Part of book/report

VOC contributions from building materials, furniture, and user equipment in low emitting and modular classrooms

Yang, Aileen; Holøs, Sverre Bjørn; Hak, Claudia; Vogt, Matthias; Schmidbauer, Norbert; Fjellheim, Øystein

This study aimed to assess whether building materials,
furniture, and user equipment are sources of pollution
that would influence the need for ventilation. Between
2017-2020, measurements were taken in four regular
classrooms in a low emitting school and four modular
classrooms in a prefabricated school. Weekly passive
sampling of volatile organic compounds (VOCs) and
aldehydes were carried out in the classrooms under
the following four conditions: 1) emptied, 2) furnished,
3) with furniture and user equipment, and 4) during
normal use. For the first three conditions, the
classrooms were measured with either no ventilation
or "low" airflow rates. Total VOC (TVOC)
concentrations were up to ten times higher in the
unventilated classroom at the prefabricated school
compared to classrooms at the low emitting school
(<450 µg/m3 for conditions 1-2). Our results show the
importance of selecting low emitting building
materials and proper ventilation.

Toward a unified terminology of processing levels for low-cost air-quality sensors

Schneider, Philipp; Bartonova, Alena; Castell, Nuria; Dauge, Franck Rene; Gerboles, Michel; Hagler, Gayle S. W.; Huglin, Christoph; Jones, Roderic L.; Khan, Sean; Lewis, Alastair C.; Mijling, Bas; Müller, Michael; Penza, Michele; Spinelle, Laurent; Stacey, Brian; Vogt, Matthias; Wesseling, Joost; Williams, Ronald W.

Mapping urban air quality using low-cost sensor networks

Schneider, Philipp; Castell, Nuria; Bartonova, Alena

Cyclic and Linear Siloxanes in Indoor Environments: Occurrence and Human Exposure

Cincinelli, Alessandra; Martellini, Tania; Scopetani, Costanza; Guerranti, C.; Katsoyiannis, Athanasios A.

An interdisciplinary view on air pollution and its impact on health and welfare in the Nordic countries

Geels, C; Andersen, M. S.; Andersson, C.; Christensen, J. H.; Forsberg, B; Frohn, LM; Gislason, T.; Hänninen, O.; Im, U; Jensen, A.; Karvosenoja, N.; Kukkonen, J.; Sofiev, M; Karppinen, A; Navrud, Ståle; Lehtomäki, H.; Lopez-Aparicio, Susana; Nielsen, O. K.; Raashcou-Nielsen, O.; Hvidtfeldt, U.; Strandell, A.; Paunu, Ville-Veikko; Pedersen, CB; Timmermann, A.; Plejdrup, M. S.; Schwarze, Per Everhard; Segersson, D.; Seifert-Dähnn, Isabel; Sigsgaard, T.; Thorsteinsson, T; Moss, A.; Vennemo, Haakon; Brandt, J.

Estimation of pollutant releases into the atmosphere is an important problem in the environmental sciences. It is typically formalized as an inverse problem using a linear model that can explain observable quantities (e.g., concentrations or deposition values) as a product of the source-receptor sensitivity (SRS) matrix obtained from an atmospheric transport model multiplied by the unknown source-term vector. Since this problem is typically ill-posed, current state-of-the-art methods are based on regularization of the problem and solution of a formulated optimization problem. This procedure depends on manual settings of uncertainties that are often very poorly quantified, effectively making them tuning parameters. We formulate a probabilistic model, that has the same maximum likelihood solution as the conventional method using pre-specified uncertainties. Replacement of the maximum likelihood solution by full Bayesian estimation also allows estimation of all tuning parameters from the measurements. The estimation procedure is based on the variational Bayes approximation which is evaluated by an iterative algorithm. The resulting method is thus very similar to the conventional approach, but with the possibility to also estimate all tuning parameters from the observations. The proposed algorithm is tested and compared with the standard methods on data from the European Tracer Experiment (ETEX) where advantages of the new method are demonstrated. A MATLAB implementation of the proposed algorithm is available for download.