UPOREDNA METODOLOGIJA KONTINUALNOG PRAĆENJA STANJA VAZDUHA U GRADU NIŠU Predrag M. Živković *, Mladen A. Tomić **, Gradimir S. Ilić *, Aleksandra D. Boričić ** *Univerzitet u Nišu, Mašinski fakultet, Aleksandra Medvedeva 14, Niš, Srbija **Visoka tehnička škola strukovnih studija, Aleksandra Medvedeva 20, Niš, Srbija Apstract: Uvećanje intenziteta saobraćaja, kao i ubrzana industrijalizacija širom sveta ima za posledicu da je kvalitet vazduha postao jedan od najbitnijih faktora svakodnevnog života. Praćenje kvaliteta vazduha je neophodan činilac za pravilno odlučivanje o kvalitetu vazduha i zagađenju. Osnova takvog istraživanja je merenje karakteristika vetra, obzirom da je vetar najuticajniji činilac u turbulentnoj difuziji polutanata u atmosferi. Najveći deo polutanata u vazduhu potiče od procesa sagorevanja, čiji izvori mogu biti međusobno jako udaljeni. Stoga je bitno uraditi kvantitativnu, kao i kvalitativnu analizu nivo zagađenja. U ovom radu je predstavljena specifična metodologija za akviziciju podataka o vetru, temperaturi i kvalitetu vazduha. Dato je poređenje izmerenih rezultata, kao i detaljan prikaz korišćenog akvizicionog softvera. Ključne reči: Kvalitet vazduha, Atmosferski uslovi, Akvizicioni softver, Podaci o vetru, CO 2.
COMPARATIVE METHODOLOGY FOR CONTINUOUS AIR QUALITY MONITORING IN THE CITY OF NIŠ Predrag M. Živković *, Mladen A. Tomić **, Gradimir S. Ilić *, Aleksandra D. Boričić ** *Univerzity of Niš, Mechanical Engineering Faculty, Aleksandra Medvedeva 14, Niš, Srbija **The School of Higher Technical Professional Education, Aleksandra Medvedeva 20, Niš, Srbija Abstract: Increase of traffic intensity as well as the rapid industry development across the world resulted in air quality becoming one of the most important influences of everyday life. Air quality monitoring is the necessary factor for proper decision making regarding air quality and pollution. Basics of such investigations are the measurement of wind characteristics, as the wind is the most influential factor in turbulent pollution diffusion into the atmosphere. The most of the air pollution originates from combustion processes, and the sources of pollution can be very distant. It is important to make quantitative, as well as qualitative analysis of the the polution levels. In this paper, specific methodology for continuous wind, temperature and air quality data acquisition is presented. Comparison of the measured results is given, as well as the detailed presentation of the characteristics of the acquisition software used. Keywords: Air Quality, Atmospheric Conditions, Acquisition Software, Wind Data, CO 2. 1. INTRODUCTION Since the Industrial Revolution, the mankind is dealing with increasing pollution problems. Main pollution sources are the side effects of manufacturing, mining, transportation, and power production. The air pollution is usually explained as the interaction of noxious aerosols, gases and particles emitted into the atmosphere with the surrounding environment. The impacts of the pollutant emissions on the environment are usually measured by their effects in the human standard of living, the number of people affected and the applied technology [1]. Since 1960, the world population has been doubled and due to improvements of standard of living and world economy. At the same time, the global number of pollutant sources like cars emissions have raised by a factor of 10 [2]. The increased air pollution level have led to important impacts on human health and well-being, as well as have caused material damages on economical/technical
structures of cultural heritage and large scale environmental phenomena as acidification, depletion of the ozone layer and an imminent global warming. The most direct impacts of air pollution are felt for those who live in cities. The United Nations report of 2006. mentions that already 48.7% of the world s population were living in urban areas in 2005. This development was expected to continue in the subsequent years, and some statistical estimations says that more than the half of the world population is living in urban areas since 2008, thus it marks the first time in history that the world have more urban residents than rural residents [3]. The present EU environmental legislation intents to control high pollutant concentrations and air quality conditions in urban environments. It is obvious that proper estimation of the pollutant emission level is of vital importance. As the refference component for the measurements, concentration of CO 2 was chosen. Reasons for such decision were the chemical inertiality and the relatively high concentration of CO 2 in the atmosphere, which allows it to be used as the tracer gas. Although CO 2 is denser than air, it can be assumed that the other components of the flue gasses will be distributed similarly. The fact is that for simulations of urban pollution distribution usually SF 6 was used, and its density is 6.12kg/m 3, which is considerably higher than 1.98kg/m 3 for CO 2 (both for atmospheric pressure and 0 0 C). As from 1. january 2006. SF 6 was banned as the tracer gas, some other solution had to be found. Under such conditions, it can be accepted that CO 2 as a main flue gas component can indicate the distribution of all the other components. 2. MEASURING METHOD In order to make as proper real-time air quality monitoring with least possible measured values, proper estimation of pollution sources was essential. As noted earlier, the main pollution sources are the traffic and the district heating plants, followed by the individual heating in households. 2.1. District heating plant The District Heating Plant "Krivi Vir" is located in the center of the City. It is surronded with high buildings, mostly over 7 floors high. About 50.000 people are directly and highly influenced by the plant. The plant power is 130MW. Situation was much improved when fuel was changed from heavy oil to natural gas in 2006. The diagram shown on the following Figure 1 represents the daily consumption of the natural gas in the District Heating Plant "Krivi Vir" for the heating season 2009/10.
250000 potroš nja gasa 200000 Nm3/dan 150000 100000 50000 0 1 Oct 2009 6 Oct 2009 11 Oct 2009 16 Oct 2009 21 Oct 2009 26 Oct 2009 31 Oct 2009 5 Nov 2009 10 Nov 2009 15 Nov 2009 20 Nov 2009 25 Nov 2009 30 Nov 2009 5 Dec 2009 10 Dec 2009 15 Dec 2009 20 Dec 2009 25 Dec 2009 30 Dec 2009 4 Jan 2010 9 Jan 2010 14 Jan 2010 19 Jan 2010 24 Jan 2010 29 Jan 2010 3 Feb 2010 8 Feb 2010 13 Feb 2010 18 Feb 2010 23 Feb 2010 28 Feb 2010 5 Mar 2010 10 Mar 2010 15 Mar 2010 20 Mar 2010 25 Mar 2010 30 Mar 2010 4 Apr 2010 9 Apr 2010 14 Apr 2010 19 Apr 2010 24 Apr 2010 29 Apr 2010 Figure 1. Natural gas consumption in the District heating plant Krivi Vir for the heating season 2009/10. Recent measurements shows that flue gasses on the exhaust collector contains 0-1ppm CO, 1-2ppm NO and about 50ppm NO 2 and NO x. This indicates that the main components are CO 2 and H 2 O, which justifies the choice of CO 2 as the tracer gas. The measured results will be used for the justifiction of the numerical experiment, which is intended to show the qualitative distribution of the pollutants, using CO 2 as the tracer gas. 2.2. Traffic intensity Jagodin Mala District Heating Plant Krivi Vir Measuring site 2 7. Juli Measuring site 1 Theater Marger City Hospital Boulevard Figure 2. Disposition of measuring sites.
Traffic counting was performed on six major crossroads in the City of Niš. The data were collected from 5AM until 1AM the next day for each location, at the end of 2009. For the location Jagodin Mala measurements were performed for 10 days, for each working day and weekend, as well as for 4 days in the summer 2009. [4]. As can be seen on the Figure 2, the measuring locations are carefully chosen and fairly represents the crossroads with highest traffic intensity in the City of Niš. This data were used to estimate the traffic intensity for each direction for all major streets in the City of Niš. It is assumed that traffic intensity for other locations and streets can be estimated on the basis of the data collected. Simultaneously with the traffic counting, CO 2 imission measurement was performed using the TESTO 454 gas analyzer with ambient CO 2 probe 0632 1240. Measurements were performed on distances 1, 3 or 5m from the street, on height of 1 m. 2.3. Wind data Wind measurements were performed on 3 locations in the city, in the period December 2008 to December 2010. On those locations, wind speed and direction, temperature and CO 2 concentration were measured on the height of 10m. Wind measurements were performed with second generation cup anemometer produced by the firm Vetrotipalnik Ljubljana, CO 2 concentration with probe TGS4161 produced by company FIGARO ENGINEERING INC., JAPAN, and temperature measurements by PT100 element with precision of 0.5 0 C. 2.4. Measuring equipment Sensors used for measurements were chosen by the specific temperature, humidity and concentration levels in the free atmosphere. The measuring period was from the end of 2008. until the end of 2010. Cup anemometer characteristics The anemometer used is is second generation cup anemometer, with direction sensor mounted on the same shaft with the speed sensor. During the measurement period, considering the City of Niš climate, there was no frosting of the sensor (during the winter period). Table 1. Cup anemometer technical data. Wind speed measurement Wind direction measurement Measuring range 0.5 50m/s Measuring range 0 360 0 Accuracy ±0.1m/s Accuracy ±0.1 0 Temperature range -40 80 0 C Temperature range -40 80 0 C Rel. humidity range 0 100%RH Rel. humidity range 0 100%RH Acquisition speed 1sample/sec Acquisition speed 1sample/sec The measuring range of the anemometer is 0.5 50m/s, with precission of 0.1m/s. Wind speed lower than 0.5m/s is measurable, but with lower accuracy. Wind direction is being measured with
precision of 22.5 0, which should be improved in the following period, by applying new sensor, mounted on another shaft. Sampling rate is 1sec, while the measuring results are being shown as average over the 5min. period. This technique was used to reduce the influences of wind turbulece on the measured results. CO 2 sensor characteristics The characteristics of the sensor used are shown in the Table 2. From this data one can notice that the sensor can operate normally in the entire measuring range, since the lowest measured value of CO 2 concentration was 392ppm, and the higher about 1000ppm. Table 2. CO 2 sensor technical data. CO2 concentration measurement Measuring range 350 10000ppmCO 2 Accuracy ±20% at 1000ppmCO 2 Temperature range -10 50 0 C Rel. humidity range 5 95%RH Acquisition speed 1sample/sec Temperature sensor characteristics The temperature sensor used is PT100 element, with precission of 0.5 0 C. This measurement was added in order to ensure the comparison of the measured data with standard ones. 2.5. Measuring results Traffic data From the data obtained by the IT Sector of MUP R. Serbia, there are 60880 vehicles registered in the City of Niš. According to the composition of this fleet of vehicles, using Copert [5] software, CO 2 emission estimation was performed. 700 600 500 400 300 200 y = 5E 07x 2 0.005x + 613.94 R 2 = 0.5127 CO 2 concentration [ppm] CO 2 emission estimation [g/km] 100 0 y = 6E 07x 2 0.0007x + 223.43 R 2 = 0.4098 0 5000 10000 [sec] 15000 Figure 3. Comparison of estimated CO 2 emission and measured CO 2 concentration on a choosen location.
Traffic counting was performed on six major crossroads in the City of Niš. The data were collected from 5AM until 1AM the next day for each location, at the end of 2009. For the location Jagodin Mala measurements were performed for 10 days, for each working day and weekend, as well as for 4 days in the summer 2009. [4]. The pollutant concentrations are intended to be estimated after the validation of the numerical experiment, having in mind the fact that the wind and atmospheric state have the crucial effect on the pollution distribution. In the Figure 3, comparison between estimated CO 2 emission and measured CO 2 concentration for one of the measured locations is presented. The results showed the existence of a relation between the measured CO 2 concentration and traffic frequency, i.e. CO 2 emission. In the Figure 4.a. measured traffic frequency for the chosen location and date is presented. Data have been averaged over 5 min intervals. Two extremes can be noticed, one in the morning, between 9 and 10 AM (morning rush-hour) and between 3 and 4 PM (afternoon rush-hour). Number of vehicles in the period from 1 to 5 AM is <50 per hour, which is negligible, comparing to ~3000 per hour during rush hours. Similar distributions were obtained for all measuring locations during work days. During weekend, distribution was normal statistical distribution. Vehicle N o 350 300 250 Jagodin Mala 29.10.09. cars trucks 200 150 100 50 0 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 a) b) Figure 4. (a) Measured frequency of vehicles on a measuring location and (b) distribution approximation for all measured locations used for Copert. Wind data Wind measurements were performed on 3 different locations near the street (to measure the influence of traffic), in the City Fortress (to indicate the influence of green and forested areas) and in the suburbia (to indicate the influence of single households on outdoor air quality).
Figure 5. Wind rose and Weibull distribution histogram on the measuring site 1 (suburban). The results from all measuring sites shows the dominant wind direction same as the Nišava river (sector 60 0 ), which flows through the choosen domain and the influence of the larger Morava river (sector 330 0 ), which is about 7km to the west from Niš. The results are very similar to the ones from the main meteorological station Niš. CO 2 data As there is large number of measurements on all measuring sites, the data comparison for the first half of April 2009. is presented. This measurements on both measuring locations shows the significant decrease of CO 2 concentration on the end of the heating season (marked with circle on both diagrams on Figure 6). This shows that the start predictions are correct, and that these measurements, after thorough analysis, can take us closer to understanding of the CO 2 distribution into the atmosphere. Figure 6. Data collected on the Measuring site 1 (suburban) and CO 2 concentration measurement on the Measuring site 3 (green area).
3. CONCLUSION In this paper, specific methodology for continuous atmospheric conditions monitoring by measuring of wind speed and direction, temperature and CO 2 concentration is presented. The proposed methodology shows large possibilities for quantitative estimation of the air quality. As concentration level of CO 2 is easily and precisely measured, the proposed methodology gives a relatively cheap and very precise system of quantitative air quality monitoring. The results obtained shows that it is possible to assess the influence of the District Heating Plant, traffic and households, which are the largest CO 2 sources in the City of Niš. As the wind is the most influential parameter for pollution diffusion processes, it is possible to combine continuous CO 2 measurements with the sampling techniques for different pollutants in order to obtain real time monitoring of the areas in which the pollution sources originates mostly from combustion processes (which is almost everywhere, except nearby the large chemical plants). Such methodology should improve our knowledge of the air quality in, primary, highly populated areas. Results of the estimated traffic induced CO 2 emission shows good agreement with the measured CO 2 concentrations near the street (Figure 3). One can notice that traffic frequency increase leads to an adequate CO 2 concentration level increase on the measuring location. The atmospheric conditions during this measurement were stable, with wind speed < 1m//s. The chosen monitoring sites covers all types of urban environment in the City of Niš, the suburbia (Monitoring Site 1), City centre (Monitoring Site 2) and green area (Monitoring site 3). It can be concluded that the methodology presented is acceptable for various monitoring schemes in urban, as well in rural and uninhabited areas. Hopefully, beter understanding of atmospheric phenomena, as well as the pollution dispersion, will lead to understanding of necesity to keep, and even improve the quality of the entire environment. ACKNOWLEDGEMENT This paper is a part of the research done within the project III 42008 supported by Ministry of Education, Science, and Technological Development of the Republic of Serbia. REFERENCES [1] Fenger J., Hertel O., Palmgren F., Urban Air Pollution European Aspects, Kluwer Academic Publishers, Dordrecht, Netherlands, 1998 [2] European Environment Agency, September, 2008, report bulletin No. 171. [3] Izarra R. G., Second moment modelling for the numerical simulation of passive scalar dispersion in urban environments, Ph.D. thesis, der Universität Siegen, Siegen, Germany, 2009.
[4] Živković P. M., Istraživanje uticaja karakteristika tehnološkog procesa u referentnom postrojenju na kvalitet vazduha u neposrednom okruženju, Ph.D. thesis, University of Niš, Serbia, 2010. [5] Ntziachristos L., Samaras Z., COPERT 3 Computer Programme to Calculate Emissions from Road Transport, Methodology and Emission Factor (version 2.1), Copenhagen: European Environment Agency; 2000, Copenhagen, Denmark