## 2 Case Study: Jordan Air Pollution Measurement Campaign  
The proposed BC proxy methodology will be tested on air quality data sets obtained from a mobile measurement campaign performed at several locations in Jordan, including the two most populated urban areas in the country: Amman and Zarqa. This campaign took place from 29 May to 4 June 2014. The sampling interval was 30 s for the aethalometer and 10 s for the remaining instruments used in this campaign. The data was then further pre-processed for analysis and proxy development by taking the average to be one minute per data point. The campaign map and other detailed information about measurement campaign are described by [PERSON] et al. [34].  
The measurements represent one of the most comprehensive mobile campaigns involving PM number concentrations and size distributions down to the UFP regime in urban areas in the Middle East and North Africa (MENA) region [35]. Understanding air pollutant sources in the area is a challenge: Amman is known as the economic and political center of the country, whereas Zarqa is one of the industrial centers. Furthermore, air pollution in the southern part of Jordan is mainly affected by dust particles due to sand resuspension from desert areas [35]. Urban air pollution in Amman and Zarqa originates from a vast range of sources, including emissions from traffic and industrial activities, local-scale household activities involving the burning of biomass (e.g., heating in the winter), and natural sources (e.g., dust resuspension). Airborne dust (super-micron PM \(>\)1 \(\upmu\)m) is a major problem, not only in Jordan, but throughout the MENA region [36]. According to the majority of anthropogenic air pollutant sources in the area, BC is likely to be an important pollutant that contributes meaningfully to total PM concentrations.  
The mobile campaign included the measurement of size-fractionated PM concentrations (10 nm-10 \(\upmu\)m) and BC mass concentrations. Table 1 lists the associated aerosol instrumentation and measured variables. Using several portable instruments that cover a wide size range and with different cutoff diameters makes it possible to derive particle number and mass concentrations in several size fractions. In this study, we focus on the following size fractions: submicron (0.01 \(\upmu\)m-1 \(\upmu\)m) particle number concentrations with three fractions in the following particle diameter ranges: 10 nm-20 nm, 20 nm-300 nm and 0.3 \(\upmu\)m-1 \(\upmu\)m. The PM\({}_{\rm x}\) was obtained from the DustTrak, which recorded PM\({}_{1}\), PM\({}_{2.5}\), and PM\({}_{10}\).  
BC concentrations were measured with a portable aethalometer (microAeth, AethLabs model AE51), which reports the BC concentration based on changes in light attenuation at a wavelength of 880 nm of particles collected on a disposable filter. The filter was replaced each day prior to the measurements. The sample flow rate was 0.1 L/min and a 2.5 \(\upmu\)m size selective inlet was used. We set the time-resolution at 30 s. The BC data was post processed to remove any spurious spikes in the concentration (e.g., \(>\)1000 \(\upmu\)g/m\({}^{3}\)) that were associated with sudden vibration of the instrument. This type of monitor (i.e., microAeth AE51) was tested against a reliable type (aethalometer AE31) and also for real-time performance in field measurements. According to [PERSON] and [PERSON] [37], negative BC levels may be present using AE51 at low actual BC levels or at a high time-resolution. Negative values can be eliminated very effectively by adopting the optimized noise-reduction averaging (ONA) algorithm.