# Analysis of the Landfill Leachate Treatment System Using Arima Models: A Case Study in a Megacity
## 1 Introduction  
The environmental impacts generated by population growth and concentration, including the significant increase in solid waste quantities, is one of the main problems facing cities worldwide [1, 2]. This trend has a negative influence on public health and natural resources close to final disposal sites for municipal solid waste (MSW) [3]. In Latin American countries, landfills are the most common final destination for MSW [4]. One of the most significant environmental problems associated with this type of facility corresponds to the liquid waste generated during its operation and closure [5, 6]. Leachate has become one of the main challenges of liquid waste treatment engineering, due to its high polluting loads (e.g., organic matter and heavy metals), and therefore it stands out as one of the areas of greatest interest during landfill management [7, 8]. In Latin America, it is often reported that the technologies implemented for the leachac transport and treatment do not meet the technical conditions for controlling environmental pollution and public health concerns [9]. This increases the operating costs of treatment systems and puts future use of existing natural resources at risk [10]. Leachate has been reported as the most significant source of landfill pollution [11]. Thus, during the development and implementation of strategies to reduce the environmental impacts of landfills, the collection and treatment of leachate should be a priority [10]. Worldwide, there are several publications of research results and comprehensive reviews focused primarily on the generation, composition, characterization, and treatment of leachate [12; 13]. Most of these publications focus on technological aspects of leachate treatment and prospects for future development [14]. However, most studies lack relevant information on time series analysis approaches to the physicochemical parameters considered during the operation of these leachate treatment systems (LTSs). Predicting the characteristics of untreated and treated leachate may be useful during optimal scheduling of LTSs. This will also be useful for maintaining stable performance during the operation of LTSs. In a few cases, staff experience and telemetry are sufficient to properly program and control the operation of these LTSs. Therefore, if future operation is to be optimally programmed, and the performance of LTSs improved, it is necessary to look for additional forecasting approaches from the time series of the measured chemical parameters. In this way, time series analyses of water quality parameters using auto-regressive integrated moving averages (ARIMA) models can be very useful. The ARIMA models have been successfully used in research for analysis in wastewater treatment plants (WTPs), and are popular due to their simplicity and robust statistical properties [15; 16; 17]. ARIMA models also provide a powerful method for accurate and reliable results without significant calibration time [18; 19; 20]. ARIMA models study the possible relationship in time between current and past data and their errors. These characteristics have favoured the application of ARIMA models to forecast the different variables in many fields of engineering and science associated with water resources [21; 22]. ARIMA models assume that the time series is generated from a linear process. This cannot be appropriate if the phenomenon under study is not linear, and indeed, this is what often happens in practice with real systems [23]. To overcome this weakness of ARIMA models with nonlinear time series, pre-processing techniques have been used [24]. For example, [PERSON] et al. [25] compared the performance of ARIMA (linear) and Artificial Neural Network (nonlinear) models to simulate time series of control parameters in a WTP. The results showed that ARIMA models were more accurate than Artificial Neural Network models in predicting daily concentrations of CO\({}_{2}\), Cl, and Ca. Studies on the use of ARIMA models in landfill leachate treatment systems are scarce.