Spatial and Seasonal Dynamics of Ship Emissions over the Yangtze River Delta and East China Sea and Their Potential Environmental Influence

2016 ◽  
Vol 50 (3) ◽  
pp. 1322-1329 ◽  
Author(s):  
Qianzhu Fan ◽  
Yan Zhang ◽  
Weichun Ma ◽  
Huixin Ma ◽  
Junlan Feng ◽  
...  
Keyword(s):  
East China Sea ◽  
Seasonal Dynamics ◽  
Yangtze River ◽  
Environmental Influence ◽  
Yangtze River Delta ◽  
River Delta ◽  
East China ◽  
The Yangtze River ◽  
Ship Emissions ◽  
The Yangtze River Delta

Genotypic diversity and antifungal susceptibility of environmental isolates of Cryptococcus neoformans from the Yangtze River Delta region of East China

2020 ◽  
Author(s):  
Min Chen ◽  
Yan Wang ◽  
Yingfang Li ◽  
Nan Hong ◽  
Xinlin Zhu ◽  
...  
Keyword(s):  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
East China ◽  
Azole Resistance ◽  
The Yangtze River ◽  
Environmental Isolates ◽  
Delta Region ◽  
Yangtze River Delta Region ◽  
The Yangtze River Delta

Abstract Although cryptococcosis is widely recognized as infection by Cryptococcus neoformans sensu lato from environmental sources, information concerning the characteristics of environmental isolates of C. neoformans s. l. and how they are related to clinical isolates is very limited, especially in East China. In this study, 61 environmental isolates of C. neoformans were recovered from pigeon (Columba livia) droppings from the Yangtze River Delta region of East China. These isolates were genotyped using the ISHAM-MLST consensus scheme and their antifungal drug susceptibilities were determined following the CLSI M27-A3 guidelines. The 61 isolates were found belonging to 13 sequence types (STs), including several novel STs such as ST254 and ST194. The dominant ST in this environmental sample was ST31, different from that of clinical strains (ST5) in this region. Azole-resistance, such as fluconazole (FLU)-resistance, was observed among our environmental C. neoformans isolates. The findings of this study expand our understanding of ecological niches, population genetic diversity, and azole-resistance characteristics of the yeast in East China. Our research lays the foundation for further comparative analysis the potential mechanisms for the observed differences between environmental and clinical populations of C. neoformans in China. Lay Summary Cryptococcosis is widely recognized as infection by Cryptococcus neoformans sensu lato from environmental sources. However, there is currently limited information about the genetic diversity and antifungal susceptibility of environmental C. neoformans s. l. isolates, including how they may differ from clinical samples. In this study, we collected 61 environmental C. neoformans isolates from domestic pigeon droppings from the Yangtze River Delta region of East China. These isolates were genotyped using multi-locus sequencing. We found a high genotypic diversity in this population of C. neoformans, with several novel genotypes and a distribution of genotypes different from that of clinical strains in this region. Azole-resistance, such as fluconazole (FLU)-resistance, was observed among our environmental C. neoformans isolates. The findings of this study expand our understanding of ecological niches, genetic diversity, and azole-resistance characteristics of the yeast in East China. Our research lays the foundation for phylogenomic analysis investigating why and how disparate population structures of C. neoformans isolates formed between environmental and clinical sources in the region.

scholarly journals Separating emission and meteorological contribution to PM<sub>2.5</sub> trends over East China during 2000–2018

2021 ◽  
Author(s):  
Qingyang Xiao ◽  
Yixuan Zheng ◽  
Guannan Geng ◽  
Cuihong Chen ◽  
Xiaomeng Huang ◽  
...  
Keyword(s):  
Pollution Control ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
Meteorological Conditions ◽  
River Delta ◽  
East China ◽  
The Yangtze River ◽  
The Yangtze River Delta ◽  
Meteorological Effects

Abstract. The contribution of meteorology and emissions to long-term PM2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used a combination of machine learning model, statistical model and chemical transport model to quantify the meteorological impacts on PM2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods, a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM2.5 retrievals as well as the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modelling system, to examine the meteorological contribution to PM2.5. We found good agreements between the GAM model estimations and the CMAQ model estimations of meteorological contribution to PM2.5 on monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual trends in meteorology-associate PM2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM2.5 pollution control across the North China Plain and central China, but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM2.5 over East China peaked at 2006 and 2011, mainly driven by the emission peaks in PM2.5 and PM2.5 precursors in these years. Although emissions dominated the long-term PM2.5 trends, the meteorology-driven anomalies also contributed −3.9 % to 2.8 % of the annual mean PM2.5 concentrations in East China estimated from the GAM model. The meteorological contributions were even higher regionally, e.g., −6.3 % to 4.9 % of the annual mean PM2.5 concentrations in the Beijing-Tianjin-Hebei region, −5.1 % to 4.3 % in the Fen-wei Plain, −4.8 % to 4.3 % in the Yangtze River Delta and −25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM2.5 and the worsening meteorological conditions in the northern part of China where air pollution was severe and population was clustered, stricter clean air actions are needed to avoid haze events in the future.

scholarly journals The Temporal and Spatial Evolution of Marathons in China from 2010 to 2018

2019 ◽  
Vol 16 (24) ◽  
pp. 5046 ◽  
Author(s):  
Yifan Zuo ◽  
Liye Zou ◽  
Mu Zhang ◽  
Lee Smith ◽  
Lin Yang ◽  
...  
Keyword(s):  
Hot Spots ◽  
Yangtze River ◽  
North China ◽  
Yangtze River Delta ◽  
River Delta ◽  
East China ◽  
The Yangtze River ◽  
Moran's I ◽  
The Core ◽  
The Yangtze River Delta

The purpose of this study is to explore the spatial distribution pattern and influencing factors of the Chinese marathon. Geographic Information System (GIS) related spatial analysis tools were used to calculate the following—averaged nearest neighbor index, nuclear density analysis and hot spot analysis among others. The spatial distribution evolution characteristics and the influencing factors of eighteen Chinese marathon events in 2010, 129 in 2015 and 342 in 2018 were analyzed. The results show that (a) in 2010 the nearest neighbor ratio was 1.164714, Moran’s I was −0.010165 (type: Random), in 2015 it was 0.502146, Moran’s I was 0.066267 (type: Clustered) and in 2018 it was 0.531149 and Moran’s I was 0.083485 (type: Clustered); (b) in 2010 there was a 333.6 km search radius; the core circle of the Yangtze River Delta was adopted. In 2015 and 2018, a search radius of 556 km was adopted, which was respectively obtained from the core circle of the Yangtze River Delta, the core circle of Beijing-Tianjin-Hebei and the core circle of East China; (c) according to the Z-value data, East China and North China in 2015 passed 95% confidence in five provinces and municipal hot spots, passed 90% confidence in three hot spots and passed 95% confidence in Chongqing Cold Point. In 2018, East China, North China, Central Region and eight other provinces and cities’ hot spots passed 95% confidence, four hot spots passed 90% confidence, the Tibet Autonomous Region cold spot passed 90% confidence. Conclusion: The overall distribution of marathon events is greater in the eastern region than the western region, greater in the southern region than the northern region and greater in coastal regions than the inland regions; the nuclear density distribution has spread from the Yangtze River Delta mononuclear circle in 2010 to the core circle of the entire East China region. Moreover, it spread to North China, Central China and South China; the distribution of hot spots spread from Shanghai, Jiangsu and Zhejiang to the entire North China and East China regions. During the past thirty-eight years of development of the Chinese marathon, it has been divided into three stages due to different political, economic and social environments.

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