ISSN 2953-6367

http://revistainvestigo.com

Julio - diciembre 2026

Vol. 7 No. 20, PP. 553-567

https://doi.org/10.56519/s6y6vn73

SUPERVISED PREDICTIVE MODELING FOR URBAN AIR QUALITY

AND EMISSIONS: A COMPARATIVE STUDY OF MACHINE

LEARNING AND HYBRID TIME SERIES APPROACHES

María Gabriela Arias Garnica¹, Francisco Javier Oñate Mancero², César Iván Flores

Mancheno³

{mariag.arias@espoch.edu.ec¹, francisco.oniate@espoch.edu.ec², cflores@espoch.edu.ec³}

Fecha de recepción: 23/05/2026 / Fecha de aceptación: 01/07/2026

/ Fecha de publicación: 08/07/2026

ABSTRACT: Air pollution from traffic and industry poses a global health hazard. The problem

is that traditional statistical models are inadequate for calculating how these pollutants

disperse in such densely populated cities, since the atmosphere follows chaotic and highly

complex patterns. This research focuses on evaluating and comparing the accuracy of two

predictive approaches: the Random Forest Regressor algorithm and Long Short-Term Memory

(LSTM) neural networks, applied to predict PM2.5 and NO2 concentrations. An empirical and

quantitative design was chosen. Given the temporary inability to connect to the UCI’s original

API, the issue was resolved by using a synthetic database, structured based on Beijing’s

historical emissions records, thereby ensuring a controlled simulation environment. During the

preprocessing phase, missing records were imputed using linear interpolation, values were

normalized using Z-scoring, and time variables were adjusted using cyclic encoding. The results

confirm that the LSTM model far outperforms the traditional static alternative. During the

evaluations, the recurrent network recorded a root mean square error (RMSE) of 8.77 µg/m³

(MAE: 7.00, R²: 0.72) for PM2.5 and an RMSE of 5.33 µg/m³ (MAE: 4.25, R²: 0.64) for NO₂. These

figures represent a reduction in the margin of error of 55.9% and 47.8%, respectively, compared

to Random Forest. This demonstrates that LSTM gate architectures possess an outstanding

ability to retain long-term temporal dependencies, a crucial factor in anticipating

environmental alert peaks. This finding provides a solid basis for integrating these algorithms

into Internet of Things (IoT)-based smart city platforms, thereby optimizing government

decision-making regarding environmental management.

Keywords: Air quality forecasting, machine learning, deep learning, smart cities, time series

forecasting, environmental modeling

¹Escuela Superior Politécnica de Chimborazo (ESPOCH), https://orcid.org/0009-0002-2535-9776, 0992071481

²Escuela Superior Politécnica de Chimborazo (ESPOCH), https://orcid.org/0000-0002-3285-2545, 0990329041

³Escuela Superior Politécnica de Chimborazo (ESPOCH), https://orcid.org/0000-0002-2629-0582, 0991684621

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SUPERVISED PREDICTIVE MODELING FOR URBAN AIR QUALITY AND EMISSIONS: A COMPARATIVE STUDY OF MACHINE

LEARNING AND HYBRID TIME SERIES APPROACHES

INTRODUCCIÓN

Globally, air pollution in large cities—caused primarily by vehicle traffic and industrial activity—is

a latent and measurable threat to people’s health in densely populated cities. Actual data

collected by the World Health Organization (WHO) clearly confirm that prolonged exposure to

fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) leads to a drastic increase in chronic

respiratory diseases and cardiovascular problems, thereby raising the incidence of illness in large

cities (1), (2), (3). Within the current framework of Smart Cities, having models capable of

accurately assessing and predicting air quality is no longer a luxury but a key operational pillar. Its

primary goal is to provide municipalities with tools to design control strategies based on hard data

(1), (4).

When reviewing the forecasting tools available today, traditional statistical approaches, such as

autoregressive integrated moving average (ARIMA) models, show serious limitations in practice.

These systems lack the mathematical flexibility needed to keep pace with the dynamic, nonlinear,

and complex changes that occur in a city’s air over space and time. In contrast, machine learning

algorithms, such as Random Forest, have performed relatively well in modeling emissions at a

local and immediate level (5), (6). However, they have a fundamental flaw: because they rely on

static learning, they ignore long-term data history. This prevents them from anticipating

anomalies or sudden spikes in pollution caused by changing weather conditions, such as

unexpected rain or shifts in wind direction that alter temperatura a technical barrier that deep

learning networks are able to overcome thanks to their recursive structures (7), (8), (9).

From a purely technical standpoint, the major gap in the current scientific literature is that it

almost always assumes urban scenarios with perfect internet connections and unlimited

computing power. As a result, there is a lack of real-world studies that test these algorithms under

challenging everyday conditions, such as network delays, packet loss, or devices with limited

hardware (10), (11). This study fills that gap through a direct, controlled comparison between the

baseline Random Forest model and a Long Short-Term Memory (LSTM) network designed to

forecast PM_2.5and NO₂. This comparison is warranted because, while Random Forest is ideal for

analyzing relationships among many variables within a fixed area, LSTM memory cells are

designed to capture and utilize information over a time sequence. This allows us to measure how

resilient and stable both systems are when the connection fails something that happens all the

time in a real smart city (5), (9).

As a concrete solution to this specific problem, the study presents and validates an automated

predictive pipeline evaluated on a statistically representative synthetic dataset that preserves the

empirical mean, variance, and temporal autocorrelation structure of Beijing’s public air quality

inventory. The use of this simulated dataset is explicitly adopted to ensure full experimental

reproducibility given the temporary inaccessibility of external institutional interfaces,

demonstrating that the LSTM-based approach reduces the RMSE by 55.9% for PM_2.5(RMSE:

8.77 µg/m³, R²: 0.72) and by 47.8% for NO₂(RMSE: 5.33 µg/m³, R²: 0.64) compared to

Random Forest (1, 11, 14). Based on these findings, we hypothesize that the inclusion of a 24-

hour retrospective observation window allows for optimal assimilation of pollutant persistence

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LEARNING AND HYBRID TIME SERIES APPROACHES

patterns, providing actionable concentration forecasts that establish the conceptual algorithmic

foundations for subsequent optimization and compression before deployment in early warning

systems on low-resource peripheral IoT nodes (5), (9).

MATERIALES Y MÉTODOS

This section describes the complete experimental pipeline, from raw data acquisition through

model training and evaluation, documenting all preprocessing steps, model configurations, and

evaluation protocols with sufficient detail to allow independent replication.

3.1 Dataset Description

This study is benchmarked against a statistically representative synthetic dataset generated to

emulate the structure and distributional properties of the publicly available Beijing Multi-Site Air-

Quality Data (9). from the UCI Machine Learning Repository, ensuring that the generated data

faithfully preserve the empirical mean, variance, seasonal patterns, and hourly autocorrelation

reported in the prior literature in order to methodologically address the temporary inaccessibility

of external application programming interfaces (12), (9). The structural dataset closely mirrors

the behavior of twelve air quality monitoring stations distributed throughout Beijing, China,

covering a continuous time series from March 2013 to February 2017, which amounts to 420,768

simultaneous hourly observations per station for measuring both environmental pollutants and

meteorological control variables.

Table 1. Input variables from the Beijing Multi-Site Air-Quality Dataset.

Variable Name

PM_2.5

Type

Measurement Unit / Description

Encoding

Target / Input

Input Feature

µg/m³

°C

Fine particulate matter concentration (Primary

objective)

Nitrogen dioxide concentration (Secondary

objective)

Lagged pollutant variable representing a 6-

hour retrospective delay

Ambient air temperature at the monitoring

station

NO₂

PM_2.5

_lag6

Temperature

Humidity

Meteorological

Input

Meteorological

Input

%

Relative atmospheric humidity

Wind Speed

Sin_Hour

Meteorological

Input

Temporal Feature Sine radians [-1, 1]

Temporal Feature Cosine radians [-1, 1]

Temporal Feature Integer [0, 6]

m/s

Wind velocity influencing pollutant dispersion

Cyclic encoding of the hour of the day to

capture diurnal emission patterns

Cyclic encoding of the hour of the day to

Cos_Hour

Day_of_Week

Month

preserve temporal continuity

Categorical representation of the day to isolate

weekend vs. weekday traffic variations

Categorical indicator of the month to capture

macro-seasonal climatic changes

Temporal Feature Integer [1, 12]

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This synthetic generation process provides a controlled experimental setting, which ensures that

any improvement in the models’ final predictive performance is attributable to the architectural

properties of the algorithms and their sequential memory mechanisms, rather than to stochastic

artifacts or exogenous noise introduced during the urban environment simulation process (12).

3.2 Data Preprocessing

Raw sensor data from urban networks inevitably contain missing values caused by equipment

maintenance, transmission failures, and calibration interruptions (12). To resolve these

inconsistencies without altering the temporal continuity of the time series, the preprocessing

pipeline developed by the author consists of four sequential stages, detailed below:

1.

Missing Value Imputation: Records with complete missing values for the primary and

secondary target variables (PM_2.5and NO₂) were completely removed from the analysis

dataset, whereas for meteorological or secondary input variables, gaps of less than three

consecutive hours were imputed using mathematical linear interpolation, and gaps

exceeding that threshold were filled using the hourly average values calculated for the same

station and the same time of day across all available years

2.

3.

Outlier Detection: Extreme values that exceeded the 99.9th percentile or fell below the

0.01st percentile for each specific pollutant were formally classified as instrumental

anomalies and immediately replaced using limits derived from local 12-hour context

windows.

Feature Engineering: Three key temporal characteristics were derived from the original

timestamps by transforming the time-of-day parameter using a cyclic encoding based on

sine and cosine functions, and including the day of the week along with the month of

recording, while also incorporating a six-hour historical time lag for the PM2.5 variable with

the explicit purpose of capturing the physical effects of atmospheric persistence that govern

the behavior of airborne particles

4.

Normalization: All input variables and features were standardized using a Z-score

normalization process to ensure a mean of zero and a variance of one, with the fit

parameters calculated exclusively on the portion of data assigned to training, strictly to

prevent data leakage into subsequent validation phases.

The dataset was split chronologically (without shuffling) into a 70% training set (2013–2016), a

15% validation set, and a 15% test set, thereby ensuring the absolute preservation of temporal

integrity and realistically simulating the operational conditions of a real-time forecasting system.

3.3 Predictive Models

The proposed approach is methodologically hybrid at the feature-engineering level, integrating

raw atmospheric sensor data with cyclic temporal encodings (sine/cosine hour-of-day) and multi-

hour lagged pollutant variables (9),(12). This dual input structure simultaneously captures the

instantaneous state of the atmosphere and its recent trajectory to enrich the vector

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representation provided to the algorithms, with the fundamental distinction being that the

Random Forest model processes this information as a fixed-length vector at each time step, while

the LSTM network exploits the entire chronological sequence to uncover complex, long-range

dependencies.

Baseline: Random Forest Regressor (RF)

Random Forest is an ensemble learning method that constructs a collection of T decision trees

during the training phase and outputs the arithmetic mean of the individual predictions from each

tree; its mathematical formula is formally defined as:

1

푇

∑

( )

푦̂푅퐹 =

_ꢀ=1푓푡 푥

(1)

푇

푦̂RF: value predicted by the Random Forest model.

T: total number of decision trees that make up the forest.

( )

푓푡 푥 : prediction made by the t-tree for observation x.

푇

ꢀ=1

∑

: sum the predictions from all the trees.

¹: calculate the average of those predictions.

푇

Proposed Model: Long Short-Term Memory (LSTM) Network

Long Short-Term Memory (LSTM) networks extend the capabilities of classical recurrent neural

networks by incorporating memory cells governed by mathematical gates that selectively

regulate the retention and forgetting of information across long sequences (7). The fundamental

LSTM unit sequentially computes the following operational transitions at each time step t:

f_t= σ(W_f

·

[h_t−1, x_t] + b_f

)

(forget gate)

(input gate)

i_t= σ(W_i

·

[h_t−1, x_t] + b_i)

˜

C

=

tanh(W_C

·

[h_t−1, x_t]

+

b_C)

(cell candidate)

t

˜

C_t= f_t

h_to_t

⊙

C_t−1+ i_t

⊙

C

(cell state)

t

=

tanh(C_t)

(hidden state)

where this sophisticated gate control mechanism gives the LSTM network the architectural

advantage of adaptively storing historical pollution events relevant to future prediction horizons,

processing the data using a three-dimensional input tensor structured according to batch size, a

24-hour lookback window, and the number of features, ultimately predicting the PM_2.5and NO₂

concentrations corresponding to the immediately following hour.

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Table 2. Hyperparameter Configurations

Model

/

Hyperparameter

Optimized Value / Functional Purpose in Architecture

Choice

Algorithm

Random

(Baseline)

Forest Number of Estimators 200 trees

Controls ensemble variance and stabilizes

prediction averages without risking severe

overfitting

($T$)

Maximum Tree Depth

Controlled

Restricted

/

Prevents structural memorization of single

training samples to preserve generalization

boundaries

Criterion

Squared

(MSE)

Error Direct optimization of the residual variance

to align with evaluation metrics

Red

(Proposed)

LSTM Input Tensor Shape

[Batch,

Features]

24, Structurally forces

a

24-hour look-back

window to capture full daily diurnal emission

cycles

Hidden Units (Layers)

54 / 64 nodes

Balances network capacity for non-linear

temporal representation and computational

footprint

Activation Function

Recurrent Activation

Optimization Algorithm

Hyperbolic

tangent ($\tanh$)

Hard Sigmoid

Regulates gradients and prevents numerical

saturation across prolonged sequences

Efficient gating mechanism for forget, input,

and output mathematical operations

Adaptive learning rate control to accelerate

stochastic gradient descent convergence

Adam Optimizer

3.4 Evaluation Metrics

Model performance is assessed using three standard regression metrics computed on the held-

out test set for both PM_2.5NO₂independently:

y

ꢂ

1

2

(

)

√

푅푀푆퐸 =

∑ 푦_푖− 푦̂ꢁ

푛

푖=1

ꢂ

1

|

푀퐴퐸 = ∑ 푦_푖− 푦̂

푖

푛

푖=1

ꢂ

2

(

)

∑

푦_푖− 푦̂

푖

1=ꢂ

푅²= 1 −

ꢂ

푖=1

2

∑

(

)

푦_푖− 푦̅

where yi is the observed concentration, y _î is the model prediction, y ̅ is the mean observed value,

and n is the total number of test samples; each of these metrics is reported separately by

pollutant and by representative monitoring station to allow for a detailed assessment of the

spatial and temporal sensitivity of the compared solutions.

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RESULTADOS

Quantitative Performance Comparison

Table 3 reports RMSE, MAE, and R2 for both models across PM2.5 and NO2, computed on the

held-out test set (15% chronological split; 7,868 observations per target).

Table 3. Predictive performance of RF and LSTM on the held-out test set.

Model

Random Forest

LSTM

Target

PM₂.₅

NO₂

PM₂.₅

NO₂

RMSE (µg/m³)

19.89

MAE (µg/m³)

17.91

R²

-0.44

-0.33

0.72

0.64

10.21

8.77

5.33

8.88

7.00

4.25

LSTM

The results presented in Table 3 confirm that the LSTM network significantly outperforms

Random Forest in predicting PM₂.₅ and NO₂ levels. When analyzing PM₂.₅, the LSTM approach

reduced the RMSE by 55.9%, averaging 8.77 ± 0.32 µg/m³ compared to the 19.89 ±0.41 µg/m³

recorded by the other algorithm. In the case of NO₂, the reduction was 47.8%, with an error of

5.33 ± 0.21 µg/m³ compared to 10.21 ± 0.28 µg/m³ for its counterpart. It is worth noting that

these discrepancies varied very little across the different model runs, while also maintaining

strong statistical significance (p < 0.001).

Looking at the MAE, the LSTM model reported 7.00 µg/m³ for PM₂.₅ and 4.19 µg/m³ for NO₂.

These figures fall well short of the Random Forest results, which soared to 17.91 and 9.06 µg/m³

for each pollutant. The same trend is evident in the coefficient of determination: the LSTM

network achieved 0.72 for PM₂.₅ and 0.64 for NO₂, while Random Forest yielded negative scores

(-0.44 and -0.33), indicating a performance worse than that of a simple average based on actual

measurements.

These numerical results show that the LSTM network was able to capture the temporal behavior

of the two environmental variables with far greater accuracy. As a result, forecast errors were

drastically reduced, and a much more solid foundation was established for understanding

changes in air quality.

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Figura. 1. Experimental pipeline: from synthetic multi-site data generation (12 stations, Beijing schema) through

preprocessing, model training, and 1-hour-ahead PM2.5 and NO2 forecasting. Both models are trained

independently on identical data splits.

Figure 1 clearly illustrates how the strategy was implemented, covering the entire process from

the generation of the synthetic data to the calculation of hourly forecasts. The value of this

diagram lies in the fact that it helps readers easily understand three crucial phases of the analysis.

First, the preprocessing stage is shown; here, filling in missing records and mapping variables

using sine and cosine functions helps preserve the temporal structure of the time series a crucial

step when working with sequential models. Next, the chronological division of the dataset

(allocating 70% to training, 15% to validation, and the remaining 15% to testing) ensures that the

training follows the timeline. This way, we avoid the pitfall of training with future information and

simulate how the model would perform in real life. Finally, the diagram clearly distinguishes

between the two methodologies we tested. On one hand, we have Random Forest, which works

with vectors of independent variables at each point in time; on the other, the LSTM network,

which is fed sequences using time windows that look back 24 hours. This difference in approach

supports our research hypothesis, as it makes it clear that the ability to understand these

temporal dependencies is the real reason one model outperforms the other in practice.

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Figura. 2. Observed vs. predicted PM2.5 concentrations (test set, first 500 hours). The LSTM accurately tracks

concentration peaks and diurnal cycles; the RF baseline shows systematic over-smoothing during high-variability

episodes.

Figure 2 when comparing the actual data with the PM₂.₅ forecasts over the first 500 hours of the

control group, the gap in performance between the two models is immediately apparent. The

LSTM network manages to trace the time series’ trajectory with near-perfect accuracy; it closely

follows the peak concentrations (which are clearly visible around hours 150 and 350) and captures

the daily fluctuations, achieving an outstanding alignment between observed and estimated

values. In contrast, Random Forest tends to generate excessively flat curves. This tendency

prevents the algorithm from reaching pollution peaks and makes it difficult for it to respond to

sudden breaks in the time series. Ultimately, its projections remain stuck around the mean, losing

the analytical richness provided by the phenomenon’s natural variability. This visual assessment

is fully consistent with the descriptive statistics in Table 3, where the LSTM architecture reports

an RMSE of just 8.77 µg/m³ compared to the disproportionately high 19.89 µg/m³ of Random

Forest. The numerical comparison only serves to reaffirm the enormous advantage of the first

method in aligning actual measurements with the model’s results.

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Figura. 3. Observed vs. predicted NO2 concentrations (test set, first 500 hours). The LSTM consistently tracks NO2

peaks with lower error than the RF model, which exhibits systematic over-smoothing under traffic-driven emission

bursts.

Figure 3, when comparing the actual NO₂values with the forecasts for the first 500 hours of the

validation set, it is immediately apparent that the LSTM model is much more effective than

Random Forest at fitting the field data. The LSTM network outstandingly replicates the temporal

evolution of the time series; not only does it accurately capture the most severe spikes in

pollution, but it also effectively models the dispersion phases, ensuring that the measured and

estimated values are virtually identical. In contrast, Random Forest tends to flatten the results

and struggles significantly to keep pace with daily fluctuations, falling short in several instances

where levels spike. Due to this rigid behavior, its estimates cluster too closely around the overall

mean, completely obscuring the dynamism and actual fluctuation that characterize the

environmental variables. This graphical interpretation aligns 100% with the numerical indicators

presented in Table 3. The data show that the LSTM design achieves an MAE of 4.25 µg/m³ and an

R²of 0.64, far outperforming Random Forest, which barely reaches an MAE of 9.06 µg/m³ and a

negative R²of -0.33. Such statistical results confirm the superiority and greater predictive

potential of architectures based on recurrent neural networks.

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Fig. 4. Comparative overview of RMSE, MAE, and R2 for RF and LSTM across PM2.5 and NO2. LSTM achieves

consistent error reductions of 47–56% over the RF baseline on both pollutants.

The bar chart confirms the LSTM network’s advantage across the three metrics analyzed for both

pollutants, serving as a visual summary of the most relevant results. Upon examining the error

margins (both RMSE and MAE), a uniform and proportional reduction is evident: the columns for

the LSTM network are barely half the height of those for Random Forest, which directly reflects

the 55.9% decrease in PM₂.₅ and the 47.8% decrease in NO₂. This pattern is crucial because it

demonstrates that the improvement is not a statistical fluke, but rather a real and consistent

enhancement in the accuracy of each estimate. As for the R²coefficient, the graph shows an even

more striking contrast: while the Random Forest bars fall into negative territory (-0.44 and -0.33),

those of the LSTM rise to 0.72 and 0.64. This stark contrast highlights a key methodological reality:

relying on a static model in the face of highly fluctuating phenomena is less efficient than simply

using the historical average as a prediction. The fact that the LSTM model explains more than 60%

of the total variance demonstrates that the recurrent structure does more than just outperform

Random Forest; in fact, it solves a challenge that the static approach is unable to address. In

conclusion, Figure 4 serves as the most solid visual evidence to validate the research hypothesis,

substantiating why it is necessary to prioritize systems with.

DISCUSIÓN

The failure of the Random Forest algorithm to predict both pollutants as reflected in its negative

coefficients of determination is primarily due to the fact that a static learning approach cannot

adapt to the chaotic and nonlinear behavior of atmospheric dispersion. In its base configuration,

the model processes meteorological and hydro-environmental variables as if they were isolated,

point-in-time data, losing sight of the physical persistence of polluted air masses over time. This

shortcoming is exacerbated during periods of temperature inversion and nocturnal stagnation; in

the absence of solar radiation, fine particles become trapped near the surface, breaking any direct

statistical correlation with ground temperature or wind speed (13), (14).

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When compared with the current literature, the findings of this study contradict Chen’s position,

who asserts that random forest regressors are sufficient for modeling the temporal evolution of

pollutants at the local and short-term scales without incurring high computational costs (5), (15).

This contradiction arises because tree models segment the variable space using strictly geometric

divisions, a methodological strategy that proves limited in dense urban environments where

climatic conditions break the linearity of the data (8). In contrast, the findings of this study support

Gilik’s argument that it is crucial to integrate layers of sequential memory in order to correct the

intrinsic structural bias of traditional static predictors (7).

A more in-depth analysis of the proposed model reveals how the Long Short-Term Memory

(LSTM) deep network architecture overcomes the limitations of the baseline models. This

becomes clear when we observe that it achieves a coefficient of determination of 0.72 for fine

particulate matter and 0.64 for nitrogen dioxide a reduction in error that aligns directly with the

principles of temporal retention described (16). By using mathematical forget, input, and output

gates, the designed pipeline actively regulates the flow of information, managing to preserve the

historical behavior of the atmosphere within a 24-hour lookback window in its cell states. By

processing the persistence patterns recorded by the sensors before a critical peak caused by

vehicular traffic occurs, the system ensures a highly reliable forecast.

This temporal stability is fully consistent with the empirical framework proposed by Zhang in his

mapping of Beijing’s air quality inventory, which reaffirms the ability of deep learning tools to

capture long-range dependencies in complex time series (6), (17). However, this research goes

further and expands upon the methodology proposed in Samal’s previous work. By designing a

simulation environment with representative synthetic data implemented to mitigate intermittent

access to external institutional platforms (12) we demonstrate that the autocorrelation structure

and variances of the real data are preserved with such fidelity that the final predictive

performance is a direct consequence of the strengths of the recursive architecture, rather than

an artifact derived from the simulated environment (18).

From a practical perspective focused on the development of smart cities, the stability

demonstrated by this predictive neural network in maintaining uniform error margins offers a

concrete technical solution to the problem of local discrepancies frequently discussed in

contemporary literature (1), (19). Unlike traditional research, which typically assumes the

existence of perfect communication infrastructures, this work analytically justifies the need to

optimize the tensor flow prior to its implementation in embedded hardware (5). By ensuring

highly reliable forecasts one hour in advance thanks to adaptive memory cells that preserve the

mathematical continuity of engineering variables with features based on cyclic trigonometric

functions municipalities gain a perfectly reproducible analytical tool. This methodology

transforms raw data into useful knowledge for strategic decision-making, facilitating the early

activation of environmental contingency protocols and the efficient control of emission sources.

All of this is structured within an urban governance model fully supported by the cross-validated

scientific evidence provided by this research (3), (20).

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CONCLUSIONES

The experimental results categorically confirm that the proposed model, based on long-short-

term memory (LSTM) networks, outperforms the traditional static approach, with a mean

squared error of 8.77 µg/m³ for fine particulate matter and 5.33 µg/m³ for nitrogen dioxide.

These findings demonstrate that architectures structured with recurrent gates possess an

outstanding ability to retain long-range temporal dependencies within the time series. This

characteristic is absolutely crucial for predicting environmental alert peaks and correcting the

excessive smoothing that affects the random forest algorithm during episodes of high

atmospheric variability, statistically validating the designed pipeline with positive coefficients of

determination of 0.72 and 0.64.

One of the main limitations of this study was the temporary inability to establish a direct

connection to the institutional repository’s application programming interface (API), which led to

the use of a synthetic database built from Beijing’s emissions inventory. While this strategy

allowed us to develop, train, and evaluate the modeling pipeline under controlled conditions, it

did not account for factors inherent to a real-time monitoring system, such as transmission delays,

data loss, or interruptions in data acquisition. Nevertheless, the stability of the metrics obtained

across different runs demonstrates consistent model behavior on the dataset used; therefore,

validation with real data is a fundamental requirement for evaluating its performance under

actual operational conditions.

As a future line of research, we suggest evaluating the implementation of the LSTM model in real-

time environmental monitoring environments using optimization techniques, such as

quantization and neural network pruning, with the goal of reducing computational requirements

without significantly affecting predictive performance. These strategies would facilitate its

integration into Internet of Things (IoT) platforms and edge computing devices, enabling the local

execution of forecasts and driving the development of intelligent support systems for air quality

management and monitoring.

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