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As indicated in Chapter 3, there are a large number of potential sources of data now available for modelling purposes. These range from historical literature references for a few compounds to highly curated databases of hundreds of thousands of compounds, available via the internet. Before including any data in an in silico model, the question of data quality must be addressed. Although it is difficult to define the quality of data in absolute terms, it is possible to assess the suitability of data for a given purpose. There are many reasons for variability within data and the degree of error that is acceptable for one model may not be the same as for another. For example generating a global model intended to pre-screen large numbers of compounds does not require the same degree of accuracy as performing an individual risk assessment for a chemical of interest. In this chapter, sources of data variability and error will be discussed and formal methods to score data quality, such as use of the Klimisch criteria, will be described. Examples of data quality issues will be given for specific endpoints relating to both environmental and human health effects. Mathematical approaches (Dempster-Schafer theory and Bayesian networks) demonstrating how this information relating to confidence in the data can be incorporated into in silico models is also discussed.

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Identification of noise sources and their ranking is a crucial part of any noise abatement program. This is a particularly difficult task when a complex source, such as a seaport, is considered. COVID -19 epidemic has had a significant impact on environmental noise related to road, rail, air and ship traffic and provided a unique opportunity to observe immediate noise reduction. In order to identify the noise sources, whose reduction was most effective in reducing noise from the port area, this study compared and quantified noise emissions between the historical and epidemic periods. Environmental noise measurements from three noise monitoring stations at the port boundary were analysed. In addition, noise emissions from ship, road, rail and industry as well as meteorological data in the historical pre – COVID -19 (January 2018 - February 2020) and COVID-19 (April 2020) period were analysed in detail. The characteristics of the noise sources mentioned, geographical data and noise measurements were used to develop and validate a noise model of the port area, which was used to calculate noise contour maps. Our results show that the reduction in noise levels observed at all monitoring stations coincides with the reduced shipping traffic. The A weighted equivalent sound pressure levels in the day, evening and night periods were reduced by 2.2 dB to 5.7 dB compared to the long-term averages, and the area of the 55 dB day-evening-night noise contour was reduced by 23 %. Compared to the historical period, the number of people exposed to noise levels above 55 dB(A) in the day-evening-night period due to shipping and industrial activities was reduced by 20% in the COVID -19 period. Such results show that environmental noise generated by moored ships is a problem for port cities that should be regulated internationally. In addition, this paper provides precise guidance on noise emission characteristics, ship categorisation and the post-processing of long-term measurement data, taking into account wind conditions and undesired sound events, which can be applied to future research at other locations near shipping ports and used to prepare strategies for noise reduction in ports.

A large data set containing values for fish, algae and Daphnia toxicity for more than 2000 chemicals and mixtures was investigated. The data set was taken from the New Chemicals Data Base of the European Union [hosted by the European Chemicals Bureau, Joint Research Centre, European Commission (http://ecb.jrc.it)]. The data are submitted by industry, according to the requirements of EU Council Directive 67/548/EEC as amended for the seventh time by EU Council Directive 92/32/EEC. The toxicities of neutral chemicals, salts, metal complexes, as well as chemical mixtures were extracted. A baseline effect was demonstrated by chemicals known to act by a narcotic mechanism of action, i.e., a relationship was observed between the toxicity and the logarithm of the octanol-water partition coefficient (log P). However, the prediction of the toxicity of more reactive chemicals was found to require the use of additional descriptors.