Emissions from an International Airport Increase Particle Number Concentrations 4-fold at 10 km Downwind

Abstract

We measured the spatial pattern of particle number (PN) concentrations downwind from the Los Angeles International Airport (LAX) with an instrumented vehicle that enabled us to cover larger areas than allowed by traditional stationary measurements. LAX emissions adversely impacted air quality much farther than reported in previous airport studies. We measured at least a 2-fold increase in PN concentrations over unimpacted baseline PN concentrations during most hours of the day in an area of about 60 km2 that extended to 16 km (10 miles) downwind and a 4- to 5-fold increase to 8–10 km (5–6 miles) downwind. Locations of maximum PN concentrations were aligned to eastern, downwind jet trajectories during prevailing westerly winds and to 8 km downwind concentrations exceeded 75 000 particles/cm3, more than the average freeway PN concentration in Los Angeles. During infrequent northerly winds, the impact area remained large but shifted to south of the airport. The freeway length that would cause an impact equivalent to that measured in this study (i.e., PN concentration increases weighted by the area impacted) was estimated to be 280–790 km. The total freeway length in Los Angeles is 1500 km. These results suggest that airport emissions are a major source of PN in Los Angeles that are of the same general magnitude as the entire urban freeway network. They also indicate that the air quality impact areas of major airports may have been seriously underestimated.

Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset

“…Therefore, a passing car could distort the result heavily as they are less interested in the local emissions of that car. Hudda et al (2014) and Choi et al (2012) used a rolling percentile to obtain a baseline concentration to discard the local peaks, as they wanted to map the larger scale variations due to the impact of an airport and a highway, respectively. However, in an urban environment with busy traffic, peak concentrations are an integral part of the air quality that people are typically exposed to.…”

“…Mobile measurements are performed with different platforms, e.g. pedestrians (Zwack et al, 2011a), bicycles (Berghmans et al, 2009;Boogaard et al, 2009;Peters et al, 2013;Sullivan and Pryor, 2014), trams (Hagemann et al, 2014;Hasenfratz et al, 2014) and cars (Westerdahl et al, 2005;Hu et al, 2012;Hudda et al, 2014). Mobile measurements are used for a range of different purposes, e.g.…”

“…Other studies address the potential of using mobile measurements to construct air pollution maps at a high spatial resolution (e.g. Hagler et al, 2010;Choi et al, 2012;Hudda et al, 2014;Peters et al, 2014). Mobile monitoring techniques also receive an increasing attention for participatory sensing and crowd-sourcing methods.…”

Sources of sub-micrometre particles near a major international airport

“…Hsu et al (2014) observed that departures and arrivals on a major runway of Green International Airport (Warwick, RI, USA) had a significant influence on UFP concentrations in a neighbourhood proximate to the end of the runway. In a study carried out at Los Angeles International Airport (CA, USA), Hudda et al (2014) concluded that emissions from the airport increase PNC by 4-to 5-fold at 8-10 km downwind of the airfield, while Shirmohammadi et al (2017) reported that the daily contributions of the airport to PNC were approximately 11 times greater than those from three surrounding freeways. reported that average PNC were 2-and 1.33-fold higher at sites 4 and 7.3 km from Boston (MA, USA) airport when winds were from the direction of the airfield compared to other directions.…”

Zone of influence for particle number concentrations at signalised traffic intersections

“…Baseline PNCs for each run were derived by taking a 30 s rolling average of the lowest 5 th percentile value of the 1s concentration time series (Hudda et al, 2014). This methodology was adopted to smooth the data and to exclude the impact of micro-scale.…”