Jason K. Levy

The effect of sandstorms and air pollution on cause- specific hospital admissions in Taipei, Taiwan

Objective: Relatively little research exists focusing on the impact of air pollution on hospital admissions in Asia compared to the extensive work conducted in the USA and Europe. The issue is of particular importance because of the frequency, intensity and health effects of Asian sandstorms. This work investigates the relation between cause-specific hospital admissions and sandstorms and air pollution in Taipei, Taiwan’s capital. Methods: Time-series analyses of asthma, pneumonia, ischaemic heart disease and cerebrovascular disease hospital admissions were performed for Taipei. An eight-year time period (1995–2002) was considered for various indicators of sandstorms and the pollutants NO2, CO, ozone, SO2, PM10, and PM2.5. Pollution effects based on single-day lags of 0, 1, 2 and 3 days were explored, along with the average of the same day and previous three days (L03). Results: The risk of ischaemic heart disease admissions was associated with several sandstorm metrics, including indicators of high PM10 levels in the Taipei area, indicators of high PM10 at a monitor designed to measure background pollution, the PM coarse fraction, and the ratio of PM10 to PM2.5. However, the lag structure of effect was not consistent across sandstorm indicators. Hospital admissions for this disease were 16–21% higher on sandstorm days compared to other days. This cause was also associated with transportation-related pollutants, NO2, CO and PM2.5. Asthma admissions rose 4.48% (95% CI 0.71% to 8.38%) per 28 μg/m3 increase in L03 PM10 levels and 7.60% (95% CI 2.87% to 12.54%) per 10 ppb increase in L03 ozone. Cerebrovascular disease admissions were associated with PM10 and CO, both at lag 3 days. SO2 exhibited no relation with admissions. Conclusions: Risk of hospital admissions in Taipei may be increased by air pollution and sandstorms. Additional research is needed to clarify the lag structure and magnitude of such effects.

Using environmental indicators to quantify the robustness of policy alternatives to uncertainty

Understanding and managing environmental risk is one of the major challenges of our time. The variability of ecological systems, wherein disturbances occur with uncertain frequency and magnitude over time and space, is essential for maintaining ecosystem integrity and resilience. This paper uses sustainable development indicators to improve multiple-objective environmental decision making under conditions of unknown variability. Specifically, the robustness to uncertainty of competing policy alternatives is assessed using multi-attribute value theory and the recently developed information gap methodology. The management of spruce-budworm outbreaks in the forests of New Brunswick, Canada, is used as an illustrative example. Numerical and theoretical results show how the minimum required return and the available prior information determine which alternative can best cope with environmental uncertainty.

A heuristic for the container loading problem: A tertiary-tree-based dynamic space decomposition approach

Increasing fuel costs, post-911 security concerns, and economic globalization provide a strong incentive for container carriers to use available container space more efficiently, thereby minimizing the number of container trips and reducing socio-economic vulnerability. A heuristic algorithm based on a tertiary tree model is proposed to handle the container loading problem (CLP) with weakly heterogeneous boxes. A dynamic space decomposition method based on the tertiary tree structure is developed to partition the remaining container space after a block of homogeneous rectangular boxes is loaded into a container. This decomposition approach, together with an optimal-fitting sequencing and an inner-right-corner-occupying placement rule, permits a holistic loading strategy to pack a container. Comparative studies with existing algorithms and an illustrative example demonstrate the efficiency of this algorithm.

Promoting Ecological Sustainability and Community Resilience in the US Gulf Coast after the 2010 Deepwater Horizon Oil Spill

Abstract On 20 April 2010, the 5000-foot-deep Macondo Mississippi Canyon Block 252 (MC252) well erupted after a blowout caused a catastrophic explosion and fire aboard the BP PLC-leased Deepwater Horizon offshore oil drilling platform (owned by Transocean Ltd.) about 40 miles (64 km) southeast of the Louisiana coast. The 2010 Deepwater Horizon Gulf Coast oil spill now imperils ecologically sensitive lands, affects the livelihoods of thousands of workers, and threatens a way of life that has been passed down for generations. An oil spill management Decision Support System (DSS) architecture is put forth that integrates the latest advances in MCDA and geomatics engineering in order to cope with the worst environmental disaster in US history. It is shown that policy-makers should improve the sustainability and resilience of Gulf Coast ecosystems and communities so that they are healthier and more robust than before the oil spill. Policy recommendations with respect to the 2010 Deepwater Horizon spill are put forth. We discuss how to best assist disrupted businesses, rehabilitate polluted ecosystems, and improve regulatory oversight of the oil and gas industry in order to prevent future oil spill disasters.

Using remote sensing to estimate sea ice thickness in the Bohai Sea, China based on ice type

It is challenging to use traditional remote sensing techniques to accurately determine the extent and thickness of ice in the Bohai Sea, on account of the presence of sea impurities (i.e. mud, salt bubbles and sand) and shape irregularities. Accordingly, we performed a series of reflectance spectra experiments to empirically link remote measurements of surface reflectance with in situ sea ice thickness measurements in the Bohai Sea. Two years of Thematic Mapper (TM) band 2 and Moderate Resolution Imaging Spectroradiometer (MODIS) band 4 data were used to distinguish between the following sea ice types, using spectral reflectance thresholds of 6.4, 9.6, 10.3 and 12.1%: (a) clean nilas ice (a thin elastic crust of ice up to 10 cm thick that, under pressure, may deform by finger rafting; (b) nilas ice and pancake ice (roughly circular accumulations of frazil ice, usually less than about 3 m in diameter, with raised rims caused by collisions); (c) grey and grey–white ice; and (d) cumulative ice (<30 cm). By establishing a relationship between sea ice type and ice thickness, a novel, practical and low-cost remote sensing technique is introduced to estimate the extent and distribution of sea ice thickness over a large spatial scale. The results obtained by remote sensing are validated with in situ ice shape measurements. The MODIS and TM data are used to distinguish between three ice thickness grades (6–9, 10–20 and 20–30 cm).

Promoting Disaster-resilient Communities: The Great Sumatra–Andaman Earthquake of 26 December 2004 and the Resulting Indian Ocean Tsunami

The human casualties and socio-economic damage associated with the Great Sumatra–Andaman Earthquake of 26 December 2004 and the resulting Indian Ocean tsunami are discussed. The Sumatra–Andaman earthquake was the largest earthquake to occur since the advent of global digital seismometry and it produced the most devastating tsunami in recorded history (and the largest humanitarian response). A reliable Indian Ocean Tsunami Warning and Mitigation System is shown to require an improved seismographic network, a real-time sea-level observing network covering the entire Indian Ocean basin, and the deployment of deep-ocean pressure sensors. It is concluded that Indian Ocean governments can achieve more tsunami-resilient communities by addressing poverty, promoting education, harnessing technological advances, investing in emergency medical and rescue services, and empowering stakeholders.

Water allocation among multiple stakeholders: conflict analysis of the Waiahole water project, Hawaii

Recent years have seen a sharp increase in the demand for water in Hawaii, which has intensified and accelerated the competition for the states water resources. Specifically, this paper uses the decision support system GMCR II to analyse the strategic aspects of a multi-party water dispute involving the allocation of Waiahole Ditch waters on Oahu, the major economic and population centre of Hawaii. The issue of surplus water allocation is unprecedented in Hawaii and thus there is no explicit water law precedent governing allocation of such water as conceived by the water administrative agencies or the judicial branch.

Advances in Decision Support Systems for Flood Disaster Management: Challenges and Opportunities

Natural variations in the global climate are governed by complex interactions among the atmosphere, oceans, and land cover. Modern climate models suggest that these variations will continue, but with larger magnitudes and greater variability due to human influences. This is expected to increase the risk of flood disaster events. To improve flood risk management, a flood decision support system architecture is proposed that capitalizes on the latest advances in remote sensing, geographic information systems, hydrologic models, numerical weather prediction, information technology, and decision theory. Specifically, the dynamic climate prediction system developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, is discussed in the context of flood management and planning in the Yangtze River valley, China.

Increasing dissolved silica trends in the Rhine River: an effect of recovery from high P loads?

The development of river dams and further human activities (causing increased nitrogen (N) and phosphorus (P) nutrient loads), are responsible for a decline in dissolved silica concentrations (DSi) in many river systems. Here, the impact of the reduction of N- and P-concentrations on DSi is examined for the Rhine River. During the last decade of the twentieth century, annual average DSi concentrations increased by ~70% in the Rhine at Bimmen/Lobith, whereas nitrate (NO3) and phosphate (PO4) concentrations decreased by approximately one third. Accordingly, decadal changes in nutrient elemental ratios shifted the river system from DSi-limitation to P-limitation. Specifically, a seasonal DSi-concentration increase is observed from May to December for the Rhine River (with exception of June). Observed increases in DSi concentration are probably due to improvements in water-basin and land-use management, specifically a reduction in point-source P discharge, leading to P-limiting conditions for diatom growth. Data of the warm season suggest that as the system is moving through the transition from P-excess to P-limitation conditions, P-limitation according to the elemental ratio DSi/total phosphorus (TP) is occurring later than for the ratio DSi/PO4-P. Latter ratio will be buffered around ~16:1 during growing season. Reduction of N fertilization is less relevant, as N-limitation with respect to DSi is not achieved, even at the end of the analyzed period, but N-limitation may be reached in the future. Analysis of discharge–DSi relationship supports the hypothesis that DSi increase is affected by increasing P-limitation during the warm period and not only due to hydrological reasons. Results suggest, however, that the influence of hydrological parameters needs to be addressed in research for DSi concentration changes due to changed nutrient loads. Despite an overall increase in water temperature of 3°C over a 50-year period, no correlation with temperature was found for the last two decades of the twentieth century, for which DSi-data were available. In conclusion, in case of eutrophied river systems with excess of P, P-reduction may lead to an increase of DSi concentrations under certain conditions. This in turn is expected to impact not only DSi-sensitive coastal-zone ecosystems impacted by eutrophication but the carbon cycle as well.

Landslide Catastrophes and Disaster Risk Reduction: A GIS Framework for Landslide Prevention and Management

As catastrophic phenomena, landslides often cause large-scale socio-economic destruction including loss of life, economic collapse, and human injury. In addition, landslides can impair the functioning of critical infrastructure and destroy cultural heritage and ecological systems. In order to build a more landslide resistant and resilient society, an original GIS-based decision support system is put forth in order to help emergency managers better prepare for and respond to landslide disasters. The GIS-based landslide monitoring and management system includes a Central Repository System (CRS), Disaster Data Processing Modules (DDPM), a Command and Control System (CCS) and a Portal Management System (PMS). This architecture provides valuable insights into landslide early warning, landslide risk and vulnerability analyses, and critical infrastructure damage assessments. Finally, internet-based communications are used to support landslide disaster modelling, monitoring and management.