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 ...
Read More
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.
Sara Haghbayan; Mohammad Reza Malek
Abstract
Extended Abstract
Introduction
Recently, different volunteered Geographic Information (VGI)databases and websites have been launched for a variety of purposes and different groups of users. Various groups and portals collect and share these data. Thus, there is a huge potential for the participation ...
Read More
Extended Abstract
Introduction
Recently, different volunteered Geographic Information (VGI)databases and websites have been launched for a variety of purposes and different groups of users. Various groups and portals collect and share these data. Thus, there is a huge potential for the participation of millions of people who can act like remote sensors and share their data with other members of the group without any cost.Therefore,diffrent users with different skill levelscan provide spatial data through personalized measurements. Various research perspectives have shown that sometimes Volunteered Geographic Information can compete with business data.The present research seeks to solve the problems in searching and finding properties, and describe indoor space using visual components in web-basedplatforms. The impact of spatial information on satisfaction of residentsortheir problems has made this research especially important.Most of related studiessought to provide models for estimationof prices, and the impact of environmental factors on the price of real estates. They also have endeavored tocreate websites for residential real estatesearch with an emphasis on descriptive information.The present research seeks to describe indoor space of residential real estate using spatial tools.In this regard, criteria like height, dimensions, topological relationships, shape, color, geographic location, and directional relationships are considered.Description of residential properties’ indoor space requires information in both spatial and descriptive dimensions. Due to the especial potential of Geospatial Information System in the simultaneous visualization of spatial and descriptive information, spatial analysis was used in the present study.
Clearly, any research is performed based on a set of presuppositions. Particularly when we seek to theoretically investigate a process like modeling or design an information system, the work scope will be very wide and serious challenges will occur without proper assumptions. The present study assumes equal spatial perception, verbal expression and visualizationabilityin all people. It is also assumed that all estate visitors havecell phones equipped with cameras and Global Positioning System and their response to qualitative relationships is better than that of quantitative relationships. Moreover,real estateis used as a synonym for apartmentin this research.
Materials & Methods
Considering the critical role of the ordinary users and the fact that survey processes are usually expensive and time consuming, volunteered spatial information environments are the most appropriate way of gathering people’s spatial perception. Not only these environments are rather easy to use, but also they simultaneously receive up-to-date information from the participant and provide them with appropriate services according to their status.
After modeling and designing, the proposed systemwas implemented in Visual Studio 2012 platform using ASP.NET framework andC#language. Server Structured Query Language (SQL) Database 2012 was usedto save spatial information. Tehran District 14 (longitude: 51.46207, latitude: 35.66905) was chosen as the study area and data collected from several residential properties was recorded in our database.
Results & Discussion
Results indicate more than 65 percent conformity between the mental image generated using the proposed method and the reality. Users’ satisfaction with the proposed model was compared with their satisfaction with three popular Iranian sites, and a foreign site regarding. The impact of tools applied in these websites was also investigated. Results indicate 78.78% satisfaction with the proposed system, which is the highest level of satisfaction as compared to other studied websites.Moreover, compared to other toolsinvestigated in the present study,virtual tours and thenmaps are more in visualization.Sincespatial perceptions depends on various parameters such aspersonal interests, spatial dimensions, gender, age, education, culture, and fields of study, different groups were investigated in the present study.
Conclusion
Using information collected inVolunteered Geographic Informationenvironments, ordinary people can share information and use each other’s experiences and opinions. This improves their knowledge level and results in a better understanding of the advantages and disadvantages of different real estates. Due to increased knowledge level, people will not select undesirable properties. This will create a competitive market and increase designers and engineers’attention to indoor space, which will consequently increase ordinary users’welfare.