The Illness Experience of Long COVID Patients: A Qualitative Study Based on the Online Q&A Community Zhihu.

Long COVID is a public health problem that cannot be ignored, and it is critical to understand the long COVID patients' living situations and support this group through their illness narratives. This study is based on grounded theory, and coded the self-produced texts of long COVID patients on the largest online Q&A community in China, Zhihu APP, in an attempt to explore the illness experiences of long COVID patients in China and to understand how they adapt to their illness and reconstruct their lives. The results show that patients face not only the threat of pain from the illness itself, but also social stigma and discrimination. Patients turn their illness experiences into motivation to move forward and reconstruct self and life by 'pushing forward the biographical flows again', 'impression management' and 'self-compassion'. These findings can help policy-makers and medical institutions to provide timely and appropriate policy support and psychological assistance to patients with long COVID, to create a supportive and inclusive social environment, and to reduce discrimination and stigma against them.

Nonlinear dynamic mechanism of regional air pollution during the COVID-19 lockdown

Based on hourly concentration of PM2.5 and O3 during the epidemic period（January 24, 2020 to May 31, 2020） in Changsha, Zhuzhou and Xiangtan, the diurnal patterns, long-term persistence, multifractality and self-organization evolution dynamics of these two pollutants were studied to reveal the internal dynamic mechanism of the occurrence and evolution of heavy pollution events during the epidemic period. Firstly, the diurnal patterns of PM2.5 and O3 concentrations were investigated. It showed that O3 showed a single peak of high concentration in the daytime and low in the night, while PM2.5 showed a single lowest peak concentration in the day and high in the night, which was different from the pattern in non-epidemic periods. Furthermore, detrended fluctuation analysis（DFA）, the multifractal detrended fluctuation analysis（MFDFA） and probability statistical analysis were applied to study the long-term persistence, multi-fractal structure of PM2.5 and O3 series. The results showed that PM2.5 and O3 series had significant long-term persistence characteristics and strong multi-fractal structures for the three cities. Meanwhile, detrended cross-correlation analysis（DCCA） and multifractal detrended cross-correlation analysis（MFDCCA） were conducted to estimate the cross-correlations between PM2.5 and O3 series. Long-term persistence as well as multifractal features at different time scales was also observed in PM2.5-O3 cross-correlations. Next, nonlinear analysis results obtained during epidemic period were compared with those obtained in the same periods of non-epidemic years of 2019 and 2018. Finally, based on the self-organized criticality（SOC） theory, the internal dynamic law of spatial and temporal evolution of PM2.5 and O3 series was discussed. Combined with the typical regional meteorological characteristics, it was found that the intrinsic dynamic mechanism of SOC may be one of the leading mechanisms of heavy air pollution episodes during the COVID-19 lockdown period. During the epidemic period, PM2.5 and O3 concentrations did not evolve independently but remained complex interactions. Under the stable meteorological conditions, the nonlinear coupling effect inside the air combined pollution might reach the dynamic critical state, thus, lead to the risk of heavy air pollution in Greater Changsha Metropolitan Region during the epidemic period.