Abundance and sources of organic nitrogen in fine (PM2.5) and coarse (PM2.5-10) particulate matter in urban Hong Kong.

Organic nitrogen (ON) in atmospheric particles is much less monitored compared to inorganic nitrogen (IN), despite its significant contribution to atmospheric N deposition budget. In this study, we expanded a newly developed instrumental method for IN and ON in PM2.5 samples to PM10 samples. We determined the quantities of ON and IN for paired PM2.5 and PM10 samples collected at an urban coastal site in Hong Kong, southern China over a year. These measurements also allowed the determination of IN and ON abundance in the coarse PM (i.e., PM2.5-10) by taking the difference between PM10 and PM2.5. The measurement results show that ON accounted for 27.6 % and 21.1 % of total N in fine and coarse particles, respectively, and was mainly (87.7 %) distributed in the fine mode at the site. The seasonal variation of ON/total N was relatively small in PM2.5 (23.6-30.4 %) while considerably larger in coarse PM (4.3-42.1 %). Analysis aided by concurrently measured source indicators revealed that sea spray, biological particle emissions, and dust mixed with anthropogenic pollutants are potentially significant sources of ON in coarse particles. Positive matrix factorization (PMF) source apportionment further revealed that industrial emissions/coal combustion (43.6 %), soil dust emission (16.3 %), fresh sea salt emission (15.2 %), and aged sea salt (24.9 %) are major sources of PMcoarse-bound ON at the site. The contributions of industrial emissions/coal combustion and soil dust emission to ON were significantly higher in autumn and winter. Fresh sea salt emissions contributed greater proportions to ON in spring and summer, while ON associated with the aged sea salt source was higher in spring and autumn. These findings have advanced our quantitative understanding of the sources of PMcoarse-bound ON, which was scarcely determined in the past. Furthermore, the ON measurement data in fine and coarse particles helps estimate ON deposition, which has been previously under-evaluated.

Estimating primary vehicular emission contributions to PM2.5 using the Chemical Mass Balance model: Accounting for gas-particle partitioning of organic aerosols and oxidation degradation of hopanes.

Particulate matter emitted from vehicles (PMvehicle) represents a major air pollution source in urban areas. Ambient measurements of hopanes and elemental carbon have traditionally been coupled with the Chemical Mass Balance (CMB) model to quantify the contributions to fine PM from diesel and gasoline vehicular emissions (VE). The organic carbon part of PMvehicle, however, undergoes gas-particle partitioning and oxidation degradation as VE move from exhaust pipe to receptor sites. This creates an issue of deviation from mass conservation in the utility of CMB. The impact of this issue on quantifying PMvehicle has remained largely uncharacterized. In this study, we incorporate in CMB the gas-particle partitioning of VE organic aerosols and hopane oxidation, which is equivalent to adopting dynamic VE source profiles. The modified version of CMB is applied to quantify primary PMvehicle contributions at a roadside and a general urban site in Hong Kong. For the roadside site, the modified CMB reports predominant PMvehicle by diesel VE, a result consistent with previous studies. For the general urban site, the apportioned gasoline contribution by the modified CMB is tripled (0.8 ± 0.5 vs. 2.7 ± 2.1 µg/m3) while the diesel contribution is reduced by one third (1.7 ± 1.2 vs. 1.1 ± 1.2 µg/m3), producing a gasoline-diesel split significantly different from that by traditional CMB (1:2 vs. 5:2). Our work strongly indicates that a static representation of VE source profiles in CMB modeling would create flawed PMvehicle estimation and demonstrates the necessity of considering gas-particle partitioning of organic aerosol and hopane oxidation degradation.

Estimating contributions of vehicular emissions to PM2.5 in a roadside environment: A multiple approach study.

Vehicular emissions (VE) are among the major sources of airborne fine particulate matter (PM2.5) in urban atmospheres, which adversely impact the environment and public health. Receptor models are widely used for estimating PM2.5 source contributions from VE (PMvehicle), but often give inconsistent results due to different modelling principles and assumptions. During December 2015-May 2017, we collected nine-months of hourly organic carbon (OC) and elemental carbon (EC) data, as well as 24-h PM2.5 speciation data including major species and organic tracers on select days from an ad hoc roadside site in Hong Kong. The weekday vs. holiday and diurnal variations of EC tracked closely with those of traffic flow volume, indicating EC as a reliable tracer for PMvehicle in this area. We applied multiple approaches to estimate the PMvehicle, including the EC-tracer method with the hourly OC-EC data, and chemical mass balance (CMB) and positive matrix factorization (PMF) analyses with the filter-based speciation data. Considering source profile variability, CMB gave the lowest PMvehicle estimate among the three approaches, possibly due to the degradation of organic markers (i.e., hopanes). The PMvehicle derived from the EC-tracer method and PMF were comparable, accounting for ~12% (3.4-4.0 µg/m3) of PM2.5 averaged across 20 samples in both approaches, but a larger sample size is needed for a more robust PMF solution. The monthly PMvehicle derived from the EC-tracer method was in the range of 3.2-6.6 µg/m3. The continuous measurement reveals a decreasing trend in PMvehicle throughout the entire sampling period, indicating the effectiveness of a recent vehicle control measures implemented by the Government in phasing out pre-Euro IV diesel commercial vehicles. This work implies that hourly OC-EC monitoring at strategically located spots is an effective way of monitoring vehicle control measures. It provides reasonable estimate of PMvehicle through comparing with other more sophisticated receptor models.