Identification of sources of pollutants in precipitation measured at the mid-Atlantic US coast using potential source contribution function (PSCF)

Lucey, Derrick W.; Hadjiiski, Lubomir M.; Hopke, Philip K.; Scudlark, Joseph R.; Church, Thomas M.

doi:10.1016/s1352-2310(01)00185-6

Cited by 42 publications

(28 citation statements)

References 20 publications

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“…The height of the air mass is also crucial for interpreting the measured ozone concentrations. As discussed in previous studies (Ma et al, 2002;, the WLG site is predominantly influenced by air from the planetary boundary layer (PBL) during daytime and from the free troposphere (FT) during nighttime, with ozone concentrations showing a daytime minimum and a nighttime maximum. Daytime and nighttime ozone at WLG show different trends, particularly in summer (0.07 ± 0.18 ppb yr −1 for daytime and 0.22 ± 0.20 ppb yr −1 for nighttime; .…”

Section: Backward Trajectory Analysismentioning

confidence: 84%

“…The potential sources of high ozone are studied using the potential source contribution function (PSCF) analysis method, which has been widely applied to detect possible source regions (Ara Begum et al, 2005;Lucey et al, 2001;Zhou et al, 2004). The PSCF analysis is performed both on PBL and on FT trajectories to detect differences in source region distributions in the PBL and in the FT or above.…”

Section: Potential Source Contribution Function Analysismentioning

confidence: 99%

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Long-term trends of surface ozone and its influencing factors at the Mt Waliguan GAW station, China – Part 2: The roles of anthropogenic emissions and climate variability

Lin

et al. 2018

Atmos. Chem. Phys.

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Abstract. Inter-annual variability and long-term trends in tropospheric ozone are both environmental and climate concerns. Ozone measured at Mt Waliguan Observatory (WLG, 3816 m a.s.l.) on the Tibetan Plateau over the period of 1994-2013 has increased significantly by 0.2-0.3 ppbv yr −1 during spring and autumn but shows a much smaller trend in winter and no significant trend in summer. Here we explore the factors driving the observed ozone changes at WLG using backward trajectory analysis, chemistry-climate model hindcast simulations (GFDL AM3), a trajectory-mapped ozonesonde data set, and several climate indices. A stratospheric ozone tracer implemented in GFDL AM3 indicates that stratosphere-to-troposphere transport (STT) can explain ∼ 60 % of the simulated springtime ozone increase at WLG, consistent with an increase in the NW air-mass frequency inferred from the trajectory analysis. Enhanced STT associated with the strengthening of the mid-latitude jet stream contributes to the observed high ozone anomalies at WLG during the springs of 1999 and 2012. During autumn, observations at WLG are more heavily influenced by polluted air masses originating from South East Asia than in the other seasons. Rising Asian anthropogenic emissions of ozone precursors are the key driver of increasing autumnal ozone observed at WLG, as supported by the GFDL AM3 model with time-varying emissions, which captures the observed ozone increase (0.26 ± 0.11 ppbv yr −1 ). AM3 simulates a greater ozone increase of 0.38 ± 0.11 ppbv yr −1 at WLG in autumn under conditions with strong transport from South East Asia and shows no significant ozone trend in autumn when anthropogenic emissions are held constant in time. During summer, WLG is mostly influenced by easterly air masses, but these trajectories do not extend to the polluted regions of eastern China and have decreased significantly over the last 2 decades, which likely explains why summertime ozone measured at WLG shows no significant trend despite ozone increases in eastern China. Analysis of the Trajectory-mapped Ozonesonde data set for the Stratosphere and Troposphere (TOST) and trajectory residence time reveals increases in direct ozone transport from the eastern sector during autumn, which adds to the autumnal ozone increase. We further examine the links of ozone variability at WLG to the quasibiennial oscillation (QBO), the East Asian summer monsoon (EASM), and the sunspot cycle. Our results suggest that the 2-3-, 3-7-, and 11-year periodicities are linked to the QBO, EASM index, and sunspot cycle, respectively. A multivariate regression analysis is performed to quantify the relative Published by Copernicus Publications on behalf of the European Geosciences Union. contributions of various factors to surface ozone concentrations at WLG. Through an observational and modelling analysis, this study demonstrates the complex relationships between surface ozone at remote locations and its dynamical and chemical influencing factors.

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Section: Backward Trajectory Analysismentioning

confidence: 84%

Section: Potential Source Contribution Function Analysismentioning

confidence: 99%

Long-term trends of surface ozone and its influencing factors at the Mt Waliguan GAW station, China – Part 2: The roles of anthropogenic emissions and climate variability

Lin

et al. 2018

Atmos. Chem. Phys.

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“…최근에는 중국의 급속한 산업발전으로 다량의 오염물질을 발생하고 있어 황사 기간 외에도 여러 연 구에서 중국 대기오염물질의 장거리 수송에 따른 국 내 대기오염의 영향에 대해 조사되었다(eg., Choi et al, 2001;Kang et al, 2004;Park and Lee, 2004;Xiao et al, 2014 and Hopke, 2004;Watson et al, 2008), Hopke et al, 1993). Lucey et al, 2001;Polissar et al, 2001;Brankov et al, 2003). 경험 가중치 W(n i, j )는 모든 격자내 평균 궤 적점 수를 기준으로 3배수보다 적은 궤적점을 갖는 격자가 큰 불확도를 갖는다고 하였고 다음과 같이 계 산된다 .…”

Section: 서 론unclassified

Potential Source of PM₁₀, PM_2.5, and OC and EC in Seoul During Spring 2016

Ham¹,

Lee²,

Wan³

et al. 2017

Atmosphere

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Organic carbon (OC) and elemental carbon (EC) in PM 2.5 were measured using Sunset OC/EC Field Analyzer at Seoul Hwangsa Monitoring Center from March to April, 2016. The mean concentrations of OC and EC during the entire period were 4.4 ± 2.0 μgC m −3 and 1.4 ± 0.6 μgC m −3 , respectively. OC/EC ratio was 3.4 ± 1.0. The average concentrations of PM 10 and PM 2.5 were 57.4 ± 25.9 and 39.7 ± 19.8 μg m , respectively, which were detected by an optical particle counter. The OC and EC peaks were observed in the morning, which were impacted by vehicle emission, however, their diurnal variations were not noticeable. This is determined to be contributed by the long-range transported OC or secondary formation via photochemical reaction by volatile organic compounds at afternoon. A conditional probability function (CPF) model was used to identify the local source of pollution. High concentrations of PM 10 and PM 2.5 were observed from the westerly wind, regardless of wind speed. When wind velocity was high, a mixing plume of dust and pollution during long-range transport from China in spring was observed. In contrast, pollution in low wind velocity was from local source, regardless of direction. To know the effect of long-range transport on pollution, a concentration weighted trajectory (CWT) model was analyzed based on a potential source contribution function (PSCF) model in which 75 percentiles high concentration was picked out for CWT analysis. PM 10 , PM 2.5 , OC, and EC were dominantly contributed from China in spring, and EC results were similar in both PSCF and CWT. In conclusion, Seoul air quality in spring was mainly affected by a mixture of local pollution and anthropogenic pollutants originated in China than the Asian dust.

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“…Such values should not be given the same weight as a value resulting from a large number of trajectory end points. 15,16,19 In this study, an empirical weight function, W (nij), was used to resolve the problem: a weight of 0.00 is given to PSCF value for the case of nij Յ 4, a weight of 0.50 for 4 Ͻ nij Յ 7, a weight of 0.75 for 7 Ͻ nij Յ 15, and a weight of 1.00 for nij Ͼ 15. We obtained these limit values empirically by applying the trial and error method.…”

Section: Pscfmentioning

confidence: 99%

Source Identification and Trends in Concentrations of Gaseous and Fine Particulate Principal Species in Seoul, South Korea

Kang

Kang²,

Lee

2006

Journal of the Air & Waste Management Association

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Ambient measurements were made using two sets of annular denuder system during the four seasons (April 2001 to February 2002 and were then compared with the results during the period of 1996 -1997 to estimate the trends and seasonal variations in concentrations of gaseous and fine particulate matter (PM 2.5 ) principal species. Annual averages of gaseous HNO 3 and NH 3 increased by 11% and 6%, respectively, compared with those of the previous study, whereas HONO and SO 2 decreased by 11% and 136%, respectively. The PM 2.5 concentration decreased by ϳ17%, 35% for SO 4 2Ϫ , and 29% for NH 4 ϩ , whereas NO 3 Ϫ increased by 21%. Organic carbon (OC) and elemental carbon (EC) were 12.8 and 5.98 g/m Ϫ3

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Identification of sources of pollutants in precipitation measured at the mid-Atlantic US coast using potential source contribution function (PSCF)

Cited by 42 publications

References 20 publications

Long-term trends of surface ozone and its influencing factors at the Mt Waliguan GAW station, China – Part 2: The roles of anthropogenic emissions and climate variability

Long-term trends of surface ozone and its influencing factors at the Mt Waliguan GAW station, China – Part 2: The roles of anthropogenic emissions and climate variability

Potential Source of PM₁₀, PM_2.5, and OC and EC in Seoul During Spring 2016

Source Identification and Trends in Concentrations of Gaseous and Fine Particulate Principal Species in Seoul, South Korea

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Identification of sources of pollutants in precipitation measured at the mid-Atlantic US coast using potential source contribution function (PSCF)

Cited by 42 publications

References 20 publications

Long-term trends of surface ozone and its influencing factors at the Mt Waliguan GAW station, China – Part 2: The roles of anthropogenic emissions and climate variability

Long-term trends of surface ozone and its influencing factors at the Mt Waliguan GAW station, China – Part 2: The roles of anthropogenic emissions and climate variability

Potential Source of PM10, PM2.5, and OC and EC in Seoul During Spring 2016

Source Identification and Trends in Concentrations of Gaseous and Fine Particulate Principal Species in Seoul, South Korea

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Product

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Potential Source of PM₁₀, PM_2.5, and OC and EC in Seoul During Spring 2016