Figure 3 shows PM2.5 pollution concentration in Chiayi City. The annual average PM2.5 concentration range is from 21.89 µg/m3 to 27.14 µg/m3, equivalent to a spatial concentration difference of 5.25 µg/m3. The highest concentration occurs in the industrial zone in the northwest, and the lowest concentration occurs in the south. Figure 4 shows the Moran’s I distribution: Moran’s I is 0.9784 > 0 and close to 1, indicating that the annual average PM2.5 concentration in Chiayi City has strong spatial clustering, and the Z score is 53.80 > 2.58, indicating that under the 99% confidence level, it has significant cluster distribution. Figure 5 shows LISA in Chiayi City, and hot spots are identified by two types of space units: the northwest industrial zone and the central and western agricultural zone.
Table 1 shows the Moran’s I result in the four seasons: The concentration distribution in the four seasons had significant clustering characteristics, and there were more obvious distribution differences in the spring and summer than in the autumn and winter. Figure 6 shows the PM2.5 concentration LISA in the four seasons in Chiayi City. The distribution of hot areas was only significantly different from other seasons in the summer, and high pollution values were clustered further north, which is speculated to be somewhat related to the prevailing wind direction of the seasons; cold areas differed in eastern Chiayi City between the summer and autumn and the spring and winter.
Table 1
Moran’s I result in the four seasons
Season | Moran’s I | Z score |
Spring | 0.9710 | 53.40 |
Summer | 0.9855 | 54.19 |
Autumn | 0.9785 | 53.79 |
Winter | 0.9814 | 53.98 |
Table 2 shows the Moran’s I result by month. All months show significant clustering at the 99% confidence level. However, the cluster intensity increased from January to June, and the maximum value of Moran’s I was 0.9893. It slowly decreased to 0.9579 before October, and in November after December, the spatial distribution is more discrete than in June.
Table 2
Moran’s I result by month
Month | Moran’s I | Z score |
January | 0.9651 | 53.07 |
February | 0.9761 | 53.68 |
March | 0.9801 | 53.90 |
April | 0.9806 | 53.96 |
May | 0.9819 | 54.00 |
June | 0.9893 | 54.40 |
July | 0.9819 | 54.00 |
August | 0.9737 | 53.55 |
September | 0.9737 | 53.55 |
October | 0.9579 | 52.67 |
November | 0.9781 | 53.78 |
December | 0.9700 | 53.34 |
From January to April, the distribution of the hot zone did not change much, and the relative change in the cold zone increased. By April, the range of the cold zone in the east was significantly reduced, and its land use included the government land, water area, and agricultural land. Starting in June, the hot area moved north. In July, the hot area was separated from the downtown area, such as Chiayi Railway Station. In October, the hot area resumed the spring and annual average hot area distribution pattern.
The inference of this study’s results is that wind direction caused the pollution source in the central and western hot area of Chiayi City to move north due to the southwest wind.
According to the Meteorological Bureau’s explanation of Taiwan’s monsoons, “Taiwan’s weather gradually warms in April each year, and the summer monsoon can blow in slowly. After June, the land temperature is high, and the low air pressure on the mainland has developed to a considerable extent. The summer monsoon blows into Taiwan from the ocean until August, the peak period.”
After September, the sun hits the southern hemisphere directly, and the cold air masses in the northern hemisphere strengthen. The parts of Siberia and Mongolia at high latitudes are colder than the ocean is. The cold air sinks, increasing the air pressure on the ground and forming high air pressure, and the Pacific Ocean has relatively low air pressure. The wind blows from inland China to the Pacific Ocean to Taiwan to form the northeast monsoon. After October, the wind direction gradually stabilizes, and it becomes unstable from December to February. After March, the monsoon gradually decays and repeats the cycle. Figures 7 to 8 show the monthly PM2.5 concentration LISA in Chiayi City.
A novel contribution of this study is that PM2.5 concentration was developed at the hourly level. Notably, most of the PM2.5 models in the literature have been annual, seasonal, monthly, or daily [16, 33–35].
Table 3 shows the Moran’s I result at hours. All the hour intervals showed significant clustering under the 99% confidence level. However, the clustering intensity of Moran’s I value ranged from 0.9597 to 0.9898, and the Z score value ranged from 52.77 to 54.41 was much greater than 2.58. The most intense clustering phenomenon occurred between 1 and 2 a.m., and the most relative dispersion occurred between 11 and 12 a.m. This result shows the obvious zoning phenomena in the PM2.5 concentration in Chiayi at any time, and understanding the location of hot and cold areas in the space is necessary. The LISA analysis method must also be used.
Table 3
Moran’s I result (hourly)
Hours | Moran’s I | Z score |
0–1 o’clock | 0.9853 | 54.17 |
1–2 o’clock | 0.9860 | 54.21 |
2–3 o’clock | 0.9871 | 54.27 |
3–4 o’clock | 0.9895 | 54.40 |
4–5 o’clock | 0.9898 | 54.41 |
5–6 o’clock | 0.9893 | 54.39 |
6–7 o’clock | 0.9893 | 54.38 |
7–8 o’clock | 0.9891 | 54.38 |
8–9 o’clock | 0.9842 | 54.11 |
9–10 o’clock | 0.9753 | 53.62 |
10–11 o’clock | 0.9668 | 53.15 |
11–12 o’clock | 0.9628 | 52.93 |
12–13 o’clock | 0.9597 | 52.77 |
13–14 o’clock | 0.9613 | 52.86 |
14–15 o’clock | 0.9627 | 52.94 |
15–16 o’clock | 0.9617 | 52.88 |
16–17 o’clock | 0.9654 | 53.09 |
17–18 o’clock | 0.9725 | 53.49 |
18–19 o’clock | 0.9776 | 53.76 |
19–20 o’clock | 0.9817 | 53.98 |
20–21 o’clock | 0.9829 | 54.04 |
21–22 o’clock | 0.9832 | 54.06 |
22–23 o’clock | 0.9840 | 54.10 |
23–24 o’clock | 0.9849 | 54.15 |
In addition to being affected by instability, the hourly average concentration in Chiayi City may also increase the mixed layer due to solar radiation. The high wind speeds starting at 8 a.m. also spreads pollutants. Figures 9 to 12 show the PM2.5 concentration LISA per hour in Chiayi City.
We performed LISA regional spatial autocorrelation analysis, under a 95% confidence level, and found different clustering characteristics in different hour intervals, especially during the day. For the PM2.5 high concentration hot zone, the difference between day and night was obvious, especially in the east of the city center, including commercial areas, residential areas, and school land, which appear relativity during the day from 9 to 10 a.m. The phenomenon of concentration clustering gradually moved to the south from 10 a.m. to 2 p.m., reached the north from 1 to 2 p.m., and gradually shrunk from 2 to 9 p.m. No significant clustering in space was observed after 9 p.m. The largest area of PM2.5 gathering hot zone in the space was in the downwind of the industrial zone; the cluster area extended to the southeast commercial area starting at 4 a.m., and the concentration of the downwind of the industrial zone gradually disappeared until 4 p.m.
The difference in distribution of PM2.5 between day and night was also observed in the cold areas where the concentration was low. PM2.5 in the cold areas in the eastern part of Chiayi City gradually disappeared after 10 a.m. while local parts were observed between 2 and 4 p.m. The occurrence of hot zones was observed, and this area was dominated by forest areas and protected areas.
A particulate matter (PM2.5) map was built in this study by using low-cost air-pollution sensor information developed by the IoT of the EPA in Taiwan. We demonstrated this approach by synergizing the data from 1 monitoring station with 287 low-cost air-pollution sensors (the amount of data entries is ∼50 million) to estimate PM2.5 concentrations from September 1, 2018, to August 31, 2019. The PM2.5 real-time observation network is of great importance under the control of air quality. Real-time data through the reduces air pollution, and it also benefits the health of residents living in Chiayi City. [36] presented results similar to those of this study. Additionally, Yougeng et al. [3] indicated that the increasing use of low-cost air quality sensor networks increased their ability to capture PM2.5 gradients at the neighborhood scale and collect more spatially and temporally resolved PM2.5 data for epidemiological and environmental justice studies.
The results of hourly PM2.5 space and time analysis can be used to understand the distribution of PM2.5 in real time through the graph. It can also be helpful for epidemiological research, such as cardiovascular or respiratory disease, especially in the analysis and discussion of immediate health shocks and diseases that may be caused by human exposure to PM2.5. Additionally, hourly real-time spatial distribution can be applied to real-time prediction of air quality in events such as air pollution and natural disasters. Nowadays, whether cities or regions are facing environmental issues such as climate change, greenhouse gases, air pollution, and natural disasters, if there are real-time spatial distribution maps to understand the current situation, damage to human activities or commercial behavior can be reduced.
Because the emission path of air pollution is composed of many complicated methods, in the past, the atmospheric diffusion model was used to predict the distribution of PM2.5. Owing to the calculation of physical chemistry, the parameters are complicated, and it is not easy to plot the map immediately. Even if the results are predicted by statistics or machine learning, it is difficult for the terrain and meteorological influences to be accurately considered; thus, the aforementioned two are combined as follows: the front-end uses atmospheric diffusion mode to accurately calculate the terrain and meteorological influences to make a base map, and the back end uses GIS and statistical reference real-time data to modify the base map and display the plot instantaneously. Some studies [19–21, 37] have presented the spatiotemporal PM2.5 “machine learning” model, and it improved the relationship between spatial and temporal predictions in the city. In further research, “machine learning” methods can also be expanded to predict the characteristics of PM2.5 concentration and include other features, for example, meteorological conditions in the city. This study also provides a reference for urban development and urban–rural planners.