Spatiotemporal Characteristics and Driving Factors of Black Carbon in Augsburg, Germany: Combination of Mobile Monitoring and Street View Images

Abstract

The study investigates the spatial pattern of black carbon (BC) at a high spatial resolution in Augsburg, Germany. Sixty two walks were performed to assess the concentrations of equivalent black carbon (eBC), ultraviolet particulate matter (UVPM), and equivalent brown carbon (eBrC) in different seasons and at different times of the day with a mobile platform (i.e., trolley). Along with BC measurements, images of street microenvironments were recorded. Meteorological parameters, including temperature, relative humidity, and wind speed, were monitored. The BC concentrations showed significant spatial heterogeneity and diurnal variations peaking in the morning and at night. The highest BC concentrations were observed near dense traffic. The correlations between BC and street views (buildings, roads, cars, and vegetation) were weak but highly significant. Moreover, meteorological factors also influenced the BC concentration. A model based on street view images and meteorological data was developed to examine the driving factors of the spatial variability of BC concentrations at a higher spatial resolution as different microenvironments based on traffic density. The best results were obtained for UVPM and eBC (71 and 70% explained variability). eBrC (53%), to which other sources besides road traffic can also make significant contributions, is modeled less well.

Analysis of mobile monitoring data from the microAeth® MA200 for measuring changes in black carbon on the roadside in Augsburg

“…To evaluate the relative performance of MA200, this study was analyzed BC data collected from multiple MA200 devices, identified individually by serial numbers (MA200-0051, MA200-0053, MA200-0059, MA200-0060, MA200-0155, MA200-0153, MA200-159) and used the MA200's onboard signal-processing to moderately reduce the noise of the BC signal. Comparative measurements of the microAeth® MA200 115 and a stationary Aethalometer (AE33, Magee Scientific, Berkeley, USA) showed good agreement of stationary measurements carried out between the individual walks (Liu et al, 2020b). Information about the date, duration, and time resolution (time base) of each MA200 device as used during measurement for this study are summarized in Table 1.…”

Analysis of mobile monitoring data from the microAeth® MA200 for measuring changes in black carbon on the roadside in Augsburg

“…S1). Meanwhile, as performed in our previous study (Liu et al, 2021), we divided the monitoring route into four microenvironment groups in Augsburg, including high traffic flow (H_Traffic, average 500-1000 vehicles per hour), medium traffic flow (M_Traffic, average 200-500 vehicles per hour), low traffic flow (L_Traffic, average 1-200 vehicles per hour), and park area (N_Traffic, average 0 vehicles per hour), according to the actual traffic density examined during the daytime and determined from the traffic flow observed by street views.…”

“…Comparative measurements of the MA200 and a stationary Aethalometer (AE33, Magee Scientific, Berkeley, USA) taken approximately 30 to 60 min between walks showed a good agreement (Pearson's r = 0.933) (Liu et al, 2021). In addition, it is worth noting that when the AE33 was used for monitoring black carbon at the same time as the MA200, the AE33 was placed in a fixed station, while the MA200 was used outdoors (in the stroller) during the individual walks, which may have presented different relative humidity and temperature values.…”

“…The LPR algorithm is a non-parametric tool similar to a moving average, but it operates on polynomial regression rather than simple averaging (Masry, 1996;Breidt and Opsomer, 2000;Kai et al, 2010). In LPR, the number of points to smooth across must be manually identified.…”

“…However, the high spatial variability of black carbon among smallscale urban blocks is difficult to characterize with existing monitoring networks which typically rely on fixed monitors (Apte et al, 2017), especially for on-road concentrations. Recently, mobile monitoring has been widely applied for the collection of real-time air quality measurements to assess local air quality and air pollutant exposures (Liu et al, 2020(Liu et al, , 2021. This method can improve the spatio-temporal resolution of measurement data in the urban environment, and it enables the collection of data such as the traffic-related air pollutant concentrations (Liu et al, 2019).…”