1. Analysis on driving factors of lake surface water temperature for major lakes in Yunnan-Guizhou Plateau

Author links open overlay panelKunYangac1ZhenyuYuab1YiLuoacShow morehttps://doi.org/10.1016/j.watres.2020.116018

Water Research Available online 23 June 2020, 116018 In Press, Journal Pre-proof

What are Journal Pre-proof articles?

• The heterogeneity of LSWT warming of 11 major plateau lakes were discussed.
  

• 6 main factors impact on LSWT were analyzed quantitively.

• LSWT changing heterogeneity was revealed from lake type and morphometry perspective.

Abstract

Lake surface water temperature (LSWT) is an important factor in lake ecological environments. It has been observed that LSWT have followed an upward trend in the last half century, which has had serious impacts on regional biodiversity and climate. It is important to understand the main reason for this phenomenon in order to have a basis for controlling and improving the regional ecological environment. In this study, the contribution rates of near surface air temperature (NSAT), surface pressure (SP), surface solar radiation (SSR), total cloud cover (TCC), wind speed (WS) and Secchi depth (SD) to LSWT of 11 naturally formed lakes in the Yunnan-Guizhou Plateau are quantified. The characteristics of and relationships between the various factors and LSWT in lakes of different types and attributes are revealed. The results show that: (1) from 2001 to 2018, most lakes were warming; the change rate of LSWT-day was higher than that of LSWT-night. The mean comprehensive warming rate (MCWR) of LSWT-day was 0.42 °C/decade, and the mean comprehensive change rate (MCCR) was 0.31 °C/decade; the MCWR of LSWT-night was 0.19 °C/decade, and the MCCR was 0.01 °C/decade. NSAT and SSR were most strongly correlated with LSWT-day/night. There were no large seasonal differences in the correlation between NSAT and LSWT-day, while seasonal differences in the correlations between NSAT with LSWT-night and SSR with LSWT-day/night were observed. (2) NSAT and SSR were the most important factors affecting LSWT-day/night changes, with contribution rates of 30.24% and 44.34%, respectively. LSWT-day was more affected by SP and SSR in small, shallow, and low-storage lakes. For larger lakes, LSWT-day was more affected by WS, while LSWT-night was greatly affected by TCC. Urban and semi-urban lakes were more affected by SSR and NSAT; for natural lakes, the decreasing SD affected the increases in LSWT, which indirectly reflects the impact of human activities. LSWT-day/night responded differently to different morphological characteristics of the lakes and different intensities of human activity.

2.Human activities and the natural environment have induced changes in the PM2.5 concentrations in Yunnan Province, China, over the past 19 years

Environmental Pollution Available online 30 May 2020, 114878In Press, Journal Pre-proof

What are Journal Pre-proof articles?

Author links open overlay panelKunYangab1MengfanTengab1YiLuoabXiaoluZhoucMiaoZhangaSunWeizhaoabqiulinLiab

https://doi.org/10.1016/j.envpol.2020.114878

• The PM2.5 concentration exhibits heterogeneity due to human activities and the natural environment.

• Springtime biomass burning has a significant influence on the surrounding air quality.

• The air quality of Yunnan Province improved after 2013.

Abstract

Fine particulate matter (PM2.5) concentrations exhibit distinct spatiotemporal heterogeneity, mainly due to the natural environment and human activities. Yunnan Province of China was selected as the research area, and a real-time measured PM2.5 concentration dataset was acquired from 41 monitoring stations in 16 major cities from February 2013 to December 2018. Aerosol optical depth (AOD) products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and data on four meteorological variables from 2000 to 2018 were employed. A novel hybrid model was constructed to estimate the historical missing PM2.5 values from 2000 to 2012, calculate the missing PM2.5 concentrations from 2012 to 2014 in some major cities, and analyze the driving factors of the PM2.5 concentration changes and causes of key pollution events in Yunnan Province over the past 19 years. The temporal analysis results indicate that the annual mean PM2.5 concentration in Yunnan Province exhibited three stages: continuous stability, a rapid increase and a rapid decrease. The year 2013 was an important breakpoint in the trend of the concentration change. The spatial analysis results reveal that the annual mean PM2.5 concentration in the north was lower than that in the south, and there was a significant difference between the east and the west. In addition, springtime biomass burning in Southeast Asia was found to be the main cause of PM2.5 pollution in Yunnan Province in spring.

3.Assessing spatiotemporal air environment degradation and improvement represented by PM2.5 in China using two-phase hybrid model

Sustainable Cities and Society Volume 59, August 2020, 102180

Author links open overlay panelKunYangab1YanShiabc1YiLuoabRuimeiLiuabWeizhaoSunbdMengzhuSunbd

https://doi.org/10.1016/j.scs.2020.102180

• Two-phase hybrid model of monthly PM2.5 was developed.

• China air environment degradation and improvement processes were revealed.

• PM2.5 pollution in China showed spatial heterogeneity obviously.

Abstract

As the largest developing country in the world, China is currently facing great pressure from poor air quality. PM2.5 is the primary pollutant causing ambient air quality problems in most cities. In the present study we developed a two-phase hybrid model, with long time scale and spatial range, that considered nonlinear, seasonal, and regional characteristics of the relationship between aerosol optical depth (AOD) and PM2.5, included meteorological parameters, and developed combinations with the ε-support vector regression (ε-SVR) and the Mind Evolutionary Computation–BP artificial neural network (MEC–BP). We used estimated and observed data to help clarify the degradation and improvement processes operating in the Chinese air environment. 2014 was an important turning point for air quality in China, as the situation changed from worsening to improving; our results are in line with the actual situation, indicating that PM2.5 pollution in China has been alleviated to a certain extent since 2013. However, during the period 2000–2017, the yearly average PM2.5 concentration in more than 60 % of China exceeded the second level criterion of the applicable national standard. Although PM2.5 pollution in China has improved to a certain extent, the air pollution situation in the country must still be regarded as severe.

4.Research papersSpatial-temporal process simulation and prediction of chlorophyll-a concentration in Dianchi Lake based on wavelet analysis and long-short term memory network

Author links open overlay panelZhenyuYuab1KunYangac1YiLuoacChunxueShangad

https://doi.org/10.1016/j.jhydrol.2019.124488

Journal of Hydrology Volume 582, March 2020, 124488

• Chlorophyll-a (Chla) higher than 100 μg/L from 2005 to 2020 have a tendency to spread.

• Wavelet mean fusion (WMF) is proposed based on wavelet domain threshold denoising (WDTD)

• WDTD, WMF and long-short term memory (LSTM) are combined to a long-term prediction model.

• The model is used to predict Chla in Dianchi Lake for 8 years (RMSE = 18.40, MAE = 13.56, R2 = 0.63)

• The model has the comprehensive prediction performance of low error and high generalization.

Abstract

With the rapid development of urbanization, the water pollution in Dianchi Lake presents the trend of combining urban and agricultural non-point source pollution, and it is more difficult to control and improve the water environment. The simulation and prediction of water quality state change is an important theoretical basis for water resources management. The data set we selected was 15 water quality parameters of 10 water quality observation sites in Dianchi Lake from 2005 to 2012. Wavelet Domain Threshold Denoising (WDTD), Wavelet Mean Fusion (WMF) and Long-Short Term Memory (LSTM) were combined to a WDTD-LSTM-WMF long-term prediction model that WMF was proposed based on WDTD in this paper. The model and geospatial analysis were used to simulate the historical change process of chlorophyll-a concentration (Chla) in Dianchi Lake and predicted the future trend of Chla. The results showed that the model has a good prediction performance of low error and high generalization (RMSE = 18.40, MAE = 13.56, R2 = 0.63). The spatial visualization analysis showed that the region with Chla higher than 100 μg/L from 2005 to 2020 had a tendency to spread from north to west and then to southwest. This is related to the urbanization development and climate change in Kunming.