بهبود دقت مدل سازی غلظت ذرات معلق (pm2.5) از طریق ادغام ایستگاه های ثابت و همراه سنجش آلودگی هوا

آلودگی هوا از جمله پدیده‌های پیچیده‌‌ای است که دارای دینامیک غیرخطی بوده و تاثیر 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 تامین 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.

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

Extended Abstract  Introduction Air pollution has become a lifethreatening hazard with severe consequences. Previous studies have indicated that longterm exposure to air pollution can pose a significant threat to human health or even cause death. Usually, air quality is monitored by groundbased 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, groundbased 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 groundbased 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 highresolution 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. Landuse regression (LUR) models are increasingly used to estimate the level of PM2.5exposure in urban areas. Landuse regression models often use data received fromgroundbased 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 groundbased 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 groundbased stations. The proposed method of the present study is to combine groundbased 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 groundbased 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 groundbased 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 groundbased stations. The present study took advantage of 14 mobile sensors along with 7 groundbased stations. Results indicated that the root mean square error of the seven groundbased 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 groundbased stations, skewness value equals 0.8803. In general, the results indicated that integrating groundbased 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 Ttest is used to examine whether or not the results were significant.  Conclusion In this study, a new framework was proposed to integrategroundbasedstations and mobile sensors with the aim of improving the accuracy of PM2.5 pollutant concentration estimation. The results of the ttest show that with only groundbased 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.