بهبود دقت مدل سازی غلظت ذرات معلق (PM2.5) از طریق ادغام ایستگاه های ثابت و همراه سنجش آلودگی هوا

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری سیستم های اطلاعات مکانی، گروه مهندسی نقشه برداری، دانشکده مهندسی عمران و حمل و نقل ، دانشگاه اصفهان

2 استادیار گروه مهندسی نقشه برداری، دانشکده مهندسی عمران و حمل و نقل، دانشگاه اصفهان

10.22131/sepehr.2021.242859

چکیده

آلودگی هوا از جمله پدیدههای پیچیده‌‌ای است که دارای دینامیک غیرخطی بوده و تأثیر پارامترهای متنوع بر رفتار آن، تجزیه و تحلیل و مدلسازی تغییرات مکانی و زمانی غلظت آلاینده‌‌ها را با دشواری‌‌های فراوانی مواجه می‌‌سازد. هدف از این مطالعه  بهبود دقت مدلسازی آلاینده‌‌های هوا به منظور مدیریت مواجهه با استفاده از داده‌‌های حاصل از حسگرهای همراه جهت مرتفع ساختن نواقص روش رگرسیون کاربری اراضی[1] است. به منظور بهبود دقت مدلسازی LUR برای تخمین غلظت PM2.5 از هفت ایستگاه ثابت و چهارده حسگر همراه استفاده گردید. منطقه مورد مطالعه شهر اصفهان است و محل نمونه‌‌برداری حسگرهای همراه در مکان‌‌هایی با بیشترین پیشبینی عدم قطعیت و بالاترین احتمالی که از یک حد آستانه معین تجاوز می‌‌کند، انتخاب شدند؛ سپس از آزمون آماری t برای بررسی معنیدار بودن و یا نبودن بهبود نتایج استفاده گردید. در این تحقیق، چارچوبی برای تأمین دقت مورد نظر با افزودن داده‌‌های حاصل از حسگرهای همراه؛ پیشنهاد شده است. نتایج حاصل از این تحقیق نشان داد که خطای جذر میانگین مربعات[2] حاصل از لایه زمین آمار هفت ایستگاهثابت پایش برابر با  1.802 و RMSE حاصل از ترکیب این ایستگاه‌‌ها با چهارده ایستگاه همراه معادل با 0.591 برآورد شد. نتایج  نشان داد که حتی با افزودن یک حسگر همراه به ایستگاه‌‌های ثابت میزان RMSE 0.113  میکروگرم بر متر مکعب کاهش می‌‌یابد و با افزودن چهارده حسگر همراه به هفت ایستگاه ثابت میزان RMSE حاصل از ساخت مدل LUR حدود سه برابر کاهش می‌‌یابد. یافته‌‌های حاصل از این تحقیق نشان داد کهبا استفاده از چارچوب پیشنهادی می‌‌توان کیفیت هوا را در هر مکان و زمان  با دقت مورد نظر تخمین زد و قدرت تفکیک بالاتری را برای محیط‌‌های ناهمگن شهری فراهم کرد.



[1]- Land Use Regression (LUR)


[2]- Root Mean Square Residual (RMSE)

کلیدواژه‌ها


عنوان مقاله [English]

Integrating ground-based air quality monitoring stations with mobile sensor units to improve the accuracy of PM2.5 concentration modeling

نویسندگان [English]

  • Sara Haghbayan 1
  • Behnam Tashayo 2
1 Department of geomatics engineering, Faculty of civil engineering and transportation, University of Isfahan
2 Faculty of civil engineering and transportation, University of Isfahan,Azadi Square, Isfahan, Iran
چکیده [English]

Extended Abstract 
Introduction
Air pollution has become a life-threatening hazard with severe consequences. Previous studies have indicated that long-term exposure to air pollution can pose a significant threat to human health or even cause death. Usually, air quality is monitored by ground-based stations that can collect data regarding temperature, humidity, pressure, and several pollutants such as Ozone (O3), Carbon Monoxide (CO), Carbon Dioxide (CO2), Sulfur dioxide (SO2), Nitrogen dioxide (NO2), and nanoparticles (e.g. PM1, PM2.5, and PM10). However, ground-based stations are costly, scattered, and often cannot cover large areas. These stations collect the concentration ofparticulate matter with a diameter of less than 2.5 µm (PM2.5) over a year.Collected data may be lost due to an unexpected shutdown of the device.  Datacollected in ground-based stations are not sufficient by their own and as a result they are modeled.  The resulting models also have flaws, so new resources are needed to solve this problem. One of these resources is the use of mobile sensors to produce high-resolution temporal and spatial air quality data. As opposed to traditional air quality monitoring stations, the use of dynamic and mobile sensors is quickly developing. These mobile sensors measure the concentration of the same air pollutants as those measured by ground stations.
Land-use regression (LUR) models are increasingly used to estimate the level of PM2.5exposure in urban areas. Land-use regression models often use data received fromground-based stations. Therefore, modeling the concentrations of particulate matter in a city leads to a significant increase in modeling error. Data from mobile sensors can increase the accuracy of this contaminant modeling process. The present study aims to improve modeling accuracy by integrating ground-based stations with mobile sensors. Therefore, using the proposed framework, we can accurately estimate air quality at any time and place and provide higher resolution estimations for heterogeneous urban environments.
 Materials & Methods
The study area covers Isfahan city. With a population of more than two million and an area of 200 square kilometers, Isfahan is located in central Iran. 13% of the total pollutants entering Isfahan belong to urban industries, 11% to domestic sources, and 76% of all pollutants belong to traffic related sources in Isfahan. Therefore, most of the PM2.5concentrations are generated by the transportation system in Isfahan. The effective solution to the air pollution problem needs to have a comprehensive understanding of the air pollution process. Such an understanding primarily depends on reliable records that can depict the temporal and spatial variations in air pollution which is not possible due to the limited number of ground-based stations. The proposed method of the present study is to combine ground-based stations with mobile sensors to increase the accuracy of PM2.5concentration estimation and modeling. One of the existing methods used to estimate PM2.5levels is land use regression. Previous studies used only ground-based stations to create this model, which was not sufficiently accurate. The present study sought to increase the accuracy of PM2.5concentration modelling in contamination values of near or beyond the threshold. Using the LUR model, a prediction map was generated usinga combination of ground-based stations and mobile sensor which helps us to reach a more accurateestimation and prediction of PM2.5concentrations in a heterogeneous region such as this city.
 Results & Discussion
Reliable and accurate estimate of temporal/spatial distribution of air pollutant concentration cannot be achieved using a limited number of ground-based stations. The present study took advantage of 14 mobile sensors along with 7 ground-based stations. Results indicated that the root mean square error of the seven ground-based stationsequaled 1.80 while the RMSE of the combination of these stations equaled 0.59. The skewness index shows asymmetry of data as compared to the standard normal distribution.This index is used to determine whether the data distribution is normal or not. Skewnessvalue of standard normal curvesequals zero. In the histogram obtained from a combination of all stations, this value is 0.11, while in the histogram obtained from the ground-based stations, skewness value equals 0.8803. In general, the results indicated that integrating ground-based stations with mobile sensors results in a PM2.5concentration distribution which looks more like a normal distribution. The normality of data distribution implies that the histogram of data frequency is approximately a normal curve, and thus T-test is used to examine whether or not the results were significant.
 Conclusion
In this study, a new framework was proposed to integrateground-basedstations and mobile sensors with the aim of improving the accuracy of PM2.5 pollutant concentration estimation. The results of the t-test show that with only ground-based stations, the actual pattern and its distribution over the city will fail. In fact, data received from mobilesensors provide additional data necessary for air pollution profiling.

کلیدواژه‌ها [English]

  • Air pollution
  • Mobile sensors
  • Ground-based stations
  • Particulate matter concentrations (PM2.5)
  • Sensor integration
1- Basagaña, X., Aguilera, I., Rivera, M., Agis, D., Foraster, M., Marrugat, J., . . . Künzli, N. (2013). 2- Measurement error in epidemiologic studies of air pollution based on land-use regression models. American Journal of Epidemiology, 178(8), 1342-1346.
3- Beelen, R., Voogt, M., Duyzer, J., Zandveld, P., & Hoek, G. (2010). Comparison of the performances of land use regression modelling and dispersion modelling in estimating small-scale variations in long-term air pollution concentrations in a Dutch urban area. Atmospheric environment, 44(36), 4614-4621.
4- Biondi, S. M., Catania, V., Monteleone, S., & Polito, C. (2017). Bus as a sensor: A mobile sensor nodes network for the air quality monitoring. Paper presented at the 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).
5- Firculescu, A.-C., & Tudose, D. S. (2015). Low-cost air quality system for urban area monitoring. Paper presented at the 2015 20th International Conference on Control Systems and Computer Science.
6- Hamburg, F. C. (1971). Some Basic Consideration in the Design of an Air Pollution Monitoring System. Journal of the Air Pollution Control Association, 21(10), 609-613.
7- Henderson, S. B., Beckerman, B., Jerrett, M., & Brauer, M. (2007). Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environmental science & technology, 41(7), 2422-2428.
8- Hennig, F., Sugiri, D., Tzivian, L., Fuks, K., Moebus, S., Jöckel, K.-H., . . . Memmesheimer, M. (2016). Comparison of land-use regression modeling with dispersion and chemistry transport modeling to assign air pollution concentrations within the Ruhr area. Atmosphere, 7(3), 48.
9- Hoek, G., Beelen, R., De Hoogh, K., Vienneau, D., Gulliver, J., Fischer, P., & Briggs, D. (2008). A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric environment, 42(33), 7561-7578.
10- Hosseini, V., & Shahbazi, H. (2016). Urban air pollution in Iran. Iranian Studies, 49(6), 1029-1046.
11- Jafari, N., Nemati, S., Hajizadeh, Y., & Abdolahnejad, A. (2017). Spatial analysis and attributable mortality to outdoor air pollutants in‎ Isfahan. Journal of health research in community, 2(4), 11-25.
12- Joanes, D., & Gill, C. (1998). Comparing measures of sample skewness and kurtosis. Journal of the Royal Statistical Society: Series D (The Statistician), 47(1), 183-189.
13- Jonidi, A., Bahari, N., nowroozi, a. a., Bahmaei, J., Rezaee, R., Khavasi, M., . . . Gholami-Borujeni, F. Monitoring and Modeling of the Concentration and Quality Index of Dust Particles in the Air of Gorgan City in 1396.
14- Kersting, J., Geierhos, M., Jung, H., & Kim, T. (2017). Internet of Things Architecture for Handling Stream Air Pollution Data. Paper presented at the IoTBDS.
15- Khan, J., Kakosimos, K., Raaschou-Nielsen, O., Brandt, J., Jensen, S. S., Ellermann, T., & Ketzel, M. (2019). Development and performance evaluation of new AirGIS–A GIS based air pollution and human exposure modelling system. Atmospheric environment, 198, 102-121.
16- Leung, Y., Leung, K.-S., Wong, M.-H., Mak, T., Cheung, K.-Y., Lo, L.-Y., . . . Dong, Y.-L. (2018). An integrated web-based air pollution decision support system–a prototype. International Journal of Geographical Information Science, 32(9), 1787-1814.
17- Leung, Y., Zhou, Y., Lam, K.-Y., Fung, T., Cheung, K.-Y., Kim, T., & Jung, H. (2019). Integration of air pollution data collected by mobile sensors and ground-based stations to derive a spatiotemporal air pollution profile of a city. International Journal of Geographical Information Science, 33(11), 2218-2240.
18- Lim, C. C., Kim, H., Vilcassim, M. R., Thurston, G. D., Gordon, T., Chen, L.-C., . . . Kim, S.-Y. (2019). Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea. Environment international, 131, 105022.
19- Mahajan, S., Kumar, P., Pinto, J. A., Riccetti, A., Schaaf, K., Camprodon, G., . . . Forino, G. (2020). A citizen science approach for enhancing public understanding of air pollution. Sustainable Cities and Society, 52, 101800.
20- Maraziotis, E., Sarotis, L., Marazioti, C., & Marazioti, P. (2008). Statistical analysis of inhalable (PM10) and fine particles (PM2. 5) concentrations in urban region of Patras, Greece. Global nest journal, 10(2), 123-131.
21- Mihăiţă, A. S., Dupont, L., Chery, O., Camargo, M., & Cai, C. (2019). Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling. Journal of cleaner production, 221, 398-418.
22- Morley, D. W., & Gulliver, J. (2018). A land use regression variable generation, modelling and prediction tool for air pollution exposure assessment. Environmental Modelling & Software, 105, 17-23.
23- Nourmoradi, H., Khaniabadi, Y. O., Goudarzi, G., Daryanoosh, S. M., Khoshgoftar, M., Omidi, F., & Armin, H. (2016). Air quality and health risks associated with exposure to particulate matter: a cross-sectional study in Khorramabad, Iran. Health scope, 5(2).
24- Prashant, K. (2005). Mass and number concentration of respirable suspended particulate matter (RSPM) on selected urban corridors of Delhi City. MSc. Thesis: Indian Institute of Technology Delhi.  
25- Rashidi, M., Rameshat, M., & Gharib, H. (2012). Air Pollution and Death Due to Cardiovascular Diseases: A Case Study of Isfahan Province of Iran. Air Pollution: A Comprehensive Perspective, 175.
26- Srivastava, S., & Sinha, I. N. (2004). Classification of air pollution dispersion models: a critical review. Paper presented at the Proceedings of National Seminar on Environmental Engineering with special emphasis on Mining Environment.
27- Tashayo, B., & Alimohammadi, A. (2016). Modeling urban air pollution with optimized hierarchical fuzzy inference system. Environmental Science and Pollution Research, 23(19), 19417-19431.
28- Weissert, L., Alberti, K., Miskell, G., Pattinson, W., Salmond, J., Henshaw, G., & Williams, D. E. (2019). Low-cost sensors and microscale land use regression: Data fusion to resolve air quality variations with high spatial and temporal resolution. Atmospheric environment, 213, 285-295.
30- Willett, W., Aoki, P., Kumar, N., Subramanian, S., & Woodruff, A. (2010). Common sense community: scaffolding mobile sensing and analysis for novice users. Paper presented at the International Conference on Pervasive Computing.
31- Zarrabi, A., Mohammadi, J., & Abdollahi, A. (2010). Evaluation of mobile and stationary sources of Isfahan air pollution (In persian). Geography, 26, 151-164.