Temporal and spatial analyses of particulate matter (PM 10 and PM 2.5 ) and its relationship with meteorological parameters over an urban city in northeast China

Li, Xiaolan; Ma, Yanhui; Wang, Yangfeng; Liu, Ningwei; Hong, Yun–Chul

doi:10.1016/j.atmosres.2017.08.023

Cited by 120 publications

(58 citation statements)

References 28 publications

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“…PM 2.5 concentrations were negatively correlated with wind speed but PM 10 concentrations were positively correlated with wind speed. With the increase of wind speed, the horizontal dispersion ability of pollutants increases, which reduces the mass concentration of PM 2.5 [45]. PM 10 is mainly distributed near the ground because of its large particle size.…”

Section: Relationship Between Pm Concentrations and Some Meteorologicmentioning

confidence: 99%

Temporal and Spatial Distribution Characteristics of Atmospheric Particulate Matter (PM10 and PM2.5) in Changchun and Analysis of Its Influencing Factors

Xie

Fang

2019

Atmosphere

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With Changchun’s economic development, atmospheric particulate pollution has become a significant challenge in Changchun. The spatiotemporal patterns of particulate matter emissions are an inherent characteristic for particulate matter emissions. By using hourly PM (particulate matter) mass concentration measured at 10 atmospheric automatic monitoring stations and meteorological parameters, the spatiotemporal distribution characteristics of particulate matter (PM10 and PM2.5) and its relationship with meteorological parameters of Changchun have been analyzed. Pollution pathways and source distribution were investigated using HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model and cluster analysis. Results indicated that the quarterly average PM2.5 and PM10 mass concentrations in Changchun were higher in the first quarter and the fourth quarter. PM concentrations observed in all seasons generally exhibited two peaks, at 07:00–10:00 and 21:00–23:00, with the exception of PM10 in spring. PM pollution was concentrated mainly in the central, northern, and western areas of Changchun in most seasons, mainly due to anthropogenic activities and soil dust transported outside the region. PM concentrations were negatively correlated with relative humidity and temperature. PM2.5 concentrations were negatively correlated with wind speed, while PM10 concentrations were positively correlated with wind speed. The results of backward trajectory clustered showed that the northwest airflow had the greatest impact on PM of Changchun, except summer.

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Section: Relationship Between Pm Concentrations and Some Meteorologicmentioning

confidence: 99%

Temporal and Spatial Distribution Characteristics of Atmospheric Particulate Matter (PM10 and PM2.5) in Changchun and Analysis of Its Influencing Factors

Xie

Fang

2019

Atmosphere

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“…Meteorology plays significant roles in air pollution formation, transport, deposition and transformation and unfavorable meteorological condition could bring severe pollution days even the total emission is reduced (Shi et al, 2018;Xu et al, 2016;Li et al, 2017c;Cai et al, 2018;Gui et al, 2019;Wang et al, 2019b). In the pollution period of December 20-26, 2015 in BTH region, about 34% increasing of PM 2.5 are believed due to adverse meteorological condition (Ma et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak

Wang

Chen

Zhu

et al. 2020

Resources, Conservation and Recycling

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527

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“…Binietoglou et al, 2015); furthermore, these properties can be used in the estimation of the aerosol radiative forcing effect in radiative transfer modelling (e.g. Lisok et al, 2018;Lolli et al, 2018) as well as for the validation of spaceborne lidar (e.g. Illingworth et al, 2015;Papagiannopoulos et al, 2016;Proestakis et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Interrelations between surface, boundary layer, and columnar aerosol properties derived in summer and early autumn over a continental urban site in Warsaw, Poland

2019

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Abstract. PollyXT Raman polarization lidar observations were performed at the Remote Sensing Laboratory (RS-Lab) in Warsaw (52.2109∘ N, 20.9826∘ E), Poland, in the framework of the European Aerosol Research Lidar Network (EARLINET) and the Aerosol, Clouds, and Trace gases Research Infrastructure (ACTRIS) projects. Data collected in July, August, and September of 2013, 2015, and 2016 were analysed using the classical Raman approach. In total, 246 sets of intact profiles, each set comprising particle extinction (α) and backscatter coefficients (β) as well as linear particle depolarization ratios (δ) at 355 nm and 532 nm, were derived for statistical investigations and stored in the EARLINET/ACTRIS database. The main analysis was focused on intensive optical properties obtained within the atmospheric boundary layer (ABL). Their interrelations were discussed for different periods: the entire day; nighttime, with respect to the nocturnal boundary layer (NL) and the residual boundary layer (RL); at sunrise, with respect to the morning transition boundary layer (MTL); and from late afternoon until sunset, with respect to the well-mixed boundary layer (WML). Within the boundary layer, the lidar-derived optical properties (entire day, 246 sets) revealed a mean aerosol optical depth (AODABL) of 0.20±0.10 at 355 nm and 0.11±0.06 at 532 nm; a mean Ångström exponent (ÅEABL) of 1.54±0.37; a mean lidar ratio (LRABL) of 48±17 sr at 355 nm and 41±15 sr at 532 nm; a mean linear particle depolarization ratio (δABL) of 0.02±0.01 at 355 nm and 0.05±0.01 at 532 nm; and a mean water vapour mixing ratio (WVABL) of 8.28±2.46 g kg−1. In addition, the lidar-derived daytime boundary layer optical properties (for the MTL and WML) were compared with the corresponding daytime columnar aerosol properties derived from the multi-filter rotating shadowband radiometer (MFR-7) measuring within the National Aerosol Research Network (PolandAOD-NET) and the CE318 sun photometer of the Aerosol Robotic NETwork (AERONET). A high linear correlation of the columnar aerosol optical depth values from the two latter instruments was obtained in Warsaw (a correlation coefficient of 0.98 with a standard deviation of 0.02). The contribution of the aerosol load in the summer and early-autumn free troposphere can result in an AODCL value that is twice as high as the AODABL over Warsaw. The occurrence of a turbulence-driven aerosol burst from the boundary layer into the free troposphere can further increase this difference. Aerosol within the ABL and in the free troposphere was interpreted based on comparisons of the properties derived at different altitudes with values reported in the literature, which were characteristic for different aerosol types, in combination with backward trajectory calculations, satellite data, and model outputs. Within the boundary layer, the aerosol consisted of either urban anthropogenic pollution (∼ 61 %) or mixtures of anthropogenic aerosol with biomass-burning aerosol (< 14 %), local pollen (< 7 %), or Arctic marine particles (< 5 %). No significant contribution of mineral dust was found in the boundary layer. The lidar-derived atmospheric boundary layer height (ABLH) and the AODABL exhibited a positive correlation (R of 0.76), associated with the local anthropogenic pollution (most pronounced for the RL and WML). A positive correlation of the AODABL and LRABL and a negative correlation of the ÅEABL and LRABL, as well as the expected negative trends for the WVABL (and surface relative humidity, RH) and δABL, were observed. Relations of the lidar-derived aerosol properties within the ABL and the surface in situ measurements of particulate matter with an aerodynamic diameter less than 10 µm (PM10) and less than 2.5 µm (PM2.5) measured by the Warsaw Regional Inspectorate for Environmental Protection (WIOS) network, and the fine-to-coarse mass ratio (FCMR) were investigated. The FCMR and surface RH showed a positive correlation even at nighttime (R of 0.71 for the MTL, 0.63 for the WML, and 0.6 for the NL), which generally lacked statistically significant relations. A weak negative correlation of the FCMR and δABL (more pronounced at 532 nm at nighttime) and no casual relation between the FCMR and ÅEABL were found. Most interestingly, distinct differences were observed for the morning transition layer (MTL) and the well-mixed layer (WML). The MTL ranged up to 0.6–1 km, and was characterized by a lower AODABL(<0.12), wetter conditions (RH 50–80 %), smaller particles (ÅEABL of 1–2.2; FCMR from 0.5 to 3), and a low LRABL of between 20 and 40 sr. The WML ranged up to 1–2.5 km and exhibited a higher AODABL (reaching up to 0.45), drier conditions (RH 25–60 %), larger particles (ÅEABL of 0.8–1.7; FCMR of 0.2–1.5), and a higher LRABL of up to 90 sr.

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Temporal and spatial analyses of particulate matter (PM 10 and PM 2.5 ) and its relationship with meteorological parameters over an urban city in northeast China

Cited by 120 publications

References 28 publications

Temporal and Spatial Distribution Characteristics of Atmospheric Particulate Matter (PM10 and PM2.5) in Changchun and Analysis of Its Influencing Factors

Temporal and Spatial Distribution Characteristics of Atmospheric Particulate Matter (PM10 and PM2.5) in Changchun and Analysis of Its Influencing Factors

Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak

Interrelations between surface, boundary layer, and columnar aerosol properties derived in summer and early autumn over a continental urban site in Warsaw, Poland

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