Urban aerosol evolution and particle formation during wintertime temperature inversions

Olofson, K. Frans G.; Andersson, Patrik; Hallquist, Mattias; Ljungström, Evert; Tang, Lin; Chen, Deliang; Pettersson, Jan B. C.

doi:10.1016/j.atmosenv.2008.09.080

Cited by 59 publications

(38 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, for excluding disturbance of the direct emission of primary particles particularly anthropogenic particles in urban areas (Shi et al, 2001), the apparent 'banana' shape of particle number concentration as a function of time and particle diameter, and preexisting particle surface concentration, precursor gas and meteorological conditions etc. have been used for supplementary criterions to identify and characterize atmospheric NPF events (Boy and Kulmala, 2002;Heintzenberg et al, 2007;Olofson et al, 2009). In this study, we adapted these criterions for identifying NPF events.…”

Section: New Particle Formation Eventmentioning

confidence: 99%

“…Identification of NPF and growth processes within urban atmosphere is more difficult than within rural or remote atmosphere (Kulmala et al, 2004a;Holmes, 2007). Numerous studies have focused on and devoted considerable efforts to NPF and growth over worldwide urban areas (Harrison et al, 2000;McMurry et al, 2000;Shi et al, 2001;Woo et al, 2001;Alam et al, 2003;Dunn et al, 2004;Zhang et al, 2004a;Shi et al, 2007;Hussein et al, 2008;Olofson et al, 2009). To date, only a few measurements on NPF events have been performed in the urban or suburban areas of China (Wehner et al, 2004;Wu et al, 2007;Liu et al, 2008;Gao et al, 2009;Yue et al, 2009;Yao et al, 2010;Shen et al, 2011;Zhang et al, 2011), which leads to an unclear outline of urban NPF and growth in this region.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aerosol Size Spectra and Particle Formation Events at Urban Shanghai in Eastern China

Cheng

Zhang

et al. 2012

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

Aerosol number size distributions between 10 nm and 10 µm were measured from October 2008 to February 2009 in Shanghai, China. The average particle number, surface and volume concentrations were 1.3 × 10 4 1/cm 3 , 6.4 × 10 2 µm 2 /cm 3 and 64 µm 3 /cm 3 , respectively. Aitken particles dominated the total number of particles, and accumulated particles were the greatest contributor to particle surface area. Particle number size distributions could be characterized by multi-lognormal functions. The average number size distributions of aerosols revealed a clear diurnal pattern of two peaks within 30-60 nm corresponding to the morning and afternoon traffic rush hours. All size particles had two peaks in mean number concentrations during the rush hours, and 10-20 nm and 20-50 nm particles had one additional peak in late morning. The new particle formation events were found on four days out of 73. The apparent formation rates varied from 0.2 to 0.5 cm 3 /s, and the growth rates of newly formed particles were 3.3-5.5 nm/h. Overall, the new particle formation events had a significant impact on particle size spectra in the nucleation and Atiken modes, but insignificant effects on particle surface and volume concentrations.

show abstract

Section: New Particle Formation Eventmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aerosol Size Spectra and Particle Formation Events at Urban Shanghai in Eastern China

Cheng

Zhang

et al. 2012

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

show abstract

“…Generally, serious pollution episodes are attributed to unfavourable meteorological conditions rather than sudden increases in the emission of pollutants. Temperature inversions are often responsible for these events (Kukkonen et al 2005;Olofson et al 2009;Nidzgorska-Lencewicz and Czarnecka 2015). Consequently, numerous studies have discussed temperature inversions with respect to air quality, particularly regarding conditions in urban valleys.…”

Section: Introductionmentioning

confidence: 99%

Temporal structure of thermal inversions in Łeba (Poland)

Czarnecka

Nidzgorska-Lencewicz

Rawicki

2018

Theor Appl Climatol

View full text Add to dashboard Cite

This study presents the detailed characteristics of thermal inversions based on a 10-year aerological measurement series (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) conducted in Łeba (Poland). The analyses included surface-based inversions (SBIs) and elevated inversions (ELIs) in the atmospheric layer up to 3000 m. In the case of SBIs, this layer extended directly from the ground level to an altitude above which the air temperature decreases with altitude, whereas for ELIs, which have a base above ground level, only the lowermost inversion layer was taken into consideration. The results of the monthly and seasonal variations in the selected parameters for air temperature inversions (thickness-ΔZ, strength-ΔT, base-Z B ) were analysed separately at night-time (00 UTC) and daytime (12 UTC). The thermal structure of the boundary layer up to 3000 m was primarily determined by ELIs, which occurred at a frequency of approximately 70% at both times during the 24-h period. The SBIs showed a pronounced temporal structure that occurred every second night throughout the year and from April to September, with a frequency similar to that of the ELI (approximately 60%). The worst vertical air exchange conditions, which resulted from the simultaneous occurrence of SBIs and ELIs, were found in 30% of nights from April to October. Elevated inversions generally formed in a layer from approximately 820 to 1200 m, which was the lowermost ELI in winter and the highest ELI in summer; however, in all seasons, the lowest base height was characteristic of daytime inversions. Both surface-based and elevated inversion layers were distinguished by comparable thicknesses, particularly for those occurring at night-time (generally within the range of 150-200 m). From November to March, greater thicknesses were identified in ELIs with lower occurrences, whereas SBIs were identified in the remaining months of the year.

show abstract

“…However with these models in general it is not possible to generate spatial distribution of pollutant concentrations. Besides, errors become considerably large when forecasting more than one day into the future (Pérez et al, 2000;Ordieres et al, 2005;Grivas and Chaloulakou, 2006). Since these models are trained with data from the past, they usually tend to be less accurate for extreme cases (very low or very high pollutant concentration) that are observed with a relative low frequency.…”

Section: Introductionmentioning

confidence: 99%

A Study of the Dynamic Behaviour of Fine Particulate Matter in Santiago, Chile

Calderón

Pérez

2015

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

We present here a study about the limitations found when trying to develop an accurate atmospheric particulate matter forecasting model based on real data, and evidence that the time series of fine particulate matter concentration exhibit deterministic chaotic behavior. We have calculated the Lyapunov exponents of PM 2.5 time series obtained from measurements from four monitoring stations located in the city of Santiago, Chile, in recent years. Values obtained for the largest Lyapunov exponents turned out to be positive and ranging between 0.3 and 0.5 which, according to the theory of chaos, is a condition for the presence of deterministic chaos and random behavior in time series. Given the shape of decay of autocorrelation functions and values of correlation dimension and Hurst exponents, random behavior can be discarded: we therefore conclude that the series are chaotic and very sensitive to initial conditions. The study presented here can be replicated in other mid-sized cities that present similar situations to the city of Santiago, where complexity of topography, meteorology and seasonal trends favor the generation of high concentration episodes of atmospheric particulate matter and where a reliable air quality forecasting model may be important for environmental management.

show abstract

Urban aerosol evolution and particle formation during wintertime temperature inversions

Cited by 59 publications

References 19 publications

Aerosol Size Spectra and Particle Formation Events at Urban Shanghai in Eastern China

Aerosol Size Spectra and Particle Formation Events at Urban Shanghai in Eastern China

Temporal structure of thermal inversions in Łeba (Poland)

A Study of the Dynamic Behaviour of Fine Particulate Matter in Santiago, Chile

Contact Info

Product

Resources

About