Source apportionment in PM2.5 in central Japan using positive matrix factorization focusing on small-scale local biomass burning

Ikemori, Fumikazu; Uranishi, Katsushige; Asakawa, Daichi; Nakatsubo, Ryohei; Makino, Masahide; Kido, Masaru; Mitamura, Noriko; Asano, Katsuyoshi; Nonaka, Suguru; Nishimura, Rie; Sugata, Seiji

doi:10.1016/j.apr.2021.01.006

Cited by 25 publications

(9 citation statements)

References 59 publications

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“…These results are comparable to BB-derived OC from the CMB model in this study. The estimated BB-derived OC concentrations are also comparable with the BB-derived OC during the same sampling periods in Tianjin (Fan et al, 2020) but higher than those at IAP in 2013/14 (Kang et al, 2018). Both of the studies applied the levoglucosan/OC ratio method to estimate the BB-derived OC, although the actual ratio in Beijing air may be very different from 8.2 %.…”

Section: Biomass Burningsupporting

confidence: 55%

Source apportionment of fine organic carbon at an urban site of Beijing using a chemical mass balance model

Liu

et al. 2021

Atmos. Chem. Phys.

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Abstract. Fine particles were sampled from 9 November to 11 December 2016 and 22 May to 24 June 2017 as part of the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) field campaigns in urban Beijing, China. Inorganic ions, trace elements, organic carbon (OC), elemental carbon (EC), and organic compounds, including biomarkers, hopanes, polycyclic aromatic hydrocarbons (PAHs), n-alkanes, and fatty acids, were determined for source apportionment in this study. Carbonaceous components contributed on average 47.2 % and 35.2 % of total reconstructed PM2.5 during the winter and summer campaigns, respectively. Secondary inorganic ions (sulfate, nitrate, ammonium; SNA) accounted for 35.0 % and 45.2 % of total PM2.5 in winter and summer. Other components including inorganic ions (K+, Na+, Cl−), geological minerals, and trace metals only contributed 13.2 % and 12.4 % of PM2.5 during the winter and summer campaigns. Fine OC was explained by seven primary sources (industrial and residential coal burning, biomass burning, gasoline and diesel vehicles, cooking, and vegetative detritus) based on a chemical mass balance (CMB) receptor model. It explained an average of 75.7 % and 56.1 % of fine OC in winter and summer, respectively. Other (unexplained) OC was compared with the secondary OC (SOC) estimated by the EC-tracer method, with correlation coefficients (R2) of 0.58 and 0.73 and slopes of 1.16 and 0.80 in winter and summer, respectively. This suggests that the unexplained OC by the CMB model was mostly associated with SOC. PM2.5 apportioned by the CMB model showed that the SNA and secondary organic matter were the two highest contributors to PM2.5. After these, coal combustion and biomass burning were also significant sources of PM2.5 in winter. The CMB results were also compared with results from the positive matrix factorization (PMF) analysis of co-located aerosol mass spectrometer (AMS) data. The CMB model was found to resolve more primary organic aerosol (OA) sources than AMS-PMF, but the latter could apportion secondary OA sources. The AMS-PMF results for major components, such as coal combustion OC and oxidized OC, correlated well with the results from the CMB model. However, discrepancies and poor agreements were found for other OC sources, such as biomass burning and cooking, some of which were not identified in AMS-PMF factors.

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Section: Biomass Burningsupporting

confidence: 55%

Source apportionment of fine organic carbon at an urban site of Beijing using a chemical mass balance model

Liu

et al. 2021

Atmos. Chem. Phys.

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“…As a long-established industrial center in China, Lanzhou has long experienced heavy coal-smoke pollution as coal is the main energy source. Among the elements with high loadings (Zn, Pb, Cd, and K), K in PM 2.5 is often related to biomass burning [ 48 , 49 , 50 ]; however, K can also occur as K 2 O (0.30–0.46 wt.%) in coal fly ash [ 51 ]. Zn, Cd, and Pb can also occur in coal fly ash [ 29 , 31 , 51 ], which may be one explanation for the high loadings of K in the coal-burning factor.…”

Section: Resultsmentioning

confidence: 99%

“…EC is recognized as a vehicle emissions marker [ 9 , 52 , 53 ]. The high loading of Zn likely reflects the resuspension of dust from brake linings and pads and tire wear [ 9 , 50 , 54 ]. Generally, Pb is the main emission marker of motor vehicles [ 16 ].…”

Section: Resultsmentioning

confidence: 99%

Source Apportionment of Fine Particulate Matter during the Day and Night in Lanzhou, NW China

Zhang

Jia

Wang

et al. 2022

IJERPH

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Source apportionment of PM2.5 in Lanzhou, China, was carried out using positive matrix factorization (PMF). Seventeen elements (Ca, Fe, K, Ti, Ba, Mn, Sr, Cd, Se, Pb, Cu, Zn, As, Ni, Co, Cr, V), water-soluble ions (Na+, NH4+, K+, Mg2+, Ca2, Cl−, NO3−, SO42−), and organic carbon (OC) and elemental carbon (EC) were analyzed. The results indicated that the mean concentration of PM2.5 was 178.63 ± 96.99 μg/m3. In winter, the PM2.5 concentration was higher during the day than at night, and the opposite was the case in summer, and the nighttime PM2.5 concentration was 1.3 times higher than during the day. Water-soluble ions were the dominant component of PM2.5 during the study. PMF source analysis revealed six sources in winter, during the day and night: salt lakes, coal combustion, vehicle emissions, secondary aerosols, soil dust, and industrial emissions. In summer, eight sources during the day and night were identified: soil dust, coal combustion, industrial emissions, vehicle emissions, secondary sulfate, salt lakes, secondary aerosols, and biomass burning. Secondary aerosols, coal combustion, and vehicle emissions were the dominant sources of PM2.5. In winter, the proportions of secondary aerosols and soil dust sources were greater during the day than at night, and the opposite was the case in summer. The coal source, industrial emissions source, and motor vehicle emissions source were greater at night than during the day in winter. This work can serve as a case study for further in-depth research on PM2.5 pollution and source apportionment in Lanzhou, China.

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“…PM 2.5 is a mixture, and its components are sophisticated and varied in different locations. The potential sources of PM 2.5 primarily consist of traffic [48][49][50], industrial activities [51,52], bushfire [53], residential energy use and biomass burning [54,55], and agricultural products and straw burning [56]. The map shows that the general spatial pattern of PM 2.5 concentrations is that PM 2.5 in urban regions (southeastern regions) tend to be higher than that in rural regions.…”

Section: Study Area and Air Pollution Datamentioning

confidence: 99%

Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

Song

2022

Remote Sensing

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Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM2.5concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations.

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Source apportionment in PM2.5 in central Japan using positive matrix factorization focusing on small-scale local biomass burning

Cited by 25 publications

References 59 publications

Source apportionment of fine organic carbon at an urban site of Beijing using a chemical mass balance model

Source apportionment of fine organic carbon at an urban site of Beijing using a chemical mass balance model

Source Apportionment of Fine Particulate Matter during the Day and Night in Lanzhou, NW China

Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

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