Background concentrations of PMs in Xinjiang, West China: An estimation based on meteorological filter method and Eckhardt algorithm

Wang, Yaqian; Zhang, Jieqiong; Zhang, Bai; Yang, Wen; Zhang, Hui; Mao, Jian; Sun, Ying; Ma, Zhenxing; Xiao, Jian; Gao, Shuang; Chen, Li

doi:10.1016/j.atmosres.2018.09.008

Cited by 12 publications

(14 citation statements)

References 16 publications

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“…Recently, increases in larger longer-lasting cloud cover and cooling have been correlated with enhanced concentrations of aerosols in ultraclean regimes (Goren and Rosenfeld, 2015). The eastern North Atlantic (ENA) Ocean is a remote region characterized by a clean marine environment and persistent subtropical marine boundary layer (MBL) clouds (Wood et al, 2015). Throughout the year, transported air masses from North and Central America, Europe, the Arctic, and North Africa (O'Dowd and Smith, 1993;Hamilton et al, 2014;Logan et al, 2014) periodically impact ENA, leading to perturbations in aerosol properties and cloud condensation nuclei concentrations.…”

Section: Aerosol and Cloud Interactions In The Eastern North Atlanticmentioning

confidence: 99%

“…In 2009, the 21-month field campaign (from April 2009 until December 2010) -Clouds, Aerosol, and Precipitation in the Marine Boundary Layer (CAP-MBL) on Graciosa Island (Azores archipelago) -provided the most extensive characterization of MBL clouds in the North Atlantic (Rémillard et al, 2012;Rémillard and Tselioudis, 2015). The observations collected during the 21 months of the deployment also highlighted a strong synoptic meteorological variability associated with seasonal variations of aerosol properties (Logan et al, 2014;Wood et al, 2015;Pennypacker and Wood, 2017;Wood et al, 2017). Following the outstanding uncertainties identified during CAP-MBL and to continue the research on aerosol-cloudprecipitation interactions on marine stratocumulus clouds, in 2013, ARM established a fixed site, known as the ENA ARM facility (Mather and Voyles, 2013;Dong et al, 2014;Logan et al, 2014;Feingold and McComiskey, 2016).…”

Section: Ground-based Aerosol Measurements In the Eastern North Atlanticmentioning

confidence: 99%

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Identifying a regional aerosol baseline in the eastern North Atlantic using collocated measurements and a mathematical algorithm to mask high-submicron-number-concentration aerosol events

et al. 2020

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Abstract. High-time-resolution measurements of in situ aerosol and cloud properties provide the ability to study regional atmospheric processes that occur on timescales of minutes to hours. However, one limitation to this approach is that continuous measurements often include periods when the data collected are not representative of the regional aerosol. Even at remote locations, submicron aerosols are pervasive in the ambient atmosphere with many sources. Therefore, periods dominated by local aerosol should be identified before conducting subsequent analyses to understand aerosol regional processes and aerosol–cloud interactions. Here, we present a novel method to validate the identification of regional baseline aerosol data by applying a mathematical algorithm to the data collected at the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility in the eastern North Atlantic (ENA). The ENA central facility (C1) includes an aerosol observing system (AOS) for the measurement of aerosol physical, optical, and chemical properties at time resolutions from seconds to minutes. A second temporary supplementary facility (S1), located ∼0.75 km from C1, was deployed for ∼1 year during the Aerosol and Cloud Experiments (ACE-ENA) campaign in 2017. First, we investigate the local aerosol at both locations. We associate periods of high submicron number concentration (Ntot) in the fine-mode condensation particle counter (CPC) and size distributions from the Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) as a function of wind direction using a meteorology sensor with local sources. Elevated concentrations of Aitken-mode (< 100 nm diameter) particles were observed in correspondence with the wind directions associated with airport operations. At ENA, the Graciosa Airport and its associated activities were found to be the main sources of high-concentration aerosol events at ENA, causing peaks in 1 min Ntot that exceeded 8000 and 10 000 cm−3 at C1, in summer and winter, respectively, and 5000 cm−3 at S1 in summer. Periods with high Ntot not associated with these wind directions were also observed. As a result, the diverse local sources at ENA yielded a poor relationship between Ntot measurements collected at C1 and S1 (R2=0.03 with a slope =0.05±0.001). As a first approach to mask these events, the time periods when the wind direction was associated with the airport operations (west to northwest and southeast to south at C1 and east to south at S1) were applied. The meteorological masks removed 38.9 % of the data at C1 and 43.4 % at S1, and they did not significantly improve the relationship between the two sites (R2=0.18 with a slope =0.06±0.001). Due to the complexity of high-Ntot events observed at ENA, we develop and validate a mathematical ENA Aerosol Mask (ENA-AM) to identify high-Ntot events using 1 min resolution data from the AOS CPC at C1 and S1. After its parameterization and application, ENA-AM generated a high correlation between Ntot in the summer at C1 and S1 (R2=0.87 with a slope =0.84±0.001). We identified the regional baseline at ENA to be 428±228 cm−3 in the summer and 346±223 cm−3 in the winter. Lastly, we compared masked measurements from the AOS with the ARM Aerial Facility (AAF) during flights over C1 in the summer to understand submicron aerosol vertical mixing over C1. The high correlation (R2=0.71 with a slope of 1.04±0.01) observed between C1 and the AAF Ntot collected within an area of 10 km surrounding ENA and at altitudes < 500 m indicated that the submicron aerosol at ENA was well mixed within the first 500 m of the marine boundary layer during the month of July during ACE-ENA. Our novel method for determining a regional aerosol baseline at ENA can be applied to other time periods and at other locations with validation by a secondary site or additional collocated measurements.

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Section: Aerosol and Cloud Interactions In The Eastern North Atlanticmentioning

confidence: 99%

Section: Ground-based Aerosol Measurements In the Eastern North Atlanticmentioning

confidence: 99%

Identifying a regional aerosol baseline in the eastern North Atlantic using collocated measurements and a mathematical algorithm to mask high-submicron-number-concentration aerosol events

et al. 2020

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“…One method to estimate the regionally representative concentrations at sites affected by local aerosol is with meteorological filters (Giostra et al, 2011;Gao et al, 2019;Wang et al, 2019b). This approach masks all data related to air masses coming from wind directions associated with sources.…”

Section: Masking Local Aerosol Sourcesmentioning

confidence: 99%

“…The challenge, however, is to identify and mask the time periods impacted by local aerosol sources without masking the regionally representative data that may include periods of long-range transport or other sources with high aerosol number concentrations. Hence, for the successful application of mathematical algorithms, it is important to know how 5 local sources impact the measurements, especially in terms of the signal change and duration of the events, to appropriately configure the algorithm (El Yazidi et al, 2018;Wang et al, 2019b). In this context, collocated and/or additional nearby aerosol and trace gas data are useful to understand the origins and pervasiveness of local aerosol and to validate the application of different masking algorithms.…”

Section: Masking Local Aerosol Sourcesmentioning

confidence: 99%

Identifying a regional aerosol baseline in the Eastern North Atlantic using collocated measurements and a mathematical algorithm to mask high submicron number concentration aerosol events

Gallo¹,

Uin²,

Springston³

et al. 2020

Preprint

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Abstract. High time-resolution measurements of in situ aerosol and cloud properties provide the ability to study regional atmospheric processes that occur on timescales of minutes to hours. However, one limitation to this approach is that continuous measurements often include periods when the data collected are not representative of the regional aerosol. Even at remote locations, submicron aerosols are pervasive in the ambient atmosphere with many sources. Therefore, periods dominated by local aerosol should be identified before conducting subsequent analyses to understand aerosol regional processes and aerosol-cloud interactions. Here, we present a novel method to validate the identification of regional baseline aerosol data by applying a mathematical algorithm to the data collected at the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) User Facility in the Eastern North Atlantic (ENA). The ENA Central facility (C1) includes an Aerosol Observing System (AOS) for the measurement of aerosol physical, optical, and chemical properties at time resolutions from seconds to minutes. A second temporary Supplementary facility (S1), located ~ 0.75 km from C1, was deployed for ~ 1 year during the Aerosol and Cloud Experiments (ACE-ENA) campaign in 2017. First, we investigate the local aerosol at both locations. We associate periods of high submicron number concentration (Ntot) in the fine mode Condensation Particle Counter (CPC) and size distributions from the Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) as a function of wind direction using a meteorology sensor with local sources. Elevated concentrations of Aitken mode (

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“…Combined with the results in Figure 2, PM 2.5 and PM 10 were the main factors of the AECC overload in Xinjiang. However, the high PM 2.5 and PM 10 levels in Xinjiang were the result of natural factors, such as drought, low rain amounts, sparse vegetation, the extensive Gobi Desert and more wind [48][49][50], with human activities having a lower impact [51,52]. At the same time, some environmental experts in Xinjiang have pointed out that the monitoring data on PM 2.5 and PM 10 in Xinjiang are too high to truly evaluate the air quality of arid regions [53][54][55].…”

Section: Comparative Analysismentioning

confidence: 99%

Comparative Study of Environmental Assessment Methods in the Evaluation of Resources and Environmental Carrying Capacity—A Case Study in Xinjiang, China

et al. 2019

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Scientifically assessing the environmental carrying capacity (ECC) based on an evaluation of environmental quality standards (EQSs) holds great significance for establishing a long-term monitoring and early warning mechanism for resources and environmental carrying capacity (RECC) and for coordinating the sustainable development of ecological environments and regional economies. However, it remains unclear whether the evaluation of EQSs based on the short board effect is suitable for environmental assessments under different natural environments. In this research, Xinjiang is used as a case study. Based on the evaluation of EQSs, the atmospheric environmental carrying capacity (AECC), water environmental carrying capacity (WECC) and comprehensive environmental carrying capacity (CECC) are assessed using the short board effect and the weighted average. Additionally, the suitability of ECC evaluation in RECC evaluation is discussed. The results show the following: (1) In the atmosphere, the overload of inhalable particles (PM10) and fine particulate matter (PM2.5) is more serious, while in water, the overload of total nitrogen (TN) is more serious. (2) The percentages of the AECC, WECC and CECC in overloaded counties and cities based on the short board effect are 84%, 45%, and 51%, respectively, while the percentages based on the weighted average are 32%, 6% and 7%, respectively. (3) The extreme effects of a single pollutant can be eliminated by the weighted average, which is more suitable for environmental assessments in Xinjiang. (4) In evaluating RECC monitoring and early warning systems, weighted average evaluation is used to assess the elements, and short board effect evaluation is used in the comprehensive integration to prevent the influence of the amplification of a single index. Thus, the interference of non-key factors in the regional system on the final evaluation results is reduced.

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Background concentrations of PMs in Xinjiang, West China: An estimation based on meteorological filter method and Eckhardt algorithm

Cited by 12 publications

References 16 publications

Identifying a regional aerosol baseline in the eastern North Atlantic using collocated measurements and a mathematical algorithm to mask high-submicron-number-concentration aerosol events

Identifying a regional aerosol baseline in the eastern North Atlantic using collocated measurements and a mathematical algorithm to mask high-submicron-number-concentration aerosol events

Identifying a regional aerosol baseline in the Eastern North Atlantic using collocated measurements and a mathematical algorithm to mask high submicron number concentration aerosol events

Comparative Study of Environmental Assessment Methods in the Evaluation of Resources and Environmental Carrying Capacity—A Case Study in Xinjiang, China

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