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Greenwald R, Sarnat JA, Fuller CH. The impact of vegetative and solid roadway barriers on particulate matter concentration in urban settings. PLoS One 2024; 19:e0296885. [PMID: 38295020 PMCID: PMC10830032 DOI: 10.1371/journal.pone.0296885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 12/19/2023] [Indexed: 02/02/2024] Open
Abstract
A potentially important approach for reducing exposure to traffic-related air pollution (TRAP) is the use of roadside barriers to reduce dispersion from highway sources to adjacent populated areas. The Trees Reducing Environmental Exposures (TREE) study investigated the effect of vegetative and solid barriers along major controlled-access highways in Atlanta, Georgia, USA by simultaneously sampling TRAP concentration at roadside locations in front of barriers and at comparison locations down-range. We measured black carbon (BC) mass concentration, particle number concentration (PNC), and the size distribution of ultrafine aerosols. Our sample sites encompassed the range of roadway barrier options in the Atlanta area: simple chain-link fences, solid barriers, and vegetative barriers. We used Generalized Linear Mixed Models (GLMMs) to estimate the effect of barrier type on the ratio of particle concentrations at the comparison site relative to the roadside site while controlling for covariates including wind direction, temperature, relative humidity, traffic volume, and distance to the roadway. Vegetative barriers exhibited the greatest TRAP reduction in terms of BC mass concentration (37% lower behind a vegetative barrier) as well as PNC (6.7% lower), and sensitivity analysis was consistent with this effect being more pronounced when the barrier was downwind of the highway. The ultrafine size distribution was comprised of modestly smaller particles on the highway side of the barrier. Non-highway particle sources were present at all sample sites, most commonly motor vehicle emissions from nearby arterials or secondary streets, which may have obscured the effect of roadside barriers.
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Affiliation(s)
- Roby Greenwald
- Population Health Sciences Department, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America
| | - Jeremy A. Sarnat
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Christina H. Fuller
- University of Georgia College of Engineering, Athens, GA, United States of America
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Chen D, Zhao W, Zhang L, Zhao Q, Zhang J, Chen F, Li H, Guan M, Zhao Y. Characterization and source apportionment for light absorption amplification of black carbon at an urban site in eastern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 865:161180. [PMID: 36581288 DOI: 10.1016/j.scitotenv.2022.161180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/06/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
The mass absorption efficiency (MAE) of black carbon (BC) could be amplified by both internal mixing and the lensing effect from non-absorbing coating, which could intensify the global warming effect of BC. In this study, a two-year-long continuous campaign with measurements of aerosol optical properties and chemical composition were conducted in Nanjing, a typical polluted city in the Yangtze River Delta (YRD) region. Relatively large MAE values were observed in 2016, and the high BC internal mixing level could be the main cause. The strong positive correlation between the ratio of non-absorbing particulate matter (NAPM) over elemental carbon (EC) and the MAE value indicated that the coating thickness of BC largely promotes its light absorption ability. The impacts of chemical component coating on MAE amplification in autumn and winter were greater than in other seasons. Multiple linear regression was performed to estimate the MAE amplification effect by internal mixing and the coating of different chemical components. Nitrate coating had the strongest impact on MAE amplification, followed by organic matter. The effects of organic matter and nitrate coatings on MAE amplification increased with the internal mixing index (IMI). Based on the positive matrix factorization (PMF) model, it was found that large decrease in the contribution of industrial emissions and coal combustion to PM2.5 from 2016 to 2017 was the main cause for MAE reduction. The novel statistical model developed in this study could be a useful tool to separate the impacts of internal mixing and non-absorbing coating.
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Affiliation(s)
- Dong Chen
- Jiangsu Provincial Academy of Environmental Science, 176 North Jiangdong Rd., Nanjing, Jiangsu 210036, China; State Key Laboratory of Pollution Control and Resource Reuse, and School of the Environment, Nanjing University, 163 Xianlin Ave., Nanjing, Jiangsu 210023, China
| | - Wenxin Zhao
- State Key Laboratory of Pollution Control and Resource Reuse, and School of the Environment, Nanjing University, 163 Xianlin Ave., Nanjing, Jiangsu 210023, China
| | - Lei Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, and School of the Environment, Nanjing University, 163 Xianlin Ave., Nanjing, Jiangsu 210023, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China.
| | - Qiuyue Zhao
- Jiangsu Provincial Academy of Environmental Science, 176 North Jiangdong Rd., Nanjing, Jiangsu 210036, China.
| | - Jie Zhang
- Jiangsu Environmental Engineering and Technology Co., Ltd., Jiangsu Environmental Protection Group Co., Ltd., 8 East Jialingjiang St., Nanjing, Jiangsu 210019, China
| | - Feng Chen
- Jiangsu Environmental Engineering and Technology Co., Ltd., Jiangsu Environmental Protection Group Co., Ltd., 8 East Jialingjiang St., Nanjing, Jiangsu 210019, China
| | - Huipeng Li
- Jiangsu Provincial Academy of Environmental Science, 176 North Jiangdong Rd., Nanjing, Jiangsu 210036, China
| | - Miao Guan
- Jiangsu Key Laboratory for Biodiversity and Biotechnology, College of Life Sciences, Nanjing Normal University, 1 Wenyuan Rd., Nanjing, Jiangsu 210023, China
| | - Yu Zhao
- State Key Laboratory of Pollution Control and Resource Reuse, and School of the Environment, Nanjing University, 163 Xianlin Ave., Nanjing, Jiangsu 210023, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China
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Chen LWA, Chow JC, Wang X, Cao J, Mao J, Watson JG. Brownness of Organic Aerosol over the United States: Evidence for Seasonal Biomass Burning and Photobleaching Effects. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:8561-8572. [PMID: 34129328 DOI: 10.1021/acs.est.0c08706] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Light-absorptivity of organic aerosol may play an important role in visibility and climate forcing, but it has not been assessed as extensively as black carbon (BC) aerosol. Based on multiwavelength thermal/optical analysis and spectral mass balance, this study quantifies BC for the U.S. Interagency Monitoring of Protected Visual Environments (IMPROVE) network while developing a brownness index (γBr) for non-BC organic carbon (OC*) to illustrate the spatiotemporal trends of light-absorbing brown carbon (BrC) content. OC* light absorption efficiencies range from 0 to 3.1 m2 gC-1 at 405 nm, corresponding to the lowest and highest BrC content of 0 and 100%, respectively. BC, OC*, and γBr explain >97% of the variability of measured spectral light absorption (405-980 nm) across 158 IMPROVE sites. Network-average OC* light absorptions at 405 nm are 50 and 28% those for BC over rural and urban areas, respectively. Larger organic fractions of light absorption occur in winter, partially due to higher organic brownness. Winter γBr exhibits a dramatic regional/urban-rural contrast consistent with anthropogenic BrC emissions from residential wood combustion. The spatial differences diminish to uniformly low γBr in summer, suggesting effective BrC photobleaching over the midlatitudes. An empirical relationship between BC, ambient temperature, and γBr is established, which can facilitate the incorporation of organic aerosol absorptivity into climate and visibility models that currently assume either zero or static organic light absorption efficiencies.
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Affiliation(s)
- Lung-Wen Antony Chen
- Department of Environmental and Occupational Health, School of Public Health, University of Nevada, Las Vegas, Las Vegas, Nevada 89154, United States
- Division of Atmospheric Sciences, Desert Research Institute, Reno, Nevada 89512, United States
| | - Judith C Chow
- Division of Atmospheric Sciences, Desert Research Institute, Reno, Nevada 89512, United States
- Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
| | - Xiaoliang Wang
- Division of Atmospheric Sciences, Desert Research Institute, Reno, Nevada 89512, United States
| | - Junji Cao
- Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
| | - Jingqiu Mao
- Department of Chemistry and Biochemistry, University of Alaska, Fairbanks, Alaska 99775, United States
| | - John G Watson
- Division of Atmospheric Sciences, Desert Research Institute, Reno, Nevada 89512, United States
- Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
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Organic Molecular Marker from Regional Biomass Burning—Direct Application to Source Apportionment Model. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10134449] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
To reduce fine particulate matter (PM2.5) level, the sources of PM2.5 in terms of the composition thereof needs to be identified. In this study, the experimental burning of ten types of biomass that are typically used in Republic of Korea, collected at the regional area were to investigate the indicated organic speciation and the results obtained therefrom were applied to the chemical mass balance (CMB) model for the study area. As a result, the organic molecular markers for the biomass burning were identified as they were varying according to chemical speciation of woods and herbaceous plants and depending upon the hard- and soft characteristics of specimens. Based on the source profile from biomass burning, major sources of PM2.5 in the study area of the present study appeared as sources of biomass burning, the secondary ions, secondary particulate matters, which is including long-distance transport, wherein the three sources occupied most over 84% of entire PM2.5. In regard to the subject area distinguished into residential area and on roads, the portion of the biomass burning appeared higher in residential area than on roads, whereas the generation from vehicles of gasoline engine and burning of meats in restaurants, etc. appeared higher on roads comparing to the residential area.
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Bray CD, Strum M, Simon H, Riddick L, Kosusko M, Menetrez M, Hays MD, Rao V. An assessment of important SPECIATE profiles in the EPA emissions modeling platform and current data gaps. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2019; 207:93-104. [PMID: 32461734 PMCID: PMC7252573 DOI: 10.1016/j.atmosenv.2019.03.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The United States (US) Environmental Protection Agency (EPA)'s SPECIATE database contains speciated particulate matter (PM) and volatile organic compound (VOC) emissions profiles. Emissions profiles from anthropogenic combustion, industry, wildfires, and agricultural sources among others are key inputs for creating chemically-resolved emissions inventories for air quality modeling. While the database and its use for air quality modeling are routinely updated and evaluated, this work sets out to systematically prioritize future improvements and communicate speciation data needs to the research community. We first identify the most prominent profiles (PM and VOC) used in the EPA's 2014 emissions modeling platform based on PM mass and VOC mass and reactivity. It is important to note that the on-road profiles were excluded from this analysis since speciation for these profiles is computed internally in the MOVES model. We then investigate these profiles further for quality and to determine whether they were being appropriately matched to source types while also considering regional variability of speciated pollutants. We then applied a quantitative needs assessment ranking system which rates the profile based on age, appropriateness (i.e. is the profile being used appropriately), prevalence in the EPA modeling platform and the quality of the reference. Our analysis shows that the highest ranked profiles (e.g. profile assignments with the highest priority for updates) include PM2.5 profiles for fires (prescribed, agricultural and wild) and VOC profiles for crude oil storage tanks and residential wood combustion of pine wood. Top ranked profiles may indicate either that there are problems with the currently available source testing or that current mappings of profiles to source categories within EPA's modeling platform need improvement. Through this process, we have identified 29 emissions sourcecategories that would benefit from updated mapping. Many of these mapping mismatches are due to lack of emissions testing for appropriate source categories. In addition, we conclude that new source emissions testing would be especially beneficial for residential wood combustion, nonroad gasoline exhaust and nonroad diesel equipment.
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Affiliation(s)
- Casey D Bray
- Contractor to the Air, Climate and Energy National Program, US EPA, RTP, NC, USA
- Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC, USA
| | - Madeleine Strum
- Office of Air Quality Planning and Standards, US EPA, RTP, NC, USA
| | - Heather Simon
- Office of Air Quality Planning and Standards, US EPA, RTP, NC, USA
| | - Lee Riddick
- National Exposure Research Laboratory, US EPA, RTP, NC, USA
| | - Mike Kosusko
- National Risk Management Research Laboratory, US EPA, RTP, NC, USA
| | - Marc Menetrez
- National Risk Management Research Laboratory, US EPA, RTP, NC, USA
| | - Michael D Hays
- National Risk Management Research Laboratory, US EPA, RTP, NC, USA
| | - Venkatesh Rao
- Office of Air Quality Planning and Standards, US EPA, RTP, NC, USA
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Bae MS, Skiles MJ, Lai AM, Olson MR, de Foy B, Schauer JJ. Assessment of forest fire impacts on carbonaceous aerosols using complementary molecular marker receptor models at two urban locations in California's San Joaquin Valley. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2019; 246:274-283. [PMID: 30557801 DOI: 10.1016/j.envpol.2018.12.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Revised: 12/05/2018] [Accepted: 12/06/2018] [Indexed: 05/03/2023]
Abstract
Two hundred sixty-three fine particulate matter (PM2.5) samples were collected over fourteen months in Fresno and Bakersfield, California. Samples were analyzed for organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC), and 160 organic molecular markers. Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) source apportionment models were applied to the results in order to understand monthly and seasonal source contributions to PM2.5 OC. Similar source categories were found from the results of the CMB and PMF models to PM2.5 OC across the sites. Six source categories with reasonably stable profiles, including biomass burning, mobile, food cooking, two different secondary organic aerosols (SOAs) (i.e., winter and summer), and forest fires were investigated. Both the CMB and the PMF models showed a strong seasonality in contributions of some sources, as well as dependence on wind transport for both sites. The overall relative source contributions to OC were 24% CMB wood smoke, 19% CMB mobile sources, 5% PMF food cooking, 2% CMB vegetative detritus, 17% PMF SOA summer, 22% PMF SOA winter, and 12% PMF forest fire. Back-trajectories using the Weather Research and Forecasting model combined with the FLEXible PARTicle dispersion model (WRF-FLEXPART) were used to further characterize wind transport. Clustering of the trajectories revealed dominant wind patterns associated with varying concentrations of the different source categories. The Comprehensive Air Quality Model with eXtensions (CAMx) was used to simulate aerosol transport from forest fires and thus confirm the impacts of individual fires, such as the Rough Fire, at the measurement sites.
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Affiliation(s)
- Min-Suk Bae
- Department of Civil & Environmental Engineering, University of Wisconsin-Madison, Madison, 53705, USA; Department of Environmental Engineering, Mokpo National University, Muan, 58554, Republic of Korea
| | - Matthew J Skiles
- Department of Civil & Environmental Engineering, University of Wisconsin-Madison, Madison, 53705, USA
| | - Alexandra M Lai
- Department of Civil & Environmental Engineering, University of Wisconsin-Madison, Madison, 53705, USA; Environmental Chemistry and Technology, University of Wisconsin-Madison, Madison, 53706, USA
| | - Michael R Olson
- Department of Civil & Environmental Engineering, University of Wisconsin-Madison, Madison, 53705, USA
| | - Benjamin de Foy
- Saint Louis University, Department of Earth & Atmospheric Sciences, Saint Louis, Missouri, 63108, USA
| | - James J Schauer
- Department of Civil & Environmental Engineering, University of Wisconsin-Madison, Madison, 53705, USA; Environmental Chemistry and Technology, University of Wisconsin-Madison, Madison, 53706, USA.
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Kelly JT, Koplitz SN, Baker KR, Holder AL, Pye HOT, Murphy BN, Bash JO, Henderson BH, Possiel N, Simon H, Eyth AM, Jang C, Phillips S, Timin B. Assessing PM 2.5 Model Performance for the Conterminous U.S. with Comparison to Model Performance Statistics from 2007-2015. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2019; 214:1-116872. [PMID: 31741655 PMCID: PMC6859642 DOI: 10.1016/j.atmosenv.2019.116872] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Previous studies have proposed that model performance statistics from earlier photochemical grid model (PGM) applications can be used to benchmark performance in new PGM applications. A challenge in implementing this approach is that limited information is available on consistently calculated model performance statistics that vary spatially and temporally over the U.S. Here, a consistent set of model performance statistics are calculated by year, season, region, and monitoring network for PM2.5 and its major components using simulations from versions 4.7.1-5.2.1 of the Community Multiscale Air Quality (CMAQ) model for years 2007-2015. The multi-year set of statistics is then used to provide quantitative context for model performance results from the 2015 simulation. Model performance for PM2.5 organic carbon in the 2015 simulation ranked high (i.e., favorable performance) in the multi-year dataset, due to factors including recent improvements in biogenic secondary organic aerosol and atmospheric mixing parameterizations in CMAQ. Model performance statistics for the Northwest region in 2015 ranked low (i.e., unfavorable performance) for many species in comparison to the 2007-2015 dataset. This finding motivated additional investigation that suggests a need for improved speciation of wildfire PM2.5emissions and modeling of boundary layer dynamics near water bodies. Several limitations were identified in the approach of benchmarking new model performance results with previous results. Since performance statistics vary widely by region and season, a simple set of national performance benchmarks (e.g., one or two targets per species and statistic) as proposed previously are inadequate to assess model performance throughout the U.S. Also, trends in model performance statistics for sulfate over the 2007 to 2015 period suggest that model performance for earlier years may not be a useful reference for assessing model performance for recent years in some cases. Comparisons of results from the 2015 base case with results from five sensitivity simulations demonstrated the importance of parameterizations of NH3 surface exchange, organic aerosol volatility and production, and emissions of crustal cations for predicting PM2.5 species concentrations.
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Affiliation(s)
- James T Kelly
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Shannon N Koplitz
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Kirk R Baker
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Amara L Holder
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Havala O T Pye
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Benjamin N Murphy
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Jesse O Bash
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Barron H Henderson
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Norm Possiel
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Heather Simon
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Alison M Eyth
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Carey Jang
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Sharon Phillips
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Brian Timin
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
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Kim S, Kim TY, Yi SM, Heo J. Source apportionment of PM 2.5 using positive matrix factorization (PMF) at a rural site in Korea. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2018. [PMID: 29533830 DOI: 10.1016/j.jenvman.2018.03.027] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
The sources of different pollutants contributing to ambient fine particles (PM2.5) on Daebu Island, Korea, were estimated. Twenty four hour integrated filter samples were collected from May 21-November 1, 2016, and analyzed for organic carbon, elemental carbon, ions, and trace elements. Positive matrix factorization was conducted on the PM2.5 chemical speciation data from the samples to define the pathways and sources of PM2.5 at the sampling site. A total of 80 samples and 24 chemical species were used to run the model and a total of nine sources were identified: secondary sulfate (29.0%), mobile (22.0%), secondary nitrate (13.2%), oil combustion (10.1%), coal combustion (9.4%), aged sea salt (7.9%), soil (5.6%), non-ferrous smelting (1.7%), and industrial activity (1.1%). Conditional probability and potential source contribution functions were then used to determine whether these sources were local or came from pollutants transported over long-range distances. The anthropogenic sources came from local emissions and originated from both industrialized and metropolitan areas, whereas the secondary inorganic aerosols were strongly influenced by the long-range transport of air pollutants from Shandong and Jiangsu provinces in China.
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Affiliation(s)
- Sunhye Kim
- Department of Environmental Health, Graduate School of Public Health, Seoul National University, Seoul, South Korea
| | - Tae-Young Kim
- Department of Environmental Health, Graduate School of Public Health, Seoul National University, Seoul, South Korea
| | - Seung-Muk Yi
- Department of Environmental Health, Graduate School of Public Health, Seoul National University, Seoul, South Korea; Institute of Health and Environment, Graduate School of Public Health, Seoul National University, Seoul, South Korea
| | - Jongbae Heo
- Institute of Health and Environment, Graduate School of Public Health, Seoul National University, Seoul, South Korea; Center for Healthy Environment Education & Research, Graduate School of Public Health, Seoul National University, Seoul, South Korea.
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Matawle JL, Pervez S, Deb MK, Shrivastava A, Tiwari S. PM 2.5 pollution from household solid fuel burning practices in Central India: 2. Application of receptor models for source apportionment. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2018; 40:145-161. [PMID: 27807676 DOI: 10.1007/s10653-016-9889-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 10/18/2016] [Indexed: 06/06/2023]
Abstract
USEPA's UNMIX, positive matrix factorization (PMF) and effective variance-chemical mass balance (EV-CMB) receptor models were applied to chemically speciated profiles of 125 indoor PM2.5 measurements, sampled longitudinally during 2012-2013 in low-income group households of Central India which uses solid fuels for cooking practices. Three step source apportionment studies were carried out to generate more confident source characterization. Firstly, UNMIX6.0 extracted initial number of source factors, which were used to execute PMF5.0 to extract source-factor profiles in second step. Finally, factor analog locally derived source profiles were supplemented to EV-CMB8.2 with indoor receptor PM2.5 chemical profile to evaluate source contribution estimates (SCEs). The results of combined use of three receptor models clearly describe that UNMIX and PMF are useful tool to extract types of source categories within small receptor dataset and EV-CMB can pick those locally derived source profiles for source apportionment which are analog to PMF-extracted source categories. The source apportionment results have also shown three fold higher relative contribution of solid fuel burning emissions to indoor PM2.5 compared to those measurements reported for normal households with LPG stoves. The previously reported influential source marker species were found to be comparatively similar to those extracted from PMF fingerprint plots. The comparison between PMF and CMB SCEs results were also found to be qualitatively similar. The performance fit measures of all three receptor models were cross-verified and validated and support each other to gain confidence in source apportionment results.
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Affiliation(s)
- Jeevan Lal Matawle
- School of Studies in Chemistry, Pt. Ravishankar Shukla University, Raipur, Chattisgarh, 492010, India
- Directorate of Geology and Mining, Chhattisgarh, Regional Laboratory, Jagdalpur, Chattisgarh, 494001, India
| | - Shamsh Pervez
- School of Studies in Chemistry, Pt. Ravishankar Shukla University, Raipur, Chattisgarh, 492010, India.
| | - Manas Kanti Deb
- School of Studies in Chemistry, Pt. Ravishankar Shukla University, Raipur, Chattisgarh, 492010, India
| | - Anjali Shrivastava
- National Environmental Engineering Research Institute, Nehru Marg, Nagpur, Maharashtra, 440020, India
| | - Suresh Tiwari
- Indian Institute of Tropical and Meteorology (IITM), New Delhi, India
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