Yi-Nan Wang

Bio: Yi-Nan Wang is an academic researcher from Nankai University. The author has contributed to research in topics: Coal combustion products. The author has an hindex of 2, co-authored 2 publications receiving 40 citations.

A Comparison of Multiple Combined Models for Source Apportionment, Including the PCA/MLR-CMB, Unmix-CMB and PMF-CMB Models

Abstract: A combined models was developed and applied to synthetic and ambient PM datasets in our prior works. In this study, multiple combined models, including the PCA/MLR-CMB, Unmix-CMB and PMF-CMB models, were developed and employed to analyzed the synthetic datasets, in order to understand 1) the accuracies of the predictions by multiple combined models; 2) the effect of Fpeak-rotation on the predictions of the PMF-CMB model; and 3) the relationship between the extracted mixed source profiles (in the first stage) and the final predictions. 50 predictions based on different combined model solutions were obtained and compared with the synthetic datasets. The average absolute errors (AAE), cluster analysis (CA), and PCA plots were applied to evaluate the precision of the predictions. These statistical methods showed that the predictions of the PCA/MLR-CMB and PMF-CMB model (with Fpeaks from 0 to 1.0) were satisfactory, those of the Unmix-CMB model were instable (some of them closely approached the synthetic values, while other them deviated from them). Additionally, it was found that the final source contributions had good correlation with their marker concentrations (obtained in the first stage), suggesting that the extracted profiles of the mixed sources can determine the final predictions of combined models.

pH of Aerosols in a Polluted Atmosphere: Source Contributions to Highly Acidic Aerosol

Abstract: Acidity (pH) plays a key role in the physical and chemical behavior of PM2.5. However, understanding of how specific PM sources impact aerosol pH is rarely considered. Performing source apportionment of PM2.5 allows a unique link of sources pH of aerosol from the polluted city. Hourly water-soluble (WS) ions of PM2.5 were measured online from December 25th, 2014 to June 19th, 2015 in a northern city in China. Five sources were resolved including secondary nitrate (41%), secondary sulfate (26%), coal combustion (14%), mineral dust (11%), and vehicle exhaust (9%). The influence of source contributions to pH was estimated by ISORROPIA-II. The lowest aerosol pH levels were found at low WS-ion levels and then increased with increasing total ion levels, until high ion levels occur, at which point the aerosol becomes more acidic as both sulfate and nitrate increase. Ammonium levels increased nearly linearly with sulfate and nitrate until approximately 20 μg m–3, supporting that the ammonium in the aerosol was mo...

Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India

Abstract: The present study investigated the comprehensive chemical composition [organic carbon (OC), elemental carbon (EC), water-soluble inorganic ionic components (WSICs), and major & trace elements] of particulate matter (PM2.5) and scrutinized their emission sources for urban region of Delhi. The 135 PM2.5 samples were collected from January 2013 to December 2014 and analyzed for chemical constituents for source apportionment study. The average concentration of PM2.5 was recorded as 121.9 ± 93.2 μg m−3 (range 25.1–429.8 μg m−3), whereas the total concentration of trace elements (Na, Ca, Mg, Al, S, Cl, K, Cr, Si, Ti, As, Br, Pb, Fe, Zn, and Mn) was accounted for ∼17% of PM2.5. Strong seasonal variation was observed in PM2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon seasons. The chemical composition of the PM2.5 was reconstructed using IMPROVE equation, which was observed to be in good agreement with the gravimetric mass. Source apportionment of PM2.5 was carried out using the following three different receptor models: principal component analysis with absolute principal component scores (PCA/APCS), which identified five major sources; UNMIX which identified four major sources; and positive matrix factorization (PMF), which explored seven major sources. The applied models were able to identify the major sources contributing to the PM2.5 and re-confirmed that secondary aerosols (SAs), soil/road dust (SD), vehicular emissions (VEs), biomass burning (BB), fossil fuel combustion (FFC), and industrial emission (IE) were dominant contributors to PM2.5 in Delhi. The influences of local and regional sources were also explored using 5-day backward air mass trajectory analysis, cluster analysis, and potential source contribution function (PSCF). Cluster and PSCF results indicated that local as well as long-transported PM2.5 from the north-west India and Pakistan were mostly pertinent.