Overview of Air Pollution Assessment in Northern Europe ( Lithuania ) by Passive Diffusion Sampling

+e regional air pollution study in Lithuania provided a comprehensive overview of air quality in Lithuania (in Vilnius (capital) and rest of territory) when 375 monitoring sites at different representative locations (urban, suburban, and residential) were equipped with diffusion samplers. +e samples were analyzed for sulfur dioxide (SO2) and nitrogen dioxide (NO2) concentration. +e measurement results show that the mean concentrations of SO2 in all investigation sites during the study period did not exceed the annual limit value of 20.0 μg·m and were below the lower assessment threshold value of 8.0 μg·m. +e mean concentrations of NO2 in Vilnius agglomeration exceeded the annual limit value of 40 μg·m at seven sites and in zone–at three sites with the intensive traffic flow, located near to highway. Comparison of SO2 and NO2 concentration levels was performed for 2004-2005 and 2010-2011. +e level of nitrogen dioxide concentrations has decreased by 34, 26, 24, and 49% during the next six years in the city of Vilnius, and the concentration of SO2 in the air environment decreased by 40–60%.


Introduction
During the past 20 years, there has been a marked improvement of the air in Europe [1].As SO 2 produced by burning of fossil fuels significantly contributes to acid deposition, it affects ecosystems and is harmful for human health.Nitrogen oxides are mostly produced during combustion by industrial facilities and the road transport sector.
Nowadays, the main goals of monitoring lie in providing useful up-to-date information to the public on pollutant concentrations in ambient air, as well as supporting economical stakeholders and decision makers in air-quality assessment and management.Instruments for air quality may change in complexity and cost.While air pollution is highest in urban zones, the monitoring efforts are typically concentrated in cities, and little sites represent the background level.e financial resources are not equal in different countries, and there are no possibilities to extend monitoring network or upgrade of equipment.e use of passive samplers greatly reduces the cost and the need of long-term measurement programs [2][3][4].Personal passive air samplers have been developed and widely used to measure gaseous air pollutants since their introduction in the late 1970s [5,6].
Monitoring of air pollution in Lithuania is organized by the Environmental Protection Agency.Currently, Lithuanian national air-monitoring network consists of one mobile, fourteen continuously operating urban stations, and three integrated monitoring (IM) stations.European Union (EU) environment law acts and legislation were applied and implemented by the National Environmental Monitoring Program (NEMP).
In this paper, within the framework of "Lithuanian Air Monitoring System Modernization Using Diffusive Samplers" (LAQMO) project for the first time were evaluated the concentrations of SO 2 and NO 2 by determining the ambient concentration using the passive sampling method at 375 sites in Lithuania.
e spatial maps of compounds using geographical information systems (GIS) were evaluated on one year measurements with diffusive air samplers.

Methodology
e analysis of concentration for SO 2 and NO 2 using diffusive samplers were set up in the urban background (residential), semiurban (mixed residential and commercial), and roadside (busy street/road and crossing) sites in order to get spatial variation in pollutants concentrations.e obtained data were compared with the acceptable levels of air pollutants that are adopted in the EU as the limit values (Table 1).

Campaigns.
e most appropriate sites for placement were determined.For purposes of taking into account the influence of weather conditions, a network of 375 passive samplers was deployed for all four seasons: autumn (September-November) 2010, winter (December-February), spring (March-May), and summer (June-August) of 2011 and were covered in 8 measurement periods (Table 2).
10% of the sampler was in duplicates, i.e., some colocated passive samplers were deployed at the sampling sites with available continuous monitors for cross correlation and calibration purposes [7].
is information was used for uncertainty calculation in the framework of GUM (Guide to the expression of Uncertainty in Measurement), applied in the laboratory of Passam Ltd., Switzerland (Table 3).
Eight sampling campaigns of 14 days were carried out in Vilnius agglomeration and zone (the rest part of Lithuania).
e locations of the monitoring sites in Vilnius and zone selected for the passive sampling is shown in Figure 1.

Description of Samplers and Measurement Uncertainty.
Passive samplers deployed in different city sites were collected after 14 days of exposure time intervals.e passive samplers were provided and analyzed by PASSAM AG (Switzerland).As supplied by the firm, the tubes are protected from sunlight by an opaque cylindrical box.ese samples have been exposed to sites with different sources of atmospheric emission and environments (Section 2.1).
Although several approaches to uncertainty evaluation exist, the indirect approach of GUM published by the ISO was used (Table 4).e permanent verification of the sampling rate, based on weight losses of permeation tubes, is an independent way of checking the overall performance of diffusive sampling systems.e output information is important for assessing measurement uncertainty.With this procedure, the requirements of ISO 9001 (process control) was fulfilled as well.Furthermore, with this procedure, long-term stability of results was guaranteed, and measurement results were comparable over time.e calculation of uncertainty started on the basis of the following measurement equation: where C u is the ambient concentration, μg•m −3 ; m d is the mass of desorbed analyte, μg; m b is the blank of analyte, μg; SR is the diffusive uptake rate, ml/min; and t is the exposure time.e input quantities and their uncertainties are defined as follows: An additional term has been introduced, which covers the uncertainties budgets of repeated measurements, microenvironmental factors, variations in the geometry of samplers, etc. u p : variation of multiple samples at the same site.e size of this term is estimated by the median of triplicate samplers in the field.u ext : external influences such as temperature, wind speed, and humidity.is term has to be taken into account, if the samplers are used in extreme conditions.
e expanded uncertainty is calculated by using a coverage factor of 2: e uncertainty of the mean of the 8 periods is calculated as follows: (4)

Spatial Interpolation.
Maps of the pollutant concentrations over the area were obtained by interpolation of the passive sampler measurements.By using custom-made automated scripts on open source GRASS GIS software (version 6.4), the following geostatistical methods commonly used for surface interpolation from randomly   [8].By comparing statistical variability of the interpolated datasets, it became obvious that with increasing search radius (N of neighboring points used in interpolation), the IDW method produced rather unstable results, the BCS method under similar conditions (increasing length of splines) produced clearly predictable results with a slight tendency of statistical "smoothing" of the interpolated grid, while AK indicated the most stable statistical results due to its ability to autocorrelate all measurements in the sample [8].
In order to streamline the process of geostatistical data analysis and operational mapping, a customized Linux shell script was developed.It uses geostatistical and mapping functions of the open source GRASS GIS software (v.surf.bspline), 4 Advances in Meteorology as well as some of the Linux OS libraries (libgdal, libgeoti , libpng, etc.) to automatically generate geostatistical grids and operational maps by iterating over each of the polygon objects (urban areas, etc.) by using standard samples of coordinated measurement points as an input.Geostatistical grids will be created in GRASS GIS environment with 10 m pixel size in the standard LKS94 CRS and masked with boundaries of the urban areas.ey will be exported from GRASS database as Float64 data type rasters in GeoTIF le format without any associated color table [8].Data indicate that at sites in the residential and recreation areas, the higher SO 2 levels were recorded in autumn, winter, and spring, when the emissions from energy production are at their highest level (Figure 4).

Vilnius Agglomeration
Conversely, the lowest SO 2 levels were measured in the summer period.erefore, the seasonal variability of concentrations should be interpreted using existing knowledge on emission and meteorological patterns.In summary, the mean sulfur dioxide concentration in Vilnius ranged from 0.2 to 3.1 μg•m −3 with an annual mean of 1.1 μg•m −3 .

Nitrogen Dioxide.
e obtained data (35 sites) during all the study period revealed that NO 2 concentrations varied considerably, which coincides with the other study depending on the distance of the measurement site from main roads caused by the large numbers of vehicles releasing NO 2 [9].For the entire study period, the mean concentration for the NO 2 ranged between 9.1 and 55.6 μg•m −3 (Figures 5 and 6).e NO 2 concentrations demonstrate a large spatial gradient (up to factor of 5), which indicate that road tra c is an important contributor to the NO 2 concentration in urban environment with a mean concentration above the NO 2 limit value of 40 μg•m −3 .However, the mean concentrations of NO 2 at sites with the minor tra c density were close to the upper assessment value of 32 μg•m −3 (Figure 6).At sites in the most visited areas with high density of motor vehicles, the mean concentration of NO 2 ranged between 26.0 and 42.1 μg•m −3 .
us, an exposure to NO 2 concentrations represents a serious As can be seen from Figure 5, higher levels of NO 2 were measured during summer at some sites in the residential and recreation areas (20.8-28.3 and 22.7-40.2μg•m −3 , respectively).As expected, NO 2 concentration was signi cantly higher in the residential and recreation areas at the sites in uenced by transport emissions.Seasonally averaged concentrations of NO 2 were generally higher during winter and spring nearly at all sites.e lowest NO 2 levels were measured in summer (Figure 5).

e Seasonal Variation of Atmospheric Sulfur Dioxide and Nitrogen Dioxide Concentrations in Zone.
e mean concentrations of NO 2 in 40 zones' territory sites, during the study period did not exceed the annual limit value of 40.0 μg•m −3 .e spatial distribution of NO 2 concentrations indicates the tendency to be the higher concentrations in the west part of Lithuania.e principal sources of nitrogen dioxide are tra c and to a lesser extent industry and households.High NO 2 levels, combined with other oxidants, have become one of the major air pollution problems in urban areas.For the entire study period (from 6 November 2010 to 4 July 2011), the mean annual concentrations of NO 2 at di erent sites in the zone were in the range from 3.6 μg•m −3 to 59.6 μg•m −3 (Figure 7).Regarding the annual limit value of 40 μg•m −3 , it was exceeded at three sites with high tra c ow in Klaipeda04 (44.6 μg•m −3 ), Klaipeda09 (44.7 μg•m −3 ), and Klaipeda11 (51.7 μg•m −3 ).At the sites,  10 Advances in Meteorology indicated that higher SO 2 levels were measured during autumn, winter, and spring at sites in the residential and recreation areas when the emissions from energy production and heating are at their highest level.Conversely, the lowest SO 2 levels were measured in summer.9 show that in Vilnius, the level of sulfur dioxide concentrations in the five years has not changed significantly.Significant decrease in SO 2 concentrations was observed at 03 and 05 in Klaipėda sites located in residential areas, while in the cities of Kedainiai and Palanga, the concentration of SO 2 in the air environment decreased by 40-60%.

Comparison of SO
e level of nitrogen dioxide concentrations has decreased by 34, 26, 24, and 49% during five years in the city of Vilnius at the sites next to traffic.Also the increase of NO 2 concentration was observed at Žirniu ˛street and at the crossroads of V. Kudirkos Street near Pamenkalnis (Figure 10).

Conclusion
Concentrations of SO 2 and NO 2 were determined over a year using the passive sampling method.For the entire study period (from 3 November 2010 to 4 July 2011), the annual mean concentrations of SO 2 ranged between 0.20 and 3.40 μg•m −3 in 40 zones territory sites.
e SO 2 annual averages were below the value of 1.50 μg•m −3 at all sampling sites (except two).ese values demonstrate rather small differences and the even regional pollution by SO 2 and its strong connection to the long-range transport of SO 2 on the regional scale.e emission of SO 2 from the local sources more or less formed the level of pollution at those sites.Mean concentrations of NO 2 ranged between 2.3 and 9.4 μg•m −3 in 40 zones territory sites.
e annual mean concentrations of NO 2 were in the range 3.0-5.0μg•m −3 at the sites in major part of the territory and were significantly below the lower assessment threshold limit value of 26.0 μg•m −3 for the annual NO 2 concentration.e highest annual average concentrations of NO 2 were measured at sites close to road with intensive traffic.

Figure 2 :Figure 3 :
Figure 2: Seasonal variation of mean SO 2 concentrations for the entire study period from 3 November 2010 to 4 July 2011 (bar lines show ±22.1% expanded uncertainty).

Figure 6 :
Figure 6: Annual mean concentrations of NO 2 in Vilnius agglomeration for the period from 3 November 2010 to 4 July 2011.

Figure 7 :
Figure 7: Annual mean concentrations of NO 2 in the zone (58 cities) for the period from 3 November 2010 to 4 July 2011.

Figure 8 :Figure 9 :Figure 10 :
Figure 8: Annual mean concentrations of SO 2 in the zone (58 cities) for the period from 3 November 2010 to 4 July 2011.
2 and NO 2 Concentration Levels for 2004-2005 and 2010-2011.e results of the 2004-2005 and 2010-2011 campaign in Figure

Table 1 :
Atmospheric air quality (μg/m 3 ) guidelines for selected air pollutants aiming to protect human health adopted by the European Union Council Directive 2008/50/EB.

Table 3 :
Uncertainty in measurements.
k is calculated as follows:

Table 4 :
Uncertainty estimation according to GUM.
(BCS; GRASS function v.surf.bspline),and kriging interpolation with automated calibration of parameters (AK; GRASS function v.krige) were tested