Change-point detection in environmental time series based on the informational approach

In this study, the Schwarz Information Criterion (SIC) is applied in order to detect change-points in the time series of surface water quality variables. The application of change-point analysis allowed detecting change-points in both the mean and the variance in series under study. Time variations in environmental data are complex and they can hinder the identification of the so-called change-points when traditional models are applied to this type of problems. The assumptions of normality and uncorrelation are not present in some time series, and so, a simulation study is carried out in order to evaluate the methodology’s performance when applied to non-normal data and/or with time correlation.

Structural Time Series Modeling: an Application to Environmental Variables

A structural time series model is one which is set up in terms of components which have a direct interpretation. In this paper, the discussion focuses on the dynamic modeling procedure based on the state space approach (associated to the Kalman filter), in the context of surface water quality monitoring, in order to analyze and evaluate the temporal evolution of the environmental variables, and thus identify trends or possible changes in water quality (change point detection). The approach is applied to environmental time series: time series of surface water quality variables in a river basin. The statistical modeling procedure is applied to monthly values of physico- chemical variables measured in a network of 8 water monitoring sites over a 15-year period (1999-2014) in the River Ave hydrological basin located in the Northwest region of Portugal.

Regional and urban air quality modelling under a climate change scenario

The better understanding of the interactions between climate change and air quality is an emerging priority for research and policy. Climate change will bring changes in the climate system, which will affect the concentration and dispersion of air pollutants. The main objective of the current study is to assess the impacts of climate change on air quality in 2050 over Portugal and Porto urban area. First, an evaluation and characterization of the air quality over mainland Portugal was performed for the period between 2002 and 2012. The results show that NO2, PM10 and O3 are the critical pollutants in Portugal. Also, the influence of meteorology on O3, NO2 and PM10 levels was investigate in the national main urban areas (Porto and Lisboa) and was verified that O3 has a statistically significant relationship with temperature in most of the components. The results also indicate that emission control strategies are primary regulators for NO2 and PM10 levels. After, understanding the national air quality problems and the influence that meteorology had in the historical air quality levels, the air quality modelling system WRF-CAMx was tested and the required inputs for the simulations were prepared to fulfil the main goal of this work. For the required air quality modelling inputs, an Emission Projections under RCP scenarios (EmiPro-RCP) model was developed to assist the estimation of future emission inventories for GHG and common air pollutants. Also, the current emissions were estimated for Portugal with a higher detailed disaggregation to improve the performance of the air quality simulations. The air quality modelling system WRF/CAMx was tested and evaluated over Portugal and Porto urban area and the results point out that is an adequate tool for the analysis of air quality under climate change. For this purpose, regional simulations of air quality during historical period and future (2045-2050) were conducted with CAMx version 6.0 to evaluate the impacts of simulated future climate and anthropogenic emission projections on air quality over the study area. The climate and the emission projections were produced under the RCP8.5 scenario. The results from the simulations point out, that if the anthropogenic emissions keep the same in 2050, the concentrations of NO2, PM10 and O3 will increase in Portugal. When, besides the climate change effects, is consider the projected anthropogenic emissions the annual mean concentrations of NO2 decrease significantly in Portugal and Porto urban area, and on the contrary the annual mean PM10 concentrations increases in Portugal and decrease in Porto urban area. The O3 results are mainly caused by the reduction of ozone precursors, getting the higher reductions in urban areas and increases in the surrounding areas. All the analysis performed for both simulations for Porto urban area support that, for PM10 and O3, there will be an increase in the occurrence of extreme values, surpassing the annual legislated parameters and having more daily exceedances. This study constitutes an innovative scientific tool to help in future air quality management in order to mitigate future climate change impacts on air quality.