Analysis of particles and carbon dioxide concentrations and fluxes in an urban area: Correlation with traffic rate and local micrometeorology

Contini, Daniele; Donateo, Antonio; Elefante, C.; Grasso, Fabio

doi:10.1016/j.atmosenv.2011.10.039

Cited by 51 publications

(32 citation statements)

References 36 publications

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“…The diurnal pattern of F p follows that of traffic intensity; however, F p do not show the drop around 10:00Á12:00 observed for traffic intensity. This behaviour, also reported in other studies (Dorsey et al, 2002;Ma˚rtensson et al, 2006;Ja¨rvi et al, 2009b;Martin et al, 2009;Contini et al, 2012), can be associated with the effect of micrometeorological parameters such as stronger turbulent mixing around midday. A significant decrease of F p from the cold to the warm period in the Road 1 and the Urban sectors is observed.…”

Section: Vertical Particle Fluxessupporting

confidence: 58%

“…Although a direct comparison between previous studies is difficult because of differences in the investigated particle size ranges and sampling designs, the observed F p appear in the same order of magnitude than those reported for other urban locations, with values varying in the range 10Á2000×10 6 m (2 s (1 (Dorsey et al, 2002;Ma˚rtensson et al, 2006;Schmidt and Klemm, 2008;Martin et al, 2009;Ja¨rvi et al, 2009b;Dahlkotter et al, 2010;Contini et al, 2012). In particular, Ma˚rtensson et al (2006) reported similar differences in F p and its daily pattern between a vegetated/residential urban area (F p levels below 200×10 6 m (2 s (1 ) and a high traffic urban area (F p levels below 1200×10 6 m (2 s (1 ).…”

Section: Vertical Particle Fluxesmentioning

confidence: 69%

“…In this context, the eddy covariance (EC) technique has recently been applied in a few cities to directly measure particle number exchange between the surface and the atmosphere (e.g. Martin et al, 2009;Ja¨rvi et al, 2009b;Contini et al, 2012). It gives the net particle flux transported by turbulent eddies to/from a wider surface area called the footprint, and it can be used to study the strength and distribution of particle sources and sinks.…”

Section: Introductionmentioning

confidence: 99%

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The effect of local sources on aerosol particle number size distribution, concentrations and fluxes in Helsinki, Finland

Ripamonti

Järvi

Mølgaard

et al. 2013

Tellus B: Chemical and Physical Meteorology

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A B S T R A C T Three years of aerosol particle number concentrations (PNCs), size distributions and vertical particle fluxes measured at the semi-urban SMEAR III station in Helsinki, Finland, were studied. The purpose is to study the local emission sources and their effect on particle concentrations and size distributions. By means of cluster analysis, six representative size distributions were identified. Their occurrence together with particle concentrations and fluxes were found to vary significantly with wind direction. Lower particle concentrations and fluxes were measured downwind from vegetated and residential areas compared to directions where the measurement site is downwind from roads passing near the measurement site. For these directions, contributions of the local sources on the measured particle concentrations and size distributions were evident. In particular, size distributions with a mode in the size range 20Á40 nm were found to be more affected by local traffic emissions, whereas the mode shifted towards larger sizes when contribution from distant sources was more evident. Using flux footprint functions, mixed vehicle fleet emission factors (EFs) were derived from the particle flux measurements. EFs of 6.03 (90.19

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Section: Vertical Particle Fluxessupporting

confidence: 58%

Section: Vertical Particle Fluxesmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The effect of local sources on aerosol particle number size distribution, concentrations and fluxes in Helsinki, Finland

Ripamonti

Järvi

Mølgaard

et al. 2013

Tellus B: Chemical and Physical Meteorology

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“…This correction depends on stability, measurement height and wind velocity, however, the value found is comparable with the average correction found in other research works using eddy correlation with different instruments and different methods of correction. For example in CONTINI et al (2012) an average correction of 33 % was found in a urban site using a CPC with a time response of 1.3s. In DAMAY et al (2009) an average correction of 30 % is found using an ELPI (with 1 Hz resolution) in an eddycorrelation system.…”

Section: Post-processing Of Datamentioning

confidence: 99%

Characterisation of PM2.5 concentrations and turbulent fluxes on a island of the Venice lagoon using high temporal resolution measurements

Donateo¹,

Contini²,

Belosi³

et al. 2012

metz

Self Cite

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This work presents an analysis of PM2.5 concentrations and vertical turbulent fluxes on an island of the Venice lagoon. Data were collected during three measurement campaigns in spring, summer and winter periods. Measurements were taken with a high-resolution optical PM2.5 detector, coupled with a micrometeorological station that allowed the evaluation of the vertical turbulent fluxes of PM2.5 using the eddy-correlation technique. The main objective of this paper is to analyse the daily and seasonal pattern in PM2.5 concentrations and fluxes and to discuss their correlation with the main meteorological and micrometeorological parameters using high temporal resolution measurements. Observed data showed a seasonal pattern in turbulent fluxes with daytime average positive value during winter and negative during summer. Deposition velocities, ranged from -60 to 20 mm/s, appeared to be mainly influenced by atmospheric stability. There were larger emissions in cases of high wind velocities blowing from water sector indicating a significant potential contribution of sea spray to PM2.5 fluxes. The local atmospheric circulation, due to the orography of the area, was characterised by diurnal winds coming from the Adriatic Sea and nocturnal wind coming from the Alps. This circulation influenced deposition velocity creating an increase of negative fluxes in the morning at the starting of the sea breeze. A diurnal pattern in concentration has been observed and it is similar for all three measurement campaigns, with higher concentrations in nocturnal periods. The daily pattern was investigated in terms of its correlation with meteorological and micro-meteorological parameters, and was found highly correlated with the diurnal pattern of boundary layer height (BLH) and with relative humidity.

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“…Pronounced monthly, weekly, and hourly PM 2.5 concentration variations have been observed from ground stations in Beijing (Song et al, 2006). PM 2.5 concentrations near the ground layer in Beijing have been shown to peak near midnight in autumn (Yang et al, 2005), while PM 2.5 and PM 10 concentrations at street locations in urban areas were found to be higher than those at other urban and rural locations (Holst et al, 2008;Liu et al, 2011;Contini et al, 2012;Keuken et al, 2013). PM 2.5 concentrations were also typically found to be lower at elevated sites surrounding the San Joaquin Valley than at monitoring sites located in the valley (McCarthy et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

A Severe Air Pollution Event from Field Burning of Agricultural Residues in Beijing, China

Lyu

Jaeger

Han

et al. 2015

Aerosol Air Qual. Res.

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Air pollutant emissions from agricultural burning are observed every year after harvest in China. While agriculture is not the main contributor to air pollution in China, agricultural activities can cause severe pollution events. Recognizing the key mechanisms involved in this process offers an opportunity to minimize pollution events caused by agricultural burning. In this paper, we review the meteorological conditions present during a selected air pollution episode and discuss those conditions using standard meteorological observations. The spatio-temporal variations of PM 2.5 concentrations following agricultural burning in Beijing were measured from October 4 to October 7, 2013. This time period coincides with a Chinese public holiday and was selected because the influence of other anthropogenic emissions on air quality was strongly reduced during those days. As a result, we were able to identify the key sources and progress of a severe air pollution event. On October 4, average PM 2.5 concentration in Beijing continuously increased from 49.7 µg m -3 at 1:00 to 302.5 µg m -3 at 23:00. Heavily polluted air (> 300 µg m -3 ) initially appeared in southeastern Beijing on the afternoon of October 4. On October 5 and in the early morning of October 6, heavily polluted air masses moved into central Beijing, the inner suburbs, and the suburbs. From 0:00 on October 6 to 15:00 on October 7, the average PM 2.5 concentration in Beijing decreased from 291.6 µg m -3 to 19.2 µg m -3. Active fire information derived from the MODIS sensors and back trajectory analysis show that field burning of agricultural residues after a harvest triggered and massively contributed to this severe air pollution event. The results improve our understanding of PM 2.5 air pollution development processes, and they provide scientific support for the Chinese government to accelerate emission reductions from the field burning of agricultural residues.

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Analysis of particles and carbon dioxide concentrations and fluxes in an urban area: Correlation with traffic rate and local micrometeorology

Cited by 51 publications

References 36 publications

The effect of local sources on aerosol particle number size distribution, concentrations and fluxes in Helsinki, Finland

The effect of local sources on aerosol particle number size distribution, concentrations and fluxes in Helsinki, Finland

Characterisation of PM2.5 concentrations and turbulent fluxes on a island of the Venice lagoon using high temporal resolution measurements

A Severe Air Pollution Event from Field Burning of Agricultural Residues in Beijing, China

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