Environmental odor studies are usually done using two approaches: nuisance impact assessment and source identification. The latter may be done using chemical analysis or sensory analysis. While sensory analyses offer many advantages, they also face the main obstacle: odor nature description still uses conventional methods based on subjective evocations as odor descriptors. This makes the sensory method ineffective especially when the expected outcome is the source identification in the context of an industrial accident. This work wants to fulfill this gap proposing to build an objective database including the odor nature description of selected potentially emitted compounds using a promising approach: the Langage des Nez® (LdN). Using definite odorous compounds as odor referents, this work provides the odor nature description of 44 compounds, reported as potential incidentally released chemical compounds in the industrial zone of Le Havre. The city of Le Havre, France, was chosen as a model due to a history of odorous emissions of industrial origins. A trained panel described the odor of each compound using up to three referents of the LdN referents collection and attributed a score to each referent. A data analysis method was developed based on the frequency of citation of the referents and the attributed scores allowing the categorization of each compound in three types of consensus categories. The data analysis results showed that around 80% of compounds were described with a good consensus, showing the LdN as a well-adapted lexicon. This study does not point to any correlation between the chemical structures of the compounds of interest and their relative referents. When compared to conventional methods, LdN revealed a more objective and precise approach. The proposed experimental method and the results provided in this work offer the first insight for time-efficient approaches to objectively describe environmental odors, especially potentially emitted odors during incidents. This work may be supplemented by abatement and mixture effect investigations for a complete understanding of odor dispersion.

通常使用两种方法进行环境气味研究：有害影响评估和来源识别。后者可以使用化学分析或感官分析来完成。尽管感官分析具有许多优点，但它们也面临主要障碍：气味性质描述仍使用基于主观唤起的传统方法作为气味描述符。这使得感觉方法无效，尤其是当预期结果是工业事故中的源识别时。这项工作希望弥补这一差距，并建议使用一种有前途的方法：Langage desNez®（LdN）建立一个客观的数据库，其中包括对某些潜在排放化合物的气味性质的描述。使用确定的气味化合物作为气味代用品，这项工作提供了44种化合物的气味性质描述，据报道是在勒阿弗尔工业区潜在释放的化学化合物。由于历史悠久的工业废气排放，法国勒阿弗尔市被选为典范。一个受过训练的小组使用多达三个LdN指代物参考物描述了每种化合物的气味，并将得分归因于每个参考物。根据被引物的引用频率和归因分数，开发了一种数据分析方法，可以将每种化合物归类为三种共有类别。数据分析结果表明，描述了约80％的化合物具有良好的一致性，表明LdN是一种适应性很好的词典。这项研究没有指出目标化合物的化学结构与其相对参照物之间的任何相关性。与传统方法相比，LdN揭示了一种更为客观和精确的方法。拟议的实验方法和这项工作中提供的结果为及时有效地客观描述环境气味，尤其是事故期间可能散发的气味的方法提供了第一见解。可以通过消减和混合效应研究来补充这项工作，以全面了解气味的扩散。