Long-term characterization of urban PM10 in Hungary

1 Hungarian Meteorological Service, Kitaibel 1, H-1024, Budapest, ferenczi.z@met.hu, 5 lakatos.m@met.hu, homolya.e@met.hu, bozo.l@met.hu 6 2 ELKH-PE Air Chemistry Research Group, Egyetem 10. H-8200, Veszprém, 7 kornelia@almos.uni-pannon.hu, amolnar@almos.uni-pannon.hu 8 3 University of Pannonia, Research Centre for Biochemical, Environmental and Chemical 9 Engineering, Egyetem 10. H-8200, Hungary, gelencs@almos.uni-pannon.hu 10 11 * Corresponding author: Tel: +36-1-3464812 12 E-mail address: ferenczi.z@met.hu 13 14


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important role in determining PM concentrations. For this reason, the analysis of local and regional 88 meteorology is important to better understand the processes that are responsible for the spatial and reported that there were negative correlations between PM10 and temperature and solar radiation, 94 while relative humidity and atmospheric pressure were positively correlated with PM10. 95 Vardoulakis and Kassomenos (2008) found a significant correlation between PM10 and solar 96 radiation during cold seasons, and these correlations became weaker during warm seasons. Most 97 of these papers finally concluded that in addition to changes in anthropogenic emissions, changes 98 in meteorology can be responsible for long-term changes in PM10 concentrations. 99 In Hungary, three important factors have essential effects on PM10 concentrations: local 100 anthropogenic emissions, long-range transport (sources outside of the country) and meteorological 101 conditions. It has been estimated that the background annual average PM10 mass concentration for 102 continental Europe is 7.0 ± 4.1 μg m -3 (Van Dingenen et al., 2004), and the natural contribution to

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and fall, depending on the local weather conditions, inhabitants use combustion equipment mostly 111 at night. In winter, the effect of residential heating must be taken into account throughout the day. 112 In our previous work (Ferenczi and Bozó, 2017), the effect of transboundary sources on the 113 air quality of the greater Budapest area was evaluated. We found that the contribution of long-range 114 (transnational) atmospheric transport to the annual cumulative PM10 concentrations in Hungary 115 was remarkably high (higher than 50%). However, during the winter, late fall and early spring, 116 episodes of very poor air quality (exceedances of PM10 limit concentrations) were always caused 117 by local (regional) emission sources in combination with special and highly unfavourable mixing of the air), which restore the importance of long-range transport in determining PM10 126 concentrations. 127 The main objectives of the present work are to determine the spatial and temporal variability 128 of the PM10 concentration in Hungarian cities, to separate the effects of emission sources and 129 meteorology on PM10 concentrations, and finally to investigate the relationships between the PM10

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Three monitoring stations with significantly different characteristics (population, type of 135 station) were selected for the detailed analysis of three cities, Budapest, Miskolc and Pécs. In these 136 cities, exceedances of PM10 limit concentrations have been reported most often (Fig. 1)  In winter, a significant difference in the PM10 concentration was found when working and 226 weekend days were compared. In Miskolc, the impact of residential heating is comparable to that 227 of traffic emissions (EMEP/CEIP), which is the reason why the effect of morning rush hour is less 228 important. Independent of the seasons, at all stations, an evening peak is typically found probably

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due to the unfavourable dispersion situation in this part of the day. The evening peak in winter is 230 much higher, presumably as a result of indoor heating. The shift of the morning peak at Miskolc 231 by about one hour from summer to winter can be explained by the shift to daylight savings time in 232 Hungary. 233 The observations show that in every season, the highest PM10 concentrations can be expected where k is the number of values included on each side of the targeted value, the window length is 268 m = 2k + 1, and A is the input time series. The separation of the original air quality time series into

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the three components (long-term, seasonal, and short-term) for the daily average PM10 is presented 270 in Fig. 3. The short-term component is attributable to weather and short-term fluctuations in 271 precursor emissions (random effect), the seasonal component is a result of changes, for example, 272 in the boundary layer height (effects that have typical seasonal variability), and the long-term trend 273 results from changes in overall emissions, atmospheric transport, and climate. 274 The effect of the local weather conditions is reflected in the short-term component. These

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of pollutant release. This study also demonstrates the need for more local measures to improve the 294 air quality in these cities. 295 To understand the contribution of each temporal component to the original daily average PM10 296 concentration value, the variance in each generated time series was determined, and their 297 contributions to the total variance in the original data were calculated (Table 1) (Table 2). 305 Although the relationship between meteorological parameters and PM10 is rather complex, we 306 found some strong relationships between them. Positive (RH, p, and GR) and negative (T, WS, and 307 PBL) correlations were found between several meteorological parameters and PM10 values for the 308 whole year. The seasonality of the correlation between PM10 and the meteorological parameters 309 was analysed separately, and large differences were found between the seasons. See Table 2  we wanted to predict.

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Based on the results of the determination of the correlation coefficients, many meteorological 342 parameters were selected for the regression analysis. that this effect could be driven by the air temperature, but we could not determine its measure. This 355 was the reason why we did not take into account the effect of emission changes in this method. 356 As a result of the analysis of the correlation coefficients, it was possible to predict the daily 357 average PM10 concentration. In our case, the dependent variable that we wanted to predict was the 358 PM10 concentration, and the independent variables were the temperature (T), wind speed (WS), 359 and boundary layer height (PBL

365
The summarized results of the regression ( In Table 5

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indices based on a contingency table. These indices are the probability of detection (POD) and false 388 alarm ratio (FAR). Table 6 shows the results of the evaluation statistics. The yearly, monthly, weekly, and daily variations in the PM10 time series were determined 407 using the data from three large cities in Hungary where the air quality is problematic. In the case 408 of yearly averages, no limit value exceedances (40 μg m -3 ) were detected after 2011. However, 409 analysing the daily average, the most limit value exceedances could be expected in winter and fall. 410 There were large differences between the cities, and the air quality varied between moderate and

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poor in both Pécs and Miskolc. There were large variances year after year, which could be 412 controlled by the meteorological conditions. Essential differences were not found between the 413 characteristics of the weekly variations. In summer, the daily variation in PM10 was very similar in 414 Budapest and Pécs, but in Miskolc, we found a characteristic peak in the morning that could be 415 attributed to local traffic. In winter, the daily variation in PM10 from every station had the same 416 features. We assumed that in the case of Miskolc, the effect of residential combustion was higher 417 than the effect of traffic, so the peak originating from morning traffic could not be identified. We      forecasted, and number of forecasted exceedances but not observed, respectively.