Built Environment & Energy Laboratory
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Xia, T., Qi, Y., Dai, X., Liu, J., Xiao, C., You, R., Lai, D., Liu, J.*, Chen, C.* (2021). Estimating long-term time-resolved indoor PM2.5 of outdoor and indoor origin using easily-obtainable inputs. Indoor Air, 31, 2020–2032. (Particle penetration, PM2.5, Building)

To evaluate the separate impacts on human health and establish effective control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM2.5 in buildings. This study used an algorithm to automatically estimate the long-term time-resolved indoor PM2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time-resolved indoor/outdoor PM2.5 concentrations and occupants’ window actions, which were easily obtained from the low-cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time-resolved infiltration factor were automatically estimated using the algorithm. The influence of outdoor PM2.5 data source and algorithm parameters on the estimated results was analyzed. The algorithm was then applied in four other apartments located in Chongqing, Shenyang, Xi’an, and Urumqi to further demonstrate its feasibility. The results provided indirect evidence, such as the plausible explanations for seasonal and spatial variation, to partially support the success of the algorithm used in real apartments. Through the analysis, this study also identified several further development directions to facilitate the practical applications of the algorithm, such as robust long-term outdoor PM2.5 monitoring using low-cost light-scattering sensors. 

An, Y., Xia, T., You, R., Lai, D., Liu, J., Chen, C.* (2021). A reinforcement learning approach for control of window behavior to reduce indoor PM2.5 concentrations in naturally ventilated buildings. Building and Environment, 200, 107978. (Particle control, PM2.5, Building)

Smart control of window behavior is a means of effectively reducing concentrations of indoor PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) in naturally ventilated residential buildings without indoor air cleaning devices. This study aimed to develop a reinforcement learning approach to automatically control window behavior in real time for mitigation of indoor PM2.5 pollution. The proposed method trains the window controller with the use of a deep Q-network (DQN) in a specific naturally ventilated apartment in the course of a month. The trained controller can then be employed to control window behavior in order to reduce the indoor PM2.5 concentrations in that apartment. The required input data for the controller are the real-time indoor and outdoor PM2.5 concentrations with a 1-min resolution, which can easily be obtained with low-cost sensors available on the market. A series of simulations were conducted in a virtual typical apartment in Beijing and a real apartment in Tianjin. The results show that, compared with the baseline I/O ratio algorithm, the proposed reinforcement learning window-control algorithm reduced the average indoor PM2.5 concentration by 12.80% in a one-year period. Furthermore, the proposed algorithm reduced the indoor PM2.5 concentrations in the real apartment by 9.11% when compared with the I/O ratio algorithm and by 7.40% when compared with real window behavior

Wang, L.^, Bian, Y.^, Lim, C.K., Lee P.K.H., Chen, C.*, Zhang, L.*, Daoud, W.A.*, Zi, Y.* (2021). Tribo-charge enhanced hybrid air filter masks for efficient particulate matter capture with greatly extended service life. Nano Energy, 85, 106015. (Particle filtration, Ultrafine particle/PM2.5, Personal use)

Face masks have been an effective and indispensable personal protective measure against particulate matter pollutants and respiratory diseases, especially the novel Coronavirus disease recently. However, disposable surgical face masks suffer from low filtration efficiency for particles ranging from nano- to micro-size, and the limited service life of ~ 4 h. Here, a nano/micro fibrous hybrid air filter mask composing of electrospun nanofibrous network and poly(3,4-ethylenedioxythiophene:poly(styrenesulfonate) coated polypropylene (PP) is proposed. Furthermore, the resultant filter is supplied with tribo-charges by a freestanding sliding triboelectric nanogenerator. Through the enhanced synergistic effect of mechanical interception and electrostatic forces, the hybrid air filter demonstrates high filtration efficiency for particle size of 11.5 nm to 2.5 µm, with a 9.3–34.68% enhancement for particles of 0.3–2.5 µm compared to pristine PP, and 48-h stable filtration efficiency of 94% (0.3–0.4 µm) and 99% (1–2.5 µm) with a low pressure drop of ~110 Pa. In addition, sterilization ability of the tribo-charge enhanced air filter is demonstrated. This work provides a facile and cost-effective approach for state-of-the-art face masks toward high filtration performance of nano- to micro- particles with greatly extended service life

Pan, Y., Chen, C.* (2021). Exploring the relationship between particle deposition and near-wall turbulence quantities in the built environment. Building and Environment, 196, 107814. (Particle deposition, PM2.5, Aircraft cabin)

Particle deposition in the built environment can cause discoloration or damage to indoor surfaces. To better understand the deposition mechanisms, this study explored the relationship between particle deposition and near-wall turbulence quantities by means of experimental measurements and numerical simulations, taking a multi-slot supply air nozzle in a commercial airplane as an example. Chamber experiments were conducted in a cabin mockup to measure the particle deposition velocity distribution on the multi-slot nozzle and the near-wall 3D air velocity components. The large-eddy simulation (LES)-Lagrangian approach was used to calculate the turbulent flow in the cabin and particle deposition on the nozzle. Correlations between the particle deposition velocity and the wall-normal turbulence kinetic energy, the wall-normal turbulence intensity, and a new turbulence quantity defined in this study were examined in the near-wall buffer, logarithmic, and outer layers. According to the simulation results, the particle deposition velocity was significantly correlated with all three turbulence quantities in the logarithmic and outer layers. Furthermore, both the experimental and simulation results indicate that the new turbulence quantity defined in this study was most closely correlated with the particle deposition velocity among the three near-wall quantities. 

Niu, Z.^, Bian, Y.^, Xia, T., Zhang, Li.*, Chen, C.* (2021). An optimization approach for fabricating electrospun nanofiber air filters with minimized pressure drop for indoor PM2.5 control. Building and Environment, 188, 107449. (Particle filtration, PM2.5, Building)

Electrospun nanofiber air filters can achieve remarkable particle filtration efficiency with low pressure drop. Therefore, they can potentially be installed in buildings for reducing indoor PM2.5 concentrations. To improve filtration performance, this study developed a design and fabrication approach for electrospun nanofiber air filters with minimized pressure drop under a target PM2.5 filtration efficiency. First, this research developed semi-empirical models for calculating the pressure drop and PM2.5 filtration efficiency of nylon electrospun nanofiber filters using the fabrication parameters of electrospinning time and nylon concentration. With the developed models, this investigation then proposed an optimization approach to minimize the pressure drop under a given PM2.5 filtration efficiency and air velocity. For a given air velocity and PM2.5 filtration efficiency, one can minimize the pressure drop by finding the optimal solution concentration, while the target PM2.5 filtration efficiency can be achieved through adjustment of the electrospinning time. Furthermore, the proposed optimization approach successfully minimized the pressure drop for 110 out of the 125 cases, with an average pressure drop reduction of 32.7%. Finally, this research numerically studied the performance of a window screen coated with the optimized nanofiber filter in reducing indoor PM2.5 of outdoor origin

Xia, T., Chen, C.* (2021). Evolution of pressure drop across electrospun nanofiber filters clogged by solid particles and its influence on indoor particulate air pollution control. Journal of Hazardous Materials, 402, 123479. (Particle filtration, PM2.5, Building)

Because of the relatively low pressure drop and high particle removal efficiency, nanofiber filter media can be potentially used for indoor particulate air pollution control. However, the influence of particle loading on the long-term performance of nanofiber filters in indoor particle control has not been well studied. This study first experimentally investigated the relationship between the pressure drop and solid particle loading mass for 42 nanofiber filter samples under various face velocities. The results show that the air resistance coefficient increased with the solid particle loading mass for the nanofiber filter media. Furthermore, the air resistance coefficient was positively associated with the face velocity, as a higher air velocity tended to make the particle cake tighter with higher resistance. Based on the experimental data, a semi-empirical equation was developed for predicting the pressure drop under different particle loading masses and face velocities. The developed semi-empirical model was then used to assess the long-term performance of an air cleaner equipped with nanofiber filter media in indoor PM2.5 control. The case study demonstrated that an air cleaner equipped with nanofiber filter media could effectively control indoor PM2.5, but the lifetime of the nanofiber filter was shorter than that of traditional HEPA filters

2020 Particle