10th International Aerosol Conference September 2 - September 7, 2018 America's Center Convention Complex St. Louis, Missouri, USA
Abstract View
Gradients in Concentration and Composition of Sub-Micron PM in a Coastal American City: Downtown Street Canyon Dominates a Large Area Emission Source in Port of Oakland CA
RISHABH SHAH, Ellis Shipley Robinson, Peishi Gu, Joshua Apte, Albert Presto, Carnegie Mellon University
Abstract Number: 1032 Working Group: Air Quality in Megacities: from Sources to Control
Abstract We deployed an Aerodyne HR-ToF-AMS along with a suite of other gas and particulate measurement instruments in the Carnegie Mellon University mobile laboratory. Repeated drives were performed over an urban domain in Oakland, CA between 10th July and 2nd August, 2017. Oakland has a somewhat unique land-use feature in that a 1 km2 downtown, a 6 km2 residential district as well as one of the largest US ports (5 km2) all lie within a short spatial transect of 4 km. Our objective was to quantify (a) spatial variations in non-refractory chemical components of PM1 to identify local emission hot-spots and (b) contribution of fresh versus photochemically processed emissions to the local PM1. The campaign-median mass-based non-refractory chemical composition of PM1 was 58% organic, 24% sulfate, 9% nitrate, with other species making up the balance.
Mobile sampling data were aggregated using artificial “magnets” located every 200 m along all city streets. Each AMS sample, once aligned in time with GPS co-ordinates, was assigned to the nearest magnet for averaging. Spatial analysis and magnet creation were done in Q-GIS software. Approximately 70% of the magnets in the domain are represented by measurements on more than 15 unique days, which means that the data should be representative of seasonal- or even annual-average spatial patterns. Bias in temporal-spatial coupling of PM1 concentrations was avoided in two ways: first, we varied the sampling route on each day, so that certain areas were not systematically sampled in morning versus afternoon. We also calculated a time-series-based estimate of background concentrations by fitting a spline to the 5th percentile of every x min of smoothed data. We observed the diurnal stability of the estimated background on a few days and its sensitivity to the value of x. We found that if x is chosen to be too small (e.g., 20 min), or too big (e.g., 9 hours), most of the spatial variability gets underestimated or overestimated, respectively. However, for x = 90, 120 and 240 min, the estimated background levels and trends remain stable. For each “magnet”, background levels were subtracted from raw data and the remaining “above background (AB)” values were used for calculating a temporal average (μΑΒ) and standard deviation (σAB).
We find that inorganic PM1 components like sulfate and ammonium are spatially stable, whereas PM1 organics are highly spatially variable. For instance, average organic aerosol concentrations in downtown Oakland are consistently higher (μΑΒ = 2.2 μgm-3) as well as internally spatially variable (σAB= 1.8) as compared to the relatively large area source, the port of Oakland (μΑΒ = 1.4 μgm-3, σAB = 1.1) as well as the largely residential West Oakland neighborhood (μΑΒ = 1.1 μgm-3, σAB = 0.8). Further, by plotting normalized organic signals of source-specific marker ions m/z 55 (indicative of cooking emissions) and m/z 57 (indicative of vehicular emissions), we find their relative abundances strongly influenced by time of day. While m/z 57 signal dominates during morning traffic rush, it is rapidly overwhelmed by m/z 55 after ~ 10 AM. This finding not only confirms the recently elsewhere investigated influence of cooking-induced emissions on local urban air quality, but also has implications for human health and exposure given that these elevated signals are found in areas and times of high human presence in urban locations. Next analyses include source-apportionment using positive matrix factorization to identify the distinct chemical source profiles (fresh and relatively aged components) and their relative contribution to local urban PM1 hot-spots.