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.

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.

Impact of climate change on "evaporation paradox" in province of Jiangsu in southeastern China.

Contrary to the common expectation that the reference evapotranspiration (ETo), which is an indicator of the atmospheric evaporation capability, increases in warming climate, the decline of the ETo has been reported worldwide, and this contradiction between the expected increasing ETo and the observed decreasing one is now termed the "evaporation paradox". Based on the updated meteorological data (1960-2019), we separately detected the spatiotemporal characteristics and the causes of the "evaporation paradox" in three subregions, namely Huaibei, Jianghuai, and Sunan, and throughout the entire province of Jiangsu in southeastern China. Different from the reported continuous unidirectional variations in the ETo, in the province of Jiangsu, it generally showed a decreasing trend before 1990 but followed an increasing trend from 1990 to 2019, which led to the different characteristics of the "evaporation paradox" in the periods from 1960 to 1989, from 1990 to 2019, and from 1960 to 2019. In the first 30 years, the reduction of the wind speed (WS) was the main reason for the decreased ETo, which consequently gave rise to the "evaporation paradox" in spring and winter in the Huaibei region and only in winter in the two other subregions and throughout the entire province. We noticed that the "evaporation paradox" in spring in the Sunan region was expressed by the decreased daily mean air temperature (Tmean) and the increased ETo which was chiefly induced by the decreased relative humidity (RH) and the increased vapor pressure deficit (VPD). After 1990, the decreased WS also dominated the decreased ETo and resulted in the "evaporation paradox" in winter in the Jianghuai region. Furthermore, the decreased sunshine hour (SH) was the main factor influencing the decreased ETo, thereby inducing the "evaporation paradox" in summer and autumn in the Jianghuai region and only in autumn in the Huaibei region and throughout the whole province from 1990 to 2019. In the whole study period from 1960 to 2019, the decreased SH was also found to be responsible for the decreased ETo and for the "evaporation paradox" in summer in all the subregions and throughout the whole province. However, regarding the "evaporation paradox" in autumn, in winter, and in the entire year in the Huaibei region and throughout the whole province, the observed decreased ETo was largely due to the reduced WS from 1960 to 2019. In summary, in addition to the air temperature, the ETo has shifted due to the other meteorological variables (especially the WS, the SH, and the VPD) and shaped the unique spatiotemporal characteristics of the "evaporation paradox" in the province of Jiangsu in southeastern China. Moreover, future studies and simulations addressing the regional climate change and hydrological cycles should take account of the changeable key meteorological variables in different subregions and seasons of the province of Jiangsu.