Influences of input data and localized source profiles on source apportionment by receptor modeling

1999 ◽  
Vol 30 ◽  
pp. S455-S456
Author(s):  
Pen-Chi Chiang ◽  
Yu-Chun Chiang ◽  
Chu-Fang Wang ◽  
Sheng-Chi Chang
Keyword(s):  
Source Apportionment ◽  
Input Data ◽  
Receptor Modeling ◽  
Localized Source ◽  
Source Profiles

Source apportionment of PM10 and PM2.5 in major urban Greek agglomerations using a hybrid source-receptor modeling process

2017 ◽  
Vol 601-602 ◽  
pp. 906-917 ◽  
Author(s):  
G. Argyropoulos ◽  
C. Samara ◽  
E. Diapouli ◽  
K. Eleftheriadis ◽  
K. Papaoikonomou ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Receptor Modeling ◽  
Modeling Process ◽  
Pm10 And Pm2.5

scholarly journals Changes in PM<sub>2.5</sub> peat combustion source profiles with atmospheric aging in an oxidation flow reactor

2019 ◽  
Vol 12 (10) ◽  
pp. 5475-5501 ◽  
Author(s):  
Judith C. Chow ◽  
Junji Cao ◽  
L.-W. Antony Chen ◽  
Xiaoliang Wang ◽  
Qiyuan Wang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Source Apportionment ◽  
Flow Reactor ◽  
Regional Scale ◽  
Ionic Species ◽  
Atmospheric Modeling ◽  
Water Soluble ◽  
Combustion Source ◽  
Tropical Regions ◽  
Source Profiles

Abstract. Smoke from laboratory chamber burning of peat fuels from Russia, Siberia, the USA (Alaska and Florida), and Malaysia representing boreal, temperate, subtropical, and tropical regions was sampled before and after passing through a potential-aerosol-mass oxidation flow reactor (PAM-OFR) to simulate intermediately aged (∼2 d) and well-aged (∼7 d) source profiles. Species abundances in PM2.5 between aged and fresh profiles varied by several orders of magnitude with two distinguishable clusters, centered around 0.1 % for reactive and ionic species and centered around 10 % for carbon. Organic carbon (OC) accounted for 58 %–85 % of PM2.5 mass in fresh profiles with low elemental carbon (EC) abundances (0.67 %–4.4 %). OC abundances decreased by 20 %–33 % for well-aged profiles, with reductions of 3 %–14 % for the volatile OC fractions (e.g., OC1 and OC2, thermally evolved at 140 and 280 ∘C). Ratios of organic matter (OM) to OC abundances increased by 12 %–19 % from intermediately aged to well-aged smoke. Ratios of ammonia (NH3) to PM2.5 decreased after intermediate aging. Well-aged NH4+ and NO3- abundances increased to 7 %–8 % of PM2.5 mass, associated with decreases in NH3, low-temperature OC, and levoglucosan abundances for Siberia, Alaska, and Everglades (Florida) peats. Elevated levoglucosan was found for Russian peats, accounting for 35 %–39 % and 20 %–25 % of PM2.5 mass for fresh and aged profiles, respectively. The water-soluble organic carbon (WSOC) fractions of PM2.5 were over 2-fold higher in fresh Russian peat (37.0±2.7 %) than in Malaysian (14.6±0.9 %) peat. While Russian peat OC emissions were largely water-soluble, Malaysian peat emissions were mostly water-insoluble, with WSOC ∕ OC ratios of 0.59–0.71 and 0.18–0.40, respectively. This study shows significant differences between fresh and aged peat combustion profiles among the four biomes that can be used to establish speciated emission inventories for atmospheric modeling and receptor model source apportionment. A sufficient aging time (∼7 d) is needed to allow gas-to-particle partitioning of semi-volatilized species, gas-phase oxidation, and particle volatilization to achieve representative source profiles for regional-scale source apportionment.

scholarly journals Sources and atmospheric dynamics of organic aerosol in New Delhi, India: insights from receptor modeling

2020 ◽  
Vol 20 (2) ◽  
pp. 735-752 ◽  
Author(s):  
Sahil Bhandari ◽  
Shahzad Gani ◽  
Kanan Patel ◽  
Dongyu S. Wang ◽  
Prashant Soni ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Chemical Characterization ◽  
Organic Aerosol ◽  
Receptor Modeling ◽  
World Health ◽  
Aerosol Composition ◽  
Time Resolved ◽  
Online Measurements ◽  
Using Data ◽  
Health Organization

Abstract. Delhi, India, is the second most populated city in the world and routinely experiences some of the highest particulate matter concentrations of any megacity on the planet, posing acute challenges to public health (World Health Organization, 2018). However, the current understanding of the sources and dynamics of PM pollution in Delhi is limited. Measurements at the Delhi Aerosol Supersite (DAS) provide long-term chemical characterization of ambient submicron aerosol in Delhi, with near-continuous online measurements of aerosol composition. Here we report on source apportionment based on positive matrix factorization (PMF), conducted on 15 months of highly time-resolved speciated submicron non-refractory PM1 (NR-PM1) between January 2017 and March 2018. We report on seasonal variability across four seasons of 2017 and interannual variability using data from the two winters and springs of 2017 and 2018. We show that a modified tracer-based organic component analysis provides an opportunity for a real-time source apportionment approach for organics in Delhi. Phase equilibrium modeling of aerosols using the extended aerosol inorganics model (E-AIM) predicts equilibrium gas-phase concentrations and allows evaluation of the importance of the ventilation coefficient (VC) and temperature in controlling primary and secondary organic aerosol. We also find that primary aerosol dominates severe air pollution episodes, and secondary aerosol dominates seasonal averages.

Correspondence estimation of the source profiles in receptor modeling

2004 ◽  
Vol 15 (6) ◽  
pp. 613-634 ◽  
Author(s):  
Byron J. Gajewski ◽  
Clifford H. Spiegelman
Keyword(s):  
Receptor Modeling ◽  
Source Profiles

Receptor Modeling for Contaminant Particle Source Apportionment in Clean Rooms

1990 ◽  
Vol 12 (4) ◽  
pp. 805-812 ◽  
Author(s):  
Yi Tian ◽  
Pratim Biswas ◽  
Sotiris E. Pratsinis ◽  
Jin Jwang Wu
Keyword(s):  
Source Apportionment ◽  
Receptor Modeling ◽  
Particle Source ◽  
Contaminant Particle

scholarly journals Receptor modeling of near-roadway aerosol mass spectrometer data in Las Vegas, Nevada, with EPA PMF

2011 ◽  
Vol 11 (8) ◽  
pp. 22909-22950 ◽  
Author(s):  
S. G. Brown ◽  
T. Lee ◽  
G. A. Norris ◽  
P. T. Roberts ◽  
J. L. Collett ◽  
...  
Keyword(s):  
Mass Spectrometer ◽  
Las Vegas ◽  
Organic Aerosol ◽  
Receptor Modeling ◽  
Factor Solution ◽  
Aerosol Mass Spectrometer ◽  
Source Profiles ◽  
Aerosol Mass ◽  
The Us ◽  
Spectrometer Data

Abstract. Ambient non-refractory PM1 aerosol particles were measured with an Aerodyne High Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-AMS) at an elementary school 20 m from the US 95 freeway in Las Vegas, Nevada, during January 2008. Additional collocated continuous measurements of black carbon (BC), carbon monoxide (CO), nitrogen oxides (NOx), and meteorological data were collected. The US Environmental Protection Agency's (EPA) positive matrix factorization (PMF) data analysis tool was used to apportion organic matter (OM) as measured by HR-AMS, and rotational tools in EPA PMF were used to better characterize the solution space and pull resolved factors toward known source profiles. Three- to six-factor solutions were resolved. The four-factor solution was the most interpretable, with the typical AMS PMF factors of hydrocarbon-like organic aerosol (HOA), low-volatility oxygenated organic aerosol (LV-OOA), biomass burning organic aerosol (BBOA), and semi-volatile oxygenated organic aerosol (SV-OOA). When the measurement site was downwind of the freeway, HOA composed about half the OM, with SV-OOA and LV-OOA accounting for the rest. Attempts to pull the PMF factor profiles toward source profiles were successful but did not qualitatively change the results, indicating that these factors are very stable. Oblique edges were present in G-space plots, suggesting that the obtained rotation may not be the most plausible one. Since solutions found by pulling the profiles or using Fpeak retained these oblique edges, there appears to be little rotational freedom in the base solution. On average, HOA made up 26 % of the OM, and it made up nearly half of the OM when the monitoring site was downwind of US 95 during morning rush hour. LV-OOA was highest in the afternoon and accounted for 26 % of the OM. BBOA occurred in the evening hours, was predominantly from the residential area to the north, and on average constituted 12 % of the OM; SV-OOA accounted for the remaining third of the OM. Use of the pulling techniques available in EPA PMF and ME-2 suggested that the four-factor solution was very stable.

scholarly journals SOURCE APPORTIONMENT OF VOCS BY RECEPTOR MODELLING IN AN URBAN SITE IN HA NOI

2018 ◽  
Vol 56 (2C) ◽  
pp. 88-95 ◽  
Author(s):  
Do Thi Nhu Ngoc
Keyword(s):  
Source Apportionment ◽  
Biomass Burning ◽  
Health Effect ◽  
Carcinogenic Risk ◽  
Atmospheric Pollutants ◽  
Urban Site ◽  
Observation Data ◽  
Source Profiles ◽  
Receptor Modelling ◽  
Gasoline Evaporation

Volatile organic compounds (VOCs) are atmospheric pollutants of concern because of the health effect including carcinogenic risk of some of their species and the contribution in the formation of tropospheric ozone. The levels of VOCs in Hanoi were demonstrated to be higher than neighboring countries by previous research. The ozone potential formation (OFP) of VOCs was also some folds higher than others. Among transportation sources, VOCs were proved to be mainly emitted from motorbikes. The contribution percentages of transportation and other sources such as industrial, biomass burning sources are still remained unknown. In this research we applied chemical mass balance (CMB) receptor modelling to determine VOCs source apportionment. One week VOCs observation data at Hanoi University of Science and Technology in June 2017 was applied for investigation. Fourteen VOC species among 55 of which were applied for CMB modelling. Transportation and biomass burning source profiles were developed by monitoring in this study. Four other source profiles, namely gasoline evaporation, industrial production, cooking and paint that were compiled or calculated from previous studies. The results showed that the main sources of VOCs were vehicular emission, biomass burning, and gasoline evaporation contributing 37 %, 21 % and 20 % for VOCs levels, respectively. Other sources contributed for the leftover. The results can support to initiate policy for future control of VOCs.

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
Keyword(s):  
Air Quality ◽  
Source Apportionment ◽  
Receptor Site ◽  
Ambient Air ◽  
Receptor Modeling ◽  
Ambient Air Quality ◽  
Receptor Models ◽  
Pmf Model ◽  
Source Contributions ◽  
Reduction Strategies

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

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