Modeling Pareto efficient PM10 control policies in Northern Italy to reduce health effects

Pisoni, Enrico; Volta, Marialuisa

doi:10.1016/j.atmosenv.2009.03.031

Cited by 23 publications

(13 citation statements)

References 17 publications

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“…Among all air pollutants, the particulate matter (PM) concentrations are of particular concern, because high PM concentrations can cause human health problems (Pisoni and Volta 2009). Epidemiological researches have shown an association between ambient concentration of PM pollution and negative effects on human health (Pope 2000;Jaecker-Voirol and Pelt 2000).…”

Section: Introductionmentioning

confidence: 99%

Using Seven Types of GM (1, 1) Model to Forecast Hourly Particulate Matter Concentration in Banciao City of Taiwan

Pai

Chen

et al. 2010

Water Air Soil Pollut

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In this study, seven types of first-order and one-variable grey differential equation model (abbreviated as GM (1, 1) model) were used to predict hourly particulate matter (PM) including PM 10 and PM 2.5 concentrations in Banciao City of Taiwan. Their prediction performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and maximum correlation coefficient (R) was 14.10%, 25.62, 5.06, and 0.96, respectively, when predicting PM 10 . When predicting PM 2.5 , the minimum MAPE, MSE, RMSE, and maximum R value of 15.24%, 11.57, 3.40, and 0.93, respectively, could be achieved. All statistical values revealed that the predicting performance of GM (1, 1, x (0) ), GM (1, 1, a), and GM (1, 1, b) outperformed other GM (1, 1) models. According to the results, it revealed that GM (1, 1) GM (1, 1) was an efficiently early warning tool for providing PM information to the inhabitants.

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Section: Introductionmentioning

confidence: 99%

Using Seven Types of GM (1, 1) Model to Forecast Hourly Particulate Matter Concentration in Banciao City of Taiwan

Pai

Chen

et al. 2010

Water Air Soil Pollut

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“…Authors (e.g., El-Assouli, 2010;Seroji, 2011;Seroji, 2012;Habeebullah, 2013;Munir et al, 2013a, b) have analysed the levels, composition, and toxicity of airborne PM in Makkah, particularly during the peak season of Hajj (Pilgrimage) when millions of Muslims visit Makkah. PM 10 , with the fine and coarse particle fractions combined, is a highly toxic traffic-related pollutant; in a number of studies (Jaecker-Voirol and Pelt, 2000;Pires et al, 2008;Pisoni and Volta, 2009;Pai et al, 2011;Habeebullah, 2013) both short-term and long-term exposures to PM 10 have been identified to have negative consequences on public health. Health damages associated with exposure to PM 10 have urged decision makers to act accordingly, however this requires better understanding of the different factors influencing PM 10 levels and accurate forecasting of its atmospheric concentrations, employing different modelling approaches.…”

Section: Introductionmentioning

confidence: 99%

Comparing the Performance of Statistical Models for Predicting PM10 Concentrations

Sayegh¹,

Munir

Habeebullah

2014

Aerosol Air Qual. Res.

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The ability to accurately model and predict the ambient concentration of Particulate Matter (PM) is essential for effective air quality management and policies development. Various statistical approaches exist for modelling air pollutant levels. In this paper, several approaches including linear, non-linear, and machine learning methods are evaluated for the prediction of urban PM 10 concentrations in the City of Makkah, Saudi Arabia. The models employed are Multiple Linear Regression Model (MLRM), Quantile Regression Model (QRM), Generalised Additive Model (GAM), and Boosted Regression Trees1-way (BRT1) and 2-way (BRT2). Several meteorological parameters and chemical species measured during 2012 are used as covariates in the models. Various statistical metrics, including the Mean Bias Error (MBE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), the fraction of prediction within a Factor of Two (FACT2), correlation coefficient (R), and Index of Agreement (IA) are calculated to compare the predictive performance of the models. Results show that both MLRM and QRM captured the mean PM 10 levels. However, QRM topped the other models in capturing the variations in PM 10 concentrations. Based on the values of error indices, QRM showed better performance in predicting hourly PM 10 concentrations. Superiority over the other models is explained by the ability of QRM to model the contribution of covariates at different quantiles of the modelled variable (here PM 10 ). In this way QRM provides a better approximation procedure compared to the other modelling approaches, which consider a single central tendency response to a set of independent variables. Numerous recent studies have used these modelling approaches, however this is the first study that compares their performance for predicting PM 10 concentrations.

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“…2 The size distributions and chemical compositions of atmospheric particles play significant roles in their transport, transformation, and removal mechanisms. 3,4 Airborne particles, especially fine particles are found to be widely associated with health problems, 5 such as increased illness and hospitalization. Therefore, a considerable amount of research is devoted to source identification and control strategy of airborne particle.…”

Section: Introductionmentioning

confidence: 99%

PM10 and their Bio-Contamination in Makkah Saudi Arabia- Case Studys

Mohammed

Attala

Habeebullah

2017

IJBSBE

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Atmospheric Particulate Matter (PM) is considered one of the most important air pollutants in terms of its health impacts, environmental degradations and visibility. Particles size, their chemical composition and atmospheric level are important factors for determining their adverse health impacts. In this paper the main objective of this work was focus on PM 10 and its bio-accumulations (microbial load) in Makkah during Hajj seasons 2016 (1437AH). This study concluded that the concentration of respirable particles (PM 10 ) and its watersoluble ions were much higher at site 2, site 1 than that at sites 3, 4 and 5 around Haram in Makkah. In addition, Man is the main source of air microbial contamination surrounding Haram in Makkah. Fungi evidence of the lack of output from natural sources of pollution. Spotted Staphylococcus bacteria were at high rates in all sites and micro-organisms at the site 2. The source of microbial contamination in all sites was the same, while the source of fungi differs especially at the site 2. The natural resources were the most predominant at the site of 2 and 1, as compared to sites 3, 4 and 5.

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Modeling Pareto efficient PM10 control policies in Northern Italy to reduce health effects

Cited by 23 publications

References 17 publications

Using Seven Types of GM (1, 1) Model to Forecast Hourly Particulate Matter Concentration in Banciao City of Taiwan

Using Seven Types of GM (1, 1) Model to Forecast Hourly Particulate Matter Concentration in Banciao City of Taiwan

Comparing the Performance of Statistical Models for Predicting PM10 Concentrations

PM10 and their Bio-Contamination in Makkah Saudi Arabia- Case Studys

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