Air Quality Trend of PM10. Statistical Models for Assessing the Air Quality Impact of Environmental Policies

Abstract

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.

Longitudinal Trends of the Annual Exposure to PM2.5 Particles in European Countries

“…The declining share of PM particles, such as street and traffic, as well as the impact of air pollution control measures were the main reasons of decreasing PM 2.5 and PM 10 exposure [ 10 ]. Another study in the Castellon region of Spain approved the falling trend of PM 10 particles concentration in urban and industrial areas due to the economic crisis, which led to a decline in industrial production [ 11 ]. Investigation of temporal changes in the annual concentration of PM 2.5 in Serbia showed a falling trend from 2001 to 2016 [ 12 ].…”

Urban Air Pollutant Monitoring through a Low-Cost Mobile Device Connected to a Smart Road

“…[12]. In addition to the assessment of the spatial representativeness of air pollutant measurements, GIS-based processing can provide tools to model their concentration [32].…”

Regression Model of PM2.5 Concentration in a Single-Family House

“…Annual and daily concentrations of particulate matter measured in urban and suburban areas often exceed World Health Organization (WHO), European Union (EU), and national levels [16][17][18]. Therefore, it is necessary to monitor particulate concentrations in order to identify sources and improve air quality in terms of reducing the concentration of pollutants from anthropic sources [19][20][21][22][23][24][25]. Monitoring of air pollution concentrations is also necessary to analyze the occurrence of related illnesses in a given area and implement appropriate preventive measures in the field of health protection [26].…”