Organic molecular markers and source contributions in a polluted municipality of north-east Italy: Extended PCA-PMF statistical approach

Padoan, Sara; Zappi, Alessandro; Adam, Thomas; Melucci, Dora; Gambaro, Andrea; Formenton, Gianni; Popovicheva, Olga; Nguyen, Dac-Loc; Schnelle-Kreis, Jürgen; Zimmermann, Ralf

doi:10.1016/j.envres.2020.109587

Cited by 24 publications

(7 citation statements)

References 44 publications

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“…The models available to solve this equation are EPA-CMB, EPA-Unmix and EPA-PMF [18]; the PMF model has become a frequently used tool in recent years, with more than 1,000 papers reporting this application [2,[19][20][21][22][23][24][25][26].…”

Section: Discussionmentioning

confidence: 99%

Chemical Characteristics and Identification of PM10 Sources in Two Lima Districts, Peru

Ilizarbe-Gonzáles

Rojas-Quincho

Torres

et al. 2020

DYNA

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This study evaluates the concentration of PM10 and PM2.5 and identification of source in the districts of San Juan de Lurigancho and Puente Piedra (PPD) in Lima-Peru. The samples were collected from April to May 2017 by the National Meteorology and Hydrology Service of Peru (Senamhi). The concentration of PM10 and PM2.5, measured by gravimetric techniques, exceeded the international (WHO) and national standards; with maximum values for PM10 and PM2.5 of 160 and 121.56 µg/ m3 in PPD and 295.06 and 154.58 µg/ m3 in SJL. Identification of sources by the Positive Matrix Factorization Model (PMF 5.0) and Principal Component Analysis (ACP), showed similar sources for both districts. In SJL, the combination of vehicular traffic and resuspension of soil dust, marine aerosol and iron and steel industry was determined, while in PPD the resuspension of soil dust, vehicular source, industrial activity and marine aerosol.

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Section: Discussionmentioning

confidence: 99%

Chemical Characteristics and Identification of PM10 Sources in Two Lima Districts, Peru

Ilizarbe-Gonzáles

Rojas-Quincho

Torres

et al. 2020

DYNA

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“…Thus, finally the PCA was carried out using R programming language [22] on the physical-chemical properties of the precursory coal (Table 2): residual moisture (Mr), ash content (A), sulfur (S), volatile matter (Vm), mean reflectance of the vitrinite (Ro), log(fluidity), and the CAQ defined with the textural description. This method has been successfully used to define geological factors related to environmental pollution [23], depositional environments and sediment supply [20], improvement of accuracy in geodetic data [24], and geochemical prospection [25]. However, for Colombian coals and cokes is the first time that this methodology is applied.…”

Section: Methodsmentioning

confidence: 99%

A Predictive Model for the Anisotropy Index of Semi-Coke Derived from the Properties of Colombia's Eastern Cordillera Coals

et al. 2020

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This study developed a theoretical model for the determination of the Coke Anisotropy Quotient (CAQ) of semi-coke from the properties of its precursor coal. This is an useful parameter to define the resistance and reactivity of semi-coke in the blast furnace. For 36 semi-coke samples, a textural analysis was performed alongside a fluidity test to determine the real CAQ. The main textures observed were: isotropic and circular for high volatile bituminous coals (HVB); lenticular and fine ribbons for the medium volatile bituminous coals (MVB); and medium and thick ribbons for the low volatile bituminous coals (LVB). The CAQ varied in a range from 1 to 11. A principal component analysis (PCA) and multiple regression allowed to discriminated the importance of certain coal properties, in determining the CAQ to be recognized and to estimate parameters of the mathematical model. The statistical analysis suggested that CAQ can be best predicted from the fluidity, volatile matter, and Ro of the parent coals. The veracity of this model result was then tested using a second dataset from Poland. This work optimizes the usefulness of standard datasets in the prediction of CAQ's offering a means of quality control that could be implemented in Colombian coke production.

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“…Instead of focusing on each observation or indicator, PMF explores the temporal pattern of all variables to find a potential common source. As a result, combining PCA and PMF to achieve an aggregate statistical summary of data is a safe way to view each observation as "independent" and as part of a temporal pattern (Padoan et al, 2020).…”

Section: Source Apportionment Using Pca and Pmf Modelingmentioning

confidence: 99%

“…On source apportionment, two chemometric techniques, principal component analysis (PCA) and positive matrix factorization (PMF) receptor model, were employed collectively. PCA is utilized to relate the air pollutants and their overall changes in the aerosol composition while PMF is used to determine the different sources of pollution and the temporal variability of each pollutant without considering their correlations (Padoan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Long-Term (2003–2019) Air Quality, Climate Variables, and Human Health Consequences in Dhaka, Bangladesh

Pavel

Zaman

Jeba

et al. 2021

Front. Sustain. Cities

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Long-term trends in air quality by studying the criteria pollutants (PM2.5, PM10, CO, O3, NO2, and SO2) and climate variables (temperature, surface pressure, and relative humidity) were depicted in this study. The 17-year (2003–2019) average values of PM2.5, PM10, CO, O3, NO2, and SO2 were 88.69 ± 9.76 μg/m3, 124.57 ± 12.75 μg/m3, 0.69 ± 0.06 ppm, 51.42 ± 1.82 ppb, 14.87 ± 2.45 ppb, and 8.76 ± 2.07 ppb, respectively. The trends among the ambient pollutants were increasingly significant (p < 0.05) except for O3 with slopes of 1.83 ± 0.15 μg/m3/year, 2.35 ± 0.24 μg/m3/year, 0.01 ± 0.002 ppm/year, 0.47 ± 0.03 ppb/year, and 0.40 ± 0.02 ppb/year for PM2.5, PM10, CO, NO2, and SO2, respectively. Pearson correlations revealed a significant association among the pollutants while a noteworthy correlation was observed between ambient pollutants and surface temperature. Principal component analysis (PCA) and positive matrix factorization (PMF) have been employed collectively to examine the main sources of the pollutants. PCA revealed similar trends for PMs and CO, as well as NO2 and SO2 being equally distributed variables. PMF receptor modeling resulted in attributing four sources to the pollutants. The factors inferred from the PMF modeling were signified as vehicular emissions, road/soil dust, biomass burning, and industrial emissions. The hazard quotient (HQ) values were not antagonistic (HQ < 1) in acute exposure levels for the three age groups (infants, children, and adults) while showing significant health risk (HQ > 1) in chronic exposure for infants and children. Children are identified as the worst sufferers among the age groups, which points to low breathing levels and high exposure to traffic pollution in Dhaka, Bangladesh.

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Organic molecular markers and source contributions in a polluted municipality of north-east Italy: Extended PCA-PMF statistical approach

Cited by 24 publications

References 44 publications

Chemical Characteristics and Identification of PM10 Sources in Two Lima Districts, Peru

Chemical Characteristics and Identification of PM10 Sources in Two Lima Districts, Peru

A Predictive Model for the Anisotropy Index of Semi-Coke Derived from the Properties of Colombia's Eastern Cordillera Coals

Long-Term (2003–2019) Air Quality, Climate Variables, and Human Health Consequences in Dhaka, Bangladesh

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