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Indoor air pollution resulting from volatile organic compounds (VOCs) is a significant health concern, especially formaldehyde. Therefore, predicting indoor formaldehyde concentration is essential for environmental control. In this research, the authors develop a thermal and wet coupling calculation model of porous fabric that considers the influence of different phases of wet components and the coupling effect of heat and humidity on formaldehyde migration. We propose a modified calculation method of the formaldehyde mass transfer characteristic parameters of fabric to obtain the diffusion coefficient D and partition coefficient K. The heat and humidity coupling model and formaldehyde mass transfer model of fabric are simultaneously solved, and the authors analyze the influence mechanism of fabric loading rate, fabric type, temperature, and humidity on indoor formaldehyde mass transfer characteristics. We study the variation trend of fabric formaldehyde mass transfer characteristics coefficient and the temporal and spatial distribution of indoor formaldehyde concentration. The theoretical model is applied to practical problems by pre-evaluating the indoor formaldehyde concentration of decorated residential buildings in typical climate areas of China before occupancy. The authors obtain the variation rule of indoor formaldehyde concentration of residential buildings under typical hot and humid climate conditions, building materials, furniture, and fabrics. To provide theoretical support for indoor environmental control and human health protection. [Display omitted] • The VOC mass transfer process and heat and moisture transfer process of fabric are considered in coupling. • The influence of different phases of wet components is considered on VOC migration. • A calculation method for fabric's VOC mass transfer characteristic parameters is proposed. • The influence mechanism of fabric loading rate, fabric type, temperature, and humidity on indoor VOC migration are analyzed. [ABSTRACT FROM AUTHOR]

China has implemented two national clean air actions in 2013–2017 and 2018–2020, respectively, with the aim of reducing primary emissions and hence improving air quality at a national level. It is important to examine the effectiveness of such emission reductions and assess the resulting changes in air quality. However, such evaluation is difficult as meteorological factors can amplify, or obscure the changes of air pollutants, in addition to the emission reduction. In this study, we applied the random forest machine learning technique to decouple meteorological influences from emissions changes, and examined the deweathered trends of air pollutants in 12 Chinese mega-cities during 2013–2020. The observed concentrations of all criteria pollutants except O 3 showed significant declines from 2013 to 2020, with PM 2.5 annual decline rates of 6–9% in most cities. In contrast, O 3 concentrations increased with annual growth rates of 1–9%. Compared with the observed results, all the pollutants showed smoothed but similar variation in trend and annual rate-of-change after weather normalization. The response of O 3 to NO 2 concentrations indicated significant regional differences in photochemical regimes, and the differences between observed and deweathered results provided implications for volatile organic compound emission reductions in O 3 pollution mitigation. We further evaluated the effectiveness of first and second clean air actions by removing the meteorological influence. We found that the meteorology can make negative or positive contribution in reducing pollutant concentrations from emission reduction, depending on type of pollutants, locations, and time period. Among the 12 mega-cities, only Beijing showed a positive meteorological contribution in amplifying reductions in main pollutants except O 3 during both clean air action periods. Considering the large and variable impact of meteorological effects in changing air quality, we suggest that similar deweathered analysis is needed as a routine policy evaluation tool on a regional basis. [Display omitted] • We used a random forest method to remove meteorological effect for air quality data. • The deweathered results show smoothed but similar variation in annual trends. • The meteorology can play positive or negative role depending on specific conditions. • Beijing shows positive meteorology in reducing pollutants for two clean air actions. [ABSTRACT FROM AUTHOR]