Urban Boundary Layer Height Characteristics and Relationship with Particulate Matter Mass Concentrations in Xi’an, Central China

Du, Cheng; Liu, Shuyan; Yu, Xiangyao; Li, Xingmin; Chen, Chuang; Yang, Peiqi; Dong, Yanfang; Dong, Zipeng; Wang, Fanqiang

doi:10.4209/aaqr.2012.10.0274

Cited by 85 publications

(45 citation statements)

References 27 publications

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“…Some previous studies Zhao et al 2009) outside of the LUR framework have demonstrated that meteorological factors have a significant impact on PM 2.5 diurnal cycle in Beijing. Our models of peak and non-peak hours were driven by the same explanatory variables and produced similar patterns of PM 2.5 concentrations, which is more likely to be interpreted by meteorological factors and boundary layer height (Du et al 2013) instead of specific sources such as traffic emission and cooking. In addition, the models of peak hours did not include more traffic variables, which might also be explained by the secondary nature of PM 2.5 .…”

Section: Diurnal Variationsmentioning

confidence: 79%

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

132

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Fine particulate matter (PM 2.5 ) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM 2.5 by building separate LUR models in Beijing. Hourly routine PM 2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 μg/m 3 (SD=13.7). PM 2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R 2 of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 μg/m 3 . This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM 2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models.

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Section: Diurnal Variationsmentioning

confidence: 79%

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

132

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“…The size-resolved dN T / dlogD a for each individual month exhibited a consistent and flat concentration profile at < 1 µm. Previous studies where a similar instrument was used have reported that pronounced diurnal variations in N T are strongly coupled with diurnal variations in meteorological variables, especially mixing layer depth Raatikainen et al, 2014;Du et al, 2013). The ab- sence of pronounced diurnal variations in N T at this particular site may be a result of weak dependence of coarse-mode TAP concentrations on meteorological parameters combined with persistent rainfall causing the washout of these particles (Radke et al, 1980;Raatikainen et al, 2014;Kanawade et al, 2014;Shika et al, 2016).…”

Section: Diurnal Patternsmentioning

confidence: 99%

Fluorescent biological aerosol particle measurements at a tropical high-altitude site in southern India during the southwest monsoon season

Valsan

Ravikrishna

Biju³

et al. 2016

Atmos. Chem. Phys.

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Abstract. An ultraviolet aerodynamic particle sizer (UV-APS) was continuously operated for the first time during two seasons to sample the contrasting winds during monsoon and winter to characterize the properties of fluorescent biological aerosol particles (FBAPs), at a high-altitude site in India. Averaged over the entire monsoon campaign (1 June–21 August 2014), the arithmetic mean number and mass concentrations of coarse-mode (> 1 µm) FBAPs were 0.02 cm−3 and 0.24 µg m−3, respectively, which corresponded to ∼  2 and 6 % of total aerosol loading, respectively. Average FBAP number size distribution exhibited a peak at ∼  3 µm, which is attributed to the fungal spores, as supported by scanning electron microscope (SEM) images, and these results are consistent with previous studies made for FBAPs. During 11 weeks of measurements the variability of the total coarse-mode particle number (TAP) concentration was high compared to that observed in FBAP number concentrations. The TAP and FBAP number concentrations measured at this site were strongly dependent on changes in wind direction and rainfall. During periods of westerly/southwesterly winds with heavy persistent rainfall, the TAP and FBAP concentrations exhibited very low values (1.3 and 0.005 cm−3, respectively) with no significant diurnal variations, whereas during periods of northerly winds with scattered rainfall FBAPs exhibited relatively high concentration values (0.05 cm−3) with pronounced diurnal variations, which were strongly coupled with diurnal variations in meteorological parameters. The campaign-averaged FBAP number concentrations were shown to correlate with daily patterns of meteorological parameters and were positively correlated with relative humidity (RH; R2  =  0.58) and negatively with temperature (R2  =  0.60) and wind speed (R2  =  0.60). We did not observe any significant positive correlation with precipitation as reported by previous researchers from selected areas. These measurement results confirm the fact that the ratio of PBAPs to TAP is strongly dependent on particle size and location and thus may constitute a significant proportion of total aerosol particles.

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“…Studies relying on numerical parameterizations were conducted by Tiwari et al (2014), who used reanalysis data, and Du et al (2013), who used routine meteorological observations to find an anticorrelation between PM 2.5 and MLH for Delhi, India, and Xi'an, China. Rost et al (2009) found a strong anticorrelation between PM 10 and MLH (derived from radiosonde data) for Stuttgart, Germany, with a coefficient of determination of R 2 > 0.95.…”

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confidence: 99%

Mixing layer height as an indicator for urban air quality?

Geiß

Wiegner²,

Bonn

et al. 2017

Atmos. Meas. Tech.

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Abstract. The mixing layer height (MLH) is a measure for the vertical turbulent exchange within the boundary layer, which is one of the controlling factors for the dilution of pollutants emitted near the ground. Based on continuous MLH measurements with a Vaisala CL51 ceilometer and measurements from an air quality network, the relationship between MLH and near-surface pollutant concentrations has been investigated. In this context the uncertainty of the MLH retrievals and the representativeness of ground-based in situ measurements are crucial. We have investigated this topic by using data from the BAERLIN2014 campaign in Berlin, Germany, conducted from June to August 2014. To derive the MLH, three versions of the proprietary software BL-VIEW and a novel approach COBOLT were compared. It was found that the overall agreement is reasonable if mean diurnal cycles are considered. The main advantage of COBOLT is the continuous detection of the MLH with a temporal resolution of 10 min and a lower number of cases when the residual layer is misinterpreted as mixing layer. We have calculated correlations between MLH as derived from the different retrievals and concentrations of pollutants (PM 10 , O 3 and NO x ) for different locations in the metropolitan area of Berlin. It was found that the correlations with PM 10 are quite different for different sites without showing a clear pattern, whereas the correlation with NO x seems to depend on the vicinity of emission sources in main roads. In the case of ozone as a secondary pollutant, a clear correlation was found. We conclude that the effects of the heterogeneity of the emission sources, chemical processing and mixing during transport exceed the differences due to different MLH retrievals. Moreover, it seems to be unrealistic to find correlations between MLH and near-surface pollutant concentrations representative for a city like Berlin (flat terrain), in particular when traffic emissions are dominant. Nevertheless it is worthwhile to use advanced MLH retrievals for ceilometer data, for example as input to dispersion models and for the validation of chemical transport models.

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Urban Boundary Layer Height Characteristics and Relationship with Particulate Matter Mass Concentrations in Xi’an, Central China

Cited by 85 publications

References 27 publications

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Fluorescent biological aerosol particle measurements at a tropical high-altitude site in southern India during the southwest monsoon season

Mixing layer height as an indicator for urban air quality?

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