**3. Results and discussion**

During the study, temperature and relative humidity averaged 20°C and 53%, respectively. It is known that higher wind speed helps ventilate the pollution from the street canyons and lower personal exposure [34]. Thus, the low winds registered during the experiment (< 2.5 m/s) offered optimal conditions to measure trapped urban pollution. Higher winds are more common during dry season months of June-August, while current conditions represent Quito weather the rest of the year. This suggests that the results of this study represent usual conditions in the city.

This suggests very high levels of short time exposure to traffic-related PM2.5 pollution, which

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It can be noted that the concentrations of PM2.5 are higher at the streets containing bicycle lanes (**Figure 2**). Statistical analysis of street level concentrations indicated that the average concentration at the streets with cycling lanes is 1.58 times higher than the rest of the streets in the district. This suggests that the personal cyclist exposure to traffic pollution is significantly higher if using existing bicycle lane infrastructure than less densely transited parallel streets, especially streets not permitting city buses (see **Figure 2**). Often, current bicycle paths are located in the most direct and widest streets, thus commonly used by city transport (diesel engines). This places cyclists in the worst air quality conditions suggesting that the users are exposed to the highest level of contamination, and may not be reaping the expected health benefits of active commuting [4, 17–22]. In a few similar studies, the route choices were evaluated in terms of personal exposure to traffic pollution [35, 36], indicating that the choices of "greener" routes significantly reduced personal exposure to direct combustion emissions, but not city background pollution. However, in the case of many cities of the developing world, there are no "greener" options, and the traffic emission levels are significantly higher [37]. Previous studies conclude the importance of the selected travel route, ventilation rate, travel speed for the personal exposure of the person to the traffic pollution, etc. [4, 17]. We confirm the importance of relocating urban bike lanes to the calmer streets, especially in the cities with

For the traffic and PM2.5 pollution correlation analysis, the vehicle counts and PM2.5 concentration data were averaged per street during all the study period. Correlation analysis is summarized in **Figure 3**. It confirms that there is a strong positive correlation (R<sup>2</sup> = 0.73) between the presence of heavy vehicles and the concentrations of PM2.5. The number of light personal vehicles and taxis also positively correlated (R2 = 0.67) with the PM2.5 concentrations

PM2.5 concentrations at the street level were also compared with the nearest air quality monitoring station (1.5 km away) representing air quality conditions for central Quito. During the study, the street level pollution was 2.5 times higher than at the monitoring site (elevated at about 10 m above the street infrastructure). The average PM2.5 concentra-

the two measurements, suggesting some relationship between the two traffic-busy areas. However, the significant difference questions current estimates of population exposure to air pollution based on monitoring network data. This especially underestimates the exposure of people that spend a considerable time outside in the street canyons (couriers, police, street vendors, etc.). This inconsistency was suggested by the previous study, where low correlation (r = 0.31 all day, r = 0.49 morning rush hours) was found between the PM2.5 pollution at a monitoring station (elevated above street level) and the surrounding

Therefore, a deeper traffic intensity and PM2.5 pollution study were performed. We compared the typical traffic at urban street infrastructure with street level PM2.5 levels. The results of the

, while at the street level, the concentra-

.There was a positive correlation (R<sup>2</sup> = 0.42) between

is of a great concern to people near traffic sources.

poor-quality fuels and technologies [17, 20].

(**Figure 3**). These findings are consistent with other studies [36].

tions at the monitoring site were 23.3 ± 8 μg/m<sup>3</sup>

tions highly varied at 58.5 ± 91 μg/m<sup>3</sup>

traffic activity [38].

The sampling route (approximately 6 h of sampling and 20 km long) covered a complete urban street infrastructure of the district, which also included sections of four existing bicycle lanes (indicated by broken lines in **Figure 2**). PM2.5 concentrations averaged per street and spatial interpolation (ordinary kriging) are represented by the same scale in **Figure 2**. The PM2.5 concentrations varied from 27 to 93 μg/m<sup>3</sup> . These levels exceed the WHO recommended levels for 24-h exposure (25 μg/m<sup>3</sup> ). Meanwhile, punctual PM2.5 concentrations (10 s averages) varied from 0 to 624 μg/m<sup>3</sup> . The sampling peaks nearly exclusively originated from the accelerating diesel buses and minibuses, often at traffic-light-controlled intersections (**Figure 2**).

**Figure 2.** PM2.5 concentrations in the urban street network of Quito district Mariscal averaged per street and spatial interpolation (ordinary kriging).

This suggests very high levels of short time exposure to traffic-related PM2.5 pollution, which is of a great concern to people near traffic sources.

**3. Results and discussion**

104 Air Pollution - Monitoring, Quantification and Removal of Gases and Particles

conditions in the city.

PM2.5 concentrations varied from 27 to 93 μg/m<sup>3</sup>

levels for 24-h exposure (25 μg/m<sup>3</sup>

varied from 0 to 624 μg/m<sup>3</sup>

interpolation (ordinary kriging).

During the study, temperature and relative humidity averaged 20°C and 53%, respectively. It is known that higher wind speed helps ventilate the pollution from the street canyons and lower personal exposure [34]. Thus, the low winds registered during the experiment (< 2.5 m/s) offered optimal conditions to measure trapped urban pollution. Higher winds are more common during dry season months of June-August, while current conditions represent Quito weather the rest of the year. This suggests that the results of this study represent usual

The sampling route (approximately 6 h of sampling and 20 km long) covered a complete urban street infrastructure of the district, which also included sections of four existing bicycle lanes (indicated by broken lines in **Figure 2**). PM2.5 concentrations averaged per street and spatial interpolation (ordinary kriging) are represented by the same scale in **Figure 2**. The

erating diesel buses and minibuses, often at traffic-light-controlled intersections (**Figure 2**).

**Figure 2.** PM2.5 concentrations in the urban street network of Quito district Mariscal averaged per street and spatial

. These levels exceed the WHO recommended

). Meanwhile, punctual PM2.5 concentrations (10 s averages)

. The sampling peaks nearly exclusively originated from the accel-

It can be noted that the concentrations of PM2.5 are higher at the streets containing bicycle lanes (**Figure 2**). Statistical analysis of street level concentrations indicated that the average concentration at the streets with cycling lanes is 1.58 times higher than the rest of the streets in the district. This suggests that the personal cyclist exposure to traffic pollution is significantly higher if using existing bicycle lane infrastructure than less densely transited parallel streets, especially streets not permitting city buses (see **Figure 2**). Often, current bicycle paths are located in the most direct and widest streets, thus commonly used by city transport (diesel engines). This places cyclists in the worst air quality conditions suggesting that the users are exposed to the highest level of contamination, and may not be reaping the expected health benefits of active commuting [4, 17–22]. In a few similar studies, the route choices were evaluated in terms of personal exposure to traffic pollution [35, 36], indicating that the choices of "greener" routes significantly reduced personal exposure to direct combustion emissions, but not city background pollution. However, in the case of many cities of the developing world, there are no "greener" options, and the traffic emission levels are significantly higher [37]. Previous studies conclude the importance of the selected travel route, ventilation rate, travel speed for the personal exposure of the person to the traffic pollution, etc. [4, 17]. We confirm the importance of relocating urban bike lanes to the calmer streets, especially in the cities with poor-quality fuels and technologies [17, 20].

For the traffic and PM2.5 pollution correlation analysis, the vehicle counts and PM2.5 concentration data were averaged per street during all the study period. Correlation analysis is summarized in **Figure 3**. It confirms that there is a strong positive correlation (R<sup>2</sup> = 0.73) between the presence of heavy vehicles and the concentrations of PM2.5. The number of light personal vehicles and taxis also positively correlated (R2 = 0.67) with the PM2.5 concentrations (**Figure 3**). These findings are consistent with other studies [36].

PM2.5 concentrations at the street level were also compared with the nearest air quality monitoring station (1.5 km away) representing air quality conditions for central Quito. During the study, the street level pollution was 2.5 times higher than at the monitoring site (elevated at about 10 m above the street infrastructure). The average PM2.5 concentrations at the monitoring site were 23.3 ± 8 μg/m<sup>3</sup> , while at the street level, the concentrations highly varied at 58.5 ± 91 μg/m<sup>3</sup> .There was a positive correlation (R<sup>2</sup> = 0.42) between the two measurements, suggesting some relationship between the two traffic-busy areas. However, the significant difference questions current estimates of population exposure to air pollution based on monitoring network data. This especially underestimates the exposure of people that spend a considerable time outside in the street canyons (couriers, police, street vendors, etc.). This inconsistency was suggested by the previous study, where low correlation (r = 0.31 all day, r = 0.49 morning rush hours) was found between the PM2.5 pollution at a monitoring station (elevated above street level) and the surrounding traffic activity [38].

Therefore, a deeper traffic intensity and PM2.5 pollution study were performed. We compared the typical traffic at urban street infrastructure with street level PM2.5 levels. The results of the

rather than on major streets, especially in developing countries using high sulfur content fuels that cause more particulate pollution. Not many cities can afford greenways (undeveloped land in or near urban area) for cycling; thus for the best solution, some lighter traffic density parallel street options can be used to redirect bicycle traffic to reduce the exposure to high concentrations of primary pollutants. Following the example of Amsterdam, Netherlands, the bicycle paths could be located on the streets of exclusively light vehicle traffic, not only reducing the risks of safety but also air pollution. This could further encourage new conversions

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To the best of our knowledge, this is the only study proposing to base urban cycling path planning on the benefits of cardiopulmonary health and offering an economic solution applicable for any country. During the study, the street level pollution in a central district of Quito was

large variation suggests an extremely high level of short time exposure to traffic-related PM2.5 pollution, which is of a great concern to people near traffic sources. The results of this study show that there is a strong positive correlation between the amount of heavy diesel vehicles (especially city busses) on the road and the concentrations of PM2.5. We also demonstrate that most of the bicycle paths in the central Quito are located on the most polluted streets. This indicates the importance of an appropriate selection of routes with low vehicular traffic load to reduce cyclists' exposure to fine particulate matter. We also conclude a high correlation between the motorized traffic intensity (Google Traffic Maps service) and PM2.5 pollution. Traffic maps offer a reliable and economic method for healthier cycling infrastructure planning in any city of the world. Therefore, this study serves as a reference for implementing control measures for public transport and for the planning of strategic routes, as well as the implementation of adequate infrastructure to support active transportation by reducing

We want to thank the Secretariat of the Environment of the Municipality of the Metropolitan District of Quito in Quito, Ecuador, especially Maria Valeria Diaz Suarez for the infinite

vehicular pollution exposure and promoting human health.

, significantly exceeding the WHO recommended levels for air quality. This

toward more active commuting.

**4. Conclusions**

58.5 ± 91 μg/m<sup>3</sup>

**Acknowledgements**

**Conflict of interest**

The authors declare no conflicts of interest.

collaboration.

**Figure 3.** PM2.5 concentrations plotted versus the counts of heavy and light vehicles in a few selected main streets, 4/6 of these streets contain bicycle paths (the highest concentrations).


**Table 1.** Values of the coefficients r according to the method and the time.

comparison are presented in **Table 1**. The coefficients r obtained from the strict correlation analysis (method 1) are 0.5 and 0.43 at 9 am and 1 pm, respectively. Since the baseline is 0.33 (three possible levels), we can conclude that the correlation is largely above the chance level and, consequently, a significant part of the air pollution is directly explained by the urban traffic. The slight decreases of the correlation in the afternoon can be explained by an augmentation of the dilution of the pollutants in the atmosphere that occurs at this time of day [38]. These results are confirmed by the weighted correlation analysis. This second method provides us with coefficients of 0.73 and 0.69 at 9 am and 1 pm, respectively. Although the baseline of this method is higher (r = 0.5) than in the first analysis, the obtained values cannot be the effect of the hazard. As expected, the correlation between traffic and PM2.5 is superior when the concentrations are recorded at the street level than at the monitoring station level. Taken together with the results presented in **Figure 3**, these findings support the hypothesis of considering traffic density in the planning of urban cycling paths.

While there is a serious traffic and physical inactivity problem in the world, one of the seemingly best solutions—cycling—might not be widely adopted due to multiple issues such as missing infrastructure, crime/safety, and environmental pollution [39]. The results of this study encourage city planners to locate cycling paths on less trafficked, light vehicle streets rather than on major streets, especially in developing countries using high sulfur content fuels that cause more particulate pollution. Not many cities can afford greenways (undeveloped land in or near urban area) for cycling; thus for the best solution, some lighter traffic density parallel street options can be used to redirect bicycle traffic to reduce the exposure to high concentrations of primary pollutants. Following the example of Amsterdam, Netherlands, the bicycle paths could be located on the streets of exclusively light vehicle traffic, not only reducing the risks of safety but also air pollution. This could further encourage new conversions toward more active commuting.
