Sara Haghbayan; Behnam Tashayo
Abstract
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 ...
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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.
