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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Environ. Sci.</journal-id>
<journal-title>Frontiers in Environmental Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Environ. Sci.</abbrev-journal-title>
<issn pub-type="epub">2296-665X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fenvs.2016.00077</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Environmental Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Air Pollution alongside Bike-Paths in Bogot&#x000E1;-Colombia</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Franco</surname> <given-names>Juan F.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn001"><sup>&#x0002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/378248/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Segura</surname> <given-names>Juli&#x000E1;n F.</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/392220/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Mura</surname> <given-names>Ivan</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/367451/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Group of Studies on Urban and Regional Sustainability, School of Engineering, Universidad de los Andes</institution> <country>Bogot&#x000E1;, Colombia</country></aff>
<aff id="aff2"><sup>2</sup><institution>Research Group on Environmental Management, School of Engineering, Universidad EAN</institution> <country>Bogot&#x000E1;, Colombia</country></aff>
<aff id="aff3"><sup>3</sup><institution>COPA Research Group, Department of Industrial Engineering, School of Engineering, Universidad de los Andes</institution> <country>Bogot&#x000E1;, Colombia</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Maurice Millet, University of Strasbourg, France</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Maria De Fatima Andrade, University of S&#x000E3;o Paulo, Brazil; Delhomme Olivier, University of Lorraine, France</p></fn>
<fn fn-type="corresp" id="fn001"><p>&#x0002A;Correspondence: Juan F. Franco <email>jffranco&#x00040;uniandes.edu.co</email></p></fn>
<fn fn-type="other" id="fn002"><p>This article was submitted to Air Pollution, a section of the journal Frontiers in Environmental Science</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>25</day>
<month>11</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="collection">
<year>2016</year>
</pub-date>
<volume>4</volume>
<elocation-id>77</elocation-id>
<history>
<date date-type="received">
<day>20</day>
<month>09</month>
<year>2016</year>
</date>
<date date-type="accepted">
<day>09</day>
<month>11</month>
<year>2016</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2016 Franco, Segura and Mura.</copyright-statement>
<copyright-year>2016</copyright-year>
<copyright-holder>Franco, Segura and Mura</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<p>The study we present in this paper aims at characterizing the range of fine particulate matter (PM<sub>2.5</sub>) and black carbon (BC) concentrations to which bike-path users in Bogot&#x000E1; are exposed to. Using a bike equipped with a DustTrak and a micro-aethalometer we measured PM<sub>2.5</sub> and BC concentration levels along bike-paths corridors, during weekdays and weekends. Experiments were conducted in fours streets, representing four typical configurations of bike-paths in the city. Traffic data for workdays was also available from local mobility authority. Results indicate that bike-paths users in Bogot&#x000E1; are exposed to air pollution levels far exceeding the threshold values established as potentially dangerous for human health. Average concentrations for PM<sub>2.5</sub> ranged between 80 and 136 ug/m<sup>3</sup> in workdays and between 30 and 72 ug/m<sup>3</sup> in weekends. BC mean concentrations were between 16 and 38 &#x003BC;g/m<sup>3</sup> during workdays and in the range 10&#x02013;32 &#x003BC;g/m<sup>3</sup> during weekends. A statistically significant difference exists in the levels of pollutants concentrations measured during workdays and weekends for all the considered bike paths. According to our results, both traffic volume and diffusions conditions, which are affected by many factors including street geometry, affect bike-path user&#x00027;s exposure levels. Taking into account the important role that bicycling is playing as an alternative transport mode in Latin American cities, we consider these results provide useful insights to increase the appreciation of the excessive bikers&#x00027; exposure to air pollution in Bogot&#x000E1;. Moreover, these findings contribute with technical elements that should lead to the inclusion of air quality variable when designing and planning sustainable urban mobility infrastructures.</p>
</abstract>
<kwd-group>
<kwd>particulate matter</kwd>
<kwd>black carbon</kwd>
<kwd>bike infrastructure</kwd>
<kwd>urban planning</kwd>
<kwd>sustainable transportation</kwd>
</kwd-group>
<counts>
<fig-count count="6"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="41"/>
<page-count count="10"/>
<word-count count="5989"/>
</counts>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>Introduction</title>
<p>Over the last decade, Bogot&#x000E1; has been recognized for its large bike-dedicated infrastructure. The city is currently endowed with more than 450 km of bike-paths and near 600 thousand trips are made by bicycle, accounting for 6% of the total daily trips (R&#x000ED;os et al., <xref ref-type="bibr" rid="B30">2015</xref>). These figures make Bogot&#x000E1; the Latin-American city with the largest number of kilometers of bike-lines, almost as the double as Sao Paulo, Ciudad de M&#x000E9;xico, and Santiago de Chile (Baumann et al., <xref ref-type="bibr" rid="B2">2013</xref>). The initial motivation for cycling in Colombian cities was primary related to economic reasons. Many people simply could not afford paying the everyday cost of public transportation. Moreover, there has been a relatively recent initiative for encouraging bicycle usage as a sustainable transport mode to face to the critical urban mobility conditions, while fostering healthier life-styles, and increasing the level of physical activity.</p>
<p>However, most of the bike-paths in Bogot&#x000E1; are located alongside busy roads, where cyclists are experimenting air quality conditions that may not be adequate for performing such physical activity. A question arises as to whether biking under such conditions may overcome the benefits of cycling. This is even more of concern, since Bogot&#x000E1; has been listed among the most polluted cities in Latin America (WHO, <xref ref-type="bibr" rid="B38">2016</xref>). Elevated concentrations of pollutants, especially respirable particulate matter (PM<sub>10</sub>), represent a constant issue (Franco R, <xref ref-type="bibr" rid="B13">2012</xref>). According to the data from the city&#x00027;s air quality monitoring network, in 2013 the annual PM<sub>10</sub> average concentration in the city ranged between 32 and 81 &#x003BC;g/m<sup>3</sup>, in the northern and western regions of the city, respectively (SDA, <xref ref-type="bibr" rid="B34">2014</xref>). In some areas of the city, both WHO reference threshold values (annual&#x02013;20 &#x003BC;g/m<sup>3</sup> and 24-h&#x02013;50 &#x003BC;g/m<sup>3</sup>) and national standards (annual&#x02013;50 &#x003BC;g/m<sup>3</sup> and 24-h&#x02013;100 &#x003BC;g/m<sup>3</sup>), are regularly exceeded.</p>
<p>This is a matter of concern, since inhalation of particulate matter has been related to various diseases. Many epidemiological studies have provided evidence of association between the exposure to PM and asthma, bronchitis and chronic obstructive pulmonary disease, as well as cardiovascular diseases (van Berlo et al., <xref ref-type="bibr" rid="B36">2012</xref>). Closeness to highly polluted locations, such as heavy traffic streets, reveals to be a risk factor for respiratory complications in vulnerable population, leading to reduction of pulmonary function (Peters et al., <xref ref-type="bibr" rid="B28">1999</xref>; Fritz and Herbarth, <xref ref-type="bibr" rid="B14">2001</xref>; Kim et al., <xref ref-type="bibr" rid="B24">2004</xref>; Calder&#x000F3;n-Garcidue&#x000F1;as et al., <xref ref-type="bibr" rid="B5">2006</xref>; Ni et al., <xref ref-type="bibr" rid="B27">2015</xref>).</p>
<p>Much like in other Latin-American cities, transportation is one of the most important sources of air pollution in Bogot&#x000E1; (WMO, <xref ref-type="bibr" rid="B39">2012</xref>). According to the city emission inventory, 65% of PM<sub>10</sub> is generated by mobile sources (SDA, <xref ref-type="bibr" rid="B31">2011</xref>), in particular from public transport and freight vehicles using diesel fuel. Although this vehicular category represents &#x0003C;5% of the actual fleet, it emits more than 60% of the PM associated to mobile sources in the city (SDA, <xref ref-type="bibr" rid="B31">2011</xref>). In terms of exposure, mobile sources imply higher negative effects than other sources (i.e., industries), as a consequence of the closer distance between the exhaust pipes and the people in transport-related microenvironments.</p>
<p>In spite of its relevance for public health, the exposure of bike-paths users to air pollution in Bogot&#x000E1; has so far received limited attention and few researches have been conducted. One previous study (Fajardo and Rojas, <xref ref-type="bibr" rid="B10">2012</xref>), drew as a conclusion the need for a more detailed characterization of the environmental conditions of bikers in the city. The main objective of our work is to measure the levels of fine particulate matter (PM<sub>2.5</sub>) and black carbon (BC) to which bike-paths users are exposed in Bogot&#x000E1;. In addition, we identified some of the factors that must be considered when designing bike-dedicated infrastructure to minimize users&#x00027; exposure.</p>
</sec>
<sec sec-type="methods" id="s2">
<title>Methods</title>
<sec>
<title>Study area and selection of the bike-paths</title>
<p>This study was conducted in the urban area of Bogot&#x000E1; D.C., Colombia&#x00027;s capital and largest city (8 million inhabitants; DNP, <xref ref-type="bibr" rid="B8">2015</xref>). It is located on the Andes mountain range, at 2600 m.a.s.l. with a temperate climate, no marked seasonal variation (Cfb, according to the K&#x000F6;ppen-Geiger classification), and with an average temperature of 13.5&#x000B0;C and an average rainfall of 866 mm/year. The city urban area and its dwellers are classified in six socio-economical strata (with stratum 1 being the lowest and stratum 6 the highest), with people living in strata 2 and 3 accounting for about 80% of the total population (DAPD, <xref ref-type="bibr" rid="B7">2007</xref>). The economy of Bogot&#x000E1; represents 25% of the GDP of the country, with prominent sectors being those of services, commerce, and manufacturing (Luna and Behrentz, <xref ref-type="bibr" rid="B25">2011</xref>).</p>
<p>More than 10 million trips occur daily within the city in both motorized and bicycle modes. Public transportation, which includes massive transport (a bus rapid transit system known as <italic>Transmilenio</italic>), conventional buses and micro-busses, and taxis, accounts for &#x0007E;60% of the daily trips (CCB, <xref ref-type="bibr" rid="B6">2015</xref>).</p>
<p>The selected bike-paths are located alongside major streets. We chose bike-paths with distinct configurations (see Table <xref ref-type="table" rid="T1">1</xref> and Figure <xref ref-type="fig" rid="F1">1</xref>). One type is a bike-path located at the center of the corridor, in the division between lanes in a two-way street. The other type is a bike-path at the right-side of the street. Cycle lanes are segregated in both cases. In addition, we considered the street geometry to represent different diffusion conditions. We chose two paths located along narrow streets (average street width less or equal than 30 m) with a consistent canyon configuration along the selected transects, and two paths located on wide streets (average street width &#x0003E;50 m) with variable configuration of flanking buildings. Moreover, we only consider bike-paths located in streets with mixed traffic where both public and private transport modes operate. These types of streets are representative for the bike-paths locations in Bogot&#x000E1;.</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p><bold>Characterization of the selected bike-path sections</bold>.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th valign="top" align="left"><bold>Street Name</bold></th>
<th valign="top" align="left"><bold>Selection Stretch</bold></th>
<th valign="top" align="center"><bold>Stretch Length (Km)</bold></th>
<th valign="top" align="left"><bold>Configuration Street/Path</bold></th>
<th valign="top" align="center"><bold>Average Street Width (m)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></bold></th>
<th valign="top" align="center"><bold>Average Bike-path Width (m)<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></bold></th>
<th valign="top" align="left"><bold>Public Transit Type</bold></th>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;"><bold>Number of Experiments</bold></th>
<th valign="top" align="center" colspan="4" style="border-bottom: thin solid #000000;"><bold>Vehicular Flow (Vehicles/hour)</bold></th>
</tr>
<tr>
<th/>
<th/>
<th/>
<th/>
<th/>
<th/>
<th/>
<th valign="top" align="center"><bold>Week days</bold></th>
<th valign="top" align="center"><bold>Week-ends<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;&#x0002A;</sup></xref></bold></th>
<th valign="top" align="center"><bold>Cars</bold></th>
<th valign="top" align="center"><bold>Motorcycle</bold></th>
<th valign="top" align="center"><bold>Buses and Trucks</bold></th>
<th valign="top" align="center"><bold>Total</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Avenida Boyac&#x000E1;</td>
<td valign="top" align="left">Between Calle 13 and Calle 80</td>
<td valign="top" align="center">6.53</td>
<td valign="top" align="left">Open with lateral bike-path</td>
<td valign="top" align="center">70</td>
<td valign="top" align="center">2.4</td>
<td valign="top" align="left">Regular collective service</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">7393</td>
<td valign="top" align="center">3767</td>
<td valign="top" align="center">1678</td>
<td valign="top" align="center">12838</td>
</tr>
<tr>
<td valign="top" align="left">Avenida Calle 26</td>
<td valign="top" align="left">Between Carrera 96 and Carrera 50</td>
<td valign="top" align="center">6.19</td>
<td valign="top" align="left">Open with central bike-path</td>
<td valign="top" align="center">86</td>
<td valign="top" align="center">2.4</td>
<td valign="top" align="left">BRT<xref ref-type="table-fn" rid="TN3"><sup>&#x0002A;&#x0002A;&#x0002A;</sup></xref> system &#x0002B; collective service</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">7465</td>
<td valign="top" align="center">1676</td>
<td valign="top" align="center">916</td>
<td valign="top" align="center">10057</td>
</tr>
<tr>
<td valign="top" align="left">Avenida Carrera 19</td>
<td valign="top" align="left">Between Calle 146 and Calle 100</td>
<td valign="top" align="center">4.42</td>
<td valign="top" align="left">Closed with central bike-path</td>
<td valign="top" align="center">26</td>
<td valign="top" align="center">2</td>
<td valign="top" align="left">Regular collective service</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">2074</td>
<td valign="top" align="center">287</td>
<td valign="top" align="center">132</td>
<td valign="top" align="center">2493</td>
</tr>
<tr>
<td valign="top" align="left">Carrera 11</td>
<td valign="top" align="left">Between Calle 100 and Calle 63</td>
<td valign="top" align="center">4.3</td>
<td valign="top" align="left">Closed with lateral bike-path</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">1.9</td>
<td valign="top" align="left">Regular collective service</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">1952</td>
<td valign="top" align="center">438</td>
<td valign="top" align="center">323</td>
<td valign="top" align="center">2713</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="TN1">
<label>&#x0002A;</label>
<p><italic>Includes both ways, for streets to which applies</italic>.</p></fn>
<fn id="TN2">
<label>&#x0002A;&#x0002A;</label>
<p><italic>In all cases, the weekend day was Sunday</italic>.</p></fn>
<fn id="TN3">
<label>&#x0002A;&#x0002A;&#x0002A;</label>
<p><italic>Bus rapid transit</italic>.</p></fn>
</table-wrap-foot>
</table-wrap>
<fig id="F1" position="float">
<label>Figure 1</label>
<caption><p><bold>Configuration of the streets and bike-paths selected for this study. (A)</bold> Open street with lateral bike-path; <bold>(B)</bold> closed street with lateral bike-path; <bold>(C)</bold> closed street with central bike-path; <bold>(D)</bold> open street with central bike-path.</p></caption>
<graphic xlink:href="fenvs-04-00077-g0001.tif"/>
</fig>
<p>Taking into account the average length of bike trips in the city (7 km according to SDM, <xref ref-type="bibr" rid="B33">2013</xref>), we selected a section of a similar distance on each bike-path to run our experiments. Safety conditions for the field team and logistic factors were also considered in the final selection of the bike-paths.</p>
</sec>
<sec>
<title>Field data collection</title>
<p>The field campaign was conducted in 2013 between April and May. For each bike-path we conducted two (or more) experiments during work days and two during weekends (always on Sundays). Each experiment consisted of a data collection along the bike-path, with a specially equipped bike for the simultaneous and real-time measurement of PM<sub>2.5</sub> and BC concentrations. Bike users were asked to bike at their average speed.</p>
<p>To measure PM<sub>2.5</sub> concentrations we used a portable photometer DustTrak 8533 (TSI Inc., USA) with a flow rate of 1.5 l/min. The DustTrak uses 90-deg light scattering to quantify the mass concentration of particles in an air stream that passes through an impactor assembly (Yanosky et al., <xref ref-type="bibr" rid="B40">2002</xref>). The instrument was factory calibrated, and zero point and flow rate were verified and/or reset according to the manufacturer&#x00027;s instructions prior to each experiment.</p>
<p>Real-time BC concentrations were obtained using a micro-aethalometer model AE51 (AethLabs Inc., USA). Operating at a flow rate of 100 ml/min, the AE51 performs the analysis by measuring the rate of change in absorption of transmitted light (880 nm) due to continuous collection of aerosol deposit on a filter media. On each experiment geographical position was register using a GARMIN 60CSX. All the instruments were set to 1-s time resolution.</p>
<p>Dates and times of each experiment are reported in Table <xref ref-type="table" rid="T2">2</xref>, together with background concentrations of PM<sub>2.5</sub> and meteorological data obtained from the city&#x00027;s air quality monitoring network for the same dates and times of the experiments.</p>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p><bold>Summary of the field data, pollutant levels and meteorological conditions</bold>.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th valign="top" align="center" colspan="5" style="border-bottom: thin solid #000000;"><bold>Experiment details</bold></th>
<th valign="top" align="center" colspan="3" style="border-bottom: thin solid #000000;"><bold>Air pollutants concentrations (</bold>&#x003BC;<bold>g/m</bold><sup><bold>3</bold></sup><bold>)</bold></th>
<th valign="top" align="center" colspan="3" style="border-bottom: thin solid #000000;"><bold>Meteorological data</bold></th>
</tr>
<tr>
<th valign="top" align="left"><bold>Street</bold></th>
<th valign="top" align="left"><bold>Day</bold></th>
<th valign="top" align="left"><bold>Date</bold></th>
<th valign="top" align="center"><bold>Start Time</bold></th>
<th valign="top" align="center"><bold>End Time</bold></th>
<th valign="top" align="center"><bold>PM<sub>2.5</sub> Average (<italic>SD</italic>)</bold></th>
<th valign="top" align="center"><bold>BC Average (<italic>SD</italic>)</bold></th>
<th valign="top" align="center"><bold>Background PM<sub>2.5</sub></bold></th>
<th valign="top" align="center"><bold>Pressure (mmHg)</bold></th>
<th valign="top" align="center"><bold>Precipitation (mm)</bold></th>
<th valign="top" align="center"><bold>Wind Speed (m/s)</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Avenida</td>
<td valign="top" align="left">Sun</td>
<td valign="top" align="left">21/04/2013</td>
<td valign="top" align="center">9:12:49</td>
<td valign="top" align="center">9:48:00</td>
<td valign="top" align="center">43.6 (61.1)</td>
<td valign="top" align="center">28.0 (35.0)</td>
<td valign="top" align="center">20.0</td>
<td valign="top" align="center">566.5</td>
<td valign="top" align="center">0.3</td>
<td valign="top" align="center">1.60</td>
</tr>
<tr>
<td valign="top" align="left">Boyac&#x000E1;</td>
<td valign="top" align="left">Wed</td>
<td valign="top" align="left">24/04/2013</td>
<td valign="top" align="center">8:20:01</td>
<td valign="top" align="center">9:04:13</td>
<td valign="top" align="center">102.4 (89.8)</td>
<td valign="top" align="center">22.5 (17.2)</td>
<td valign="top" align="center">39.0</td>
<td valign="top" align="center">565.6</td>
<td valign="top" align="center">0.1</td>
<td valign="top" align="center">1.23</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Thu</td>
<td valign="top" align="left">25/04/2013</td>
<td valign="top" align="center">8:23:18</td>
<td valign="top" align="center">8:52:00</td>
<td valign="top" align="center">156.2 (143.5)</td>
<td valign="top" align="center">48.8 (44.0)</td>
<td valign="top" align="center">18.0</td>
<td valign="top" align="center">565.5</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">0.87</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Sun</td>
<td valign="top" align="left">28/04/2013</td>
<td valign="top" align="center">8:32:00</td>
<td valign="top" align="center">9:06:25</td>
<td valign="top" align="center">87.5 (65.6)</td>
<td valign="top" align="center">37.6 (32.24)</td>
<td valign="top" align="center">43.0</td>
<td valign="top" align="center">564.8</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">1.38</td>
</tr>
<tr>
<td valign="top" align="left">Calle 26</td>
<td valign="top" align="left">Sun</td>
<td valign="top" align="left">21/04/2013</td>
<td valign="top" align="center">7:50:00</td>
<td valign="top" align="center">8:25:00</td>
<td valign="top" align="center">19.0 (19.9)</td>
<td valign="top" align="center">12.2 (13.9)</td>
<td valign="top" align="center">21.0</td>
<td valign="top" align="center">566.5</td>
<td valign="top" align="center">0.2</td>
<td valign="top" align="center">1.14</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Wed</td>
<td valign="top" align="left">24/04/2013</td>
<td valign="top" align="center">7:11:28</td>
<td valign="top" align="center">7:45:00</td>
<td valign="top" align="center">160.5 (44.7)</td>
<td valign="top" align="center">26.5 (19.2)</td>
<td valign="top" align="center">42.0</td>
<td valign="top" align="center">565.6</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">1.21</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Thu</td>
<td valign="top" align="left">25/04/2013</td>
<td valign="top" align="center">7:27:09</td>
<td valign="top" align="center">7:54:00</td>
<td valign="top" align="center">105.8 (40.5)</td>
<td valign="top" align="center">48.8 (23.0)</td>
<td valign="top" align="center">18.0</td>
<td valign="top" align="center">565.5</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">0.87</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Sun</td>
<td valign="top" align="left">28/04/2013</td>
<td valign="top" align="center">7:24:00</td>
<td valign="top" align="center">7:54:00</td>
<td valign="top" align="center">83.5 (15.1)</td>
<td valign="top" align="center">22.7 (17.6)</td>
<td valign="top" align="center">40.0</td>
<td valign="top" align="center">564.8</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">1.29</td>
</tr>
<tr>
<td valign="top" align="left">Carrera 19</td>
<td valign="top" align="left">Sun</td>
<td valign="top" align="left">5/05/2013</td>
<td valign="top" align="center">7:18:42</td>
<td valign="top" align="center">7:45:27</td>
<td valign="top" align="center">27.2 (39.8)</td>
<td valign="top" align="center">11.0 (14.1)</td>
<td valign="top" align="center">24.0</td>
<td valign="top" align="center">566.3</td>
<td valign="top" align="center">0.8</td>
<td valign="top" align="center">1.27</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Thu</td>
<td valign="top" align="left">9/05/2013</td>
<td valign="top" align="center">7:37:08</td>
<td valign="top" align="center">8:04:08</td>
<td valign="top" align="center">100.5 (228.9)</td>
<td valign="top" align="center">22.4 (32.1)</td>
<td valign="top" align="center">37.0</td>
<td valign="top" align="center">566.4</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">1.22</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Sun</td>
<td valign="top" align="left">12/05/2013</td>
<td valign="top" align="center">7:12:16</td>
<td valign="top" align="center">7:31:59</td>
<td valign="top" align="center">35.3 (33.2)</td>
<td valign="top" align="center">8.6 (6.3)</td>
<td valign="top" align="center">39.0</td>
<td valign="top" align="center">565.0</td>
<td valign="top" align="center">0.1</td>
<td valign="top" align="center">1.27</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Wed</td>
<td valign="top" align="left">15/05/2013</td>
<td valign="top" align="center">7:20:17</td>
<td valign="top" align="center">7:40:07</td>
<td valign="top" align="center">91.2 (158.1)</td>
<td valign="top" align="center">13.4 (15.7)</td>
<td valign="top" align="center">40.0</td>
<td valign="top" align="center">565.0</td>
<td valign="top" align="center">0.1</td>
<td valign="top" align="center">1.46</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Thu</td>
<td valign="top" align="left">23/05/2013</td>
<td valign="top" align="center">7:01:11</td>
<td valign="top" align="center">7:22:53</td>
<td valign="top" align="center">63.5 (67.8)</td>
<td valign="top" align="center">11.4 (9.8)</td>
<td valign="top" align="center">45.0</td>
<td valign="top" align="center">565.5</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">0.91</td>
</tr>
<tr>
<td valign="top" align="left">Carrera 11</td>
<td valign="top" align="left">Sun</td>
<td valign="top" align="left">5/05/2013</td>
<td valign="top" align="center">7:54:51</td>
<td valign="top" align="center">8:18:06</td>
<td valign="top" align="center">51.5 (76.3)</td>
<td valign="top" align="center">24.1 (28.3)</td>
<td valign="top" align="center">18.0</td>
<td valign="top" align="center">566.3</td>
<td valign="top" align="center">0.5</td>
<td valign="top" align="center">1.25</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Thu</td>
<td valign="top" align="left">9/05/2013</td>
<td valign="top" align="center">8:15:29</td>
<td valign="top" align="center">8:40:11</td>
<td valign="top" align="center">136.1 (156.5)</td>
<td valign="top" align="center">44.3 (65.4)</td>
<td valign="top" align="center">26.0</td>
<td valign="top" align="center">566.4</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">1.18</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Sun</td>
<td valign="top" align="left">12/05/2013</td>
<td valign="top" align="center">7:37:59</td>
<td valign="top" align="center">7:56:04</td>
<td valign="top" align="center">79.8 (173.6)</td>
<td valign="top" align="center">12.6 (12.1)</td>
<td valign="top" align="center">39.0</td>
<td valign="top" align="center">565.0</td>
<td valign="top" align="center">0.1</td>
<td valign="top" align="center">1.27</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Wed</td>
<td valign="top" align="left">15/05/2013</td>
<td valign="top" align="center">7:46:32</td>
<td valign="top" align="center">8:07:01</td>
<td valign="top" align="center">113.1 (192.1)</td>
<td valign="top" align="center">19.6 (21.4)</td>
<td valign="top" align="center">40.0</td>
<td valign="top" align="center">565.0</td>
<td valign="top" align="center">0.1</td>
<td valign="top" align="center">1.46</td>
</tr>
<tr>
<td/>
<td valign="top" align="left">Thu</td>
<td valign="top" align="left">23/05/2013</td>
<td valign="top" align="center">7:32:25</td>
<td valign="top" align="center">7:55:09</td>
<td valign="top" align="center">135.3 (167.9)</td>
<td valign="top" align="center">16.3 (14.1)</td>
<td valign="top" align="center">45.0</td>
<td valign="top" align="center">565.5</td>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">0.91</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>We selected a time frame between 6:00 and 9:30 a.m. to do the experiments. According to the hourly distribution of the bike trips in the city (SDM, <xref ref-type="bibr" rid="B32">2012</xref>), the peak of the bike trips corresponds to that period (about 60,000 trips/hour). Data regarding vehicular volume at the corridors was obtained from the transport local authorities. This information is reported in Table <xref ref-type="table" rid="T1">1</xref> and in all cases corresponds to workdays.</p>
</sec>
<sec>
<title>Field data processing</title>
<p>Field data was downloaded using the specific software of each measuring instrument. A total of 33,700 observations were obtained. Each of them consisted of a tuple with a measured level of PM<sub>2.5</sub>, BC, and the GPS coordinates.</p>
<p>All the observations were submitted to a process of filtering and validation. The filtering consisted in the identification and removal of spurious values, generated by equipment errors. Null and negative values were removed, as well as extreme ones outside of the data trend (e.g., spikes), which were obviously spurious measurements determined by mechanical stresses affecting the data collection devices. The validation process implemented a cross-check of pollutant levels, identifying, and removing all observations for which the PM<sub>2.5</sub> was not greater than that of BC. This validation processes removed 11% of the observations.</p>
<p>We computed descriptive statistics of the measured data to identify its variability range, average standard deviation, distribution, and to determine the percentage of observations above the threshold values (WHO and national regulations).</p>
<p>We analyzed pollution levels with their geographical position, to identify differences in exposure levels along the bike-path and analyze possible impacts from crossings, traffic lights, and bus stops on the air quality.</p>
<p>A different analysis considered an aggregation of data to compute the average values of PM<sub>2.5</sub> and BC during workdays and weekends. For the estimation of the average measures, we used confidence intervals to determine a measure of the error or uncertainty. As it was not possible to assume that the measurement data was normally distributed, we used empirical bootstrap confidence intervals (Efron and Tibshirani, <xref ref-type="bibr" rid="B9">1993</xref>) at a 95% confidence level.</p>
<p>We then used a boxplot visualization (with standard settings, Q1, Q2, and Q3, whiskers at 1.5 times the interquartile range, outliers not plotted) of the measured concentrations of air pollutants to determine possible differences in their distributions across bike-paths and with respect to the day of the measurement (i.e., workday or weekend).</p>
<p>For each of the bike-paths, we ran a one-way ANOVA to ascertain the existence of a statistical difference between the averages of the concentrations of PM<sub>2.5</sub> and BC measured during workdays and weekends. This analysis led us to conduct a more detailed graphical characterization of the distributions of the air contaminants, of their correlation, and of the differences between the amounts measured during workdays and weekends.</p>
</sec>
</sec>
<sec id="s3">
<title>Results and discussion</title>
<p>Table <xref ref-type="table" rid="T3">3</xref> shows the descriptive statistics for the total number of valid observations of PM<sub>2.5</sub> and BC. For PM<sub>2.5</sub>, more than 75% of the measurements are above the 24-h threshold value of 25 &#x003BC;g/m<sup>3</sup> set by WHO, with extreme values beyond 1000 &#x003BC;g/m<sup>3</sup>. The measurements of BC concentrations raise similar worries (i.e., 33% of the measurements are higher than 25 &#x003BC;g/m<sup>3</sup>).</p>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p><bold>Descriptive statistics</bold>.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th valign="top" align="center"><bold>Min</bold></th>
<th valign="top" align="center"><bold>Median</bold></th>
<th valign="top" align="center"><bold>Mean</bold></th>
<th valign="top" align="center"><bold>Max</bold></th>
<th valign="top" align="center"><bold>Standard Deviation</bold></th>
<th valign="top" align="center"><bold>Coeff. Variation</bold></th>
<th valign="top" align="center"><bold>% &#x0003E;25 &#x003BC;g/m<sup>3</sup></bold></th>
<th valign="top" align="center"><bold>% &#x0003E;50 &#x003BC;g/m<sup>3</sup></bold></th>
<th valign="top" align="center"><bold>% &#x0003E;100 &#x003BC;g/m<sup>3</sup></bold></th>
<th valign="top" align="center"><bold>% &#x0003E;200 &#x003BC;g/m<sup>3</sup></bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">PM<sub>2.5</sub>(&#x003BC;g/m<sup>3</sup>)</td>
<td valign="top" align="center">1.0</td>
<td valign="top" align="center">68.0</td>
<td valign="top" align="center">92.6</td>
<td valign="top" align="center">3604.0</td>
<td valign="top" align="center">115.2</td>
<td valign="top" align="center">1.2</td>
<td valign="top" align="center">77</td>
<td valign="top" align="center">60</td>
<td valign="top" align="center">31</td>
<td valign="top" align="center">8</td>
</tr>
<tr>
<td valign="top" align="left">BC (&#x003BC;g/m<sup>3</sup>)</td>
<td valign="top" align="center">1.1</td>
<td valign="top" align="center">15.3</td>
<td valign="top" align="center">25.6</td>
<td valign="top" align="center">3157.0</td>
<td valign="top" align="center">39.2</td>
<td valign="top" align="center">1.6</td>
<td valign="top" align="center">34</td>
<td valign="top" align="center">13</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">1</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Figure <xref ref-type="fig" rid="F2">2</xref> shows the histograms for PM<sub>2.5</sub> and BC considering all the samples (for both measurements the rightmost part of the distribution was cut for the sake of visualization). Both distributions of values are exhibiting a long tail, as also indicated by the coefficient of variation &#x0003E;1 (see Table <xref ref-type="table" rid="T3">3</xref>). Moreover, the distribution of PM<sub>2.5</sub> shows multimodality, which can be attributed to the presence of multiple emitting sources along the bike-paths.</p>
<fig id="F2" position="float">
<label>Figure 2</label>
<caption><p><bold>Histograms of PM<sub><bold>2.5</bold></sub> and BC concentrations measured along the bike-paths</bold>.</p></caption>
<graphic xlink:href="fenvs-04-00077-g0002.tif"/>
</fig>
<p>Both pollutants show highly dynamical patterns in its concentrations, with many swift peaks. Figure <xref ref-type="fig" rid="F3">3</xref> shows a georeferenced display of PM<sub>2.5</sub> concentrations along the four bike-paths (for one specific experiment). Different levels of each pollutant are represented by using a color scale, were the nuances of red are assigned to the highest concentration levels and those of blue to the lowest ones. Bikers are exposed to highly variable pollution levels during their bike ride.</p>
<fig id="F3" position="float">
<label>Figure 3</label>
<caption><p><bold>Concentrations of PM<sub><bold>2.5</bold></sub> measured during data collection experiments along the four selected bike-paths</bold>.</p></caption>
<graphic xlink:href="fenvs-04-00077-g0003.tif"/>
</fig>
<p>For those particular experiments we reported in Figure <xref ref-type="fig" rid="F3">3</xref>, the highest concentrations were recorded in the proximity to the main road crossing (e.g., along bike-path c), but also close to traffic lights and public transportation bus stops along the street (e.g., along bike-path a). The results of this analysis, as well as the proximity of the bike-paths to the street, indicate that the concentrations of air pollutants to which bikers are exposed are highly influenced by vehicular traffic emissions.</p>
<p>Figure <xref ref-type="fig" rid="F4">4</xref> presents the average and the 95% confidence interval for PM<sub>2.5</sub> and BC concentrations, classed by workdays and weekends. These charts confirm cyclists are consistently exposed to high concentrations of both pollutants, irrespective of the type of day. For PM<sub>2.5</sub>,the average concentration is within 80 and 136 &#x003BC;g/m<sup>3</sup> for workdays. These levels are three to five times higher than those suggested by the WHO (if we take the 24 h&#x02013;25 &#x003BC;g/m<sup>3</sup> as a reference value). During weekends, the concentrations of this pollutant ranged between 30 and 72 &#x003BC;g/m<sup>3</sup>.</p>
<fig id="F4" position="float">
<label>Figure 4</label>
<caption><p><bold>Average concentrations and 95% confidence intervals for PM<sub><bold>2.5</bold></sub> (&#x003BC;g/m<sup><bold>3</bold></sup>) and BC (&#x003BC;g/m<sup><bold>3</bold></sup>) (all the bike-paths, classed by work days and weekends)</bold>.</p></caption>
<graphic xlink:href="fenvs-04-00077-g0004.tif"/>
</fig>
<p>The average concentrations of BC were between 16 and 38 &#x003BC;g/m<sup>3</sup> during workdays and in the range of 10&#x02013;32 &#x003BC;g/m<sup>3</sup> during weekends. Such air pollution levels are relevant from a public health point of view, considering the positive association of the exposure to particle related pollutants with respiratory and cardiovascular problems (Calder&#x000F3;n-Garcidue&#x000F1;as et al., <xref ref-type="bibr" rid="B4">2003</xref>; Kim, <xref ref-type="bibr" rid="B23">2004</xref>; WHO, <xref ref-type="bibr" rid="B18">2005</xref>; Holguin et al., <xref ref-type="bibr" rid="B19">2007</xref>).</p>
<p>We compared the results of this study with the measured concentrations to which bike users are exposed to in other cities of the world. The PM<sub>2.5</sub> levels found in Bogot&#x000E1; are up to one order of magnitude higher than those measured in the proximity of streets along bike-paths in cities such as Minneapolis and Berkeley in the United States (Jarjour et al., <xref ref-type="bibr" rid="B22">2013</xref>; Hankey and Marshall, <xref ref-type="bibr" rid="B17">2015</xref>). They are up to three times higher than the concentrations recorded in European cities (Adams et al., <xref ref-type="bibr" rid="B1">2001</xref>; Zuurbier et al., <xref ref-type="bibr" rid="B41">2010</xref>); in the same range of those measured in Beijing, China (Huang et al., <xref ref-type="bibr" rid="B20">2012</xref>), and lesser than those reported for Indian cities, such as New Delhi (Goel et al., <xref ref-type="bibr" rid="B15">2015</xref>). Similarly, BC concentrations measured in this study were up to two orders of magnitude higher than those observed in North American cities (Jarjour et al., <xref ref-type="bibr" rid="B22">2013</xref>; MacNaughton et al., <xref ref-type="bibr" rid="B26">2014</xref>; Hankey and Marshall, <xref ref-type="bibr" rid="B17">2015</xref>). Table <xref ref-type="table" rid="T4">4</xref> presents a summary of the studies cited above.</p>
<table-wrap position="float" id="T4">
<label>Table 4</label>
<caption><p><bold>International studies assessing the exposure levels of bike-path users</bold>.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th valign="top" align="left"><bold>Authors</bold></th>
<th valign="top" align="left"><bold>Location</bold></th>
<th valign="top" align="left"><bold>Particle size measured</bold></th>
<th valign="top" align="left"><bold>Equipment</bold></th>
<th valign="top" align="left"><bold>Mean</bold></th>
<th valign="top" align="left"><bold>Minimum</bold></th>
<th valign="top" align="left"><bold>Maximum</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Adams et al., <xref ref-type="bibr" rid="B1">2001</xref></td>
<td valign="top" align="left">London, UK</td>
<td valign="top" align="left">PM<sub>2.5</sub></td>
<td valign="top" align="left">Gravimetric High Flow Personal Sampler (HFPS)</td>
<td valign="top" align="left">34.5 &#x003BC;g/m<sup>3</sup> (Summer)<break/> 23.5 &#x003BC;g/m<sup>3</sup> (Winter)</td>
<td valign="top" align="left">13.3 &#x003BC;g/m<sup>3</sup> (Summer)<break/> 6.8 &#x003BC;g/m<sup>3</sup>(Winter)</td>
<td valign="top" align="left">68.7 &#x003BC;g/m<sup>3</sup> (Summer)<break/> 76.2 &#x003BC;g/m<sup>3</sup> (Winter)</td>
</tr>
<tr>
<td valign="top" align="left">Zuurbier et al., <xref ref-type="bibr" rid="B41">2010</xref></td>
<td valign="top" align="left">Amhem, Netherlands</td>
<td valign="top" align="left">PM<sub>2.5</sub></td>
<td valign="top" align="left">Real time active sampling DataRAMs model 1200 with a PM<sub>2.5</sub> cyclone model GK 2.05</td>
<td valign="top" align="left">71.7 &#x003BC;g/m<sup>3</sup> (Low-traffic bicycle) 72.3 &#x003BC;g/m<sup>3</sup> (High-traffic bicycle)</td>
<td valign="top" align="center">&#x02013;</td>
<td valign="top" align="center">&#x02013;</td>
</tr>
<tr>
<td valign="top" align="left">Huang et al., <xref ref-type="bibr" rid="B20">2012</xref></td>
<td valign="top" align="left">Beijing, China</td>
<td valign="top" align="left">PM<sub>2.5</sub></td>
<td valign="top" align="left">Portable Aerosol Spectrometer Model LD-6S</td>
<td valign="top" align="left">49.1 &#x003BC;g/m<sup>3</sup></td>
<td valign="top" align="left">18.96 &#x003BC;g/m<sup>3</sup></td>
<td valign="top" align="left">112.47 &#x003BC;g/m<sup>3</sup></td>
</tr>
<tr>
<td valign="top" align="left">Jarjour et al., <xref ref-type="bibr" rid="B22">2013</xref></td>
<td valign="top" align="left">Berkeley, USA</td>
<td valign="top" align="left">PM<sub>2.5</sub></td>
<td valign="top" align="left">DustTrak 8520</td>
<td valign="top" align="left">4.88 &#x003BC;g/m<sup>3</sup> (Low Traffic)<break/> 5.12 &#x003BC;g/m<sup>3</sup> (High Traffic)</td>
<td valign="top" align="left">2.25 &#x003BC;g/m<sup>3</sup> (Low Traffic)<break/> 2.25 &#x003BC;g/m<sup>3</sup> (High Traffic)</td>
<td valign="top" align="left">20.96 &#x003BC;g/m<sup>3</sup>(Low Traffic)<break/> 27.40 &#x003BC;g/m<sup>3</sup> (High Traffic)</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">BC</td>
<td valign="top" align="left">microAeth AE51</td>
<td valign="top" align="left">1.76 &#x003BC;g/m<sup>3</sup> (Low Traffic)<break/> 2.06 &#x003BC;g/m<sup>3</sup>(High Traffic)</td>
<td valign="top" align="left">0.11 &#x003BC;g/m<sup>3</sup>(Low Traffic)<break/> 0.1 &#x003BC;g/m<sup>3</sup>(High Traffic)</td>
<td valign="top" align="left">63.83 &#x003BC;g/m<sup>3</sup>(Low Traffic)<break/> 53.53 &#x003BC;g/m<sup>3</sup> (High Traffic)</td>
</tr>
<tr>
<td valign="top" align="left">MacNaughton et al., <xref ref-type="bibr" rid="B26">2014</xref></td>
<td valign="top" align="left">Boston, USA</td>
<td valign="top" align="left">BC</td>
<td valign="top" align="left">microAeth AE51</td>
<td valign="top" align="left">1.67 &#x003BC;g/m<sup>3</sup> (Bike-path)<break/> 2.36 &#x003BC;g/m<sup>3</sup>(Bike-lane)<break/> 1.98 &#x003BC;g/m<sup>3</sup> (Designated Bike-lane)</td>
<td valign="top" align="center">&#x02013;</td>
<td valign="top" align="center">&#x02013;</td>
</tr>
<tr>
<td valign="top" align="left">Hankey and Marshall, <xref ref-type="bibr" rid="B17">2015</xref></td>
<td valign="top" align="left">Minneapolis, USA</td>
<td valign="top" align="left">PM<sub>2.5</sub></td>
<td valign="top" align="left">DustTrak 8530</td>
<td valign="top" align="left">10.9 &#x003BC;g/m<sup>3</sup> (Morning) 9.4 &#x003BC;g/m<sup>3</sup>(Afternoon)</td>
<td valign="top" align="center">&#x02013;</td>
<td valign="top" align="center">&#x02013;</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">BC</td>
<td valign="top" align="left">microAeth AE51</td>
<td valign="top" align="left">2.7 &#x003BC;g/m<sup>3</sup> (Morning) 0.8 &#x003BC;g/m<sup>3</sup> (Afternoon)</td>
<td valign="top" align="center">&#x02013;</td>
<td valign="top" align="center">&#x02013;</td>
</tr>
<tr>
<td valign="top" align="left">Goel et al., <xref ref-type="bibr" rid="B15">2015</xref></td>
<td valign="top" align="left">Delhi, India</td>
<td valign="top" align="left">PM<sub>2.5</sub></td>
<td valign="top" align="left">DustTrak 8533</td>
<td valign="top" align="left">347 &#x003BC;g/m<sup>3</sup> (January) 285 &#x003BC;g/m<sup>3</sup> (February)</td>
<td valign="top" align="center">&#x02013;</td>
<td valign="top" align="center">&#x02013;</td>
</tr>
<tr>
<td valign="top" align="left">This work</td>
<td valign="top" align="left">Bogot&#x000E1;, Colombia</td>
<td valign="top" align="left">PM<sub>2.5</sub></td>
<td valign="top" align="left">DustTrak 8533</td>
<td valign="top" align="left">80&#x02013;136 &#x003BC;g/m<sup>3</sup> (Weekdays)<break/> 30&#x02013;72 &#x003BC;g/m<sup>3</sup> (Weekdays)</td>
<td/>
<td/>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">BC</td>
<td valign="top" align="left">microAeth AE51</td>
<td valign="top" align="left">16&#x02013;38 &#x003BC;g/m<sup>3</sup> (Weekdays)<break/> 10&#x02013;32 &#x003BC;g/m<sup>3</sup> (Weekdays)</td>
<td/>
<td/>
</tr>
</tbody>
</table>
</table-wrap>
<p>In a previous study conducted in Bogot&#x000E1;, Fajardo and Rojas (<xref ref-type="bibr" rid="B10">2012</xref>) determined the concentration levels of PM<sub>10</sub> in two sites along a bike-path located on a major city avenue. They reported PM<sub>10</sub> average concentrations between 77 and 108 &#x003BC;g/m<sup>3</sup>, and concluded that people who regularly use bikes in Bogot&#x000E1; can inhale doses of particulate matter between 45 and 65% higher than the doses inhaled by people who do not use bikes.</p>
<p>Accounting for the day of the week when each experiment was conducted, and the traffic volume, we can conclude that the dynamics of vehicular traffic during the week has a significant impact on the PM<sub>2.5</sub> and BC concentrations in the proximity of streets. A statistically significant difference exists in pollutant concentrations measured during workdays and weekends at each of the four streets considered (see confidence intervals in Figure <xref ref-type="fig" rid="F4">4</xref>).</p>
<p>For PM<sub>2.5</sub>, in streets such as Carrera 19, the average concentration during workdays is about three times the one measured during weekends, where the vehicular flow is lesser (according to local mobility authorities, Sundays&#x00027; total public transit traffic is &#x0007E;50% lesser than in workdays). For BC, streets that bear a higher share of heavy traffic during workdays, such as Calle 26 and Avenida Boyac&#x000E1;, were those with the highest BC concentrations. We compared means of workdays and weekends for each street by a one-way ANOVA test, and in each case we found that the null hypothesis (i.e., mean concentrations are the same) can be rejected with a very small <italic>p</italic>-value (&#x0003C;5&#x000D7;10<sup>&#x02212;8</sup>). These conclusion has been remarked by other authors (Berghmans et al., <xref ref-type="bibr" rid="B3">2008</xref>; Int Panis et al., <xref ref-type="bibr" rid="B21">2010</xref>; Fajardo and Rojas, <xref ref-type="bibr" rid="B10">2012</xref>). Figure <xref ref-type="fig" rid="F5">5</xref> provides a graphical appreciation of the differences in the distribution of the measured pollutants during workdays and weekends.</p>
<fig id="F5" position="float">
<label>Figure 5</label>
<caption><p><bold>Comparison between the distribution of concentrations of PM<sub><bold>2.5</bold></sub> and BC measured in workdays and weekends (all the bike-paths)</bold>. The borders of the boxes indicate the first quartile (Q1), second (median), and third quartile (Q3) of the distribution, and the whiskers are located at 1.5 times the Q3&#x02013;Q1 distance from the closest quartile.</p></caption>
<graphic xlink:href="fenvs-04-00077-g0005.tif"/>
</fig>
<p>Figure <xref ref-type="fig" rid="F6">6</xref> shows scatter plots for the pairwise concentrations of BC and PM<sub>2.5</sub>&#x02013;BC measured during workdays and weekends for the bike&#x02013;paths along Carrera 19 and Calle 26. There is not graphical evidence of significant correlation between the two measurements. This could be explained by the simultaneous co-existence of different emitting sources. Similar results have been obtained for the other two bike-paths (data not shown).</p>
<fig id="F6" position="float">
<label>Figure 6</label>
<caption><p><bold>Scatter plots and distributions of PM<sub><bold>2.5</bold></sub> and BC concentrations measured along two bike-paths, during work days and weekend</bold>.</p></caption>
<graphic xlink:href="fenvs-04-00077-g0006.tif"/>
</fig>
<p>The charts at the top and at the right of the scatter plots in Figure <xref ref-type="fig" rid="F6">6</xref> are providing the distribution of the measured concentrations. The distribution for PM<sub>2.5</sub>&#x02013;BC for Calle 26 shows a very pronounced bimodality, both during workdays and weekend days. This important avenue of Bogot&#x000E1; is very wide, with large stretches of fluid vehicular traffic. However, it has several intersections with mayor confluent streets, where traffic jams are constant. Such diverse conditions may be a cause of the prominent bimodality.</p>
<p>It is also important to remark the impact that the usage of paths located on narrow, closed geometry streets may have on bikers&#x00027; level of exposure to air pollution. Even though the total vehicular traffic along the narrowest street considered in this study (Carrera 11) is about four times lesser than the one in the largest and open geometry one (Calle 26), the concentrations of PM<sub>2.5</sub> and BC (as measured during workdays) were practically equivalent. Even if several other elements are still to be analyzed at a finer level, these results suggest that the geometry of the streets can significantly affect air pollutant dispersion, increasing levels of exposure for bike-path users.</p>
<p>While promoting the bicycle as an alternative transport mode in Bogot&#x000E1;, it would be significant to stress the importance of reducing air pollution exposure levels of cyclists. It appears advisable to consider using secondary, low traffic level streets, for new bike dedicated infrastructure, as well as physical barriers in heavy-traffic roads were existing bike-paths are located. Also, it is urgent to actively promote emission control systems on mobile sources such as particulate filters, as well as to encourage the massive use of face filter masks among bike users. This fact clearly indicates the need of more accurate studies on the interrelations between mobility strategies and their effects on public health.</p>
</sec>
<sec id="s4">
<title>Uncertainty and results representativeness</title>
<p>It is well-documented that DustTrak and Aethalometer monitors readings may diverge from the standard PM determination methods (Ramachandran et al., <xref ref-type="bibr" rid="B29">2000</xref>; Yanosky et al., <xref ref-type="bibr" rid="B40">2002</xref>; Wallace et al., <xref ref-type="bibr" rid="B37">2011</xref>). The readings of photometers such as DustTrak monitors may overestimate the concentration of airborne particulate matter compared to gravimetric techniques (Gorner et al., <xref ref-type="bibr" rid="B16">1995</xref>; Ramachandran et al., <xref ref-type="bibr" rid="B29">2000</xref>; Tasi&#x00107; et al., <xref ref-type="bibr" rid="B35">2012</xref>). We previously reported results from specific experiments carried out to compare and validate the two methods for mass concentrations measurement in Bogot&#x000E1; (Franco et al., <xref ref-type="bibr" rid="B11">2009</xref>, <xref ref-type="bibr" rid="B12">2013</xref>). Even though those local comparisons were only conducted to assess PM<sub>10</sub> concentrations, we believe that under our study conditions, DustTrak monitors provided representative information on the range of PM concentration. In addition, other authors (Wallace et al., <xref ref-type="bibr" rid="B37">2011</xref>) have shown that continuous monitors such as DustTrak and Aethalometer can provide reliable information for environmental studies.</p>
<p>The authors are aware of the representativeness limits of the results in terms of the number of experiments and bike-paths addressed. However, they believe this pilot study can provide useful data for an improved understanding of air quality conditions for bikers in Bogot&#x000E1; and in other Latin American cities.</p>
</sec>
<sec sec-type="conclusions" id="s5">
<title>Conclusions</title>
<p>For the very first time for Bogot&#x000E1;, we quantified in this study PM<sub>2.5</sub> and BC concentrations at bike-paths located alongside major streets. Bicycle is a sustainable mode of transport and it has important health benefits for the population. However, the pollution levels indicate the existence of low air quality conditions and show that cyclists are exposed to concentrations that largely exceed the threshold values established as potentially dangerous for human health. We consider these results provide useful insights to increase the appreciation of this problem in Latin American cities and to contribute with technical elements that lead to the inclusion of air quality variables when designing and planning sustainable urban mobility infrastructures.</p>
</sec>
<sec id="s6">
<title>Author contributions</title>
<p>JF, JS, and IM were involved in field work design. JF and JS were in charge of the data collecting process. JF and IM were in charge of the data processing process. JS contributed in structuring the paper, and JF and IM wrote the final version. All authors supported the experiments, critically commented, and revised the manuscript.</p>
<sec>
<title>Conflict of interest statement</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
</sec>
</body>
<back>
<ack>
<p>The authors acknowledge the financial support provided by Universidad EAN for the data collection campaign and the partial funding of the Colombian Department of Science, Technology and Innovation (COLCIENCIAS) under the figure of a junior-scientist agreement. In particular, we thank the contribution of professor Daniel Gomez as an in-house supervisor for the agreement between Universidad EAN and COLCIENCIAS.</p>
</ack>
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