An evaluation model of indoor PM2.5 dynamic characteristics considering human activities

Abstract

Healthy indoor air quality is a basic standard for a good living environment, the air exchange rate (a), particle penetration coefficient (p), and deposition rate (k) of indoor PM2.5 are the main parameters for the design of fresh air ventilation systems like air cleaning facilities. Accurate cleaning methods help to provide qualified indoor air quality and reduce energy consumption. However, most of the measurements and evaluations have been completed in an enclosed indoor environment that fails to consider the impact of indoor activities and lacks the personalization of cleaning methods. Therefore, parameters should not be fixed but dynamically set according to the size, function, and human activities of different indoor spaces. In order to provide dynamic parameters, this paper proposes an indoor PM2.5 dynamic characteristic evaluation model considering human activities based on the real-time monitoring data and the principle of mass conservations. First, the indoor dynamic equilibrium equation of PM2.5 under the influence of human activities was established, and the dynamic ranges of a, p, and k were obtained by solving differential equation groups. In addition, the indoor environment without human activities was used as the control group for analysis. The differences between the experimental and control groups were analyzed, and the validity of the model was verified based on the measured data. The experiment results showed that the fluctuations of p were relatively small (p = 0.9), and the fluctuations of a and k were large (a = 2.5 ± 2.26, k = 1.63 ± 2.07) in a natural ventilated indoor environment with human activities, which were different from the enclosed indoor environment. This study proposed the influence of human activities for the first time, which can provide a reference for the parameter design of indoor fresh air ventilation systems and provide technical support and decision-making service for the prediction of indoor PM2.5 under natural ventilation.