Influence of indoor transport and mixing time scales on the performance of sensor systems for characterizing contaminant releases

Optimizing real-time sensor systems to detect and identify relevant characteristics of an indoor contaminant event is a challenging task. The interpretation of incoming sensor data is confounded by uncertainties in building operation, in the forces driving contaminant transport, and in the physical parameters governing transport. In addition, simulation tools used by the sensor interpretation algorithm introduce modeling uncertainties. This paper explores how the time scales inherent in contaminant transport influence the information that can be extracted from real-time sensor data. In particular, we identify three time scales (within room mixing, room-to-room transport, and removal from the building) and study how they affect the ability of a Bayesian Monte Carlo (BMC) sensor interpretation algorithm to identify the release location and release mass from a set of experimental data, recorded in a multi-floor building. The research shows that some limitations in the BMC approach do not depend on details of the models or the algorithmic implementation, but rather on the physics of contaminant transport. These inherent constraints have implications for the design of sensor systems.     

An integrated methodology for estimation of forest fire-loss using geospatial information

These days, wildfires are prevalent in almost all areas of the world. Researchers have been actively analyzing wildfire damage using a variety of satellite images and geospatial datasets. This paper presents a method for detailed estimation of wildfire losses using various geospatial datasets and an actual case of wildfire at Kang-Won-Do, Republic of Korea in 2005. A set of infrared (IR) aerial images acquired after the wildfire were used to visually delineate the damaged regions, and information on forest type, diameter class, age class, and canopy density within the damaged regions was retrieved from GIS layers of the Korean national forest inventory. Approximate tree heights were computed from airborne LIDAR and verified by ground LIDAR datasets. The corresponding stand volumes were computed using tree volume equations (TVE). The proposed algorithm can efficiently estimate fire loss using the geospatial information; in the present case, the total fire loss was estimated as $5.9 million, which is a more accurate estimate than $4.5 million based on conventional approach. The proposed method can be claimed as a powerful alternative for estimating damage caused by wildfires, because the aerial image interpretation can delineate and analyze damaged regions in a comprehensive and consistent manner; moreover, LIDAR datasets and national forest inventory data can significantly reduce field work.     

Organic matter source discrimination by humic acid characterization: Synchronous scan fluorescence spectroscopy and Ferrate(VI)

In this study, seven soil and sedimentary humic acid samples were analyzed by synchronous scan fluorescence (SSF) spectroscopy. The spectra of these humic acids were compared to each other and characterized, based on three major SSF peaks centered at approximately 281, 367 and 470 nm. Intensity ratios were calculated based on these peaks that were used to numerically assist in source discrimination. All humic acid samples were then reacted with Ferrate(VI) and were again analyzed with SSF. Upon the addition of Ferrate(VI) SSF spectra were obtained which more readily differentiated humic acid source. This method will assist geochemists and water management districts in tracing sources of organic matter to receiving water bodies and may aid in the elucidation of the chemical nature of humic acids.     

Aquatic arsenic: Toxicity,speciation, transformations,and remediation

This paper reviews the current knowledge on the toxicity, speciation and biogeochemistry of arsenic in aquatic environmental systems. The toxicity of arsenic is highly dependent on the chemical speciation. The effects of pH, E_h, adsorbing surfaces, biological mediation, organic matter, and key inorganic substances such as sulfide and phosphate combine in a complex and interwoven dynamic fashion to produce unique assemblages of arsenic species. The number of different arsenic species found in environmental samples and an understanding of the transformations between arsenic species has increased over the past few decades as a result of new and refined analytical methods. Changes in arsenic speciation and in total arsenic content of foods upon processing have suggested possible risks associated with processed and unprocessed food. Arsenic removal from water using adsorbents, chemical oxidation, photolysis and photocatalytic oxidation techniques is also reviewed.     

Development finance in the 1990s for Latin American resource exporting countries

As a result of the debt crisis, per capita income in Latin America in 1990 was 10% below its 1981 level. Many resource exporting countries in the region have undertaken wide and deep reforms in order, among other reasons, to attract and retain long-term capital to fuel growth prospects. One newly proposed long-term instrument for development finance and risk management is the commodity bond. Given the sovereign risk component, the costly premiums that must be paid to insure it and more efficient alternative instruments for both issuers and investors, commodity bonds are unlikely to generate the required long-term capital needed to enhance growth prospects. With the continuing globalization of the goods, factors and currency markets, long-term capital can be attracted and maintained in both developed and developing countries by an attractive investment environment characterized by a competitive microeconomy, a stable macroeconomy, strong global linkages, and a serious programme to improve physical and social infrastructure.     

Chile's resource sectors

Chilean economic reforms, introduced in the 1970s and early 1980s, discarded decades of state intervention in the economy, but were accompanied by considerable economic pain and social stress from domestic and international sources. In the 1990s, Chile, supported by a competitive micro-economy, a stable macro-economy, strong linkages with the world economy and the return to democratic government, is exploiting its world-class resource sectors to propel economic growth in order to address social problems. However, the prospects for raising living standards in Chile and other resource-exporting developing countries are critically dependent on a favourable external environment.     

Rapidly locating and characterizing pollutant releases in buildings

Releases of airborne contaminants in or near a building can lead to significant human exposures unless prompt response measures are taken. However, possible responses can include conflicting strategies, such as shutting the ventilation system off versus running it in a purge mode or having occupants evacuate versus sheltering in place. The proper choice depends in part on knowing the source locations, the amounts released, and the likely future dispersion routes of the pollutants. We present an approach that estimates this information in real time. It applies Bayesian statistics to interpret measurements of airborne pollutant concentrations from multiple sensors placed in the building and computes best estimates and uncertainties of the release conditions. The algorithm is fast, capable of continuously updating the estimates as measurements stream in from sensors. We demonstrate the approach using a hypothetical pollutant release in a five-room building. Unknowns to the interpretation algorithm include location, duration, and strength of the source, and some building and weather conditions. Two sensor sampling plans and three levels of data quality are examined. Data interpretation in all examples is rapid; however, locating and characterizing the source with high probability depends on the amount and quality of data and the sampling plan.     

Monitoring and modeling of disinfection by-products (DBPs)

In the United States, the newly promulgateddisinfectant/disinfection by-product (D/DBP) regulationsforce water treatment utilities to be more concerned withfinished and distributed water qualities. In this study,monitoring of DBP formation was conducted from three Frenchwater treatment plants trying to assess DBP variationsthrough time and space. Compared to the in-plant totaltrihalomethanes (TTHM) levels, TTHM levels in thedistribution system increased from less than 150% to morethan 300%. Significant variations for TTHM and bromate(BrO₃ ^-) levels throughout the seasons were alsoobserved; generally higher levels in the summer and lowerlevels in the winter. Combining chemical DBP models(empirical power functional models) and hydraulicsimulations, DBPs including TTHM and BrO₃ ^- weresuccessfully simulated from the full-scale monitoring data,indicating that empirical DBP model can be a potential toolto access DBP formation in actual plants. This study alsoprovides the protocols to assess DBP simulations in thewater treatment systems.     

Rapidly Locating and Characterizing Pollutant Releases in Buildings

Abstract

Releases of airborne contaminants in or near a building can lead to significant human exposures unless prompt response measures are taken. However, possible responses can include conflicting strategies, such as shutting the ventilation system off versus running it in a purge mode or having occupants evacuate versus sheltering in place. The proper choice depends in part on knowing the source locations, the amounts released, and the likely future dispersion routes of the pollutants. We present an approach that estimates this information in real time. It applies Bayesian statistics to interpret measurements of airborne pollutant concentrations from multiple sensors placed in the building and computes best estimates and uncertainties of the release conditions. The algorithm is fast, capable of continuously updating the estimates as measurements stream in from sensors. We demonstrate the approach using a hypothetical pollutant release in a five-room building. Unknowns to the interpretation algorithm include location, duration, and strength of the source, and some building and weather conditions. Two sensor sampling plans and three levels of data quality are examined. Data interpretation in all examples is rapid; however, locating and characterizing the source with high probability depends on the amount and quality of data and the sampling plan.