Alternative modelling options

Applying this exact approach in a different setting will require a long-term mobile measure-ment campaign. In itself, this is a huge restriction, but the benefits of the mobile noise-BC biking are large as such. The fact that this approach works is an incentive to perform exten-sive mobile noise-BC measurement campaigns. City wide noise mapping results both in the required underlying dataset to build the gam models and has the potential to improve the indoor model by including the local traffic dynamics (see 4.6.6.1).

An alternative approach, replacing the underlying gam model with a direct model for local traffic assessment at the facade to indoor concentration is another option (see future work, 6.1.1). In Section 4.7, a pilot case will be evaluated. The noise levels at a high exposed facade are used to predict the outdoor BC exposure at the facade. More extended campaigns, cov-ering multiple dwellings with various traffic situations over longer periods, including outdoor and indoor measurements for multiple air pollutants can result in an alternative direct mod-el, including better knowledge of the spatial and temporal variability of the I/O ratios.

4.6.6.6 Limitations

The limitations can be split into three sets: fundamental unknowns, unavailable data and limitations of the validation data. The main fundamental limitation of the indoor model is the origin of the Black Carbon data to convert the noise map into a local contribution to BC at the facade of the dwellings: the bicycle data. The 1/r² distance to source relation is similar for noise and air pollution but is only valid in its most basic form (see 4.6.2). The actual

dis-tance to source relation from roadside (bicyclist) to dwelling facade (outdoor at dwelling) could not be verified and identifies the second fundamental limitation. The third fundamen-tal limitation is the extrapolation of the rush hour bicycle model to a diurnal pattern based on a standard diurnal pattern, this is also not proven.

The model suffers also from unavailable data. No dwelling specific information is included in the model, not for the traffic and not for the ventilation properties of the dwelling. The most important missing data is however a background measurement location not including a diurnal pattern. This is identified as the main data limitation.

Some of the limitations link back to the external validation set. The sample of the 31 households in the external validation set. None of the dwellings was located close the major roads (<100m) or highways (<300m) where the major road or highway is expected to have a significant impact. Extending the validation data to these spatial configurations is crucial. It is also not expected that the presented model will work in these extreme settings without modifications. Noise measurements can however distinguish between continuous noise sources and local traffic events. Splitting the BCBAloc component into actual local traffic in the local street and midrange influences from major sources at medium distance is a poten-tial future development.

4.6.7 Conclusions

The indoor exposure estimation is based on a long-term bicycle exposure experiment. The in-traffic measurements are mapped to an LDEN noise map and used to estimate a BC concen-tration with a highly improved spatial resolution. The noise map acts in this land-use regres-sion like approach (LUR) as a distance weighted traffic assessment sensitive to the combina-tion of exposure to local and more distant high density roads. A full continuous traffic evalu-ation with extreme distance to source effects emerges. The background adjusted approach is able to disassemble the exposure in two components. A background component is express-ing the long-range and long-term evolution of the BC concentration, strongly determined by the meteorological effects. The local traffic component is resolved in the noise map based gam model. A seasonal adjustment of the I/O ratio is fitted and validated with external data, resolving all remaining temperature depending influence. In the provided solution, the back-ground component includes a diurnal pattern. The components are therefore not as inde-pendent as desired. Unfortunately no alternative location for the background BC concentra-tion with lower diurnal pattern was available. This limitaconcentra-tion in the input data reduces the quality of this part of the exercise significantly. Despite all these restrictions, the model reaches a Spearman’s correlation of 0.65 on the individual activities and a slope of 0.78.

This section is a numerical experiment based on available data only. It nevertheless illus-trates the potential of the background adjusted approach. Dedicated measurements will be necessary to provide scientific validation.

4.6.8 References

Allen R.W., Adar S.D., Avol E., Cohen M., Curl C.L., et al. 2012. Modeling the Residential Infiltration of Out-door PM2.5 in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Environmental Health Perspectives, 120, 824-830.

Beelen, R., Hoek, G., Vienneau, D., Eeftens, M., Dimakopoulou, K., Pedeli, X., ... & de Hoogh, K. (2013).

Development of NO 2 and NO x land use regression models for estimating air pollution exposure in 36 study areas in Europe–the ESCAPE project. Atmospheric Environment, 72, 10-23.

Can A., Van Renterghem T., Rademaker M., Dauwe S., Thomas P., et al. 2011. Sampling approaches to predict urban street noise levels using fixed and temporary microphones. Journal of Environmental Monitoring, 13, 2710-2719.

Can A., Dekoninck L. & Botteldooren D. 2014. Measurement network for urban noise assessment: Com-parison of mobile measurements and spatial interpolation approaches. Applied Acoustics, 83, 32-39.

Chen C. & Zhao B. 2011. Review of relationship between indoor and outdoor particles: I/O ratio, infiltra-tion factor and penetrainfiltra-tion factor. Atmospheric Environment, 45, 275-288.

Dauwe S., Van Renterghem T., Botteldooren D. & Dhoedt B. 2012. Multiagent-Based Data Fusion in Envi-ronmental Monitoring Networks. International Journal of Distributed Sensor Networks.

Dons E., Panis L.I., Van Poppel M., Theunis J., Willems H., et al. 2011. Impact of time-activity patterns on personal exposure to black carbon. Atmospheric Environment, 45, 3594-3602.

Dons, E., 2013. Air pollution exposure assessment through personal monitoring and activity-based model-ing. PhD Thesis. Hasselt University, Diepenbeek, p. 302.

EEA Technical report No 18/2013, Status of BC monitoring in ambient air in Europe, ISSN 1725-2237 Hanninen O., Hoek G., Mallone S., Chellini E., Katsouyanni K., et al. 2011. Seasonal patterns of outdoor PM

infiltration into indoor environments: review and meta-analysis of available studies from different climatological zones in Europe. Air Quality Atmosphere and Health, 4, 221-233.

Hoek G., Kos G., Harrison R.M., de Hartog J., Meliefste K., et al. 2008. Indoor-outdoor relationships of particle number and mass in four European cities. Atmospheric Environment, 42.

Meier, R., Eeftens, M., Phuleria, H. C., Ineichen, A., Corradi, E., Davey, M., ... & Künzli, N. (2015). Differ-ences in indoor versus outdoor concentrations of ultrafine particles, PM2. 5, PMabsorbance and NO2 in Swiss homes. Journal of Exposure Science and Environmental Epidemiology.

Sajani, S. Z., Ricciardelli, I., Trentini, A., Bacco, D., Maccone, C., Castellazzi, S., ... & Harrison, R. M. (2015).

Spatial and indoor/outdoor gradients in urban concentrations of ultrafine particles and PM 2.5 mass and chemical components. Atmospheric Environment, 103, 307-320.

Van Renterghem T., Thomas P., Dominguez F., Dauwe S., Touhafi A., et al. 2011. On the ability of consum-er electronics microphones for environmental noise monitoring. Journal of Environmental Monito-ring, 13, 544-552.

Wei W., Botteldooren D., Van Renterghem T., Hornikx M., Forssen J., et al. 2014. Urban Background Noise Mapping: The General Model. Acta Acustica United with Acustica, 100, 1098-1111.