Elsevier

Atmospheric Environment

Volume 244, 1 January 2021, 117915
Atmospheric Environment

Mobile air quality measurements using bicycle to obtain spatial distribution and high temporal resolution in and around the city center of Stuttgart

https://doi.org/10.1016/j.atmosenv.2020.117915Get rights and content

Highlights

  • Local traffic is identified as a vital source of air pollution in the studied area.

  • Higher pollutant concentrations close to the federal highways and hotspots.

  • Lower concentration levels in the park and areas with less traffic.

Abstract

Cities often have a problem regarding the air quality because various emissions are produced within these cities and local emission sources among others relate directly to the air quality. As an addition to stationary monitoring stations, mobile measurements can provide valuable information regarding spatial distribution of pollutants. In this research, a case study is presented to illustrate the design and implementation of mobile measurement platform with a bicycle. Different compact measuring devices were selected and installed on the bicycle for mobile measurements along a designated route in Stuttgart, Germany. The measured pollutants included Particulate Matter (PM), Ultrafine Particles (UFP), Black Carbon (BC), nitrogen oxides (NO, NO2 and NOX) and ozone (O3). Meteorological parameters such as air temperature, relative humidity, wind speed, wind direction, solar radiation and air pressure were also measured. These measured parameters were allocated to the location using a GPS device. The measurements were carried out during February 2018. The measurement route covered part of the city center with high traffic roads as well as side roads and a park. The results show a very high spatial variability and the pollutant concentration was influenced by different factors such as local traffic, measurement location, meteorological conditions, etc. For NO, NO2 and UFP, the concentrations in the park (which can be considered as urban background level) were only around 30%–50% of the measured concentrations directly on the roadside. For PM10, the differences between the concentrations on the road and in urban background were less pronounced but still clearly measurable. The mobile measurement platform with the bicycle provided an opportunity to have a spatial distribution of a larger study area and proved to be highly adaptive and flexible method.

Introduction

Mobile measurements are an ever-growing trend for the evaluation of urban emissions, local air quality and air pollutant exposure. Unlike the stationary air quality monitoring methods, mobile measurements with portable and precise measurement devices provides the opportunity to study the behaviour of air pollutants and to investigate the spatial distribution with high temporal and spatial resolution in corresponding study areas. The city of Stuttgart in Germany is very famous because of its air pollution problems. The main reasons of pollution in Stuttgart are heavy traffic and high population density in combination with a topography that favours the accumulation of air pollutants. Stuttgart is a valley surrounded by mountains from three sides, which causes poor ventilation and hinders the dispersion and dilution of pollutants (Regierungspräsidium Stuttgart, 2018). The traffic density in Stuttgart is very high. At some locations on the highway like B14, which passes through Stuttgart, around 100,000 vehicles on average drive every day (LUBW, 2018). The high traffic load leads to high emission of nitrogen oxides from the vehicle exhaust and PM from the abrasion of brakes and tires as well as PM and UFP from the exhausts (Baumbach, 1996). Apart from this, the city of Stuttgart does not have any ring road that causes all the traffic to pass unnecessarily through the city. Human settlement in most part of Stuttgart is very dense and high buildings form narrow canyons. This causes poor ventilation, which leads to poor dispersion and dilution of air pollutants.

Mobile measurement technique has been applied previously as well using different platforms. A mobile measurement platform AERO-TRAM, built on the roof of a tram, was developed by the Institute for Meteorology and Climate Research in Karlsruhe. They measured the NOX and PM concentrations. In this study two routes were chosen, both not only through the city but also in the neighbouring area. The results showed that the NOX concentration reduced to 70% when the distance from the city center increased. PM on the other hand reduced up to 50% (Hagemann et al., 2014). The researchers of Computer Engineering and Networks Laboratory in Switzerland investigated the UFP concentration using public transport in 2013 (Hasenfratz et al., 2014). There were mobile measurements performed in Leipzig in the year 2011. The measurement route for these measurements covered park areas, traffic intersections, walkways along a street and a pedestrian zone. It was found out that the main source of air pollution in the investigated area was local traffic. A significant decline in PM concentrations was observed at a distance of 20–30 m off the main road. In the parks and the pedestrian zones relatively low concentration was measured (Birmili et al., 2013). The Aeroflex (bicycle) was used for the mobile air quality measurements in Belgium. The air quality parameters such as UFP, PM and BC were investigated in this study (Elen et al., 2012). The University of Applied Sciences in Düsseldorf performed mobile measurements and measured PM and UFP. Two measurement routes were investigated, one through an outer limit of the low emission zone and the other through the city center. From the investigations carried out in the year 2010, it could be found that the PM concentrations were only 12% higher on the main road as compared to the secondary road. The UFP on the other hand were found to be about 45% higher on the main road than on the secondary road (Vogel et al., 2011).

The “Government Authority for the Environment, Baden-Württemberg” (LUBW) is responsible for the operation and maintenance of the ambient air-monitoring network. In 2018, the relevant air pollutants were measured at five monitoring stations in Stuttgart, which include urban background and traffic monitoring stations (Stadtklima Stuttgart, 2018). In addition to the permanently existing monitoring stations, so-called spot monitoring stations are installed at selected locations, which are considered to be the hotspots. The purpose of the spot monitoring station is the detection of traffic-related air pollution at urban stress centers. In Stuttgart, there are currently two spot monitoring stations namely Stuttgart Hohenheimer Strasse and Stuttgart “Am Neckartor”. (Stadtklima Stuttgart, 2018). This monitoring station Stuttgart “Am Neckartor” which is close to the city center on the busy federal highway B14 is already in frequent focus of both professional world and public. At this monitoring station the highest NO2 and PM10 pollution levels in the whole Germany were measured in the recent years (LUBW, 2017). Including passive samplers, NO2 was measured at 11 places in Stuttgart in 2018 (LUBW, 2019).

The German environmental pollution regulation (39th BImSchV) which implements Directive 2008/50/EC into German law sets the following limit values for NO2 i.e. the hourly average NO2 concentration must not exceed the limit value of 200 μg/m³ for more than 18 times in a year. The annual mean concentration of NO2 must not be higher than 40 μg/m³. Fig. 1 shows the NO2 annual average concentration at the LUBW monitoring station Stuttgart “Am Neckartor” and Hohenheimer Strasse in the period from 2005 to 2019 (Stadtklima Stuttgart, 2018). It can be seen that the NO2 annual average concentration at the monitoring station “Am Neckartor” was regularly between 53 and 121 μg/m³, i.e. by a factor of around 1.5 to 3 above the permitted limit. The permitted limit was also clearly exceeded at the other spot monitoring station Hohenheimer Strasse.

The ambient air limit values for PM10 and PM2.5 are also defined in above mentioned regulation. The long-term ambient air limit value averaged over one calendar year for PM10 is 40 μg/m³ and for PM2.5 is 25 μg/m³ that is valid since 2015. The short-term ambient air limit value is the daily average value for PM10 which is 50 μg/m³. It may not be exceeded 35 times in one calendar year (Directive, 2008/50/EC). The count of days with exceeding daily mean PM10 concentration at two Stuttgart spot monitoring stations for the years 2005–2019 is shown in Fig. 2. As late as 2005, the daily limit value of PM10, which is 50 μg/m³, was exceeded on every other day of the year (187 times) at the monitoring station “Am Neckartor” when a maximum of 35 days of exceedances were allowed per year. However, a decreasing trend of exceedances can also be clearly observed. Only in the last two years, the number of exceedances at the monitoring station Am Neckartor was under the allowed limit of 35 exceeding days per year (see Fig. 3).

Investigations done by the LUBW on the emission sources have shown that 52% of NO2 emission at the location of Stuttgart “Am Neckartor” is attributable to the local road traffic. The contribution to air pollution from the local sources (local road traffic, small and medium combustion plants, others, etc.) is very high with 56% as compared to the air pollution from other sources from regional and urban background (LUBW, 2017a, LUBW, 2017b). For PM10, at the location Stuttgart “Am Neckartor”, 47% of the emission is caused by local road traffic (exhaust fumes, abrasion and re-suspension) and approx. 2% by small and medium combustion plants and less than 1% by industry. The remaining 50% are to be assigned to the overall background level that is the combination of urban and large-scale background sources (LUBW, 2017a, LUBW, 2017b).

The federal highway B14, which passes directly by the spot monitoring station Stuttgart “Am Neckartor” is an important traffic pathway in the area of Stuttgart. It consists of two directions of travel and has three lanes in each direction. On some sections later, the road has up to five lanes in each direction. According to a traffic survey conducted by the LUBW in 2018, around 70,000 vehicles on average pass by the monitoring station Stuttgart “Am Neckartor” each day. The proportion of heavy commercial vehicles is around 3%. On Sundays, the average daily traffic decreases by up to 31% and the share of heavy commercial vehicles even decrease by 87% (LUBW, 2018).

This situation clearly indicates the air quality problem in the city of Stuttgart. Hence, the necessity of air quality measurements that provide good spatial distribution and high temporal resolution is evident. For this problem, mobile measurements using a bicycle equipped with relevant air pollutants and meteorological measurement devices provided the optimum solution. The task of this study was to use mobile measurements in order to investigate some part of the city center of Stuttgart and its surroundings for two main reasons: Firstly, to confirm that stationary monitoring station is representative for the area under study. Secondly, to find if there are other hotspots that might not be covered by the stationary measurements. The route for mobile measurements was selected so that along with the main federal highways, the side roads and parks were also covered in order to observe the variation in the pollutant concentrations.

In this investigation, different compact measurement devices were selected in order to realize the mobile measurements with a bicycle. These devices were installed on the bicycle. The measurement devices included an aerosol spectrometer working on the principle of light scattering for measuring PM, Condensate Particle Counter (CPC) for measuring UFP, aethalometer working on the principle of light absorption for measuring BC, nitrogen oxide monitor for measuring NO2 and NO by direct radiation absorbance and ozone monitor for measuring O3 by light absorption. Meteorological parameters such as air temperature, relative humidity, wind speed, wind direction, solar radiation and air pressure were measured with the aid of a compact weather station. A GPS device was used in order to relate the measured parameters to the location and a video camera was also part of this system to relate the measured data to special events.

The devices used during these measurements were designed especially for mobile use, which is why they have a relatively low weight compared to stationary measuring devices and can be operated with rechargeable batteries. In addition, they are very insensitive to external influences such as shocks and larger temperature changes. The total weight of the measurement system excluding the bicycle was around 20 kg. Keeping the route and the weight of the whole system in mind, an electric bicycle was chosen for these measurements. The devices used for these mobile measurements are listed in Table 1. The measuring principle on which these devices operate is also mentioned in this table.

Fig. 3 shows the bicycle along with equipment used to perform the mobile measurements. The GPS and the camera were installed on the front of the bicycle. Since the driver could influence the PM measurements, therefore the aerosol spectrometer and the aethalometer were put in the front basket. The gas measurement devices for measuring NO2, NO, NOX and O3, the Condensate Particle Counter (CPC) for measuring UFP and the data logging system were placed inside the box on the back of the bicycle. All of the measurement devices were operated with batteries. The weather station was installed on the rod at the back of the bicycle.

Since the measurements should be carried out at the same distance from the road as people are exposed to the pollutants, therefore the measurements were carried out intentionally with bicycle as a measuring vehicle and not with a car. The spot monitoring station Stuttgart “Am Neckartor” that is located on the route is also at a distance of about 3 m away from the roadside. Therefore, to be compatible with measurements of the stationary monitoring station, it is better to perform the measurements away from the source rather than measuring directly on the roads. The closer you get to the source of pollution, the stronger is the gradient to which the concentrations are subjected. Fig. 4 shows the entire measurement area in and around the city center of Stuttgart. The blue line shows the bicycle route, the white circles with numbers show the location of passive samplers installed on the bicycle route. The route started from the park where the ground station was built for the measurement activity. Then the route went through the park on the other side of train lines, on a side road parallel to the federal highway B27 travelling from north to south and then turning to the federal highway B27. Moving from west to south while covering the city center, the route turned towards the federal highway B14. After going for a short distance on the side road parallel to the federal highway B14, the route came back again to B14 near the monitoring station Stuttgart “Am Neckartor” and returned to the side road that led the route back to the starting point in the park. The total route length was approximately 12 km. The PM measurements becomes critical at relatively high speeds above 10 km per hour, hence the bicycle was driven with a speed of not more than 10 km per hour. The time required to complete one round was between 1.2 and 1.5 h. The measurements took place throughout the day and night with occasional stops for checking the devices, to calibrate them and to download the data from them (see Fig. 5).

The measurements were performed in worst-case scenario when high pollutant concentrations were expected. The measurement days were selected by keeping specific weather conditions in mind such as high-pressure situations with clear sky and no rain. In these situations, surface inversion can build up which trap the pollutants and restrict their transport. The measurement period of the mobile measurements lasted for around one week from 18th February to February 24, 2018. This included 8 days of bicycle measurements with 43 rounds on the complete route of around 12 km. The bicycle measurements lasted for a short time period and the situation varied from one ride to another. So in order to validate the results, the average of the measurement results was then compared to a long-term observation method, e.g. in this case the NO2 passive samplers were installed on the bicycle route for a longer period from 18th February until March 4, 2018. For the passive samplers, an average value was obtained for the whole measurement period of two weeks. As part of the quality assurance of the measured data, the gas devices were calibrated regularly using a gas phase titration system. For the PM, UFP and BC measurements, the measurement devices were compared in advance with calibrated devices and with the stationary monitoring stations.

Section snippets

Results and discussion

These measurements provided high temporal resolution and spatial distribution of the measured parameters. The time resolution of the measured parameters was 1 s. In order to plot the results, a software ArcGIS was used. The result figures show the route on the map and the pollutant concentration is plotted on the route. The route is divided in 50 m sections. All the parameters measured within the 50 m section are averaged and presented on the route as a coloured circle. The legend for each

Conclusions

In this investigation, we were able to show that a bicycle can be used for mobile air quality measurements, as it is a highly adaptive and flexible method. With this platform, it is possible to have a spatial distribution of a larger study area in order to get an understanding of the air quality situation of this study area. This technique is also suitable to find hotspots with respect to air pollutants. It was concluded that the information obtained from stationary air quality hotspot

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was performed under the project Urban Climate Under Change [UC]2 funded by the Federal Ministry of Education and Research (BMBF) Germany.

References (17)

  • R. Hagemann et al.

    Spatial variability of particle number concentration and NOx in the Karlsruhe (Germany) area obtained with the mobile laboratory 'AERO-TRAM

    Atmos. Environ.

    (2014)
  • G. Baumbach

    Air Quality Control

    (1996)
  • W. Birmili et al.

    Micro-scale variability of urban particle number and mass concentration in Leipzig, Germany

    Meteorol. Z.

    (2013)
  • W. Birmili et al.

    Long-term observations of tropospheric particle number size distributions and equivalent black carbon mass concentrations in the German Ultrafine Aerosol Network (GUAN)

    Earth Syst. Sci. Data

    (2016)
  • B. Elen et al.

    The aeroflex: a bicycle for mobile air quality measurements

    Sensors

    (2013)
  • M. Hangartner et al.

    Review of the Application of Diffusive Samplers for the Measurement of Nitrogen Dioxide in Ambient Air in the European Union

    (2009)
  • D. Hasenfratz et al.

    Pushing the Spatio-Temporal Resolution Limit of Urban Air Pollution Maps

    (2014)
  • (2017)
There are more references available in the full text version of this article.

Cited by (18)

  • Complex networks from time series data allow an efficient historical stage division of urban air quality information

    2021, Applied Mathematics and Computation
    Citation Excerpt :

    At present, scholars have analyzed several urban air quality issues, including advance monitoring methods, factors, and statistical information. Some experts have proposed a series of monitoring and estimation methods for air quality indexes (AQIs), such as collection or monitoring systems [4,5], outdoor mobile collection devices [6,7], deep learning technologies [8], and wireless monitoring networks [9]. Numerous studies have focused on different factors, such as advance evaluation standards [10], morphology, temperature [11], gas emissions [12], social environments, and economic conditions [13].

View all citing articles on Scopus
View full text