Integrating metal–organic frameworks (MOFs) into electrospun nanofiber filters has become an effective method for improving particle filtration efficiency. This study hypothesized that there is an optimal amount of MOFs that can be integrated into electrospun nanofiber filters to achieve the maximum particle removal efficiency while minimizing the corresponding MOF synthesis time. To test the hypothesis, this study systematically explored the influence of the time-dependent in situ growing process of zeolitic imidazolate framework-67 (ZIF-67), a typical type of MOFs, on the filtration performance of polyacrylonitrile (PAN) electrospun nanofibers. The results show that the surface morphology and chemical composition of the PAN/ZIF-67 hybrid nanofiber filters gradually changed with the reaction time. For PAN/ZIF-67 hybrid nanofiber filters with relatively low initial PM0.3–0.4 filtration efficiency, a reaction time of only 5 min was sufficient for the synthesis of the amount of ZIF-67 that maximized the PM0.3–0.4 filtration efficiency. However, for thick filters with high original PM0.3–0.4 filtration efficiency (>90%), the integration of ZIF-67 was not necessary, because the efficiency enhancement would not be significant. In addition, the enhancement of filtration efficiency for ultrafine particles was positively correlated with the amount of incorporated ZIF-67. In summary, this study shortened the synthesis time of the in situ incorporation of MOFs into electrospun nanofiber filters from more than 10 h (reported in the literature) to only 5 min.

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.