Fraction of coarse particles (PM2.5 10) contributed 89 (with mean particle mass concentration, PMC ≈ 133 ± 17 μg m−3), 83 (100 ± 29 μg m−3), and 70%5959 ± 12 μg m−3) of total PMC during the fixed-site, mobile monitoring and sequential measurements, respectively, compared with only 50% (mean 12±± 6 μg m−3) during the background measurements.
In Section 4, we introduce the mobile monitoring network and measurements using the monitoring equipment to assess the influence of AGC on spectrum sensing.
This relates to the mobile monitoring equipment where the measurement data is provided in a spectrogram representation corrupted by noise from the AGC.
These studies rely on airborne and tower measurements, and are at times supplemented by ground measurements such as mobile monitoring.
To analyze the effect of non-stationary AGC noise, we use the measurement data obtained from mobile monitoring vehicles used by the Dutch regulator.
Relating the test measurement back to the mobile monitoring readings sheds light on the AGC distortion in the spectrogram of Figure 5.
The models were based on NO and NO2 measurements at 116 sites, PM2.5 measurements at a subset of 25 locations, and mobile monitoring of black carbon concentrations at a subset of 39 sites.
LUR models of TRAP are developed by regressing measurements from dense monitoring networks or mobile monitoring against spatial covariates including distances to or densities of land uses (e.g., road networks, topography, and land cover).
In addition, mobile monitoring in each of the study areas during the study period showed high concordance with measurements taken by the permanent monitors in the same area (ARPA Lombardia 2006).
In our measurement campaign, the geometric means of PM2.5 and PM10 along the mobile monitoring path were 21 and 49 μg/m, respectively, in the neighborhood with highest SES and 39 and 96 μg/m, respectively, in the neighborhood with lowest SES and highest population density.
