# Evaluation of Air Pollutants in Extra-Long Road Tunnel with the Combination of Pollutants Nonlinear Evolution and Machine Learning Method

^{1}

^{2}

^{*}

## Abstract

**:**

_{2}, PM

_{2.5}, PM

_{10}) were analyzed using the Multifractal Detrended Fluctuation Analysis approach. Using the Random Forest model, the impacts of traffic and environmental parameters on air quality were quantified. The findings indicated that COVID-19 had a considerable impact on tunnel traffic, although the variance in pollutant concentration was not very noteworthy. The bidirectional effect of traffic was the main reason for this phenomenon. The Canonical Correlation Analysis was unable to quantify the correlation between pollutants and environmental parameters. The pollutant concentration evolution has a steady power-law distribution structure. Further, an inverse Random Forest model was proposed to predict air pollutants. Compared with other prediction models (baseline and machine learning), the proposed model provided higher goodness of fit and lower prediction error, and the prediction accuracy was higher under the semi-enclosed structure of the tunnel. The relative deviations between the predictions and measured data are less than 5%. These findings ascertain the nonlinear evolutionary mechanisms of pollutants inside the expressway tunnel, thus eventually improving tunnel environmental sustainability. The data in this paper can be used to clarify the changes in the traffic environment under the COVID-19 lockdown.

## 1. Introduction

_{3}levels in road tunnels are several times higher than those in ordinary roads [12]. At the same time, the traffic of heavy trucks in mountainous areas and their proportion of the total traffic in road tunnels increased significantly, exacerbating the levels of CO, NO

_{2}, SO

_{2}, and particulate matter in the air of such buildings [13,14,15,16]. Song et al. conducted a two-week field test and found a linear relationship between the proportion of heavy vehicle traffic and pollutant levels, with the PM

_{2.5}, NO, NO

_{2}, NO

_{x}, and CO being 75, 81, 24, 65, and 33 times more intense than those from light-duty vehicles, respectively [17]. In addition, such buildings hurt the local environment, especially in residential and environmental areas [18,19]. These findings highlight the importance of understanding the dynamics of pollutants in extra-long road tunnels to help derive an appropriate response to control traffic pollution.

_{10}components of tunnels, and road slope and road roughness are the fundamental factors determining non-tail gas emissions of roads [15]. Hou et al. performed in situ sampling of aerosol particles in a tunnel in a coastal city in southern China and analyzed the particles using transmission electron microscopy and energy dispersive X-ray spectrometry. They suggested that the aging of particles is weaker than the atmospheric environment due to the absence of photochemical reactions inside the tunnel [41]. Based on the monitoring results for the Caldecott Tunnel in San Francisco, Dallmann et al. quantified the emission factors of motor vehicles, including medium- and heavy-duty trucks [42]. Tong et al. performed continuous and single-point measurements in five urban road tunnels and showed that the wind speed inside the tunnel depends strongly on the vehicle speed [43]. Xu et al. used a multiple fractal detrended fluctuation analysis (MF-DFA) model to analyze the collected aerosol data. The data were decomposed and analyzed. The aerosol level was found to have multifractal properties and long-term persistence [44]. On-site monitoring allows for quick and direct access to first-hand data. However, the single field monitoring scenario and short monitoring period limit the field measurement results.

## 2. Tunnel Description

^{2}. The G18 Expressway, which opened to traffic at the end of 2019, is a crucial link to the Beijing-Tianjin-Hebei area. The tunnel is an essential conduit for carrying coal from Shanxi Province to the Beijing-Tianjin-Hebei area. The tunnel adopts the longitudinal mechanical ventilation system (as shown in Figure 1) and is a double-hole single-way tunnel. The northbound tunnel was chosen as the sample tunnel, as it is an uphill tunnel with a gradient of +2.15%, in which more pollutants are generated by vehicles running in it. Furthermore, the distance between the southbound and northbound tunnels is higher than 50 m, and pollutant channeling may be ignored. The operating power of the jet fan is 37 kW with the 1450 r/min for operating speed and 99,000 m

^{3}/h of airflow. The surrounding rock is Granite (density: 2650 kg/m

^{3}); C25 shotcrete (thickness 15 cm) was used in the initial lining. The secondary lining is made of C35 mold concrete (45 cm thick). The pavement structure is divided into two layers; the lower is 6 cm thick SBS composite modified asphalt (ARHM-20), and the upper is 4 cm thick flame retardant asphalt (SMA-13).

## 3. Experimental Method

#### 3.1. Traffic Data

#### 3.2. Environmental Parameters

#### 3.3. Pollutant Concentration

## 4. Methodology

#### 4.1. Multifractal Detrended Fluctuation Analysis Method (MFDFA)

- (1)
- For the pollutant concentration time series x
_{t}, t = 1, 2, 3, …, N, construct the cumulative deviation series Y_{i}. The time series Y_{i}is divided equidistantly into N_{s}intervals.$${Y}_{i}={\displaystyle \sum _{t=1}^{i}\left({x}_{t}-{\overline{x}}_{t}\right)}$$ - (2)
- To obtain the mean square error F
^{2}(v, s), the local trend of the 2N_{s}subintervals is calculated by fitting each subinterval v (v = 1, 2, …, 2N_{s}) with the least-squares method. The y_{v}(i) is the fitted polynomial for the v segment of data in Equation (2).$$\{\begin{array}{c}{F}^{2}\left(v,s\right)=\frac{1}{s}{\displaystyle \sum _{i=1}^{s}{\left\{Y\left[\left(v-1\right)s+i\right]-{y}_{v}\left(i\right)\right\}}^{2}}\begin{array}{cc}& v=1,2,\cdots ,N\end{array}\\ {F}^{2}\left(v,s\right)=\frac{1}{s}{\displaystyle \sum _{i=1}^{s}{\left\{Y\left[N-\left(v-{N}_{s}\right)s+i\right]-{y}_{v}\left(i\right)\right\}}^{2}}\begin{array}{cc}& v=N+1,\cdots ,2N\end{array}\end{array}$$ - (3)
- The fluctuation function F
_{q}(s) of order q is calculated, as shown in Equation (3).>$$\{\begin{array}{c}{F}_{q}\left(s\right)={\left\{\frac{1}{2{N}_{s}}{\displaystyle \sum _{v=1}^{2{N}_{s}}{\left[{F}^{2}\left(s,v\right)\right]}^{q/2}}\right\}}^{1/q}\begin{array}{cc}& q\ne 0\end{array}\\ {F}_{0}\left(s\right)=\mathrm{exp}\left\{\frac{1}{4{N}_{s}}{\displaystyle \sum _{v=1}^{2{N}_{s}}\mathrm{ln}\left[{F}^{2}\left(s,v\right)\right]}\right\}\begin{array}{cc}& q=0\end{array}\end{array}$$ - (4)
- The power-law relationship between the volatility function F
_{q}(s) of order q and the time scale s holds when the time series x_{t}has self-similarity, as shown in Equations (4) and (5).$$\begin{array}{c}{F}_{q}(s)\propto {s}^{h(q)}\\ \mathrm{ln}{F}_{q}(s)=a\mathrm{ln}s+b\end{array}$$$$h(q)=\frac{\mathrm{log}{F}_{q}(s)}{\mathrm{log}s}$$$$\begin{array}{l}\tau (q)=qh(q)-1\\ D(q)=\frac{\tau (q)}{q-1}=\frac{qh(q)-1}{q-1}\end{array}$$

_{max}− ∆α

_{min}) and ∆h (∆h = h

_{max}− h

_{min}), according to the multifractal theory, can be used to characterize the multifractal strength.

#### 4.2. Random Forest Model (RF)

_{2}, PM

_{2.5}, PM

_{10}are the dependent variables. The environmental parameters and the daily traffic are independent variables (as shown in Table 2). Wind speed has little effect on vehicle emissions despite its evident impact on pollutant concentration. Similarly, the wind direction is parallel to the tunnel axis ([20]), so the wind speed and direction are not included in the independent variables. The training set was randomly selected with 70% of the data, and the remaining 30% was used as the validation set. The number of trees was 200, the split variables per node were four, and the model used the MSE split criterion.

## 5. Results and Discussion

#### 5.1. Traffic Characteristic

#### 5.2. Monitoring Results

#### 5.2.1. Pollutant Concentrations

_{2}, PM

_{2.5}, PM

_{10}) concentration difference between entrance and exit are shown in Figure 5. The concentration data were processed to the interval [0, 1] using the normalization method to compare the pollutant concentration data characterized by different magnitudes. Non-aerosol pollutants (CO, VOCs, NO

_{2}) correspond to the level of epidemic control. The control level is stricter, the pollutant concentration is lower. Interestingly, CO concentration peaks at the end of the relatively stringent level-II control (29 April 2020). Traffic flow, particularly LDVs, increased significantly in the short term, resulting in higher CO concentration. The CO concentration then levels off as the desire to travel decreases. Aerosol pollutants show the opposite characteristics, where the more stringent the epidemic control, the higher the concentrations instead, except for level-I control. It should be noted that the pollutant concentration differences were not significant, and the mechanical ventilation system was inactive during the observed period. The maximum difference ratio between the average pollutant concentration in different periods ($({\overline{c}}_{\mathrm{max}}-{\overline{c}}_{\mathrm{min}})/{\overline{c}}_{\mathrm{min}}$) is 35% for CO, 28% for VOCs,25% for NO

_{2}, 52% for PM

_{2.5}, 52% for PM

_{10}.

#### 5.2.2. Tunnel Environment Parameters

_{LDV}, N

_{HDV}, N

_{LDT}, N

_{MDT}, N

_{HDT}, are daily traffic of LDV, HDV, LDT, MDT, HDT, respectively.

#### 5.3. Relationship between Pollutant Concentrations and Environmental Parameters

_{2}increased, while PM

_{2.5}and PM

_{10}decreased. The correlation between relative humidity and pollutant concentrations is low, with a positive correlation with CO, and a negative correlation with other pollutants. Due to the linear relationship between temperature and air pressure, the correlation between temperature and pollutant concentrations is not analyzed here to avoid multicollinearity and air pressure.

_{2}concentrations evidenced the perspective. Therefore, despite considerable fluctuations in daily traffic, the variation of pollutant concentrations is slight. Remarkably, aerosol pollutants decrease with increasing daily traffic. Compared to the pollutant concentrations on the day when the maximal average daily wind speed was 8.49 m/s, the CO concentration decreased by 30.9%, 28.5% for VOCs, 21.4% for NO

_{2}, 4.2% for PM

_{2.5}, 4.1% for PM

_{10}on the day with the lowest average daily wind speed of 2.27 m/s. In addition, to avoid multicollinearity, it is necessary to further analyze pollutant emission factors using RF models based on the analysis of pollutant concentration evolution mechanisms.

#### 5.4. Pollutants’ Nonlinear Evolution

_{q}(s) and s for each pollutant. They all have significant power-law scaling relationships within the monitoring period. The DFA scaling exponent a was estimated linearly using the Least Squares Method. All DFA scaling exponents are greater than 1.4 (as shown in Figure 8f).

_{2.5}is relatively weak.

_{max}− ∆α

_{min}). The larger the value of ∆α, the stronger the multifractal, which implies the strength of the long-term persistence in actual pollutant concentration changes. The ∆f reflects the frequency change of the maximum and minimum fluctuations in the long-term persistence of pollutants.

_{2}> PM

_{10}> PM

_{2.5}. This is partly because the YEL Tunnel is located in a mountainous area with high forest cover and the long-term nature of VOCs released by vegetation. Another reason is the photochemical reaction of VOCs. CO is slightly higher than VOCs in data dispersion. The long-term fluctuation of CO is the most significant. The distribution homogeneity of dynamic change in PM

_{2.5}and PM

_{10}is the most uneven. α

_{0}is the abscissa of the extreme point in the multifractal spectrum.

#### 5.5. Prediction of Air Pollutants in YEL Tunnel

_{total}(mg/km) is the traffic emissions. ∆C (mg/m

^{3}) is the difference in pollutant concentration between the outlet and inlet. A (m

^{2}) is the cross-sectional area of the tunnel (99.47 m

^{2}). v (m/s) is the air velocity parallel to the tunnel alignment. T is for one day. L is the tunnel length (5.6773 km).

^{2}calculated for each RF model clarified the model’s fit quality. The established RF models were validated using the partitioned validation dataset (1 January 2020 to 31 January 2021). The RF model generalization ability was certified by the 10-Fold Cross-validation (as shown in Figure 11).

_{2.5}has the most significant difference with a 12.04% increase in MSE and a 13.58% increase in MAE, while the MSE value for VOCs decreased by 11.26% and the MAE value decreased by 6.19%. Overall, the 10-Fold Cross-validation results show very little difference from the original test results, indicating the RF model generalizes well. The model can explain most of the fluctuations in pollutant emissions.

_{2}, PM

_{2.5}, PM

_{10,}respectively. The YEL Tunnel is the main transportation corridor for heavy-industrial raw materials in North China. PM sources include coal dust, secondary dust, clutch, and tire wear. However, all these factors are closely related to traffic flow. From the perspective of traffic flow of different vehicle types, the HDT has the largest influence on the generation of various pollutants, contributing 61.54%, 58.76%, 82.38%, 39.26%, 40.61% to the output results of CO, VOCs, NO

_{2}, PM

_{2.5}, PM

_{10,}respectively. Analyzing the reason, firstly, the traffic flow of HDT in the ERL Tunnel is least affected by the control policy; secondly, the emission coefficient of HDT is high. Its influence weight on NO

_{2}is the largest, so HDT is the main source of NO

_{2}emissions. The contributions of all environmental parameters to the output results of CO, VOCs, NO

_{2}, PM

_{2.5}, PM

_{10}are 15.45%, 3.76%, 6.34%, 23.53%, 22.40%, respectively. Among these, the effects of air pressure and precipitation are negligible. However, CO and aerosol pollutant emissions are sensitive to temperature and humidity, as vehicle engines have different working efficiency under different temperatures and relative humidity. CO and aerosol pollutant emissions will increase as the low-temperature condition causes poor fuel atomization. The high-temperature condition causes premature combustion, which leads to larger CO and aerosol pollutants emissions. Similarly, an increase in relative humidity favors the production of particulate matter. Temperature contributes 10.35% to CO generation, 19.98% to PM

_{2.5}and 16.90% to PM

_{10}. Relative humidity is 5.04% for CO, 3.13% for PM

_{2.5}and 5.43% for PM

_{10}. To sum up, the HDT flow is the controlling factor for emissions of each pollutant.

^{2}and MAPE, the established RF model shows good accuracy. The maximum R

^{2}is 0.9948, and the minimum is 0.9514, as shown in Table 6. Combined with the calculation results on the validation data set, the RF model has better prediction accuracy for CO, COVs, NO

_{2}. In particular, the calculation accuracy for aerosol pollutants slightly improves the prediction period, which proves that the RF model has a good generalization ability (compared to 10 cross-validation results).

^{2}) for CO is 0.9825, R

^{2}= 0.9825 for VOCs, R

^{2}= 0.9903 for NO

_{2}, R

^{2}= 0.9758 for PM

_{2.5}, and R

^{2}= 0.9845 for PM

_{10}. Therefore, the method of back-calculating pollutant concentrations by predicting traffic emissions is implementable, and the results are highly accurate. Compared to the results for pollutant emissions, the concentrations of aerosol pollutants have a little improvement in the prediction accuracy. In combination with the MFDFA results, the long-term persistence of aerosol pollutant concentrations is minimized. Therefore the prediction accuracy is improved by converting emissions to concentrations due to the effect of traffic wind. Collectively, the prediction results for level-3 control are better than the others, mainly due to the relatively stable traffic flow in the situation. Overall, the RF model’s good performance indicates the prediction results’ reliability.

## 6. Conclusions

- (1)
- Different epidemic control levels had different degrees of impact on daily traffic for different types of vehicles, with the HDT only showing a stronger response to level-I control. The same control level also had different effects on traffic flow in different periods.
- (2)
- The pollutant concentrations did not fluctuate significantly during the observation period. Typical correlation analysis results show wind speed largely influences pollutants concentration, ranging from −0.523 to 0.673. The correlations between aerosol pollutant concentrations and wind speed are negative, and the aerosol pollutants are more likely to be discharged from the tunnel. The traffic wind dilutes the pollutant concentrations, and the higher the traffic wind, the higher the pollutant emissions.
- (3)
- The MFDFA results indicate that the pollutant concentrations inside the tunnel exhibit long-term persistence, with CO concentrations being the most significant (h
_{co}(2) = 1.791), and relatively weak for PM_{2.5}concentrations (h_{PM2.5}(2) = 1.602). The evolution of each pollutant has a stable power-law distribution structure, and the pollutant evolution may have the Self-Organized Critical (SOC) state. - (4)
- Through the validation results for the 10-Fold CV and different mathematical models, the created RF models demonstrated high prediction accuracy and generalization ability. The HDT traffic flow was the controlling factor for each pollutant (39.26 to 82.38%). The concentration inversion findings revealed that the prediction accuracy (R
^{2}) is 0.9942 for CO, 0.9825 for VOCs, 0.9903 for NO_{2}, 0.9758 for PM_{2.5}, and 0.9845 for PM_{10}.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 4.**The YEL Tunnel traffic characteristics. (

**a**) Variation of traffic volume by vehicle type. (

**b**) The proportion of traffic volume for each vehicle under different control levels. (

**c**) Traffic volume characteristics for each vehicle type. (

**d**) Daily mean of each vehicle under different control levels.

**Figure 6.**Monitoring results of environmental parameters inside the tunnel. (

**a**) Changes of Temperature and pressure with date; Relationship between precipitation and relative humidity. (

**b**) Changes of wind speed and relative humidity with date.

**Figure 7.**The Canonical Correlation Analysis between pollutants and environmental parameters (The significance levels of the correlation coefficients are all less than 0.0001).

**Figure 8.**DFA results of various pollutant concentrations. (

**a**) The relationship between Fq(s) and s for CO. (

**b**) The relationship between Fq(s) and s for VOCs. (

**c**) The relationship between Fq(s) and s for NO

_{2}. (

**d**) The relationship between Fq(s) and s for PM

_{10}. (

**e**) The relationship between Fq(s) and s for PM

_{2.5}. (

**f**) The scaling exponent a for all pollutants.

**Figure 9.**Calculation results of the MFDFA parameters. (

**a**) The relationship between h(q) and q. (

**b**) The relationship between τ(q) and q. (

**c**) The relationship between f(α) and α. (

**d**) ∆α/∆f(α) for all pollutants.

**Figure 13.**Direct prediction of traffic pollutant masses and indirect prediction of concentrations by RF models. (

**a**) Predicted results for traffic pollution emissions. (

**b**) Indirect prediction results for CO concentration. (

**c**) Indirect prediction results for VOCs concentrationv. (

**d**) Indirect prediction results for NO

_{2}concentration. (

**e**) Indirect prediction results for PM

_{2.5}concentration. (

**f**) Indirect prediction results for PM

_{10}concentration.

Project | Instrument | Producers | Range | Resolution | Accuracy |
---|---|---|---|---|---|

Temperature | Kestrel 5500 | Kestrel | −29~70 °C | 0.1 °C | 0.5 °C |

Relative humidity | 10~90% | 0.1 | 2% | ||

Air pressure | 700~1100 hPa | 0.1 hPa/mb | 1.5 hPa/mb | ||

Wind speed | 0.6~40 m/s | 0.1 m/s | 3% | ||

AR866A | SMART SENSOR | 0~30 m/s | 0.01 m/s | 1% | |

CO | HFP-1201 | Huafan (Xi’an) | 0~1000 pPM | 1 pPM | 3% |

VOCs | HYPERSENSE 1000 | Peking ZhongHA | 0~1000 mg/m^{3} | 0.1 μg/m^{3} | 3% |

NO_{2} | PAC7000-NO_{2} | Draeger Company | 0~50 pPM | 0.1 pPM | 3% |

PM_{2.5} | HW-N1 | Hanvon | 0~999.9 μg/m^{3} | 0.1 μg/m^{3} | 5% |

PM_{10} | HW-M1 | Hanvon | 0~999.9 μg/m^{3} | 0.1 μg/m^{3} | 5% |

Abbreviation | Variables | Units |
---|---|---|

Environmental parameters | ||

Temp | Air temperature | °C |

RH | Relative humidity | % |

Pressure | Atmospheric pressure | hPa |

Precipitation | Precipitation in Baoding area | mm |

Vehicle parameters | ||

N-1 | Count of LDV vehicle per day | n.a. |

N-2 | Count of HDV vehicle per day | n.a. |

N-3 | Count of LDT vehicle per day | n.a. |

N-4 | Count of MDT vehicle per day | n.a. |

N-5 | Count of HDT vehicle per day | n.a. |

Prediction variables | ||

CO_e | Vehicle emissions for CO | g/km |

VOCs_e | Vehicle emissions for VOCs | g/km |

NO_{2}_e | Vehicle emissions for NO_{2} | g/km |

PM_{2.5}_e | Vehicle emissions for PM_{2.5} | g/km |

PM_{10}_e | Vehicle emissions for PM_{10} | g/km |

No. | Level | Period | No. | Level | Period |
---|---|---|---|---|---|

1 | N (1) | 1 January 2020–23 January 2020 | 6 | Ⅲ (6) | 6 August 2020–1 January 2021 |

2 | Ⅰ (2) | 24 January 2020–29 April 2020 | 7 | Ⅱ (7) | 2 January 2021–23 January 2021 |

3 | Ⅱ (3) | 30 April 2020–5 June 2020 | 8 | Ⅰ (8) | 24 January 2021–7 February 2021 |

4 | Ⅲ (4) | 6 June 2020–15 June 2020 | 9 | Ⅱ (9) | 8 February 2021–20 February 2021 |

5 | Ⅱ (5) | 16 June 2020–5 August 2020 | 10 | Ⅲ (10) | 21 February 2021–31 July 2021 |

Code | MSE | RMSE | MAE | R^{2} | MAPE | MSE * | RMSE * | MAE * | R^{2} * | MAPE * |
---|---|---|---|---|---|---|---|---|---|---|

CO | 9,359,226.8 | 3059.3 | 2126.8 | 0.9983 | 1.0388 | 9,988,660.5 | 3160.5 | 2219.4 | 0.9971 | 1.5187 |

VOCs | 1346.4 | 36.7 | 22.8 | 0.9882 | 1.8102 | 1194.7 | 34.6 | 21.3 | 0.9893 | 1.6982 |

NO_{2} | 3,066,479.4 | 1751.1 | 1065.1 | 0.9805 | 2.3059 | 2,820,593.7 | 1679.5 | 993.4 | 0.9826 | 2.1507 |

PM_{2.5} | 5611.1 | 74.9 | 39.7 | 0.9632 | 3.5809 | 6286.6 | 79.3 | 45.1 | 0.9503 | 3.8625 |

PM_{10} | 5709.1 | 75.6 | 46.2 | 0.9698 | 3.4651 | 6212.3 | 78.8 | 48.2 | 0.9636 | 3.6166 |

Model | The Goodness of Fit (R^{2}) | ||||
---|---|---|---|---|---|

CO | VOCs | NO_{2} | PM_{2.5} | PM_{10} | |

MLR | 0.4021 | 0.5259 | 0.3805 | 0.3462 | 0.3025 |

PR | 0.5005 | 0.5188 | 0.3966 | 0.2218 | 0.2564 |

RF | 0.9983 | 0.9882 | 0.9805 | 0.9632 | 0.9698 |

CART | 0.9860 | 0.9021 | 0.9410 | 0.8905 | 0.8582 |

XGB | 0.9905 | 0.9941 | 0.9606 | 0.9153 | 0.9055 |

Code | MSE | RMSE | MAE | R^{2} | MAPE |
---|---|---|---|---|---|

CO | 10,761,006.4 | 3280.4 | 2490.1 | 0.9948 | 1.6591 |

VOCs | 2102.6 | 45.99 | 30.3 | 0.9852 | 2.4129 |

NO_{2} | 3,247,377.2 | 1802.0 | 1089.2 | 0.9803 | 2.3583 |

PM_{2.5} | 6964.5 | 83.5 | 46.3 | 0.9514 | 3.8052 |

PM_{10} | 5308.7 | 72.9 | 45.6 | 0.9734 | 3.3862 |

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**MDPI and ACS Style**

Chang, H.; Ren, R.; Wang, Y.; Li, J.
Evaluation of Air Pollutants in Extra-Long Road Tunnel with the Combination of Pollutants Nonlinear Evolution and Machine Learning Method. *Sustainability* **2022**, *14*, 10710.
https://doi.org/10.3390/su141710710

**AMA Style**

Chang H, Ren R, Wang Y, Li J.
Evaluation of Air Pollutants in Extra-Long Road Tunnel with the Combination of Pollutants Nonlinear Evolution and Machine Learning Method. *Sustainability*. 2022; 14(17):10710.
https://doi.org/10.3390/su141710710

**Chicago/Turabian Style**

Chang, Hongtao, Rui Ren, Yaqiong Wang, and Jiaqi Li.
2022. "Evaluation of Air Pollutants in Extra-Long Road Tunnel with the Combination of Pollutants Nonlinear Evolution and Machine Learning Method" *Sustainability* 14, no. 17: 10710.
https://doi.org/10.3390/su141710710