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Different methods have been used to estimate exposure of participants to urban traffic-related air pollution in previous studies. Dispersion models can provide information that satisfies both the spatial coverage and temporal variation, but good performance from dispersion modelling requires good input data. In the approach of LUR models it is relatively simple to interpolate a limited number of measurements to a larger population [ 15 ].

However, both approaches do not account for personal activities such as time spent in traffic and activity level which will influence exposure considerably [ 16 ]. A platform that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure was presented in [ 18 ]. The authors also proposed using mobile phones with a built-in GPS Global Positioning System receiver and an accelerometer for identifying individual transportation modes including whether an individual is stationary, walking, running, biking, or in motorized transport [ 19 , 20 ].

The CitiSense project [ 22 ] had a mobile air quality system that enabled users to track their personal air quality exposure [ 23 , 24 ]. The above studies did not target population level exposure. Studies using mobile air pollution sensors are only active on a relatively small number of vehicles for which the estimated pollution levels will be derived.

Through this brief review of the health effects of traffic, we identified: i exposure to traffic-related emissions may cause major adverse health effects on the population at or near air polluted roadways; ii existing exposure models do not accurately characterize the exposure fields; and iii these disadvantages in exposure estimation can attenuate health effect estimates. To address these issues, studies are needed to characterize the personal exposure to traffic-related air pollution, and to better understand the linkages between traffic-related air pollution and public health effects. In this study, we propose a conceptual framework to improve exposure assessment by using existing, low cost mobile phone data to obtain individual trajectories to further estimate the concentration of the traffic emission in the roadway network and its contribution to exposure for the urban population.

Our proposed methodology is based upon recent advances in social and information networks using Statistical Physics [ 25 ] and the techniques of Traffic Engineering in mobile phone networks [ 26 ]. The advantage of the proposed study is that we target the entire network of mobile phone users to calculate pollution levels, exposure and health effects. We believe that this has the potential to open up new perspectives in public health research. An overview of work process for modelling traffic-related air pollution contribution to individual exposure by mobile phone tracking.

The science of individual human trajectories is a new research field based on the recent availability of empirical data on human mobility. Developments in this field have been influenced by results in the multidisciplinary field of network physics [ 27 , 28 ]. Databases and technologies that capture human mobility by mobile phone traces are now available and many interesting features have been discovered including the characteristics of individual human trajectories [ 29 ], the interaction between individual mobility and social networks [ 30 ], and meso-scopic structure i.

Modern network physics is starting to influence public health research. By analysing data from over 7. Basic mobile phone based positioning systems include: location of nearest base station; trilateration of two or more neighbouring base stations by signal strength or Time Of Arrival TOA ; multilateration hyperbolic lateration by Time Difference Of Arrival TDOA ; and network based triangulation by Angle Of Arrival AOA using multiple antennas at a base station.

More methods are available with third generation mobile systems [ 34 ]. Thiragarajan et al. The road segments are the hidden states of the system and the noisy data is the input to the algorithm. The mobile phone data are run through the algorithm twice, first to find the mobile phone area sequence and then to find the road segment sequence [ 38 ]. Processed position data provided by mobile phones are as accurate as GPS reading intervals of 2 minutes and have much lower energy consumption [ 39 ].

According to studies on probabilistic models for mobile phone trajectory estimation from Thiagarajan [ 39 ], the misplaced road segment error is approximately only 45 metres. The car navigation company TomTom [ 44 ], has one million GPS users and uses about 80 million other data sources such as GSM to monitor real time traffic and give advice on the best route, taking into account traffic congestion. Google Maps [ 45 ] gets live traffic information displaying road way traffic conditions for main roads in many countries updated every five to ten minutes.

Close to real-time traffic maps are provided by all larger search companies including Yahoo, Bing, Baidu and Yandex. For example Yandex also provide real time video cameras of selected junctions in Moscow, and traffic map forecast at 15 minutes intervals up to one hour. Microscopic traffic simulation [ 47 ] is a field closely related to trajectory mapping. Several simulation models combining microscopic traffic modelling with simulation of air pollution concentrations in street canyons [ 48 - 51 ] make it feasible to calculate traffic-related air pollution by mapped personal trajectories.

This paper combines the above presented unrelated technologies to form a new approach in urban traffic-related air pollution and public health impact research. We develop a concept for air pollution research based on trajectory mapping by using an empirical model that articulate the overall work process, system architecture, traffic-related air pollution and exposure modelling. We also discuss the main challenges that might be faced by applying such an approach to study the traffic-related air pollution to individual exposure and its public health impact assessment.

The work process is composed of six parts. The work process is described in detail below. In the suggested approach, the air pollution data can be directly gathered from a static network of pollution monitoring sites across the city and air pollution maps are generated by interpolating between them.

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People can then check their exposure levels by mapping their individual trajectory into the pollution map. However, there are some limitations of using static monitoring networks, i. As a possible complementing source of information, high spatial resolution satellite observations can be used. While satellite measurements have many advantages and are developing rapidly, they do have some major limitations, particularly when considering intra-urban resolution.

Although most operational satellite products relevant for air quality applications currently do not yet possess the spatial resolution necessary to directly resolve fine spatial detail at the urban scale, their output can nonetheless be very valuable for improving the output of local and regional chemical transport models through data assimilation schemes, and as such indirectly improve urban-scale air quality estimates. Furthermore, research is currently ongoing on producing higher-resolution m satellite-based PM measurements at the urban scale [ 52 ].

Finally, significant progress with regards to spatial resolution achievable from space-based platforms will further be made in the near future with new instruments such as TROPOMI The TROPOspheric Monitoring Instrument on the Sentinel-5 precursor platform, which will open up entirely new applications for space-based air quality monitoring, in particular over megacities and other large urban agglomerations.

Trajectory mapping uses individual human trajectories generated by advanced mobile phone GSM GPS-free tracking systems. The tracking system should be capable of accurately locating mobile phones on road segments in a roadway network by using traffic engineering tracking systems. From frequent location updates, it is possible to track the approximate real-time speed of the mobile phones.

This technology is partly qualified, but to date has not been implemented for large populations. Computational capacity may also be a limitation as one PC calculates a one hour individual trajectory in about two minutes [ 39 ]. There are ways to use one individual trajectory for calculating other trajectories on the same road segment at approximately the same time to make the computation of a large number of trajectories feasible. Commercial algorithms are likely to be faster on commuter freeway systems where the vehicles, on average, are travelling on a small set of roadways and stay on the same roadway for quite a long time.

A mobile phone signal does not identify a vehicle. The vehicle type can be estimated by drawing randomly or conditioned to trajectories such as bus stop patterns to identify that it is a bus from a national vehicle type distribution. These systems are based on Radio Frequency Identification RFID [ 56 ] and give the exact date and time for passing a certain check point.

Cross correlation of AVI and mobile phone data results in one-to-one assignment of a mobile phone to an identified vehicle.

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The vehicle type, age, etc. Individual human trajectories can be further used to generate vehicle trajectories on road segments in a roadway network by using traffic engineering tracking systems. Emissions from each individual vehicle can be calculated along the vehicle trajectories.

Dispersion of traffic air pollution in urban areas can be modelled numerically by taking into account the recirculation effect of street canyons, meteorological conditions, pollution type, traffic counts and types, and other parameters. In this case, various dispersion models e. Moreover, there is a need for high resolution input data for the models about the meteorology in the city, including temperature and wind speed and directions. Exposure data can be acquired by evaluating the pollution field along the trajectories in space and time.

In this case, a micro-environment based exposure model can be applied [ 60 ]. Mobile phone-based mobility tracking used to model traffic pollution contributions to both individual chronic and acute exposure can open new fields of research. This approach can be further developed to estimate the accumulated dose of air pollutants in the human body, and to assess the health impact by coupling individual trajectories to individual physiological information e. The trajectory based system proposed in this paper will pick up such correlations.

Therefore, the individual trajectory approach would be expected to more accurately identify the individuals with the greatest health risk. Thus, this may open up new perspectives in empirical public health research. It needs to be noted that the mobile phone is used to identify the person and a person-related vehicle.

The potential to characterize traffic-related microenvironments, e. On the Information Communication Technology ICT infrastructure part, the raw data are the mobile phone base stations in communication with individual mobile phones. These raw data are used to efficiently generate individual mapped trajectories showing the positions of individual mobile phones through time. Mapping algorithms are used to locate the moving positions onto road segments. The mapped trajectories are used as input to algorithms to construct the pollution field and estimate exposure for people moving in the pollution field.

System architecture for obtaining mapped trajectories and public health impact. It consists of a two-way communication: server side mobile phone base stations — raw data — mapped trajectories and person side mobile phone — individual trajectory, emission, exposure - health effect. At the population-scale one may investigate high risk groups and check if they share common travel patterns. This may be used to identify the population fraction i. The goal of this section is to review some modelling approaches that can be used in this proposed approach, to calculate the traffic-related air pollution and its contribution to individual exposure.

Each given model is kept simple and current at a conceptual level, so that each model can be replaced by more advanced models in specific studies. A simplifying concept of a roadway air pollution model is to treat the emission from many vehicles as one line source. The pollution is dispersed away from the line source to the entire model domain and not just near roads by the mechanisms of diffusion and convection move with the local velocity of the air. However, due to the dispersive spreading the highest pollution levels are found at the line source.

Geo-statistical interpolation models can be implemented to estimate the traffic-related air pollution based on a statistical interpolation of a dense, well-distributed, monitoring work [ 64 ]. These models allow the estimation of pollution concentrations over several time intervals, but this is limited only by the number of available measurement periods [ 64 ]. The hybrid models are those that combine personal or regional monitoring with other air pollution exposure methods [ 64 ].

Most studies were conducted in European cities, and in San Diego [ 65 ], e. No matter which of the above given models is applied, in order to calculate real-time varying traffic-related air pollution, the methods for estimating the traffic emission, emission rates, emission factors, traffic flows, and individual exposure to dynamic traffic air pollution need to be addressed explicitly. The roadway air dispersion models are usually used to estimate traffic-related air pollution. It generally relies on Gaussian plume equation [ 68 ], including wind speed, wind direction, traffic wakes and meteorological air stability.

Many tools have been developed to estimate the vehicle emission factors covering a broad range of pollutants and allow multiple scale analysis, such as the U. One challenge in roadway air dispersion modelling is the transformation from a Lagrangian formulation [ 74 ] where each vehicle is a particle with a given speed to an Eulerian formulation [ 75 ] where each road segment has a vehicle density with a local flow velocity. This particular challenge has been investigated by the UC Berkeley Mobile Millennium project [ 72 ] and an Eulerian—Lagrangian cell transmission model for air traffic flow has been developed [ 76 ].

In traffic flow conditions, the two most important densities are the critical density n c and the traffic jam density n jam. The maximum density achievable under free flow is n c , while the minimum density achieved under jamming congestion is n jam. In general, traffic jam density is about seven times the critical density. Traffic is, however, a complex phenomenon that is described by physics concepts such as synchronization and self-organized criticality SOC.

Kerner [ 77 ] divided traffic flow into three phases from low to high density: 1 free flow, 2 synchronized congested flow, and 3 wide moving jam wide jam that moves upstream. The traffic density-interaction between the vehicle wakes i. One may fill in these gaps using statistical distributions. Linking individual trajectories to vehicles can be done as follows: i Identify bus drivers and passengers by their characteristic trajectory pattern.

They follow a pre-defined route with a given stopping pattern. It is important to correctly estimate the number of persons travelling by bus in order to avoid over estimating the number of vehicles. This is to avoid overestimating the number of vehicles with passengers. The road network is discretized into piecewise linear road segments. The pollution from each segment is modelled by the linear source equation. The pollutant concentration for people on the road segment is given by the characteristic mid-segment pollution.

The vehicle densities number per length are estimated by the procedure above. If the road is a freeway with many lanes one may consider splitting the two driving directions into two line sources and sum the pollution from the two directions taking into account the centre-to-centre distance between the lane directions.

For near roadway exposure one may select bands parallel to the roadway network and take into account overlapping contributions from the nearest neighbouring road s. Given a close to real-time continuous pollution field, it is straightforward to calculate the direct exposure to air pollution in the transport network.

In this context, a microenvironment e. Where X i is the total exposure for person i over the specified period of time, C j is the pollutant concentration in microenvironment j , t ij is the residence time of the person i in microenvironment j , and J is the total number of microenvironments. The resulting population exposure may then be used to relate the number of hospital admissions to exposure. A constraint for using the microenvironment based exposure models see equation above is that the residence time of the person termed the time—activity pattern needs to be known together with the pollution concentrations in each of the microenvironments at the time when the person is present.

In this article, however, by using mobile phone tracking we could get both individual time-activity pattern and real time continuous pollution concentration along the traffic network. Two alternatives to an individual trajectory based system are: 1 one may measure real-time air pollution by large-scale implementation of a Wireless Sensor Network WSN [ 79 - 82 ], in which hundreds or thousands of small personal environmental sensors carried by the public rely on cell phones to shuttle information to central computers where it will be analysed, made anonymous and reflected back to individuals, authorities and public health agencies [ 83 ]; and 2 one may rely on stationary based, monitored pollution data together with geo-statistical models and use GPS information [ 84 - 86 ] provided by smart phone applications to track air pollution exposure of a sub-population [ 87 - 89 ] as mentioned earlier in this article.

Alternative 1 uses individual trajectories. One may expect that an actual measurement of the air pollution exposure by WSN would be somewhat better than a calculated individual trajectory based system as proposed in this article. This is a point in favour of the WSN-based micro sensor approach. Alternative 2 for an entire city would require stationary measurements at nearly all road segments in the city. Although this could provide high quality data, it is certainly extremely costly due to the extremely high density of measurement stations.

The cost of up-scaling a mobile phone trajectory system is only the cost of incremental computational power. This is very low since the individual data already exists and can be uploaded from the mobile phone or downloaded from the mobile phone service provider. Less computational challenging studies have already included six million phone users of a Call Detailed Records CDR data base [ 90 ]. The detailed traffic trajectories need more computational power, but the algorithms are cost-efficient and are likely very well suited for parallel algorithms, since each trajectory is calculated independently of the other trajectories.

Trajectory based data have many opportunities for reducing costs by building on synergies with commercial traffic monitoring systems. Traffic monitoring companies, including the largest search engine companies, may want to show their social responsibility by releasing their data for public health research in the same way as mobile phone tracking data are released for human mobility research.

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In this way the public health research could be obtained at almost no extra cost. More realistic, we believe that traffic monitoring companies will offer new free services to calculate real time pollution exposure and health risk maps in large cities that can be used by traffic map users with GPS smart phones. We have developed a model to estimate traffic emissions from traffic map data.

This modelling is planned to be published in another article. Based on this approach a mobile phone tracking system has the potential of being highly competitive economically for population-wide traffic pollution health studies. Trajectory based individual exposure analysis opens up new perspectives in public health research. Natural questions that arise include: Who is most at health risk? A natural extension of the trajectory based individual exposure analysis is to do research on optimal regulatory, policy change and urban planning to reduce air pollution.

What are the most efficient ways to transform an existing urban environment into a city with a low air pollution health risk? In polluted cities there are many different options for improving air quality. One may develop trajectory based dynamic computer simulation models where urban planning concepts are evaluated based on simulated performance with regard to cost and improved public health.

This approach is similar to dynamic petroleum reservoir simulation by streamlines [ 94 ]. A vision for mobile phone tracking technology is to do global studies that incorporate the global gaps in population of mobile phone users. The gaps in the network, that is, those that do not have a mobile phone, may be inferred directly from the trajectories from home to kindergarten, to school, to work, and scale this number of trajectories to estimate the effect from the whole population. An advantage of this approach is that it is effortless for citizens as the global population does not need to adjust their activity pattern or wear any measurement devices to participate in the study.

There are some limitations to our approach that need to be addressed when used in real practice.

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First, the proposed approach is mainly targeted at the person who has a mobile phone. This is a limitation for those people that do not tend to have mobile phones e. The biased samples are not easily worked out with statistical methods if they are not randomly distributed, which is the case here. Second, the risks arise first from the fact that in order to get the individual human trajectory, the data we need is mostly about people, where they have been, at what times, how often.

Therefore privacy and security is a major concern, and needs to be addressed before the opportunities of trajectory mapping can be created. Encryption and anonymization can provide a level of privacy [ 95 ]. In this study, anonymous data are enough to construct individual human trajectories, pertaining to groups of people. Many studies that address privacy issues in a spatio-temporal data mining context have already been published. In the Geographic Privacy-Aware Knowledge Discovery and Delivery GeoPKDD project, Giannotti and Pedreschi [ 96 ] investigated the various scientific and technological issues of mobility data, open problems, roadmap, and concluded that the privacy issues related to the ICTs can only be addressed through an alliance of technology, legal regulations and social norms.

In the meantime, increasingly sophisticated privacy-preserving data mining techniques are being studied and further developed [ 97 ]. The project Citisense designed a permissioning system that allows users to configure publicly accessible time Windows of their data [ 98 ]. Mun et al. Furthermore, an alternative to deal with privacy issues related to tracking of individual trajectories is to collect a population-wide data in the form of willing participation, as done by PEIR [ 17 , 18 ] mentioned above, where only individuals that are motivated will take part in the data collection.

In this approach, the methods to empower citizens at population level to participate in such studies may need to be further developed. So this approach also suffers from similar data problems to the more conventional dispersion models.

On the other hand, such challenges may indicate that the approach of modelling traffic-related air pollution and its implications for public health impact assessment through mobile phone tracking will require broad cooperation within inter- and multidisciplinary disciplines. We conclude that there is an opportunity to calculate traffic air pollution and its public health impact by mobile phone trajectory mapping.

This approach is promising due to the following characteristics: i Low cost : There is no cost from the observation part. The cost is mainly in data power to compute mobile phone trajectories and emission dispersion modelling in real time.

Combination of low cost and standardized mobile phone data makes it is feasible to do population-wide traffic pollution studies. The system is suited for developing countries that have a low fraction of mobile phones with GPS. This allows identification of high vehicle density congestion events that may contribute significantly to health risk iii Effortless citizen participation : Each trajectory reflects one individual citizen.

Therefore, this system opens up the possibility of providing information to each individual citizen. One may develop applications for automatic web server feedback to citizens who want to get the data of their own trajectories or want to contribute toward a training data set of true ground GPS measurements. We believe that using the trajectories of individual mobile phones opens up new perspectives on urban dynamics and public health research. HYL and ES planed this work and wrote the manuscript. MJK contributed content and provided editorial input.

All authors approved the final version. Special thanks to William A. Thanks to Philipp Schneider at NILU for his help to revise the paragraph on how remote sensing from satellite improve the spatial resolution of air pollutants in urban areas. Any remaining errors are the responsibility of the authors. National Center for Biotechnology Information , U. Your Cart.

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