Thomas Ertl

Bio: Thomas Ertl is an academic researcher from University of Natural Resources and Life Sciences, Vienna. The author has contributed to research in topics: Wastewater & Heat recovery ventilation. The author has an hindex of 11, co-authored 42 publications receiving 420 citations. Previous affiliations of Thomas Ertl include Life Sciences Institute & University of Agricultural Sciences, Dharwad.

The consistency of visual sewer inspection data

Abstract: In common with most infrastructure systems, sewers are often inspected visually. Currently, the results from these inspections inform decisions for significant investments regarding sewer rehabilitation or replacement. In practice, the quality of the data and its analysis are not questioned although psychological research indicates that, as a consequence of the use of subjective analysis of the collected images, errors are inevitable. This article assesses the quality of the analysis of visual sewer inspection data by analysing data reproducibility; three types of capabilities to subjectively assess data are distinguished: the recognition of defects, the description of defects according to a prescribed coding system and the interpretation of sewer inspection reports. The introduced uncertainty is studied using three types of data: inspector examination results of sewer inspection courses, data gathered in day-to-day practice, and the results of repetitive interpretation of the inspection results. After a thorough analysis of the data it can be concluded that for all cases visual sewer inspection data proved poorly reproducible. For the recognition of defects, it was found that the probability of a false positive is in the order of a few percent, the probability of a false negative is in the order of 25%.

Mapping thermal energy resource potentials from wastewater treatment plants.

Abstract: Wastewater heat recovery via heat exchangers and heat pumps constitutes an environmentally friendly, approved and economically competitive, but often underestimated technology. By introducing the spatial dimension in feasibility studies, the results of calculations change considerably. This paper presents a methodology to estimate thermal energy resource potentials of wastewater treatment plants taking spatial contexts into account. In close proximity to settlement areas, wastewater energy can ideally be applied for heating in mixed-function areas, which very likely have a continuous heat demand and allow for an increased amount of full-load hours compared to most single-use areas. For the Austrian case, it is demonstrated that the proposed methodology leads to feasible results and that the suggested technology might reduce up to 17% of the Austrian global warming potential of room heating. The method is transferrable to other countries as the input data and calculation formula are made available. A broad application of wastewater energy with regard to spatial structures and spatial development potentials can lead to (1) increasing energy efficiency by using a maximum of waste heat and (2) a significant reduction of (fossil) energy consumption which results in a considerable reduction of the global warming potential of the heat supply (GWP) if electricity from renewables is used for the operation of heat pumps.

Uncommon heavy metals, metalloids and their plant toxicity: a review

Abstract: Heavy metals still represent a group of dangerous pollutants, to which close attention is paid. Many heavy metals are essential as important constituents of pigments and enzymes, mainly zinc, nickel and copper. However, all metals, especially cadmium, lead, mercury and copper, are toxic at high concentration because of disrupting enzyme functions, replacing essential metals in pigments or producing reactive oxygen species. The toxicity of less common heavy metals and metalloids, such as thallium, arsenic, chromium, antimony, selenium and bismuth, has been investigated. Here, we review the phytotoxicity of thallium, chromium, antimony, selenium, bismuth, and other rare heavy metals and metalloids such as tellurium, germanium, gallium, scandium, gold, platinum group metals (palladium, platinum and rhodium), technetium, tungsten, uranium, thorium, and rare earth elements yttrium and lanthanum, and the 14 lanthanides cerium, dysprosium, erbium, europium, gadolinium, holmium, lutetium, neodymium, promethium, praseodymium, samarium, terbium, thulium and ytterbium.

Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?

Abstract: . In hydrological climate-change impact studies, regional climate models (RCMs) are commonly used to transfer large-scale global climate model (GCM) data to smaller scales and to provide more detailed regional information. Due to systematic and random model errors, however, RCM simulations often show considerable deviations from observations. This has led to the development of a number of correction approaches that rely on the assumption that RCM errors do not change over time. It is in principle not possible to test whether this underlying assumption of error stationarity is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well correction methods perform for conditions different from those used for calibration with the relatively simple differential split-sample test. For five Swedish catchments, precipitation and temperature simulations from 15 different RCMs driven by ERA40 (the 40 yr reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF)) were corrected with different commonly used bias correction methods. We then performed differential split-sample tests by dividing the data series into cold and warm respective dry and wet years. This enabled us to cross-evaluate the performance of different correction procedures under systematically varying climate conditions. The differential split-sample test identified major differences in the ability of the applied correction methods to reduce model errors and to cope with non-stationary biases. More advanced correction methods performed better, whereas large deviations remained for climate model simulations corrected with simpler approaches. Therefore, we question the use of simple correction methods such as the widely used delta-change approach and linear transformation for RCM-based climate-change impact studies. Instead, we recommend using higher-skill correction methods such as distribution mapping.