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Making air quality indices comparable – assessment of 10 years of air pollutant levels in western Europe ab
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Hanna Leona Lokys , Jürgen Junk & Andreas Krein a
Department Environment and Agro-biotechnologies (EVA), Centre de Recherche Public – Gabriel Lippmann, Belvaux, Luxembourg b
Climatology Working Group, University of Münster, Münster, Germany Published online: 27 Mar 2014.
To cite this article: Hanna Leona Lokys, Jürgen Junk & Andreas Krein (2014): Making air quality indices comparable – assessment of 10 years of air pollutant levels in western Europe, International Journal of Environmental Health Research, DOI: 10.1080/09603123.2014.893568 To link to this article: http://dx.doi.org/10.1080/09603123.2014.893568
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International Journal of Environmental Health Research, 2014 http://dx.doi.org/10.1080/09603123.2014.893568
Making air quality indices comparable – assessment of 10 years of air pollutant levels in western Europe Hanna Leona Lokysa,b*, Jürgen Junka and Andreas Kreina Department Environment and Agro-biotechnologies (EVA), Centre de Recherche Public – Gabriel Lippmann, Belvaux, Luxembourg; bClimatology Working Group, University of Münster, Münster, Germany
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(Received 10 October 2013; final version received 20 December 2013) To address the incomparability of the large number of existing air quality indices, we propose a new normalization method that is suited to directly compare air quality indices based on the common European World Health Organization (WHO) air quality guidelines for NO2, O3, and PM10. Using this method, we compared three air quality indices based on the European guidelines, related them to another air quality index based on the relative risk concept, and used them to assess the air quality and its trends in northwest central Europe. The average air quality in the area of investigation is below the recommended European guidelines. The majority of index values exceeding this threshold are caused by PM10, which is also, in most cases, responsible for the degrading trends in air quality. Eleven out of 29 stations tested showed significant trends, of which eight indicated trends towards better air quality. Keywords: air quality index; health impact; normalization method; relative risk; western Europe
Introduction Exposure to ambient air pollution is an environmental problem (Pope et al. 2002; Alastuey et al. 2004; Mayer & Kalberlah 2009; COMEAP 2010), and populations worldwide are exposed to pollution levels that affect their health and often exceed the recommended European Environment Agency (EEA) air quality guideline limit values (EEA 2013). According to the World Health Organization (WHO) air quality guidelines for Europe (WHO 2005), clean air is considered as a basic requirement for human health and well-being. This is supported by the large number of recent studies (e.g. Rohr & Wyzga 2012; Wong et al. 2012; Dimitriou et al. 2013). To assess the overall air quality, different indices have been developed (Plaia & Ruggieri 2010). They address the problem of air pollution with many purposes; e.g. estimating the health impact of air quality, informing the public about current pollution levels, long-term monitoring of air quality, or to evaluate urban planning management strategies (Hodges et al. 2008). Most of the air quality indices differ in the calculation method (e.g. number of pollutants determining the final index value) and also in the number, range, area of origin, and – if they are health impact-related – the corresponding health impact associated with different index classes. A direct comparison of the different indices is not possible. A solution for this problem was suggested by van den Elshout et al. *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
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(2008), with the creation of a “Common Air Quality Index” (CAQI) for the whole of Europe. This index is based on the guideline values published in the European Directive 2008/50/EC, which was defined to protect and enhance human well-being. All indices based on these guideline values can be considered as health impact-related air quality indices. Nevertheless, according to van den Elshout et al. (2008), indices of this kind often do not have a clear link with observed health effects. Other health impact-related indices do not refer to guideline values, but are based on the relative risk (RR) concept that describes the increase of morbidity and mortality that is caused by exposure to ambient air pollution; e.g. the “Air Pollution Index” (API) by Cairncross et al. (2007). The RR is commonly used in epidemiology and is defined as the ratio of the risk among exposed populations to the risk among unexposed populations to be affected by an adverse health effect (Sistrom & Garvan 2004). Despite these efforts, currently three out of five citizens do not feel they are sufficiently informed about air quality in their country (EEA 2013). This might result from the large number of existing indices that are mostly incomparable. Therefore, this study presents a new statistical method making direct comparisons between different air quality indices possible. We focus on the commonly used guideline-based air quality indices, e.g. the “Daily Air Quality Index” (DAQx) (Mayer et al. 2004), the CAQI (van den Elshout et al. 2008), or the “Multi Pollutant Index” (MPI) (Gurjar et al. 2008). The fact that they are based on the same guidelines and aim to inform about air quality and its health impact implies that the results, even if derived with different calculation and aggregation methods, should be comparable via normalization. The common reference values used for the normalization were taken from the current valid WHO recommendations for NO2, O3, and PM10 (WHO 2005). These three health impact-related air quality indices, based on the European guideline values (Directive 2008/50/EC), were also used to assess the air quality in the so-called “Greater Region” (Belgium, France, Germany, and Luxembourg). In addition, the “Aggregate Risk Index” (ARI) (Sicard et al. 2011) – an aggregated index based on the RR concept – is used to further analyze the impact relatedness of the guideline-based indices. Materials and methods Area of investigation and data-sets In this study, 29 official air quality monitoring stations in the “Greater Region” were analyzed covering the period from 2001 to 2010 (Figure 1). All emission data-sets were retrieved from the European AirBase Network, the air quality information system maintained by the EEA (EEA AirBase). Data-sets for each station included the concentrations of NO2, SO2, and O3 on an hourly basis and PM10 on an hourly or daily basis. Some stations additionally provide hourly measurements of CO and/or hourly or daily measurements of PM2.5. Additional meteorological parameters for Rhineland-Palatinate (western Germany) were directly retrieved from the ZIMEN network (Zentrales Immissionsmessnetz Rheinland-Pfalz). At some ZIMEN stations, the measurement of PM10 was replaced by PM2.5 during the period of investigation. To maintain a continuous set of PM10 values, PM2.5 can be converted to PM10 because of fairly constant ratios at individual sites (Van Dingenen et al. 2004). Ratios between PM2.5 and PM10 of 0.59 for summer (September–February) and 0.69 for winter (March–August) were derived from daily values (N = 3217). Only stations with comparable station characteristics were used to derive these ratios. These
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Figure 1. Overview of air quality stations used for the study in the “Greater Region.” Circles represent cluster 1, triangles cluster 2, and squares represent cluster 3. White symbols represent background stations, gray symbols represent industrial stations, and black symbols represent traffic stations.
ratios were also used to convert the PM2.5 measurements into PM10 values at stations where only PM2.5 measurements were available. According to the European Directive 2008/50/EC, the data were aggregated to daily values only for cases with at least 75 % of valid hourly data. To identify stations with similar pollutant characteristics, a hierarchical cluster analysis based on the Ward algorithm (Ward 1963) was used. The method minimizes the total variance within clusters. Therefore, each step merges the clusters with a minimum distance between each other based on the Euclidian distance. Instead of clustering the stations by daily data, which would only group stations that show the same characteristics on the same day, all integer percentiles of O3 maximum daily 8 h running mean and PM10 daily mean were used in the cluster analysis. Thus, the clusters include stations that exhibit a similar distribution of pollution over the analyzed period. The resulting clusters correspond well to the station type provided by the AirBase Network. Cluster one contains mostly urban and suburban background stations, while cluster 2 represents rural background stations. The third identified cluster mostly combines the traffic and industrial stations (Figure 1). For the following analyses, one station per cluster has been selected according to maximum data availability and maximum spatial distribution. Air quality indices There are many criteria that can be used to group air quality indices, e.g. health relatedness, purpose of the index, or exposure time to air pollutants (Plaia & Ruggieri 2010).
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Besides these differentiators, air quality indices can also be distinguished into two groups by the number of determining pollutants (Plaia & Ruggieri 2010). The indices of the first group are determined only by the pollutant with the highest subindex value (non-aggregating group), whereas the second group of indices takes the conjoint effect of different pollutants into account (aggregating group). In our study, three health impact-related air quality indices, based on the European guideline values (Directive 2008/50/EC), were used to compare the air quality in the “Greater Region.” The impact relation of these three indices has been derived from cohort studies and meta-analyses of epidemiological studies as mentioned in the WHO air quality guidelines for Europe (WHO 2005). The DAQx (Mayer et al. 2004) and the CAQI (van den Elshout et al. 2008) belong to the first, non-aggregating group. The third index is the MPI (Gurjar et al. 2008), which belongs to the second group of air quality indices and uses an aggregation method to determine the final index value. In addition, the aggregated index ARI (Sicard et al. 2012), based on the RR concept, is used to assess the impact relatedness of the three guideline-based indices DAQx, CAQI, and MPI. The DAQx was developed for southwest Germany and its calculation concept is used in several regions of Germany under different names, e.g. LQI or LuQx (Griem & Kalberlah 2000), to inform the public about the current situation of the daily air quality. Subindices for the pollutants NO2, SO2, CO, O3, and PM10 are calculated according to Equation (1), DAQxup DAQxlow (1) DAQxi ¼ ðCinst: Clow Þ þ DAQxlow Cup Clow where DAQxi is the subindex for each pollutant i, DAQxup and DAQxlow are the upper and lower index values of an index class, Cup and Clow are the upper and lower concentrations that belong to the index class, and Cinst. is the measured concentration of the pollutant. The index classes were derived from multiple epidemiological and toxicological investigations and range – in six classes – from 1 (very good) to 6 (very poor), where the values of DAQxlow for index class 5 correspond to the European standards (Directive 2008/50/EC) for each pollutant. A table with upper and lower class limits according to Mayer et al. (2004) can be taken from the supplement. DAQx values were calculated only for days where valid measurements of NO2, PM10, and O3 were available. According to the calculation method in combination with the table, all concentrations that lead to an index value superior to 5.5 are assigned 6. The CAQI was developed to compare air quality within Europe (van den Elshout et al. 2008). It can be calculated on a daily or hourly basis and distinguishes traffic and city background stations. For traffic stations, NO2 and PM10 are mandatory for calculation and CO is auxiliary, whereas for city background stations NO2, PM10, and O3 are mandatory and CO and SO2 are auxiliary pollutants. The CAQI subindices are calculated by linear interpolation between the index class thresholds in the same way as the DAQx subindices, but the class limit values are different (Table see supplement). The CAQI ranges from 0 (very low) to > 100 (very high) in 5 classes. The upper threshold of the medium class (CAQI = 75) corresponds to the European standards (Directive 2008/50/EC) for each pollutant, except for SO2 where the guideline value for maximum hourly values is 350 μg m−3 but the concentration leading to a CAQI subindex of 75 is 300 μg m−3 (van den Elshout et al. 2008).
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The aggregating MPI is calculated according to Equation (2), 1 X ACi GCi MPI ¼ n GCi
5
(2)
where n is the number of pollutants used to calculate the MPI, ACi is the concentration of the pollutant i, and GCi is the guideline value for the specific pollutant (Gurjar et al. 2008). To make the index comparable with the two other indices, we used the European guideline values from Directive 2008/50/EC for the calculations. This index was originally developed to compare the air quality amongst mega cities worldwide, but the flexibility of the calculations makes it easily adaptable for various situations, including our air quality assessment for the “Greater Region.” Contrary to the first two indices, the MPI has no fixed upper threshold value. The lowest theoretically possible index value is –1, which corresponds to ambient air that is free from all the pollutants covered by the index. If all pollutants meet the guideline values, the MPI would result in 0. MPI values above 0, indicating that one or more pollutants have exceeded the guideline values. To ensure that the indices remain comparable, only NO2, PM10, and O3 were used for calculation. According to various studies (van den Elshout et al. 2008; COMEAP 2011; Dimitriou et al. 2013), SO2 and CO no longer pose an important public health hazard, and hence were not included in our study. The ARI was developed by Sicard et al. (2011, 2012) and is based on the Cairncross’ concept (Cairncross et al. 2007) adapted for the European region by adjusting the individual class limits for each pollutant. As an aggregated index, the ARI is calculated according to Equation (3), X ARI ¼ ai Ci (3) i
where Ci is the measured concentration of pollutant i and ai is a conversion factor calculated based on the corresponding RR. The conversion factors vary for different health endpoints. Each ARI class represents an increase in the RR by 3 % (Sicard et al. 2012). The correlation between the ARI and the other three indices will be used to assess the health impact relation of the three guideline-based indices. Normalization approach A direct comparison of DAQx, MPI, and CAQI is not possible because of their different scales, number of classes, and threshold values. Therefore, a standardization which normalizes all indices of the range from zero to one would lead to wrong results. This is caused by the fact that the indices have two values in common. First, no pollution leads all indices to their lowest index value, whereas a pollution level that meets the European guidelines will lead to index values of 5 for the DAQx, 75 for CAQI, and 0 for MPI. Second, the index value corresponding to the guideline value is, for every index, located at a different location. For the DAQx it is approximately at 83 % of the total index range, whereas for the CAQI the corresponding value is located at 75 %. Applying onestage normalization, the normalized index value corresponding to the guideline value would, for every index, result in a different value. Therefore, two-stage normalization was chosen to make the indices directly comparable. Stage one normalized the range from zero till the index value correspondent to the guideline value. On stage two, the index values from the guideline value up to the maximum index value were also normalized (Figure 2). As the MPI has no fixed upper threshold for the individual pollutants, and thus does not have a defined maximum index value, the measured
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Figure 2. Two-step normalization method to enable direct comparison of the guidelines-based air quality indices.
absolute maximum plus 10 % was used as the maximum index value for the normalization. The values of both ranges were divided by 2 and 0.5 was added to the values of stage two to spread the normalized index in the range from zero to one. The value of 0.5 of each normalized index now represents the index corresponding to the European guideline values (Directive 2008/50/EC). Trend analysis In order to assess the history of the air quality in the “Greater Region” during the period from 2001 to 2010, trend analyses were carried out for all indices as well as for the pollutants NO2, O3, and PM10. Therefore, the MAKESENS tool from the Finnish Meteorological Institute was used (Salmi et al. 2002). It provides a template for the determination of trends in annual time series of atmospheric concentrations. The tool is based on the Mann–Kendall test, which is a non-parametric statistical test to detect monotonic trends within a time series with no seasonal or other cycle (Sicard et al. 2010). To estimate the slope of the detected trend, the Sen’s method is used. The tests were chosen because both methods are non-sensitive for missing values and the data does not need to have a particular distribution. In addition, the Sen’s method is robust against single data errors or outliers (Salmi et al. 2002). Results and discussion Comparison of the air quality indices DAQx, CAQI, and MPI To analyze the different characteristics of the three guideline-based air quality indices, one station of each cluster was analyzed in detail. Differences in the index characteristics between the three clusters are evident at all three indices (Figure 3). The rural background site Hunsrück-Leisel (cluster 2) shows the most distinct annual cycle, while the annual cycle is less pronounced at the other two sites. Amongst the indices, the MPI exhibits the least pronounced annual cycle. The determining pollutant at Hunsrück-Leisel is O3 for all three indices over the whole measurement period, while the other two sites show differences amongst the
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Figure 3. (Color online) Absolute DAQx, CAQI, and MPI values based on the European guideline values (2008/50/EC) for Thionville (cluster 1), Hunsrück-Leisel (cluster 2), and Koblenz (cluster 3) from 2001 to 2010. Colors show the dominating pollutant. Bar plots show the number of days dominated by each pollutant per year.
indices and time. This corresponds well with the results of Mayer et al. (2004), who showed that sites with low DAQx index values are mainly determined by O3. As the sites become more influenced by traffic or industrial emissions, the determining pollutants shift towards PM10 and NO2. Throughout all indices, it is obvious that the influence of PM10 on the local air quality in Thionville is increasing. According to the comparison of the air quality indices at the three exemplarily analyzed cluster stations, the DAQx is more NO2-dominated (57 % of the valid measurements are determined by NO2 in Koblenz), whilst the CAQI stresses more the influence of PM10 (57 % of the valid measurements are determined by PM10 in Koblenz). The MPI as an aggregating index is always influenced by all pollutants, so the determining pollutant in this index describes the pollutant that contributes most to the resulting index value. Due to the calculation method of the MPI, the dominant pollutant is always the one with the highest percentage of the guideline value. The differences in the dominant pollutants at the DAQx and CAQI result from the different calculation tables of the indices. Nevertheless, the two non-aggregating air quality indices show the strongest linear correlation between each other (Figure 4), indicated by R2 of 0.885. The correlation between these indices and the aggregating MPI is slightly lower, with R2 at 0.760 (DAQx) and 0.778 (CAQI), respectively. The spread between the DAQx and CAQI decreases from clean air to polluted air, whilst it grows along this gradient for the correlation between the non-aggregating indices and the MPI. This can be explained by the different weighting of the pollutants by the calculation table. The spread between the DAQx, respectively the CAQI, and the MPI increases with increasing air pollution. This is a consequence of the different calculation methods of the air quality indices. Low MPI values can occur only if all
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Figure 4. Scatterplots and linear regression of the air quality indices of all sites for the period from 2001 to 2010.
pollutants show low levels, so in these cases the difference between an index that is only determined by one pollutant and an aggregating one is not large. However, high MPI values can be reached by different combinations of pollutants. One of these combinations is, for example, one strongly dominating pollutant accompanied by the other pollutants being at low concentrations. This results in a good correlation between the aggregating and non-aggregating indices. On the other hand, high MPI values can also be reached by a combination of pollutants at a level which leads to a similar, mid-level subindex. In this case the correlation between the MPI and the DAQx, respectively the CAQI, would be much lower, as the non-aggregating indices would result in a better air quality than the aggregating one. DAQx, MPI, and CAQI in relation to the RR concept As all the analyzed air quality indices are based on different calculation tables it is important to evaluate the resulting air quality index according to the informative value concerning public health. Therefore, the three guideline-based air quality indices were compared to the RR-based index ARI. Figure 5 shows that the non-aggregating indices do not correlate well with the ARI, especially in the higher index range. Correlation coefficients were calculated for the whole data-set, but single stations did not provide significantly better results. At high index values, the non-aggregating indices correspond
Figure 5. Scatterplots and regression of the air quality indices of all sites based on the European directive (Directive 2008/50/EC) and the RR based air quality index ARI for the period from 2001 until 2010.
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to a range of more than 4 ARI classes. As each index class represents an increased RR of 3 %, the variation in the health outcome at a certain index value is, therefore, more than 12 %. The MPI as an aggregating air quality index with an R2 of 0.806 performs better in this context. Therefore, we conclude that an aggregating air quality index is more suitable to get information about the RR increase caused by the ambient air pollutants. Further studies that analyze the direct correlations between the air quality indices (normalized and not normalized) and adverse health effects would help to get a better comprehension on this particular topic. Direct index comparison with a normalization approach So far, studies on air quality indices either compared them in a descriptive way (Leeuw & Mol 2005; Dimitriou et al. 2013) or created new air quality indices (Kyrkilis et al. 2007; Sicard et al. 2011; Wong et al. 2012) that were adjusted to regional pollution characteristics or regulations. To simplify a direct comparison of air quality within Europe on the basis of air quality indices, we propose a new normalization method. To enable a more objective comparison of the air quality indices, they were normalized to the same scale, making a direct comparison of the selected stations from the three clusters possible (Figure 6). Comparing Figures 3 and 6, it is obvious that after the normalization the structure (shape, annual cycles, peak, and variations) of the time series remains the same and the method is suited to compare the indices. According to the structure of the MPI, the linear correlation coefficient for the normalized and non-normalized index is 1, but the normalized and non-normalized DAQx and CAQI exhibit an R2 > 0.9.
Figure 6. (Color online) Daily values of normalized air quality indices at Thionville (cluster 1), Hunsrück-Leisel (cluster 2), and Koblenz (cluster 3) for the period from 2001 to 2010. Colors show the dominating pollutant. Index values below European guideline equivalents are marked in gray.
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At all stations, the DAQx expresses the highest median value (0.31–0.34 normalized), resulting in the worst air quality rating. The median of the normalized CAQI is slightly lower at all stations (0.24–0.29), whilst the normalized MPI exhibits the lowest median at all stations (0.13–0.18). The normalized DAQx and CAQI show several days (between 51 and 171 days at the three stations that were analyzed in detail), where the indices exceed 0.5, highlighting that at least one pollutant exceeds the European guideline values. In comparison, the normalized MPI shows only very few values above 0.5 at Thionville (n = 3) and Koblenz (n = 6). At the rural background site Hunsrück-Leisel, the MPI never exceeds an index value of 0.5, reaching only values below 0.4. This leads to the result that on days where the non-aggregating indices exhibit values above 0.5 they are only caused by one pollutant, while the other pollutant concentrations are much lower. As seen in Figure 6, the pollutant causing the most normalized index values above 0.5 is PM10. In clusters 1 and 3 only a few index values above 0.5 are caused by O3 and even fewer by NO2. Most of the exceedances only occur at the non-aggregating indices, indicating that on these days the concentration of other pollutants is well below the European guideline value. At the rural background site Hunsrück-Leisel, most index values above 0.5 are caused by O3. Here, the aggregating MPI does not reach values above 0.5, indicating that the overall pollutant concentration at that site is lower than at the other two sites. Air quality assessment for the Greater Region Apart from Buchholz et al. (2010), long-term and transnational studies on air quality based on different air quality indices have, up to our knowledge, not been established for the region under investigation. Our assessment of the air quality is based on the three air quality indices mentioned above. According to the DAQx, the overall air quality can be characterized as “satisfying” (index class 3). The long-term average DAQx value during the periods 2001–2010 at all stations is 3.04 (± 0.17) that corresponds well with the average DAQx values, between 3.02 and 3.19, published by Mayer et al. (2004) for southwest Germany in 1998. With an average DAQx of 3.02 (± 0.14) for cluster one (mainly urban background) and 3.22 (± 0.25) for cluster three (mainly traffic), the overall air quality at these clusters is inferior to cluster two (mainly rural background), where the mean DAQx is 2.95 with a standard deviation of 0.11. Similar to the DAQx, the CAQI rates the average air pollution in the region for all clusters as “low” (43.41 ± 4.64). The average index values for the clusters show the same characteristics as the DAQx, indicating that cluster two is the least polluted cluster (40.35 ± 2.95), whereas cluster three is the most polluted one (48.35 ± 6.80). Cluster one exhibits an average CAQI of 43.40 with a standard deviation of 3.27. The average MPI for the “Greater Region” is –0.66 (± 0.05). Compared to the cities analyzed by Gurjar et al. (2008) this indicates much better air quality. None of the stations tested in this study reach similar high MPI values, where the lowest MPI was – 0.37, whereas the highest MPI at the stations in the “Greater Region” was –0.54 (± 0.02). Concerning the clusters, the MPI shows the same characteristics as the two non-aggregating indices. Cluster two is the one with the best air quality (MPI –0.70 ± 0.03), followed by cluster one (MPI –0.65 ± 0.04) and cluster three (MPI –0.60 ± 0.06). Dimitriou et al. (2013) analyzed the air quality within Europe for 2006 and 2007 on the basis of the PI introduced by Murena (2004) and the “API” by Cairncross et al. (2007). The PI is based on the same calculation method as the DAQx and the CAQI
AirBase ID
DERP021
FR01001
FR01019
FR01020
DERP014
DERP015
DERP013
DERP025
FR01008
BETR001
Cluster
1
1
1
1
2
2
2
2
2
3
DAQx CAQI MPI DAQx CAQI MPI DAQx CAQI MPI DAQx CAQI MPI DAQx
DAQx CAQI MPI DAQx CAQI MPI DAQx CAQI MPI DAQx CAQI MPI DAQx CAQI MPI
Index
0.32 0.25 0.14 0.32 0.26 0.15 0.35 0.31 0.19 0.36 0.31 0.15
0.33 0.28 0.17 0.33 0.29 0.15 0.33 0.28 0.16 0.33 0.26 0.15 0.32 0.25 0.14
2001
0.32 0.25 0.14 0.33 0.28 0.16 0.35 0.31 0.19 0.32 0.24 0.11
0.33 0.30 0.18 0.36 0.33 0.18 0.32 0.26 0.14 0.34 0.28 0.17 0.32 0.26 0.14
2002
0.35 0.30 0.16 0.36 0.33 0.18 0.40 0.36 0.22 0.36 0.29 0.14
0.38 0.36 0.21 0.35 0.32 0.19 0.34 0.28 0.15 0.35 0.31 0.18 0.35 0.30 0.16
2003
0.31 0.25 0.13 0.33 0.28 0.16 0.36 0.32 0.19 0.31 0.27 0.13
0.35 0.32 0.19 0.33 0.27 0.16 0.32 0.25 0.14 0.33 0.28 0.16 0.32 0.25 0.14
2004
0.32 0.26 0.14 0.33 0.28 0.15 0.35 0.31 0.19 0.35 0.29 0.17
0.34 0.30 0.19 0.34 0.28 0.17 0.33 0.26 0.15 0.34 0.29 0.19 0.32 0.26 0.14
2005
0.33 0.27 0.14 0.34 0.28 0.16 0.37 0.33 0.19 0.35 0.31 0.17 0.39
0.34 0.30 0.18 0.31 0.26 0.15 0.33 0.27 0.16 0.34 0.30 0.18 0.33 0.27 0.14
2006
0.31 0.24 0.13 0.31 0.26 0.14 0.34 0.29 0.17 0.34 0.31 0.18 0.40
0.33 0.28 0.17 0.31 0.25 0.14 0.34 0.28 0.18 0.34 0.29 0.18 0.32 0.25 0.13
2007
0.31 0.24 0.12 0.32 0.25 0.14 0.34 0.29 0.17 0.34 0.31 0.18 0.38
0.33 0.28 0.17 0.31 0.26 0.15 0.32 0.26 0.16 0.34 0.28 0.18 0.32 0.25 0.13
2008
0.37
0.32 0.25 0.13 0.32 0.26 0.14 0.35 0.29 0.18
0.34 0.28 0.17 0.32 0.28 0.16 0.33 0.28 0.17 0.35 0.32 0.19 0.32 0.25 0.13
2009
0.37
0.32 0.25 0.13 0.33 0.27 0.14 0.35 0.30 0.17
0.33 0.28 0.17 0.32 0.27 0.16 0.34 0.28 0.17 0.36 0.36 0.21 0.32 0.26 0.13
2010
↑ ↓
↓
↓
↓
↓
↑ ↑
↑
↓ ↓
↓
Sig.
(Continued)
−0.00091 −0.00216 −0.00146 −0.00089 −0.00187 −0.00212 −0.00109 −0.00342 −0.00260 −0.00204 0.00567 0.00676 −0.00624
−0.00098 −0.00365 −0.00172 −0.00334 −0.00674 −0.00190 0.00060 0.00118 0.00316 0.00150 0.00600 0.00398 −0.00021 −0.00018 −0.00142
Q
Table 1. Annual average of normalized air quality indices and results of the trend analysis (4 < n < 9: direct comparison of S with the theoretical distribution of S derived by Mann and Kendall (Salmi et al. 2002).
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AirBase ID
BETWOL1
Cluster
3
DAQx CAQI MPI
CAQI MPI
Index
2001
2002
2003
2004
2005
0.37 0.33 0.21
0.37 0.22
2006
0.37 0.34 0.21
0.38 0.22
2007
0.36 0.32 0.20
0.37 0.22
2008
0.35 0.33 0.20
0.35 0.21
2009
0.35 0.32 0.19
0.34 0.21
2010
↓
↓
↓
Sig.
−0.00568 −0.00454 −0.00471
−0.00918 −0.00471
Q
Notes: Arrows indicate significant increasing or decreasing trends for 2001–2010 (p < 0.1). Q describes the slope of the determined trend. The table only shows results for stations with at least one significant trend result.
(Continued).
Table 1.
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but uses a different calculation table. The API uses the same RR approach as the ARI but its calculation table was originally designed for South Africa. Three of the 14 stations analyzed are located within or close to our study area (Belgium and Germany). Due to the different index class thresholds a direct comparison with the PI is not possible, but the results for the API at these three stations (more than 50 % of the days indicate a RR increase between 6.1 and 10.6 %) are similar to our ARI results (mean index class 2–3, RR increase of 6.0–11.9 %) in these regions. The analysis shows that NO2 and O3 concentrations depend mostly on the station type, whereas PM10 showed similar characteristics within the whole area of investigation. Our trend analysis (Table 1) reveals a more detailed picture of the evolution of the overall air quality situation in the “Greater Region” during the last 10 years and enables, due to the normalization proposed before, a direct comparison of the trends and their slopes in all air quality indices. All significant trends could be determined as well for the non-normalized (not shown here) as for the normalized indices; so, the trend analysis of the normalized indices has the added benefit of allowing a comparison of the Qvalue, which describes the slope of the trend. All indices were tested for trends and show similar results. Eleven out of 29 stations show a significant linear trend for at least one air quality index. Eight of these stations exhibit a trend towards better air quality, whereas three stations show a trend towards worse air quality. Cluster 1 (mainly urban and suburban background) and cluster 2 (mainly rural background) exhibit trends in both directions, whereas cluster 3 (mainly industry and traffic) shows only trends towards better air quality. The three stations that exhibit aggravating air quality are located in France. At 10 out of the 11 stations, the regression for all three indices shows a slope with the same algebraic sign, even if no significant trend could be determined. It is shown that all indices are well suited to determine the trends in air quality. Statistically significant positive trends in the air quality indices (Table 1) always correlate with significant positive trends in PM10 levels (Table see supplement), whereas significantly negative trends of air quality indices often correlate with significant negative trends in either NO2 concentrations or PM10 levels. On the other hand, significant trends in one or more pollutants do not always lead to a significant trend in the air quality indices. This indicates that studies of single pollutants, instead of air quality indices, might not give a holistic picture of air quality and its effect on human health. Conclusions The air quality indices (DAQx, CAQI, and MPI), in their original form, are not directly comparable because they differ in index ranges, number of classes, and calculation methods. Nevertheless, they all tend to inform stakeholders and the general public about air quality and potential health impacts. The new normalization method proposed in our study offers an easy and objective way to compare these, or any other, air quality indices based on the European Directive 2008/50/EC guideline values. As the normalization does not change the structure of the air quality indices as well as their trends, it is well suited for a direct index comparison. It could be shown that the non-aggregating air quality indices DAQx and CAQI indicate, in general, inferior air quality than the aggregating index MPI. In our study, we compared the actual health impact relation of the indices by comparing them to the RR-based index ARI. Results of this correlation indicate that the MPI is most consistent with the RR approach, while both non-aggregating indices
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(DAQx and CAQI) have problems indicating the correct health outcome. This is also stated by van den Elshout et al. (2008), who concluded that the CAQI suffers from the problem of having no clear link with health effects. Therefore, the non-aggregating air quality indices are less suitable to investigate relationships between morbidity and mortality levels, and the current air quality. Nevertheless, they can serve as a good indicator of air quality for policy-makers and stakeholders, as they shall ensure the reduction of every pollutant and to inform the public about the real health impacts. End-users must be aware of these facts and must carefully choose an air quality index appropriate for the relevant purpose or research question; e.g. in order to either focus more on average pollution via aggregating indices or on peaks of at least one pollutant via non-aggregating air quality indices. Supplementary material The supplementary material for this paper is available online at http://dx.doi.10.1080/ 09603123.2014.893568. Acknowledgment We gratefully acknowledge the financial support of the National Research Fund in Luxembourg for the PhD scholarship of Hanna Lokys (4965163). Parts of the work have been done in the framework of the “Small Particles – environmental behaviour and toxicity of nanomaterials and particulate matter” (SMALL) project.
References Alastuey A, Querol X, Rodríguez S, Plana F, Lopez-Soler A, Ruiz C, Mantilla E. 2004. Monitoring of atmospheric particulate matter around sources of secondary inorganic aerosol. Atmos Environ. 38:4979–4992. Buchholz S, Junk J, Krein A, Heinemann G, Hoffmann L. 2010. Air pollution characteristics associated with mesoscale atmospheric patterns in northwest continental Europe. Atmos Environ. 44:5183–5190. Cairncross EK, John J, Zunckel M. 2007. A novel air pollution index based on the relative risk of daily mortality associated with short-term exposure to common air pollutants. Atmos Environ. 41:8442–8454. COMEAP. 2010. The mortality effects of long term exposure to particulate air pollution in the UK. London: Report produced by the Health Protection Agency for the Committee on the medical effects of air pollutants; p. 98. COMEAP. 2011. Review of the UK air quality index. London: Report produced by the health protection agency for the Committee on the medical effects of air pollutants; p. 118. ISBN 978-0-85951-699-0. Dimitriou K, Paschalidou AK, Kassomenos PA. 2013. Assessing air quality with regards to its effect on human health in the European Union through air quality indices. Ecol. Indic. 27:108–115. Directive 2008/50/EC of the European Parliament and of the council of 21 May 2008 on ambient air quality and cleaner air for Europe. Offic J EU. 152:1–44. EEA. 2013. Every breath we take – improving air quality in Europe. Copenhagen: EEA Signals, European Environment Agency. EEA AirBase [Internet]. Copenhagen: European Environment Agency; [cited 2013 Apr 18]. Available from: http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-qualitydatabase-6 Griem P, Kalberlah F. 2000. Ableitung eines tages- und wirkungsbezogenen Luftqualitätsindexes [Development of an impact related air quality index]. Karlsruhe: Landesanstalt für Umweltschutz Baden-Württemberg.
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Gurjar BR, Butler TM, Lawrence MG, Lelieveld J. 2008. Evaluation of emissions and air quality in megacities. Atmos Environ. 42:1593–1606. Hodges N, Obszynska J, Lad C, Swaton R. 2008. Air quality management guidebook. Leicester: Citeair. Kyrkilis G, Chaloulakou A, Kassomenos PA. 2007. Development of an aggregate air quality index for an urban mediterranean agglomeration: relation to potential health effects. Environ int. 33:670–676. Leeuw FD and Mol W. 2005. Air quality and air quality idices: a world apart? Bilthoven: European Topic Centre on air and climate change ETC/ACC Technical Paper 2005/5; p. 34. Mayer H, Kalberlah F. 2009. Two impact related air quality indices as tools to assess the daily and long-term air pollution. Int J Environ Pollut. 36:19–29. Mayer H, Makra L, Kalberlah F, Ahrens D, Reuter U. 2004. Air stress and air quality indices. Meteorologische Zeitschrift. 13:395–403. Murena F. 2004. Measuring air quality over large urban areas: development and application of an air pollution index at the urban area of Naples. Atmos Environ. 38:6195–6202. Plaia A, Ruggieri M. 2010. Air quality indices: a review. Rev Environ Sci Bio/Technol. 10:165–179. Pope CA, Burnett RT, Thurston GD. 2002. Pollution-related mortality and educational level – reply. JAMA. 288:830–830. Rohr AC, Wyzga RE. 2012. Attributing health effects to individual particulate matter constituents. Atmos Environ. 62:130–152. Salmi T, Määttä A, Anttila P, Ruoho-Airola T, Amnell T. 2002. Detecting trends of annual values of atmospheric pollutants by the Mann–Kendall test and Sen’s slope estimates – the excel template application MAKESENS. Helsinki: Publications on Air Quality, Finnish Meteorological Institute No. 31. p. 35. Sicard P, Lesne O, Alexandre N, Mangin A, Collomp R. 2011. Air quality trends and potential health effects – development of an aggregate risk index. Atmos Environ. 45:1145–1153. Sicard P, Mangin A, Hebel P, Malléa P. 2010. Detection and estimation trends linked to air quality and mortality on French Riviera over the 1990–2005 period. Sci Tot Environ. 408:1943–1950. Sicard P, Talbot C, Lesne O, Mangin A, Alexandre N, Collomp R. 2012. The aggregate risk index: an intuitive tool providing the health risks of air pollution to health care community and public. Atmos Environ. 46:11–16. Sistrom CL, Garvan CW. 2004. Proportions, odds, and risk. Radiology. 230:12–19. van den Elshout S, Léger K, Nussio F. 2008. Comparing urban air quality in Europe in real time. Environ Int. 34:720–726. Van Dingenen R, Raes F, Putaud J-P, Baltensperger U, Charron A, Facchini MC, Decesari S, Fuzzi S, Gehrig R, Hansson H-C, et al. 2004. A European aerosol phenomenology – 1: physical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe. Atmos Environ. 38:2561–2577. Ward JH, Jr. 1963. Hierarchical grouping to optimize an objective function. J Am Stat Assoc. 58:236–244. WHO. 2005. WHO air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Geneva: WHO/SDE/PHE/OEH/06.02. Wong TW, Tam WWS, Yu ITS, Lau AKH, Pang SW, Wong AHS. 2012. Developing a risk-based air quality health index. Atmos Environ. 76:52–58. ZIMEN Network of Rhineland Palatine [Internet]. Mainz: Landesamt für Umwelt, Wasserwirtschaft und Gewerbeaufsicht; [cited 2013 Apr 18]. Available from: http://www. luft-rlp.de/aktuell/messwerte/
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Making air quality indices comparable – assessment of 10 years of air pollutant levels in western Europe ab
a
a
Hanna Leona Lokys , Jürgen Junk & Andreas Krein a
Department Environment and Agro-biotechnologies (EVA), Centre de Recherche Public – Gabriel Lippmann, Belvaux, Luxembourg b
Climatology Working Group, University of Münster, Münster, Germany Published online: 27 Mar 2014.
To cite this article: Hanna Leona Lokys, Jürgen Junk & Andreas Krein (2014): Making air quality indices comparable – assessment of 10 years of air pollutant levels in western Europe, International Journal of Environmental Health Research, DOI: 10.1080/09603123.2014.893568 To link to this article: http://dx.doi.org/10.1080/09603123.2014.893568
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International Journal of Environmental Health Research, 2014 http://dx.doi.org/10.1080/09603123.2014.893568
Making air quality indices comparable – assessment of 10 years of air pollutant levels in western Europe Hanna Leona Lokysa,b*, Jürgen Junka and Andreas Kreina Department Environment and Agro-biotechnologies (EVA), Centre de Recherche Public – Gabriel Lippmann, Belvaux, Luxembourg; bClimatology Working Group, University of Münster, Münster, Germany
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(Received 10 October 2013; final version received 20 December 2013) To address the incomparability of the large number of existing air quality indices, we propose a new normalization method that is suited to directly compare air quality indices based on the common European World Health Organization (WHO) air quality guidelines for NO2, O3, and PM10. Using this method, we compared three air quality indices based on the European guidelines, related them to another air quality index based on the relative risk concept, and used them to assess the air quality and its trends in northwest central Europe. The average air quality in the area of investigation is below the recommended European guidelines. The majority of index values exceeding this threshold are caused by PM10, which is also, in most cases, responsible for the degrading trends in air quality. Eleven out of 29 stations tested showed significant trends, of which eight indicated trends towards better air quality. Keywords: air quality index; health impact; normalization method; relative risk; western Europe
Introduction Exposure to ambient air pollution is an environmental problem (Pope et al. 2002; Alastuey et al. 2004; Mayer & Kalberlah 2009; COMEAP 2010), and populations worldwide are exposed to pollution levels that affect their health and often exceed the recommended European Environment Agency (EEA) air quality guideline limit values (EEA 2013). According to the World Health Organization (WHO) air quality guidelines for Europe (WHO 2005), clean air is considered as a basic requirement for human health and well-being. This is supported by the large number of recent studies (e.g. Rohr & Wyzga 2012; Wong et al. 2012; Dimitriou et al. 2013). To assess the overall air quality, different indices have been developed (Plaia & Ruggieri 2010). They address the problem of air pollution with many purposes; e.g. estimating the health impact of air quality, informing the public about current pollution levels, long-term monitoring of air quality, or to evaluate urban planning management strategies (Hodges et al. 2008). Most of the air quality indices differ in the calculation method (e.g. number of pollutants determining the final index value) and also in the number, range, area of origin, and – if they are health impact-related – the corresponding health impact associated with different index classes. A direct comparison of the different indices is not possible. A solution for this problem was suggested by van den Elshout et al. *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
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(2008), with the creation of a “Common Air Quality Index” (CAQI) for the whole of Europe. This index is based on the guideline values published in the European Directive 2008/50/EC, which was defined to protect and enhance human well-being. All indices based on these guideline values can be considered as health impact-related air quality indices. Nevertheless, according to van den Elshout et al. (2008), indices of this kind often do not have a clear link with observed health effects. Other health impact-related indices do not refer to guideline values, but are based on the relative risk (RR) concept that describes the increase of morbidity and mortality that is caused by exposure to ambient air pollution; e.g. the “Air Pollution Index” (API) by Cairncross et al. (2007). The RR is commonly used in epidemiology and is defined as the ratio of the risk among exposed populations to the risk among unexposed populations to be affected by an adverse health effect (Sistrom & Garvan 2004). Despite these efforts, currently three out of five citizens do not feel they are sufficiently informed about air quality in their country (EEA 2013). This might result from the large number of existing indices that are mostly incomparable. Therefore, this study presents a new statistical method making direct comparisons between different air quality indices possible. We focus on the commonly used guideline-based air quality indices, e.g. the “Daily Air Quality Index” (DAQx) (Mayer et al. 2004), the CAQI (van den Elshout et al. 2008), or the “Multi Pollutant Index” (MPI) (Gurjar et al. 2008). The fact that they are based on the same guidelines and aim to inform about air quality and its health impact implies that the results, even if derived with different calculation and aggregation methods, should be comparable via normalization. The common reference values used for the normalization were taken from the current valid WHO recommendations for NO2, O3, and PM10 (WHO 2005). These three health impact-related air quality indices, based on the European guideline values (Directive 2008/50/EC), were also used to assess the air quality in the so-called “Greater Region” (Belgium, France, Germany, and Luxembourg). In addition, the “Aggregate Risk Index” (ARI) (Sicard et al. 2011) – an aggregated index based on the RR concept – is used to further analyze the impact relatedness of the guideline-based indices. Materials and methods Area of investigation and data-sets In this study, 29 official air quality monitoring stations in the “Greater Region” were analyzed covering the period from 2001 to 2010 (Figure 1). All emission data-sets were retrieved from the European AirBase Network, the air quality information system maintained by the EEA (EEA AirBase). Data-sets for each station included the concentrations of NO2, SO2, and O3 on an hourly basis and PM10 on an hourly or daily basis. Some stations additionally provide hourly measurements of CO and/or hourly or daily measurements of PM2.5. Additional meteorological parameters for Rhineland-Palatinate (western Germany) were directly retrieved from the ZIMEN network (Zentrales Immissionsmessnetz Rheinland-Pfalz). At some ZIMEN stations, the measurement of PM10 was replaced by PM2.5 during the period of investigation. To maintain a continuous set of PM10 values, PM2.5 can be converted to PM10 because of fairly constant ratios at individual sites (Van Dingenen et al. 2004). Ratios between PM2.5 and PM10 of 0.59 for summer (September–February) and 0.69 for winter (March–August) were derived from daily values (N = 3217). Only stations with comparable station characteristics were used to derive these ratios. These
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Figure 1. Overview of air quality stations used for the study in the “Greater Region.” Circles represent cluster 1, triangles cluster 2, and squares represent cluster 3. White symbols represent background stations, gray symbols represent industrial stations, and black symbols represent traffic stations.
ratios were also used to convert the PM2.5 measurements into PM10 values at stations where only PM2.5 measurements were available. According to the European Directive 2008/50/EC, the data were aggregated to daily values only for cases with at least 75 % of valid hourly data. To identify stations with similar pollutant characteristics, a hierarchical cluster analysis based on the Ward algorithm (Ward 1963) was used. The method minimizes the total variance within clusters. Therefore, each step merges the clusters with a minimum distance between each other based on the Euclidian distance. Instead of clustering the stations by daily data, which would only group stations that show the same characteristics on the same day, all integer percentiles of O3 maximum daily 8 h running mean and PM10 daily mean were used in the cluster analysis. Thus, the clusters include stations that exhibit a similar distribution of pollution over the analyzed period. The resulting clusters correspond well to the station type provided by the AirBase Network. Cluster one contains mostly urban and suburban background stations, while cluster 2 represents rural background stations. The third identified cluster mostly combines the traffic and industrial stations (Figure 1). For the following analyses, one station per cluster has been selected according to maximum data availability and maximum spatial distribution. Air quality indices There are many criteria that can be used to group air quality indices, e.g. health relatedness, purpose of the index, or exposure time to air pollutants (Plaia & Ruggieri 2010).
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Besides these differentiators, air quality indices can also be distinguished into two groups by the number of determining pollutants (Plaia & Ruggieri 2010). The indices of the first group are determined only by the pollutant with the highest subindex value (non-aggregating group), whereas the second group of indices takes the conjoint effect of different pollutants into account (aggregating group). In our study, three health impact-related air quality indices, based on the European guideline values (Directive 2008/50/EC), were used to compare the air quality in the “Greater Region.” The impact relation of these three indices has been derived from cohort studies and meta-analyses of epidemiological studies as mentioned in the WHO air quality guidelines for Europe (WHO 2005). The DAQx (Mayer et al. 2004) and the CAQI (van den Elshout et al. 2008) belong to the first, non-aggregating group. The third index is the MPI (Gurjar et al. 2008), which belongs to the second group of air quality indices and uses an aggregation method to determine the final index value. In addition, the aggregated index ARI (Sicard et al. 2012), based on the RR concept, is used to assess the impact relatedness of the three guideline-based indices DAQx, CAQI, and MPI. The DAQx was developed for southwest Germany and its calculation concept is used in several regions of Germany under different names, e.g. LQI or LuQx (Griem & Kalberlah 2000), to inform the public about the current situation of the daily air quality. Subindices for the pollutants NO2, SO2, CO, O3, and PM10 are calculated according to Equation (1), DAQxup DAQxlow (1) DAQxi ¼ ðCinst: Clow Þ þ DAQxlow Cup Clow where DAQxi is the subindex for each pollutant i, DAQxup and DAQxlow are the upper and lower index values of an index class, Cup and Clow are the upper and lower concentrations that belong to the index class, and Cinst. is the measured concentration of the pollutant. The index classes were derived from multiple epidemiological and toxicological investigations and range – in six classes – from 1 (very good) to 6 (very poor), where the values of DAQxlow for index class 5 correspond to the European standards (Directive 2008/50/EC) for each pollutant. A table with upper and lower class limits according to Mayer et al. (2004) can be taken from the supplement. DAQx values were calculated only for days where valid measurements of NO2, PM10, and O3 were available. According to the calculation method in combination with the table, all concentrations that lead to an index value superior to 5.5 are assigned 6. The CAQI was developed to compare air quality within Europe (van den Elshout et al. 2008). It can be calculated on a daily or hourly basis and distinguishes traffic and city background stations. For traffic stations, NO2 and PM10 are mandatory for calculation and CO is auxiliary, whereas for city background stations NO2, PM10, and O3 are mandatory and CO and SO2 are auxiliary pollutants. The CAQI subindices are calculated by linear interpolation between the index class thresholds in the same way as the DAQx subindices, but the class limit values are different (Table see supplement). The CAQI ranges from 0 (very low) to > 100 (very high) in 5 classes. The upper threshold of the medium class (CAQI = 75) corresponds to the European standards (Directive 2008/50/EC) for each pollutant, except for SO2 where the guideline value for maximum hourly values is 350 μg m−3 but the concentration leading to a CAQI subindex of 75 is 300 μg m−3 (van den Elshout et al. 2008).
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The aggregating MPI is calculated according to Equation (2), 1 X ACi GCi MPI ¼ n GCi
5
(2)
where n is the number of pollutants used to calculate the MPI, ACi is the concentration of the pollutant i, and GCi is the guideline value for the specific pollutant (Gurjar et al. 2008). To make the index comparable with the two other indices, we used the European guideline values from Directive 2008/50/EC for the calculations. This index was originally developed to compare the air quality amongst mega cities worldwide, but the flexibility of the calculations makes it easily adaptable for various situations, including our air quality assessment for the “Greater Region.” Contrary to the first two indices, the MPI has no fixed upper threshold value. The lowest theoretically possible index value is –1, which corresponds to ambient air that is free from all the pollutants covered by the index. If all pollutants meet the guideline values, the MPI would result in 0. MPI values above 0, indicating that one or more pollutants have exceeded the guideline values. To ensure that the indices remain comparable, only NO2, PM10, and O3 were used for calculation. According to various studies (van den Elshout et al. 2008; COMEAP 2011; Dimitriou et al. 2013), SO2 and CO no longer pose an important public health hazard, and hence were not included in our study. The ARI was developed by Sicard et al. (2011, 2012) and is based on the Cairncross’ concept (Cairncross et al. 2007) adapted for the European region by adjusting the individual class limits for each pollutant. As an aggregated index, the ARI is calculated according to Equation (3), X ARI ¼ ai Ci (3) i
where Ci is the measured concentration of pollutant i and ai is a conversion factor calculated based on the corresponding RR. The conversion factors vary for different health endpoints. Each ARI class represents an increase in the RR by 3 % (Sicard et al. 2012). The correlation between the ARI and the other three indices will be used to assess the health impact relation of the three guideline-based indices. Normalization approach A direct comparison of DAQx, MPI, and CAQI is not possible because of their different scales, number of classes, and threshold values. Therefore, a standardization which normalizes all indices of the range from zero to one would lead to wrong results. This is caused by the fact that the indices have two values in common. First, no pollution leads all indices to their lowest index value, whereas a pollution level that meets the European guidelines will lead to index values of 5 for the DAQx, 75 for CAQI, and 0 for MPI. Second, the index value corresponding to the guideline value is, for every index, located at a different location. For the DAQx it is approximately at 83 % of the total index range, whereas for the CAQI the corresponding value is located at 75 %. Applying onestage normalization, the normalized index value corresponding to the guideline value would, for every index, result in a different value. Therefore, two-stage normalization was chosen to make the indices directly comparable. Stage one normalized the range from zero till the index value correspondent to the guideline value. On stage two, the index values from the guideline value up to the maximum index value were also normalized (Figure 2). As the MPI has no fixed upper threshold for the individual pollutants, and thus does not have a defined maximum index value, the measured
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Figure 2. Two-step normalization method to enable direct comparison of the guidelines-based air quality indices.
absolute maximum plus 10 % was used as the maximum index value for the normalization. The values of both ranges were divided by 2 and 0.5 was added to the values of stage two to spread the normalized index in the range from zero to one. The value of 0.5 of each normalized index now represents the index corresponding to the European guideline values (Directive 2008/50/EC). Trend analysis In order to assess the history of the air quality in the “Greater Region” during the period from 2001 to 2010, trend analyses were carried out for all indices as well as for the pollutants NO2, O3, and PM10. Therefore, the MAKESENS tool from the Finnish Meteorological Institute was used (Salmi et al. 2002). It provides a template for the determination of trends in annual time series of atmospheric concentrations. The tool is based on the Mann–Kendall test, which is a non-parametric statistical test to detect monotonic trends within a time series with no seasonal or other cycle (Sicard et al. 2010). To estimate the slope of the detected trend, the Sen’s method is used. The tests were chosen because both methods are non-sensitive for missing values and the data does not need to have a particular distribution. In addition, the Sen’s method is robust against single data errors or outliers (Salmi et al. 2002). Results and discussion Comparison of the air quality indices DAQx, CAQI, and MPI To analyze the different characteristics of the three guideline-based air quality indices, one station of each cluster was analyzed in detail. Differences in the index characteristics between the three clusters are evident at all three indices (Figure 3). The rural background site Hunsrück-Leisel (cluster 2) shows the most distinct annual cycle, while the annual cycle is less pronounced at the other two sites. Amongst the indices, the MPI exhibits the least pronounced annual cycle. The determining pollutant at Hunsrück-Leisel is O3 for all three indices over the whole measurement period, while the other two sites show differences amongst the
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Figure 3. (Color online) Absolute DAQx, CAQI, and MPI values based on the European guideline values (2008/50/EC) for Thionville (cluster 1), Hunsrück-Leisel (cluster 2), and Koblenz (cluster 3) from 2001 to 2010. Colors show the dominating pollutant. Bar plots show the number of days dominated by each pollutant per year.
indices and time. This corresponds well with the results of Mayer et al. (2004), who showed that sites with low DAQx index values are mainly determined by O3. As the sites become more influenced by traffic or industrial emissions, the determining pollutants shift towards PM10 and NO2. Throughout all indices, it is obvious that the influence of PM10 on the local air quality in Thionville is increasing. According to the comparison of the air quality indices at the three exemplarily analyzed cluster stations, the DAQx is more NO2-dominated (57 % of the valid measurements are determined by NO2 in Koblenz), whilst the CAQI stresses more the influence of PM10 (57 % of the valid measurements are determined by PM10 in Koblenz). The MPI as an aggregating index is always influenced by all pollutants, so the determining pollutant in this index describes the pollutant that contributes most to the resulting index value. Due to the calculation method of the MPI, the dominant pollutant is always the one with the highest percentage of the guideline value. The differences in the dominant pollutants at the DAQx and CAQI result from the different calculation tables of the indices. Nevertheless, the two non-aggregating air quality indices show the strongest linear correlation between each other (Figure 4), indicated by R2 of 0.885. The correlation between these indices and the aggregating MPI is slightly lower, with R2 at 0.760 (DAQx) and 0.778 (CAQI), respectively. The spread between the DAQx and CAQI decreases from clean air to polluted air, whilst it grows along this gradient for the correlation between the non-aggregating indices and the MPI. This can be explained by the different weighting of the pollutants by the calculation table. The spread between the DAQx, respectively the CAQI, and the MPI increases with increasing air pollution. This is a consequence of the different calculation methods of the air quality indices. Low MPI values can occur only if all
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Figure 4. Scatterplots and linear regression of the air quality indices of all sites for the period from 2001 to 2010.
pollutants show low levels, so in these cases the difference between an index that is only determined by one pollutant and an aggregating one is not large. However, high MPI values can be reached by different combinations of pollutants. One of these combinations is, for example, one strongly dominating pollutant accompanied by the other pollutants being at low concentrations. This results in a good correlation between the aggregating and non-aggregating indices. On the other hand, high MPI values can also be reached by a combination of pollutants at a level which leads to a similar, mid-level subindex. In this case the correlation between the MPI and the DAQx, respectively the CAQI, would be much lower, as the non-aggregating indices would result in a better air quality than the aggregating one. DAQx, MPI, and CAQI in relation to the RR concept As all the analyzed air quality indices are based on different calculation tables it is important to evaluate the resulting air quality index according to the informative value concerning public health. Therefore, the three guideline-based air quality indices were compared to the RR-based index ARI. Figure 5 shows that the non-aggregating indices do not correlate well with the ARI, especially in the higher index range. Correlation coefficients were calculated for the whole data-set, but single stations did not provide significantly better results. At high index values, the non-aggregating indices correspond
Figure 5. Scatterplots and regression of the air quality indices of all sites based on the European directive (Directive 2008/50/EC) and the RR based air quality index ARI for the period from 2001 until 2010.
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to a range of more than 4 ARI classes. As each index class represents an increased RR of 3 %, the variation in the health outcome at a certain index value is, therefore, more than 12 %. The MPI as an aggregating air quality index with an R2 of 0.806 performs better in this context. Therefore, we conclude that an aggregating air quality index is more suitable to get information about the RR increase caused by the ambient air pollutants. Further studies that analyze the direct correlations between the air quality indices (normalized and not normalized) and adverse health effects would help to get a better comprehension on this particular topic. Direct index comparison with a normalization approach So far, studies on air quality indices either compared them in a descriptive way (Leeuw & Mol 2005; Dimitriou et al. 2013) or created new air quality indices (Kyrkilis et al. 2007; Sicard et al. 2011; Wong et al. 2012) that were adjusted to regional pollution characteristics or regulations. To simplify a direct comparison of air quality within Europe on the basis of air quality indices, we propose a new normalization method. To enable a more objective comparison of the air quality indices, they were normalized to the same scale, making a direct comparison of the selected stations from the three clusters possible (Figure 6). Comparing Figures 3 and 6, it is obvious that after the normalization the structure (shape, annual cycles, peak, and variations) of the time series remains the same and the method is suited to compare the indices. According to the structure of the MPI, the linear correlation coefficient for the normalized and non-normalized index is 1, but the normalized and non-normalized DAQx and CAQI exhibit an R2 > 0.9.
Figure 6. (Color online) Daily values of normalized air quality indices at Thionville (cluster 1), Hunsrück-Leisel (cluster 2), and Koblenz (cluster 3) for the period from 2001 to 2010. Colors show the dominating pollutant. Index values below European guideline equivalents are marked in gray.
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At all stations, the DAQx expresses the highest median value (0.31–0.34 normalized), resulting in the worst air quality rating. The median of the normalized CAQI is slightly lower at all stations (0.24–0.29), whilst the normalized MPI exhibits the lowest median at all stations (0.13–0.18). The normalized DAQx and CAQI show several days (between 51 and 171 days at the three stations that were analyzed in detail), where the indices exceed 0.5, highlighting that at least one pollutant exceeds the European guideline values. In comparison, the normalized MPI shows only very few values above 0.5 at Thionville (n = 3) and Koblenz (n = 6). At the rural background site Hunsrück-Leisel, the MPI never exceeds an index value of 0.5, reaching only values below 0.4. This leads to the result that on days where the non-aggregating indices exhibit values above 0.5 they are only caused by one pollutant, while the other pollutant concentrations are much lower. As seen in Figure 6, the pollutant causing the most normalized index values above 0.5 is PM10. In clusters 1 and 3 only a few index values above 0.5 are caused by O3 and even fewer by NO2. Most of the exceedances only occur at the non-aggregating indices, indicating that on these days the concentration of other pollutants is well below the European guideline value. At the rural background site Hunsrück-Leisel, most index values above 0.5 are caused by O3. Here, the aggregating MPI does not reach values above 0.5, indicating that the overall pollutant concentration at that site is lower than at the other two sites. Air quality assessment for the Greater Region Apart from Buchholz et al. (2010), long-term and transnational studies on air quality based on different air quality indices have, up to our knowledge, not been established for the region under investigation. Our assessment of the air quality is based on the three air quality indices mentioned above. According to the DAQx, the overall air quality can be characterized as “satisfying” (index class 3). The long-term average DAQx value during the periods 2001–2010 at all stations is 3.04 (± 0.17) that corresponds well with the average DAQx values, between 3.02 and 3.19, published by Mayer et al. (2004) for southwest Germany in 1998. With an average DAQx of 3.02 (± 0.14) for cluster one (mainly urban background) and 3.22 (± 0.25) for cluster three (mainly traffic), the overall air quality at these clusters is inferior to cluster two (mainly rural background), where the mean DAQx is 2.95 with a standard deviation of 0.11. Similar to the DAQx, the CAQI rates the average air pollution in the region for all clusters as “low” (43.41 ± 4.64). The average index values for the clusters show the same characteristics as the DAQx, indicating that cluster two is the least polluted cluster (40.35 ± 2.95), whereas cluster three is the most polluted one (48.35 ± 6.80). Cluster one exhibits an average CAQI of 43.40 with a standard deviation of 3.27. The average MPI for the “Greater Region” is –0.66 (± 0.05). Compared to the cities analyzed by Gurjar et al. (2008) this indicates much better air quality. None of the stations tested in this study reach similar high MPI values, where the lowest MPI was – 0.37, whereas the highest MPI at the stations in the “Greater Region” was –0.54 (± 0.02). Concerning the clusters, the MPI shows the same characteristics as the two non-aggregating indices. Cluster two is the one with the best air quality (MPI –0.70 ± 0.03), followed by cluster one (MPI –0.65 ± 0.04) and cluster three (MPI –0.60 ± 0.06). Dimitriou et al. (2013) analyzed the air quality within Europe for 2006 and 2007 on the basis of the PI introduced by Murena (2004) and the “API” by Cairncross et al. (2007). The PI is based on the same calculation method as the DAQx and the CAQI
AirBase ID
DERP021
FR01001
FR01019
FR01020
DERP014
DERP015
DERP013
DERP025
FR01008
BETR001
Cluster
1
1
1
1
2
2
2
2
2
3
DAQx CAQI MPI DAQx CAQI MPI DAQx CAQI MPI DAQx CAQI MPI DAQx
DAQx CAQI MPI DAQx CAQI MPI DAQx CAQI MPI DAQx CAQI MPI DAQx CAQI MPI
Index
0.32 0.25 0.14 0.32 0.26 0.15 0.35 0.31 0.19 0.36 0.31 0.15
0.33 0.28 0.17 0.33 0.29 0.15 0.33 0.28 0.16 0.33 0.26 0.15 0.32 0.25 0.14
2001
0.32 0.25 0.14 0.33 0.28 0.16 0.35 0.31 0.19 0.32 0.24 0.11
0.33 0.30 0.18 0.36 0.33 0.18 0.32 0.26 0.14 0.34 0.28 0.17 0.32 0.26 0.14
2002
0.35 0.30 0.16 0.36 0.33 0.18 0.40 0.36 0.22 0.36 0.29 0.14
0.38 0.36 0.21 0.35 0.32 0.19 0.34 0.28 0.15 0.35 0.31 0.18 0.35 0.30 0.16
2003
0.31 0.25 0.13 0.33 0.28 0.16 0.36 0.32 0.19 0.31 0.27 0.13
0.35 0.32 0.19 0.33 0.27 0.16 0.32 0.25 0.14 0.33 0.28 0.16 0.32 0.25 0.14
2004
0.32 0.26 0.14 0.33 0.28 0.15 0.35 0.31 0.19 0.35 0.29 0.17
0.34 0.30 0.19 0.34 0.28 0.17 0.33 0.26 0.15 0.34 0.29 0.19 0.32 0.26 0.14
2005
0.33 0.27 0.14 0.34 0.28 0.16 0.37 0.33 0.19 0.35 0.31 0.17 0.39
0.34 0.30 0.18 0.31 0.26 0.15 0.33 0.27 0.16 0.34 0.30 0.18 0.33 0.27 0.14
2006
0.31 0.24 0.13 0.31 0.26 0.14 0.34 0.29 0.17 0.34 0.31 0.18 0.40
0.33 0.28 0.17 0.31 0.25 0.14 0.34 0.28 0.18 0.34 0.29 0.18 0.32 0.25 0.13
2007
0.31 0.24 0.12 0.32 0.25 0.14 0.34 0.29 0.17 0.34 0.31 0.18 0.38
0.33 0.28 0.17 0.31 0.26 0.15 0.32 0.26 0.16 0.34 0.28 0.18 0.32 0.25 0.13
2008
0.37
0.32 0.25 0.13 0.32 0.26 0.14 0.35 0.29 0.18
0.34 0.28 0.17 0.32 0.28 0.16 0.33 0.28 0.17 0.35 0.32 0.19 0.32 0.25 0.13
2009
0.37
0.32 0.25 0.13 0.33 0.27 0.14 0.35 0.30 0.17
0.33 0.28 0.17 0.32 0.27 0.16 0.34 0.28 0.17 0.36 0.36 0.21 0.32 0.26 0.13
2010
↑ ↓
↓
↓
↓
↓
↑ ↑
↑
↓ ↓
↓
Sig.
(Continued)
−0.00091 −0.00216 −0.00146 −0.00089 −0.00187 −0.00212 −0.00109 −0.00342 −0.00260 −0.00204 0.00567 0.00676 −0.00624
−0.00098 −0.00365 −0.00172 −0.00334 −0.00674 −0.00190 0.00060 0.00118 0.00316 0.00150 0.00600 0.00398 −0.00021 −0.00018 −0.00142
Q
Table 1. Annual average of normalized air quality indices and results of the trend analysis (4 < n < 9: direct comparison of S with the theoretical distribution of S derived by Mann and Kendall (Salmi et al. 2002).
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AirBase ID
BETWOL1
Cluster
3
DAQx CAQI MPI
CAQI MPI
Index
2001
2002
2003
2004
2005
0.37 0.33 0.21
0.37 0.22
2006
0.37 0.34 0.21
0.38 0.22
2007
0.36 0.32 0.20
0.37 0.22
2008
0.35 0.33 0.20
0.35 0.21
2009
0.35 0.32 0.19
0.34 0.21
2010
↓
↓
↓
Sig.
−0.00568 −0.00454 −0.00471
−0.00918 −0.00471
Q
Notes: Arrows indicate significant increasing or decreasing trends for 2001–2010 (p < 0.1). Q describes the slope of the determined trend. The table only shows results for stations with at least one significant trend result.
(Continued).
Table 1.
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12 H.L. Lokys et al.
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but uses a different calculation table. The API uses the same RR approach as the ARI but its calculation table was originally designed for South Africa. Three of the 14 stations analyzed are located within or close to our study area (Belgium and Germany). Due to the different index class thresholds a direct comparison with the PI is not possible, but the results for the API at these three stations (more than 50 % of the days indicate a RR increase between 6.1 and 10.6 %) are similar to our ARI results (mean index class 2–3, RR increase of 6.0–11.9 %) in these regions. The analysis shows that NO2 and O3 concentrations depend mostly on the station type, whereas PM10 showed similar characteristics within the whole area of investigation. Our trend analysis (Table 1) reveals a more detailed picture of the evolution of the overall air quality situation in the “Greater Region” during the last 10 years and enables, due to the normalization proposed before, a direct comparison of the trends and their slopes in all air quality indices. All significant trends could be determined as well for the non-normalized (not shown here) as for the normalized indices; so, the trend analysis of the normalized indices has the added benefit of allowing a comparison of the Qvalue, which describes the slope of the trend. All indices were tested for trends and show similar results. Eleven out of 29 stations show a significant linear trend for at least one air quality index. Eight of these stations exhibit a trend towards better air quality, whereas three stations show a trend towards worse air quality. Cluster 1 (mainly urban and suburban background) and cluster 2 (mainly rural background) exhibit trends in both directions, whereas cluster 3 (mainly industry and traffic) shows only trends towards better air quality. The three stations that exhibit aggravating air quality are located in France. At 10 out of the 11 stations, the regression for all three indices shows a slope with the same algebraic sign, even if no significant trend could be determined. It is shown that all indices are well suited to determine the trends in air quality. Statistically significant positive trends in the air quality indices (Table 1) always correlate with significant positive trends in PM10 levels (Table see supplement), whereas significantly negative trends of air quality indices often correlate with significant negative trends in either NO2 concentrations or PM10 levels. On the other hand, significant trends in one or more pollutants do not always lead to a significant trend in the air quality indices. This indicates that studies of single pollutants, instead of air quality indices, might not give a holistic picture of air quality and its effect on human health. Conclusions The air quality indices (DAQx, CAQI, and MPI), in their original form, are not directly comparable because they differ in index ranges, number of classes, and calculation methods. Nevertheless, they all tend to inform stakeholders and the general public about air quality and potential health impacts. The new normalization method proposed in our study offers an easy and objective way to compare these, or any other, air quality indices based on the European Directive 2008/50/EC guideline values. As the normalization does not change the structure of the air quality indices as well as their trends, it is well suited for a direct index comparison. It could be shown that the non-aggregating air quality indices DAQx and CAQI indicate, in general, inferior air quality than the aggregating index MPI. In our study, we compared the actual health impact relation of the indices by comparing them to the RR-based index ARI. Results of this correlation indicate that the MPI is most consistent with the RR approach, while both non-aggregating indices
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(DAQx and CAQI) have problems indicating the correct health outcome. This is also stated by van den Elshout et al. (2008), who concluded that the CAQI suffers from the problem of having no clear link with health effects. Therefore, the non-aggregating air quality indices are less suitable to investigate relationships between morbidity and mortality levels, and the current air quality. Nevertheless, they can serve as a good indicator of air quality for policy-makers and stakeholders, as they shall ensure the reduction of every pollutant and to inform the public about the real health impacts. End-users must be aware of these facts and must carefully choose an air quality index appropriate for the relevant purpose or research question; e.g. in order to either focus more on average pollution via aggregating indices or on peaks of at least one pollutant via non-aggregating air quality indices. Supplementary material The supplementary material for this paper is available online at http://dx.doi.10.1080/ 09603123.2014.893568. Acknowledgment We gratefully acknowledge the financial support of the National Research Fund in Luxembourg for the PhD scholarship of Hanna Lokys (4965163). Parts of the work have been done in the framework of the “Small Particles – environmental behaviour and toxicity of nanomaterials and particulate matter” (SMALL) project.
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