Atmospheric gaseous aromatic hydrocarbons in eastern China based on mobile measurements: Spatial distribution, secondary formation potential and source apportionment

Biogenic emission as a potential source of atmospheric aromatic hydrocarbons: Insights from a cyanobacterial bloom-occurring eutrophic lake

As important precursors of ozone (O3) and secondary organic aerosol (SOA), reactive aromatic hydrocarbons (AHs) have typically been classified as anthropogenic air pollutants. However, biogenic emission can also be a potential source of atmospheric AHs. Herein, field observations in a eutrophic lake were combined with laboratory incubation experiments to investigate the biogenic AH emission. Field work showed that the water-air fluxes of AHs measured at sites with high cyanobacteria abundance could reach an order of magnitude greater than those at sites with low cyanobacteria abundance, suggesting that cyanobacteria could be the important contributor to measured AHs. Laboratory incubation experiments further confirmed the AH emission of cyanobacteria and revealed that the emission could change significantly over the lifespan of cyanobacteria and varied to their growing conditions. By combining field observations and laboratory incubation experiments, it has been suggested that the emission of different AH species from cyanobacteria could be modulated by variable biogeochemical mechanisms and that the biochemical process of toluene could be different from that of other AHs. This study investigates AH emissions from inland aquatic ecosystem and suggests that biogenic emission could be a potential source of atmospheric AHs.

Mapping Source-Specific Air Pollution Exposures Using Positive Matrix Factorization Applied to Multipollutant Mobile Monitoring in Seattle, WA

Mobile monitoring strategies are increasingly used to provide fine spatial estimates of multiple air pollutant concentrations. This study demonstrates a novel approach using positive matrix factorization (PMF) applied to multipollutant mobile monitoring data to assess source-specific air pollution exposures and to estimate associated emission factors. Data were collected from one-year mobile monitoring, with an average of 26 repeated measures of size-resolved particle number counts (PNC), PM2.5, BC, NO2, and CO2 at 309 sites in Seattle from 2019 to 2020. PMF was used to characterize underlying source-related factors. The sources associated with these six factors included emissions from aviation, diesel trucks, gasoline/hybrid vehicles, oil combustion, wood combustion, and accumulation mode aerosols. Fuel-based emission factors for three transportation-related sources were also estimated. This study reveals that PNC of ultrafine particles with size <18, 18-42, and 42-178 nm was dominated by features associated with aircraft, diesel trucks, and both oil and wood combustion. Gasoline and hybrid vehicles contributed the most to CO2 and NO2 concentrations. This approach can also be extended to other metropolitan areas, enhancing the exposure assessment in epidemiology studies.

New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment

To effectively control pollution and improve water quality, it is essential to accurately analyze the potential pollution sources in rivers. The study proposes a hypothesis that land use can influence the identification and apportionment of pollution sources and tested it in two areas with different types of water pollution and land use. The redundancy analysis (RDA) results showed that the response mechanisms of water quality to land use differed among regions. In both regions, the results indicated that the water quality response relationship to land use provided important objective evidence for pollution source identification, and the RDA tool optimized the procedure of source analysis for receptor models. Positive matrix decomposition (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models identified five and four pollution sources along with their corresponding characteristic parameters. PMF attributed agricultural nonpoint sources (23.8%) and domestic wastewater (32.7%) as the major sources in regions 1 and 2, respectively, while APCS-MLR identified mixed sources in both regions. In terms of model performance parameters, PMF demonstrated better-fit coefficients (R²) than APCS-MLR and had a lower error rate and proportion of unidentified sources. The results show that considering the effect of land use in the source analysis can overcome the subjectivity of the receptor model and improve the accuracy of pollution source identification and apportionment. The results of the study can help managers clarify the priorities of pollution prevention and control, and provide a new methodology for water environment management in similar watersheds.