Investigating the discrepancy

The discrepancy is mainly expressed in the absolute predicted exposure value, not in the correlation in the total trip fit evaluations. In this section the potential origins of the discrep-ancy are evaluated. As mentioned before no control was available on the actual use and set-tings of the ventilation system. The people in PSC were however asked to avoid the in-vehicle circulation and set the ventilation to their normal operation. This unknown factor can influence the in-vehicle exposure strongly for individual trips but it is not expected to affect the measurements to the determined magnitude. Two new cars, driven by sales representa-tives, collected a large portion of the dataset. The bulk of their contribution was collected off-peak and large parts were collected on highways and major roads. Their measurements were collected in spring of 2013. This was a much colder and windy period compared to oth-er years. The potential influence of these atypical weathoth-er conditions woth-ere tested by build-ing the gam model without the two sales persons (persons 6 and 7), but the validation did not improve when removing these two persons (Appendix E2.3).

An extensive study by Fruin builds a model to predict the air flow rate based on age, mile-age, speed of the car and a manufacture specific adjustment (Fruin et al., 2011). The dis-crepancy between the participants in this participatory campaign can partially be related to the age and the vehicle of the participants. In the Appendix E2.4, a variant of the

BCR_LDENWH model is presented including the year of build of the vehicle of the partici-pant. This approach fits the individual differences between the participants with the age of the vehicle, attempting to isolate the influence of the vehicle from the participant specific trip behaviour. The model shows strong improvement of the deviance explained and trip fit quality. A vehicle age attribution is tested on the external participatory campaign. Vehicle age is in the EX_PSC only available in an indirect manner. For each household, the number of vehicles is known, fuel type and an annotation referring to the age of the car ‘before 2009’ is available. It is unknown which person drives which vehicle at any time in the campaign. Yet this data was attributed to the trips based on gender. The newest car is attributed to the man if two vehicles are available to the household. The adjusted model validation results in an increase for Q1, median, mean and Q3 of 32% to 38%, 49% to 50%, 68% to 74% and 75%

to 81% compared to the BCR_LDENWH validation. The correlation of the trip average expo-sure fit increased from 0.47 to 0.5 (Spearman) and is equal for Pearson’s correlation. Despite the poor quality of the vehicle information in the EX_PSC, the validation improves slightly.

This is an indication that age and/or quality of the ventilation system of the vehicle is part of the discrepancy in the validation. It does not explain the large discrepancy in the validation, only closes the gap with about 6%.

The two participatory campaigns were performed with three years difference in time. In 2009 more stringent EU-legislation (Euro 5) reduced particulate matter emission limits for diesel vehicles. The EU limits for PM exhaust is reduced from 0.025 g/km to 0.005 g/km be-tween Euro 4 and Euro 5. The new legislation was hardly influencing the fleet composition at the time of the external participatory campaign (2010-2011). In 2013 strong changes in the vehicle fleet emission can be expected due to the Euro5 legislation. In Appendix E3.1, a de-tailed estimate of the modified fleet is documented. Data on the fleet composition by envi-ronmental class is only available since 2011. An estimate of the evolution from 2010 to 2013 sets the minimum reduction of the vehicle fleet emission to 33%, not accounting the effect of more mileage performed by newer vehicles. A reduction of at least 40% of the PM emis-sion due to the Euro5 legislation can be expected over this short span of time. External evi-dence of the effectiveness of the Euro5 legislation was found in the BC concentration in the measurements performed by the Flemish Government. The results are shown in Appendix 3.2. A reduction of 30 to 40% is found for BC during rush hour and 20 to 30 % in the off-peak periods. The strongest decreases are found at sites with high traffic exposure.

A third potential relevant discrepancy is found in the differences in the distribution of the street canyon index. In the PSC campaign relatively few measurements were performed in build-up area. The main part of the PSC campaign is performed outside the cities. The effect of this potential effect cannot be quantified.

In an adjusted model BCR_LDENWH_VH_AGE_EU5 the two quantified discrepancies are added to the BCR_LDENWH model. It combines the adjustment for the vehicle age and quali-ty and an emission reduction at the source is presented. The external validation is visible in

Figure 4.5.7. The details of the implementation of the age of the vehicle and the changes in the vehicle PM emission are available in Appendix 3.2).

Figure 4.5.7: External validation based on BCR_LDENWH_VH_AGE_EU5: Total trip fit versus measurement (A), average trip fit versus measurement (B), relative trip fit distribution (C) and relative trip fit distribution (D).

The Q1, median, mean and Q3 of the relative trip fit are increasing from 38% to 53%, 50%

to 79%, 74% to 104% and 81% to 114% compared to the BCR_LDENWH_AGE validation. The correlation of the trip average exposure remains the same, the total trip correlations in-crease slightly. The average is inin-creased with 36%, from 68% to 104% similar to the inin-crease at the near roadside long-term measurement location. The IQR is 51%.

4.5.5 Discussion

In-vehicle exposure dynamics: In section 4.5.3.1 the complexity of the in-vehicle exposure is illustrated. The technique to evaluate the in-vehicle exposure in an extreme detailed tem-poral resolution, combined with equally detailed traffic attribution, results in important in-formation. The approach chosen in this article, modelling real-life in-vehicle personal expo-sure, contrasts with other approaches to evaluate predesigned in-vehicle exposure experi-ments. Most focus on a single aspect of the complex in-vehicle exposure dynamics. From a scientific point of view, the chosen approach will fail in quantifying the individual relations between the different relevant parameters influencing in-vehicle exposure. For epidemiolog-ical extrapolation however, the total combined effects of all potential influences will deter-mine the actual personal exposure. This approach fits in the so-called ‘data science’ field, an emerging scientific field with a focus on data exploration to extract relevant relationships from the gathered data without the need for an analytical or a physical solution for the re-sulting relations. The high temporal resolution of the measurements and model covariates can disentangle the biases in the participatory sensing data and direct new measurements campaigns towards improved attribution and sampling.

Noise maps as ubiquitous traffic data source: The evaluation in section 4.5.3.2 has shown the potential of noise maps as a proxy for the local traffic data. The differences between the traffic based models and the noise map based models is not statistically significant. This is not surprizing since the noise maps is based on the identical set of traffic data. The quality of

the spatial attribution of both the noise and traffic is identical due to the ‘map matching’

data cleaning procedure, linking the traffic to the actual physical road. This is important to produce valid noise maps. An important advantage of the noise mapping approach is the availability of such spatially detailed noise maps. All EU cities larger than 50,000 inhabitants are required these maps to be compliant with the END noise directive. An import synergy is possible.

Noise maps accumulate the noise exposure from different roads when they are in each other’s vicinity. This results in higher values at crossings and complex traffic situations, matching the areas with high traffic dynamics. In this way the additive nature of the noise maps seems to mimic the traffic emission behaviour. It was not possible to fully quantify the benefits of this built-in property.

External validation: The external validation underestimates the exposure by almost 50 %.

The correlations are however very good. Three discrepancies with significant potential are identified:

 vehicle age and quality influencing the indoor PM levels due to changes of the vehi-cle intake filters and ventilation

 a potential bias in the sampling (more in-city measurements in the external data)

 the reduced emission of the vehicle fleet

The potential biases could only be investigated due to the detailed spatiotemporal attrib-ution of the measurements. The origin of the discrepancies requires further research. The vehicle age and air intake filter quality will complicate exposure estimates even more in the future. Recent publications mention the improved quality and active development of UFP filters for vehicle ventilation systems. A reduction of 80 to 90% is achieved with certain spe-cific filters (Lee, 2014). It is unknown to what extent these types of filters or less performant filter are available in the operational vehicle fleets at this moment. The change in the PM emission standard for diesel vehicles (Euro5) is the most plausible argument for the discrep-ancy in the external validation. Background measurements at remote and kerbside locations detect a decrease in the BC concentrations similar to the estimate based on the evolution of the vehicle fleet. Adjusting for the changed emission of the vehicle fleet is valid approach.

The actual underlying spatial and temporal changes of the emission due to the Euro5 legisla-tion is unknown but the chosen correclegisla-tion curve, adjusted stronger for high exposure epi-sodes in the instantaneous model seems to explain the discrepancy between the two meas-urement campaigns. Further investigation of the changing emission dynamics of the vehicle fleet over time is crucial.

Participatory sensing: It is clear that the vehicle is a complex micro-environment with strong interactions with outside and driving conditions and strongly influenced by vehicle and driver specific parameters. As a result, this large set of influences can include unex-pected biases in the sampling, including route choice, time of day, meteorology and age and

quality of the vehicles. The fast changing environment due to effective legislation adds more complexity. The strongest limitation in the measurement campaign is the small sample of vehicles. The high complexity of the in-vehicle exposure, combined with fast changing fleet emission and individual vehicle air intake quality hampers the quantification of each individ-ual covariate. Large participatory campaigns are necessary to quantify the trend of personal exposure in the vehicles. The approach taken in this PhD, to sample and predict real-life conditions, will be important to be able to quantify the combined effects of the fast changing external influences and the high traffic dynamics and route sensitive in-vehicle exposure.

4.5.6 Conclusions

A large participatory sensing campaign of in-vehicle BC exposure is conducted and a high resolution spatiotemporal model is build. Several relations influencing the internal dynamics of the in-vehicle exposure can be detected (effects of speed, acceleration, wind speed, tem-perature, traffic density, diurnal pattern of PM dynamics…). The traffic dynamics and diurnal traffic patterns do not resolve the diurnal patterns of the in-vehicle exposure at full. Traffic attribution is achieved through traffic counts and through noise mapping. An in-vehicle ex-posure model based on six parameters with LDEN in combination with a fitted diurnal pattern, resolves spatiotemporal variability. Noise maps are a valid proxy for air pollution without actual knowledge of the local traffic dynamics. The noise maps are widely available due to the EU END directive and enable a strong synergy in local policy making for cities and ag-glomerations with available noise maps.

The external validation with a four year old participatory campaign showed strong under-estimations but the data analysis revealed two main components explaining the discrepan-cies: vehicle age and quality and PM emission changes due to the recent EU euro5 legisla-tion. Adjusting for changes in vehicle quality and changed emission due to the effective legis-lation is possible. The chosen approach to sample real life exposure, combined with high resolution spatiotemporal modelling can provide good prediction models without the need of disentangling the underlying complex interaction of the underlying parameters.

4.5.7 References

Can A., Dekoninck L., Rademaker M., Van Renterghem T., De Baets B., et al. 2011. Noise measurements as proxies for traffic parameters in monitoring networks. Science of the Total Environment, 410, 198-204.

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.

Carslaw D.C., Beevers S.D. & Tate J.E. 2007. Modelling and assessing trends in traffic-related emissions using a generalised additive modelling approach. Atmospheric Environment, 41, 5289-5299.

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. Interna-tional Journal of Distributed Sensor Networks, Volume 2012 (2012), http://dx.doi.org/10.1155/2012/324935

de Nazelle A., Seto E., Donaire-Gonzalez D., Mendez M., Matamala J., et al. 2013. Improving estimates of air pollution exposure through ubiquitous sensing technologies. Environmental Pollution, 176, 92-99.

Dekoninck L., Botteldooren D. & Int Panis L. 2013. An instantaneous spatiotemporal model to predict a bicyclist's Black Carbon exposure based on mobile noise measurements. Atmospheric Environment, 79, 623-631.

Dekoninck L., Botteldooren D. & Int Panis L. 2015. Applicability of a noise-based model to estimate in-traffic exposure to black carbon and particle number concentrations in different cultures. Environ-ment International, 74, 89-98.

Dons E., Int Panis L., Van Poppel M., Theunis J. & Wets G. 2012. Personal exposure to Black Carbon in transport microenvironments. Atmospheric Environment, 55, 392-398

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., Temmerman P., Van Poppel M., Bellemans T., Wets G., et al. 2013. Street characteristics and traffic factors determining road users' exposure to black carbon. Science of the Total Environment, 447, 72-79.

EU Commission, The Environmental Noise Directive (2002/49/EC), http://ec.europa.eu/environment/noise/directive.htm.

Fruin S.A., Hudda N., Sioutas C. & Defino R.J. 2011. Predictive Model for Vehicle Air Exchange Rates Based on a Large, Representative Sample. Environmental Science & Technology, 45, 3569-3575.

Hudda N., Eckel S.R., Knibbs L.D., Sioutas C., Delfino R.J., et al. 2012. Linking in-vehicle ultrafine particle exposures to on-road concentrations. Atmospheric Environment, 59, 578-586.

Janssen N.A.H., Hoek G., Simic-Lawson M., Fischer P., van Bree L., et al. 2011. Black Carbon as an Addi-tional Indicator of the Adverse Health Effects of Airborne Particles Compared with PM10 and PM2.5. Environmental Health Perspectives, 119, 1691-1699.

Karner A.A., Eisinger D.S. & Niemeier D.A. 2010. Near-Roadway Air Quality: Synthesizing the Findings from Real-World Data. Environmental Science & Technology, 44, 5334-5344.

Knibbs L.D., de Dear R.J. & Morawska L. 2010. Effect of Cabin Ventilation Rate on Ultrafine Particle Expo-sure Inside Automobiles. Environmental Science & Technology, 44, 3546-3551.

Lee E.S. & Zhu Y. 2014. Application of a High-Efficiency Cabin Air Filter for Simultaneous Mitigation of Ultrafine Particle and Carbon Dioxide Exposures Inside Passenger Vehicles. Environmental Science

& Technology, 48, 2328-2335.

Li L.F., Wu J., Hudda N., Sioutas C., Fruin S.A., et al. 2013. Modeling the Concentrations of On-Road Air Pollutants in Southern California. Environmental Science & Technology, 47, 9291-9299.

WHO Europe, 2012: Health effects of black carbon, ISBN: 978 92 890 0265 3.

Wood S.N. 2006. On confidence intervals for generalized additive models based on penalized regression splines. Australian & New Zealand Journal of Statistics, 48.

Xu B., Wu Y., Lin Z. & Chen Z. 2014. Investigation of Air Humidity Affecting Filtration Efficiency and Pres-sure Drop of Vehicle Cabin Air Filters. Aerosol and Air Quality Research, 14, 1066-1073.