## 3 Materials and Methods
### Weather Station Design and Development
\begin{table}
\begin{tabular}{c c} \hline \hline
**Recommendation (Components)** & **Implemented Solution (Components)** \\ \hline Data acquisition (sensing instruments) & Specialized low-cost sensors \\ \hline Plug-and-play connectors & JST connector \\ \hline CPU & STM ARM Cortex-M3 \\ \hline Modern & ESP8266 module WiFi for one-way communication \\ \hline RTC clock & Internal RTC clock \\ \hline Power Supply & Internal battery \\  & PV module \\  & AC plug \\ \hline Sampling one minute at least & \
\begin{tabular}{c} Configurable measurement interval \\ 30 s, 1 min, 10 min, 30 min, 1 h, 12 h, 24 h \\ \end{tabular} \\ \hline Available memory for hundreds of days &
\begin{tabular}{c} 32 GB SD Card up to 10 years for \\ measurements each 30 s \\ \end{tabular} \\ \hline
**Recommendation (Design)** & **Implemented solution (Design)** \\ \hline Easy component replacement & Plug-and-Play sensors, components, display, and MCU. \\ \hline Stand-alone AWS & All-in-one design \\ \hline Visual indicators & s \\ \hline Extreme weather-proof case & IP65 case to cover all the components \\ \hline Sensors exposed to air but covered from & 3D PLA case to protect the sensors exposed \\ direct sunlight & to air \\ \hline Supporting structure between 1.25 m and 2 m & 1.6 m height mast \\ \hline \end{tabular}
\end{table} 
Table 4: Recommendations and proposed solutions. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Hour** & **Second** & **Date** & **Month** & **Year** & **AT** & **RH** & **AP** & **CO2** & **PM 1** & **PM 2.5** & **PM 10** \\ \hline
[h] & [s] & [DD] & [MM] & [YY] & [\({}^{\circ}\)C] & [RH\%] & [hPa] & [ppm] & [\(\mathrm{\SIUnitSymbolMicro}\)/\(\mathrm{m}^{3}\)] & [\(\mathrm{\SIUnitSymbolMicro}\)/\(\mathrm{m}^{3}\)] & [\(\mathrm{\SIUnitSymbolMicro}\)/\(\mathrm{m}^{3}\)] & [\(\mathrm{\SIUnitSymbolMicro}\)/\(\mathrm{m}^{3}\)] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Order of the columns stored in a CSV file. As stated before, the forecast ARIMA model is univariate; hence, a single variable is considered to predict values from the same variable. For instance, if temperature values are to be expected, previous temperature values are considered in the model. Each set of values (temperature, PM 1, PM 2.5, CO\({}_{2}\), etc.) is regarded as a time series since every measurement has a timestamp and is evenly spaced. This space between values is configured with the button that sets the measurement interval described in the human-machine interface. The time series (marked as green in Figure 4) represents all the values collected from the device from a single variable. The following step was to split the dataset into train and test data. The first part trains the model; the other compares the predicted values and quantifies the model's performance (Figure 5). Furthermore, since the ARIMA model requires the time series to be stationary, the mean and standard deviation must be constant; therefore, a statistical test called Augmented Dickey-Fuller was implemented to assess this requisite. If the time series was non-stationary, a different method was applied to turn it into stationary. This method subtracts the previous value from the current value [33]. The following equation shows the first-order differencing process:  
\[z_{t}=y_{t}-y_{t-1} \tag{1}\]  
where:  
* \(z_{t}\) is the new time series storing the differences of the original time series. * \(y_{t}\) is the current value of the original time series. * \(y_{t-1}\) is the first previous value of the actual time series at time t.  
Once this method is applied, the ADF test can be used to the time series \(Zt\) to determine if it is stationary. When this requisite is accomplished, the order for the model must be estimated. This model is made up of three parts: Autoregressive AR(p), Integrated I(d), and Moving Average MA(q); therefore, three parameters must be estimated: p, d, and q [34]. The \"d\" parameter represents the number of times the differencing method was applied to the time series.