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  • Petschick, Lara  (2)
  • Stehle, Sebastian  (2)
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  • 1
    In: Data, MDPI AG, Vol. 4, No. 4 ( 2019-12-14), p. 150-
    Abstract: Regulatory threshold levels (RTL) represent robust benchmarks for assessing risks of pesticides, e.g., in surface waters. However, comprehensive scientific risk evaluations comparing RTL to measured environmental concentrations (MEC) of pesticides in surface waters were yet restricted to a low number of pesticides, as RTL are only available after extensive review of regulatory documents. Thus, the aim of the present study was to model RTL equivalents (RTLe) for aquatic organisms from publicly accessible ecotoxicological effect databases. We developed a model that applies validity criteria in accordance with official US EPA review guidelines and validated the model against a set of manually retrieved RTL (n = 49). Model application yielded 1283 RTLe (n = 676 for pesticides, plus 607 additional RTLe for other use types). In a case study, the usability of RTLe was demonstrated for a set of 27 insecticides by comparing RTLe and RTL exceedance rates for 3001 MEC from US surface waters. The provided dataset enables thorough risk assessments of surface water exposure data for a comprehensive number of substances. Especially regions without established pesticide regulations may benefit from this dataset by using it as a baseline information for pesticide risk assessment and for the identification of priority substances or potential high-risk regions.
    Type of Medium: Online Resource
    ISSN: 2306-5729
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2856531-9
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  • 2
    Online Resource
    Online Resource
    MDPI AG ; 2019
    In:  Data Vol. 4, No. 1 ( 2019-02-22), p. 34-
    In: Data, MDPI AG, Vol. 4, No. 1 ( 2019-02-22), p. 34-
    Abstract: Assessing the impact of chemicals on the environment and addressing subsequent issues are two central challenges to their safe use. Environmental data are continuously expanding, requiring flexible, scalable, and extendable data management solutions that can harmonize multiple data sources with potentially differing nomenclatures or levels of specificity. Here, we present the methodological steps taken to construct a rule-based labeled property graph database, the “Meta-analysis of the Global Impact of Chemicals” (MAGIC) graph, for potential environmental impact chemicals (PEIC) and its subsequent application harmonizing multiple large-scale databases. The resulting data encompass 16,739 unique PEICs attributed to their corresponding chemical class, stereo-chemical information, valid synonyms, use types, unique identifiers (e.g., Chemical Abstract Service registry number CAS RN), and others. These data provide researchers with additional chemical information for a large amount of PEICs and can also be publicly accessed using a web interface. Our analysis has shown that data harmonization can increase up to 98% when using the MAGIC graph approach compared to relational data systems for datasets with different nomenclatures. The graph database system and its data appear more suitable for large-scale analysis where traditional (i.e., relational) data systems are reaching conceptional limitations.
    Type of Medium: Online Resource
    ISSN: 2306-5729
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2856531-9
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
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