Investigation of quantitative predictions of precipitation amounts and forecasts of drought events are conducive to facilitating early drought warnings. However, there has been limited research into or modern statistical analyses of precipitation and drought over Northeast China, one of the most important grain production regions. Therefore, a case study at three meteorological sites which represent three different climate types was explored, and we used time series analysis of monthly precipitation and the grey theory methods for annual precipitation during 1967–2017. Wavelet transformation (WT), autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) methods were utilized to depict the time series, and a new hybrid model wavelet-ARIMA-LSTM (W-AL) of monthly precipitation time series was developed. In addition, GM (1, 1) and DGM (1, 1) of the China Z-Index (CZI) based on annual precipitation were introduced to forecast drought events, because grey system theory specializes in a small sample and results in poor information. The results revealed that (1) W-AL exhibited higher prediction accuracy in monthly precipitation forecasting than ARIMA and LSTM; (2) CZI values calculated through annual precipitation suggested that more slight drought events occurred in Changchun while moderate drought occurred more frequently in Linjiang and Qian Gorlos; (3) GM (1, 1) performed better than DGM (1, 1) in drought event forecasting.
With global warming, the increasing number and intensity of extreme precipitation events are a global trend. The IPCC AR5 (2012) points out that the global average temperature has increased by 0.12°C every 10 years in the past 60 years (1951-2012). The temperature in Northeast China has increased by 1.75-2.5°C and is one of the most sensitive regions to climate change in the world (IPCC, 2012; Qin and Stocker, 2014). Northeast China is the most important area for food production in China and is also an important base for national security. Therefore, it is particularly important to study the mechanism of the formation of extreme precipitation in Northeast China. Extreme value theory (EVT) can successfully characterize climatic and hydrological extremes and has been widely used in meteorology and hydrology (
Heavy air pollution can impact plant growth, human health, and weather/climate. Air quality predictions, especially PM 2.5 predictions, have become increasingly important for controlling air pollution and maintaining sustainable development (
The biological treatment of source-separated human urine to produce biofuel, nutraceutical, and high-value chemicals is getting increasing attention. Especially, photoautotrophic microalgae can use human urine as media to achieve environmentally and economically viable large-scale cultivation. This review presents a comprehensive overview of the up-to-date advancements in microalgae cultivation employing urine in photobioreactors (PBRs). The standard matrices describing algal growth and nutrient removal/recovery have been summarized to provide a platform for fair comparison among different studies. Specific consideration has been given to the critical operating factors to understand how the PBRs should be maintained to achieve high efficiencies. Finally, we discuss the perspectives that emphasize the impacts of co-existing bacteria, contamination by human metabolites, and genetic engineering on the practical microalgal biomass production in urine.
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