Characterization and source apportionment of particle number concentration at a semi-urban tropical environment

Abstract

Principal component analysis (PCA) and correlation have been used to study the variability of particle mass and particle number concentrations (PNC) in a tropical semi-urban environment. PNC and mass concentration (diameter in the range of 0.25->32.0 μm) have been measured from 1 February to 26 February 2013 using an in situ Grimm aerosol sampler. We found that the 24-h average total suspended particulates (TSP), particulate matter ≤10 μm (PM10), particulate matter ≤2.5 μm (PM2.5) and particulate matter ≤1 μm (PM1) were 14.37 ± 4.43, 14.11 ± 4.39, 12.53 ± 4.13 and 10.53 ± 3.98 μg m(-3), respectively. PNC in the accumulation mode (<500 nm) was the most abundant (at about 99 %). Five principal components (PCs) resulted from the PCA analysis where PC1 (43.8 % variance) predominates with PNC in the fine and sub-microme tre range. PC2, PC3, PC4 and PC5 explain 16.5, 12.4, 6.0 and 5.6 % of the variance to address the coarse, coarser, accumulation and giant fraction of PNC, respectively. Our particle distribution results show good agreement with the moderate resolution imaging spectroradiometer (MODIS) distribution.

Fine particulate matter in the tropical environment: monsoonal effects, source apportionment, and health risk assessment

“…In this study, we used a value of 0.4 for C, which, according to Ogulei et al (2006b), produced the best Q value as it is the closest to theoretical value and physically interpretable results. Other main researchers have also applied this procedure for the calculation of uncertainty (Harrison et al, 2011;Hedberg et al, 2005;Khan et al, 2015b). An additional 5 % uncertainty was added to cover any methodological errors during the preparation of filter papers, gravimetric mass measurements, and preparing the calibration curves.…”

“…Widely used multivariate methods are (a) a chemical mass balance model (CMB) (Watson et al, 1990), (b) positive matrix factorization (PMF) (Paatero, 1997;Paatero and Tapper, 1994), (c) Unmix (Henry, 1987), (d) principal component analysis coupled with absolute principal component score (PCA-APCS) (Thurston and Spengler, 1985), (e) pragmatic mass closure (PMC) (Harrison et al, 2003), and (f) a new source-type identification method for PM 2.5 known as Reduction and Species Clustering Using Episodes (ReSCUE) (Vedantham et al, 2014). PMF is the most reliable method for source-type identification for the following reasons: (i) it uses a weighted least-squares fit and estimates error of the measured data and can impose non-negativity constraints weighing each data point individually (Paatero, 1997;Paatero and Tapper, 1994), (ii) a priori knowledge of pollutants is not necessary, and (iii) it is able to deal with missing values, noisy data, outliers, and values below detection limit (Baumann et al, 2008;Khan et al, 2012Khan et al, , 2015bPolissar et al, 1998a, b). A recent study by Gibson et al (2015) suggested that PMF can resolve PM 2.5 concentrations even below 2 µg m −3 more accurately compared to PMC and CMB.…”

Transportation characteristics of motor vehicle pollutants near Beijing typical expressway

“…The number of the particles with the diameter greater than 1 m is very few. As reported by Khan et al [16], more than 90% particle number concentration is dominated by fine particles.…”

Comprehensive assessment of PM2.5 physicochemical properties during the Southeast Asia dry season (southwest monsoon)

“…During the southwest monsoon, which is usually associated with the dry season, a high density of biomass fire or wildfires hotspots were reported in the upwind of the sampling location [ Khan et al ., , , , ; Sahani et al ., ]. The dominant boundary‐transported air was from the South Sumatra region.…”