شناسایی نقاط داغ غلظت ذرات معلق(PM 2.5) هوای مشهد

نوع مقاله : مقاله پژوهشی

نویسندگان

1 مرکز پایش آلاینده های زیست محیطی مشهد

2 کارشناس مسئول بخش هوا، اداره کل محیط زیست خراسان رضوی

چکیده

امروزه آلودگی هوابه بزرگترین معضل زیست محیطی در کلانشهر ها تبدیل شده است. بر اساس برآورد سازمان بهداشت جهانی (WHO) از هر 10 نفر 9 نفر هوای ناسالم تنفس می کنند. بر اساس این گزارش سالانه بالغ بر 7 میلیون نفر قربانی آلودگی هوا می گردند که هزینه این مرگ و میر 225 میلیارد دلار برآورد شده است. در این تحقیق الگوی پراکنش pm2.5 به عنوان آلاینده اصلی کلانشهر مشهد با استفاده از تحلیل خود همبستگی فضایی مورد بررسی قرار گرفته است. بدین منظور برای یک دوره 5 ساله60 نقشه ماهانه پراکنش pm2.5  ترسیم گردید سپس نقشه های فصلی و سالانه از ترکیب نقشه ها ماهانه بدست آورده شد. با استفاده از آماره محلی موران(LMI) و شاخص گتیس ارد جی (Getis –Ord-Gi) نقاط داغ غلظت ذرات معلق شهر مشهد شناسایی شد. بر اساس یافته های تحقیق نقاط داغ (Hot Spot) با 22.3 درصد از مساحت کل شهر در شرق و جنوب شرق و نقاط سرد (Cold Spot) با 25.5 درصد در شمال غرب مشهد شکل گرفته اند. یک چنین الگویی در مقیاس فصلی نیز وجود دارد. نتایج محاسبات انجام شده بین دو لکه متمایز داغ و سرد نشان داد ، تعداد روزهای ناسالم در نقاط داغ چهار برابر نقاط سرد است. این نسبت در غلظت NO2حدوداً چهار برابر، SO2 سه برابروPM2.5,10 و CO دو برابر بیشتر است. همچنین 21 درصد جمعیت بیشتر، 30 درصد مساحت و تعداد کاربری های صنعتی و خدماتی بیشتر در نقاط داغ نسبت به نقاط سرد بیشتر است. از طرفی برخورداری کمتر از (70 درصد کمتر) پارک ها و فضاهای سبز (62 درصد کمتر) و همچنین ارتفاع 114 متری کمتر بین دو منطقه را می توان در تراکم بالای PM2.5  و شکل گیری نقاط داغ در شرق و جنوب شرق مشهد موثر دانست.

کلیدواژه‌ها


عنوان مقاله [English]

Identification of hot spots PM2.5 in Mashhad air pollution

نویسندگان [English]

  • reza esmaili 1
  • Farrokh Legha Amini 2
1 شهرداری مشهد و اداره کل حفاظت محیط زیست خراسان رضوی
2 Master of Environmental Science, Department of the Environment Khorasan Razavi Province
چکیده [English]

Introduction
Air pollution has become a major environmental problem in most parts of the world, especially in the metropolises of developing countries, along with population growth, agricultural development, urban development, industrial development and increasing motor transportation (Joulaei et al., 2017).
Among air pollutants, PM2.5 is one of the most dangerous pollutants. Numerous studies have shown that PM2.5 can damage human lung tissue and increase the risk of chronic respiratory, cardiovascular and cancer diseases (Wang and etal, 2019; Vinikoor-Imler and etal, 2011).
 Statistical and spatial methods have been widely used to identify Spatial-temporal patterns of various air pollutants (Alijani, 2015)
The purpose of this study was to use temporal and spatial analysis methods and Geographic Information System (GIS) affecting the emission of PM2.5 in Mashhad in a period of 5 years.
.
Materials and Methods
Mashhad City in Northeastern of Iran, the second largest metropolis in Iran, has 23 air quality monitoring stations. In this study, was used of Geographic Information System (GIS), geo-statistical functions, spatial analysis, geographical processes and sub-tools of each  to identified the spatial and temporal patterns of the main air pollutant in Mashhad.
 
Daily data of particulate matter smaller than 2.5 microns (PM2.5) were collected from air quality stations in the 5-year period (2014-2019). Initially, the data was verified.  Then the PM2.5 concentrations were calculated for each station daily and monthly scale. Maps were used by Geographic Information System (GIS). In the next step, local spatial correlation analysis or Local Moran Index (LMI) and hot spots analysis was performed by Getis –Ord-Gi statistic for this pollutant.
 
Results 
Daily and monthly average concentration of pollutants in the late spring and early summer, showed sometimes winds with dust, so that the PM2.5 concentration is increased. Seasonal maps were prepared using the Algebra Map function.
In the spring and summer, the Western and North-Western regions of Mashhad have the lowest density of particulars maters (19.3 μg / m3). The comparison of the average concentration of PM2.5 in different seasons of the year shows that autumn has the highest concentration.
In the winter, the intensity of PM2.5 concentration has decreased. However, in the winter, with an average concentration of 30 μg / m3 and it is in the second rank of air pollution after autumn and the lowest concentration is located in the North-West and West of Mashhad as in previous seasons.
According to the local spatial autocorrelation analysis, the concentration of particulate matter in the Eastern areas of the city is significantly higher than its neighboring areas, which is marked on the map with high cluster (HH) and low clusters (LL) are spread in the North-West.
The annual map of PM2.5 concentration was drawn by combination of seasonal maps in a 5-year period.
This map provides a more complete understanding of how PM2.5 is distributed in the air of Mashhad. According to this map, the eastern and southeastern regions of Mashhad with an average concentration of 36.8 μg / m3 have the highest concentration than the Western and North-Western.
The study of effective factors in the emission of PM pollutants in the Mashhad city showed that the total length of the urban transportation network in hot spots is 283.287 meters and the average speed of vehicles in this section is 36 kilometers per hour. While, the total length of the network in the cold spot is 424342 meters and the average speed of cars is 46 kilometers per hour.
Emissions in cold spots are also lower than in high-traffic areas due to longer communication lines, faster vehicle speeds and light traffic.
Geographical point of view show that, Mashhad plain is located between two the Binalood mountains in the south and the Hezar Masjed mountains in the north, which causes the formation of a special pattern and canalization of wind from the South-East to the North-West. High density of suburban population, more agricultural lands, rural roads, sand mines, brick kilns and cement factories in the East and South-East of the city due to high concentration of PM2.5 and the formation of hot spots in this area. However, the location of the cold spot in the North-Western regions of Mashhad was due to lower population density, much more green space per capita, higher altitude, greater distance from industries, adjective to Torqabeh and Shandiz areas.
Conclusion
Spatial autocorrelation analysis showed that the most Particulate Matter (PM2.5) or hot spots in East and Southeast Mashhad, and 22.3 percent of the total area of the city. On a seasonal scale, hotspots are the largest in spring with 25% of the total area of the city, then winter with 24%, summer with 21.8% and finally autumn with 17.2%. Against, areas with low concentrations of PM2.5 (cold spots) have been formed in the North-West of Mashhad. On an annual scale, 25.5% of the city is in the cold spots and 22.3% is in the hot spots. The population is 21% and the area of industrial - service uses is 30% more in the hot spot than the cold spot, which directly increases the emission of air pollutants in hot spots. While, in the cold spots the parks area is 70%, residential land use is 67%, the total area of green spaces is 62% is more than hot spots. On the other hand, other factors such highways and main roads, the length of the transportation network, increase the speed of vehicles in these areas.

کلیدواژه‌ها [English]

  • Air pollution
  • Spatial Autocorrelation Analysis
  • Particular Materials smaller than 2.5 microns
  • Hot Spot
  1. Alijani B., 2015, Spatial Analysis, Journal of Spatial Analysis Environmental Hazards, 2,3, pp.1-14.
  2. Allahyari S., Assadi SN., Esmaily H., 2014, Assessment of air pollution condition and compare in different areas of Mashhad in winter 2011, Journal of North Khorasan University of Medical Sciences, 6(1): 17, pp.7-16.
  3. Bahari R., Abbaspour R. A., Pahlavani P., 2015, Zoning of particle contamination  by local statistical models in GIS (Case Study, Tehran City), Jouranal of Geomatics Science and Techniques, 3, pp. 165-173.
  4. Charmzan M., Esmaili R., Mohammadi M., 2018, Evaluation of the best interpolation algorithm in spatial distribution of air pollutants, First National Conference on Modern Studies and Research in Environmental and Management Sciences, Motahar Institute of Higher Education, Mashhad, Iran.
  5. Chel Jung M., Park J., Kim S., 2019, Spatial Relationships between Urban Structures and Air Pollution in Korea, Sustainability, 11, 476, pp. 1-17.
  6. Chu HJ., Huang B., Lin CY., 2015, modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship, Atmospheric Environment, 102, pp. 176-182.
  7. Ebtekar T., 1974, Motor vehicles and air pollution, Journal of Environmental Studies, 1 (1), 123-143.
  8. Ebtekar T., 1975, Large cities air pollution, Journal of Environmental Studies, 3 (3), 82-77.
  9. Esmaili R., 2018, Determination of air pollution’s homogenous areas in Mashhad, Journal of Natural Environmental Hazards, 7 (16), pp. 227-240.
  10. Esmaili R., 2013 , Application of Cluster Analysis in Grouping of Air Pollution Measuring Stations Case Study: Mashhad City, Second National Conference on Air and Sound Pollution Management, Sharif University of Technology, Tehran.
  11. Fang Ch., Lin H., Li G., Sun D., Miao Zh., 2015, estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models, sustainability, 7, pp. 15570-15591.
  12. Habibi R., Alesheikh A.A., Mohammadian A., Sharif M., 2017, “An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran, International Journal of Geo-Information, 7, 270, pp. 2-14.
  13. Hosseinzadeh S. R., Doostan R., Haghighat Ziaberi S. M., Haghighat Ziaberi S. M., 2013, Investigation of synoptic patterns affecting air pollution in Mashhad metropolis, Journal of Geography and Regional Development , 21, pp. 81-101.
  14. Joulaei F., Peiravi R., Esmaily H., Ketabi D., Motealemi A., 2017, Variations of Carbon Monoxide and Particulate Matter Concentration in Mashhad, Iran, during 2016, Jouranl of Health Research in Community, 3(3), pp. 34-45.
  15. Ketabi D., Esmaili R., Alidadi H., Peirovi R., Joulae, F., 2016, Evaluation of Mashad City Air Quality Based on Air Quality Index (AQI), 2015, Journal of Research in Environmental Health, 2(1), pp. 228-236.
  16. Khosravi Y., Bahri A., 2018, Use of Spatial Statistics Techniques to Investigate Spatio-temporal Changes in Chlorophyll a Concentration in the Persian Gulf, Journal Of Marine Biology, 10, 37, pp. 33-37.
  17. Lin, G., Fu, J., Jiang, D., Hu, W., Dong, D., Huang, Y., Zhao, M., 2014 Spatio-temporal variation of PM2.5 Concentrations and their relationship with geographic and socioeconomic factors in China, Int. J. Environ. Res. Public Health, 11,pp. 173-168.
  18. Liu W., Xu Z., Yang T., 2018, Health effects of air pollution in China, Int. J. Environ. Res. Public Health, 15, 1471, pp. 1-15.
  19. Mashhad Environmental Pollution Monitoring Center, Extensive Report on Environmental Pollutants of Mashhad in 2014- 2018, WWW.epmc.mashhad.ir.
  20. Miri M., Mohammadi A., Nemati S., Abdolahnejad A., Nikoonahad A., 2016, The Trend of Air Quality Index (AQI) Variations in Mashhad Metropolis Using GISJournal of Health research in Community, 2(1), pp. 12-20.
  21. Nadian M., Mirzaei R., Soltani Mohammadi S., 2018, Application of Moran's / Autocorrelation in Spatial-Temporal Analysis of PM2.5 Pollutant (A case Study: Tehran City), Journal of Environmental Health Engineering; 5(3), pp. 197-213.
  22. Nunez-Alonso D., Vicente Perez-Arribas L., Manzoor S., O.Caceres J., 2019,  Statistical Tools for Air Pollution Assessment: Multivariate and Spatial Analysis Studies in the Madrid Degion, Journal of Analytical in Chemistry, 1-9.
  23. Peeters A., Zude M., Käthner J., Ünlü M., Kanber R., 2015, Hetzroni A, Gebbers R, Ben-Gal A. Getis–Ord’s hot-and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data. Computers and Electronics in Agriculture, 111, pp.140-50.
  24. Pourkhabaz H. R., Javanmardi S., 2014, Determination of concentration and source of toxic metals in urban areas (Case study: Mashhad), Journal of Geography and Environmental Planning, 25, 3 (55), pp. 207-216.
  25. Pu, H.; Luo, K.; Wang, P.; Wang, S.; Kang, S., 2017 spatial variation of air quality index and urban driving factors Linkage: Evidence from Chinese cities, Environ. Sci. Pollut. Res. Int., 24, 4457-4468.
  26. Roshani M., Abbasian M., Naderi M., Shahbazi H., Torbatian S., Karimi E., Nakata Sh., ,2018,Tehran Annual Air Quality Report period of March 2017- March 2018, Tehran Air Quality Control Company, QM97/02/01(U)/1, pp. 1-307.
  27. Shahbazi H., Mostatazadeh A., Ahmadi M., Taghvaei S., Babaei M., Salamat Y., Afshin H., Hoseini V., 2015, emission inventory of Tehran City  Base Year of 2013, vol.1, Tehran Air Quality Control Company, QM/94/04/03/(U)/01, pp. 1-65.
  28. Taghavi H., 2012, Temporal and Spatial Distribution of Mashhad Air Pollution Index Pollutants and its Affecting Factors, Master Thesis, Ferdowsi University of Mashhad, Iran.
  29. Tian Y., Yao X., Chen L., 2019, Analysis of spatial and seasonal distributions of air pollutants by Incorporating urban morphological characteristics, Computers, Environment and Urban Systems, 75, pp 35-48.
  30. Tobler, W. 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography. 46 (2). pp. 234-240
  31. US EPA, 2014, National Ambient Air Quality Standards (NAAQS), pp.1-2. http://epa.gov/air/criteria.html
  32. Vinikoor-Imler, L.C., Davis, J.A., Luben, T.J., 2011, an ecologic analysis of county-level PM2.5 concentrations and lung cancer incidence and mortality. Int. J. Environ. Res. Public Health, 8, pp. 1865-1871.
  33. Wang Y., Duan X., Wang L., 2019, Spatial-Temporal Evolution of PM2.5 Concentration and its Socioeconomic Influence Factors in Chinese Cities in 2014–2017, Int. J. Environ. Res. and Public Health, 16, 985, pp. 1-18.
  34. Wang Z. and Fang C., 2016, Spatial-temporal characteristics and determinants of PM2.5 in the Bohai Rim Urban Agglomeration, Chemosphere, 148, pp. 148-162.
  35. World Health Organization (WHO), 2016, Ambient air pollution: a global assessment of exposure and burden of disease, pp. 1-130.
  36. Yamada, I., and Thill, J. C., 2007. Local indicators of network‐constrained clusters in spatial point patterns.Geographical Analysis, 39 (3): 268-292.