Supporting the improvement of air quality management practices: The "FAIRMODE pilot" activity

Assessing the Impact of Local Policies on PM2.5 Concentration Levels: Application to 10 European Cities

In this paper, we propose a methodology to evaluate the effectiveness of local emission reduction policies on PM2.5 concentration levels. In particular, we look at the impact of emission reduction policies at different scales (from urban to EU scale) on different PM2.5 baseline concentration levels. The methodology, based on a post-processing of air quality model simulations, is applied to 10 cities in Europe to understand on which sources local actions are effective to improve air quality, and over which concentration ranges. The results show that local actions are effective on low-level concentrations in some cities (e.g., Rome), whereas in other cases, policies are more effective on high-level concentrations (e.g., Krakow). This means that, in specific geographical areas, a coordinated approach (among cities or even at different administration levels) would be needed to significantly improve air quality. At last, we show that the effectiveness of local actions on urban air pollution is highly city-dependent.

Assessing air pollution in European cities to support a citizen centered approach to air quality management

European cities have made significant progress over the last decades towards clean air. Despite this progress, several cities are still facing acute air pollution episodes, with various urban areas frequently exceeding air quality levels allowed by the European legal standards and WHO guidelines. In this paper, six European cities/ regions (Bristol, UK; Amsterdam, NL; Sosnowiec, PL; Ljubljana, SI; Aveiro, PT; Liguria, IT) are studied in terms of air quality, namely particulate matter, nitrogen dioxide and ozone. The concentrations trends from 2008 to 2017 in the different typology of monitoring stations are addressed, together with the knowledge of daily, weekly and seasonal pollution patterns to better understand the city specific profiles and to characterize pollutant dynamics and variations in multiple locations. Additionally, an analysis of the duration and severity of air pollution episodes is also discussed, followed by an analysis of the fulfillment of the legislated limit values. Each of our 6 case study locations face different air pollution problems, but all these case studies have made some progress in reducing ambient concentrations. In Bristol, there have been strong downward trends in many air pollutants, but the levels of NO₂ remain persistently high and of concern. In recent years, decreasing concentration levels point to some success of Amsterdam air quality policies. PM₁₀ exceedances are a seasonal pollution problem in Ljubljana, Sosnowiec and Aveiro region (even if with different levels of severity). While, exceedances of NO₂ and O₃ concentrations are still problematic in Liguria region. The main findings of this paper are particular relevant to define and compare future citizen-led strategies and policy initiatives that may be implemented to improve and fulfill the EU legislation and the WHO guidelines.

A methodological framework for improving air quality monitoring network layout. Applications to environment management

This work aims to provide a methodology framework which allows to improve the performance and efficiency of an air quality monitoring network (AQMN). It requires to be constituted by a minimum and reliable number of measurement sites. Nevertheless, the AQMN efficiency should be assessed over time, as a consequence of the possible emergence of new emission sources of air pollutants, which could lead to variations on their spatial distribution within the target area. PM₁₀ particles data monitored by the Community of Madrid's (Spain) AQMN between 2008 and 2017 were used to develop a methodology to optimize the AQMN performance. The annual spatial distribution of average PM₁₀ levels over the studied period monitored by all current stations vs those more representative was provided by a geographic information system (GIS), and the percentage of similarity between both postulates was quantified using simple linear regression (> 95%). As one innovative tool of this study, the practical application of the proposed methodology was validated using PM₁₀ particles data measured by AQMN during 2007 and 2018, reaching a similitude degree higher than 95%. The influence of temporal variation on the proposed methodological framework was around 20%. The proposed methodology sets criteria for identifying non-redundant stations within AQMN, it is also able to appropriately assess the representativeness of fixed monitoring sites within an AQMN and it complements the guidelines set by European legislation on air pollutants monitoring at fixed stations, which could help to tackle efforts to improve the air quality management.

Analysis and Modelling of PM2.5 Temporal and Spatial Behaviors in European Cities

Particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) is associated with adverse effects on human health (e.g., fatal cardiovascular and respiratory diseases), and environmental concerns (e.g., visibility impairment and damage in ecosystems). This study aimed to evaluate temporal and spatial trends and behaviors of PM2.5 concentrations in different European locations. Statistical threshold models using Artificial Neural Networks (ANN) defined by Genetic Algorithms (GA) were also applied for an urban centre site in Istanbul, to evaluate the influence of meteorological variables and PM10 concentrations on PM2.5 concentrations. Lower PM2.5 concentrations were observed in northern Europe. The highest values were found at traffic-related sites. PM2.5 concentrations were usually higher during the winter and tended to present strong increases during rush hours. PM2.5/PM10 ratios were slightly higher at background sites and the lower values were found in northern Europe (Helsinki and Stockholm). Ratios were usually higher during cold months and during the night. The statistical model (ANN + GA) allowed evaluating the combined effect of different explanatory variables (temperature, wind speed, relative humidity, air pressure and PM10 concentrations) on PM2.5 concentrations, under different regimes defined by relative humidity (threshold value of 79.1%). Important information about the temporal and spatial trends and behaviors related to PM2.5 concentrations in different European locations was developed.