Chemical Characterization of PM1 at a Regional Background Site in the Western Mediterranean

Galindo, Nuria; Yubero, Eduardo; Nicolás, J.F.; Crespo, J.; Soler, Rubén

doi:10.4209/aaqr.2015.05.0302

Cited by 17 publications

(9 citation statements)

References 43 publications

(59 reference statements)

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“…PM 1 at the regional background station of Mt. Aitana, Iberian Peninsula was mainly composed of organic matter and ammonium sulfate, while EC and nitrate were minor components (Galindo et al, 2016).…”

Section: Introductionmentioning

confidence: 95%

Development of an Automated System (PPWD/PILS) for Studying PM2.5 Water-Soluble Ions and Precursor Gases: Field Measurements in Two Cities, Taiwan

Liu

Lin

et al. 2017

Aerosol Air Qual. Res.

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An automated system consisting of a particle-into-liquid sampler (PILS) and a parallel plate wet denuder (PPWD) coupled with an ion chromatography was used for simultaneous measurement of ambient water-soluble ions in PM 2.5 and precursor gases. The performance of the PPWD/PILS was validated by comparing it with the PDS (porous metal denuder sampler) for precursor gases (NH 3 , HONO, HNO 3 and SO 2 ) and PM 2.5 ionic species (NH 4 + , NO 3 -, SO 4 2-, Na + , Cl -and K + ) measured in Taipei and Hsinchu Cities of Taiwan. Good correlations were demonstrated with linear regression slopes ranging from 0.92 to 1.04 and 0.84 to 0.97 as well as R 2 ranging from 0.76 to 0.83 and 0.89 to 0.94, for precursor gases and PM 2.5 ions, respectively. The accuracy of the current system for precursor gases outperforms the other commercial systems. Field continuous data showed that NH 3 was the most abundant precursor gas with the diurnal pattern peaking at low nocturnal boundary heights and during rush hours with local traffic emissions in Taipei, and with the pattern peaking only at mid-day associated with regional sources in Hsinchu. A reverse diurnal pattern for HONO in Taipei reflected the daytime photolysis and its nocturnal heterogeneous reaction, while its concentration was relatively constant at very low level in Hsinchu. SO 4 2-, NH 4 + and NO 3 -exhibited very similar diurnal patterns with the mean concentrations of 4.56 ± 3.14, 1.55 ± 1.16 and 0.52 ± 0.5 µg m -3 in Taipei, and 7.95 ± 5.52, 2.41 ± 1.95 and 0.96 ± 1.10 µg m -3 in Hsinchu, respectively. Correspondingly high concentrations of major ions to precursor gases were associated with the photochemical secondary aerosol formations and heavy traffic in Taipei. Based on an ammonia-rich atmosphere and high SOR values, (NH 4 ) 2 SO 4 and NH 4 NO 3 were inferred to be the dominant inorganic salts in PM 2.5 at both sites, which were also verified by the ion balance analysis.

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Section: Introductionmentioning

confidence: 95%

Development of an Automated System (PPWD/PILS) for Studying PM2.5 Water-Soluble Ions and Precursor Gases: Field Measurements in Two Cities, Taiwan

Liu

Lin

et al. 2017

Aerosol Air Qual. Res.

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show abstract

“…Furthermore, meteorological parameters such as temperature, wind speed and direction, relative humidity, and rainfall have an impact on air quality [4][5][6]. As a citation, several studies have reported the potential impact of meteorological factors on the ambient air quality [4,[7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Clustering the Concentrations of PM10 and O3: Application of Spatiotemporal Model–Based Clustering

Saeipourdizaj

Musavi

Gholampour

et al. 2021

Environ Model Assess

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Air pollution data are large-scale datasets that can be analyzed in low scales by clustering to recognize the pattern of pollution and have simpler and more comprehensible interpretations. So, this study aims to cluster the days of the year 2017 according to the hourly O 3 and PM 10 amounts collected from four stations of Tabriz by using spatiotemporal mixture model-based clustering (STMC). Besides, mixture model-based clustering with temporal dimension (TMC) and mixture model-based clustering without considering spatiotemporal dimensions (MC) were utilized to compare with STMC. To evaluate the efficiency of these three models, and obtain the optimal number of clusters in each model, BIC and ICL criteria were used. According to BIC and ICL, STMC outperforms TMC and MC. Three clusters for O 3 and four clusters for PM 10 were selected as the optimal number of clusters to fit STMC models. Regarding PM 10 , the average concentration was the highest in cluster 4. Regarding O 3 , all summer days were in cluster 3, and the average concentration of this cluster was the highest. Cluster 2 had the lowest concentration with a high difference from clusters 1 and 3, and its average temperature was the lowest. Autumn days make up about 84% of this cluster. The clustering of polluted and clean days into separate groups and observing the effect of meteorological factors on the amount of concentration in each cluster clearly prove the efficiency of the model. Results of STMC showed that the efficiency of clustering in air pollution data increases by considering both spatiotemporal dimensions.

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“…Moreover, meteorological factors affect air quality, e.g., temperature, speed and direction of wind, relative humidity, and rainfall [4][5][6]. As a citation, several studies have reported the potential impact of meteorological factors on the ambient air quality [4,[7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Clustering the Concentrations of PM10 and O3: Application of Spatio-temporal Model-based Clustering

Saeipourdizaj

Musavi

Gholampour

et al. 2021

Preprint

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Air pollution data are large-scale dataset which can be analyzed in low scales by clustering to recognize the pattern of pollution and have simpler and more comprehensible interpretation. So, this study aims to cluster the days of year 2017 according to the hourly O3 and PM10 amounts collected from four stations of Tabriz by using spatio-temporal mixture model-based clustering (STMC). Besides, mixture model-based clustering with temporal dimension (TMC) and mixture model-based clustering without considering spatio-temporal dimensions (MC) were utilized to compare with STMC. To evaluate the efficiency of these three models and obtain the optimal number of clusters in each model, BIC and ICL criteria were used. According to BIC and ICL, STMC outperforms TMC and MC. Three clusters for O3 and four clusters for PM10 were selected as the optimal number of clusters to fit STMC models. Regarding PM10, the average concentration was the highest in cluster 4. Regarding O3, all summer days were in cluster 3 and the average concentration of this cluster was the highest. Cluster 2 had the lowest concentration with a high difference from clusters 1 and 3 and its average temperature was the lowest. Autumn days make up about 84% of this cluster. The clustering of polluted and clean days into separate groups and observing the effect of meteorological factors on the amount of concentration in each cluster clearly prove the efficiency of the model. Results of STMC showed that efficiency of clustering in air pollution data increases by considering both spatio-temporal dimensions.

show abstract

Chemical Characterization of PM1 at a Regional Background Site in the Western Mediterranean

Cited by 17 publications

References 43 publications

Development of an Automated System (PPWD/PILS) for Studying PM2.5 Water-Soluble Ions and Precursor Gases: Field Measurements in Two Cities, Taiwan

Development of an Automated System (PPWD/PILS) for Studying PM2.5 Water-Soluble Ions and Precursor Gases: Field Measurements in Two Cities, Taiwan

Clustering the Concentrations of PM10 and O3: Application of Spatiotemporal Model–Based Clustering

Clustering the Concentrations of PM10 and O3: Application of Spatio-temporal Model-based Clustering

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