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.… Click to show full abstract
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.
               
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