scholarly journals Introduction and Application of the PMF Model to Estimate the Source Apportionment of PM2.5 at Various Sites

1970 ◽  
Vol 8 (3) ◽  
pp. 25-31 ◽  
Author(s):  
Injo Hwang

To manage ambient air quality and establish effective emissions reduction strategies, it is necessary to identify sources and to apportion the ambient PM mass. To do so, receptor models have been developed that analyze various measured properties of the pollutants at the receptor site, identify the sources, and estimate their contributions. Receptor modeling is based on a mathematical model that analyzes the physicochemical properties of gaseous and/or particulate pollutants at various atmospheric receptors. Among the multivariate receptor models used for PM source identification and apportionment, positive matrix factorization (PMF) has been developed by Paatero in 1997. PMF have been developed for providing a new approach to multivariate receptor modeling based on explicit least-squares technique. Also, PMF shown to be a powerful technique relative to traditional multivariate receptor models. PMF has been implemented in two different algorithms: PMF2 (or PMF3) and the multilinear engine (ME). Since the release of PMF2 and ME, these programs have been successfully applied to assess ambient PM source contributions at many locations in the world. In this study, I would like to introduce about outline of the PMF model and application of the PMF model to estimate the source apportionment of ambient PM2.5 at various sampling sites in USA and Korea. This study suggests the possible role for maintain and manage ambient air quality and achieve reasonable air pollution strategies. DOI: http://dx.doi.org/10.3126/jie.v8i3.5928 JIE 2011; 8(3): 25-31

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 121 ◽  
Author(s):  
Jun Hu ◽  
Han Wang ◽  
Jingqiao Zhang ◽  
Meng Zhang ◽  
Hefeng Zhang ◽  
...  

Beijing-Tianjin-Hebei (BTH) and its surrounding areas are one of the most polluted regions in China. Xingtai, as a heavy industrial city of BTH and its surrounding areas, has been experiencing a severe PM2.5 pollution in recent years, characterized by extremely high concentrations of PM2.5. In 2014, PM2.5 mass concentrations monitored by online instruments in urban areas of Xingtai were 116, 77, 128, and 200 µg m−3 in spring, summer, autumn and winter, respectively, with annually average concentrations of 130 µg m−3 exhibiting 3.7 times higher than National Ambient Air Quality Standard (NAAQS) value for PM2.5 (35 µg m−3). To identify PM2.5 emission sources, ambient PM2.5 samples were collected during both cold and warm periods in 2014 in urban areas of Xingtai. Organic carbon (OC), sulfate, nitrate, ammonium and elemental carbon (EC) were the dominant components of PM2.5, accounting for 13%, 11%, 12%, 11% and 8% in the cold period, respectively, and 11%, 12%, 9%, 6%, and 5% in the warm period, respectively. Source apportionment results indicated that coal combustion (24.4%) was the largest PM2.5 emission source, followed by secondary sulfate (22.2%), secondary nitrate (18.4%), vehicle exhaust dust (12.4%), fugitive dust (9.7%), construction dust (5.5%), soil dust (3.4%) and metallurgy dust (1.6%). Based on PM2.5 source apportionment results, some emission control measures, such as replacing bulk coal with clean energy sources, controlling coal consumption by coal-fired boiler upgrades, halting operations of unlicensed small polluters, and controlling fugitive and VOCs emission, were proposed to be implemented in order to improve Xingtai’s ambient air quality.


2012 ◽  
Vol 14 (11) ◽  
pp. 2939 ◽  
Author(s):  
M. Escudero ◽  
A. Alastuey ◽  
T. Moreno ◽  
X. Querol ◽  
P. Pérez

2013 ◽  
Vol 6 (3) ◽  
pp. 4941-4969 ◽  
Author(s):  
D. Đorđević ◽  
S. Petrović ◽  
D. Relić ◽  
A. Mihajlidi-Zelić

Abstract. Two receptor modeling techniques were applied to a common data set of daily measurements of 11 elements in particulate matter (PM). Samples of PM were collected in the 5 yr period at an urban site located at the sea coast. In the vicinity of the sampling site traffic is a permanent but not significant anthropogenic source. In this study we used both the Unmix and PMF receptor models for evaluation of the sources contribution. Unmix found thirteen solutions for several combinations of species, but four solutions satisfy the criteria Min R2 > 0.8 and Min S/N > 2. Unmix identified three and four sources in satisfactory solutions. The PMF model has given 3 possible solutions and by further analysis the best solution of four sources was selected. F peak refinement enabled finding a more realistic solution that includes re-suspension and traffic as dominant source contributions. The results given in this study are in accordance with the results of Enrichment Factors analysis presented in our previous work.


2020 ◽  
Vol 20 (5) ◽  
pp. 2927-2951 ◽  
Author(s):  
Md. Robiul Islam ◽  
Thilina Jayarathne ◽  
Isobel J. Simpson ◽  
Benjamin Werden ◽  
John Maben ◽  
...  

Abstract. The Kathmandu Valley in Nepal is a bowl-shaped urban basin that experiences severe air pollution that poses health risks to its 3.5 million inhabitants. As part of the Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE), ambient air quality in the Kathmandu Valley was investigated from 11 to 24 April 2015, during the pre-monsoon season. Ambient concentrations of fine and coarse particulate matter (PM2.5 and PM10, respectively), online PM1, inorganic trace gases (NH3, HNO3, SO2, and HCl), and carbon-containing gases (CO2, CO, CH4, and 93 non-methane volatile organic compounds; NMVOCs) were quantified at a semi-urban location near the center of the valley. Concentrations and ratios of NMVOC indicated origins primarily from poorly maintained vehicle emissions, biomass burning, and solvent/gasoline evaporation. During those 2 weeks, daily average PM2.5 concentrations ranged from 30 to 207 µg m−3, which exceeded the World Health Organization 24 h guideline by factors of 1.2 to 8.3. On average, the non-water mass of PM2.5 was composed of organic matter (48 %), elemental carbon (13 %), sulfate (16 %), nitrate (4 %), ammonium (9 %), chloride (2 %), calcium (1 %), magnesium (0.05 %), and potassium (1 %). Large diurnal variability in temperature and relative humidity drove corresponding variability in aerosol liquid water content, the gas–aerosol phase partitioning of NH3, HNO3, and HCl, and aerosol solution pH. The observed levels of gas-phase halogens suggest that multiphase halogen-radical chemistry involving both Cl and Br impacted regional air quality. To gain insight into the origins of organic carbon (OC), molecular markers for primary and secondary sources were quantified. Levoglucosan (averaging 1230±1154 ng m−3), 1,3,5-triphenylbenzene (0.8±0.6 ng m−3), cholesterol (2.9±6.6 ng m−3), stigmastanol (1.0 ±0.8 ng m−3), and cis-pinonic acid (4.5±1.9 ng m−3) indicate contributions from biomass burning, garbage burning, food cooking, cow dung burning, and monoterpene secondary organic aerosol, respectively. Drawing on source profiles developed in NAMaSTE, chemical mass balance (CMB) source apportionment modeling was used to estimate contributions to OC from major primary sources including garbage burning (18±5 %), biomass burning (17±10 %) inclusive of open burning and biomass-fueled cooking stoves, and internal-combustion (gasoline and diesel) engines (18±9 %). Model sensitivity tests with newly developed source profiles indicated contributions from biomass burning within a factor of 2 of previous estimates but greater contributions from garbage burning (up to three times), indicating large potential impacts of garbage burning on regional air quality and the need for further evaluation of this source. Contributions of secondary organic carbon (SOC) to PM2.5 OC included those originating from anthropogenic precursors such as naphthalene (10±4 %) and methylnaphthalene (0.3±0.1 %) and biogenic precursors for monoterpenes (0.13±0.07 %) and sesquiterpenes (5±2 %). An average of 25 % of the PM2.5 OC was unapportioned, indicating the presence of additional sources (e.g., evaporative and/or industrial emissions such as brick kilns, food cooking, and other types of SOC) and/or underestimation of the contributions from the identified source types. The source apportionment results indicate that anthropogenic combustion sources (including biomass burning, garbage burning, and fossil fuel combustion) were the greatest contributors to PM2.5 and, as such, should be considered primary targets for controlling ambient PM pollution.


2019 ◽  
Author(s):  
Md. Robiul Islam ◽  
Thilina Jayarathne ◽  
Isobel J. Simpson ◽  
Benjamin Werden ◽  
John Maben ◽  
...  

Abstract. The Kathmandu Valley in Nepal is a bowl-shaped urban basin that experiences severe air pollution that poses health risks to its 3.5 million inhabitants. As part of the Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE), ambient air quality in the Kathmandu Valley was investigated from 11 to 24 April 2015, during the pre-monsoon season. Ambient concentrations of fine and coarse particulate matter (PM2.5 and PM10, respectively), online PM1, inorganic trace gases (NH3, HNO3, SO2, and HCl), and carbon-containing gases (CO2, CO, CH4, and 85 non-methane volatile organic compounds; NMVOC) were quantified at a semi-urban location near the center of the valley. Concentrations and ratios of NMVOC indicated that origins primarily from poorly-maintained vehicle emissions, biomass burning, and solvent/gasoline evaporation. During those two weeks, daily average PM2.5 concentrations ranged from 30 to 207 µg m−3, which exceeded the World Health Organization 24 hour guideline by factors of 1.2 to 8.3. On average, the non-water mass of PM2.5 was composed of organic matter (48 %), elemental carbon (13 %), sulfate (16 %), nitrate (4 %), ammonium (9 %), chloride (2 %), calcium (1 %), magnesium (0.05 %), and potassium (1 %). Large diurnal variability in temperature and relative humidity drove corresponding variability in aerosol liquid water content, the gas-aerosol phase partitioning of NH3, HNO3, and HCl, and aerosol solution pH. The observed levels of gas-phase halogens suggest that multiphase halogen-radical chemistry involving both Cl and Br impacted regional air quality. To gain insight into the origins of organic carbon (OC), molecular markers for primary and secondary sources were quantified. Levoglucosan (1230 ± 1153 ng m−3), 1,3,5-triphenylbenzene (0.8 ±0.5 ng m−3), cholesterol (3.0 ± 6.7 ng m−3), stigmastanol (1.4 ± 6.7 ng m−3), and cis-pinonic acid (4.5 ± 0.6 ng m−3) indicate contributions from biomass burning, garbage burning, food cooking, cow-dung burning, and monoterpene secondary organic aerosol, respectively. Drawing on source profiles developed in NAMaSTE, chemical mass balance (CMB) source apportionment modeling was used to estimate contributions to OC from major primary sources including garbage burning (18 ± 5 %), biomass burning (17 ± 10 %) inclusive of open burning and biomass-fueled cooking stoves, and internal-combustion (gasoline and diesel) engines (18 ± 9 %). Model sensitivity tests with newly-developed source profiles indicated contributions from biomass burning within a factor of two of previous estimates, but relatively greater contributions from garbage burning (up to three times), indicating large potential impacts of garbage burning on regional air quality and the need for further evaluation of this source. Contributions of secondary organic carbon (SOC) to PM2.5 OC included those originating from anthropogenic precursors for naphthalene (10 ± 4 %) and methylnaphthalene (0.3 ± 0.1 %) and biogenic precursors for monoterpenes (0.13 ± 0.07 %) and sesquiterpenes (5 ± 2 %). An average of 25 % of the PM2.5 OC was unapportioned, indicating the presence of additional sources (e.g., evaporative and/or industrial emissions such as brick kilns, food cooking, and other types of SOC) or underestimation of the contributions from the identified source types. The source apportionment results indicate that anthropogenic combustion sources (including biomass burning, garbage burning, and fossil-fuel combustion) were the greatest contributors to PM2.5 and, as such, should be considered primary targets for controlling ambient PM pollution.


2018 ◽  
Vol 243 ◽  
pp. 1791-1801 ◽  
Author(s):  
Shedrack R. Nayebare ◽  
Omar S. Aburizaiza ◽  
Azhar Siddique ◽  
David O. Carpenter ◽  
Mirza M. Hussain ◽  
...  

Author(s):  
J. B. Moran ◽  
J. L. Miller

The Clean Air Act Amendments of 1970 provide the basis for a dramatic change in Federal air quality programs. The Act establishes new standards for motor vehicles and requires EPA to establish national ambient air quality standards, standards of performance for new stationary sources of pollution, and standards for stationary sources emitting hazardous substances. Further, it establishes procedures which allow states to set emission standards for existing sources in order to achieve national ambient air quality standards. The Act also permits the Administrator of EPA to register fuels and fuel additives and to regulate the use of motor vehicle fuels or fuel additives which pose a hazard to public health or welfare.National air quality standards for particulate matter have been established. Asbestos, mercury, and beryllium have been designated as hazardous air pollutants for which Federal emission standards have been proposed.


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