Spatial and temporal variations of currently used pesticides (CUPs) concentrations in ambient air in Wallonia, Belgium.

Pesticides are among the most widely used chemicals thus contributing to a global contamination of the environment. Studies in North America and Europe have reported ambient air concentrations of Currently Used Pesticides in rural and urban locations as well as in remote areas. Monitoring pesticides in air is required for a better understanding of human exposure through inhalation and to assess potential health effects related to this exposure pathway. In this study, 46 pesticides were analyzed in ambient air in sampling stations distributed over Wallonia during a year, from May 2015 to May 2016. Different typologies were defined for sampling sites (remote areas, urban sites, agricultural sites, livestock area, and sites with other professional uses). Ambient air was sampled for 14 days with an active air sampler at a flow rate of 4 m³/h. Quartz filters and PUF/XAD-2/PUF cartridges were used to sample both gas and particulate phase pesticides. On the 46 pesticides studied, 6 insecticides, 18 herbicides and 18 fungicides were detected. Herbicides were measured in 68.3% of samples throughout the year, whereas fungicides and insecticides were measured in 62.6% and 13.2% of the samples, respectively. The highest mean concentrations for all pesticides were measured in spring-summer, whereas few pesticides were measured at low concentrations in winter. Six pesticides were measured in the remote sampling station at lower concentrations than in all other sites highlighting volatility of these pesticides. The highest number of different pesticides and the highest concentrations were measured in agricultural stations, where uses of plant protection products are higher. Finally, less volatile pesticides were only detected near application areas and at low concentrations. Together, these results provide better insight on the spatial and temporal variations of pesticides concentrations in ambient air, which were related to pesticides uses as well to atmospheric volatility and persistence.

A detailed workflow to develop QIIME2-formatted reference databases for taxonomic analysis of DNA metabarcoding data.

BACKGROUND: The DNA metabarcoding approach has become one of the most used techniques to study the taxa composition of various sample types. To deal with the high amount of data generated by the high-throughput sequencing process, a bioinformatics workflow is required and the QIIME2 platform has emerged as one of the most reliable and commonly used. However, only some pre-formatted reference databases dedicated to a few barcode sequences are available to assign taxonomy. If users want to develop a new custom reference database, several bottlenecks still need to be addressed and a detailed procedure explaining how to develop and format such a database is currently missing. In consequence, this work is aimed at presenting a detailed workflow explaining from start to finish how to develop such a curated reference database for any barcode sequence. RESULTS: We developed DB4Q2, a detailed workflow that allowed development of plant reference databases dedicated to ITS2 and rbcL, two commonly used barcode sequences in plant metabarcoding studies. This workflow addresses several of the main bottlenecks connected with the development of a curated reference database. The detailed and commented structure of DB4Q2 offers the possibility of developing reference databases even without extensive bioinformatics skills, and avoids 'black box' systems that are sometimes encountered. Some filtering steps have been included to discard presumably fungal and misidentified sequences. The flexible character of DB4Q2 allows several key sequence processing steps to be included or not, and downloading issues can be avoided. Benchmarking the databases developed using DB4Q2 revealed that they performed well compared to previously published reference datasets. CONCLUSION: This study presents DB4Q2, a detailed procedure to develop custom reference databases in order to carry out taxonomic analyses with QIIME2, but also with other bioinformatics platforms if desired. This work also provides ready-to-use plant ITS2 and rbcL databases for which the prediction accuracy has been assessed and compared to that of other published databases.