Air pollutants can cause serious effects on human health, especially fine particles in air, which can easily cause respiratory diseases, such as asthma and bronchitis, and also increase the probability of lung cancer and heart disease. The disease surveillance of the residents around the chemical plant is an effective means to understand the effects of the discharged pollutant on the health of the surrounding residents. The research area, Shanghai Chemical Industry Park is located at the junction of Jinshan and Fengxian, as the South Center of the six industrial bases of Shanghai. Applying the spatial distribution of population obtained from the extraction of residential land by using GF-2 data, and the disease data after cleaning treatment, can reach to the spatial distribution of the above two diseases. Through the integration of spatial characteristics and attribute characteristics, the disease surveillance of the surrounding residents can be realized, which directly reflects the impact of chemical industry on the health of the surrounding residents.
KEYWORDS: Calibration, Monte Carlo methods, Data modeling, Roads, Information science, Remote sensing, Climatology, Diffusion, Resistance, Local area networks
With the rapid development of urbanization, the dynamic evolution of urban expansion has become one of the hot topics throughout the world. Thus, modeling and predicting the urban expansion in the future is one of the effective methods for the study of urban growth. Based on the rapid urbanization in Shanghai, our study uses four years of land use data (1995, 2000, 2005 and 2010), DEM and two years of traffic roads data (2005 and 2010) to obtain the optimal parameters of urban growth through model calibration. And the results of calibration were used to simulate and predict the land use change in 2040 under different scenarios of excluded layers. The results show that the urban growth in Shanghai is more often grow along the edge of existing urban centers and the transportation network with the relatively high spread coefficient (43) and road coefficient (66), while the dispersion, breed and slope coefficient are relatively low. The SLEUTH simulation with these five parameters possessed satisfactory capability of predicting land use changes with the kappa coefficient of 0.8628 and an appropriate Lee-Sallee index of 0.8139. The result shows that the urban areas in Shanghai increase significantly in 2040, while the rural area, grass and other construction area are decreased. Therefore, SLEUTH can better predict the spatial changes of land use and provide some theoretical support and decision-making basis for the urban-rural planning in Shanghai.
Fine particles less than 2.5 microns in aerodynamic diameter (PM2.5) has found to threat human health and environment. The formation and diffusion of PM2.5 are closely related to the meteorological elements. Many scholars have studied the influence mechanism of meteorological elements to PM2.5. However, most of these researches mainly focus on some serious short-term atmospheric pollution, long-term research is rare. In addition, the impact of meteorological elements on PM2.5 has regional characteristics. This paper takes Shanghai as study area, applying PM2.5 concentrations from China environmental monitoring stations and reanalysis meteorological data from 2014 to 2016.. Through qualitative and quantitative analysis, this paper got the change characteristics of PM2.5 in Shanghai in recent three years, and the correlation between PM2.5 and relative humidity, temperature, wind and boundary layer height. Relative humidity is positively correlated with PM2.5, while U wind is negatively correlated with PM2.5. And there are seasonal differences in the correlation between PM2.5 and temperature, V wind and boundary layer height.
People in Huaihe River Basin and Shanghai have been suffering from severe air pollution of nitrogen dioxide and sulfur dioxide due to the development of heavy industry. Traditional ambient monitoring station measurements can provide real-time accurate data, but it is limited due to the less number of monitoring sites. Satellite observation data from remote sensing can provide a wide range opollutants concentrations in long-time sequence. Top-down approaches based on satellite data can be effectively applied to estimate the ground concentrations of pollutants. In this paper, the tropospheric pollutants columns from the Ozone Monitoring Instrument(OMI) were used to analyse the seasonal variation of NO2 and SO2 in 2015. Moreover, the ground-level NO2 and SO2 concentrations of the Huaihe River Basin and Shanghai at this time were estimated by the data and meteorological data. The results show that: the concentrations of NO2 and SO2 are highest in winter, and high-value areas are mainly located in Shandong and Northern Henan. Estimating the ground-level NO2 and SO2 concentrations based on satellite observations is reliable with the validation R2 0.48 and 0.47 respectively. Finally, The spatial distribution of satellite-derived annual mean NO2 and SO2 has a similar characteristics to the satellite columns.
Fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) has been widely considered to
be one of the main pollutant threating human health. Ground-level PM2.5 monitoring can provide
accurate point data, but its value is hard to scale up to large scale. In this respects, satellite data with
large coverage areas and long term range, could enhance our ability to estimate PM2.5 concentration. In
this study, a Multilinear correlation model (MLC) based on MODIS AOD level 2 data was developed
to estimate PM2.5 concentration in Northeastern China from 2013-2016, then ground-level PM2.5
monitoring data from 15 stations covering study area were used for validation. Results showed that 1)
the annual PM2.5 is 63.98μg/m2, AOD values agreed well with estimated PM2.5 concentration, 2) the
spatial variations of PM2.5 were not clear, while the temporal dynamic of PM2.5 were observed, the
highest values were observed in winter, opposite to what were observed in fall. 3) the MLC model
coupled with meteorological data could improve the precision of PM2.5 estimations. Therefore, we
suggest that the developed MLC model is useful for the PM2.5 estimations in northeastern China.
Over the past decade, China has experienced a rapid increase in urbanization. The urban built-up areas
(population) of Shanghai increased by 16.1% (22.9%) from 2006 to 2015. This study aims to analyze
the variations of tropospheric NO2 over Yangtze River Delta region and the impacts of rapid
urbanization during 2006-2015. The results indicate that tropospheric NO2 vertical column density
(VCD) of all cities in the study area showed an increasing trend during 2006-2011 whereas a
decreasing trend during 2011-2015. Most cities showed a lower tropospheric NO2 VCD value in 2015
compared to that in 2006, except for Changzhou and Nantong. Shanghai and Ningbo are two hotspots
where the tropospheric NO2 VCD decreased most significantly, at a rate of 22% and 19%, respectively.
This effect could be ascribed to the implementation of harsh emission control policies therein. Similar
seasonal variability was observed over all cities, with larger values observed in the summer and smaller
values shown in the winter. Further investigations show that the observed increasing trend of
tropospheric NO2 during 2006-2011 could be largely explained by rapid urbanization linked to car
ownership, GDP, power consumption, population and total industrial output. Such effect was not
prominent after 2011, mainly due to the implementation of emission control strategies.
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