Study of PBLH and Its Correlation with Particulate Matter from One-Year Observation over Nanjing, Southeast China

Qu, Yawei; Han, Yong; Wu, Yonghua; Gao, Peng; Wang, Tijian

doi:10.3390/rs9070668

Cited by 66 publications

(32 citation statements)

References 56 publications

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“…We also use the PBLH data obtained from the Modern Era-Retrospective Reanalysis for Research and Applications (MERRA) reanalysis dataset to generate a PBLH climatology with a spatial resolution of 2/3 • × 1/2 • (longitudelatitude). The MERRA reanalysis data use a new version of the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5), which is a state-of-the-art system coupling a global atmospheric general circulation model (GEOS-5 AGCM) to NCEP's Grid-point Statistical Interpolation (GSI) analysis (Rienecker et al, 2011). Compared with other reanalysis products (e.g., ECMWF), MERRA PBLHs have relatively high temporal and spatial resolution, and are widely used in multiple studies (e.g., Jordan et al, 2010;McGrath-Spangler and Denning, 2012;Kennedy et al, 2011).…”

Section: Pblh Obtained From Merra Reanalysis Datamentioning

confidence: 99%

Relationships between the planetary boundary layer height and surface pollutants derived from lidar observations over China: regional pattern and influencing factors

Su¹,

Li²,

Kahn³

2018

Atmos. Chem. Phys.

243

137

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Abstract. The frequent occurrence of severe air pollution episodes in China has been a great concern and thus the focus of intensive studies. Planetary boundary layer height (PBLH) is a key factor in the vertical mixing and dilution of near-surface pollutants. However, the relationship between PBLH and surface pollutants, especially particulate matter (PM) concentration across China, is not yet well understood. We investigate this issue at ∼1600 surface stations using PBLH derived from space-borne and ground-based lidar, and discuss the influence of topography and meteorological variables on the PBLH–PM relationship. Albeit the PBLH–PM correlations are roughly negative for most cases, their magnitude, significance, and even sign vary considerably with location, season, and meteorological conditions. Weak or even uncorrelated PBLH–PM relationships are found over clean regions (e.g., Pearl River Delta), whereas nonlinearly negative responses of PM to PBLH evolution are found over polluted regions (e.g., North China Plain). Relatively strong PBLH–PM interactions are found when the PBLH is shallow and PM concentration is high, which typically corresponds to wintertime cases. Correlations are much weaker over the highlands than the plains regions, which may be associated with lighter pollution loading at higher elevations and contributions from mountain breezes. The influence of horizontal transport on surface PM is considered as well, manifested as a negative correlation between surface PM and wind speed over the whole nation. Strong wind with clean upwind air plays a dominant role in removing pollutants, and leads to obscure PBLH–PM relationships. A ventilation rate is used to jointly consider horizontal and vertical dispersion, which has the largest impact on surface pollutant accumulation over the North China Plain. As such, this study contributes to improved understanding of aerosol–planetary boundary layer (PBL) interactions and thus our ability to forecast surface air pollution.

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Section: Pblh Obtained From Merra Reanalysis Datamentioning

confidence: 99%

Relationships between the planetary boundary layer height and surface pollutants derived from lidar observations over China: regional pattern and influencing factors

Su¹,

Li²,

Kahn³

2018

Atmos. Chem. Phys.

243

137

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“…However, the processes governing the boundary layer evolution are still poorly resolved and/or often not represented in the current forecast models. Especially, continuous observations of changes in the ABLH with high spatial and temporal resolution are desirable to support weather and air quality prediction [16].…”

Section: Introductionmentioning

confidence: 99%

“…In general, the knowledge of the long-term daytime and nighttime boundary layer variability is still rather limited [51]. Added to this, the long-term seasonal and annual statistics at particular places of interest are seldom in existing studies [29,44], especially their combination with other parameters related to the air quality or the optical properties of the atmosphere [11,12,16,52]. Additionally, boundary layer clouds, being a part of ABL, play an important role in the ABL variability [53,54].…”

Section: Introductionmentioning

confidence: 99%

Variability of the Boundary Layer Over an Urban Continental Site Based on 10 Years of Active Remote Sensing Observations in Warsaw

et al. 2020

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Atmospheric boundary layer height (ABLH) was observed by the CHM15k ceilometer (January 2008 to October 2013) and the PollyXT lidar (July 2013 to December 2018) over the European Aerosol Research LIdar NETwork to Establish an Aerosol Climatology (EARLINET) site at the Remote Sensing Laboratory (RS-Lab) in Warsaw, Poland. Out of a maximum number of 4017 observational days within this period, a subset of quasi-continuous measurements conducted with these instruments at the same wavelength (1064 nm) was carefully chosen. This provided a data sample of 1841 diurnal cycle ABLH observations. The ABLHs were derived from ceilometer and lidar signals using the wavelet covariance transform method (WCT), gradient method (GDT), and standard deviation method (STD). For comparisons, the rawinsondes of the World Meteorological Organization (WMO 12374 site in Legionowo, 25 km distance to the RS-Lab) were used. The ABLHs derived from rawinsondes by the skew-T-log-p method and the bulk Richardson (bulk-Ri) method had a linear correlation coefficient (R2) of 0.9 and standard deviation (SD) of 0.32 km. A comparison of the ABLHs obtained for different methods and instruments indicated a relatively good agreement. The ABLHs estimated from the rawinsondes with the bulk-Ri method had the highest correlations, R2 of 0.80 and 0.70 with the ABLHs determined using the WCT method on ceilometer and lidar signals, respectively. The three methods applied to the simultaneous, collocated lidar, and ceilometer observations (July to October 2013) showed good agreement, especially for the WCT method (R2 of 0.94, SD of 0.19 km). A scaling threshold-based algorithm was proposed to homogenize ceilometer and lidar datasets, which were applied on the lidar data, and significantly improved the coherence of the results (R2 of 0.98, SD of 0.11 km). The difference of ABLH between clear-sky and cloudy conditions was on average below 230 m for the ceilometer and below 70 m for the lidar retrievals. The statistical analysis of the long-term observations indicated that the monthly mean ABLHs varied throughout the year between 0.6 and 1.8 km. The seasonal mean ABLH was of 1.16 ± 0.16 km in spring, 1.34 ± 0.15 km in summer, 0.99 ± 0.11 km in autumn, and 0.73 ± 0.08 km in winter. In spring and summer, the daytime and nighttime ABLHs appeared mainly in a frequency distribution range of 0.6 to 1.0 km. In winter, the distribution was common between 0.2 and 0.6 km. In autumn, it was relatively balanced between 0.2 and 1.2 km. The annual mean ABLHs maintained between 0.77 and 1.16 km, whereby the mean heights of the well-mixed, residual, and nocturnal layer were 1.14 ± 0.11, 1.27 ± 0.09, and 0.71 ± 0.06 km, respectively (for clear-sky conditions). For the whole observation period, the ABLHs below 1 km constituted more than 60% of the retrievals. A strong seasonal change of the monthly mean ABLH diurnal cycle was evident; a mild weakly defined autumn diurnal cycle, followed by a somewhat flat winter diurnal cycle, then a sharp transition to a spring diurnal cycle, and a high bell-like summer diurnal cycle. A prolonged summertime was manifested by the September cycle being more similar to the summer than autumn cycles.

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“…In addition to several meteorological factors that affect visibility such as humidity, temperature and wind speed (Chung-Yih Kuo et al, 2013), the height of the planetary boundary layer also influences the concentration of particulates in the atmosphere which in turn affects visibility. According to research conducted by Qu, et al (2017), low PBL heights often occur when conditions are low wind speeds and high humidity, which will cause high concentrations of particulates in the air and low visibility.…”

Section: Figure 4 Visibility In Palangka Rayamentioning

confidence: 99%

The impact of forest fire on air-quality and visibility in Palangka Raya

Sumaryati¹,

Cholianawati²,

Indrawati³

2019

J. Phys.: Theor. Appl.

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It has been analyzed impact of forest fire on the air quality using PM10 parameter and visibility during 2000 -2014 in Palangka Raya, Central Kalimantan province. Palangka Raya is an affected forest fire area with a monsoonal rainfall type which has one peak of the rainy season in January and one peak of the dry season in August. Drought condition has an impact on rising forest fire intensity causes increasing of PM10 concentration and decresing of visibility in July to November moreover when there is an El Niño phenomenon. The result of PM10 analysis shows that the air quality index in Palangka Raya during December -June is in a good level category and still below the ambient air quality standard with an average concentration of 19 µg/m3. The impact of forest fire on declining air quality due to increasing of PM10 concentration occurred in July -November with an average concentration rising of 129 µg/m3. The El Niño phenomenon rises the PM10 concentration due to increasing of forest fires, but the increasing of PM10 is not comparable to the strength of El Niño, because of combustion condition and and human activities that play a role in forest fires. The worst impact of El Niño occurred in 2002, although the El Niño strength was only moderate, which is a half the time from July to November Palangka Raya covered air quality with dangerous levels with PM10 concentrations of more than µg/m3. A high PM10 concentration environment reduces the visibility significantly, which is visibility in the no fire condition about 8 km, but when the huge forest fire the visibility drops to 0.1 km.

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Study of PBLH and Its Correlation with Particulate Matter from One-Year Observation over Nanjing, Southeast China

Cited by 66 publications

References 56 publications

Relationships between the planetary boundary layer height and surface pollutants derived from lidar observations over China: regional pattern and influencing factors

Relationships between the planetary boundary layer height and surface pollutants derived from lidar observations over China: regional pattern and influencing factors

Variability of the Boundary Layer Over an Urban Continental Site Based on 10 Years of Active Remote Sensing Observations in Warsaw

The impact of forest fire on air-quality and visibility in Palangka Raya

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