Assesment of PM10 pollution episodes in a ceramic cluster (NE Spain): Proposal of a new quality index for PM10, As, Cd, Ni and Pb

Vicente, A.B.; Sanfeliu, T.; Jordán, M. M.

doi:10.1016/j.jenvman.2012.04.032

Cited by 27 publications

(11 citation statements)

References 40 publications

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“…It essentially constitutes a loading of Co and As (to a lesser extent). As is regarded as a marker element of coal combustion and coke-making processes [31,32]; however, the PCA doesn't confirm this source. It would seem that the As in the water-soluble fraction originated from a source other than coal combustion processes.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 95%

Determination of water-soluble elements in PM2.5, PM10, and PM2.5-10 collected in the surroundings of power plants

Zajusz-Zubek

Mainka

Kaczmarek

2018

E3S Web of Conferences

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Abstract. The analysis reported in this study was performed to characterize the concentrations and water-soluble content of trace elements (As, Cd, Co, Cr, Hg, Mn, Ni, Pb, Sb and Se) in PM2.5, PM10 and PM2.5-10 samples collected in the surroundings of power plants in southern Poland. The solubility of trace elements bound to PM2.5 and PM10 was higher than for PM2.5-10, and in most cases, significant differences were revealed in the relative percentage concentrations of the water-soluble fractions. The occurrence of Cd, Cr, Mn, Ni, Pb and Se in first PCA (Principal Component Analysis) factor (PC1) − indicate coal combustion processes as the potential source of these elements. Other factors indicate two further anthropogenic sources: the resuspension of road dust due to vehicular activities and waste burning in domestic sources − factor (PC2), and, soil dust sources affected by fugitive dust from the mining processes and unpaved roads, as well as transportation and deposition of coal −factor (PC3).

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Section: Principal Component Analysis (Pca)mentioning

confidence: 95%

Determination of water-soluble elements in PM2.5, PM10, and PM2.5-10 collected in the surroundings of power plants

Zajusz-Zubek

Mainka

Kaczmarek

2018

E3S Web of Conferences

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“…Vicente et al discussed a new quality index for a variety of pollutants. However, they did not provide any details for air quality monitoring and prediction [ 12 ]. Sowlat et al examined that many research types are being carried out on the AQI because air pollution is increasing at an infinite pace [ 13 ].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living

Pandya

Ghayvat²,

Sur

et al. 2020

Sensors

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Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.

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“…PM10 and PM2.5, with aerodynamic diameters less than 10 μm and 2.5 μm, respectively, can be inhaled into the respiratory tract leading to critical health problems. As such, the concentration of PM10 has been monitored extensively in urban areas in many western countries [4] [5] followed by many Asian countries [6] [7].…”

Section: Introductionmentioning

confidence: 99%

The Air Quality, Characterization of Polycyclic Aromatic Hydrocarbon, Organic Carbon, and Diurnal Variation of Particulate Matter over Riyadh City

Modaihsh¹,

Mahjoub²,

Nadeem³

et al. 2016

JEP

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Concentrations of airborneparticulate matter (PM10 & PM2.5) were measured over a period extended from January 2012 to October 2013 at two sites: a suburban area, Riyadh airport (AP) and an urban area, King Fahd road district (KF) (densely trafficked site) in Riyadh city, Saudi Arabia. The quality of ambient air using the U.S. Environmental Protection Agency (USEPA) Air Quality Index (AQI) was evaluated during this period. The average AQI values exceeded 100 on 6 months out of 11 in AP district. At KF district the condition was worse, and AQI values exceeded 100 on all months. This emphasizes the fact that PM concentrations over Riyadh can be regarded as a real environmental problem that could be a severe risk to quality of life and threaten human health. A higher-volume air sampler at the two sites was used to collect particulate matter (PM10 and PM2.5) for chemical analysis of the PM for PAHs and total organic carbon. At KF station the mean concentrations' values of total PAHs in PM2.5 and PM10 were 4.89 ng/m 3 and 12.96 ng/m 3 respectively. At the AP station the corresponding values of total PAHs in PM2.5 and PM10 were 8.07 ng/m 3 and 11.91 ng/m 3 respectively. The PAHs did not exceed the air criteria of World Health Organization indicating the air pollution of PAHs in Riyadh District was not serious. As for the total organic carbon the results demonstrated that the concentration ranged from 0.211 to 0.506 g/kg at KF station and from 0.054 to 0.342 g/kg at AP station. On an annual basis, OC concentrations were two times higher for the city center site (KF) compared to the rural AP site. This can most likely be explained by increased emissions from higher traffic at KF site. The diurnal variability of PM was mainly associated with the severity of pollution. * Corresponding author. A. S. Modaihsh et al. 1199

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Assesment of PM10 pollution episodes in a ceramic cluster (NE Spain): Proposal of a new quality index for PM10, As, Cd, Ni and Pb

Cited by 27 publications

References 40 publications

Determination of water-soluble elements in PM2.5, PM10, and PM2.5-10 collected in the surroundings of power plants

Determination of water-soluble elements in PM2.5, PM10, and PM2.5-10 collected in the surroundings of power plants

Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living

The Air Quality, Characterization of Polycyclic Aromatic Hydrocarbon, Organic Carbon, and Diurnal Variation of Particulate Matter over Riyadh City

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