Tianpeng Hu

Levels, compositions, sources and health risk of PM2.5-bound PAHs were measured at a regional monitoring site of Huang-gang in the winter of 2018. Samples were collected every morning, afternoon, evening and late night (LN). The average concentrations of PM2.5 and PAHs were 110.4 ± 48.3 μg m-3 and 25.6± 12.0 ng m-3, respectively. The concentration of PM2.5 was in the order of evening > morning > afternoon > LN, while PAHs concentration was in the order of evening > LN > morning > afternoon. According to PM2.5 concentration and its changing trend, the haze can be divided into three stages: early (low concentration, January 13–15, EHZ), medium (high concentration, January 16–22, MHZ) and late (concentration decreased rapidly, January 23–24, LHZ). A positive correlation (P < 0.01) was observed between PM2.5 and ∑16PAHs concentration only in MHZ. Besides, PAHs concentration was positively related to NOX concentration, CO concentration and temperature but negatively correlated with RH, independent of O3 concentration. The ratios of OC/EC indicated that the secondary aerosol had a great influence on the formation of haze. Three sources of PAHs including traffic emission, coal combustion and biomass combustion were extracted and quantified, accounting for 54.4%, 13.3% and 32.3% of PAHs, respectively. Potential source contribution function (PSCF) and concentration weighted trajectory (CWT) indicated the transmission of PM2.5 from Wuhan and local pollutant emission were crucial to the formation of haze. BaPeq concentration combined with ILCR values was used to evaluate health risk and the results showed this haze had potential health risk to both adults and children.

Oil and natural gas are important for energy supply around the world. The exploring, drilling, transportation and processing in oil and gas regions can release a lot of volatile organic compounds (VOCs). To understand the VOC levels, compositions and sources in such regions, an oil and gas station in northwest China was chosen as the research site and 57 VOCs designated as the photochemical precursors were continuously measured for an entire year (September 2014–August 2015) using an online monitoring system. The average concentration of total VOCs was 297 ± 372 ppbv and the main contributor was alkanes, accounting for 87.5 % of the total VOCs. According to the propylene-equivalent concentration and maximum incremental reactivity methods, alkanes were identified as the most important VOC groups for the ozone formation potential. Positive matrix factorization (PMF) analysis showed that the annual average contributions from natural gas, fuel evaporation, combustion sources, oil refining processes and asphalt (anthropogenic and natural sources) to the total VOCs were 62.6 ± 3.04, 21.5 ± .99, 10.9 ± 1.57, 3.8 ± 0.50 and 1.3 ± 0.69 %, respectively. The five identified VOC sources exhibited various diurnal patterns due to their different emission patterns and the impact of meteorological parameters. Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) models based on backward trajectory analysis indicated that the five identified sources had similar geographic origins. Raster analysis based on CWT analysis indicated that the local emissions contributed 48.4–74.6 % to the total VOCs. Based on the high-resolution observation data, this study clearly described and analyzed the temporal variation in VOC emission characteristics at a typical oil and gas field, which exhibited different VOC levels, compositions and origins compared with those in urban and industrial areas.

The spatial distribution of polycyclic aromatic hydrocarbons (PAHs) and their source contributions employing receptor models has been widely reported. However, the temporal distribution of PAH source contributions is less studied. Thus, in this paper, three receptor models including principle component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and Unmix were used to PAH source apportionment study in a sediment core from Honghu Lake, China. Sixteen USEPA priority PAHs in 37 sliced sediment layers (1-cm interval) were measured, with the concentrations of ∑₁₆PAH (sum of 16 PAHs) ranging from 93.0 to 431 ng g⁻¹. The source apportionment results derived from three receptor models were similar, with three common sources: mixed sources of biomass burning and coal combustion (31.0–41.4% on average), petroleum combustion (31.8–45.5%), and oil leakage (13.1–21.3%). The PMF model segregated an additional source: domestic coal combustion (contributed 20.9% to the ∑₁₆PAHs). Four aspects including intra-comparison, inter-comparison, source numbers and compositions, and source contributions were considered in comparison study. The results indicated that the PMF model was most reasonable in PAH source apportionment research in this study.

The international journal of Aerosol and Air Quality Research (AAQR) covers all aspects of aerosol science and technology, atmospheric science and air quality related issues.