Statistical modelling of district-level residential electricity use in NSW, Australia

Electricity network investment and asset management require accurate estimation of future demand in energy consumption within specified service areas. For this purpose, simple models are typically developed to predict future trends in electricity consumption using various methods and assumptions. This paper presents a statistical model to predict electricity consumption in the residential sector at the Census Collection District (CCD) level over the state of New South Wales, Australia, based on spatial building and household characteristics. Residential household demographic and building data from the Australian Bureau of Statistics (ABS) and actual electricity consumption data from electricity companies are merged for 74 % of the 12,000 CCDs in the state. Eighty percent of the merged dataset is randomly set aside to establish the model using regression analysis, and the remaining 20 % is used to independently test the accuracy of model prediction against actual consumption. In 90 % of the cases, the predicted consumption is shown to be within 5 kWh per dwelling per day from actual values, with an overall state accuracy of ?1.15 %. Given a future scenario with a shift in climate zone and a growth in population, the model is used to identify the geographical or service areas that are most likely to have increased electricity consumption. Such geographical representation can be of great benefit when assessing alternatives to the centralised generation of energy; having such a model gives a quantifiable method to selecting the ‘most’ appropriate system when a review or upgrade of the network infrastructure is required.     

Standard inocula preparations reduce the bacterial diversity and reliability of regulatory biodegradation tests

OECD ready biodegradability tests have been central to understanding the biodegradation of chemicals from a regulatory perspective for many decades. They are not fit for contemporary prioritisation of chemicals based on persistence, however, due to the low concentration of inocula used, short duration and high variability between tests. Two OECD standard inoculum pretreatment methods (settlement and filtration) were investigated to observe their effect on the probability of biodegradation and associated changes in bacterial community structure and diversity of inocula sourced from the activated sludge process of wastewater treatment plants. Both settlement and filtration were shown to dramatically and significantly reduce the probability and increase the variability of biodegradation of 4-nitrophenol compared to the use of unprocessed inocula. These differences were associated with a significant hundred-fold reduction in cell numbers and solids content and a significant shift in bacterial community structure that was sometimes accompanied by significant reductions in detectable operational taxonomic unit richness and evenness. The natural variation (between different environments) and variation due to differential selection of bacterial communities (by different pretreatment methods) is offered as an explanation for the historical high variability in standard OECD ready biodegradability tests.     

Bayesian importance parameter modeling of misaligned predictors: soil metal measures related to residential history and intellectual disability in children

In this paper, we propose a novel spatial importance parameter hierarchical logistic regression modeling approach that includes measurement error from misalignment. We apply this model to study the relationship between the estimated concentration of soil metals at the residence of mothers and the development of intellectual disability (ID) in their children. The data consist of monthly computerized claims data about the prenatal experience of pregnant women living in nine areas within South Carolina and insured by Medicaid during January 1, 1996 and December 31, 2001 and the outcome of ID in their children during early childhood. We excluded mother-child pairs if the mother moved to an unknown location during pregnancy. We identified an association of the ID outcome with arsenic (As) and mercury (Hg) concentration in soil during pregnancy, controlling for infant sex, maternal race, mother’s age, and gestational weeks at delivery. There is some indication that Hg has a slightly higher importance in the third and fourth months of pregnancy, while As has a more uniform effect over all the months with a suggestion of a slight increase in risk in later months.     

Polyaromatic hydrocarbon exposure: an ecological impact ambiguity

Polyaromatic hydrocarbons (PAHs) represent a fraction of petroleum hydrocarbons and are currently one of the foremost sources of generating energy in today’s contemporary society. However, evidence highlighted in this review show that PAH pollution, as a result of oil spills, hazardous PAH-contaminated working environments and technologies which do not efficiently utilise fuels, as well as natural sources of emissions (e.g. forest fires) may have significant health implications for all taxa. The extent of damage to organisms from PAH exposure is dependent on numerous factors including degree and type of PAH exposure, nature of the environment contaminated (i.e. terrestrial or aquatic), the ability of an organism to relocate to pristine environments, type and sensitivity of organism to specific hydrocarbon fractions and ability of the organism to metabolise different PAH fractions. The review highlights the fact that studies on the potential damage of PAHs should be carried out using mixtures of hydrocarbons as opposed to individual hydrocarbon fractions due to the scarcity of individual fractions being a sole contaminant. Furthermore, potential damage of PAH-contaminated sites should be assessed using an entire ecological impact outlook of the affected area.     

3-D Flow Modeling of a Hazardous Waste Incinerator

A three-dimensional flow model of a hazardous waste incinerator kiln and vertical secondary combustion chamber arrangement was constructed to evaluate critical system parameters. The chamber wall configuration, location and arrangement of burners, and the positioning of the outlet duct were examined to determine the critical secondary combustion chamber gas residence time and mixing of the combustion flows. The scale model consisted of the rotary kiln as a primary combustion chamber, the secondary combustion chamber, two burners, and the exhaust ducting. Flue gas velocities in the model inlets and outlet were maintained to provide a Reynolds numbers equal to the full size unit. Patterns of smoke which were injected into the model inlets were viewed to evaluate flow mixing. Slow motion playback of video tape was used to determine the minimum residence time of flow in the high temperature combustion zone. The results of the model study were used to complete the engineering of a waste incineration system.     

An Empirical Model to Predict Styrene Emissions from Fiber-Reinforced Plastics Fabrication Processes

ABSTRACT

Styrene is a designated hazardous air pollutant, per the 1990 Clean Air Act Amendments. It is also a tropospheric ozone precursor. Fiber-reinforced plastics (FRP) fabrication is the primary source of anthropogenic styrene emissions in the United States. This paper describes an empirical model designed to predict styrene emission factors for selected FRP fabrication processes. The model highlights 10 relevant parameters impacting styrene emission factors for FRP processes, and helps identify future areas of FRP pollution prevention (P2) research. In most cases, the number of these parameters with greatest impact on styrene emission factors can be limited to four or five. Seven different emission studies were evaluated and used as model inputs.