Abstract
Effective identification of pollution sources is particularly important for indoor air quality. Accurate estimation of source strength is the basis for source effective identification. This paper proposes an optimization method for the deconvolution process in the source strength inverse calculation. In the scheme, the concept of time resolution was defined, and combined with different filtering positions and filtering algorithms. The measures to reduce effects of measurement noise were quantitatively analyzed. Additionally, the performances of nine deconvolution inverse algorithms under experimental and simulated conditions were evaluated and scored. The hybrid algorithms were proposed and compared with single algorithms including Tikhonov regularization and iterative methods. Results showed that for the filtering position and algorithm, Butterworth filtering performed better, and different filtering positions had little effect on the inverse calculation. For the calculation time step, the optimal Tr (time resolution) was 0.667% and 1.33% in the simulation and experiment, respectively. The hybrid algorithms were found to not perform better than the single algorithms, and the SART (simultaneous algebraic reconstruction technique) algorithm from CAT (computer assisted tomography) yielded better performances in the accuracy and stability of source strength identification. The relative errors of the inverse calculation for source strength were typically below 25% using the optimization scheme.
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References
Andersen AH, Kak AC (1984). Simultaneous algebraic reconstruction technique (SART): A superior implementation of the art algorithm. Ultrasonic Imaging, 6: 81–94.
Aster RC, Borchers B, Thurber CH (2005). Parameter Estimation and Inverse Problems. Oxford, UK: Academic Press.
Bhatti P, Newcomer L, Onstad L, et al. (2011). Wood dust exposure and risk of lung cancer. Occupational and Environmental Medicine, 68: 599–604.
Chan JF-W, Yuan S, Kok K-H, et al. (2020). A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster. The Lancet, 395: 514–523.
Chata F, Belut E, Maillet D, et al. (2017). Estimation of an aerosol source in forced ventilation through prior identification of a convolutive model. International Journal of Heat and Mass Transfer, 108: 1623–1633.
Chen C, Zhao Y, Zhao B (2018). Emission rates of multiple air pollutants generated from Chinese residential cooking. Environmental Science & Technology, 52: 1081–1087.
Cheng L (2015). Iterative tomographic algorithms of gas diffusion distribution reconstruction based on incomplete projection data. Master Thesis, Hefei University of Technology, Chinese. (in Chinese).
Chung J, Nagy JG, O’Leary DP (2008). A weighted-GCV method for Lanczos-hybrid regularization. Electronic Transactions on Numerical Analysis, 28: 149–167.
Cui J, Lang J, Chen T, et al. (2019). Investigating the impacts of atmospheric diffusion conditions on source parameter identification based on an optimized inverse modelling method. Atmospheric Environment, 205: 19–29.
Emmerich SJ, Nabinger SJ, Gupte A (2003). Comparison of measured and predicted tracer gas concentrations in a townhouse. NIST Interagency/Internal Report (NISTIR), 7035. Gaithersburg, MD, USA: National Institute of Standards and Technology.
EPA (2017). Report on the environment about indoor air quality: What are the trends in indoor air quality and their effects on human health? U.S. Environmental Protection Agency. Available at https://www.epa.gov/report-environment/indoor-air-quality.
Etheridge DW, Sandberg M (1996). Building Ventilation: Theory and Measurement: Chapters 12&13. Chichester, UK: John Wiley & Sons.
Feng Q, Cai H, Chen Z, et al. (2019). Experimental study on a comprehensive particle swarm optimization method for locating contaminant sources in dynamic indoor environments with mechanical ventilation. Energy & Buildings, 196: 145–156.
Han B, Dou Y, Ding L (2012). Electrical resistivity tomography by using a hybrid regularization. Chinese Journal of Geophysics, 55(3): 970–980. (in Chinese)
Hancock DG, Langley ME, Chia KL, et al. (2015). Wood dust exposure and lung cancer risk: A meta-analysis. Occupational and Environmental Medicine, 72: 889–898.
Hansen PC (1994). REGULARIZATION TOOLS: A Matlab package for analysis and solution of discrete ill-posed problems. Numerical Algorithms, 6: 1–35.
Hansen C (2010). Discrete Inverse Problems: Insight and Algorithms. Lyngby, Denmark: The Society for Industrial and Applied Mathematics (SIAM).
Hiyama K, Kato S, Ishida Y (2010). Thermal simulation: Response factor analysis using three-dimensional CFD in the simulation of air conditioning control. Building Simulation, 3: 195–203.
Kaiser JF, Reed WA (1977). Data smoothing using low-pass digital filters. Review of Scientific Instruments, 48: 1447–1457.
Kathirgamanathan P, McKibbin R, McLachlan RI (2004). Source release-rate estimation of atmospheric pollution from a non-steady point source at a known location. Environmental Modeling & Assessment, 9: 33–42.
Lawson CL, Hanson RJ (1995). Solving Least Squares Problems. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
Li Y, Duan S, Yu ITS, et al. (2005). Multi-zone modeling of probable SARS virus transmission by airflow between Flats in Block E, Amoy Gardens. Indoor Air, 15: 96–111.
Li J, Li H, Ma Y, et al. (2018). Spatiotemporal distribution of indoor particulate matter concentration with a low-cost sensor network. Building and Environment, 127: 138–147.
Li F, Cai H, Xu J, et al. (2020a). Gas distribution map** for indoor environments based on laser absorption spectroscopy: Development of an improved tomographic algorithm. Building and Environment, 172: 106724.
Li F, Liu X, Liu J, et al. (2020b). Solutions to mitigate the impact of measurement noise on the air pollution source strength estimation in a multi-zone building. Building Simulation, 13: 1329–1337.
Liang W, Yang X (2013). Indoor formaldehyde in real buildings: Emission source identification, overall emission rate estimation, concentration increase and decay patterns. Building and Environment, 69: 114–120.
Liu X, Li F, Cai H, et al. (2019). Dynamical source term estimation in a multi-compartment building under time-varying airflow. Building and Environment, 160: 106162.
Manal K, Rose W (2007). A general solution for the time delay introduced by a low-pass Butterworth digital filter: An application to musculoskeletal modeling. Journal of Biomechanics, 40: 678–681.
Mao S, Lang J, Chen T, et al. (2020). Impacts of typical atmospheric dispersion schemes on source inversion. Atmospheric Environment, 232: 117572.
Paige CC, Saunders MA (1982). LSQR: An algorithm for sparse linear equations and sparse least squares. ACM Transactions on Mathematical Software, 8: 43–71.
Pang L, Wang W, Qu H, et al. (2014). Approach to identifying a sudden continuous emission pollutant source based on single sensor with noise. Indoor and Built Environment, 23: 955–970.
Sandberg M, Blomqvist C (1985). A quantitative estimate of the accuracy of tracer gas methods for the determination of the ventilation flow rate in buildings. Building and Environment, 20: 139–150.
Shepp LA, Vardi Y (1982). Maximum likelihood reconstruction for emission tomography. IEEE Transactions on Medical Imaging, 1: 113–122.
Sherman MH, Grimsrud DT, Condon PE, et al. (1980). Air infiltration measurement techniques. Berkeley. In: Proceedings First Air Infiltration Centre Conference, Windsor, UK.
Shi Y (2012). The application of the Butterworth low-pass digital filter on experimental data processing. In: Proceedings of the International Conference in Electrics, Communication and Automatic Control Proceedings.
Sreedharan P, Sohn MD, Gadgil AJ, et al. (2006). Systems approach to evaluating sensor characteristics for real-time monitoring of high-risk indoor contaminant releases. Atmospheric Environment, 40: 3490–3502.
Tsantaki E, Smyrnakis E, Constantinidis TC, et al. (2020). Indoor air quality and sick building syndrome in a university setting: A case study in Greece. International Journal of Environmental Health Research, https://doi.org/10.1080/09603123.2020.1789567
Wang L, You X (2015). Identification of indoor contaminant source location by a single concentration sensor. Air Quality, Atmosphere & Health, 8: 115–122.
Wang H, Lu S, Cheng J, et al. (2017). Inverse modeling of indoor instantaneous airborne contaminant source location with adjoint probability-based method under dynamic airflow field. Building and Environment, 117: 178–190.
Yan J, Grantham M, Pantelic J, et al. (2018). Infectious virus in exhaled breath of symptomatic seasonal influenza cases from a college community. Proceedings of the National Academy of Sciences of the United States of America, 115: 1081–1086.
Yang Y, Feng Q, Cai H, et al. (2019). Experimental study on three single-robot active olfaction algorithms for locating contaminant sources in indoor environments with no strong airflow. Building and Environment, 155: 320–333.
Yin S (2011). Quantitatively identify unsteady gas pollutant releases in indoor environment by inverse CFD modeling. Master Thesis, Dalian University of Technology, China. (in Chinese)
Young DL, Tsai CC, Chen CW, et al. (2008). The method of fundamental solutions and condition number analysis for inverse problems of Laplace equation. Computers & Mathematics with Applications, 55: 1189–1200.
Yu B (2006). Open Path Fourier Transform Infrared Spectroscopy (OP-FTIR) for Atmospheric Environment Monitoring. Master Thesis, Nan**g University of Science and Technology, Nan**g. (in Chinese).
Zhang TT, Yin S, Wang S (2013). An inverse method based on CFD to quantify the temporal release rate of a continuously released pollutant source. Atmospheric Environment, 77: 62–77.
Zhuang J, Li F, Liu X, et al. (2021). An experiment-based impulse response method to characterize airborne pollutant sources in a scaled multi-zone building. Atmospheric Environment, 251: 118272.
Acknowledgements
This study was supported by the National Natural Science Foundation of China (No. 51708286), the Natural Science Foundation of Jiangsu Province (No. BK20180701) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. SJCX20_0325).
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Li, M., Li, F., **g, Y. et al. Estimation of pollutant sources in multi-zone buildings through different deconvolution algorithms. Build. Simul. 15, 817–830 (2022). https://doi.org/10.1007/s12273-021-0826-3
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DOI: https://doi.org/10.1007/s12273-021-0826-3