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Air Quality and Source Apportionment

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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (29 February 2016) | Viewed by 58394

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Special Issue Editor

Dr. Rebecca Sheesley

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Guest Editor

Department of Environmental Science, Baylor University, Waco, TX 76798, USA
Interests: atmospheric particulate matter; organic aerosol; mega-cities; arctic aerosol; black carbon; molecular tracers; isotopes; anthropogenic aerosol; biogenic aerosol

Special Issue Information

Dear Colleagues,

Atmospheric particulate matter (PM) is known to have far-ranging impacts, from human health to climate forcing. The characterization of emission sources and the quantification of specific source impacts to PM concentrations significantly enhance our understanding of and our ability to eventually predict the fate and transport of atmospheric PM and its associated impacts on humans and the environment. The source apportionment of PM has been realized through combinations of chemical analysis (of elemental tracers, particle size, isotopic composition, and organic composition via unique tracers and molecular fingerprints) and numerical modeling (e.g., diagnostic source ratios, chemical mass balance modeling, positive matrix factorization, and Monte Carlo simulations).

Recent advances in source apportionment applications have contributed unique combinations of chemical and numerical techniques for determining the contributions of specific sources, including diesel exhaust and biomass burning. These advances also identify and help characterize the contributions of previously uncharacterized sources. Numerical modeling has also enabled estimations of contributions of emission sources to atmospherically processed PM in urban and rural regions.

Manuscripts for this Special Issue on source apportionment will be accepted if they relate to advances in chemical characterization, numerical modeling, and applications in uncharacterized regions. Regions in need of additional studies include South and East Asia, the Polar regions, and the interface of urban and agricultural areas.

Dr. Rebecca J. Sheesley
Guest Editor

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Published Papers (10 papers)

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Research

2731 KiB

Open AccessArticle

Source Apportionment of Sulfate and Nitrate over the Pearl River Delta Region in China

by Xingcheng Lu and Jimmy C. H. Fung

Atmosphere 2016, 7(8), 98; https://doi.org/10.3390/atmos7080098 - 27 Jul 2016

Cited by 25 | Viewed by 5275

Abstract

In this work, the Weather Research Forecast (WRF)–Sparse Matrix Operator Kernel Emission (SMOKE)–Comprehensive Air Quality Model with Extensions (CAMx) modeling system with particulate source apportionment technology (PSAT) module was used to study and analyze the source apportionment of sulfate and nitrate particulate matter in the Pearl River Delta region (PRD). The results show that superregional transport was an important contributor for both sulfates and nitrates in all 10 cities in this region in both February (winter) and August (summer). Especially in February, the average super-regional contribution of sulfate and nitrate reached up to 80% and 56% respectively. For the local and regional source category, power plant emissions (coal-fired and oil-fired) and industry emissions were important for sulfate formation in this region. Industry emissions and mobile emissions are important for nitrate formation in this region. In August, the sum of these two sources contributed around over 60% of local and regional nitrate. The contributions from power plant emissions and marine emissions became important in August due to the southerly prevailing wind direction. Area sources and biogenic emissions were negligible for sulfate and nitrate formation in this region. Our results reveal that cross-province cooperation is necessary for control of sulfates and nitrates in this region. Full article

(This article belongs to the Special Issue Air Quality and Source Apportionment)

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2900 KiB

Open AccessArticle

Detailed Source-Specific Molecular Composition of Ambient Aerosol Organic Matter Using Ultrahigh Resolution Mass Spectrometry and ¹H NMR

by Amanda S. Willoughby, Andrew S. Wozniak and Patrick G. Hatcher

Atmosphere 2016, 7(6), 79; https://doi.org/10.3390/atmos7060079 - 03 Jun 2016

Cited by 33 | Viewed by 6901

Abstract

Organic aerosols (OA) are universally regarded as an important component of the atmosphere that have far-ranging impacts on climate forcing and human health. Many of these impacts are related to OA molecular characteristics. Despite the acknowledged importance, current uncertainties related to the source apportionment of molecular properties and environmental impacts make it difficult to confidently predict the net impacts of OA. Here we evaluate the specific molecular compounds as well as bulk structural properties of total suspended particulates in ambient OA collected from key emission sources (marine, biomass burning, and urban) using ultrahigh resolution mass spectrometry (UHR-MS) and proton nuclear magnetic resonance spectroscopy (¹H NMR). UHR-MS and ¹H NMR show that OA within each source is structurally diverse, and the molecular characteristics are described in detail. Principal component analysis (PCA) revealed that (1) aromatic nitrogen species are distinguishing components for these biomass burning aerosols; (2) these urban aerosols are distinguished by having formulas with high O/C ratios and lesser aromatic and condensed aromatic formulas; and (3) these marine aerosols are distinguished by lipid-like compounds of likely marine biological origin. This study provides a unique qualitative approach for enhancing the chemical characterization of OA necessary for molecular source apportionment. Full article

(This article belongs to the Special Issue Air Quality and Source Apportionment)

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3192 KiB

Open AccessArticle

The Spatial Variation of Dust Particulate Matter Concentrations during Two Icelandic Dust Storms in 2015

by Pavla Dagsson-Waldhauserova, Agnes Ösp Magnusdottir, Haraldur Olafsson and Olafur Arnalds

Atmosphere 2016, 7(6), 77; https://doi.org/10.3390/atmos7060077 - 03 Jun 2016

Cited by 20 | Viewed by 5130

Abstract

Particulate matter mass concentrations and size fractions of PM₁, PM_2.5, PM₄, PM₁₀, and PM₁₅ measured in transversal horizontal profile of two dust storms in southwestern Iceland are presented. Images from a camera network were used to estimate the visibility and spatial extent of measured dust events. Numerical simulations were used to calculate the total dust flux from the sources as 180,000 and 280,000 tons for each storm. The mean PM₁₅ concentrations inside of the dust plumes varied from 10 to 1600 µg·m⁻³ (PM₁₀ = 7 to 583 µg·m⁻³). The mean PM₁ concentrations were 97–241 µg·m⁻³ with a maximum of 261 µg·m⁻³ for the first storm. The PM₁/PM_2.5 ratios of >0.9 and PM₁/PM₁₀ ratios of 0.34–0.63 show that suspension of volcanic materials in Iceland causes air pollution with extremely high PM₁ concentrations, similar to polluted urban areas in Europe or Asia. Icelandic volcanic dust consists of a higher proportion of submicron particles compared to crustal dust. Both dust storms occurred in relatively densely inhabited areas of Iceland. First results on size partitioning of Icelandic dust presented here should challenge health authorities to enhance research in relation to dust and shows the need for public dust warning systems. Full article

(This article belongs to the Special Issue Air Quality and Source Apportionment)

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4262 KiB

Open AccessArticle

Composition and Sources of Particulate Matter Measured near Houston, TX: Anthropogenic-Biogenic Interactions

by Jeffrey K. Bean, Cameron B. Faxon, Yu Jun Leong, Henry William Wallace, Basak Karakurt Cevik, Stephanie Ortiz, Manjula R. Canagaratna, Sascha Usenko, Rebecca J. Sheesley, Robert J. Griffin and Lea Hildebrandt Ruiz

Atmosphere 2016, 7(5), 73; https://doi.org/10.3390/atmos7050073 - 23 May 2016

Cited by 16 | Viewed by 6772

Abstract

Particulate matter was measured in Conroe, Texas (~60 km north of downtown Houston, Texas) during the September 2013 DISCOVER-AQ campaign to determine the sources of particulate matter in the region. The measurement site is influenced by high biogenic emission rates as well as transport of anthropogenic pollutants from the Houston metropolitan area and is therefore an ideal location to study anthropogenic-biogenic interactions. Data from an Aerosol Chemical Speciation Monitor (ACSM) suggest that on average 64 percent of non-refractory PM₁ was organic material, including a high fraction (27%–41%) of organic nitrates. There was little diurnal variation in the concentrations of ammonium sulfate; however, concentrations of organic and organic nitrate aerosol were consistently higher at night than during the day. Potential explanations for the higher organic aerosol loadings at night include changing boundary layer height, increased partitioning to the particle phase at lower temperatures, and differences between daytime and nighttime chemical processes such as nitrate radical chemistry. Positive matrix factorization was applied to the organic aerosol mass spectra measured by the ACSM and three factors were resolved—two factors representing oxygenated organic aerosol and one factor representing hydrocarbon-like organic aerosol. The factors suggest that the measured aerosol was well mixed and highly processed, consistent with the distance from the site to major aerosol sources, as well as the high photochemical activity. Full article

(This article belongs to the Special Issue Air Quality and Source Apportionment)

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1926 KiB

Open AccessArticle

Wintertime Residential Biomass Burning in Las Vegas, Nevada; Marker Components and Apportionment Methods

by Steven G. Brown, Taehyoung Lee, Paul T. Roberts and Jeffrey L. Collett

Atmosphere 2016, 7(4), 58; https://doi.org/10.3390/atmos7040058 - 19 Apr 2016

Cited by 19 | Viewed by 5368

Abstract

We characterized residential biomass burning contributions to fine particle concentrations via multiple methods at Fyfe Elementary School in Las Vegas, Nevada, during January 2008: with levoglucosan on quartz fiber filters; with water soluble potassium (K⁺) measured using a particle-into-liquid system with ion chromatography (PILS-IC); and with the fragment C₂H₄O₂⁺ from an Aerodyne High Resolution Aerosol Mass Spectrometer (HR-AMS). A Magee Scientific Aethalometer was also used to determine aerosol absorption at the UV (370 nm) and black carbon (BC, 880 nm) channels, where UV-BC difference is indicative of biomass burning (BB). Levoglucosan and AMS C₂H₄O₂⁺ measurements were strongly correlated (r² = 0.92); K⁺ correlated well with C₂H₄O₂⁺ (r² = 0.86) during the evening but not during other times. While K⁺ may be an indicator of BB, it is not necessarily a unique tracer, as non-BB sources appear to contribute significantly to K⁺ and can change from day to day. Low correlation was seen between UV-BC difference and other indicators, possibly because of an overwhelming influence of freeway emissions on BC concentrations. Given the sampling location—next to a twelve-lane freeway—urban-scale biomass burning was found to be a surprisingly large source of aerosol: overnight BB organic aerosol contributed between 26% and 33% of the organic aerosol mass. Full article

(This article belongs to the Special Issue Air Quality and Source Apportionment)

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3083 KiB

Open AccessArticle

Effect of Pollution Controls on Atmospheric PM_2.5 Composition during Universiade in Shenzhen, China

by Nitika Dewan, Yu-Qin Wang, Yuan-Xun Zhang, Yang Zhang, Ling-Yan He, Xiao-Feng Huang and Brian J. Majestic

Atmosphere 2016, 7(4), 57; https://doi.org/10.3390/atmos7040057 - 14 Apr 2016

Cited by 13 | Viewed by 5459

Abstract

The 16th Universiade, an international multi-sport event, was hosted in Shenzhen, China from 12 to 23 August 2011. During this time, officials instituted the Pearl River Delta action plan in order to enhance the air quality of Shenzhen. To determine the effect of these controls, the current study examined the trace elements, water-soluble ions, and stable lead isotopic ratios in atmospheric particulate matter (PM) collected during the controlled (when the restrictions were in place) and uncontrolled periods. Fine particles (PM_2.5) were collected at two sampling sites in Shenzhen: “LG”—a residential building in the Longgang District, with significant point sources around it and “PU”—Peking University Shenzhen Graduate School in the Nanshan District, with no significant point sources. Results from this study showed a significant increase in the concentrations of elements during the uncontrolled periods. For instance, samples at the LG site showed (controlled to uncontrolled periods) concentrations (in ng·m⁻³) of: Fe (152 to 290), As (3.65 to 8.38), Pb (9.52 to 70.8), and Zn (98.6 to 286). Similarly, samples at the PU site showed elemental concentrations (in ng·m⁻³) of: Fe (114 to 301), As (0.634 to 8.36), Pb (4.86 to 58.1), and Zn (29.5 to 259). Soluble Fe ranged from 7%–15% for the total measured Fe, indicating an urban source of Fe. Ambient PM_2.5 collected at the PU site has an average ²⁰⁶Pb/²⁰⁴Pb ratio of 18.257 and 18.260 during controlled and uncontrolled periods, respectively. The LG site has an average ²⁰⁶Pb/²⁰⁴Pb ratio of 18.183 and 18.030 during controlled and uncontrolled periods, respectively. The ²⁰⁶Pb/²⁰⁴Pb ratios at the PU and the LG sites during the controlled and uncontrolled periods were similar, indicating a common Pb source. To characterize the sources of trace elements, principal component analysis was applied to the elements and ions. Although the relative importance of each component varied, the major sources for both sites were identified as residual oil combustion, secondary inorganic aerosols, sea spray, and combustion. The PM_2.5 levels were severely decreased during the controlled period, but it is unclear if this was a result of the controls or change in meteorology. Full article

(This article belongs to the Special Issue Air Quality and Source Apportionment)

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2133 KiB

Open AccessArticle

Comparison of Land-Use Regression Modeling with Dispersion and Chemistry Transport Modeling to Assign Air Pollution Concentrations within the Ruhr Area

by Frauke Hennig, Dorothea Sugiri, Lilian Tzivian, Kateryna Fuks, Susanne Moebus, Karl-Heinz Jöckel, Danielle Vienneau, Thomas A.J. Kuhlbusch, Kees De Hoogh, Michael Memmesheimer, Hermann Jakobs, Ulrich Quass and Barbara Hoffmann

Atmosphere 2016, 7(3), 48; https://doi.org/10.3390/atmos7030048 - 19 Mar 2016

Cited by 34 | Viewed by 7409

Abstract

Two commonly used models to assess air pollution concentration for investigating health effects of air pollution in epidemiological studies are Land Use Regression (LUR) models and Dispersion and Chemistry Transport Models (DCTM). Both modeling approaches have been applied in the Ruhr area, Germany, a location where multiple cohort studies are being conducted. Application of these different modelling approaches leads to differences in exposure estimation and interpretation due to the specific characteristics of each model. We aimed to compare both model approaches by means of their respective aims, modeling characteristics, validation, temporal and spatial resolution, and agreement of residential exposure estimation, referring to the air pollutants PM_2.5, PM₁₀, and NO₂. Residential exposure referred to air pollution exposure at residences of participants of the Heinz Nixdorf Recall Study, located in the Ruhr area. The point-specific ESCAPE (European Study of Cohorts on Air Pollution Effects)-LUR aims to temporally estimate stable long-term exposure to local, mostly traffic-related air pollution with respect to very small-scale spatial variations (≤100 m). In contrast, the EURAD (European Air Pollution Dispersion)-CTM aims to estimate a time-varying average air pollutant concentration in a small area (i.e., 1 km²), taking into account a range of major sources, e.g., traffic, industry, meteorological conditions, and transport. Overall agreement between EURAD-CTM and ESCAPE-LUR was weak to moderate on a residential basis. Restricting EURAD-CTM to sources of local traffic only, respective agreement was good. The possibility of combining the strengths of both applications will be the next step to enhance exposure assessment. Full article

(This article belongs to the Special Issue Air Quality and Source Apportionment)

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3990 KiB

Open AccessArticle

Reconstructing Fire Records from Ground-Based Routine Aerosol Monitoring

by Hongmei Zhao, Daniel Q. Tong, Pius Lee, Hyuncheol Kim and Hang Lei

Atmosphere 2016, 7(3), 43; https://doi.org/10.3390/atmos7030043 - 14 Mar 2016

Cited by 4 | Viewed by 4963

Abstract

Long-term fire records are important to understanding the trend of biomass burning and its interactions with air quality and climate at regional and global scales. Traditionally, such data have been compiled from ground surveys or satellite remote sensing. To obtain aerosol information during a fire event to use in analyzing air quality, we propose a new method of developing a long-term fire record for the contiguous United States using an unconventional data source: ground-based aerosol monitoring. Assisted by satellite fire detection, the mass concentration, size distribution, and chemical composition data of surface aerosols collected from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network are examined to identify distinct aerosol characteristics during satellite-detected fire and non-fire periods. During a fire episode, elevated aerosol concentrations and heavy smoke are usually recorded by ground monitors and satellite sensors. Based on the unique physical and chemical characteristics of fire-dominated aerosols reported in the literature, we analyzed the surface aerosol observations from the IMPROVE network during satellite-detected fire events to establish a set of indicators to identify fire events from routine aerosol monitoring data. Five fire identification criteria were chosen: (1) high concentrations of PM_2.5 and PM₁₀ (particles smaller than 2.5 and 10 in diameters, respectively); (2) a high PM_2.5/PM₁₀ ratio; (3) high organic carbon (OC/PM_2.5) and elemental carbon (EC/PM_2.5) ratios; (4) a high potassium (K/PM_2.5) ratio; and (5) a low soil/PM_2.5 ratio. Using these criteria, we are able to identify a number of fire episodes close to 15 IMPROVE monitors from 2001 to 2011. Most of these monitors are located in the Western and Central United States. In any given year within the study period fire events often occurred between April and September, especially in the two months of April and September. This ground-based fire climatology is also consistent with that derived from satellite retrievals. This study demonstrates that it is feasible to reconstruct historic records of fire events based on continuous ground aerosol monitoring. This dataset can provide not only fire activity information but also fire-induced aerosol surface concentrations and chemical composition that can be used to verify satellite-based products and evaluate air quality and climate modeling results. However, caution needs to be exercised because these indicators are based on a limited number of fire events, and the proposed methodology should be further tested and confirmed in future research. Full article

(This article belongs to the Special Issue Air Quality and Source Apportionment)

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1150 KiB

Open AccessArticle

Potential Sources of Trace Metals and Ionic Species in PM_2.5 in Guadalajara, Mexico: A Case Study during Dry Season

by Mario Alfonso Murillo-Tovar, Hugo Saldarriaga-Noreña, Leonel Hernández-Mena, Arturo Campos-Ramos, Beatriz Cárdenas-González, Jesús Efren Ospina-Noreña, Ricardo Cosío-Ramírez, José De Jesús Díaz-Torres and Winston Smith

Atmosphere 2015, 6(12), 1858-1870; https://doi.org/10.3390/atmos6121834 - 01 Dec 2015

Cited by 14 | Viewed by 5572

Abstract

This study was conducted from May 25 to June 6, 2009 at a downtown location (Centro) and an urban sector (Miravalle) site in the Metropolitan Zone of Guadalajara (MZG) in Mexico. The atmospheric concentrations of PM_2.5 and its elemental and inorganic components were analyzed to identify their potential sources during the warm dry season. The daily measurements of PM_2.5 (24 h) exceeded the WHO (World Health Organization) air quality guidelines (25 μg·m⁻³). The most abundant element was found to be Fe, accounting for 59.8% and 72.2% of total metals mass in Centro and Miravalle, respectively. The enrichment factor (EF) analysis showed a more significant contribution of non-crustal sources to the elements in ambient PM_2.5 in Centro than in the Miravalle site. Particularly, the highest enrichment of Cu suggested motor vehicle-related emissions in Centro. The most abundant secondary ionic species (NO³⁻; SO₄²⁻ and NH₄⁺) and the ratio NO³⁻/SO₄²⁻ corroborated the important impact of mobile sources to fine particles at the sampling sites. In addition, the ion balance indicated that particles collected in Miravalle experienced neutralization processes likely due to a higher contribution of geological material. Other important contributors to PM_2.5 included biomass burning by emissions transported from the forest into the city. Full article

(This article belongs to the Special Issue Air Quality and Source Apportionment)

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758 KiB

Open AccessArticle

Forecasting Urban Air Quality via a Back-Propagation Neural Network and a Selection Sample Rule

by Yonghong Liu, Qianru Zhu, Dawen Yao and Weijia Xu

Atmosphere 2015, 6(7), 891-907; https://doi.org/10.3390/atmos6070891 - 09 Jul 2015

Cited by 18 | Viewed by 4792

Abstract

In this paper, based on a sample selection rule and a Back Propagation (BP) neural network, a new model of forecasting daily SO₂, NO₂, and PM₁₀ concentration in seven sites of Guangzhou was developed using data from January 2006 to April 2012. A meteorological similarity principle was applied in the development of the sample selection rule. The key meteorological factors influencing SO₂, NO₂, and PM₁₀ daily concentrations as well as weight matrices and threshold matrices were determined. A basic model was then developed based on the improved BP neural network. Improving the basic model, identification of the factor variation consistency was added in the rule, and seven sets of sensitivity experiments in one of the seven sites were conducted to obtain the selected model. A comparison of the basic model from May 2011 to April 2012 in one site showed that the selected model for PM₁₀ displayed better forecasting performance, with Mean Absolute Percentage Error (MAPE) values decreasing by 4% and R² values increasing from 0.53 to 0.68. Evaluations conducted at the six other sites revealed a similar performance. On the whole, the analysis showed that the models presented here could provide local authorities with reliable and precise predictions and alarms about air quality if used at an operational scale. Full article

(This article belongs to the Special Issue Air Quality and Source Apportionment)

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