Air Quality Trend of PM10. Statistical Models for Assessing the Air Quality Impact of Environmental Policies
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Department of Agricultural and Environmental Sciences, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain
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Department of Mathematics, Statistics Area, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain
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IMAC, Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain
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CIBER of Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
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Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, 17004 Girona, Spain
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Institute of New Imaging Technologies (INIT), Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain
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Author to whom correspondence should be addressed.
Sustainability 2019, 11(20), 5857; https://doi.org/10.3390/su11205857
Received: 11 September 2019 / Revised: 16 October 2019 / Accepted: 17 October 2019 / Published: 22 October 2019
(This article belongs to the Special Issue Sustainable City: Innovative Technologies for Air Quality Monitoring and Assessment)
A statistical modelling of PM10 concentration (2006–2015) is applied to understand the behaviour, to know the influence of the variables to exposure risk, to treat the missing data to evaluate air quality, and to estimate data for those sites where they are not available. The study area, Castellón region (Spain), is a strategic area in the framework of EU pollution control. A decrease of PM10 is observed for industrial and urban stations. In the case of rural stations, the levels remain constant throughout the study period. The contribution of anthropogenic sources has been estimated through the PM10 background of the study area. The behaviour of PM10 annual trend is tri-modal for industrial and urban stations and bi-modal in the case of rural stations. The EU Normative suggests that 90% of the data per year are necessary to control air quality. Thus, interpolation statistical methods are presented to fill missing data: Linear Interpolation, Exponential Interpolation, and Kalman Smoothing. This study also focuses on testing the goodness of these methods in order to find the ones that better approach the gaps. After analyzing graphically and using the RMSE the last method is confirmed to be the best option.
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Keywords:
PM10; trend; interpolation methods; Kalman Smoothing
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MDPI and ACS Style
Vicente, A.B.; Juan, P.; Meseguer, S.; Serra, L.; Trilles, S. Air Quality Trend of PM10. Statistical Models for Assessing the Air Quality Impact of Environmental Policies. Sustainability 2019, 11, 5857. https://doi.org/10.3390/su11205857
AMA Style
Vicente AB, Juan P, Meseguer S, Serra L, Trilles S. Air Quality Trend of PM10. Statistical Models for Assessing the Air Quality Impact of Environmental Policies. Sustainability. 2019; 11(20):5857. https://doi.org/10.3390/su11205857
Chicago/Turabian StyleVicente, Ana B., Pablo Juan, Sergi Meseguer, Laura Serra, and Sergio Trilles. 2019. "Air Quality Trend of PM10. Statistical Models for Assessing the Air Quality Impact of Environmental Policies" Sustainability 11, no. 20: 5857. https://doi.org/10.3390/su11205857
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