Statistics for Spatial Data.

「脆弱性(Vulnerability)」とは何か

[i] An inventory of air pollutant emissions in Asia in the year 2000 is developed to support atmospheric modeling and analysis of observations taken during the TRACE-P experiment funded by the National Aeronautics and Space Administration (NASA) and the ACE-Asia experiment funded by the National Science Foundation (NSF) and the National Oceanic and Atmospheric Administration (NOAA). Emissions are estimated for all major anthropogenic sources, including biomass burning, in 64 regions of Asia. We estimate total Asian emissions as follows: 34.3 Tg SO 2 , 26.8 Tg NO x , 9870 Tg CO 2 , 279 Tg CO, 107 Tg CH 4 , 52.2 Tg NMVOC, 2.54 Tg black carbon (BC), 10.4 Tg organic carbon (OC), and 27.5 Tg NH 3 . In addition, NMVOC are speciated into 19 subcategories according to functional groups and reactivity. Thus we are able to identify the major source regions and types for many of the significant gaseous and particle emissions that influence pollutant concentrations in the vicinity of the TRACE-P and ACE-Asia field measurements. Emissions in China dominate the signature of pollutant concentrations in this region, so special emphasis has been placed on the development of emission estimates for China. China's emissions are determined to be as follows: 20.4 Tg SO 2 , 11.4 Tg NO x , 3820 Tg CO 2 , 116 Tg CO, 38.4 Tg CH 4 , 17.4 Tg NMVOC, 1.05 Tg BC, 3.4 Tg OC, and 13.6 Tg NH 3 . Emissions are gridded at a variety of spatial resolutions from 1° × 1° to 30 s x 30 s, using the exact locations of large point sources and surrogate GIS distributions of urban and rural population, road networks, landcover, ship lanes, etc. The gridded emission estimates have been used as inputs to atmospheric simulation models and have proven to be generally robust in comparison with field observations, though there is reason to think that emissions of CO and possibly BC may be underestimated. Monthly emission estimates for China are developed for each species to aid TRACE-P and ACE-Asia data interpretation. During the observation period of March/ April, emissions are roughly at their average values (one twelfth of annual). Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of ±16% for SO 2 to a high of ±450% for OC.

An inventory of gaseous and primary aerosol emissions in Asia in the year 2000 : NASA global tropospheric experiment transport and chemical evolution over the pacific (TRACE-P): Measurements and analysis (TRACEP1)

Mercury (Hg) is a global pollutant that affects human and ecosystem health. We synthesize understanding of sources, atmosphere-land-ocean Hg dynamics and health effects, and consider the implications of Hg-control policies. Primary anthropogenic Hg emissions greatly exceed natural geogenic sources, resulting in increases in Hg reservoirs and subsequent secondary Hg emissions that facilitate its global distribution. The ultimate fate of emitted Hg is primarily recalcitrant soil pools and deep ocean waters and sediments. Transfers of Hg emissions to largely unavailable reservoirs occur over the time scale of centuries, and are primarily mediated through atmospheric exchanges of wet/dry deposition and evasion from vegetation, soil organic matter and ocean surfaces. A key link between inorganic Hg inputs and exposure of humans and wildlife is the net production of methylmercury, which occurs mainly in reducing zones in freshwater, terrestrial, and coastal environments, and the subsurface ocean. Elevated human exposure to methylmercury primarily results from consumption of estuarine and marine fish. Developing fetuses are most at risk from this neurotoxin but health effects of highly exposed populations and wildlife are also a concern. Integration of Hg science with national and international policy efforts is needed to target efforts and evaluate efficacy.

http://pubs.acs.org/doi/pdf/10.1021/es305071v

Mercury as a Global Pollutant: Sources, Pathways, and Effects

A positive temperature coefficient is the term which has been used to indicate that an increase in solubility occurs as the temperature is raised, whereas a negative coefficient indicates a decrease in solubility with rise in temperature.

Effect of Temperature

BACKGROUND

Although access to cancer care is known to influence patient outcomes, to the authors' knowledge, little is known regarding geographic access to cancer care, and how it may vary by population characteristics. This study estimated travel time to specialized cancer care settings for the continental U.S. population and calculated per capita oncologist supply.



METHODS

The closest travel times were estimated using a network analysis of the road distance weighted by travel speeds from the population or geographic centroid of every ZIP area in the continental U.S. to that of the nearest cancer care setting under consideration: National Cancer Institute (NCI)-designated Cancer Centers, academic medical centers, and oncologists. Alaska and Hawaii were excluded because travel in these states is often not road-based. Population and geographic characteristics including race/ethnicity, income, education, and region were derived from U.S. Census 2000 data and from rural-urban commuting area classifications. Oncologist supply per 100,000 residents in Hospital Referral Regions (pHRRs) was estimated by region.



RESULTS

Travel times of ≤1 hour were estimated for 45.2% of the population to the nearest NCI Cancer Center, 69.4% to the nearest academic-based care, and 91.8% to any specialized cancer care. Native Americans, nonurban dwellers, and residents in the South had the longest travel times to the nearest NCI Cancer Center compared with the overall U.S. population (median [interquartile range (IQR)] in minutes: 155 [62–308], 173 [111–257], and 164 [70–272], vs 78 [27–172], respectively). Travel burdens persisted for Native Americans and nonurban populations across all 3 cancer care settings. For all population strata, travel times markedly increased as the degree of cancer care specialization increased. The median oncologist supply for pHRRs was 2.83 per 100,000 individuals.



CONCLUSIONS

There are population groups with limited access to the most specialized cancer care settings. Cancer 2008. © 2008 American Cancer Society.

https://acsjournals.onlinelibrary.wiley.com/doi/pdf/10.1002/cncr.23229

Geographic access to cancer care in the U.S.

Cellular automata for simulating land use changes based on support vector machines

http://www.geosimulation.cn/Papers/EcoModellingKernelCA.pdf

Simulating complex urban development using kernel-based non-linear cellular automata

[1] During 1960–2010, the air temperature in the arid region of northwest China had a significant rising trend (P < 0.001), at a rate of 0.343°C/decade, higher than the average of China (0.25°C/decade) and that of the entire globe (0.13°C/decade) for the same period. Based on the analysis of the data from 74 meteorological stations in the region for 1960–2010, we found that among the four seasons the temperature change of winter has been playing the most important role in the yearly change in this region. We also found that the winter temperature in this region has a strong association with the Siberian High (correlation coefficient: R = −0.715) and the greenhouse gas emission (R = 0.51), and between the two the former is stronger. We thus suggest that the weakening of the Siberian High during the 1980s to 1990s on top of the steady increasing of the greenhouse emission is the main reason for the higher rate of the temperature rise in the arid region of the northwest China.

/pdf/why-does-the-temperature-rise-faster-in-the-arid-region-of-144ndbr1zb.pdf

Why does the temperature rise faster in the arid region of northwest China

Biomass is distributed over extensive areas. Therefore, transportation cost is a critical factor in planning new biomass power plants. This paper presents a case study of using remote sensing and geographical information systems (GIS) to evaluate the feasibility of setting up new biomass power plants and optimizing the locations of plants in Guangdong, China. In this study, the biologically available biomass was estimated from MODIS/Terra remote sensing data. The amount of biomass that is usable for energy production was then derived using a model incorporating factors including vegetation type, ecological retaining, economical competition, and harvest cost. GIS was employed to define the supply area of each candidate site based on transportation distance along roads. The amount of usable biomass within the supply area was calculated and optimal sites were identified accordingly. This study presents a procedural framework for taking advantage of spatial information technologies to achieve more scientific planning in bioenergy power plant construction.

Xun Shi

Papers

Geographic access to cancer care in the U.S.

Cellular automata for simulating land use changes based on support vector machines

Simulating complex urban development using kernel-based non-linear cellular automata

Why does the temperature rise faster in the arid region of northwest China

Using spatial information technologies to select sites for biomass power plants : A case study in Guangdong Province, China