A Thermodynamic Geography: Night-Time Satellite Imagery as a Proxy Measure of Emergy

Night-time satellite imagery enables the measurement, visualization, and mapping of energy consumption in an area. In this paper, an index of the “sum of lights” as observed by night-time satellite imagery within national boundaries is compared with the emergy of the nations. Emergy is a measure of the solar energy equivalent used, directly or indirectly, to support the processes that characterize the economic activity in a country. Emergy has renewable and non-renewable components. Our results show that the non-renewable component of national emergy use is positively correlated with night-time satellite imagery. This relationship can be used to produce emergy density maps which enable the incorporation of spatially explicit representations of emergy in geographic information systems. The region of Abruzzo (Italy) is used to demonstrate this relationship as a spatially disaggregate case.     

Modelling remediation scenarios in historical mining catchments

Local remediation measures, particularly those undertaken in historical mining areas, can often be ineffective or even deleterious because erosion and sedimentation processes operate at spatial scales beyond those typically used in point-source remediation. Based on realistic simulations of a hybrid landscape evolution model combined with stochastic rainfall generation, we demonstrate that similar remediation strategies may result in differing effects across three contrasting European catchments depending on their topographic and hydrologic regimes. Based on these results, we propose a conceptual model of catchment-scale remediation effectiveness based on three basic catchment characteristics: the degree of contaminant source coupling, the ratio of contaminated to non-contaminated sediment delivery, and the frequency of sediment transport events.     

U.S. National PM2.5 Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks

The U.S. Environmental Protection Agency (EPA) initiated the national PM_2.5 Chemical Speciation Monitoring Network (CSN) in 2000 to support evaluation of long-term trends and to better quantify the impact of sources on particulate matter (PM) concentrations in the size range below 2.5 μm aerodynamic diameter (PM_2.5; fine particles). The network peaked at more than 260 sites in 2005. In response to the 1999 Regional Haze Rule and the need to better understand the regional transport of PM, EPA also augmented the long-existing Interagency Monitoring of Protected Visual Environments (IMPROVE) visibility monitoring network in 2000, adding nearly 100 additional IMPROVE sites in rural Class 1 Areas across the country. Both networks measure the major chemical components of PM_2.5 using historically accepted filter-based methods. Components measured by both networks include major anions, carbonaceous material, and a series of trace elements. CSN also measures ammonium and other cations directly, whereas IMPROVE estimates ammonium assuming complete neutralization of the measured sulfate and nitrate. IMPROVE also measures chloride and nitrite. In general, the field and laboratory approaches used in the two networks are similar; however, there are numerous, often subtle differences in sampling and chemical analysis methods, shipping, and quality control practices. These could potentially affect merging the two data sets when used to understand better the impact of sources on PM concentrations and the regional nature and long-range transport of PM_2.5. This paper describes, for the first time in the peer-reviewed literature, these networks as they have existed since 2000, outlines differences in field and laboratory approaches, provides a summary of the analytical parameters that address data uncertainty, and summarizes major network changes since the inception of CSN.

ImplicationsTwo long-term chemical speciation particle monitoring networks have operated simultaneously in the United States since 2001, when the EPA began regular operations of its PM_2.5 Chemical Speciation Monitoring Network (IMPROVE began in 1988). These networks use similar field sampling and analytical methods, but there are numerous, often subtle differences in equipment and methodologies that can affect the results. This paper describes these networks since 2000 (inception of CSN) and their differences, and summarizes the analytical parameters that address data uncertainty, providing researchers and policymakers with background information they may need (e.g., for 2018 PM_2.5 designation and State Implementation Plan process; McCarthy, 2013) to assess results from each network and decide how these data sets can be mutually employed for enhanced analyses. Changes in CSN and IMPROVE that have occurred over the years also are described.     

Preventing corruption in humanitarian assistance: perceptions, gaps and challenges

Corruption is a threat to the purpose of humanitarian assistance. Until fairly recently, humanitarian assistance has not been considered an important arena in broader efforts aimed at curbing corruption, and corruption has not always been considered a particularly important concern for humanitarian assistance despite the obviously challenging nature of the context of humanitarian emergencies. Corruption, though, is a threat to humanitarian action because it can prevent assistance from getting to the people who most need it, and because it can potentially undermine public support for such assistance. This paper examines perceptions of corruption and its affects, documents best practices, and outlines gaps in understanding. It suggests recommendations for improving the capacity of humanitarian agencies to prevent and manage the risk of corruption. Agencies have taken steps to combat corruption and improve accountability--downwards and upwards--but scope remains for improvement and for greater sharing of learning and good practice.     

The Effects of Some Air Pollutants and Meteorological Conditions on Airborne Algae and Protozoa

The purpose of this investigation was to determine the effects of meteorological conditions and specific air pollutants on the viability of airborne algae and protozoa. Such investigations will be of interest to medical researchers because these organisms are the source of many allergies. The three air pollutants that were continuously measured and recorded were sulfur, hydrocarbons, and particulate maiter. During the experiment, 25 different species of algae and 19 species of protozoa were collected from the atmosphere and cultured at the Westinghouse Environmental Station Laboratory in Raleigh, North Carolina. The algae and protozoa were collected over a one-year period (Jan-Dec 1971) by using a sequential sampler that moved air through a membrane filter at the rate of 15 ft³/hr. Every two hours a new filter was sequentially moved in to replace the old one. The results indicated a relationship between wind speed, wind direction, temperature, dew-point, particulate matter, barometric pressure, and rainfall to the percent frequency of positive culture tubes and number of cells/ft³ of air. Further studies are necessary to determine the interrelationships between the physical and chemical character of various air masses and their effect on the survival of algae and protozoa.     

A Pilot Indoor-Outdoor Study of Organic Particulate Matter and Particulate Mutagenicity

Abstract

Many large metropolitan areas experience elevated concentrations of ground-level ozone pollution during the summertime “smog season”. Local environmental or health agencies often need to make daily air pollution forecasts for public advisories and for input into decisions regarding abatement measures and air quality management. Such forecasts are usually based on statistical relationships between weather conditions and ambient air pollution concentrations. Multivariate linear regression models have been widely used for this purpose, and well-specified regressions can provide reasonable results. However, pollution-weather relationships are typically complex and nonlinear—especially for ozone—properties that might be better captured by neural networks. This study investigates the potential for using neural networks to forecast ozone pollution, as compared to traditional regression models. Multiple regression models and neural networks are examined for a range of cities under different climate and ozone regimes, enabling a comparative study of the two approaches. Model comparison statistics indicate that neural network techniques are somewhat (but not dramatically) better than regression models for daily ozone prediction, and that all types of models are sensitive to different weather-ozone regimes and the role of persistence in aiding predictions.     

Particulate,Carbon Monoxide,and Acid Emission Factors for Residential Wood Burning Stoves

Emissions from residential wood burning stoves are of Increasing concern in many areas. This concern is due to the magnitude of the emissions and the toxic and chemical characteristics of the pollutants. Recent testing of standard and new technology woodstoves has provided data for developing a family of particulate and carbon monoxide emission factor curves. This testing has also provided data illustrating the acidity of woodstove emissions. The particulate and carbon monoxide curves relate the actual stove emissions to the stove size and operating parameters of burn rate, fuel loading, and fuel moisture. Curves relating stove types to the acidity of emissions have also been constructed.

Test data show actual emissions vary from 3 to 50 grams per kilogram for particles and from 50 to 300 grams per kilogram for carbon monoxide. Since woodstove emissions are the largest single category of particulate emissions in many areas, it Is essential that these emissions be quantified specifically for geographic regions, allowing meaningful impact analysis modeling to be accomplished. Emission factors for particles and carbon monoxide are presented from several stove sizes and burn rates.

The acidic nature of woodstove emissions has been clearly demonstrated. Tests indicate woodstove flue gas condensate solutions to be predominantly in the 2.8 to 4.2 pH range. Condensate solutions from conventional woodstoves exhibited the characteristic buffering capacity of carboxylic acids when titrations were performed with a strong base. The environmental impact of buffered acidic woodstove emissions is not currently well understood; however, it is possible with the data presented here to make semi-quantitative estimates of acid emissions from particulate and carbon monoxide emission factors and wood use inventories.     

Indoor/Outdoor Measurements of Volatile Organic Compounds in the Kanawha Valley of West Virginia

The Kanawha Valley region of West Virginia which is comprised of Charleston and surrounding communities Is the center of a heavily industrialized area known for its chemical manufacturing. As part of a larger study designed to investigate the Impact of the chemical industry on human exposures to volatile organic compounds (VOC), a study of the relationship between indoor and outdoor concentrations was conducted. Thirty-five homes were selected for monitoring from among volunteers; approximately ten in each of three distinct population-industry centers and four outside the Valley to act as controls. Monitoring was performed using passive, badge samplers with a three-week monitoring period. Two separate questionnaires were administered: one for characterization of the residence; and one to characterize source use during monitoring. Participants were also asked to keep a record of their activities with respect to in-home, outdoors and other Indoor environments. Analysis of the samplers was performed by solvent extraction followed by gas chromatography using a flame-ionization detector. Results suggest that indoor VOC concentrations are higher than outdoor concentrations. Additionally, certain ventilation-related parameters were identified that afforded some predictive power for indoor concentrations. No statistically significant differences between regions were identified.