Spatiotemporal distribution characteristics of PM2.5 concentration in China from 2000 to 2018 and its impact on population.

PM2.5 is an important indicator reflecting changes in air quality. In recent years, affected by climate change and human activities, the problem of environmental pollution has become more and more prominent. In this study, the PM2.5 data from 2000 to 2018 obtained by satellite remote sensing inversion algorithm were selected to analyze the temporal and spatial distribution of PM2.5 in China. The results show that the areas with higher PM2.5 concentrations were mainly in the North China, the Sichuan Basin, and the Tarim Basin. The areas with a significant increase in PM2.5 were mainly in the Northeast China, while the areas with a significant decrease were mainly in the Sichuan Basin and southeastern Gansu. The change of PM2.5 in southern China was not significantly correlated with the change of population and economy, while PM2.5 in Northeast China increases with the increase of population and economy. In 2000, 2005, 2010, and 2015, the proportion of the population polluted by PM2.5 was 8.65%, 7.2%, 22.99%, and 9.75%, respectively. The year with the highest percentage (37.63%) of population when air quality reached EXCELLENT was 2015. When the PM2.5 spatial cluster number was six, it can better reflect the PM2.5 spatial distribution state. The places with large changes in PM2.5 spatial clustering were mainly in the Northeast China, Sichuan Basin, and Tarim Basin, which were also areas with large changes in PM2.5. This study provides an important reference for atmospheric environmental monitoring and protection.

Influence and prediction of PM2.5 through multiple environmental variables in China.

Fine particulate matter (PM2.5) is an important indicator to measure the degree of air pollution. With the pursuit of sustainable development of China's economy and society, air pollution has been paid more and more attention. The spatial distribution of PM2.5 is affected by multiple factors. In this study, we selected Normalized Difference Vegetation Index (NDVI), precipitation, temperature, wind speed and elevation data to analyze the impact of each variable on PM2.5 in different regions of China. The results show that the high-value areas of PM2.5 were mainly concentrated in the North China Plain, the middle and lower reaches of the Yangtze River Plain, the Sichuan Basin, and the Tarim Basin. PM2.5 showed an upward trend in North China, Northeast China and Northwest China, while in most of South China, especially the Sichuan Basin, PM2.5 showed a downward trend. Therefore, the northern region of China needs to take measures to curb the growth of PM2.5. In Northwest China, wind speed and temperature had a greater impact on PM2.5. In North China, wind speed had a greater impact on PM2.5. In southern China, temperature and NDVI had a greater impact on PM2.5. The deep learning model can better simulate the spatial distribution of PM2.5 based on the selected variables. The clustering effect of single variable is better than multivariate spatial information clustering based on principal component analysis (PCA). It is difficult to explain which variable has the greatest impact on PCA clustering. This study can provide an important reference for PM2.5 prevention and control in different regions of China.

Monitoring hydrological drought using long-term satellite-based precipitation data.

Long-term (over 30a) satellite-based quantitative rainfall estimate (SRE) products provide an ideal data source for hydrological drought monitoring. This study mainly explores the suitability of the two long-term SREs, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and the Climate Hazards Group (CHG) Infrared Precipitation with Stations (CHIRPS), for hydrological drought monitoring. A hydrological drought index called the standardized streamflow index (SSI) was used as an example and the Grid-based Xinanjiang (GXAJ) hydrological model was used for streamflow generation of the SREs. A middle size basin in the humid region of south China was selected as case study. The obtained results show that both SREs present acceptable performances for hydrological modeling, and CHIRPS outperformed PERSIANN-CDR. SSIs calculated by the SRE simulations generally fit well with the trend of observation-based on SSI but apparent deviations in drought intensity were also found. In contrast to hydrological modeling, performance of the SRE-based SSI showed almost no change after model recalibration. Both SREs generally present acceptable classification accuracy but tended to underestimate the levels of drought types. Both SREs accurately captured the beginning, end, and duration of this drought event; however, several deviations were found in severity and intensity estimation of the drought event. In general, both SREs are suitable for hydrological drought monitoring. Although the CHIRPS generally presented better performance, the PERSIANN-CDR is still adequate for hydrological drought monitoring.

Does drought in China show a significant decreasing trend from 1961 to 2009?

In recent decades, the occurrence and severity of drought in China has had devastating impact on social and economic development. The increase in drought has been attributed to global warming. We used the high-accuracy self-calibrating Palmer Drought Severity Index (scPDSI) to investigate the variation in drought in China between 1961 and 2009 using the Mann-Kendall (MK), continuous wavelet transform (CWT) and the rotated empirical orthogonal function (REOF) methods. We also analyzed the relationship between the rotated principal component time series (RPCs) and 74 circulation indices. The results revealed that: 1) all of China experienced a significant wet trend at annual and seasonal scale; an abrupt change in the drought pattern occurred around 1970 with a 2-8-year significant period; 2) eight major sub-climate regions were identified: Northwest China, Northeast-Inner Mongolia Plateau, Greater Khingan Range area, Northern Tibetan Plateau, Southern Tibetan Plateau, Central China, Huang-Huai-Hai Plain and Southeast China. Of these regions, the Southern Tibetan Plateau experienced a significant wet trend, but the Northeast-Inner Mongolia Plateau and Northern Tibetan Plateau became significantly drier. Using either annual or seasonal scales, Northwest China became significantly wetter and Central China became more arid. In addition, the period of each sub-climate region shared a significant 2-8-year band; 3) the polar vortex exhibited dominant patterns that affected most areas of China. The Pacific Decadal Oscillation had a significant influence on drought evolution, especially for Northwest China and the Huang-Huai-Hai plain. Additionally, the El Niño-Southern Oscillation also affected drought evolution, and the Central China was impacted by the Indian Ocean Dipole.

Preparation and Characterization of Biochars from Eichornia crassipes for Cadmium Removal in Aqueous Solutions.

The study investigated the preparation and characterization of biochars from water hyacinth at 300°C to 700°C for cadmium (Cd) removal from aqueous solutions. The adsorption process was dominated by oxygen-containing functional groups with irregular surfaces via esterification reactions. Furthermore, the mineral components in the biochars also contributed to Cd absorption through precipitation. Parameters such as the effects of solution pH, contact time, and initial concentration were studied. The optimum pH value was observed at 5.0, in which nearly 90% of Cd was removed. The maximum Cd adsorption capacities based on the Langmuir isotherm were calculated at 49.837, 36.899, and 25.826 mg g(-1). The adsorption processes of the biochars followed the pseudo-second-order kinetics, with the equilibrium achieved around 5 h. The biochar from E. crassipes is a promising adsorbent for the treatment of wastewater, which can in turn convert one environmental problem to a new cleaning Technology.