PM10 source apportionment in California's San Joaquin valley

Chow, Judith C.; Watson, John G.; Lowenthal, Douglas H.; Solomon, Paul A.; Magliano, Karen L.; Ziman, Steven D.; Richards, L.W.

doi:10.1016/0960-1686(92)90350-t

Cited by 192 publications

(147 citation statements)

References 8 publications

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“…Alternatively, SOA may be represented by a "single constituent source profile" containing only OC. 35,102,103 It accounts for OC not associated with any other sources and leads to results similar to those of Schauer and Cass. 101 A tracer solution CMB was tested by Gray et al 104 using EC as the marker for primary source types.…”

Section: Quantification Of Soasupporting

confidence: 70%

Source Apportionment: Findings from the U.S. Supersites Program

Watson

Chen

Chow

et al. 2008

Journal of the Air & Waste Management Association

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220

132

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Receptor models are used to identify and quantify source contributions to particulate matter and volatile organic compounds based on measurements of many chemical components at receptor sites. These components are selected based on their consistent appearance in some source types and their absence in others. UNMIX, positive matrix factorization (PMF), and effective variance are different solutions to the chemical mass balance (CMB) receptor model equations and are implemented on available software. In their more general form, the CMB equations allow spatial, temporal, transport, and particle size profiles to be combined with chemical source profiles for improved source resolution. Although UNMIX and PMF do not use source profiles explicitly as input data, they still require measured profiles to justify their derived source factors. The U.S. Supersites Program provided advanced datasets to apply these CMB solutions in different urban areas. Still lacking are better characterization of source emissions, new methods to estimate profile changes between source and receptor, and systematic sensitivity tests of deviations from receptor model assumptions. INTRODUCTIONReceptor-oriented source apportionment models infer source contributions and atmospheric processes from air quality measurements. Receptor models complement, rather than replace, source-oriented dispersion and chemical transformation models that begin with source emission rates to estimate ambient concentrations. 1,2 Source and receptor models are mathematical representations of reality, requiring simplifying assumptions that create uncertainty. Applying both types of models to the same situation allows them to be improved when their results diverge and lends confidence to their results when they agree. 3

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Section: Quantification Of Soasupporting

confidence: 70%

Source Apportionment: Findings from the U.S. Supersites Program

Watson

Chen

Chow

et al. 2008

Journal of the Air & Waste Management Association

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220

132

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“…2 While primary (emitted directly) chemical constituents such as elemental carbon (EC) and crustal components (e.g., silicon, iron) displayed spatial variations, the concentrations of inorganic secondary aerosols such as NH 4 NO 3 and NH 4 SO 4 were much more uniform throughout the valley. These observations suggest that particle concentrations in the SJV are determined by regional-scale interaction of source emissions, chemical transformation, vertical mixing, horizontal transport, and deposition.…”

Section: Introductionmentioning

confidence: 99%

“…1 The San Joaquin Valley (SJV) in central California frequently experiences elevated PM 10 (particles with aerodynamic diameters less than 10 µm) concentrations during the fall and winter months. Past studies 2,3 have shown that wintertime particulate matter (PM) concentrations were primarily in the PM 2.5 size fraction (particles with aerodynamic diameters less than 2.5 µm), while during the remainder of the year PM 10 consisted of nearly equal parts of PM 2.5 and coarse particles (PM 10 minus PM 2.5 ). The 1995 Integrated Monitoring Study (IMS-95) was conducted as part of CRPAQS to improve the understanding of emissions and atmospheric processes that influence particle formation and distribution in the SJV.…”

Section: Introductionmentioning

confidence: 99%

“…2 The IMS-95 winter study (December 9, 1995-January 6, 1996) included (1) 3-hr-duration aerosol (PM 2.5 , PM 10 ) and reactive precursor gas (NH 3 , nitric acid [HNO 3 ]) measurements at four core sites; (2) 24-hr PM 10 and NH 3 measurements at 42 satellite sites;) (3) 24-hr PM 2.5 , PM 10 , and NH 3 measurements at 11 boundary sites; (4) 6-hr size-resolved (0.05-3.5m in nine size fractions) mass and chemical measurements at one site; (5) fog chemistry measurements at three sites; (6) surface-and upper-air meteorological measurements at 20 sites; and (7) micrometeorological surface measurements at 38 sites. 6 This paper examines spatial and temporal variations of 3-hr-average PM chemical components and gaseous precursors, and explains these variations in terms of emissions sources, chemical transformation, and meteorological characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Receptor model source apportionments 2 showed that the highest PM 2.5 and PM 10 concentrations in urban areas during winter contained significant contributions from residential wood combustion, motor vehicle exhaust, and ammonium sulfate (NH 4 SO 4 ), with ammonium nitrate (NH 4 NO 3 ) accounting for nearly half of the PM 10 . NH 4 SO 4 and NH 4 NO 3 were of secondary origin, formed in the atmosphere from direct emissions of gaseous sulfur dioxide, oxides of nitrogen (NO x ), and ammonia (NH 3 ).…”

Section: Introductionmentioning

confidence: 99%

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Temporal Variations of PM_2.5, PM₁₀, and Gaseous Precursors during the 1995 Integrated Monitoring Study in Central California

Chow

Watson

Lowenthal

et al. 1999

Journal of the Air & Waste Management Association

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The spatial and temporal distributions of particle mass and its chemical constituents are essential for understanding the source-receptor relationships as well as the chemical, physical, and meteorological processes that result in elevated particulate concentrations in California's San Joaquin Valley (SJV). Fine particulate matter (PM 2.5 ), coarse particulate matter (PM 10 ), and aerosol precursor gases were sampled on a 3-hr time base at two urban (Bakersfield and Fresno) and two non-urban (Kern Wildlife Refuge and Chowchilla) core sites in the SJV during the winter of 1995-1996. Day-to-day variations of PM 2.5 and PM 10 and their chemical constituents were influenced by the synopticscale meteorology and were coherent among the four core IMPLICATIONS Concentrations of fine particulates (PM 2.5 ) in California's SJV during winter result from a complex interaction of source emissions, chemical transformation, and meteorological factors. While concentrations of primary pollutants like elemental and organic carbons are higher in urban than in rural areas, vertical mixing and transport aloft significantly impact their concentrations in rural areas. Concentrations of ammonium nitrate, a significant component of PM 2.5 , and ammonium sulfate, are much more spatially homogeneous. These compounds, produced from gaseous oxides of sulfur and nitrogen, are transported from urban areas, photochemically oxidized, and combined with ammonia produced in outlying areas. An understanding of the mechanisms responsible for elevated PM 2.5 concentrations will be necessary for attaining new federal compliance standards for PM 2.5 in the SJV.sites. Under non-rainy conditions, similar diurnal variations of PM 2.5 and coarse aerosol were found at the two urban sites, with concentrations peaking during the nighttime hours. Conversely, PM 2.5 and coarse aerosol peaked during the morning and afternoon hours at the two nonurban sites. Under rainy and foggy conditions, these diurnal patterns were absent or greatly suppressed.In the urban areas, elevated concentrations of primary pollutants (e.g., organic and elemental carbons) during the late afternoon and nighttime hours reflected the impact from residential wood combustion and motor vehicle exhaust. During the daytime, these concentrations decreased as the mixed layer deepened. Increases of secondary nitrate and sulfate concentrations were found during the daylight hours as a result of photochemical reactions. At the non-urban sites, the same increases in secondary aerosol concentrations occurred during the daylight hours but with a discernable lag time. Concentrations of the primary pollutants also increased at the non-urban sites during the daytime. These observations are attributed to mixing aloft of primary aerosols and secondary precursor gases in urban areas followed by rapid transport aloft to non-urban areas coupled with photochemical conversion.

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Robust estimation of chemical profiles of air-borne particulate matter

2000

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We present a modi_cation of the Chemical Mass Balance model commonly used for apportioning pollutants measured at a receptor site to particular sources\ given pro_le data from these sources [ The standard E}ective Variance model is included as a special case[ We present a package of estimation methods for these models^a {robustness option| is highlighted[ A simulation study is carried out to compare and contrast the various approaches[ Copyright Þ 1990 John Wiley + Sons\ Ltd[ KEY WORDS] ambient pro_les^chemical mass balance^e}ective variance^Generalized M!estimate^iteratively reweighted least squares^least absolute deviations^least squares^maximum likelihood^New! tonÐRaphson^regression^source pro_les 0[ INTRODUCTION Inhalable particulate matter "PM# in the atmosphere is a major environmental and pubic health concern in North America and elsewhere "Burnett et al[\ 0884^Dockery et al[\ 0882#[ PM collected at a receptor site is generally grouped according to size fractions] _ne "³1[4 mg# and coarse "1[4Ð 09 mg#[ The chemicals associated with these PM fractions o}er unique challenges beyond their physico!chemical attributes because they represent complex mixtures of often multiple point sources of pollutants "see Hopke\ 0880 and references therein#[ The objectives for monitoring air quality at a receptor site then become those of sampling "de_ning frequency and duration#\ estimation "determining ambient chemical concentrations# and apportionment "allocating the total ambient particulate mass among all regional sources detected at the receptor\ both natural and anthropogenic\ given the sources| chemical pro_les#[ These activities are often further Correspondence to] Zack Florence\ FVA Inc[\ 0205 Slater Street\ Victoria\ BC V7X 1P8\ Canada[

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PM10 source apportionment in California's San Joaquin valley

Cited by 192 publications

References 8 publications

Source Apportionment: Findings from the U.S. Supersites Program

Source Apportionment: Findings from the U.S. Supersites Program

Temporal Variations of PM_2.5, PM₁₀, and Gaseous Precursors during the 1995 Integrated Monitoring Study in Central California

Robust estimation of chemical profiles of air-borne particulate matter

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PM10 source apportionment in California's San Joaquin valley

Cited by 192 publications

References 8 publications

Source Apportionment: Findings from the U.S. Supersites Program

Source Apportionment: Findings from the U.S. Supersites Program

Temporal Variations of PM2.5, PM10, and Gaseous Precursors during the 1995 Integrated Monitoring Study in Central California

Robust estimation of chemical profiles of air-borne particulate matter

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Product

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Temporal Variations of PM_2.5, PM₁₀, and Gaseous Precursors during the 1995 Integrated Monitoring Study in Central California