US EPA

7712257 

Journal Article 

Seasonal and site-specific variation in particulate matter pollution in Lithuania 

Dedele, A; Miskinyte, A 

2019 

Yes 

Atmospheric Pollution Research
ISSN: 1309-1042 

10 

3 

768-775 

10.1016/j.apr.2018.12.004 

WOS:000466482900011 

The levels of particulate matter tend to exceed the limit values in many countries across the world. Epidemiological studies have found the associations between particulate matter pollution and human health effects. Therefore, the accurate assessment of particulate matter pollution in urban areas is essential for evaluating the risk to human health. Particulate matter concentration varies between different seasons and it is important to understand the differences in pollution sources in each season and their contributions to the total particulate matter pollution in the city or in the different areas of the city. Better understanding of these variations of particulate matter would lead to an improvement of the assessment of exposure to air pollution. In present study, particles less than 10 mu m in aerodynamic diameter (PM10) were seasonally modelled using the ADMS-Urban model and compared with a measurement campaign to estimate seasonal differences in site-specific concentrations of PM(10 )in Kaunas city, Lithuania. The statistical evaluation of model performance was calculated for seasonal and annual concentrations of PM(10 )for 20 measurement sites. The results of the study showed that ADMS-Urban model performed well in predicting PM(10 )concentrations, with annual mean measured and modelled concentrations of 30.3 and 25.7 mu g/m(3), respectively. The RMSE was 5.0 mu g/m(3), the index of agreement was 0.72, the correlation coefficient was 0.87, and the relative percentile error 15%. The results of the site-specific analysis of PM10 indicated that ADMS-Urban model considers different characteristics of sites relatively well and can be applied for exposure estimates in cohort studies. 

Air pollution; Particulate matter; PM10; Measurements; Dispersion modelling