Representativeness of Urban Highest Polluted Zones for Sitting Traffic-Oriented Air Monitoring Stations in a Chinese City

Costabile, Francesca; Desantis, Franco; Weimin, Hong; Liu, Fenglei; Salvatori, Rosamaria; Wang, Fenjuan; Allegrini, I.

doi:10.1299/jsmeb.49.35

Cited by 11 publications

(8 citation statements)

References 5 publications

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“…Apart from CO, the other three gas-phase concentrations, SO 2 , NO 2 , and O 3 , also contribute to PM 2.5 concentration, though they appear to be less important than the above two variables, CO and aerosols. Sulfur dioxide and nitrogen dioxide are mainly released by traffic and industrial emissions in urban areas [ 76 , 77 ]. Ozone is formed when oxygen molecules are combined with atoms released from nitrogen dioxide molecules in hot days [ 78 – 80 ].…”

Section: Resultsmentioning

confidence: 99%

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

et al. 2015

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Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5.

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Section: Resultsmentioning

confidence: 99%

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

et al. 2015

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“…This variability often results in inconsistent comparisons for air quality data among different sites, and, consequently, inconsistent actions to control air quality and to assess people exposure. A better understanding of the "representativeness" of air quality measurements with respect to exposure assessment is therefore important [5][6][7] In this study we investigated the urban air pollution distribution in Lanzhou, China. Lanzhou, in the Gansu province, is about 1,800 km SW of Beijing, with a popula *Address correspondence to this author at the CNR-IIA, Via Salaria km 29,300, Monterotondo -Rome, Italy; Tel: +39 06 90672272; Fax: +39 06 90672661; E-mail: bertoni@iia.cnr.it tion of about 3 million inhabitants.…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution of Urban Air Pollution in Lanzhou, China~!2009-07-23~!2010-01-27~!2010-04-02~!

Costabile¹,

Bertoni²,

Santis³

et al. 2010

TOEPTJ

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Urban air quality, and its variability in space and time, is a critical issue for human exposure and health related studies. In this work, we investigated the intra-urban distribution of SO 2 , NO x , NO 2 , Benzene, Toluene, and Xylenes in ambient air in one of the world's most polluted cities, Lanzhou, China. Measurements were contemporarily carried out at forty locations in four seasonal campaigns during the period 2005-2006. Diffusive samplers allowing a sampling period of one month were used. As a general finding, the selected air pollutant concentrations often exceeded the EU limit values, and varied significantly with space, time, and proximity to relevant point emission sources. A statistical analysis revealed that monthly air pollutant concentrations were normally distributed in space. This suggests that the spatial distribution of urban air pollution was governed by the spatial diffusion of emission sources, at least for its apparent average behaviour. This result might be relevant to link urban air quality measurements and human exposure assessment. To gain insights into the properties of the urban air pollution distribution in space, makes the assessment of homogeneous areas of air pollution in the urban area less critical -and, consequently, allows a proper selection of the sites where to monitor air quality for health-effects studies.

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“…3781, Figure 1 and Figure 2) [24][25][26][27] was used to measure the concentration of particles in air with a diameter larger than 6 nm (and smaller than 3 µm), N 6, Representativeness criteria with respect to emission sources drove the selection of these two sites [29,30]. Parco della Cittadella was selected as representative of urban background concentrations, the closest traffic emission www.witpress.com, ISSN 1743-3541 (on-line) source being around 100 meters away.…”

Section: Methodsmentioning

confidence: 99%

Spatial distribution of ultrafine particles at urban scale: the road-to-ambient stage

Costabile¹,

Zani²,

Allegrini³

2008

WIT Transactions on Ecology and the Environment

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The spatial variability of ultrafine particles (UFPs) is believed to be an important issue to assess urban air pollution fate and exposure in connection with traffic motorised emissions. In this work, the high-time resolution total number concentration of UFPs was measured at traffic-oriented and urban background locations in a middle-size city in Italy. The major objective was to study valuable connections with local traffic sources, as well as measurement sites' representativeness. On the one hand, it was found that the total concentration at the traffic site can be representative of vehicle exhaust sources in ambient air. On the other hand, it was possible to identify three prevailing contributions for the total UFPs number concentration at urban scale: a very low urban background concentration, a significant contribution due to local traffic sources, and a significant contribution due to secondary transformation processes closely linked to meteorology.

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Representativeness of Urban Highest Polluted Zones for Sitting Traffic-Oriented Air Monitoring Stations in a Chinese City

Cited by 11 publications

References 5 publications

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

Spatial Distribution of Urban Air Pollution in Lanzhou, China~!2009-07-23~!2010-01-27~!2010-04-02~!

Spatial distribution of ultrafine particles at urban scale: the road-to-ambient stage

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