# A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas
## 5 Conclusions and Future Work  
Recently, people's awareness about air pollution has increased. There is an urgent need for air quality forecasting. In this paper, we discussed the relationship between spatial-temporal data and PM2.5 values, and forecasted air quality using a spatial-temporal combination approach. In this approach, both recent and similar references were considered for temporal air quality forecasting. According to the experimental results, considering recent references can only forecast air quality in the first six hours. It is necessary to consider 15 recent and 15 similar references to forecast PM2.5 after six hours. While the experimental results show that the forecast error decreases with the increase in reference data, it shows a tendency to converge. To reduce the amount of calculations, we did not use a larger reference amount. For the spatial air quality forecast, nearby monitoring devices with similar meteorological features were considered as references to forecast the PM2.5 values. This can cause the trend of the PM2.5 forecast value to be similar for nearby devices for a period of time. The results showed that our proposed approach outperforms methods based on well-established IDW and PCC measurements and other simple methods in terms of the forecast error. The results of our proposed approach are convergent and stable. While we only forecasted the PM2.5 value for 48 h, our proposed approach could forecast further PM2.5 values. In fact, there are many events that can cause sudden increases in PM2.5 value, such as someone smoking or burning something. Sources and sinks of PM2.5 are also important. In the future, we will collect more data for seasonal analyses, and we will try to analyze the causality of PM2.5 value and traffic flow, human mobility, points of interest, natural disasters, terrain, etc. Furthermore, we will try to discover how weather conditions affect air quality, and will consider adding weight to each feature. For the spatial method, there is still a long way to go. We will test, with devices, which has the highest correlation, which are in similar terrains, and which are in similar weather conditions in terms of the spatial method. Additionally, we found that PM2.5 in the same or opposite wind directions is similar. We will also consider the relationship of upwind and leeward to improve the spatial forecast mechanism.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Grade** & **Good** & **Moderate** & **Unhealthy for Sensitive Groups** & **Unhealthy** & **Very Unhealthy** & **Hazardous** \\ \hline PM2.5 (\(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)) & 0.0–15.4 & 15.5–35.4 & 35.5–54.4 & 54.5–150.4 & 150.5–250.4 & 250.5–500.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grade of PM2.5 from Environmental Protection Administration Executive [PERSON] (R.O.C.).  
Figure 15: Acceptable error and each grade’s standard deviation from our results.  
**Author Contributions:** Conceptualization, E.H.-C.L. and C.-Y.L.; methodology, E.H.-C.L. and C.-Y.L.; software, C.-Y.L.; validation, E.H.-C.L. and C.-Y.L.; formal analysis, E.H.-C.L. and C.-Y.L.; investigation, C.-Y.L.; resources, E.H.-C.L.; data curation, E.H.-C.L.; writing--original draft preparation, E.H.-C.L. and C.-Y.L.; writing--review and editing, E.H.-C.L.; visualization, C.-Y.L.; supervision, E.H.-C.L.; project administration, E.H.-C.L.; funding acquisition, E.H.-C.L. Both authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Ministry of Science and Technology, Taiwan, R.O.C., grant number MOST 109-2121-M-006-013-MY2 and MOST 109-2121-M-006-005-.  
**Conflicts of Interest:** The authors declare no conflict of interest.