SPATIAL DISTRIBUTION CHARACTERISTICS OF SUBTERRANEAN HOT WATER EVALUATED USING WATER QUALITY CONCENTRATION DATA

2013 ◽  
Author(s):  
Takamitsu Kajisa
Keyword(s):  
Water Quality ◽  
Spatial Distribution ◽  
Hot Water ◽  
Distribution Characteristics ◽  
Concentration Data ◽  
Spatial Distribution Characteristics

Spatial Distribution Characteristics and Hourly Forecast of PM2.5 Pollution in Summer in Beijing

2014 ◽  
Vol 1073-1076 ◽  
pp. 874-880
Author(s):  
Li Sha Wang ◽  
Jie Li ◽  
Li Na Zhang ◽  
Yuan Hua Chen ◽  
Yu Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Spatial Distribution ◽  
Air Quality ◽  
Quality Monitoring ◽  
Atmospheric Environment ◽  
Distribution Characteristics ◽  
Air Quality Monitoring ◽  
Concentration Data ◽  
Spatial Distribution Characteristics ◽  
June July

The fine particulate matter (PM2.5) has become the primary pollutant of atmospheric environment of Beijing now, therefore to know the spatial distribution characteristics and to make accurate and refined forecast of PM2.5 is very important. In this study, the spatial distribution characteristics of PM2.5 concentration in summer in Beijing was derived by using Kriging interpolation method based on the data from 30 air quality monitoring stations all over Beijing during June, July and August 2014. In general, the PM2.5 pollution is higher in the south and east, and is lower in the north and west, but when there is south or southeast wind blowing, the situation could be totally the opposite. Based on the hourly PM2.5 concentration data from one representative air quality monitoring station and the meteorological data from a nearby meteorological station during June, July and August 2014, the hourly PM2.5 concentration was forecasted up to 168 hours ahead by using BP (Error-back propagation) and RBF (Radial basis function) neural network methods. The results show RBF neural network method is more efficient, the curve trends of the forecasted values are similar with the curve trends of the monitored values and the forecasted values have significant linear relationship with the monitored values, which demonstrates the possibility of hourly forecast for PM2.5 pollution.

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