Preliminary Analysis

14 PM2.5 Sensing and Data Analysis

In opportunistic sensing, data collection and data transmission happen when the con-ditions of the equipment are satisfied. Opportunistic sensing mainly depends on the envi-ronment. This sensing architecture could simplify wireless heterogeneous sensor network design and lead to a more effective data collection [19]. Nowadays, GPS, bluetooth, Wi-Fi, smart label, smart phone and various kinds of sensors have all become important approaches to opportunistic sensing, from which a series of research hot spots [33–37], have been de-rived. Because opportunistic sensing is mainly "human centered, unanimous", it makes it challenging to take care of energy consumption, security and privacy . Another characteristic of opportunistic sensing is their intersection and fusion with sociology and psychology, which develops new concepts including the calculation of social perception and that of emotion[20, 21]. There are some hot researches on the application aspect, such as environ-mental monitoring[22–27] and health care. Another application of opportunistic sensing was found in air quality sensing system. Wi-Fi, GPS and air sensor were installed on the bus.

The transmission of air sensing data only takes place when the bus arrives at the bus station.

However, there are few research works that thoroughly consider the frame and structure of opportunistic sensing calculation.

2.4 Preliminary Analysis

The main inspiration behind this project is the LASS (Location Aware Sensing System) community. This community engages the people to participate in PM2.5 sensing and also encourages them to try and develop sensing devices by themselves. The project facilitates PM2.5 monitoring at a finer spatiotemporal granularity and enriches data analysis by making sure that all the measurement data are available freely to everyone [15]. The devices are installed in buildings with reliable Internet connection and power source. In addition, the data (https://pm25.lass-net.org/en/) are easily accessible which makes data analysis easy. The

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2.4 Preliminary Analysis 15

sensing devices in Airbox Project are designed and developed by professional manufacturers.

The industrial product level devices are made in close cooperation with Edimax Inc. and Realtek Inc. in Taiwan. The devices are based on Realtek Ameba development board.

The device contains a PMS5003 PM2.5 sensor and a HTS221 temperature/humidity sensor.

Another version of deployed device is called MAPS (Micro Air Pollution Sensing System) which is developed by Network Research Lab at Institute of Information Science, Academia Sinica, Taiwan. It is based on MediaTek LinkIt Smart 7688 Duo development board. It has a PMS5003 sensor for PM2.5 and BME 280 for temperature/humidity. The data sensing part of the framework is shown in Figure 2.1. There are three major components of the sensing system. 1. Data Producers comprise the sensors which provide sensed data. The hardware and the source codes are open source so that people can create such devices themselves.

2. Transit Centers act as data brokers for the data sent from the data producer to data users. Multiple data brokers can be used to achieve scalability and fault tolerance.

3. Data Users are those who use the sensed data, analyze it and create different types of applications.

Fig. 2.1 Flowchart for PM2.5 Sensing Framework.

In this section, we will discuss how the PM2.5 data changes over time, the inflection points, the variation patterns that is observed due to wind speed, wind direction, time of the

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16 PM2.5 Sensing and Data Analysis

hour mean and 95% confidence interval in mean

Fig. 2.2 Hourly and daily PM2.5 variations

day and many more factors. For this study, the data from Shillin station of EPA Taiwan is used. Figure 2.2 shows the PM2.5 variations for the month of November. It can be observed from Figure 2.2 that sometimes there is a trend in PM2.5 variations. For example, during the weekends it can be assumed that most people would go out which means more traffic and more pollutants. So, higher PM2.5 would be observed during the weekends rather than the weekdays. Similarly, during the morning and late evening PM2.5 would be higher as people would be going and coming back from work. Such trends are easy to observe. But sometimes there are certain inflection points (sudden spikes). Inflection points can be considered as sudden increase in the PM2.5 values which might be caused by environmental factors or

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Fig. 2.3 Variation in PM2.5 based on wind speed

human activities. As these incidents are rare, so it is very difficult to model them using a conventional forecast model. One year data for the monitoring station was studied and we tried to analyze how the air quality is effected by the wind speed. The plot based on the wind direction for different months of the year is shown in Figure 2.3. It can be observed that for most of the study period, PM2.5 is higher when the wind direction is North-West. It actually points to the fact that there is a lot of smog coming from the north of Taiwan which constitutes a major proportion of air pollutants.