondong Oh,et al.  A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors , 2017, Inf. Fusion.

[6]  Bin Zhou,et al.  A numerical investigation on the mixing factor and particle deposition velocity for enclosed spaces under natural ventilation , 2019, Building Simulation.

[7]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[8]  Wei Liu,et al.  A Markov chain model for predicting transient particle transport in enclosed environments , 2015, Building and Environment.

[9]  D. Maillet,et al.  Estimation of an aerosol source in forced ventilation through prior identification of a convolutive model , 2017 .

[10]  Enbo Wang,et al.  Inverse tracking of an airborne pollutant source location in a residential apartment by joint simulation of CFD and a multizone model , 2019, Building Simulation.

[11]  Jelena Srebric,et al.  Real-Time Identification of Indoor Pollutant Source Positions Based on Neural Network Locator of Contaminant Sources and Optimized Sensor Networks , 2010, Journal of the Air & Waste Management Association.

[12]  Zhilong Chen,et al.  Experimental study on a comprehensive particle swarm optimization method for locating contaminant sources in dynamic indoor environments with mechanical ventilation , 2019, Energy and Buildings.

[13]  James W. Axley,et al.  Multizone Airflow Modeling in Buildings: History and Theory , 2007 .

[14]  M. Nicas,et al.  Markov modeling of contaminant concentrations in indoor air. , 2000, AIHAJ : a journal for the science of occupational and environmental health and safety.

[15]  J. Kaiser,et al.  Data smoothing using low‐pass digital filters , 1977 .

[16]  D. L. Young,et al.  The method of fundamental solutions and condition number analysis for inverse problems of Laplace equation , 2008, Comput. Math. Appl..

[17]  Z. Zhai,et al.  Sensitivity analysis of the probability-based inverse modeling method for indoor contaminant tracking , 2016 .

[18]  Xianting Li,et al.  An experimental and numerical study on a multi-robot source localization method independent of airflow information in dynamic indoor environments , 2020 .

[19]  Xianting Li,et al.  Source localization in dynamic indoor environments with natural ventilation: An experimental study of a particle swarm optimization-based multi-robot olfaction method , 2019, Building and Environment.

[20]  P. Kathirgamanathan,et al.  Source release rate estimation of atmospheric pollution from a non-steady point source - Part 2: Source at an unknown location , 2003 .

[21]  Z. Zhai,et al.  Inverse identification of multiple outdoor pollutant sources with a mobile sensor , 2017 .

[22]  Fei Li,et al.  Experimental study on three single-robot active olfaction algorithms for locating contaminant sources in indoor environments with no strong airflow , 2019, Building and Environment.

[23]  Chenyang Lu,et al.  Spatiotemporal distribution of indoor particulate matter concentration with a low-cost sensor network , 2018 .

[24]  Z. Zhai,et al.  Identifying decaying contaminant source location in building HVAC system using the adjoint probability method , 2018, Building Simulation.

[25]  T Zhang,et al.  Inverse identification of the release location, temporal rates, and sensor alarming time of an airborne pollutant source. , 2015, Indoor air.

[26]  Per Christian Hansen,et al.  REGULARIZATION TOOLS: A Matlab package for analysis and solution of discrete ill-posed problems , 1994, Numerical Algorithms.

[27]  Mark A. Lukas,et al.  Robust GCV choice of the regularization parameter for correlated data , 2010 .

[28]  Xue-Yi You,et al.  Identification of indoor contaminant source location by a single concentration sensor , 2014, Air Quality, Atmosphere & Health.

[29]  Xianting Li,et al.  Towards locating time-varying indoor particle sources: Development of two multi-robot olfaction methods based on whale optimization algorithm , 2019 .

[30]  Shugang Wang,et al.  An inverse method based on CFD to quantify the temporal release rate of a continuously released pollutant source , 2013 .

[31]  Xiaoping Zheng,et al.  Inverse calculation approaches for source determination in hazardous chemical releases , 2011 .