Measurement of atmospheric optical parameters on U.S. Atlantic coast sites, ships, and Bermuda during TARFOX

Смирнов, А. И.; Holben, B. N.; Dubovik, Оleg; O’Neill, N. T.; Remer, L. A.; Eck, T. F.; Slutsker, I.; Savoie, Dennis L.

doi:10.1029/1999jd901067

Cited by 54 publications

(47 citation statements)

References 21 publications

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“…Note, to have the correlation analysis results in one table (Table 2), we show the correlation coefficients of u Â f(rh)/m and v Â f(rh)/m with PM 2.5 Â f(rh)/m (e.g., left-side of equation 5 assuming Q ext H as constant); similar results are found when correlation coefficients are computed for u (or v) and PM 2.5 . The positive correlation between precipitiable water and surface PM 2.5 is consistent with some early studies that showed surface PM 2.5 is positively correlated with AOD that in turn is positively correlated with precipitiable water (Wang and Christopher, 2003;Smirnov et al, 2000). Interestingly, while horizontal wind (u) is negatively correlated surface PM 2.5 (Table 2), meridional wind (v) shows positive correlation, likely reflecting more polluted source from the south.…”

Section: Regression Analysissupporting

confidence: 78%

Potential application of VIIRS Day/Night Band for monitoring nighttime surface PM 2.5 air quality from space

Wang

Aegerter

et al. 2016

Atmospheric Environment

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h i g h l i g h t sVIIRS Day/Night Band (DNB) is much more sensitive to aerosols than to water vapor Modeling of outdoor light transfer in nighttime atmosphere for VIIRS DNB DNB potential for estimating surface PM 2.5 is shown qualitatively and quantitatively PM 2.5 at VIIRS night overpass time is much closer to daily-mean PM 2.5 than at daytime Strategies for future DNB remote sensing of aerosols are elaborated a r t i c l e i n f o A pilot study is conducted to illustrate the potential of using radiance data collected by the Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polarorbiting Partnership (S-NPP) satellite for particulate matter (PM) air quality monitoring at night. The study focuses on the moonless and cloudless nights in Atlanta, Georgia during AugusteOctober 2012. We show with radiative transfer calculations that DNB at night is sensitive to the change of aerosols and much less sensitive to the change of water vapor in the atmosphere illuminated by common outdoor light bulbs at the surface. We further show both qualitatively that the contrast of DNB images can indicate the change of air quality at the urban scale, and quantitatively that change of light intensity during the night (as characterized by VIIRS DNB) reflects the change of surface PM 2.5 . Compared to four meteorological variables (u and v components of surface wind speed, surface pressure, and columnar water vapor amount) that can be obtained from surface measurements, the DNB light intensity is the only variable that shows either the largest or second largest correlation with surface PM 2.5 measured at 5 different sites. A simple multivariate regression model with consideration of the change of DNB light intensity can yield improved estimate of surface PM 2.5 as compared to the model with consideration of meteorological variables only. Cross validation of this DNB-based regression model shows that the estimated surface PM 2.5 concentration has nearly no bias and a linear correlation coefficient (R) of 0.67 with respect to the corresponding hourly observed surface PM 2.5 concentration. Furthermore, groundbased observations support that surface PM 2.5 concentration at the VIIRS night overpass (~1:00 am local) time is representative of daily-mean PM 2.5 air quality (R ¼ 0.82 and mean bias of À0.1 mg m À3 ).While the potential appears promising, mapping surface PM 2.5 from space with visible light at night still face various challenges and the strategies to address some of these challenges are elaborated for future studies.

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Section: Regression Analysissupporting

confidence: 78%

Potential application of VIIRS Day/Night Band for monitoring nighttime surface PM 2.5 air quality from space

Wang

Aegerter

et al. 2016

Atmospheric Environment

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“…The dependency of α on the size distribution has been widely used to distinguish among desert dust (e.g. Eck et al, 1999;Dubovik et al, 2002), urban-industrial Smirnov et al, 2000;Dubovik et al, 2002;Kubilay et al, 2003), and biomass burning aerosols (Kaufman et al, 1998;Eck et al, 1999Eck et al, , 2001Reid et al, 1999;Dubovik et al, 2002;Balis et al, 2003). Two main groups of particles can be identified in Fig.…”

Section: Instrumentation and Data Retrievalmentioning

confidence: 99%

Aerosol optical properties at Lampedusa (Central Mediterranean). 1. Influence of transport and identification of different aerosol types

et al. 2006

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Abstract. Aerosol optical depth andÅngström exponent were obtained from multi filter rotating shadowband radiometer (MFRSR) observations carried out at the island of Lampedusa, in the Central Mediterranean, in the period July 2001-September 2003. The average aerosol optical depth at 495.7 nm, τ , is 0.24±0.14; the averageÅngström exponent, α, is 0.86±0.63. The observed values of τ range from 0.03 to 1.13, and the values of α vary from −0.32 to 2.05, indicating a large variability in aerosol content and size. In cloudfree conditions, 36% of the airmasses come from Africa, 25% from Central-Eastern Europe, and 19% from Western patterns over the Mediterranean, the efficiency of the aerosol production mechanisms, and the variability of the particles' residence time produce a distinct seasonal cycle of aerosol optical depths andÅngström exponent values. Particles originating from all sectors show a summer maximum in aerosol optical depth. The summer increase in optical depth for European aerosols is linked with an increment in the values of α, that indicates an enhancement in the number of fine particles. The summer maximum of τ for African particles is associated with a weak reduction in theÅngström exponent, suggesting an increase in the total number of particles and a relatively more intense transport of large particles. The observations were classified according to the aerosol optical properties, and two main classes have been identified: desert dust and biomass burning/urban-industrial aerosols. Values of τ and α averaged over the whole observing period are 0.37 and 0.15 for desert dust, and 0.27 and 1.77 for urbanindustrial/biomass burning aerosols.

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“…Climate and meteorology have a strong influence on temporal and spatial variability in turbidity at a variety of scales. At the microscale, higher humidity can enhance hygroscopic aerosol growth (Pilinis et al, 1995;Kotchenruther et al, 1999;Kay and Box, 2000;Smirnov et al, 2000;Holben et al, 2001;Ramaswamy et al, 2001). Figure 10 demonstrates that the predominant summer maxima/winter minima turbidity trend over Germany is paralleled by a summer maxima/winter minima trend in precipitable water.…”

Section: Germanymentioning

confidence: 99%

Comparison of aerosol and climate variability over Germany and South Africa

Power

Goyal

2003

Intl Journal of Climatology

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A radiation-based model is employed to evaluate seasonal, interannual, and spatial variability within monthly averaged turbidity (the total column amount of aerosol) at eight stations in South Africa and 13 stations in Germany. Germany has a strong seasonal turbidity trend of summer maxima and winter minima. In South Africa the seasonal turbidity trend is weaker, but, on average, turbidity peaks in spring and is lowest in autumn and winter. Over the last several decades, turbidity over Germany has decreased significantly at seven stations. The impacts of the El Chichón and Pinatubo volcanic eruptions are evident in the turbidity signatures over Germany. Four stations in South Africa show long-term trends in turbidity, but there is no consistent trend. Turbidity in Germany appears to be more influenced by aerosols of non-local origin than in South Africa, where the aerosol load may be of more local origin. Long-term monthly averages ofÅngström's turbidity coefficient β over Germany range between 0.019 and 0.143, and long-term annual averages range between 0.064 and 0.116. Over South Africa, turbidity is noticeably lower: long-term monthly averaged β ranges between 0.004 and 0.071, and the long-term annually averaged β ranges between 0.013 and 0.047. When averaged across all stations in each of the respective countries, turbidity is, on average, between 2.6 and 5.8 times higher in Germany than in South Africa, depending on the time of year.The climate of South Africa is clearly more humid than Germany, although both countries have strong seasonal trends in humidity. South Africa has become more humid over the last several decades. The decrease in turbidity at stations in Germany appears to be due to a reduction in aerosol emissions and/or changes in climatic factors other than humidity.

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Measurement of atmospheric optical parameters on U.S. Atlantic coast sites, ships, and Bermuda during TARFOX

Cited by 54 publications

References 21 publications

Potential application of VIIRS Day/Night Band for monitoring nighttime surface PM 2.5 air quality from space

Potential application of VIIRS Day/Night Band for monitoring nighttime surface PM 2.5 air quality from space

Aerosol optical properties at Lampedusa (Central Mediterranean). 1. Influence of transport and identification of different aerosol types

Comparison of aerosol and climate variability over Germany and South Africa

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