Climate modulation of external forcing factors on air quality change in Eastern China: Implications for PM2.5 seasonal prediction.

Meteorological conditions significantly influence the frequency and duration of air pollution events, making the prediction of seasonal variations of PM2.5 concentration crucial for air quality control. This study analyzed the spatiotemporal variations of PM2.5 concentration anomalies over the past 39 years (1980-2018) in winter (November to January) over eastern China based on the empirical orthogonal function (EOF) method. Regression analysis is conducted on external forcing factors such as sea ice, sea temperature, and snow cover in the pre-autumn (September to October) using the time series of the first three modes. Nine key factors were selected, which further led to establishing a model for predicting winter PM2.5 concentration in eastern China using the long short-term memory deep learning algorithm (LSTM). Independent verification revealed that the predicted and observed PM2.5 concentration distributions were consistent, with the absolute value of deviation within 15 µg·m-3 between 2016 and 2018. The correlation coefficients between the predicted and observed values were between 0.42 and 0.93 over eight key cities in the past 10 years (2009-2018). The contribution rates of the nine factors to PM2.5 concentration were calculated to explore their impact on PM2.5 concentration during winter. The Arctic sea ice (ASI) was found to be the key contributor to the winter PM2.5 concentration in eastern China. The predictors can be monitored in real time; hence, the model provides a real-time predictive tool, improving the prospects of predicting seasonal PM2.5 pollution, especially in vulnerable regions such as eastern China.

Synergetic roadmap of carbon neutrality and clean air for China.

It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction, air quality improvement, and improved health. In the context of carbon peak, carbon neutrality, and clean air policies, this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 indicators. The 18 indicators cover the following five aspects: air pollution and associated weather-climate conditions, progress in structural transition, sources, inks, and mitigation pathway of atmospheric composition, health impacts and benefits of coordinated control, and synergetic governance system and practices. By tracking the progress in each indicator, this perspective presents the major accomplishment of coordinated control, identifies the emerging challenges toward the synergetic governance, and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.

PM2.5 extended-range forecast based on MJO and S2S using LightGBM.

We developed an extended-range fine particulate matter (PM2.5) prediction model in Shanghai using the light gradient-boosting machine (LightGBM) algorithm based on PM2.5 historical data, meteorological observational data, Subseasonal-to-Seasonal Prediction Project (S2S) forecasts and Madden-Julian Oscillation (MJO) monitoring data. The analysis and prediction results demonstrated that the MJO improved the predictive skill of the extended-range PM2.5 forecast. The MJO indexes, namely, real-time multivariate MJO series 1 (RMM1) and real-time multivariate MJO series 2 (RMM2), ranked the first, and seventh, respectively, in terms of the predictive contribution of all meteorological predictors. When the MJO was not introduced, the correlation coefficients for the forecasts on lead times of 11-40 days ranged from 0.27 to 0.55, and the root mean square errors (RMSEs) ranged from 23.4 to 31.8 µg/m3. After the MJO was introduced, the correlation coefficients for the 11-40 day forecast ranged from 0.31 to 0.56, among which those for the 16-40 day forecast substantially improved, and the RMSEs ranged from 23.2 to 28.7 µg/m3. When comparing the prediction scores, such as percent correct (PC), critical success index (CSI), and equitable threat score (ETS), the forecast model was more accurate when it introduced the MJO. A novel aspect of this study is to investigate the effects of the MJO mechanism on the meteorological conditions of air pollution in eastern China through advanced regression analysis. The MJO indexes RMM1 and RMM2 considerably impacted the geopotential height field of 28°-40° at 300-250 hPa 45 days in advance. When RMM1 increased and RMM2 decreased 45 days in advance, the 500 hPa geopotential height field weakened accordingly, and the bottom of the 500 hPa trough moved southward; thus cold air was more easily transported southward and the upstream air pollutants were transported to eastern China. With a weak ground pressure field and dry air at low altitudes, the westerly wind component increased, which led to the easier formation of a weather configuration favorable for the accumulation and transport of air pollution, thus resulting in an increase in PM2.5 concentration in the region. These findings can guide forecasters regarding the utility of MJO and S2S for subseasonal air pollution outlooks.

MERRA-2 PM2.5 mass concentration reconstruction in China mainland based on LightGBM machine learning.

MERRA-2 developed by the National Aeronautics and Space Administration (NASA) provides the long-term record of surface PM2.5 mass concentration since 1980s, but needs great improvement over mainland China according to recent studies. In this study, a newly developed light gradient boosting machine (LGBM) model is introduced to correct the MERRA-2 PM2.5 record over mainland China by incorporating the meteorological reanalysis and satellite AOD retrievals. A 40-year surface PM2.5 record covering mainland China is reconstructed from 1980 to 2019, providing a new dataset for exploring the interactions between climate variability and air pollution. The new record exhibits not only much better magnitude but also more excellent variabilities of surface PM2.5 loading compared to original MERRA-2 products. The correlation coefficient, the root-mean-square error and the mean error between the observed and reconstructed records are 0.8, less than 28.5 µg·m-3, and 0.33 µg·m-3, respectively, which are much better than those of 0.27, 45.8 µg·m-3, and 1.64 µg·m-3 between the observed and MERRA-2 PM2.5 records. The PM2.5 record with longer term and higher accuracy developed in this study provides a better base for the research on the climate change variability and air pollution in mainland China. However, limitations of the reconstructed record still exist, especially in the Tibetan Plateau and marine regions with very sparse surface measurements, which need further correction in the future studies.

Opposite interdecadal variations of wintertime haze occurrence over North China Plain and Yangtze River Delta regions in 1980-2013.

In this study the interdecadal variations of haze pollution during recent 34 years from 1980 to 2013 over eastern China are investigated. It is found that opposite interdecadal variations of wintertime haze days (WHDs) between the North China Plain region (NCP) and the Yangtze River Delta region (YRD) occurred in this period, i.e., a decreased interdecadal trend of anomalous WHDs appeared in NCP and an increased trend in YRD. Meteorological factors driven the opposite interdecadal variations of anomalous WHDs are revealed. In the phase of interdecadal variations with more WHDs in NCP and less in YRD before 1996, there were weaker northerly winds in the lower troposphere over eastern China accompanied with a weakened Siberia High and mid-latitude westerly jet. The decreased northerly winds are conductive to the local accumulation of air pollutants in NCP and inhibition of air pollutants to be transported from North China to YRD. Consequently, more WHDs appeared in YRD and less in NCP. On the contrary, in the phase of interdecadal variations with less WHDs in NCP and more in YRD after 1996, stronger northerly winds associated with strengthened Siberia High and westerly jet, appeared over eastern China. The increased northerly winds are favorable for the dispersion of air pollutants in NCP and transport more pollutants from North China to the downstream area in YRD, resulting in less WHDs in NCP and more in YRD.

Long-term exposure to PM2.5 lowers influenza virus resistance via down-regulating pulmonary macrophage Kdm6a and mediates histones modification in IL-6 and IFN-ß promoter regions.

Atmospheric particulates, especially PM2.5, not only damage the respiratory system, but also play important roles in pulmonary immunity. China is influenced by atmospheric diffusion conditions, industrial manufacturers, and heating and discharging. PM2.5 levels in the air rise substantially in the winter, which is also a period of flu high-incidence. Although an epidemiological link exists between PM2.5 and flu, we do not understand how long-term PM2.5 inhalation affects pulmonary immunity and the influenza virus response. Our study has prepared an in vivo PM2.5 mouse pharyngeal wall drop-in model and has found that PM2.5 exposure leads to mouse inflammatory injuries and furthers influenza A infection. Our results suggest that short-term exposure to PM2.5 significantly enhances the survival rate of influenza A-contaminated mice, while long-term PM2.5 inhalation lowers the capacity of pulmonary macrophages to secrete IL-6 and IFN-ß. A disorder in the pulmonary innate defense system results in increased death rates following influenza infection. On a macromolecular level, this mechamism involves Kdm6a down-regulation after long-term exposure to PM 2.5 and a resultant increase in H3K4 and H3K9 methylation in IL-6 and IFN-ß promoter regions. In summary, PM2.5 causes injuries of lung tissue cells and downregulates immune defense mechanisms in the lung.

Long-term follow-up with Ross procedure at a single institution in China.

BACKGROUND: Although the Ross operation requires double-valve replacement for aortic valve disease, it has been shown to provide excellent hemodynamic results and is associated with low morbidity and mortality rates. We reviewed our long-term experience after completion of the Ross procedure. METHODS: Between October 1994 and February 2009, 58 consecutive patients underwent a Ross procedure at our institution. The right ventricular outñow tract was repaired with a cryopreserved pulmonary homograft. All patients were scheduled for a yearly study thereafter that ended at the time of death or at closure of the follow-up visit. Mean follow-up was 8.2 ± 3.5 years (range: 1.8 to 16.2 years). RESULTS: There were two early deaths (3%) and one late death (2%). Actuarial survival at 16 years was 94.8 ± 3.1%. One patient required reoperation and died during reoperation 1.5 years after his first Ross operation. Of the 55 survival patients, 42 patients (76%) were in NYHA functional class I and 13 patients (24%) were in NYHA functional class II. Grade 1 or grade 2 autograft regurgitation was observed in seven patients (12%) at 1 year after the surgery. The sinus of Valsalva diameters were all <40 mm in these seven patients. Freedom from hemodynamically relevant autograft regurgitation was 88 ± 2.8% at 16 years. After surgery, left ventricular function was significantly improved and left ventricular end-diastolic diameter recovered to normal over the long term. None of the patients required reoperation due to pulmonary homograft failure. CONCLUSIONS: The Ross procedure can be safely performed in patients with aortic valve disease. To date, mortality, morbidity, and reoperation rates are very low. Reasons for these superior results may include the following: (1) the diameter of the aortic valve annulus matching that of the pulmonary valve and (2) patients were monitored with antihypertensive medications to keep systolic blood pressure under 120 mm Hg to delay pressure lesions to the pulmonary autograft.