# An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
## 4 Data Processing
### PM2.5 and PM10 Mass Concentration Estimation  
The satellite instrument detects radiance from the mixture of surface reflection and atmospheric scattering caused by elements in the air, like gases and particles. We make certain assumptions to estimate aerosol optical depth (AOD), including the exploitation of derived surface reflectivity and aerosol attributes like phase procedure or single scattering albedo, influenced by the angular distribution of scattered light and primarily determined by particle size [34]. The MODIS instrument is specifically designed to retrieve aerosol properties and utilizes data from seven different wavelengths spanning from 0.47 to 2.13 um. This range of wavelengths provides valuable information about both the size of aerosol particles and AOD. The MODIS algorithm considers surface reflectivity and makes assumptions regarding specific aerosol and atmospheric properties. The AOD products are obtained by analyzing the average radiance observed within a 10 km\({}^{2}\) area derived from 400 pixels with 500 m per pixel resolution. We excluded 50% and 20% of the brightest and darkest pixels from the analysis, respectively. The potential of using AOD, extracted from satellite images, for estimating values of PM mass concentrations at the ground level is currently under investigation. This study examined the relationship between the values of PM mass concentration at the ground level and AOD\({}_{blue}\) and AOD\({}_{green}\) values (MODIS blue and green bands) using the linear regression technique with multiple variables. The analysis period spanned from 1 May to 31 October 2022. However, it should be noted that the availability of the MODIS AOD product was limited to certain days within this timeframe. Furthermore, due to partial image coverage, some areas of the study region had no data pixels due to cloud cover or unfavorable weather conditions. To derive the daily average PM2.5 and PM10 mass concentrations for each ground station, measurements were taken from 10:00 to 11:00 a.m. The associating AOD values of MODIS blue and green bands were also extracted. Therefore, 57 observations were gathered as data for the model, depicted in Figure 7 and described in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **N** & **Max** & **Min** & **Mean** & **StdEv** \\ \hline PM2.5 (\(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)) & 69 & 30 & 10 & 15.19 & 5.40 \\ \hline PM10 (\(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)) & 69 & 31 & 10 & 15.58 & 5.29 \\ \hline AOD\({}_{blue}\) & 69 & 976 & 95 & 367.38 & 172.07 \\ \hline AOD\({}_{green}\) & 69 & 709 & 65 & 258.54 & 125.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Details of observations from 1 May to 31 October 2022. Figure 6: The LST images on 9 June 2022 in the Dong Thap province. The dataset's multivariable linear regression reveals a relatively low correlation between values of PM mass concentrations at ground level and AOD values of MODIS blue and green bands. Specifically, the PM2.5 regression model exhibits a correlation coefficient of 0.700 and a root mean square error (RMSE) of 4.474 ug/m3, while the PM10 model demonstrates a correlation coefficient of 0.492 and an RMSE of 6.093 ug/m3, likely due to the presence of certain anomalies. To address this issue, an analysis using the RANSAC algorithm has been conducted to eliminate these anomalies [35]. Consequently, the dataset for the PM2.5 and PM10 regression models consists of 53 and 48 observations, respectively, as depicted in Table 3. As a result, the multivariable linear regression models for ground-level PM measurements and MODIS AODblue and AODgreen values exhibit stronger correlations and are represented by Equations (9) and (10). The PM2.5 regression model displays a correlation coefficient of 0.806 and an RMSE of 4.110 ug/m3, while the PM10 model exhibits a correlation coefficient of 0.676 and an RMSE of 5.741 ug/m3.