Center for Applications of Spatial Information Technologies in Public Health

Beijing, China

Center for Applications of Spatial Information Technologies in Public Health

Beijing, China
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Zheng S.,Beijing Normal University | Zheng S.,University of Chinese Academy of Sciences | Zheng S.,Max Planck Institute for Chemistry | Pozzer A.,Max Planck Institute for Chemistry | And 3 more authors.
Atmospheric Chemistry and Physics | Year: 2015

Beijing, the capital of China, is a densely populated city with poor air quality. The impact of high pollutant concentrations, in particular of aerosol particles, on human health is of major concern. The present study uses aerosol optical depth (AOD) as proxy to estimate long-term PM2.5 and subsequently estimates the premature mortality due to PM2.5. We use the AOD from 2001 to 2012 from the Aerosol Robotic Network (AERONET) site in Beijing and the ground-based PM2.5 observations from the US embassy in Beijing from 2010 to 2011 to establish a relationship between PM2.5 and AOD. By including the atmospheric boundary layer height and relative humidity in the comparative analysis, the correlation (R2) increases from 0.28 to 0.62. We evaluate 12 years of PM2.5 data for the Beijing central area using an estimated linear relationship with AOD and calculate the yearly premature mortality by different diseases attributable to PM2.5. The estimated average total mortality due to PM2.5 is about 5100 individuals per year for the period 2001-2012 in the Beijing central area, and for the period 2010-2012 the per capita mortality for all ages due to PM2.5 is around 15 per 10 000 person-years, which underscores the urgent need for air pollution abatement. © 2015 Author(s).


Reid P.C.,Sir Alister Hardy Foundation for Ocean Science | Reid P.C.,University of Plymouth | Reid P.C.,Marine Biological Association of The United Kingdom | Hari R.E.,Eawag - Swiss Federal Institute of Aquatic Science and Technology | And 37 more authors.
Global Change Biology | Year: 2016

Despite evidence from a number of Earth systems that abrupt temporal changes known as regime shifts are important, their nature, scale and mechanisms remain poorly documented and understood. Applying principal component analysis, change-point analysis and a sequential t-test analysis of regime shifts to 72 time series, we confirm that the 1980s regime shift represented a major change in the Earth's biophysical systems from the upper atmosphere to the depths of the ocean and from the Arctic to the Antarctic, and occurred at slightly different times around the world. Using historical climate model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and statistical modelling of historical temperatures, we then demonstrate that this event was triggered by rapid global warming from anthropogenic plus natural forcing, the latter associated with the recovery from the El Chichón volcanic eruption. The shift in temperature that occurred at this time is hypothesized as the main forcing for a cascade of abrupt environmental changes. Within the context of the last century or more, the 1980s event was unique in terms of its global scope and scale; our observed consequences imply that if unavoidable natural events such as major volcanic eruptions interact with anthropogenic warming unforeseen multiplier effects may occur. © 2016 John Wiley & Sons Ltd.


Yan H.,National Meteorological Center | Yan H.,University of Virginia | Wang S.-Q.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Lu H.-Q.,National Meteorological Center | And 6 more authors.
Journal of Geophysical Research: Atmospheres | Year: 2014

Vegetation effects are currently disregarded in Palmer Drought Severity Index (PDSI), and the sensitivity of PDSI to the choice of potential evaporation (EP) parameterization is often a concern. We developed a revised self-calibrating PDSI model that replaces EP with leaf area index-based total evapotranspiration (ARTS E0). It also included a simple snowmelt module. Using a unique satellite leaf area index data set and climate data, we calculated and compared ARTS E0, three other types of EP (i.e., Thornthwaite EP-Th, Allen EP-Al, and Penman-Monteith EP-PM), and corresponding PDSI values (i.e., PDSI-ARTS, PDSI-Th, PDSI-Al, and PDSI-PM) for the period 1982-2011. The results of PDSI-ARTS, PDSI-Al, and PDSI-PM show that global land became wetter mainly due to increased precipitation and El Niño-Southern Oscillation (ENSO) effect for the period, which confirms the ongoing intensification of global hydrologic cycle with global temperature increase. However, only PDSI-Th gave a trend of global drying, which confirms that PDSI-Th overestimates the global drying in response to global warming; i.e., PDSI values are sensitive to the parameterizations for Ep. Thus, ARTS E0, EP-Al, and EP-PM are preferred to EP-Th in global drought monitoring. In short, global warming affects global drought condition in two opposite ways. One is to contribute to the increases of EP and hence drought; the other is to increase global precipitation that contributes to global wetting. These results suggest that precipitation trend and its interaction with global warming and ENSO should be given much attention to correctly quantify past and future trends of drought. ©2014. American Geophysical Union. All Rights Reserved.


Cao C.,Beijing Normal University | Cao C.,Center for Applications of Spatial Information Technologies in Public Health | Liu D.,Beijing Normal University | Liu D.,University of Chinese Academy of Sciences | And 7 more authors.
Geomatics, Natural Hazards and Risk | Year: 2016

The primary goal of this paper is to discuss an integrated approach to efficiently obtaining earthquake damage information. We developed a framework to rapidly obtain earthquake damage information using post-earthquake airborne optical images. The framework is a standard process that includes data selection, preprocessing, damage factor identification, damage factor evaluation and the development of an earthquake damage information map. We can obtain damage information on severely affected regions using this framework, which will aid in planning rescue and rehabilitation efforts following disasters. We used the integrated approach to obtain damage information using the Lushan earthquake (magnitude 7.0, 20 April 2013) as a case study. The result were as follows: (1) 644 collapsed buildings and 4599 damaged buildings accounted for 13.90% and 96.24%, respectively, of the total number of buildings in the study area; (2) 334 landslides (total area of 691,674.5 m2) were detected and were found at greater probabilities at elevations of 1400–1500 m and higher slope; (3) no secondary disasters, such as barrier lakes, were detected; (4) 15 damaged sections (total of 306 m) were detected in the lifelines, and road sections that are at a high risk of damage (total of 2.4 km) were identified; and (5) key structures, including Yuxi River Dam and three bridges, were intact. Integrating the earthquake damage factor information generated a comprehensive Lushan earthquake damage information map. The integrated approach was proven to be effective using the Lushan earthquake as a case study and can be applied to assess earthquake damage to facilitate efficient rescue efforts. © 2015 Taylor & Francis.


Sitch S.,University of Exeter | Friedlingstein P.,University of Exeter | Gruber N.,ETH Zurich | Jones S.D.,University of East Anglia | And 29 more authors.
Biogeosciences | Year: 2015

The land and ocean absorb on average just over half of the anthropogenic emissions of carbon dioxide (CO2) every year. These CO2 "sinks" are modulated by climate change and variability. Here we use a suite of nine dynamic global vegetation models (DGVMs) and four ocean biogeochemical general circulation models (OBGCMs) to estimate trends driven by global and regional climate and atmospheric CO2 in land and oceanic CO2 exchanges with the atmosphere over the period 1990-2009, to attribute these trends to underlying processes in the models, and to quantify the uncertainty and level of inter-model agreement. The models were forced with reconstructed climate fields and observed global atmospheric CO2; land use and land cover changes are not included for the DGVMs. Over the period 1990-2009, the DGVMs simulate a mean global land carbon sink of g'2.4 ± 0.7 Pg C yrg'1 with a small significant trend of g'0.06 ± 0.03 Pg C yrg'2 (increasing sink). Over the more limited period 1990-2004, the ocean models simulate a mean ocean sink of g'2.2 ± 0.2 Pg C yrg'1 with a trend in the net C uptake that is indistinguishable from zero (g'0.01 ± 0.02 Pg C yrg'2). The two ocean models that extended the simulations until 2009 suggest a slightly stronger, but still small, trend of g'0.02 ± 0.01 Pg C yrg'2. Trends from land and ocean models compare favourably to the land greenness trends from remote sensing, atmospheric inversion results, and the residual land sink required to close the global carbon budget. Trends in the land sink are driven by increasing net primary production (NPP), whose statistically significant trend of 0.22 ± 0.08 Pg C yrg'2 exceeds a significant trend in heterotrophic respiration of 0.16 ± 0.05 Pg C yrg'2 - primarily as a consequence of widespread CO2 fertilisation of plant production. Most of the land-based trend in simulated net carbon uptake originates from natural ecosystems in the tropics (g'0.04 ± 0.01 Pg C yrg'2), with almost no trend over the northern land region, where recent warming and reduced rainfall offsets the positive impact of elevated atmospheric CO2 and changes in growing season length on carbon storage. The small uptake trend in the ocean models emerges because climate variability and change, and in particular increasing sea surface temperatures, tend to counter\-act the trend in ocean uptake driven by the increase in atmospheric CO2. Large uncertainty remains in the magnitude and sign of modelled carbon trends in several regions, as well as regarding the influence of land use and land cover changes on regional trends. © 2015 Author(s).


Zheng S.,Beijing Normal University | Zheng S.,University of Chinese Academy of Sciences | Cao C.-X.,Beijing Normal University | Cao C.-X.,Center for Applications of Spatial Information Technologies in Public Health | Singh R.P.,Chapman University
Science of the Total Environment | Year: 2014

Air quality in mega cities is one of the major concerns due to serious health issues and its indirect impact to the climate. Among mega cities, Beijing city is considered as one of the densely populated cities with extremely poor air quality. The meteorological parameters (wind, surface temperature, air temperature and relative humidity) control the dynamics and dispersion of air pollution. China National Environmental Monitoring Centre (CNEMC) started air pollution index (API) as of 2000 to evaluate air quality, but over the years, it was felt that the air quality is not well represented by API. Recently, the Ministry of Environmental Protection (MEP) of the People's Republic of China (PRC) started using a new index "air quality index (AQI)" from January 2013. We have compared API and AQI with three different MODIS (MODIS - Moderate Resolution Imaging SpectroRadiometer, onboard the Terra/Aqua satellites) AOD (aerosol optical depth) products for ten months, January-October, 2013. The correlation between AQI and Aqua Deep Blue AOD was found to be reasonably good as compared with API, mainly due to inclusion of PM2.5 in the calculation of AQI. In addition, for every month, the correlation coefficient between AQI and Aqua Deep Blue AOD was found to be relatively higher in the month of February to May. According to the monthly average distribution of precipitation, temperature, and PM10, the air quality in the months of June-September was better as compared to those in the months of February-May. AQI and Aqua Deep Blue AOD show highly polluted days associated with dust event, representing true air quality of Beijing. © 2013 Elsevier B.V.


Ichii K.,Fukushima University | Ichii K.,Japan National Institute of Environmental Studies | Kondo M.,Fukushima University | Okabe Y.,Fukushima University | And 8 more authors.
Remote Sensing | Year: 2013

Past changes in gross primary productivity (GPP) were assessed using historical satellite observations based on the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series and four terrestrial biosphere models to identify the trends and driving mechanisms related to GPP and NDVI in Asia. A satellite-based time-series data analysis showed that approximately 40% of the area has experienced a significant increase in the NDVI, while only a few areas have experienced a significant decreasing trend over the last 30 years. The increases in the NDVI are dominant in the sub-continental regions of Siberia, East Asia, and India. Simulations using the terrestrial biosphere models also showed significant increases in GPP, similar to the results for the NDVI, in boreal and temperate regions. A modeled sensitivity analysis showed that the increases in GPP are explained by increased temperature and precipitation in Siberia. Precipitation, solar radiation and CO2 fertilization are important factors in the tropical regions. However, the relative contributions of each factor to GPP changes are different among the models. © 2013 by the authors.


Tian H.,CAS Institute of Remote Sensing | Tian H.,Center for Applications of Spatial Information Technologies in Public Health | Tian H.,University of Chinese Academy of Sciences | Cao C.,CAS Institute of Remote Sensing | And 10 more authors.
International Journal of Remote Sensing | Year: 2014

Accurate assessment of phytoplankton chlorophyll-a (chl-a) concentration in turbid waters by means of remote sensing is challenging because of the optical complexity of case 2 waters. We applied a bio-optical model of the form [R–1(λ1) – R–1(λ2)](λ3), where R(λi) is the remote-sensing reflectance at wavelength λi, to estimate chl-a concentration in coastal waters. The objectives of this article are (1) to validate the three-band bio-optical model using a data set collected in coastal waters, (2) to evaluate the extent to which the three-band bio-optical model could be applied to the spectral radiometer (SR) ISI921VF-512T data and the hyperspectral imager (HSI) data on board the Chinese HJ-1A satellite, (3) to evaluate the application prospects of HJ-1A HSI data in case 2 waters chl-a concentration mapping. The three-band model was calibrated using three SR spectral bands (λ1 = 664.9 nm, λ2 = 706.54 nm, and λ3 = 737.33 nm) and three HJ-1A HSI spectral bands (λ1 = 637.725 nm, λ2 = 711.495 nm, and λ3 = 753.750 nm). We assessed the accuracy of chl-a prediction with 21 in situ sample plots. Chl-a predicted by SR data was strongly correlated with observed chl-a (R2 = 0.93, root mean square error (RMSE) = 0.48 mg m–3, coefficient of variation (CV) (RMSE/mean(chl-amea)) = 3.72%). Chl-a predicted by HJ-1A HSI data was also closely correlated with observed chl-a (R2 = 0.78, RMSE = 0.45 mg m–3, CV (RMSE/mean(chl-amea)) = 7.51%). These findings demonstrate that the HJ-1A HSI data are promising for quantitative monitoring of chl-a in coastal case-2 waters. © 2014, © 2014 Taylor & Francis.


Catalano F.,ENEA | Alessandri A.,ENEA | De Felice M.,ENEA | Zhu Z.,CAS Institute of Remote Sensing | And 2 more authors.
Earth System Dynamics | Year: 2016

The temporal variance of soil moisture, vegetation and evapotranspiration over land has been recognized to be strongly connected to the temporal variance of precipitation. However, the feedbacks and couplings between these variables are still not well understood and quantified. Furthermore, soil moisture and vegetation processes are associated with a memory and therefore they may have important implications for predictability. In this study we apply a generalized linear method, specifically designed to assess the reciprocal forcing between connected fields, to the latest available observational data sets of global precipitation, evapotranspiration, vegetation and soil moisture content. For the first time a long global observational data set is used to investigate the spatial and temporal land variability and to characterize the relationships and feedbacks between land and precipitation. The variables considered show a significant coupling among each other. The analysis of the response of precipitation to soil moisture evidences a robust coupling between these two variables. In particular, the first two modes of variability in the precipitation forced by soil moisture appear to have a strong link with volcanic eruptions and El Niño-Southern Oscillation (ENSO) cycles, respectively, and these links are modulated by the effects of evapotranspiration and vegetation. It is suggested that vegetation state and soil moisture provide a biophysical memory of ENSO and major volcanic eruptions, revealed through delayed feedbacks on rainfall patterns. The third mode of variability reveals a trend very similar to the trend of the inter-hemispheric contrast in sea surface temperature (SST) and appears to be connected to greening/browning trends of vegetation over the last three decades. © 2016 Author(s).

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