1. Introduction
Off-road construction machinery plays an irreplaceable role in infrastructure construction, land development, and agricultural production [
1]. However, the accompanying increase in emissions from off-road machinery has exacerbated the environmental impacts, particularly the impact of atmospheric pollution [
2,
3]. Vehicle exhaust emissions have long been recognized as a significant factor affecting air quality, with the increasing proportion of off-road machinery emissions in the global vehicle emission total drawing heightened attention [
4]. Therefore, the accurate prediction and control of transient emissions from off-road construction machinery have become urgent requirements for current environmental protection and climate change mitigation efforts.
Off-road machinery encompasses construction machinery, agricultural machinery, etc., with their emission of hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NOₓ), and particulate matter (PM) accounting for 18–29% of total global vehicle emissions [
5]. Statistics indicate that the emissions from construction machinery constitute 28.2%, 31.3%, and 32.5% of the total emissions from off-road machinery [
6]. Among these pollutants, nitrogen oxides are significant atmospheric pollutants with crucial implications for ozone layer depletion and acid rain formation [
7,
8]. Studies have shown that the emissions of off-road mobile machinery, such as excavators, loaders, and forklifts, increase with engine power, with diesel engine emissions accumulating the highest amount of exhaust pollutants [
9,
10]. Hence, establishing a comprehensive evaluation and control system for off-road mobile machinery emissions that can reduce the overall pollutant emissions and improve air quality has become a pressing environmental concern.
In general, the methods used to measure vehicle emissions include chassis and engine dynamometer testing [
11], road tunnel measurements [
12], optical remote sensing systems [
13], plume chasing measurements [
14], and portable emission measurement systems (PEMSs) [
15]. In recent years, PEMSs have been utilized to measure the emission characteristics of off-road mobile machinery, proving to be an effective method for characterizing the actual emissions levels [
16,
17,
18]. To accurately assess the pollution emissions from mobile machinery on actual roads, researchers have proposed various deep learning models for predicting mobile machinery emissions. Singh and Dubey [
19] proposed an LSTM-based CO
2 emission prediction model that can utilize vehicle onboard diagnostic (OBD) port data for the remote emission monitoring of vehicles. Yu et al. [
20] introduced a method for the accurate prediction of nitrogen oxide emissions from diesel vehicles using adaptive noise (CEEMDAN) and long short-term memory (LSTM) networks. Shin et al. [
21] presented an LSTM-based emission prediction model for the accurate forecasting of transient NOx concentrations in diesel engines. Zhang et al. [
22] developed a time series model based on wavelet transform and LSTM networks to predict engine emissions, including CO, HC, and NO emissions. Wang et al. [
5] utilized a PEMS to measure and analyze the exhaust pollutant emission characteristics of engineering machinery under different operating conditions. Zhang et al. [
23] utilized PEMS and global positioning system (GPS) data to investigate vehicle emission characteristics and developed an LSTM-based model for predicting carbon dioxide emissions. Xie et al. [
24] proposed a parallel attention-based LSTM emission prediction model integrating PEMS and OBD systems.
The role of PEMS in assessing vehicle emissions is critical, but there are some challenges with their practical application. Firstly, the datasets collected by PEMS can be affected by outliers and zero-level offsets, especially over long sampling periods [
24], which can significantly affect the accuracy and reliability of the emission data. Secondly, the high cost of PEMS has limited its accessibility to research institutions. To the best of our knowledge, research on the exhaust emissions of off-road machinery has not been fully carried out. Moreover, traditional models often lack the ability to learn from time series data, as they do not retain past information, thus limiting their predictive power for long-term time series data.
To effectively reduce emissions from off-road machinery, this study analyzes the relationship between variations in the external operating conditions of off-road construction machinery and changes in the emission characteristics. It also explores suitable emission prediction methods and effective emission reduction measures for different types of off-road mobile machinery under transient conditions. Based on LSTM network models, a predictive method tailored to the transient emission characteristics of off-road construction machinery has been developed. By integrating comprehensive data preprocessing, including data compensation and local linear regression, with LSTM predictive models, this study can enhance the accuracy of predicting vehicular emissions, particularly for off-road machinery. This research may provide new insights for controlling emissions from off-road construction machinery and support environmental monitoring and informed policy making to mitigate the environmental footprint of off-road machinery.
3. Data Preprocessing
Emissions data preprocessing was used to eliminate the data anomalies caused by equipment malfunctions and sensor anomalies. In addition, it helps to make the data more standardized, which is essential for the study of emission prediction models for construction machinery.
3.1. Data Compensation
Due to sensor failures or partial data loss during monitoring, the recorded emission data in the monitoring system are incomplete. Some data are lost during the data transmission and updating process. Therefore, it is necessary to compensate for the missing data before analyzing the emission data. Compensating for incomplete data records could significantly enhance the prediction performance. The process of compensating for missing data mainly consists of three steps:
- (1)
Perform data inspection on the original data sequence L to identify the positions of missing data points (xi, yi).
- (2)
Select the key data points of (
x1,
y1) and (
x2,
y2) nearest to the missing value (
xi,
yi) from the data sequence
L, and perform linear interpolation using these two points. Subsequently, add this interpolated data point (
x’i,
y’i) to the data sequence
L to replace (
xi,
yi), resulting in an updated dataset
L’. The coordinates of the updated data points are as follows:
- (3)
Repeat steps (1) and (2) until there are no incomplete data points in data sequence L.
3.2. Outlier Detection
The emissions data of off-road construction machinery typically exhibit strong non-stationarity, which may stem from various factors such as varying operating conditions, mechanical wear and tear, differences in fuel quality, maintenance practices, and environmental influences. In addition, frequent acceleration, deceleration, and aerodynamics often put the engine in transient operating conditions. Due to the non-stationarity of the emissions data, selecting appropriate outlier detection methods is crucial. Techniques like local linear regression can better adapt to the non-stationarity of the data, thereby enhancing the accuracy and reliability of outlier detection.
Local linear regression is a non-parametric regression method that, in making predictions, takes into account the local data points surrounding each data point to fit a local linear model. This approach enables the more effective capture of the local characteristics and nonlinear relationships present in the data. Quadratic local linear regression is a variant of local linear regression that fits a quadratic polynomial model around local data points to capture the nonlinear relationships in the data and improve the data quality and analysis reliability. The model for quadratic local linear regression can be presented as follows:
where
is the response variable of the
i-th data point,
xi is the predictor variable of the
i-th data point,
β0(
xi),
β1(
xi), and
β2(
xi) are the local quadratic linear coefficients at
xi, and
ϵi is the error term.
The objective of quadratic local linear regression is to minimize the following local weighted square error:
where
wi is a weight function that adjusts the influence of each data point during the fitting process, which can be defined using Gaussian or triangular kernel functions. After the iterative parameter adjustments, the optimal fitting model is derived for prediction and analysis.
3.3. Normalization
The emission data were normalized to ensure that all features were on the same scale. Normalization ensures smooth training and not performing normalization might also cause a vanishing gradient problem while training out the LSTM Network. Min–max normalization has been used for normalizing the data. The formula for min–max normalization is as follows:
where
is the
j-th feature value of the
i-th input sample and
is the corresponding normalized value.
4. LSTM-Based Emissions Forecasting Model
The transient emission concentration from off-road construction machinery can vary significantly due to the dynamic and unpredictable nature of construction sites. Factors such as load variations, speed changes, and idling periods contribute to irregular patterns and sudden changes in emission concentrations, making accurate prediction challenging. The complexity of predicting emissions also lies in capturing the intricate nonlinear relationships among factors like the engine load, operating conditions, fuel quality, and environmental influences. The non-stationary nature of emission data further complicates prediction efforts, as traditional models may struggle to adapt to the rapid changes and irregular patterns inherent in off-road machinery emissions.
Deep learning techniques for predicting emission concentrations from off-road machinery offer notable advantages over traditional methods. Deep learning models excel in automatically learning complex features, capturing nonlinear relationships, handling temporal dynamics, scaling with data size, and adapting to changing patterns. These capabilities enable deep learning models to effectively capture the dynamic and non-stationary nature of emission concentration variations, facilitating the accurate predictions crucial for enhancing environmental monitoring and management practices in off-road settings.
The emission prediction model for off-road construction machinery is shown in
Figure 4. The LSTM network used in this research includes one input layer,
n hidden layers, and one fully connected output layer. To improve the prediction accuracy, the Adam optimization algorithm is implemented to optimize the LSTM model. Moreover, dropouts are set after each hidden layer to prevent over-fitting. The activation functions of the hidden layer are the sigmoid and tanh functions, and the activation function of the output layer is the linear function.
6. Conclusions
A long short-term memory network model is proposed to predict the emission of CO, NO, NO2, and NOx from four kinds of off-road construction machinery. The model considers nine input features, which are the vehicle speed, ambient temperature, ambient relative humidity, emission flow rate, emission temperature, and emission pressure differential. The predicted results of the model are CO, NO, NO2, and NOx. When some emission data samples are incomplete, a method based on linear interpolation is used to compensate for the missing data. Local linear regression is applied to smooth the raw emission time series, which exhibit strong non-stationary characteristics, and to detect outliers. For each outlier, a local linear fit is used to update the database. The preprocessed data are then input into the proposed LSTM model to obtain the emission concentrations of CO, NO, NO2, and NOx. The emission data measured using a portable emission measurement system are used to train the proposed LSTM-based emission prediction model, and the predicted results are compared with the experimental results of four types of off-road construction machinery, including a forklift, loader, tractor, and excavator. The experimental results show that the proposed LSTM-based model can predict the emission concentrations of different off-road construction machinery under transient operating conditions.