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Article

ARIMA Analysis of PM Concentrations during the COVID-19 Isolation in a High-Altitude Latin American Megacity

by
David Santiago Hernández-Medina
,
Carlos Alfonso Zafra-Mejía
* and
Hugo Alexander Rondón-Quintana
Grupo de Investigación en Ingeniería Ambiental-GIIAUD, Facultad del Medio Ambiente y Recursos Naturales, Universidad Distrital Francisco José de Caldas, Bogotá E-110321, Colombia
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(6), 683; https://doi.org/10.3390/atmos15060683
Submission received: 29 April 2024 / Revised: 27 May 2024 / Accepted: 29 May 2024 / Published: 2 June 2024
(This article belongs to the Special Issue Urban Air Pollution, Meteorological Conditions and Human Health)
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Abstract

:
The COVID-19 pandemic precipitated a unique period of social isolation, presenting an unprecedented opportunity to scrutinize the influence of human activities on urban air quality. This study employs ARIMA models to explore the impact of COVID-19 isolation measures on the PM10 and PM2.5 concentrations in a high-altitude Latin American megacity (Bogota, Colombia). Three isolation scenarios were examined: strict (5 months), sectorized (1 months), and flexible (2 months). Our findings indicate that strict isolation measures exert a more pronounced effect on the short-term simulated concentrations of PM10 and PM2.5 (PM10: −47.3%; PM2.5: −54%) compared to the long-term effects (PM10: −29.4%; PM2.5: −28.3%). The ARIMA models suggest that strict isolation measures tend to diminish the persistence of the PM10 and PM2.5 concentrations over time, both in the short and long term. In the short term, strict isolation measures appear to augment the variation in the PM10 and PM2.5 concentrations, with a more substantial increase observed for PM2.5. Conversely, in the long term, these measures seem to reduce the variations in the PM concentrations, indicating a more stable behavior that is less susceptible to abrupt peaks. The differences in the reduction in the PM10 and PM2.5 concentrations between the strict and flexible isolation scenarios were 23.8% and 12.8%, respectively. This research provides valuable insights into the potential for strategic isolation measures to improve the air quality in urban environments.

1. Introduction

Air pollution, a global concern requiring urgent attention, imposes a significant economic strain on public health systems [1]. In 2019, it was responsible for an estimated 4.2 million premature deaths, with 38% due to ischemic heart disease, 20% to heart attacks, and 43% to chronic obstructive pulmonary disease [2]. Particulate Matter (PM), a key air pollutant, has a significant impact on public health. PM, comprising small-diameter particles of inorganic and organic substances, originates from coal combustion, thermal power plants, and industrial activities [3]. The severe effects of PM on the pulmonary and cardiac systems persist even at low exposure levels, particularly for particles with diameters ≤ 10 µm (PM10) and ≤2.5 µm (PM2.5) [4]. The smallest particles (PM2.5) evidence the greatest problems, because they can penetrate deep into the lungs and even reach the bloodstream. This phenomenon increases their toxicity, because they can cause or worsen respiratory and cardiovascular problems [5]. This issue is exacerbated in low- and middle-income countries, where a large segment of the population is exposed to PM. As a result, there is a significant economic burden due to the increased mortality and morbidity rates arising from cardiovascular and respiratory diseases [1]. The main sources of PM10 and PM2.5 in urban environments are primary combustion, such as vehicle emissions, coal combustion, biomass burning, secondary aerosol formation, industrial emissions, and dust sources [6].
The COVID-19 pandemic precipitated an acute respiratory disease, prompting the global implementation of social isolation measures to curb the spread of this novel biological agent [7]. These unprecedented social restrictions profoundly influenced global economic dynamics, social interactions, and urban environments [4]. This period of social isolation provided a unique opportunity to study the impact of human activities on urban air quality. The behavior of pollution emission sources underwent significant changes during the pandemic. Mobile sources (e.g., vehicles) [8,9], stationary sources (e.g., factories) [10,11], and fugitive sources (e.g., construction sites) [12,13] all responded to the isolation measures imposed by COVID-19. Consequently, the urban concentrations of PM10 and PM2.5 exhibited notable reductions [14,15]. These reductions were attributed to measures such as limiting vehicular traffic, restricting public transportation mobility, and curbing non-essential industrial production [16]. However, not all cities experienced uniform air quality changes. Some reported increases in the PM10 and PM2.5 concentrations [17,18]. The factors contributing to these increases included heightened household utility usage (e.g., natural gas and electricity) and the economic reactivation following the isolation period [19]. For instance, during the strict lockdowns in Indian cities like Chennai, New Delhi, and Kolkata, the PM10 and PM2.5 concentrations decreased by approximately 65% and 73%, respectively [20]. In contrast, Saudi Arabia witnessed PM concentration increases, likely due to regional phenomena like sandstorms and urban dust resuspension [17]. In the megacity of Bogota, Colombia, strict isolation measures led to a substantial reduction in PM10, NO2, and PM2.5 concentrations—approximately 39%, 63%, and 34%, respectively [7]. While meteorological conditions also played a role, historical observations during this period suggest that changes in human behavior were the primary drivers of the improved air quality [7,21].
The fluctuations in the PM concentrations within our study megacity during the COVID-19 isolation provide a unique lens through which to examine this air pollutant’s behavior under conditions of reduced anthropogenic activity. This analysis holds particular significance given PM’s central role in the megacity’s air quality monitoring and control efforts [22]. PM’s relevance in our megacity stems from its frequent exceedances of the permissible limit values for both PM10 and PM2.5. While these elevated concentrations are often associated with human activities, they may also result from the unfavorable meteorological conditions that hinder atmospheric dispersion. Factors such as persistent thermal inversions, reduced precipitation, and wind patterns conducive to the influx of external pollutants all contribute to these exceedances [23,24]. Moreover, regional events—such as biomass burning in nearby crops—can introduce external air pollutants [25]. Therefore, our investigation sheds light on the dynamics of PM in a megacity during a unique period of reduced human activity, emphasizing the need for comprehensive air quality management strategies. While there is some literature on air quality changes during COVID-19, few studies have focused on high-altitude urban environments where the atmospheric dynamics can differ significantly.
During efforts to prevent and mitigate air pollution events, governmental entities and research centers have increasingly turned to modeling techniques to generate early warnings of air quality deterioration [26]. These models serve as valuable tools for air pollution control and management [27]. Simulation models such as Weather Research and Forecasting (WRF) or the WRF-CMAQ require a substantial amount of air quality data and meteorological information to achieve accurate predictions [26,28]. To address this need, researchers have explored the individual modeling of air pollutants using time series analysis—a statistical approach based on continuous observations of a variable of interest over a specific time interval [29,30]. Among the time series analysis techniques, Autoregressive, Integrated, and Moving Average (ARIMA) models play a prominent role. ARIMA models allow for the study of air pollutants individually and over time by analyzing correlations within the data series itself [31]. The ARIMA model comprises three key components: (1) an Autoregressive (AR) component, which accounts for the regressors of the time series and reflects the model’s memory [32,33]; (2) an Integrated (I) component, which is associated with the number of differences needed to make the time series stationary (i.e., independent of time) [33]; and (3) a Moving Average (MA) component, which addresses random shocks and inherent variability within the time series [33]. The ARIMA structure facilitates the simulation of parameter behavior in the presence of external variables by capturing linear trends within the time series [34]. By leveraging these models, researchers can enhance our understanding of air pollutant dynamics and contribute to effective pollution management strategies.
The application of ARIMA models in analyzing urban PM concentrations has garnered attention across several studies [35,36]. Notably, the optimal ARIMA simulations of the PM concentrations were observed at an hourly timescale, during short time intervals, and in the urban areas characterized by low pollutant persistence (where the autoregressive [AR] term was less than 4) [26,37]. Sensitivity analyses revealed that the ARIMA models performed well when dealing with a PM concentration series exhibiting substantial temporal variability [38]. This variability in the PM concentrations was closely linked to unfavorable meteorological conditions for air pollutant dispersion [31] and regional particle transport phenomena [39]. Megacities provided an ideal testing ground, where persistent PM10 (with AR terms exceeding 8) and variable PM10 (with moving average [MA] terms exceeding 8) were observed in the areas dominated by impervious surfaces [40]. Furthermore, the ARIMA models were pitted against other modeling approaches (such as Long Short-Term Memory [LSTM], Random Forest Regression [RFR], and Support Vector Regression [SVR]) to explore the concentration behavior of PM during the COVID-19 isolation. The ARIMA models demonstrated a satisfactory performance within this context [41]. Moreover, the historical PM2.5 concentration series were compared with those observed during the COVID-19 isolation. The latter exhibited higher persistence and variability, underscoring the unique impact of pandemic-related behavioral changes on urban air quality [4].
The objective of this work is to investigate by means of ARIMA models the effect of COVID-19 isolation measurements on the PM10 and PM2.5 concentrations in Bogota, Colombia—a megacity located at a high altitude. Leveraging the ARIMA models, we explore the following three distinct scenarios for the PM concentrations: (1) the historical series, examining pre-pandemic data; (2) pre-isolation, assessing the PM concentrations before the initial isolation measures; and (3) during isolation, analyzing the impact of various social isolation strategies (strict, sectorized, and flexible) implemented by the city administration. Our study holds practical significance in the field of urban pollution for the following reasons. (1) The Evaluation of ARIMA Models: We assess the utility of ARIMA models in studying urban air quality interventions. These models allow us to dissect the temporal dynamics of the PM concentrations. (2) A High-Altitude Context: Bogota’s unique high-altitude conditions introduce additional complexities. We investigate how COVID-19 isolation influenced the PM levels in this challenging environment. (3) Temporal Structure Analysis: By examining different types of social isolation measures, we gain insights into the temporal behavior captured by our ARIMA models. Lastly, this research contributes to our understanding of the pandemic-induced changes in air quality and informs effective pollution management strategies.

2. Materials and Methods

2.1. Study Site Description

The megacity under investigation is situated in Colombia (South America, 4°35′53″ N–74°4′33″ W). Bogota, the capital of Colombia, is positioned at an average elevation of 2640 masl and is recognized as one of the most densely populated megacities in Latin America, with a density of 272 inhabitants/Ha. This density surpasses the Latin American average by a factor of 2.5 [42]. Owing to its proximity to the equator, this megacity does not experience distinct seasons and maintains an average annual temperature ranging between 14 and 15 °C. Its elevated location results in hourly temperature fluctuations reaching up to 17.9 °C within a single day. The annual precipitation pattern reveals two periods of increased precipitation: March–May and September–November [43]. For the purpose of air quality monitoring, four stations were selected (RMCAB, http://rmcab.ambientebogota.gov.co/home/map (accessed on 1 February 2022). These stations are identified as follows (Figure 1): Centro de Alto Rendimiento—CAR, Kennedy—KEN, Las Ferias—LAF, and Tunal—TUN. The selection criteria for these stations included comprehensive coverage of the megacity and data availability exceeding 90% for the PM10 and PM2.5 concentrations. The characteristics of these monitoring stations are detailed in Table 1.

2.2. Data Collection

The time series data procured from the monitoring stations provided hourly information on the PM10 and PM2.5 concentrations, relative humidity (%), precipitation (mm), temperature (°C), wind speed (m/s), and wind direction (°). This study spanned a period of five years, from 1 January 2017 to 31 December 2021, a timeline chosen in accordance with the occurrence of the COVID-19 isolation episodes within the megacity. The method employed for the collection of PM10 and PM2.5 samples, aimed at determining the presence of inorganic compounds in the air, adhered to the guidelines established by U.S. EPA/625/R-96/010a [44]. Furthermore, the monitoring system was designed in compliance with the guidelines set forth in CFR 40, Part 50, Appendices J and L, for the automatic measurement of PM10 (Met One Bam 1020) and PM2.5 (Thermo Scientific FH62C14-DHS, MA, USA) [45]. The system utilized Beta Ray Attenuation as the principle of measurement.

2.3. Data Analysis

The analysis of the data was conducted in three phases. In the first phase, an exploratory analysis of the hourly time series of atmospheric pollutants and meteorological parameters was carried out. Initially, the non-normal distribution (p-value > 0.050) of the time series was examined using a Kolmogorov–Smirnov test [46]. The correlation between the variables considered was also studied using Spearman’s coefficient [47]. These correlations between PM10 and PM2.5 and the meteorological parameters allowed for the study of the influence of meteorological conditions and anthropogenic activity due to COVID-19 isolation. Three scenarios of human activity were considered: (1) pre-isolation, (2) isolation, and (3) historical. Descriptive statistics (mean, median, and standard deviation) were calculated for the time series under study. The IBM SPSS Statistics V.25.0 software [48] was used in this study. Lastly, wind roses, pollution roses, and polar roses were developed for the PM10 and PM2.5 concentrations at each of the monitoring stations using the OpenAir package of the R software [12,15].
In the second phase, the missing data in the time series of atmospheric pollutants and meteorological parameters were filled in. It was previously confirmed that all the time series had more than 75% of the data. The random nature of the missing data was detected using the Visualization and Imputation of Missing Values (VIM) package of the R 4.2.1 software [49]. The missing data were filled in using the Multivariate Imputation by Chained Equations (MICE) and Nonparametric Missing Value Imputation using Random Forest (MissForest) packages of the R 4.2.1 software [50]. The MICE package used various methods (Predictive Mean, Bayesian Linear Regression, and Logistic Regression) to fill in the missing data [51]. The MissForest package filled in the missing data by creating random forests with the observed data, then iterated again until the minimum error was obtained [52].
In the third phase, the ARIMA models were developed for the PM10 and PM2.5 concentrations. The timescales considered were as follows: daily (24 h moving average), weekly (120 h moving average), and monthly (720 h moving average). The development of the ARIMA models also considered three scenarios: (1) before the first isolation (E1), (2) during the different types of isolation (E2.1 = strict, E2.2 = sectorized, and E2.3 = flexible), and (3) the historical trend (E3) (Table 2). The development of the ARIMA models was based on the methodology reported by Box and Jenkins [53]. This methodology considered the following stages: identification, parameter estimation, assumption verification, and model use [53]. From the temporal length of the isolation scenarios considered (less than 5 months), it was assumed in this study that the weekly timescale was the most appropriate. The ARIMA modeling of the PM10 and PM2.5 concentrations was performed using IBM SPSS Statistics V.25.0 software [54].
During the development of the ARIMA models, the PM concentration time series underwent differencing and transformation (square root and logarithmic) according to their time structure. This process was performed to obtain a stationary series (independence). Subsequently, the model identification and parameter estimation were carried out, leading to the determination of the p, d, and q orders of the ARIMA models. The previous stages were carried out using the IBM SPSS Statistics V.25.0 Expert Modeler Tool [54]. Once the p, d, and q terms were identified, compliance with the Ljung–Box statistic (p-value > 0.05) was verified in the model obtained. Compliance with the Ljung–Box test indicated that the residuals of the model were equal or close to zero and that its variance was constant, becoming white noise, confirming the development of a model that adequately described the observed variable [33,55]. If the Ljung–Box statistic was not met, the p, d, and q terms of the initial model were modified until a suitable ARIMA model was obtained. Once the ARIMA model was identified, estimated, and verified, the goodness of fit coefficients (RMSE: root mean square error, MAE: mean percentage error, MAPE: maximum mean percentage error, and R2) were reviewed, with emphasis placed on the Bayesian Information Criterion (BIC) [54]. This last statistic allowed the selection of the model with the highest goodness of fit using the fewest possible terms [56]. All statistics were estimated with a confidence level of 95%.
Table 2. ARIMA analysis scenarios for COVID-19 isolation.
Table 2. ARIMA analysis scenarios for COVID-19 isolation.
ScenarioStart DateEnd DateCharacteristics
Pre-isolation
E1		1 January 202025 March 2020Without any type of isolation. Usual behavior of anthropic activities.
Isolation
E2		Strict
E2.1		25 March 202027 August 2020Controlled outflow for primary activities (health, services, and supply). 41% reduction in population mobility [57]. 85–90% reduction in vehicular transport. Staggered reduction in isolation (12 June 2023). 65–70% reduction in vehicular transport [58].
Sectorized
E2.2		5 January 20212 February 2020Weekly isolation by sector in the city. 63.3% of work activity was remote. Access to closed spaces, stores, and public areas continued to be restricted [59].
Flexible
E2.3		10 April 20207 June 2020Reactivation of the economic sectors of manufacturing, construction, restaurants, and educational centers. Control of maximum capacity in transportation, public places, and commercial establishments. By the end of the scenario, general isolation was repealed [60].
Historical
E3		Strict
E3.1		25 March 2017–201927 August 2017–2019Historical concentrations of atmospheric pollutants for the same periods (without COVID-19). Three previous years according to other authors’ considerations [14,61,62].
Sectorized
E3.2		5 January 2017–20192 February 2017–2019
Flexible
E3.3		10 April 2017–20197 June 2017–2019
Following the development of the ARIMA models, the time series simulated under scenarios E1, E2, and E3 were examined. With this information on the PM10 and PM2.5 concentrations, the following analyses were performed: (1) A non-parametric comparison (a Mann–Whitney U test) [63] was conducted on the simulated time series for scenarios E2 and E3 (E2.1–E3.1, E2.2–E3.2, and E2.3–E3.3). This allowed for an evaluation of similar behavior between the time series of the isolation period (with COVID-19) and the historical period (without COVID-19). (2) The percentage changes were calculated for the simulated weekly concentrations in the short and long term. The short-term percent changes were obtained by comparing the weekly PM10 and PM2.5 concentrations between scenarios E1 (pre-isolation) and E2 (isolation): E1–E2.1, E1–E2.2, and E1–E2.3. The long-term percent change was obtained by comparing the weekly PM10 and PM2.5 concentrations between scenarios E2 (isolation) and E3 (historical): E2.1–E3.1, E2.2–E3.2, and E2.3–E3.3. The aim was to quantify the change in the trends of the PM10 and PM2.5 concentrations during the isolation scenarios [64]. (3) The behavior of the p, d, and q terms and the goodness of fit statistics (BIC, RMSE, MAPE, and R2) of the ARIMA models developed for both the short and long term were analyzed. With this, the occurrence of changes in the ARIMA temporal structure of the weekly PM10 and PM2.5 concentrations during the COVID-19 isolation was evaluated. This was done in relation to the other established analysis scenarios.

3. Results and Discussion

3.1. PM Concentrations

The results showed that the lowest PM concentrations at the monitoring stations were observed during scenario E2.1 (strict isolation): CAR/PM10 = 15.7 µg/m3, KEN/PM10 = 33.9 µg/m3, LAF/PM10 = 17.0 µg/m3, TUN/PM10 = 25.9 µg/m3, CAR/PM2.5 = 9.38 µg/m3, KEN/PM2.5 = 16.9 µg/m3, LAF/PM2.5 = 9.40 µg/m3, and TUN/PM2.5 =10.3 µg/m3 (Figure 2). This trend was possibly associated with the strict isolation measures and temporary closure of non-primary economic activities observed during this scenario. However, sudden increases in the PM concentrations were observed between the months of March–April and June–July 2020, which were probably associated with regional PM transport events (e.g., forest biomass burning) [25]. The above trend was more evident in PM10 compared to PM2.5. During the E2.3 flexible isolation scenario, a decrease in the PM concentrations was also observed, although this decrease was smaller compared to the E2.1 scenario (PM10 = 6.92%; PM2.5 = 5.79%). This lower reduction in the PM concentrations was possibly associated with the controlled reactivation of the economic sectors of manufacturing, construction, restaurants, and educational centers, due to flexible isolation (Table 2) [59].
The findings showed that the highest concentrations of PM at the monitoring stations were observed during the E1 scenario (pre-isolation): CAR/PM10 = 31.3 µg/m3, KEN/PM10 = 47.8 µg/m3, LAF/PM10 = 36.8 µg/m3, TUN/PM10 = 49.9 µg/m3, CAR/PM2.5 = 20.6 µg/m3, KEN/PM2.5 = 29.6 µg/m3, LAF/PM2.5 = 22.9 µg/m3, and TUN/PM2.5 = 22.1 µg/m3 (Figure 2). During the E2.2 scenario of sectorized isolation, the PM concentrations also tended to increase, although this increase was smaller compared to the E1 scenario (PM10 = 23.9%; PM2.5 = 45.3%). The E2.2 scenario showed a different behavior in relation to the other isolation scenarios (E2), as increases in the PM concentrations were observed (Table 2). This trend was similar to that observed during the E3.2 scenario of historical behavior, although the increases in the PM concentrations were lower (PM10 = 21.3%; PM2.5 = 23.0%). The results suggested that this trend was related to the period of occurrence of these two scenarios (E2.2 and E3.2). That is, the two scenarios developed during the first three months of each year, a period of time in which it was common to detect episodes of regional PM transport (e.g., forest fires and Saharan dust) and meteorological conditions where there was a decrease in precipitation and wind speed [23,25]. These meteorological conditions probably did not favor the dispersion of pollutants, which facilitated the formation and accumulation of PM in the urban atmosphere. This behavior has also been reported in cities under similar meteorological conditions [65].
The findings showed strong positive correlations between the PM10 and PM2.5 concentrations (Spearman rs between 0.71 and 0.89) at all the monitoring stations during the E3 scenario (historical scenario). This trend suggested a similar historical behavior in the formation and transport of PM10 (the coarse fraction) and PM2.5 (the fine fraction), which has also been reported in other studies [66]. However, during the isolation scenarios (E2), a decrease in the Spearman correlation coefficients (rs between 0.50 and 0.76) was evidenced. The previous trend suggested a change in the activity of PM10 and PM2.5 pollution sources during the isolation scenarios. On average, there was a 20% decrease in the magnitude of the Spearman correlation coefficients during the isolation scenarios (E2). This decrease in the correlation between the PM10 and PM2.5 concentrations was possibly associated with the intervention of external factors during the formation and transport of PM. There were studies that associated this trend with the following external factors: (1) differential meteorological conditions that increased the concentrations of a specific PM fraction [48,67]; (2) regional PM transport events, which increased PM concentrations [68,69]; and (3) changes in the baseline level of atmospheric emissions [11,70]. Therefore, the results suggested that the possible changes in the PM concentrations during the isolation scenarios (E2) could not be attributed exclusively to the implemented restrictions (Table 2).

3.2. Meteorological Analysis

The results showed that the lowest relative humidity occurred during the E2.2 scenario of sectorized isolation (CAR = 64.7%, KEN = 59.9%, LAF = 60.2%, and TUN = 57.8%). This scenario also showed the least precipitation (CAR = 15.1 mm, KEN = 20.5 mm, LAF = 10.3 mm, and TUN = 9.20 mm). The E2.2 scenario of sectorized isolation showed a different behavior in the PM concentrations compared to the other isolation scenarios (E2). That is, an increase in the PM concentrations was observed, possibly associated with the decrease in precipitation and relative humidity, which probably led to less favorable conditions for the vertical dispersion of this air pollutant, similar conditions to those reported in Milan (Italy) [60] and Sao Paulo (Brazil) [64] during the COVID-19 isolation. This trend was also similar to that observed during the E3.2 scenario of historical behavior, although the increases in the PM concentrations were lower in the E2.2 scenario (PM10 = 21.3%; PM2.5 = 23.0%). On the other hand, the findings showed that the highest relative humidity occurred during the E2.1 (CAR = 67.2%, KEN = 63.0%, LAF = 62.6%, and TUN = 62.7%) and E2.3 (CAR = 67.6%, KEN = 63.9%, LAF = 64.3%, and TUN = 63.9%) scenarios of strict and flexible isolation, respectively. These two scenarios were also the ones that showed the highest precipitation (351/233 mm, 983/370 mm, 202/293 mm, and 376/264 mm, respectively). The results suggested that during the E2.1 scenarios of strict isolation and E2.3 of flexible isolation, the decrease in the PM concentrations were, in part, influenced by the observed meteorological conditions, which favored the washing of the pollutants in the atmosphere [17]. However, the effect on the PM concentrations of the restrictions established during these two isolation scenarios (Table 2) should not be forgotten.
On average, the findings showed that wind speeds tended to be slightly higher (4.74%) during the isolation scenarios (E2) compared to the historical scenarios (E3). However, a Mann–Whitney U test allowed the visualization of the non-existence of significant differences (p-value > 0.050) in the wind speed between these two scenarios. In relation to the wind direction, the results showed a predominance from the SE at all study stations. Nevertheless, as there were isolation scenarios less than five months, the predominant wind direction tended to change during these periods (SSE, SSW, S, and NE). The findings showed that during the scenario of the lower concentrations of PM10 and PM2.5 (E2.1—strict isolation) the predominant wind direction at the monitoring stations was between SSE and SE. During the E2.3 scenario (flexible isolation), a decrease in the PM concentrations was also observed, as was a predominance in the wind direction between SSE and E. In general, the results suggested that during the scenarios of lower PM concentrations (E2.1 and E2.3) the wind tended to come from the east. In contrast, during the scenario of higher PM concentrations (E1—pre-isolation) the predominant wind direction at the monitoring stations tended to be between WNW and SW. During the E2.2 scenario, the PM concentrations tended to increase. Under this scenario, the predominant wind direction tended to be between SSW and WNW. This trend was similar to that observed during the E3.2 scenario of historical behavior, in which an increase in the PM concentrations was evidenced. In general, the findings suggested that during the scenarios of increasing PM concentrations (E1, E2.2, and E3.2) the wind tended to come from the west. This trend was more evident for PM2.5 than for PM10 (Figure 3). Lastly, the results suggested that the E2.2 scenario of sectorized isolation (where there was an increase in the PM concentration) could have been influenced by the regional transport of PM (with the forest biomass burning). Other authors have also reported scenarios where urban PM concentrations were influenced by adverse weather conditions that caused the dispersion of air pollutants [67,70].
The results showed that the meteorological variable that best correlated with the PM10 (rs ≤ 0.32) and PM2.5 (rs ≤ 0.32) concentrations during the historical scenario (E3) was wind direction. Significant positive correlations from weak to medium (rs for PM10 up to 0.32 and rs for PM2.5 up to 0.32) were observed with this variable. However, during the isolation scenarios (E2), these correlations tended to increase. This increase in correlations was greater for PM2.5 compared to PM10. Significant positive correlations for PM10 and PM2.5 up to 0.40 and 0.52 (around medium), respectively, were evidenced. The results suggested that during the isolation scenario (E2) there was possibly an external contribution of PM (due to regional transport) in the megacity under study, especially for the concentrations in the southwest (KEN) and south (TUN) of the megacity. From the observed increase in the correlations, the external contribution of PM2.5 was possibly greater compared to the contribution of PM10. Borhani et al. [66] reported a similar scenario in Tehran (Iran), where during the isolation, regional transport contributed increases in suspended particles, especially for PM2.5, which came from industrial and stationary emission sources. The findings also suggested that the increase in the PM concentrations during the E2.2 scenario (sectorized isolation) compared to the other isolation scenarios (E2.1 and E2.3) could be related to the episodes of regional PM transport and increases in the PM emission levels in the sectors of the megacity that had no restrictions (Table 2). Lastly, this study showed very weak or non-significant correlations of the PM concentrations with precipitation, relative humidity, wind speed, and temperature during the isolation scenarios considered.

3.3. Short- and Long-Term Analysis

ARIMA models were developed for each of the considered scenarios (E1, E2, and E3). The models were developed under a weekly timeframe (168 h moving average; Table 3). The results, based on a Kolmogorov–Smirnov test, showed that all the variables exhibited a non-normal distribution (p-value < 0.05).
The results showed that, during scenario 2.1 of strict isolation, the greatest short-term reduction (compared to E1) was observed in the simulated concentrations of PM10 and PM2.5. On average, the short-term reductions in the PM10 and PM2.5 concentrations for the LAF, CAR, TUN, and KEN stations were as follows: 55.7/60.6%, 51.9/56.5%, 49.9/54.8%, and 31.6/44.1%, respectively (Figure 4). In relation to the long-term trend (compared to E3.1), the findings showed a smaller reduction in the simulated concentrations of PM10 and PM2.5 during scenario E2.1. On average, the long-term reductions in the PM10 and PM2.5 concentrations for the CAR, LAF, KEN, and TUN stations were as follows: 34.2/24.7%, 37.3/25.3%, 24.4/25.2%, and 21.7/37.9%, respectively. The findings confirmed that there was a short- and long-term reduction in the PM concentrations during scenario E2.1 of strict isolation. Indeed, this trend was primarily related to the strict isolation measures implemented due to COVID-19. A Mann–Whitney U test indicated the existence of significant differences (p-value > 0.050) between the simulated PM concentrations for scenarios E2.1 and E3.1. This behavior possibly confirmed the incidence of the implemented strict isolation measures. However, the influence of factors such as the regional transport of PM from hotspots and the emissions from industrial activities external to the megacity was not ruled out [14,71].
The findings showed that the greatest short-term reductions in the simulated PM concentrations during the E2.1 scenario of strict isolation tended to occur for PM2.5 compared to PM10. On average, the short-term reductions (compared to E1) in the PM2.5 and PM10 concentrations during the E2.1 scenario were as follows (significant differences, p-value < 0.050): 54/47.3%, respectively (Figure 4). Indeed, this short-term trend in the PM2.5 concentrations was possibly related to the established strict isolation restrictions. In other words, the strict isolation measures had a greater short-term effect on PM2.5 compared to PM10. In the long term (compared to E3.1), the reduction in the PM2.5 and PM10 concentrations was similar (no significant differences, p-value = 0.235): 28.3/29.4%, respectively. The results suggested that this long-term trend was possibly more associated with those regional PM transport processes that typically occurred during the months of March–April and June–July [21]. These regional transport processes possibly tended to increase the PM2.5 concentrations, which made the decrease in the PM2.5 and PM10 concentrations similar. Short-term measures were important for assessing immediate exposure and health risks, while long-term measures were essential for understanding trends and the cumulative effects of air pollution over time [64].
In the E2.2 scenario of sectorized isolation, the results revealed a short-term reduction (compared to E1) in the simulated PM concentrations (significant differences, p-value < 0.050). The reduction in the PM10 and PM2.5 concentrations for the LAF, TUN, and CAR stations were as follows: 29.4/28.0%, 19.3/32.6%, and 13.3/28.5%, respectively. On the other hand, the KEN station did not show a short-term reduction (no significant difference, p-value = 0.670) in the PM10 concentrations (0.01%), although it did experience a reduction in the PM2.5 concentrations (21.8%). In general, in the long term (compared to E3.2), the PM10 concentrations also tended to decrease during the E2.2 scenario of sectorized isolation (significant differences, p-value < 0.050). The decrease in the concentrations for the LAF, CAR, and KEN stations were as follows: 27.6%, 13.8%, and 3.11%, respectively. At the TUN station, no reduction was observed (no significant difference, p-value = 0.753) in the PM10 concentration; that is, a slight increase of 1.50% in the PM10 concentration was evidenced. In relation to the PM2.5 concentrations, the results showed reductions of 29.4%, 14.9%, and 9.79% in the concentrations for the TUN, CAR, and KEN stations, respectively. At the LAF station, no reduction was observed (no significant difference, p-value = 0.510) in the PM2.5 concentration; that is, a slight increase of 0.55% was evidenced.
Therefore, the results revealed that the greatest short-term reductions (compared to E1) in the simulated PM concentrations during the E2.2 scenario of sectorized isolation tended to occur for PM2.5 rather than PM10. On average, the short-term reductions in the PM2.5 and PM10 concentrations during the E2.2 scenario were as follows: 29.7/20.7%, respectively (Figure 4). However, at the KEN station, no significant short-term reduction was observed in the PM10 concentrations, but for the PM2.5 concentrations this reduction was significant during the E2.2 scenario of sectorized isolation. In the long term (compared to E3.2), the reduction in the PM2.5 and PM10 concentrations were as follows: 18.0/14.8%, respectively. However, at the TUN and LAF stations, no reductions were observed in the PM10 and PM2.5 concentrations. These short- and long-term trends in the reduction in the concentrations during the E2.2 scenario were possibly related to the established sectorized isolation measures (Table 2). In other words, under this scenario of sectorized isolation, the short- and long-term reductions in the PM2.5 (45/36.4%) and PM10 (56.2/49.7%) concentrations were smaller compared to the E2.1 scenario of strict isolation.
Additionally, it was observed during the E2.2 scenario of sectorized isolation that in the central (CAR), western (LAF), and southern (TUN) zones of the megacity, reductions in the PM concentrations occurred both in the short and long term. In the southwestern zone (KEN), the smallest reductions in the PM concentrations were observed. Thus, the results suggested that in the center, west, and south of the megacity, the restrictions due to sectorized isolation had a greater effect compared to the southwestern zone. This spatial trend was possibly related to the influence of the sectorized isolation measures and land use [40,72]. Moreover, during this scenario, climatic conditions possibly also influenced the smaller reduction in the PM concentrations. That is, a higher occurrence of low wind speeds (<1m/s), a decrease in precipitation, the development of thermal inversions, and the regional transport of PM were reported [7,73,74].
In the E2.3 flexible isolation scenario, the results evidenced a short-term reduction (compared to E1) in the simulated PM concentrations (significant differences, p-value < 0.050). The average reduction in the PM10 and PM2.5 concentrations for the LAF, TUN, CAR, and KEN stations were as follows: 50.9/62.2%, 48.6/60.3%, 42.9/50.6%, and 21.5/40.8%, respectively. In the long term (compared to E3.3), the findings also showed a reduction in the simulated PM10 and PM2.5 concentrations during the E2.3 scenario. On average, the reductions in the PM10 and PM2.5 concentrations for the TUN, LAF, CAR, and KEN stations were as follows: 26.4/51.4%, 38.3/36.8%, 31.6/26.6%, and 18.4/25.7%, respectively. The findings suggested that there was a short- and long-term reduction in the PM concentrations during the E2.3 flexible isolation scenario. A Mann–Whitney U test suggested the existence of significant differences (p-value > 0.050) between the simulated PM concentrations for the E2.3 and E3.3 scenarios. Indeed, this trend was mainly related to the flexible isolation measures implemented as a result of COVID-19. For example, it was reported that 63.3% of the population worked remotely [60]. However, meteorology possibly also influenced the reduction in the PM concentrations during this flexible isolation scenario.
The results revealed that the greatest short-term reductions (compared to E1) in the simulated PM concentrations during the E2.3 flexible isolation scenario tended to occur for PM2.5 compared to PM10. On average, the short-term reductions in the PM2.5 and PM10 concentrations during the E2.3 flexible isolation scenario were as follows: 53.5/41%, respectively (Figure 3). In the long term (compared to E3.3), the reduction in the PM2.5 and PM10 concentrations were as follows: 35.1/28.7%, respectively. Indeed, these short- and long-term trends in the reduction in the concentrations during the E2.3 scenario were possibly related to the established flexible isolation measures (Table 2), the observed climatic conditions, and the regional contribution of PM. Under this flexible isolation scenario, the short- and long-term reductions in the PM2.5 (1.01 times lower and 1.24 times higher) and PM10 (1.15 times lower and 1.02 times lower) concentrations were similar or greater comparatively with the E2.1 strict isolation scenario.

3.4. ARIMA Models

The analysis scenarios considered were comparatively evaluated based on variations in the autoregressive (AR), difference (I), and moving averages (MA) terms of the developed ARIMA models.
In relation to the E2.1 strict isolation scenario, the results showed AR = 1 in the ARIMA models developed for the PM10 concentrations. In the short term (E1), the findings showed AR terms between 2 and 6 for the PM10 concentrations (Table 3). The results suggested that, in the short term, strict isolation measures tended to decrease the magnitude of the AR term (phenomenon memory). In other words, strict isolation measures tended to reduce the persistence over time of the PM10 concentrations. Persistence in an ARIMA model refers to the model’s ability to capture and predict the temporal relationship between the past and present values of a time series [31]. High persistence could indicate a time series with a trend or pattern that persisted over time, while low persistence could indicate a more volatile time series or with more abrupt changes [75]. In relation to the PM2.5 concentrations, the results showed AR terms between 0 and 1 during the E2.1 strict isolation scenario. In the short term, the AR terms varied between 0 and 11. The results suggested that, in the short term, strict isolation measures tended to reduce the persistence over time of the PM2.5 concentrations. Thus, it was suggested that, in the short term, strict isolation measures generated a greater reduction in the persistence of PM2.5 compared to PM10. This lower persistence in the finer fraction (PM2.5) possibly decreased the toxic effects of PM on human health.
Additionally, in the long term, the results showed AR terms between 1 and 7 in the ARIMA models developed for the PM10 concentrations (Table 3). The results suggested that, in the long term, strict isolation measures tended to reduce the persistence over time of the PM10 concentrations. In relation to the PM2.5 concentrations, in the long term, the findings showed that the AR terms varied between 0 and 1. Thus, it was suggested that, in the long term, the strict isolation measures implemented possibly did not significantly change the persistence over time of the PM2.5 concentrations. The long-term persistence of the PM2.5 concentrations could have been influenced by the following complex factors: regional PM transport patterns and meteorological conditions [76].
The results showed that during the E2.1 strict isolation scenario, the I term in the ARIMA models varied between 1 and 2 for the PM10 and PM2.5 concentrations, respectively. In the short term (E1), a similar trend was observed for the PM10 and PM2.5 concentrations (Table 3). The results suggested that the strict isolation measures implemented did not comparatively change the decreasing trend in the PM10 and PM2.5 concentrations observed in the short term (Figure 2). In the long term (E3.1), the ARIMA models showed I terms = 1 and between 1 and 2 for the PM10 and PM2.5 concentrations, respectively. The results suggested that, in the long term, the strict isolation measures implemented probably had a greater effect on the PM10 concentrations compared to the PM2.5 concentrations. Namely, during the strict isolation scenario, a greater decreasing trend was suggested for the PM10 concentrations compared to the PM2.5 concentrations.
In relation to the MA term of the ARIMA models developed during the E2.1 strict isolation scenario, the findings evidenced a variation in its magnitude between 3 and 5 and between 6 and 12 for the PM10 and PM2.5 concentrations, respectively (Table 3). In the short term (E1), a variation in the MA term was observed between 1 and 7 for the PM10 and PM2.5 concentrations, respectively. In general, the results suggested that, in the short term, strict isolation measures tended to increase the variations in the PM10 and PM2.5 concentrations. This increase in the variation of the concentrations was greater for PM2.5 compared to PM10. The increase in the variations of the PM10 and PM2.5 concentrations did not indicate an increase in the PM concentrations in the short term [77]. The variations in the concentrations in the short term were probably related to factors such as climatic effects and specific external events that were reported in the previous sections. In the long term (E3.1), a variation in the MA term was observed between 1 and 12 and between 5 and 15 for the PM10 and PM2.5 concentrations, respectively. In general, the findings suggested that, in the long term, strict isolation measures (E2.1) tended to decrease the variations in the PM10 and PM2.5 concentrations. It is probable that the PM concentrations were more stable and less prone to sudden peaks during this strict isolation scenario.
During the E2.2 sectorized isolation scenario, the results showed AR terms between 0 and 4 and between 0 and 1 in the ARIMA models developed for the PM10 and PM2.5 concentrations, respectively. In the short term (E1), the findings showed AR terms between 2 and 6 and between 0 and 11 for the PM10 and PM2.5 concentrations, respectively (Table 3). The results suggested that, in the short term, sectorized isolation measures tended to decrease the magnitude of the AR term. That is, sectorized isolation measures tended to reduce the persistence over time of the PM10 and PM2.5 concentrations. It was also suggested that, in the short term, sectorized isolation measures comparatively generated a greater reduction in the persistence of PM2.5 compared to PM10. In the long term (E3.2), the results showed AR terms between 0 and 1 and between 0 and 2 for the PM10 and PM2.5 concentrations, respectively. The results suggested that, in the long term, sectorized isolation measures tended to reduce the persistence over time of the PM10 concentrations. In relation to the PM2.5 concentrations, in the long term, the findings suggested that sectorized isolation measures did not considerably change their persistence over time. The long-term persistence of the PM2.5 concentrations may have been influenced by complex factors such as regional PM transport patterns and the meteorological conditions observed [78]. The results showed that there were changes in the wind patterns during the sectorized isolation scenario, which likely influenced the dispersion and accumulation of PM2.5. This behavior could have affected its long-term persistence. Moreover, the long-term persistence in the PM concentrations possibly depended on factors such as the duration and intensity of isolation measures, the composition of PM sources, and the subsequent economic recovery [79].
The results showed that during the E2.2 sectorized isolation scenario, the I term in the ARIMA models was equal to 2 for the PM10 and PM2.5 concentrations. In the short term (E1), it was observed that the I term varied between 1 and 2 for the PM10 and PM2.5 concentrations (Table 3). Comparatively, the results suggested that the sectorized isolation measures implemented did not considerably change the increasing trend in the PM2.5 concentrations observed in the short term (Figure 2). However, with the I terms = 1 in the short term (E1) in some ARIMA models, a lesser increasing trend in the PM2.5 concentrations was suggested during this scenario. In relation to PM10, the interpretation required further analysis. Namely, in the CAR and LAF stations, an increasing trend (+) was observed, and, in the KEN and TUN stations, a decreasing trend (−) was observed in the concentrations. Therefore, the results suggested that the ARIMA models indicated with the I term the magnitude of the trend in the PM10 concentrations but did not indicate whether the trend was increasing (+) or decreasing (−). In the long term (E3.2), the ARIMA models showed I terms between 1 and 2 for the PM10 and PM2.5 concentrations. These ARIMA findings could initially suggest that, in the long term, sectorized isolation measures possibly did not change the trend in the PM10 and PM2.5 concentrations. However, in general, during the sectorized isolation scenario (E2.2), a decreasing trend (−) was observed, and, in the long term, an increasing trend (+) was observed in the PM10 and PM2.5 concentrations.
In relation to the MA term of the ARIMA models for the E2.2 sectorized isolation scenario, the findings evidenced a variation between 1 and 9 and between 1 and 7 for the PM10 and PM2.5 concentrations, respectively (Table 3). In the short term (E1), a variation in the MA term was observed between 1 and 7 for the PM10 and PM2.5 concentrations. In general, the results suggested that, in the short term, sectorized isolation measures did not tend to considerably influence the variations in the PM10 and PM2.5 concentrations. However, for the PM10 concentrations a slight increase in the variation of their concentrations was observed during the E2.2 sectorized isolation scenario. In the long term (E3.2), a variation in the MA term was observed between 0 and 6 and between 1 and 8 for the PM10 and PM2.5 concentrations, respectively. In general, the findings suggested that, in the long term, sectorized isolation measures (E2.2) tended to slightly increase and decrease the variations in the PM10 and PM2.5 concentrations, respectively. In other words, during this sectorized isolation scenario, the PM2.5 concentrations were slightly more stable compared to the PM10 concentrations.
During the E2.3 flexible isolation scenario, the results showed AR terms between 0 and 8 and between 1 and 2 in the ARIMA models developed for the PM10 and PM2.5 concentrations, respectively (Table 3). In the short term (E1), the findings showed AR terms between 2 and 6 and between 0 and 11 for the PM10 and PM2.5 concentrations, respectively. The results suggested that, in the short term, flexible isolation measures tended to increase and decrease the magnitude of the AR term for the PM10 and PM2.5 concentrations, respectively. Namely, flexible isolation measures tended to increase and reduce the persistence over time of the PM10 and PM2.5 concentrations, respectively. Under this E2.3 flexible isolation scenario, a greater difference in the persistence of the PM10 and PM2.5 concentrations was hinted at. It was also suggested that, in the short term, flexible isolation measures comparatively generated a greater reduction in the persistence of PM2.5 compared to PM10. In the long term (E3.3), the results showed AR terms = 1 and between 1 and 2 for the PM10 and PM2.5 concentrations, respectively. The results hinted that, in the long term, flexible isolation measures tended to increase the persistence over time of the PM10 concentrations. In relation to the PM2.5 concentrations in the long term, the findings hinted that flexible isolation measures did not change their persistence over time. Indeed, the long-term persistence in the PM concentrations probably depended on factors such as the duration and intensity of isolation measures, the composition of PM sources, and the subsequent economic recovery [80].
The results showed that, during the E2.3 flexible isolation scenario, the I term in the ARIMA models was equal to 1 for the PM10 and PM2.5 concentrations. In the short term (E1), it was observed that the I term varied between 1 and 2 for the PM10 and PM2.5 concentrations (Table 3). Initially, the results could suggest that flexible isolation measures did not considerably change the increasing trend in the PM2.5 concentrations observed in the short term. However, the trend observed in the PM10 and PM2.5 concentrations during the E2.3 flexible isolation scenario was decreasing (Figure 2). In other words, the trends in magnitude were similar, but in the short term it was increasing (+) and during the E2.3 flexible isolation scenario it was decreasing (−). In the long term (E3.3), the ARIMA models showed I terms = 1 for the PM10 and PM2.5 concentrations. These ARIMA findings suggested that, in the long term, flexible isolation measures possibly did not change the decreasing trend in the PM10 and PM2.5 concentrations.
In relation to the MA term of the ARIMA models for the E2.3 flexible isolation scenario, the findings evidenced a variation between 4 and 14 and between 1 and 15 for the PM10 and PM2.5 concentrations, respectively (Table 3). In the short term (E1), a variation in the MA term between 1 and 7 was observed for the PM10 and PM2.5 concentrations. The results suggested that, in the short term, flexible isolation measures tended to considerably influence the variations in the PM10 and PM2.5 concentrations. Namely, these flexible isolation measures possibly generated a greater variation in the PM10 and PM2.5 concentrations in the short term. In the long term (E3.3), a variation in the MA term between 1 and 11 and between 1 and 8 was observed for the PM10 and PM2.5 concentrations, respectively. The findings suggested that, in the long term, flexible isolation measures tended to increase the variations in the PM10 and PM2.5 concentrations, respectively. This influence on the variation of the concentrations was more evident for PM2.5 compared to PM10.

4. Conclusions

The findings of this study allowed for the following conclusions to be drawn from the sequentially established COVID-19 isolation scenarios (strict, sectorized, and flexible) in the high-altitude megacity under study. This is based on the analysis of the PM concentrations using ARIMA models.
The results suggest an average order in the observed concentrations of PM10 and PM2.5 according to the considered isolation scenarios (compared to the historical trend): sectorized (−28.9% and −31.7%) > strict (−29.4% and −28.3%) > flexible (−8.52% and −12.4%). The change in the sequence between the isolation scenarios is likely related to the occurrence of particular weather conditions (wind direction and precipitation) and regional PM transport episodes (forest fires and Sahara dust) during the sectorized isolation scenario. The differences in the reduction in the PM10 and PM2.5 concentrations between the strict and flexible isolation scenarios were 23.8% and 12.8%, respectively.
The findings reveal that strict isolation measures have a greater effect on the simulated concentrations of PM10 and PM2.5 in the short term (PM10: −47.3% and PM2.5: −54%) than in the long term (PM10: −29.4% and PM2.5: −28.3%). In the short term, these isolation measures have a greater effect on the PM2.5 concentrations compared to the PM10 concentrations. In the long term, the effects of these strict isolation measures on the PM10 and PM2.5 concentrations are similar.
The ARIMA models suggest that strict isolation measures tend to decrease the persistence over time of the PM10 and PM2.5 concentrations both in the short and long term. The models also suggest that these isolation measures do not significantly modify the decreasing trend of the PM10 and PM2.5 concentrations in the short term. However, in the long term, these isolation measures have a greater effect on the PM10 concentrations compared to the PM2.5 concentrations, suggesting a greater downward trend of the PM10 concentrations. Lastly, the ARIMA models reveal that, in the short term, strict isolation measures tend to increase the variation in the PM10 and PM2.5 concentrations, with a greater increase in the case of PM2.5. Conversely, in the long term, these isolation measures tend to decrease the variations in the PM concentrations, suggesting a more stable behavior that is less prone to sudden peaks.
The ARIMA analysis suggests that flexible isolation measures tend to increase the persistence over time of the PM10 concentrations in the short term, while they tend to decrease the persistence of the PM2.5 concentrations. In the long term, an increase in the persistence of the PM10 concentrations is suggested, while no changes in the persistence of PM2.5 are hinted at. Lastly, the ARIMA models reveal that flexible isolation measures tend to increase the variation in the PM10 and PM2.5 concentrations both in the short and long term.
Finally, this study is relevant because it highlights the effectiveness of isolation measures to reduce urban PM10 and PM2.5 concentrations. Strict measures produce significant short-term reductions, while flexible measures affect the persistence of PM over time. These findings are significant for visualizing the air quality management strategies in megacities.

Author Contributions

Conceptualization, D.S.H.-M. and C.A.Z.-M.; methodology, D.S.H.-M. and C.A.Z.-M.; software, D.S.H.-M. and C.A.Z.-M.; validation, D.S.H.-M. and C.A.Z.-M.; formal analysis, D.S.H.-M., C.A.Z.-M. and H.A.R.-Q.; investigation, D.S.H.-M. and C.A.Z.-M.; resources, C.A.Z.-M. and H.A.R.-Q.; data curation, D.S.H.-M., C.A.Z.-M. and H.A.R.-Q.; writing—original draft preparation, D.S.H.-M.; writing—review and editing, C.A.Z.-M. and H.A.R.-Q.; visualization, D.S.H.-M., C.A.Z.-M. and H.A.R.-Q.; supervision, C.A.Z.-M.; project administration, C.A.Z.-M.; funding acquisition, C.A.Z.-M. and H.A.R.-Q. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Acknowledgments

The authors wish to acknowledge the support provided by the Environmental Engineering Research Group (GIIAUD) of the Universidad Distrital Francisco José de Caldas (Colombia).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location of monitoring stations in the megacity of study (Bogota, Colombia).
Figure 1. Location of monitoring stations in the megacity of study (Bogota, Colombia).
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Figure 2. Behavior of PM10 and PM2.5 concentrations during the study scenarios (E1, E2, and E3): (a): CAR, (b) KEN, (c) LAF, and (d) TUN. OBS: observed concentrations during the study scenarios (E1 and E2) and E3: observed historical concentrations.
Figure 2. Behavior of PM10 and PM2.5 concentrations during the study scenarios (E1, E2, and E3): (a): CAR, (b) KEN, (c) LAF, and (d) TUN. OBS: observed concentrations during the study scenarios (E1 and E2) and E3: observed historical concentrations.
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Figure 3. Polar plots for (a) PM10 and (b) PM2.5 concentrations for each monitoring station and analysis scenario. WS: wind speed (m/s).
Figure 3. Polar plots for (a) PM10 and (b) PM2.5 concentrations for each monitoring station and analysis scenario. WS: wind speed (m/s).
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Figure 4. Average percent change in simulated PM10 and PM2.5 concentrations during the E2 isolation scenario. E2.1—strict isolation, E2.2 —sectorized isolation, and E2.3—flexible isolation. E1—short term and E3—long term.
Figure 4. Average percent change in simulated PM10 and PM2.5 concentrations during the E2 isolation scenario. E2.1—strict isolation, E2.2 —sectorized isolation, and E2.3—flexible isolation. E1—short term and E3—long term.
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Table 1. Characteristics of selected air quality monitoring stations.
Table 1. Characteristics of selected air quality monitoring stations.
CharacteristicMonitoring Stations
CARKENLAFTUN
Coordinates4°39′30.48″ N4°37′30.18″ N4°41′26.52″ N4°34′34.41″ N
74°5′2.28″ W74°9′40.80″ W74°4′56.94″ W74°7′51.44″ W
Atmospheric pollutantsPM10, PM2.5PM10, PM2.5 PM10, PM2.5PM10, PM2.5
Meteorological variablesVV, DV, T, Pr, HRVV, DV, T, Pr, RS, HR, PsVV, DV, T, Pr, HR, PsVV, DV, T, Pr, RS, HR
Altitude (masl)2577258025522589
Height of sampling (m)4.67.04.63.0
Type of monitoring stationBackgroundBackgroundTrafficBackground
Mean annual relative humidity (%)67.663.163.262.6
Mean annual precipitation (mm)11487971147980
Mean annual temperature (°C)14.915.814.414.9
Mean annual wind speed (m/s)1.212.151.871.36
Predominant annual wind direction (°)204 (SSW)196 (S)140 (SE)175 (S)
Land use (%)R: 54.9R: 38.4R: 53.1R: 41.9
I: 4.40I: 5.80I: 7.10I: 0.60
D: 3.80D: 37.1D: 10.0D: 8.90
C: 9.60C: 18.4C: 5.20C: 31.3
P: 8.30P: 0.20P: 2.80P: 17.0
Land category (%)Urban: 100Urban: 93.5Urban: 95.8Urban: 71.5
Urban sprawl: 6.55Urban sprawl: 4.14Protection: 28.5
Population density (Inhabitants/Ha)123268227183
Note: VV—wind speed, DV—wind direction, T—temperature, Pr—precipitation, RS—solar radiation, Ps—atmospheric pressure, R—residential, I—industrial, D—residential and services, C—commercial, and P—protection.
Table 3. ARIMA models developed for PM10 and PM2.5 concentrations during the scenarios considered.
Table 3. ARIMA models developed for PM10 and PM2.5 concentrations during the scenarios considered.
Station AR (p)I (d)MA (q)TransformationR2MAERMSEMAPEQ’p-ValueDFBIC
E1
CARPM10612Square root0.9990.0430.0570.15118.0990.05310−5.689
PM2.51121None0.9990.0390.0550.21911.4610.0756−5.762
KENPM10221None0.9990.0560.0790.12120.6830.14715−5.060
PM2.5211None0.9990.0590.0780.21311.0370.75015−5.086
LAFPM10327None0.9990.0690.0990.19314.3800.0728−4.584
PM2.5027Square root0.9990.0550.0730.27117.4320.09611−5.207
TUNPM10211None0.9990.0950.1320.19916.9910.31915−4.027
PM2.5211None0.9990.0460.0670.24419.1170.20815−5.381
E2.1
CARPM10113Natural logarithm0.9990.0280.0400.19921.1920.09714−6.440
PM2.5118Natural logarithm0.9990.0260.0380.34714.6350.1019−6.525
KENPM10123Natural logarithm0.9990.0420.0560.13016.8150.26614−5.764
PM2.5026None0.9990.0460.0590.2967.8640.79612−5.628
LAFPM10124Natural logarithm0.9990.0300.0440.19710.6170.64313−6.243
PM2.5026None0.9990.0240.0340.31612.1180.43612−6.746
TUNPM10115Square root0.9990.0640.0920.26717.6240.12812−4.755
PM2.50212None0.9990.0330.0460.3854.1950.6506−6.107
E3.1
CARPM10211None0.9990.0400.0530.18317.0740.31415−5.869
PM2.51213Square root0.9990.0240.0320.2251.9090.7524−6.854
KENPM10711Natural logarithm0.9990.0460.0620.10614.7880.14010−5.533
PM2.51113Natural logarithm0.9990.0320.0420.1466.4200.1704−6.318
LAFPM101112None0.9990.0430.0580.1729.5720.0885−5.664
PM2.50215None0.9990.0270.0360.2571.8410.6063−6.598
TUNPM10411Natural logarithm0.9990.0520.0700.16816.9500.20213−5.293
PM2.5115Natural logarithm0.9990.0350.0470.23312.6160.39812−6.115
E2.2
CARPM10029Natural logarithm0.9990.0610.0830.23710.0850.3449−4.844
PM2.5121None0.9990.0330.0460.25424.1880.08516−6.108
KENPM10021None0.9990.0540.0750.11825.5170.08417−5.172
PM2.5023None0.9990.0560.0730.25812.1800.66515−5.192
LAFPM10124Natural logarithm0.9990.0540.0750.21617.9420.16013−5.112
PM2.5027Natural logarithm0.9990.0470.0610.29718.5590.06911−5.485
TUNPM10421Natural logarithm0.9990.0870.1250.2296.5610.0873−3.959
PM2.5111None0.9990.0430.0600.31610.7740.82316−5.596
E3.2
CARPM10121Square root0.9990.0400.0540.1297.8910.95216−5.790
PM2.5022None0.9990.0260.0350.15725.9300.05516−6.676
KENPM10110Natural logarithm0.9990.0530.0710.10814.3180.64417−5.261
PM2.5211Natural logarithm0.9990.0350.0460.14110.8170.76515−6.094
LAFPM10026Natural logarithm0.9990.0520.0690.14420.2230.06312−5.247
PM2.5118Natural logarithm0.9990.0300.0380.18614.7340.0989−6.403
TUNPM10110None0.9980.0580.0770.14824.6390.10317−5.116
PM2.5117Natural logarithm0.9990.0380.0520.1848.9400.53810−5.809
E2.3
CARPM10124None0.9990.0500.0740.30020.4240.08513−5.179
PM2.5112Square root0.9990.0280.0390.30318.9760.21515−6.487
KENPM100214Square root0.9990.0530.0730.1428.0730.0894−5.136
PM2.5121None0.9990.0520.0680.31612.9710.67516−5.369
LAFPM10828Natural logarithm0.9990.0370.0540.2165.4480.0662−5.741
PM2.5222None0.9990.0280.0390.39413.0600.52214−6.440
TUNPM101112Natural logarithm0.9990.0750.1070.3035.3070.3805−4.394
PM2.51115Square root0.9990.0340.0490.4502.7090.2582−5.921
E3.3
CARPM10114None0.9990.0440.0570.17215.5000.27713−5.694
PM2.5211None0.9990.0280.0350.20718.5870.23315−6.656
KENPM10111None0.9990.0510.0670.11019.0920.26416−5.375
PM2.5117Natural logarithm0.9990.0350.0460.1508.8400.54710−6.114
LAFPM101112None0.9990.0460.0610.1595.4330.3655−5.514
PM2.5217None0.9990.0300.0390.22716.4310.0589−6.441
TUNPM101111Natural logarithm0.9990.0570.0740.1647.0410.3176−5.128
PM2.5118Natural logarithm0.9990.0380.0500.2198.7810.4589−5.921
Note: AR—autoregressive, I—integrated, MA—moving average, RMSE—root mean square error, MAE—mean percentage error, MAPE—maximum mean percentage error, R2—determination coefficient, Q’—Ljung–Box statistic, p-value—p-value, DF—degrees of freedom, and BIC—Bayesian Information Criterion.
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Hernández-Medina, D.S.; Zafra-Mejía, C.A.; Rondón-Quintana, H.A. ARIMA Analysis of PM Concentrations during the COVID-19 Isolation in a High-Altitude Latin American Megacity. Atmosphere 2024, 15, 683. https://doi.org/10.3390/atmos15060683

AMA Style

Hernández-Medina DS, Zafra-Mejía CA, Rondón-Quintana HA. ARIMA Analysis of PM Concentrations during the COVID-19 Isolation in a High-Altitude Latin American Megacity. Atmosphere. 2024; 15(6):683. https://doi.org/10.3390/atmos15060683

Chicago/Turabian Style

Hernández-Medina, David Santiago, Carlos Alfonso Zafra-Mejía, and Hugo Alexander Rondón-Quintana. 2024. "ARIMA Analysis of PM Concentrations during the COVID-19 Isolation in a High-Altitude Latin American Megacity" Atmosphere 15, no. 6: 683. https://doi.org/10.3390/atmos15060683

APA Style

Hernández-Medina, D. S., Zafra-Mejía, C. A., & Rondón-Quintana, H. A. (2024). ARIMA Analysis of PM Concentrations during the COVID-19 Isolation in a High-Altitude Latin American Megacity. Atmosphere, 15(6), 683. https://doi.org/10.3390/atmos15060683

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