Analysis on the Temporal Distribution Characteristics of Air Pollution and Its Impact on Human Health under the Noticeable Variation of Residents' Travel Behavior: A Case of Guangzhou, China

The impact of COVID-19 pandemic lockdowns on air quality index: a systematic review

During the outbreak of the novel coronavirus disease 2019 (COVID-19), many countries implemented lockdown policies to control its transmission. These restrictions provided an opportunity to rest and recover the environment. This systematic review (SR) aimed to evaluate the impact of COVID-19 lockdowns on the Air Quality Index (AQI) in countries worldwide. ScienceDirect and PubMed were searched using relevant keywords to identify studies published until March 2020. Overall, 20 studies were included in the SR based on the eligibility criteria. The results show that COVID-19-related lockdown policies positively affect AQI by restricting air-polluting activities, such as transportation, industry, and construction. However, it is important to note that these policies are ineffective in controlling sources of natural air pollution and local dust. The findings of this study emphasize the need for policymakers to approve legislation limiting the sources of air pollutants.

Analysis of the Temporal Distribution Characteristics of PM2.5 Concentration and Risk Evaluation of Its Inhalation Exposure

PM_2.5 poses a threat to human health. It is important to evaluate the potential risk of PM_2.5 inhalation exposure when people are located in different spatiotemporal activity locations. In this study, the PM_2.5 concentration was detected by the atmospheric cruise monitoring system (ACMS), a new detection technology used for city-wide PM_2.5 concentration monitoring. People were divided into eight categories of five typical activity patterns, including rest (R), sedentary behavior (SB), light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA). The PM_2.5 inhalation exposure risk was then estimated for these typical activities. The research results showed that the time sequence data of the ACMS had a similar tendency to change as those of the traditional air quality monitoring stations (AQMS). Although both passed the stationarity test, the relative error (RE) of the monthly average PM_2.5 concentration between the ACMS and AQMS was 7.5-14%. RE was usually lower when the individual air quality index (IAQI) of PM_2.5 was higher. Otherwise, RE was higher. The research results also showed that PM_2.5 exposure was positively correlated with PM_2.5 concentration, respiration rate, and human activity patterns. Because adults had a higher monthly average potential exposure (MAPE) than minors and that males had a higher MAPE than females. The potential exposure generated by LPA and MPA reached 50.76% of the total potential exposure (TPE). VPA brought about a 14.7% increase in the TPE. The research findings are helpful to understand the temporal distribution characteristics of PM_2.5 concentrations and guide the potential risk evaluation of PM_2.5 inhalation exposure.

No Change of Pneumocystis jirovecii Pneumonia after the COVID-19 Pandemic: Multicenter Time-Series Analyses

Consolidated infection control measures imposed by the government and hospitals during COVID-19 pandemic resulted in a sharp decline of respiratory viruses. Based on the issue of whether Pneumocystis jirovecii could be transmitted by airborne and acquired from the environment, we assessed changes in P. jirovecii pneumonia (PCP) cases in a hospital setting before and after COVID-19. We retrospectively collected data of PCP-confirmed inpatients aged ≥18 years (N = 2922) in four university-affiliated hospitals between January 2015 and June 2021. The index and intervention dates were defined as the first time of P. jirovecii diagnosis and January 2020, respectively. We predicted PCP cases for post-COVID-19 and obtained the difference (residuals) between forecasted and observed cases using the autoregressive integrated moving average (ARIMA) and the Bayesian structural time-series (BSTS) models. Overall, the average of observed PCP cases per month in each year were 36.1 and 47.3 for pre- and post-COVID-19, respectively. The estimate for residuals in the ARIMA model was not significantly different in the total PCP-confirmed inpatients (7.4%, p = 0.765). The forecasted PCP cases by the BSTS model were not significantly different from the observed cases in the post-COVID-19 (−0.6%, 95% credible interval; −9.6~9.1%, p = 0.450). The unprecedented strict non-pharmacological interventions did not affect PCP cases.