Journal
SUSTAINABILITY
Volume 11, Issue 20, Pages -Publisher
MDPI
DOI: 10.3390/su11205857
Keywords
PM10; trend; interpolation methods; Kalman Smoothing
Funding
- Generalitat Valenciana through the Subvenciones para la realizacion de proyectos de I+D+i desarrollados por grupos de investigacion emergentes program [GV/2019/016]
- postdoctoral programme PINV2018-Universitat Jaume I [POSDOC-B/2018/12]
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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, Castellon 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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