Modal comparison exposure studies typically use the same routes or origins and destinations across modes and fix other travel characteristics (e.g. departure time). While potentially informative, these comparisons are not always realistic because pollution exposure is also affected by intrinsic modal travel differences. The more realistic modal comparisons allow self-selected routes or direct active travelers to use representative routes for their mode – but local transportation network characteristics may affect the results. Bicycle travel patterns are different from motorized ones because of distinct traveler characteristics, trip distances, and route preferences (Plaut 2005, Broach et al. 2012). Real-world bicycle trips tend to be shorter and in higher-density parts of a city than trips using motorized modes. Bicycle trips are also highly seasonal (Nankervis 1999), so a different distribution of meteorological conditions could be expected by mode, with a systematic influence on exposure concentrations. Most bicycle exposure studies occur during warmer months when a greater proportion of bicycling occurs (see Appendix B), but the joint seasonality of mode splits and pollution levels should be considered when comparing travelers’ exposures – especially for year-round bicyclists.
5.2 Noise Barrier
Understanding the influence of traffic and the physical environment on UFP concentrations is an essential component of evaluating human exposure to pollution in urban environments. In this study, we examine the UFP exposure of bicyclists and pedestrians on multiuse paths adjacent to freeway noise barriers. Using concurrent traffic, wind and air quality measurements, UFP levels were found to be significantly higher along multiuse paths placed in front of noise barriers (on the freeway side) than those behind the noise barriers (on the residential side). The shielding effectiveness of the noise barrier varied by study site and day, but the effectiveness did not appear to relate to traffic or wind conditions. The barrier was also consistently effective at reducing pathway concentrations for highly varying exposed-side concentrations. Utilizing pre- existing freeway right-of-way near noise barriers for bicycle/pedestrian pathways is a cost- effective way to provide non-motorized transportation facilities and promote the use of active transportation modes, especially in urban environments where space is at a premium. But these facilities can expose bicyclists and pedestrians to UFP levels much higher than urban
While bicyclists and other active travelers obtain health benefits from increased physical activity, they also risk an uptake of traffic-relatedairpollution. But pollution exposure for urbanbicyclists is not well understood due to a lack of direct measurements and insufficient analysis of the determinants of exposure. This knowledge gap impedes pollution-conscious transportation planning, design, and health impact assessment. The research presented in this report generates new connections between transportationsystem characteristics and pollution exposure for bicyclists. T he primary research questions are: 1) How does urbanbicyclists’ exposure to airpollution vary with roadway and travel characteristics? and 2) T o what extent can transportation-related strategies reduce exposure? Novel methods to collect and integrate bicycle, rider, traffic, and environmental data are also introduced. Bicyclist exposure concentrations and travel characteristics were collected on a wide range of facilities in Portland, OR. High-resolution trajectory and pollution data were then integrated with roadway and traffic data. Models of exposure were estimated from the on-road data. Important new quantifications in the models include the effects of facility type, average daily traffic (ADT), stop-and-go conditions, and industrial corridors on multi-pollutant exposure. Findings from this research and the literature are distilled so that they can be incorporated into bicycle network design guidelines.
affect obesity and cardio-metabolic disorders. Traffic could influence perceived safety and thereby affect the amount of active travel by foot or by bike. In this in- stance, we hypothesize that higher traffic could reduce physical activity, and as noted above, this could posi- tively change energy balance. Previous research on this and similar cohorts has demonstrated that traffic can negatively affect active travel  and that this may lead to higher levels of obesity . Another pathway could operate through perceived safety, noise and vibration, which all have the potential to increase stress. Stress has been associated with higher intakes of fat and carbohy- drates and with cortisol and sleep dis-regulation that can affect the diet. All of these pathways, if they lead to altered eating habits, could contribute to obesity. In recent re- search on the same cohort, we showed that stress in the family is linked to small increases in BMI growth . Fi- nally, there is the impact of environmental and traffic- relatedpollution. Here the effect could operate through systemic inflammation to increase pro-obesogenic path- ways mentioned above  or through the formation of chronic diseases that might lessen physical activity and have themselves been associated with obesity in the case of asthma [24,25]. Some components of traffic-relatedairpollution may contain endocrine disruptors that could be obesogens. This pathway might be enhanced through other obesogen exposures from other environ- mental sources such as phthalates . This framework is used to guide our statistical modeling in terms of selecting variables to test for confounding and to help interpret our results where specific variables are unavail- able for analyses (e.g., biomarkers of obesogen exposures).
Airpollution exposure assessment is often a tradeoff between achieving coverage and identifying the intricacies of exposure. As airpollution exposure can vary greatly spatiotemporally, the role of one’s microenvironment is a major factor in overall exposure, as TRAP levels have been shown to fluctuate even across relatively small distances (Ryan & LeMasters, 2007). Efforts to model airpollution exposure based on single 24 or 48 hour exposure windows may misclassify exposure due to variability between days, especially if the sampling period captures a rare contributor to airpollution, like an haze event or higher than normal time in a vehicle, or misses a regular contributor to airpollution that occurs outside of the sampling period. Furthermore, weather, seasonal variation, and alterations in personal behaviors can all contribute to the complexity of accurately assessing airpollution exposure. This study examined the utility of three, 24-hour time points, each roughly 2 weeks apart, to explore the role of the microenvironment on PM 2.5 exposure among pregnant women. Not surprisingly, the
Three factors are used as instrumental variables for teleworking. The first is the job position which refers to whether the individual is on a management position, production position or training- supervising position. This variable can be correlated with the teleworking, since managers or supervisors, are older, more educated and more flexible to work at home some days of the week or month. In addition, we argue that the job position cannot have a direct effect on airpollution and traffic, but the effect can go only indirectly through teleworking. The second variable is whether the individual has a computer at home or not. This can be significantly correlated with the teleworking scheme, since technology, computing and internet is the major determinant of using this employment scheme. As we mentioned before, the possession of the computer at home cannot cause directly the traffic volume or the airpollution. The social class of the individual is the third variable used as an instrument. More specifically, the social-professional class refers to whether the individual belongs in one of the following categories: Legislators; senior officials; managers; professionals; technicians and associate professionals; clerical officers; service workers; market sales workers; skilled agricultural and fishery worker; craft and related trades workers; plant and machine operator assemblers; and elementary occupations. This variable can be correlated with teleworking, since managers, clerical officers and service workers for instance, are more likely to participate in the teleworking scheme than those who belong to plant and machine operator assemblers and the elementary occupations. More details on the variables employed in the analysis, and, the correlation amongst teleworking and the main outcomes of interest-airpollution and traffic- are described and discussed in the next section.
directly identify the victims of complex substances and mixtures with cumulative toxicity, such as smoking or air pollutants. Neither are the health-relevant characteristics of the exposure unanimously defined, nor are the health outcomes specifically linked to airpollution only. Therefore, uncertainty remains an inherent characteristic of any attempt to derive attributable cases. We prudently dealt with uncertainty, deriving the number of cases “at least” attributable to airpollution. We did not include all health outcomes associated with ambient air. For mortality, we ignored potential effects
characterize the causes determining the pollution phenomena .Across the world, increasing population and rapid industrialization has caused significant environmental degradation. It ranges across air, water, noise and land pollution. Carbon Monoxide (CO), Carbon Dioxide (CO2),Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Particulate Matter (PM), Lead (pb) ,Ammonia (NH3), Ground level Ozone (O3) are the primary cause of airpollution. Development of airpollution monitoring system will be beneficial to control and measure pollutionrelated parameters. Conventional strategies for measurement of airpollution parameters are more precise yet expensive and restricted to spatial area, it is not possible to deploy measurement instruments in large number. In remote locations i.e. glaciers where less or no network connectivity is found then data communication and collection becomes major issue. Because of the communication issue, data needs to be collected manually at fixed location which is time consuming . Many studies on human health have concluded that environmental stress is a major factor for morbidity and has a negative impact on the quality of life especially in urban areas (e.g.).One of the major challenges in these studies is to obtain or estimate high resolution (spatial and temporal) air quality data to be able to analyze the correlation between health and the exact air to which people are exposed. Among all the airborne pollutants (Sox, NOx ,CO, NH3 O3, etc.). Recently there has been a growing attention to study particulate matters due to their significant adverse impact on human health. In urban environments, this measure is closely linked to urbantraffic conditions . Recent development of electronics has realized the vision of using wireless communication in devices used for monitoring wide range of real life parameters, such as temperature, pressure, and airpollution. These
Chronic respiratory diseases are responsible for 4.2 million deaths a year worldwide, nearly 80% of which occur in low- or middle-income countries . The burden of chronic respiratory diseases such as asthma and COPD is un- known in Africa  but should be high due to environmental conditions such as airpollution. Urbanization rates in Africa are among the highest in the world, with more than half of the population expected to reside in urban areas by 2035 . Trafficrelated to airpollution is a major part of pollution sources in big African cities. While the use of biomass fuel for domestic cooking is the main source of pollution in most African rural areas . The United Nations (UN) es- timates that roads account for up to 90% of urbanairpollution in developing countries . The United Nations Environment Programme (UNEP) also es- timated that more than 600 million people in urban areas around the world, the majority of them in developing countries, particularly in Africa, were exposed to dangerous levels of air pollutants generated by traffic .
Asthma is a burdensome disease which is often cited as the most common chronic disease in childhood. Traffic-relatedairpollution (TRAP) may be an important exposure in the development of childhood asthma. However, the burden of childhood asthma attributable to TRAP is poorly documented. Using a land-use regression (LUR) model, we estimated the childhood (birth-18 years old) population exposure to the following three air pollutants in Bradford, UK: Particulate Matter equal or less than 2.5 micrometers in diameter (PM 2.5 ), Particulate Matter equal or less than 10 micrometers in diameter (PM 10 ) and Black Carbon (BC). We assigned exposures at the lowest census tract level: the ‘output area’. We extracted national and local childhood asthma incidence rates from the literature and used meta-analytic exposure-response functions to calculate the relative risk, popula- tion attributable fraction of childhood asthma in association with each pollutant and the number of childhood asthma cases attributable to each pollutant. We investigated the impacts of reducing air pollutants at each output area to comply with the World Health Organization’s (WHO) air quality guidelines. At the output area level, the annual mean PM 2.5 , PM 10 and BC concentrations were 10.40 mg/m 3 , 16.63 mg/m 3 and 1.07 10 5 m 1 , respectively. Depending on the pollutant, the estimated number of attributable childhood asthma cases varied between 279 and 612 annually, representing between 15% and 33% of all cases in the city. Between 7% and 12% of annual childhood asthma cases were specifi- cally attributable to TRAP. Compliance with the WHO air quality guidelines prevented up to 29 cases. Using national versus local baseline childhood asthma incidence rates with differing underlying asthma definitions resulted in up to 322% as many attributable cases.
compounds in . Jung et al. proposed an airpollution geo- sensor network to monitor several air pollutants in . The system consists of 24 sensors and 10 routers, and provides alarm messages depending on the detected pollutants. Gao et al. proposed a wireless mesh network to cover a given geographic area using embedded microprocessors consisting of sensors and wireless communication in . Kwon et al. proposed another outdoor airpollution monitoring system in . This system uses ZigBee networks to transmit the sensed pollutant density levels. The above systems are all airpollution systems utilizing mobile sensors to achieve high coverage, but they all need proprietary equipment to accommodate the movement requirements of the system. Gil-Castineira et al. proposed an airpollution detection system based on the public transportationsystem and tested it in a small scale experiment . However, there is no sensor deployment algorithm that can efficiently utilize the available resources. So in this paper, we formulate an optimization problem to deploy the sensors so as to utilize them efficiently. The optimization problem is solved with CRO.
The term of “transport integration” is used in different contexts in many publications. Several detailed definitions and areas of integration are presented by different authors, including: (Dydkowski G. 2005, Givoni M., Banister D. 2010, Hine J. 2000, Hull A. 2005, Ibrahim M. 2003, Janic M., Reggiani A. 2001, May et al 2006, May T. 1993, Potter S., Skinner M. 2000, Starowicz W. 2000, Stead D. 2003, Underdal A. 1980, Vigar G.). In general, transport integration denominates such technical, economic, organizational, policy – based and informational concepts and solutions that assure the continuity of travels from door to door (Janic M., Reggiani A. 2001). Transport integration is focused on: connecting different transportation modes operating in a certain transportationsystem, providing solutions to facilitate passengers’ / goods transfer between the modes and assuring safe, smooth and efficient flow of passengers / goods from their origins to their destinations (GUIDE 1999, Ibrahim M. 2003). Based on the results of the EU projects (Hilferink P., Roest Crollius A., Van Elburg J. - C. 2003, Żak J., Fierek S., Kruszyński M. 2014) integration of an urban public transportation is defined as an organizational process by which elements of the passenger public transportationsystem (network and infrastructure, fares and ticketing systems, information and marketing components) and a variety of carriers who serve different transportation modes, interact more closely and efficiently, to generate an overall improvement in service quality level and enhanced performance of the combined public and individual transportation. In general, the implementation of different transport integration solutions may result in the following benefits (Prospects 2003): reduction of travel times, transportation costs, traffic congestion and environmental pollution. Transport integrating solutions may improve the urban public transportationsystem accessibility and overall competitiveness as well as assure better utilization of different transportation means and infrastructure.
Chapter two of WHO, 1999 deals comprehensively with the relationship between information on air quality and population exposure. Herein it is mentioned that “Air quality assessment in general and specifically air quality monitoring should produce information that can be interpreted to indicate population exposure. Correctly determining population exposure requires knowing the population distribution and location of air monitoring stations to identify the population concentrations to which the population and different population subgroups in particular are exposed. Not only hot spots or areas where maximum concentrations are expected but also representative community sites where most of the population lives should be monitored. Monitoring ambient air quality that means outdoor air, and the monitoring sites are more or less fixed at selected locations. The population moves into, out of and across the community every day. The exposure estimated by using the ambient air concentration levels is the potential exposure of the population”. Various methods to assess population exposure using ambient air quality monitoring data are described in this WHO monograph too 2 .
researcher to expand the understanding concerning the distribution of the pollutants in some locations or areas and to understand the factors that influence the trends and significance [Rahman et al., 2015]. Estimation of small-area variations in trafficpollution are important to the exposure experience of the population and may detect health effects that would have gone unnoticed with other exposure estimates [Watmough et al., 2014]. Despite increasing urban development and anthropogenic activities, monitored data on urbanairpollution are sparse in Nigeria and many developing countries [Baumbach et al., 1995;Gupta et al., 2006; Abam and Unachukwu, 2009], hence the collection of accurate and reliable data necessary for the evaluation of urbanair quality is therefore very important. This study aim to collect, analyze and map the gaseous trafficrelatedair pollutants (CO, NO x , NO 2 and SO 2 ) at road junctions, intersections, and motor garages in order to facilitate the management of air pollutions in the study area.
Daily pollution concentrations were obtained from: (1) the London Air Quality Network (www.londonair.org.uk); (2) the UK Particle Concentrations and Numbers Network (http://uk-air.defra.gov.uk/networks/network-info? view = particle); (3) the ClearfLo 23 project that measured pollutant concentrations at seven locations across London and the South East of England; and (4) by a receptor modelling exercise to isolate the urban increment from regional background concentrations. Data on over 100 pollutant metrics were assembled. From these data we selected, a priori, the most appropriate metrics to act as markers of a range of traf ﬁc sources in our main analyses. This selection was based on the analyses of temporal patterns and correlations between the metrics, knowledge of local emission sources and reference to the existing literature. Supplementary Table S1 online provides details regarding the rationale for the selection of these metrics and of their measurement methods. In brief, (1) oxides of nitrogen (NO X ) was selected as a general indicator of traf ﬁc pollution as road transport represented ~ 47% of NO X emissions in 2010 compared with 16% for space heating; 24 (2) carbon monoxide (CO) was selected as an indicator of petrol engine exhaust as in London it is derived predominately from incomplete petrol combustion; 25 (3) EC in PM 10 (mass of particles with aerodynamic diameter o10 μm) and BC in PM 2.5 (mass of particles with aerodynamic diameter o2.5 μm) were selected as markers of emissions from diesel vehicles; 26 (4) copper (Cu) was selected as an indicator of brake wear as it is generally the most abundant element in brake linings and in brake dust; 27 (5) zinc (Zn) was selected as an indicator of tyre wear as it is the only element in tyres with concentrations above those found in crustal material; 27 and (6) aluminium (Al) was selected as the indicator species for mineral dust including road wear. 28 All of the above pollutants were measured at the central London background monitoring site at North Kensington. All measurements were 24-h averages except for CO, which were 8-h averages. We assessed the speci ﬁcity of each trafﬁc indicator from other sources by calculating a mean kerbside enrichment factor. This was de ﬁned as: kerbside enrich- ment factor = ((roadside) − (background)/(background) using the London Marylebone Road monitoring site to indicate roadside concentrations and the North Kensington site to indicate background concentrations.
In Havana, transport is blamed as a likely source of pollution issues, which is usually supported on arguments referring to a vehicle fleet mainly made of old cars (i.e., most models are American from the 1950s or Russian from the 1980s) with poor technical conditions. Most of the existing studies are based on measurements from passive samplers collected for 24 h, which may not be representative of conditions where pollutant concen- trations (particles or gases) fluctuate or are not homogeneous, such as transport-relatedpollution. The goal of this paper is to explore the transport-generated pollution by examining short-time correlations between traffic flows, pollutant concentrations and meteorological parameters. To do that, statistical relationships among all variables were analyzed, which revealed that PM 10 , NO 2 and SO 2 concentration levels are influenced by
The third question is how these quotas will be allocated. Should they be allocated free of charge? If not, the entities affected by the scheme will have to buy all the permits they need on the market: in the event of the total available quantity on the market being small, it is equivalent to setting up a quota auction. Economically, this is the most efficient solution as it obliges actors to reveal their preferences. It is also consistent with the polluter-pays principle and creates a usable financial resource. However, as with congestion charging it immediately increases the financial burden on the actors involved: this would eliminate the essential acceptability advantage that driving rights could have over congestion charging. Consequently, at least some of the quotas would have to be allocated free of charge as a visible and immediate compensation in order to facilitate this instrument’s acceptability. If the quotas are allocated free of charge, to whom should they be allocated and with what distribution method? The problem is that although in theory these methods do not threaten the effectiveness of the instrument, they ultimately determine the financial burden on the participating entities. Will these entities be vehicle owners or inhabitants? Choosing the latter would amount to compensating inhabitants for the consequences of congestion and pollution. This would involve those who travel little, pedestrians and public transport users and not only motorists, which would improve the acceptability of the scheme.
On- and near-road UFP concentrations are further determined by location-specific meteor- ological variables. Temperature, wind speed and wind direction are the most frequently re- ported. In-transit studies have reported negative correlations between temperature and UFP concentrations (correlation coefficients around -0.76), with stronger relationships for cycling than for automobiles (Knibbs et al., 2011). This likely also explains the higher median UFP concentrations during cycling than during car driving in the cooler morning rush-hours in Basel. Higher wind speed is usually associated with more dilution of particles resulting in lower concentrations (Kaur and Nieuwenhuijsen, 2009; Knibbs and de Dear, 2010). Comparisons of transportation modes and respective concentrations are constrained as measurements have been conducted during various times of the day, week, and year. It is well known from fixed-site measurements that UFP show diurnal, weekly and seasonal patterns typical of temporal patterns of traffic density and meteorological conditions. In ur- ban areas, highest ambient UFP levels are usually observed during morning weekday rush hour with a second, less distinct, peak during afternoon rush hours (Borsós et al., 2012; Morawska et al., 2008). This was also seen in Basel (Figure 4 in article 1). Not all studies carried out measurements within the same time period in the different transportation modes (e.g. Boogaard et al., 2009; Int Panis et al., 2010) and therefore relative differences in UFP concentrations were potentially misclassified. To the best of my knowledge, our study was the first multimodal in-transit study to include a weekend sampling period. We found less contrast in UFP concentrations between modes during weekend than during weekday rush and non-rush hour.
The sensors that were needed as part of the original project proposal included O 3 , NO 2 , and CO. These three sensor requirements were used as a basis for research on potential sensor manufacturers. There were several factors that were important to the project; (a) cost, (b) sensitivity, and (c) compatibility. The team chose a Raspberry Pi for a control center and data storage. The sensors needed to be able to connect with the Pi in order to respond to the data collection code written by one of the team members. Alphasense sensors are high quality, in comparison to a “hobby” level air quality sensor. Cost, sensitivity, performance range, and linearity (the amount of error change) for each sensor can be seen below in Table 2.1. This table also includes both temperature and humidity range, important factors to consider since Ohio is a temperate climate. The sensors are electrochemical; a certain voltage is generated in the presence of an electrochemically active gas, this current is directly proportional to the amount of gas in ppm.