Acousto-Ultrasonic Assessment of Internal Decay in Glulam Beams

An acousto-ultrasonic (AU) through-transmission technique was evaluated for assessing brown-rot decay in Douglas-fir glulam beams that had been removed from service. The effect of decay on different AU signal features was compared to that from normal variations in wood, such as growth ring angle, knots, and moisture gradient. The analysis was based on measurement of velocity, attenuation, shape, and frequency content of the received signals. All of the studied signal features were correlated with the degree of decay; however, they were affected by natural characteristics of wood. Attenuation and signal shape were more affected by the growth ring angle variations and knots than were velocity and frequency features. The effect of knots depended upon size, type, orientation, and distance from the surface. A steep moisture gradient obscured the detection of small degrees of decay, with the greatest effect on signal shape and frequency parameters. This study suggests that multiple signal feature analysis can be used to distinguish decay from certain types of natural wood characteristics such as growth ring angle variations and knots

Electrical impedance and image analysis methods in detecting and measuring Scots pine heartwood from a log end during tree harvesting

Scots pine (Pinus sylvestris L.) heartwood is naturally durable wood material which has not been fully utilized in the wood industry. Currently, there are no practical measurement methods for detecting and measuring heartwood in a tree harvesting. The objective of this study was to evaluate the applicability of an electrical impedance spectroscopy and an image analysis of a log end face for pine heartwood measurements from the harvesting perspective. Both methods were tested with a fresh wood material which was collected during the harvesting operations. The results indicate that both methods have potential to measure the heartwood from processed stems with an average heartwood diameter error being less than two centimeters for each method. However, the image analysis of the log end face is only appropriate when visible contrast between the heartwood and a sapwood exists. Our findings indicate that the studied heartwood detection methods show great potential in measuring the heartwood of the stem in the harvesting phase which would ideally benefit later links in wood value chains.Peer reviewe

Rapid changes in biomass burning aerosols by atmospheric oxidation

Peer reviewe

Spatial, temporal and source contribution assessments of black carbon over the northern interior of South Africa

After carbon dioxide (CO2) aerosol black carbon (BC) is considered to be the second most important contributor to global warming. This paper presents equivalent black carbon (eBC) (derived from an optical absorption method) data collected from three sites in the interior of South Africa where continuous measurements were conducted, i.e. Elandsfontein, Welgegund and Marikana, as well elemental carbon (EC) (determined by evolved carbon method) data at five sites where samples were collected once a month on a filter and analysed offline, i.e. Louis Trichardt, Skukuza, Vaal Triangle, Amersfoort and Botsalano. Analyses of eBC and EC spatial mass concentration patterns across the eight sites indicate that the mass concentrations in the South African interior are in general higher than what has been reported for the developed world and that different sources are likely to influence different sites. The mean eBC or EC mass concentrations for the background sites (Welgegund, Louis Trichardt, Skukuza, Botsalano) and sites influenced by industrial activities and/or nearby settlements (Elandsfontein, Marikana, Vaal Triangle and Amersfoort) ranged between 0.7 and 1.1, and 1.3 and 1.4 ae gm 3, respectively. Similar seasonal patterns were observed at all three sites where continuous measurement data were collected (Elandsfontein, Marikana and Welgegund), with the highest eBC mass concentrations measured from June to October, indicating contributions from household combustion in the cold winter months (June-August), as well as savannah and grassland fires during the dry season (May to mid-October). Diurnal patterns of eBC at Elandsfontein, Marikana and Welgegund indicated maximum concentrations in the early mornings and late evenings, and minima during daytime. From the patterns it could be deduced that for Marikana and Welgegund, household combustion, as well as savannah and grassland fires, were the most significant sources, respectively. Possible contributing sources were explored in greater detail for Elandsfontein, with five main sources being identified as coal-fired power stations, pyrometallurgical smelters, traffic, household combustion, as well as savannah and grassland fires. Industries on the Mpumalanga Highveld are often blamed for all forms of pollution, due to the NO2 hotspot over this area that is attributed to NOx emissions from industries and vehicle emissions from the Johannesburg-Pretoria megacity. However, a comparison of source strengths indicated that household combustion as well as savannah and grassland fires were the most significant sources of eBC, par-ticularly during winter and spring months, while coal-fired power stations, pyrometallurgical smelters and traffic contribute to eBC mass concentration levels year round.Peer reviewe

Differences in aerosol absorption Ångström exponents between correction algorithms for particle soot absorption photometer measured on South African Highveld

Peer reviewe

Classification of Wood Chips Using Electrical Impedance Spectroscopy and Machine Learning

Wood chips are extensively utilised as raw material for the pulp and bio-fuel industry, and advanced material analyses may improve the processes in utilizing these products. Electrical impedance spectroscopy (EIS) combined with machine learning was used in order to analyse heartwood content of pine chips and bark content of birch chips. A novel electrode system integrated in a sampling container was developed for the testing using frequency range 42 Hz–5 MHz. Three electrode pairs were used to measure the samples in x-, y- and z-direction. Three machine learning methods were used: K-nearest neighbor (KNN), decision tree (DT) and support vector machines (SVM). The heartwood content of pine chips and bark content of birch chips were classified with an accuracy of 91% using EIS from pure materials combined with a k-nearest neighbour classifier. When using mixed materials and multiple classes, 73% correct classification for pine heartwood content (four groups) and 64% for birch bark content (five groups) were achieved