Source apportionment of visibility degradation problems in Brisbane (Australia) using the multiple linear regression techniques

Chan, Y.C.; Simpson, R.W.; McTainsh, Grant Harvey; Vowles, P.D.; Cohen, David D.; Bailey, G.M.

doi:10.1016/s1352-2310(99)00091-6

Cited by 147 publications

(86 citation statements)

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“…For the 4th Street site, 25 species (OC, EC, S, NO 3 Ϫ , NH 4 ϩ , Al, Ba, Br, Ca, Cl, Cr, Cu, Fe, K, Mg, Mn, Na ϩ , Ni, Pb, Si, Sn, Sr, Ti, V, and Zn) were selected, and the weak variables (Cr, Mg, Pb, Sr, Sn, and V) were downweighted. For the Jackson Street site, 27 species (OC, EC, S, NO 3 Ϫ , NH 4 ϩ , Al, As, Ba, Br, Ca, Cd, Cl, Cr, Cu, Fe, K, Mg, Mn, Na ϩ , Ni, Pb, Si, Sn, Sr, Ti, V, and Zn) were selected, and the weak variables (As, Ba, Cd, Cr, Mg, Mn, Pb, Sr, Sn, and V) were downweighted. Tables 1 and 2 also present the MDL (ng/m 3 ), number of BDL (%), and S/N ratios for the species at the two San Jose STN sites.…”

Section: Resultsmentioning

confidence: 99%

“…The major species contributing to the eighth source included OC, EC, NO 3 Ϫ , Fe, Na ϩ , Si, Zn, Ca, and Cu. This source was assigned to diesel emissions.…”

Section: Source Contributions At 4th Streetmentioning

confidence: 99%

“…The species contributing to this source as markers included OC, EC, NO 3 Ϫ , Fe, Na ϩ , and Ni. Ni is generally associated with oil combustion, 13 …”

Section: Source Contributions At 4th Streetmentioning

confidence: 99%

“…Table 4 presents a comparison of seasonal contributions for each source and the average source contribution for the whole period of sampling using the PMF model at the Jackson Street site in San Jose. The species contributing to the first source as markers included OC, EC, NO 3 Ϫ , K, Fe, and Ba, and this profile was assigned to wood combustion. Again, the winter contributions were higher than the other seasonal contributions (winter 8.92 g/m 3 Ͼ fall 5.68 g/m 3 Ͼ spring 2.04 g/m 3 Ͼ summer 1.11 g/m 3 ).…”

Section: Source Contributions At Jackson Streetmentioning

confidence: 99%

“…[1][2][3] Because particulate matter (PM) has adverse human health and welfare effects, 4,5 many countries are developing and implementing environmental policies to control PM by establishing ambient air quality standards for PM. In 1997, U.S. Environmental Protection Agency (EPA) promulgated a National Ambient Air Quality Standards for PM with aerodynamic diameters Ͻ2.5 m (PM 2.5 ).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Source Apportionments of Fine Particulate Matter at Two San Jose Speciation Trends Network Sites

Hwang

Hopke

2006

Journal of the Air & Waste Management Association

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In this study, the chemical composition of fine particulate matter samples collected at U.S. Environmental Protection Agency Speciation Trends Network sites in San Jose, CA, from February 2000 to February 2005 were analyzed. A San Jose site was initially established at 4th Street and then subsequently moved to Jackson Street in mid-2002. These sites are ϳ1 km apart. There were no known major changes in the nature of the sources in the area over this period. The study used positive matrix factorization model to extract the source profiles and their mass contributions and to compare the results for the congruence of the source apportionments between these two nearby sites. In the case of the 4th Street site, the average mass was apportioned to wood combustion (32.1 Ϯ 2.5%), secondary nitrate (22.3 Ϯ 2%), secondary sulfate (10.7 Ϯ 0.6%), fresh sea salt (7.7 Ϯ 0.9%), gasoline vehicles (7.3 Ϯ 0.5%), aged sea salt (6.8 Ϯ 0.4%), road dust (6.7 Ϯ 0.7%), diesel emissions (3.9 Ϯ 0.3%), and a Ni-related industrial source (2.5 Ϯ 0.4%). At the Jackson Street site, the average mass was apportioned to wood combustion (33.6 Ϯ 2.6%), secondary nitrate (20.3 Ϯ 1.9%), secondary sulfate (13.9 Ϯ 0.9%), aged sea salt (12.4 Ϯ 0.7%), gasoline vehicle (8.3 Ϯ 0.6%), fresh sea salt (5.3 Ϯ 0.5%), diesel emission (3.2 Ϯ 0.3%), road dust (1.9 Ϯ 0.1%), and Ni-related industrial source (1.3 Ϯ 0.1%). Conditional probability function analysis was used to help identify local sources. These results suggested that moving the sampling site a short distance had little effect on the nature of the resolved source types although some differences in their quantitative impacts were obtained in the positive matrix factorization analyses.

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Section: Resultsmentioning

confidence: 99%

“…The major species contributing to the eighth source included OC, EC, NO 3 Ϫ , Fe, Na ϩ , Si, Zn, Ca, and Cu. This source was assigned to diesel emissions.…”

Section: Source Contributions At 4th Streetmentioning

confidence: 99%

“…The species contributing to this source as markers included OC, EC, NO 3 Ϫ , Fe, Na ϩ , and Ni. Ni is generally associated with oil combustion, 13 …”

Section: Source Contributions At 4th Streetmentioning

confidence: 99%

Section: Source Contributions At Jackson Streetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Source Apportionments of Fine Particulate Matter at Two San Jose Speciation Trends Network Sites

Hwang

Hopke

2006

Journal of the Air & Waste Management Association

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Association of the Indoor Environment With Dry Eye Metrics

et al. 2020

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ry eye is a multifactorial chronic disease of the tears and ocular surface. 1 Worldwide, the prevalence of dry eye ranges from 5% to 50% and varies owing to population characteristics, disease definition, and other risk factors. 2 In the United States, approximately 16.4 million adults (6.8%) have been diagnosed with dry eye. 3 Symptoms of dry eye are heterogeneous and include painful symptoms (ie, dryness, burning, irritation) and vision-related symptoms (ie, poor or fluctuating vision), which can negatively affect physical health, mental health, and quality of life. 2 Signs of dry eye are likewise heterogeneous and include decreased tear production, increased tear evaporation, inflammation, and high tear osmolarity. 4 Numerous risk factors for dry eye have been iden-tified, including age, sex, comorbidities such as depression and arthritis, and medications such as antihistamines. 2 Dry eye is also influenced by environmental factors, including wind, 5 high temperature, 6 low humidity, 5 high altitude, 7 and air pollution. 5 In a study of 500 hospital-based individuals in India, 6 wind and high temperature exposure, as assessed by self-report, correlated with dry eye prevalence (odds ratios, 2.15 and 1.91, respectively). In a study of 3.41 million US veterans, the risk of a diagnosis of dry eye was 6% and 7% higher in zip codes where wind speed and humidity were 1 SD less than the mean, respectively. The risk of a dry eye diagnosis was also 13% higher in zip codes where aerosol optical depth, a measure of atmospheric aerosols that include air-IMPORTANCE The ocular surface is continuously exposed to the environment. Although studies have focused on associations between outdoor environmental conditions and dry eye, information on associations between the indoor environment and dry eye is lacking.OBJECTIVE To determine associations between the indoor environment and dry eye. DESIGN, SETTING, AND PARTICIPANTSThis prospective cross-sectional study sample of 97 veterans with a wide range of dry eye metrics was recruited from the Miami Veterans Affairs Healthcare eye clinic from October 19, 2017, to August 30, 2018. Dry eye metrics were first evaluated in the clinic, followed by indoor home environmental metrics within 1 week using a handheld particle counter. Data were analyzed from October 19, 2017, to August 30, 2018.MAIN OUTCOMES AND MEASURES Symptoms of dry eye were assessed with standardized questionnaires. Dry eye signs were assessed via standard examination. Indoor environmental metrics included temperature, humidity, and particulate matter mass and count. RESULTSOf the 97 participants included in the analysis, 81 (84%) were men, with a mean (SD) age of 58.2 (11.9) years. Dry eye symptoms were in the moderate range with a mean (SD) Ocular Surface Disease Index (OSDI) score of 31.

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Chemical apportionment of aerosol optical properties during the Asia‐Pacific Economic Cooperation summit in Beijing, China

Han

Chen

et al. 2015

JGR Atmospheres

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We have investigated the chemical and optical properties of aerosol particles during the 2014 Asia‐Pacific Economic Cooperation (APEC) summit in Beijing, China, using the highly time‐resolved measurements by a high‐resolution aerosol mass spectrometer and a cavity attenuated phase shift extinction monitor. The average (±σ) extinction coefficient (bext) and absorption coefficient (bap) were 186.5 (±184.5) M m−1 and 23.3 (±21.9) M m−1 during APEC, which were decreased by 63% and 56%, respectively, compared to those before APEC primarily due to strict emission controls. The aerosol composition and size distributions showed substantial changes during APEC; as a response, the mass scattering efficiency (MSE) of PM1 was decreased from 4.7 m2 g−1 to 3.5 m2 g−1. Comparatively, the average single‐scattering albedo (SSA) remained relatively unchanged, illustrating the synchronous reductions of bext and bap during APEC. MSE and SSA were found to increase as function of the oxidation degree of organic aerosol (OA), indicating a change of aerosol optical properties during the aging processes. The empirical relationships between chemical composition and particle extinction were established using a multiple linear regression model. Our results showed the largest contribution of ammonium nitrate to particle extinction, accounting for 35.1% and 29.3% before and during APEC, respectively. This result highlights the important role of ammonium nitrate in the formation of severe haze pollution during this study period. We also observed very different optical properties of primary and secondary aerosol. Owing to emission controls in Beijing and surrounding regions and also partly the influences of meteorological changes, the average bext of secondary aerosol during APEC was decreased by 71% from 372.3 M m−1 to 108.5 M m−1, whereas that of primary aerosol mainly from cooking, traffic, and biomass burning emissions showed a smaller reduction from 136.7 M m−1 to 71.3 M m−1. As a result, the contribution of primary aerosol to particle extinction increased from 26.8% to 39.6%, elucidating an enhanced role of local primary sources in visibility deterioration during APEC. Further analysis of chemically resolved particle extinction showed that the extinction contributions of aerosol species varied greatly between different air masses but generally with ammonium nitrate, ammonium sulfate, and secondary OA being the three major contributors.

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Source apportionment of visibility degradation problems in Brisbane (Australia) using the multiple linear regression techniques

Cited by 147 publications

References 0 publications

Comparison of Source Apportionments of Fine Particulate Matter at Two San Jose Speciation Trends Network Sites

Comparison of Source Apportionments of Fine Particulate Matter at Two San Jose Speciation Trends Network Sites

Association of the Indoor Environment With Dry Eye Metrics

Chemical apportionment of aerosol optical properties during the Asia‐Pacific Economic Cooperation summit in Beijing, China

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