This paper discusses the analysis of the severe dust storm that occurred over Beijing from 26th April to 3rd May in 2012 with the use of combined satellite observations and ground-based measurements. In this study, we analyze the pollution characteristics of particulate matters near ground, with the main focus on spatio-temporal and vertical distributions of aerosol during this event by using ground-based Aerosol Robotic Network (AERONET), MODerate resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data. Results show that the Aerosol Optical Depth (AOD) measured at 550 nm from the AERONET Beijing station has an ascending trend with a peak value of 2.5 on 1st May. Moreover, the AOD variation from the MODIS data agrees well with AERONET observations during the same time period. In addition, the vertical distribution of total attenuated backscatter coefficient (TABC), volume depolarization ratio (VDR) and color ratio (CR) of CALIPSO data are comprehensively analyzed. Results from these analyses show that the dust mainly accumulates in the layer at altitudes of 1.5 to 4.5 km on 1st May. In this dust layer, the values of TABC are generally around 0.002~0.0045 km-1sr-1 and VDR and CR are typically around 0.1~0.5 and 0.6~1.4 respectively. Thus, the combined satellite and ground-based observations are of great use for monitoring and analyzing air quality with high accuracy.
With the fast urbanization process, how does the vegetation environment change in one of the most economically developed metropolis, Shanghai in East China? To answer this question, there is a pressing demand to explore the non-stationary relationship between socio-economic conditions and vegetation across Shanghai. In this study, environmental data on vegetation cover, the Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery in 2003 were integrated with socio-economic data to reflect the city’s vegetative conditions at the census block group level. To explore regional variations in the relationship of vegetation and socio-economic conditions, Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models were applied to characterize mean NDVI against three independent socio-economic variables, an urban land use ratio, Gross Domestic Product (GDP) and population density. The study results show that a considerable distinctive spatial variation exists in the relationship for each model. The GWR model has superior effects and higher precision than the OLS model at the census block group scale. So, it is more suitable to account for local effects and geographical variations. This study also indicates that unreasonable excessive urbanization, together with non-sustainable economic development, has a negative influence of vegetation vigor for some neighborhoods in Shanghai.
Population dynamics has major impacts on regional ecosystem and socioeconomic development. Its prediction has become a key step for assessing ecosystems and socioeconomic development. Using the population data of Yangtze River Delta, a model created by Back Propagation (BP) neural network were adapted to probe and describe the dynamic evolution, and the Moran Index was used in analyzing spatial autocorrelation quantitatively.
Increased CO2 (carbon dioxide) has been considered as one of key factors of global warming. Intending
to describe the capability of CO2 measurement by space-borne sensors quantitatively, this paper
compares two data sets of CO2 monthly products retrieved from AIRS and SCIAMACHY over China
from 2003 to 2005. The increasing trend of CO2 concentration can be detected consistently from both of
the data sets. However, the seasonal variation of AIRS CO2 is larger than SCIAMACHY CO2 because the
former represents CO2 existing in the mid-troposphere while the latter represents in the
lower-troposphere. CO2 concentration reaches its yearly maximum in spring (April and May) and
reaches its yearly minimum in late-autumn and winter (October to December and January) for both data
sets. The coverage of AIRS monthly CO2 is much better than that of SCIAMACHY over China and it
shows that Xinjiang, Tibet, Inner Mongolia and northeast China have higher values than other regions in
China especially in April and May due to local climate and vegetation growth process.
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