scholarly journals Spatial distribution and seasonal variation characteristics of global atmospheric moisture recycling

2014 ◽  
Vol 63 (9) ◽  
pp. 099201
Author(s):  
Su Tao ◽  
Lu Zhen-Yu ◽  
Zhou Jie ◽  
Hou Wei ◽  
Li Yue ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Seasonal Variation ◽  
Atmospheric Moisture ◽  
Moisture Recycling ◽  
Variation Characteristics

scholarly journals Seasonal Variation Characteristics of Bacteria and Fungi in PM2.5 in Typical Basin Cities of Xi’an and Linfen, China

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 809
Author(s):  
Sen Wang ◽  
Wanyu Liu ◽  
Jun Li ◽  
Haotian Sun ◽  
Yali Qian ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Relative Abundance ◽  
Pathogenic Bacteria ◽  
High Throughput Sequencing ◽  
Fine Particulate Matter ◽  
Ecological Functions ◽  
Factors Affecting ◽  
Bacterial Phyla ◽  
Variation Characteristics ◽  
Bacteria And Fungi

Microorganisms existing in airborne fine particulate matter (PM2.5) have key implications in biogeochemical cycling and human health. In this study, PM2.5 samples, collected in the typical basin cities of Xi’an and Linfen, China, were analyzed through high-throughput sequencing to understand microbial seasonal variation characteristics and ecological functions. For bacteria, the highest richness and diversity were identified in autumn. The bacterial phyla were dominated by Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes. Metabolism was the most abundant pathway, with the highest relative abundance found in autumn. Pathogenic bacteria (Pseudomonas, Acinetobacter, Serratia, and Delftia) were positively correlated with most disease-related pathways. Besides, C cycling dominated in spring and summer, while N cycling dominated in autumn and winter. The relative abundance of S cycling was highest during winter in Linfen. For fungi, the highest richness was found in summer. Basidiomycota and Ascomycota mainly constituted the fungal phyla. Moreover, temperature (T) and sulfur dioxide (SO2) in Xi’an, and T, SO2, and nitrogen dioxide (NO2) in Linfen were the key factors affecting microbial community structures, which were associated with different pollution characteristics in Xi’an and Linfen. Overall, these results provide an important reference for the research into airborne microbial seasonal variations, along with their ecological functions and health impacts.

scholarly journals Temporal-spatial Distribution Characteristics of Air Pollutants in Chengdu Economic Region, China

2019 ◽  
Vol 118 ◽  
pp. 04027
Author(s):  
Hongjin Tong ◽  
Sha Liu ◽  
Ruixue Liao ◽  
Xiaomei Wei ◽  
Kangli Che ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Temporal Variation ◽  
Air Pollutants ◽  
Interpolation Method ◽  
Central Area ◽  
Average Concentration ◽  
Distribution Characteristics ◽  
Economic Region ◽  
Spatial Distribution Characteristics ◽  
Variation Characteristics

The previous characteristics researches of air pollution were almost based on data from national environmental monitoring stations in 2015. The temporal variation curves of air pollutants and the ArcGIS grid interpolation method were used to analyze the spatial-temporal variation of air pollutants in five cities of Chengdu economic region. In 2015, the monthly change trends of PM2.5, PM10, CO, NO2 and NO of air pollutants in Chengdu economic region were basically the same. The maximum monthly average concentration was in January or December, and the minimum was in May to September. The temporal variation of SO2 was characterized by little fluctuation of monthly concentration. The temporal variation characteristics of O3 were opposite to other pollutants. The spatial distribution of PM10 and PM2.5 was characterized by the largest concentration in Chengdu and the southwest of Meishan, in which they were mainly concentrated in the central area of Chengdu in winter. The average concentration of CO in Chengdu was the largest, followed by Deyang and Mianyang, and Meishan and Ziyang was the smallest. The concentrations of NO2 and NO in Chengdu were the largest, while those in Ziyang were the smallest. The spatial distribution characteristics of O3 were different from other pollutants. The areas with the largest concentration of O3 were Ziyang and a small part of west in Chengdu. The spatial distribution of SO2 was characterized by the largest concentration of SO2 in Ziyang, the lowest concentration in Mianyang and Deyang.

scholarly journals Seasonal Variation in the Spatial Distribution of Basking Sharks (Cetorhinus maximus) in the Lower Bay of Fundy, Canada

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82074 ◽  
Author(s):  
Zachary A. Siders ◽  
Andrew J. Westgate ◽  
David W. Johnston ◽  
Laurie D. Murison ◽  
Heather N. Koopman
Keyword(s):  
Spatial Distribution ◽  
Seasonal Variation ◽  
Bay Of Fundy ◽  
Cetorhinus Maximus

Spatial Distribution and Seasonal Variation of Organophosphate Esters in Air above the Bohai and Yellow Seas, China

2017 ◽  
Vol 52 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Jing Li ◽  
Jianhui Tang ◽  
Wenying Mi ◽  
Chongguo Tian ◽  
Kay-Christian Emeis ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Seasonal Variation ◽  
Organophosphate Esters

Crop Decision Tree Classification Extraction Based on MODIS NDVI in Beijing

2014 ◽  
Vol 955-959 ◽  
pp. 787-790 ◽  
Author(s):  
Shi Wei Dong ◽  
Dan Feng Sun ◽  
Hong Li
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Seasonal Variation ◽  
Decision Tree ◽  
Land Surface ◽  
Satellite Images ◽  
Overwhelming Majority ◽  
Modis Ndvi ◽  
Decision Tree Classification ◽  
Double Cropping

Time-series satellite images can reflect the seasonal variation from vegetation on land surface. Single cropping and double cropping were extracted by decision tree classification based on MODIS NDVI of Beijing in 2007, and spatial distribution of dominant crops in Beijing was obtained. The dominant crops of single cropping were maize, wheat and vegetable, and the overwhelming majority of crops with double cropping were wheat-maize. The results showed that this method could effectively determine the dominated crops in Beijing.

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