Application of Positive Matrix Factorization in the Identification of the Sources of PM2.5 in Taipei City
Abstract
:1. Introduction
2. Methodology
2.1. Monitoring Experiment
2.2. PMF Model
3. Results and Discussion
3.1. Analysis of the Time Series of Monitoring Values
3.2. Automatic Continuous Hourly Monitoring: Daily Trends
- (A)
- Fine particulate matter (PM2.5)
- (B)
- Sulfur oxides (SO2, SO42−)
- (C)
- NH3/NH4+
- (D)
- Carbon (EC/OC)
3.3. PM Pollution Source Classification
- Factor 1
- This factor includes four major characteristic species: NO3−, EC, SO42− and OC. The major source of EC and NO3− is vehicles, particularly exhaust emitted by vehicles with a diesel engine.
- Factor 2
- This factor is composed of four major characteristic species: Na+, Cl−, Mg2+ and Ca2+. These major characteristic species can be classified as originating from sea spray.
- Factor 3
- The major characteristic species included in this factor are Co, V, As, Ga, and Se, which are fuel indicators. As, Ga, and Se might also be coal indicators. After integrating the major characteristic species of this factor, the emissions can be attributed to oil boilers used by hospitals, hotels and restaurants in the city [36,37].
- Factor 4
- This factor incorporates the following major characteristic species: NH4+, SO42−, Ni, and Ba. These species are mainly derived from pollutants emitted by industrial entities. One example is pollutants transported by air masses, which undergo photochemical reactions and attach to PM. These types of pollutants might be PM2.5 derived from large-scale industrial sources (e.g., power plants and petrochemical plants) at locations upwind of the metropolitan area.
- Factor 5
- The major characteristic species incorporated in this factor are Cr, Ca, Zn, Cu, Fe, and Mn. These elements originate from the Earth’s crust. Fe and Mn are indicator species of dust and are mainly derived from street dust.
- Factor 6
- The major characteristic species incorporated in this factor are OC, OC, Au, Hg, and Pb. According to the VOC emission inventory in Taipei, gasoline vehicles and motorcycles are the major source of these species. Because the sampling site is near a road, OC might originate from the exhaust of gasoline vehicles, including motorcycles [38,39,40].
3.4. Vertical PM2.5 Concentration Trends
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Date | OC | EC | SO42− | NH4+ | NO3− | Other Anions | Heavy Metals | All Chemical Components | PM2.5 |
---|---|---|---|---|---|---|---|---|---|
8 August | 2.90 | 0.91 | 4.09 | 2.12 | 1.04 | 0.86 | 0.55 | 12.47 | 11.64 |
9 August | 2.27 | 0.66 | 3.35 | 1.83 | 0.81 | 0.75 | 0.47 | 10.14 | 9.95 |
10 August | 2.30 | 0.69 | 2.98 | 1.71 | 0.73 | 0.67 | 0.39 | 9.46 | 8.95 |
11 August | 2.81 | 0.94 | 4.08 | 2.23 | 0.96 | 0.90 | 0.43 | 12.36 | 13.96 |
12 August | 3.00 | 0.99 | 5.93 | 2.92 | 1.16 | 0.93 | 0.48 | 15.40 | 16.13 |
13 August | 2.88 | 0.84 | 6.92 | 3.36 | 1.17 | 0.93 | 0.43 | 16.52 | 16.96 |
14 August | 2.83 | 0.95 | 4.96 | 2.59 | 1.02 | 0.80 | 0.42 | 13.57 | 12.63 |
15 August | 2.58 | 1.03 | 3.28 | 1.60 | 0.67 | 0.66 | 0.39 | 10.22 | 11.29 |
16 August | 2.44 | 1.07 | 5.37 | 2.50 | 0.99 | 0.70 | 0.37 | 13.44 | 13.21 |
17 August | 2.83 | 1.45 | 9.25 | 4.29 | 1.45 | 0.87 | 0.44 | 20.58 | 21.38 |
18 August | 3.46 | 1.30 | 9.68 | 4.55 | 1.51 | 0.87 | 0.51 | 21.89 | 21.67 |
19 August | 3.96 | 1.07 | 11.72 | 5.49 | 1.91 | 0.85 | 0.49 | 25.50 | 27.13 |
20 August | 3.15 | 0.73 | 8.44 | 3.80 | 0.88 | 0.69 | 0.40 | 18.10 | 18.50 |
21 August | 1.29 | 0.51 | 3.68 | 1.71 | 0.47 | 0.93 | 0.14 | 8.73 | 6.54 |
22 August | 1.10 | 0.26 | 0.62 | 0.48 | 0.28 | 1.33 | 0.07 | 4.14 | 5.25 |
23 August | 1.62 | 0.70 | 1.78 | 0.96 | 0.62 | 0.69 | 0.12 | 6.50 | 6.75 |
24 August | 2.74 | 1.06 | 4.35 | 2.06 | 1.04 | 0.68 | 0.50 | 12.42 | 13.41 |
25 August | 2.04 | 0.62 | 2.81 | 1.47 | 0.06 | 0.55 | 0.16 | 7.72 | 8.50 |
26 August | 1.29 | 0.19 | 0.73 | 0.55 | 0.02 | 0.45 | 0.10 | 3.33 | 2.63 |
27 August | 2.21 | 0.50 | 4.23 | 2.16 | 0.06 | 0.54 | 0.19 | 9.90 | 13.22 |
28 August | 1.69 | 0.43 | 2.90 | 1.44 | 0.50 | 0.54 | 0.21 | 7.70 | 7.63 |
29 August | 1.11 | 0.36 | 1.97 | 1.22 | 0.35 | 0.51 | 0.09 | 5.61 | 3.96 |
30 August | 1.06 | 0.37 | 2.45 | 1.50 | 0.40 | 0.70 | 0.09 | 6.56 | 4.43 |
Mean | 2.33 | 0.77 | 4.59 | 2.28 | 0.79 | 0.76 | 0.32 | 11.84 | 11.99 |
Percent | 19.7% | 6.5% | 38.8% | 19.3% | 6.7% | 6.4% | 2.7% | 100% (98.75%) |
Factor | Major Characteristic Species | Possible Emission Sources | Contribution Percentage (%) |
---|---|---|---|
Factor 1 | NO3−, EC, SO42−, OC | Diesel vehicle exhaust Exhaust emissions | 32.8% |
Factor 2 | Na+, Cl−, Mg2+, Ca2+ | Sea salt spray | 5.0% |
Factor 3 | Co, V, As, Ga, Se | Boiler combustion | 3.9% |
Factor 4 | NH4+, SO42−, Ni, Ba | NH4+, NO3− Emissions transported from large-scale pollution sources | 40.0% |
Factor 5 | Cr, Ca, Zn, Cu, Fe, Mn | Crustal elements, street dust | 3.2% |
Factor 6 | OC, EC, Au, Hg, Pb | Gasoline vehicle exhaust | 15.1% |
Total | 100.0% |
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Ho, W.-Y.; Tseng, K.-H.; Liou, M.-L.; Chan, C.-C.; Wang, C.-h. Application of Positive Matrix Factorization in the Identification of the Sources of PM2.5 in Taipei City. Int. J. Environ. Res. Public Health 2018, 15, 1305. https://doi.org/10.3390/ijerph15071305
Ho W-Y, Tseng K-H, Liou M-L, Chan C-C, Wang C-h. Application of Positive Matrix Factorization in the Identification of the Sources of PM2.5 in Taipei City. International Journal of Environmental Research and Public Health. 2018; 15(7):1305. https://doi.org/10.3390/ijerph15071305
Chicago/Turabian StyleHo, Wen-Yuan, Kuo-Hsin Tseng, Ming-Lone Liou, Chang-Chuan Chan, and Chia-hung Wang. 2018. "Application of Positive Matrix Factorization in the Identification of the Sources of PM2.5 in Taipei City" International Journal of Environmental Research and Public Health 15, no. 7: 1305. https://doi.org/10.3390/ijerph15071305