Quantifying the Environmental Impact of Vehicle Emissions Due to Traffic Diversion Plans for Road Infrastructure Construction Projects: A Case Study in China

Abstract

Current LCA-based environmental impact assessments rarely consider the environmental impact of traffic network deterioration due to temporary road closures during road infrastructure construction processes. This study proposes a quantification method to evaluate the environmental impact of traffic diversions during the road infrastructure construction process. The environmental impact assessment method ReCiPe 2016 was selected to evaluate the environmental impact of pollutant emissions from deteriorated traffic conditions. Ten types of traffic emissions were estimated by emission factors and traffic conditions. A case study quantified the potential environmental impact of traffic emissions resulting from four diversion plans based on an actual bridge-construction case study in Chongqing city of China. Results revealed that different diversion plans could lead to different final environmental impacts. “Global warming” dominated both “Human health” and “Ecosystems” impacts. In the “Human health” category, more than 95% of the environmental impact was contributed by global warming. Similarly, the impact of “Global warming” was higher than 75% in the “Ecosystems” category. CO₂ emissions were the main contributor to the overall “Global warming” impact in all four diversion plans. The traffic speed under traffic diversions before and during road infrastructure construction processes is the major factor influencing the overall environmental impact (endpoint).

1. Introduction

Mitigating greenhouse gas and pollutant emissions has been regarded as a priority worldwide. As a developing country, China aims to reach its carbon peak by 2030 and carbon neutrality by 2060; however, this goal is challenged by the acceleration of urbanization [1]. With the progress of urbanization, China is experiencing rapid urban population growth and urban land-use expansion, leading to an increase in associated transport activities at the urban level. Based on the China Energy Report [2], 13.7% of total energy consumption in China was from the transportation sector in 2016. Although developing clean-energy transportation has been a long-term goal for China, the volume of vehicle emissions is still expected to increase due to the rapid growth of private vehicle ownership [3].

The environmental impact of transportation infrastructure can last for a long lifespan, from the materials production stage to the final waste disposal stage [4]. As a common tool to assess the environmental burden of construction projects from cradle to grave, life cycle assessment (LCA) is often applied in life cycle environmental analysis by identifying goal and scope, life cycle inventory (LCI), life cycle impact assessment (LCIA) and their interpretation [5,6]. Current studies have adopted LCA-based analysis to evaluate the environmental impact of transportation infrastructure [7,8,9,10,11]. However, the current LCA-based studies often underestimate the impact of deteriorating traffic conditions on the life cycle of transportation infrastructure [12]. For example, O’Born et al. [13] presented an LCA-based method to quantify road infrastructure energy use and greenhouse gas emissions and highlighted the importance of considering future traffic conditions at the early planning stage. Indeed, considering the nature of road infrastructure, its environmental impact should not omit the consequential influence of traffic conditions [14], especially for roads under construction in urban areas.

Temporary road closures due to road infrastructure construction projects can potentially lead to a remarkable increase in the final environmental impact caused by the deteriorated traffic conditions of the surrounding traffic network [12]. Inti et al. [15] considered traffic delays in the impact analysis of pavement construction processes and reported an increased global warming potential. Liu et al. [16] evaluated the environmental impact of four maintenance plans for a highway project in China. The results indicated that the increase in CO₂ emissions was significantly related to traffic delays caused by lane closures, whereas the analysis uncertainty was largely influenced by traffic volume, work-zone duration and vehicle operating conditions. Similarly, Mehrabani et al. [17] pointed out that the pollutant emissions in road maintenance activities were mainly caused by on-site activities and delayed traffic due to lane closures. As a solution for temporary road closures during construction processes, Pedneault et al. [18] stated that traffic diversions could inevitably influence the surrounding traffic network, remarkably influencing the environment. Daily traffic volume, diversion distance and disruption period are three key factors that directly impact the release of carbon emissions from traffic diversions [19]. Similarly, Pedneault et al. [18] reported that traffic volume and detour distance could largely influence the overall environmental impact of road infrastructure maintenance from the whole life cycle perspective. Therefore, it is necessary to consider rationally diverting traffic flows and minimizing the environmental impact of road closures in the early planning stage of urban road infrastructure construction projects. Although traffic deterioration due to road closures in the road construction process has been emphasized for its potential environment impact, the method of quantifying the environment impact of different diversion plans in the early planning stage of an urban road infrastructure construction project has been rarely studied.

A vehicle’s primary pollutant emissions include nitrogen oxides (NO_X), volatile organic compounds (VOCs), carbon monoxide (CO), carbon dioxide (CO₂), sulfur dioxide (SO₂) and particulate matter (PM) [3,20,21]. However, the vehicle’s pollutant emission rate significantly depends on short-term events, such as speed, acceleration and deacceleration [20,22]. Zhang et al. [23] estimated light-duty vehicle emissions under three traffic conditions (free-flow, work-zone and rush-hour conditions). They found that the transitional periods (deacceleration/acceleration between free-flow and congested conditions) were associated with higher vehicular emission rates. To accurately quantify vehicular emissions, vehicular emission models, such as COPERT [24], MOVES [25], CMEM [26] and PHEM [27], have been established based on the American and European databases and further applied in China’s contexts [28,29]. These models can be separated into emission factor models (based on fixed driving cycles) and emissions rate models (based on vehicle specific power), such as COPERT and MOVES models, respectively [30]. Although the COPERT model has long been criticized for not considering actual vehicle operating modes on roads [31], it has been widely calibrated and validated for Chinese vehicles [32,33] and can effectively estimate traffic emissions at the street level with actual traffic speeds [21]. Meanwhile, with the capability of estimating fuel consumption under congested traffic conditions [34], the COPERT model demonstrates the potential for assessing traffic pollutant emissions due to road infrastructure construction projects.

To sum up, the construction of road infrastructure (e.g., maintenance process) is critical for long-term carbon mitigation advantages [35], but the environmental impact of the changes in traffic conditions due to traffic diversions cannot be underestimated in urban areas. Quantifying the environmental impact of deterioration in traffic conditions due to road infrastructure construction processes is becoming essential for evaluating the life-cycle impact of road infrastructure. However, the impact of traffic network deterioration is often omitted in the current environmental impact analysis studies. This paper aimed to quantify the environmental impact of deteriorated traffic conditions due to traffic diversions in urban road infrastructure construction projects. The COPERT V model with proven effectiveness in the Chinese context [36] was adopted in this paper to estimate vehicle pollution emission factors. The original novelty is to propose a quantification method to evaluate the environmental impact of traffic diversions during the road infrastructure construction process. The paper is organized as follows: Section 2 presents the quantification framework and identifies the quantitative equations and the bridge-construction case study in Chongqing city, China. Section 3 presents the results of the environmental impact assessment of traffic emissions under four diversion plans for the case study. A sensitivity analysis follows to explore the performance and identify the main parameters affecting the variation in model output. The influential factors that might affect the accuracy and performance are discussed in Section 4.

2. Methods

2.1. Goal and System Boundary

This study aimed to quantify increases in environmental impact from deteriorated traffic conditions due to traffic diversions for urban road infrastructure construction projects. Ten types of traffic emissions, including nitrogen oxides (NO_X), volatile organic compounds (VOCs), carbon monoxide (CO), carbon dioxide (CO₂), sulfur dioxide (SO₂), particulate matter (PM), nitrous oxide (N₂O), methane (CH₄), ammonia (NH₃) and non-methane volatile organic compounds (NMVOCs), were deduced as functions of average traffic speed and mean cumulative mileage. In this study, a case study analysis was conducted to evaluate the environmental impact of deterioration in traffic conditions caused by traffic diversions for an actual bridge infrastructure construction project in an urban area of Chongqing, China. A more detailed case description can be found in Section 2.4. The system boundary was established at peak hours of working days during the construction process in the road infrastructure construction, use and demolishment phases (Figure 1). Direct waste gas emissions from traffic diversions under deteriorated traffic conditions were considered. Figure 1 demonstrates the environmental impact assessment framework for traffic diversions. The framework considers the fuel consumption as input and 10 vehicle emissions as direct output. The environmental impact of 10 vehicle emissions was further assessed using the environmental impact assessment method ReCiPe 2016. The assessment midpoint and endpoint were selected by considering the potential impact of 10 outputs (vehicle emissions).

2.2. Environmental Impact Assessment Method

The environmental impact assessment method ReCiPe 2016 [37] was selected to evaluate the environmental impact of pollutant emissions from deteriorated traffic conditions. The hierarchist perspective was selected, as it represents a moderate time perspective, and substances are only included if there is a scientific consensus regarding their effects [38]. Seventeen midpoint categories and three endpoint categories are identified in the ReCiPe 2016 method [37]. Figure 1 shows six selected midpoint and two selected endpoint categories potentially impacted by vehicle emissions. Meanwhile, normalization and weighting processes enabled unified compatibility, which resulted in the relative significance of the environmental impact in the global impact of the year 2010 [39].

2.3. Traffic Condition Deterioration Assessment Method

➀

Assessing vehicle emission factors with the COPERT V model

The COPERT V model, based on European emission standards, reflects typical road conditions and estimates emission factors as a function of speed and mean cumulative mileage [34]. Since the Chinese vehicle emission standards generally correspond to European standards [40], this study used the emission factors identified in the COPERT V model for the Euro 5 standard (i.e., China 5). Additionally, since this study only focused on the environmental impact of on-road vehicles due to traffic diversions, only hot emissions occurring when the vehicle engine was operating in the normal regime were considered. Based on the hot emission factor [41], the emission factors of pollution emissions (CO, NO_x, VOCs, CH₄, PM) and fuel consumption (FC) were calculated by Equation (1).

$$e{f}_{\mathrm{FC}, \mathrm{CO}, {\mathrm{NO}}_{\mathrm{x}}, \mathrm{VOCs}, {\mathrm{CH}}_4, \mathrm{PM}}=\left(\alpha v^2+\beta v+\gamma +\delta /v\right)/\left(\epsilon v^2+\zeta v+\eta \right)\times \left(1-RF\right)$$

(1)

where, ef is the emission factor of pollution emissions and fuel consumption of a vehicle (g/(vehicle·km)); seven parameters (α, β, γ, δ, ε, ζ, η) were constant values retrieved from the COPERT V model for the Euro 5 standard, as shown in

Table A1. Pollution emission factors in COPERT V model for Petrol Medium passenger cars [31].

	$\mathit{\alpha }$	$\mathit{\beta }$	$\mathit{\gamma }$	$\mathit{\delta }$	$\mathit{\epsilon }$	$\mathit{\zeta }$	$\mathit{\eta }$
CO	0.00045	−0.10208	6.87693	10.38385	0.00162	−0.43756	30.33733
CH4	0.00000	0.00000	2.87000	0.00000	0.00000	0.00000	1000.00000
NOx	−0.00031	0.10306	0.23906	−0.33928	0.03454	1.98601	1.26376
VOCs	0.00000	−0.00071	0.04525	0.17307	0.00007	−0.04754	6.21205
PM	0.00000	0.00000	0.00040	0.00000	0.00000	0.00000	1.00000
FC	0.00013	0.00549	2.61920	0.00000	−0.00009	0.02358	0.34430

in Appendix A (for medium-size petrol passenger cars); v is the vehicle travel speed (km/h); RF is a reduction factor, if applicable.

The emission factors of CO₂ and SO₂ emissions were multiples of the fuel consumption emission factor, as shown in Equations (2) and (3).

$$e{f}_{C{O}_2}=3.169\times e{f}_{FC}$$

(2)

$$e{f}_{S{O}_2}=e{f}_{FC}\times {10}^{-5}$$

(3)

Meanwhile, the emission factors of nitrous oxide (N₂O) and ammonia (NH₃) were calculated as functions of the mean cumulative mileage of a particular vehicle type (Equation (4)), as adopted by the COPERT model. The cumulative mileage could be expressed as annual mileage times the years of service life of a vehicle [41].

$$e{f}_{N_{2}O, N{H}_3 }=\left[a\times CMileage+b\right]\times e{f}_{Base }$$

(4)

where $e{f}_{Base }$ is a constant value retrieved from the COPERT V model (mg/(vehicle·km)); two parameters (a and b) are constant values for a given vehicle type. The above values are shown in

Table A2. The parameters in COPERT V model for Petrol Medium passenger cars [31].

	$\mathit{a}$	$\mathit{b}$	$\mathit{e}{\mathit{f}}_{\mathit{B}\mathit{a}\mathit{s}\mathit{e} }$
N2O	0.000000783	0.861	2.4
NH3	0.00000173	0.955	4.1

in Appendix A (for Euro 5 petrol passenger cars under hot urban conditions).

Finally, the emission of non-methane volatile organic compounds (NMVOCs) was deduced by the difference between VOCs and CH₄ emissions, as deduced in Equation (5).

$$E_{NMVOCs}=E_{VOCs}-E_{{\mathit{CH}}_4}$$

(5)

where $E_{NMVOC,VOCs,{\mathit{CH}}_4}$ comprises emissions of NMVOC, VOCs and CH₄ (mg/(vehicle)).

➁

Identifying vehicle speeds and traffic flow

Identifying traffic vehicle speed changes and diversion route flow before and during the construction process is essential for deducing the overall vehicle emissions due to deteriorated traffic conditions. The traffic speed and flow on a given road can be deduced by on-site monitoring [42] and model prediction [43]. Some classical speed-flow models could be considered for approximating traffic conditions based on the relationship between traffic speed and traffic flow rate, such as Greenshields’ Speed-Flow Curve [44], Edie’s Speed-Density model [45] and HCM’s Speed-Flow Curves [46]. This case study adopted the traffic-condition index system proposed by [47] based on urban traffic situations in China (

Table 1. The traffic-condition index system [47].

Condition	Very Smooth	Smooth	Slightly Congested	Congested	Heavily Congested
Road Saturation	≤0.4	(0.4, 0.6)	(0.6, 0.7)	(0.7, 0.8)	>0.8
Average Road Speed (km/h)	>45	(35, 45)	(25, 35)	(15, 25)	≤15

). The road saturation was deduced by the traffic flow and the maximum road capacity. The maximum road traffic capacity and predicted traffic flow of diversion routes were directly retrieved from the local transportation bureau in this case study.

2.4. Case Description and Data Sources

This case study quantified the environmental impact of pollutant emissions from deteriorated traffic conditions due to traffic diversions for an actual bridge construction project in Chongqing, China. The duration of the whole project was expected to be 210 days, including 150 working days. The bridge construction project resulted in the temporary closure of gateways from Nan’an District to Jiangbei District. As initially planned, the traffic flows that usually passed the under-construction bridge were expected to be diverted in either of two directions (i.e., the Inner Ring Expressway North or ChaoTianMen Bridge) through four potential traffic diversion plans (Figure 2). The diversion plans were proposed by the local transportation bureau to direct the traffic flow across the river via two bridges, and the selection of diversion routes mainly considered road capacity.

Specifically, Figure 2 shows the four traffic diversion plans proposed in the early planning stage of the project, which would divert the original traffic flow coming from the south side to Jiangbei District via different routes. As a result, seven critical routes would share the daily commuting burden (Figure 3). They were Danguang road (Road A), Tushan road (Road B), Nanbin road (Road C), Danzishi new street (Road D), Shangxin street (Road E), Inner Ring Expressway (Road F) and Chaotianmen bridge (Bridge A). The length of each critical route is shown in

Table 2. The length of each critical route in the case study.

ROAD	Road A	Road B	Road C	Road D	Road E	Road F	Bridge A
Length (km)	1.2	3.5	3.5	1.5	0.8	4.5	1.5

. Therefore, the main differences between Plan A/B and Plan C/D would be that Plan A/B directed the traffic flow to Bridge A, while Plan C/D directed the traffic flow to Road F. Meanwhile, Plan A/C would direct the traffic to Road B, while Plan B/D directed it to Road C.

The information on the average traffic network saturation and traffic flow of seven critical routes at peak hours before and during the bridge construction process was collected by traffic flow monitoring devices and numeric estimation, respectively, and directly provided by the local transportation bureau, as listed in

Table 3. Peak-hour traffic conditions under four diversion plans before and during construction.

	Road	Road A	Road B	Road C	Road D	Road E	Road F	Bridge A
Plans and Traffic
Before Construction	Saturation	0.46	0.66	0.61	0.55	0.77	0.76	0.48
	Flow (vehicle/h)	1368	1818	1954	1596	2030	6679	4226
Plan A	Saturation	0.61	0.85	0.68	0.82	0.79	0.78	0.75
	Flow (vehicle/h)	1814	2341	2178	2379	2083	6855	6603
Plan B	Saturation	0.65	0.71	0.82	0.82	0.85	0.79	0.75
	Flow (vehicle/h)	1933	1956	2627	2379	2241	6943	6603
Plan C	Saturation	0.72	0.85	0.68	0.61	0.79	0.85	0.62
	Flow (vehicle/h)	2141	2341	2178	1770	2083	7470	5459
Plan D	Saturation	0.72	0.71	0.82	0.65	0.85	0.85	0.62
	Flow (vehicle/h)	2141	1956	2627	1886	2241	7470	5459

.

To compare the environmental impact of pollution emissions under four traffic diversion plans and simplify the calculation process for the case study, three assumptions were made, as follows:

• The vehicles on-road were light duty gasoline vehicles (LDGV) only;
• The average urban traffic speeds under “very smooth”, “smooth”, “slightly congested”, “congested” and “heavily congested” conditions were 50 km/s, 40 km/s, 30 km/s, 20 km/s and 10 km/h;
• The mean cumulative mileage of one type of vehicle was 100,000 km.

To quantify the increased environment impact of seven critical routes due to road closures, all of the LCI data were calculated based on the methods presented in Section 2. The summary inventory list of vehicle emissions and fuel consumption under four diversion plans and the situation before construction are shown in

Table A3. Life cycle inventory of four diversion plans and the situation before construction.

		Unit	Before Construction	Plan A	Plan B	Plan C	Plan D
Raw material	Gasoline	kg	360.12	471.77	493.12	534.01	522.72
Emissions to air	CO	kg	27.30	32.16	32.76	33.35	33.77
	NOx	kg	3.96	5.14	5.36	5.65	5.84
	VOCs	kg	0.74	0.98	1.03	1.10	1.14
	CH4	kg	0.32	0.37	0.37	0.37	0.37
	PM	kg	0.04	0.05	0.05	0.05	0.05
	CO2	kg	1141.22	1495.04	1562.68	1692.27	1751.56
	SO2	kg	0.02	0.02	0.02	0.03	0.03
	N2O	kg	0.25	0.29	0.29	0.29	0.29
	NH3	kg	0.51	0.59	0.60	0.60	0.60
	NMVOCs	kg	0.42	0.62	0.66	0.73	0.76

in Appendix A. Generally, the volume of vehicle emissions into the air due to the diversion plans was higher than that of the case before construction.

Table 4. Differences in vehicle emissions between diversion plans and the reference case.

		Unit	Increase for Plan A	Increase for Plan B	Increase for Plan C	Increase for Plan D
Emissions into air	CO	kg	4.86	5.46	6.05	6.47
	NOx	kg	1.19	1.40	1.70	1.88
	VOCs	kg	0.25	0.29	0.36	0.40
	CH4	kg	0.05	0.06	0.05	0.06
	PM	kg	0.01	0.01	0.01	0.01
	CO2	kg	353.82	421.46	551.05	610.34
	SO2	kg	0.01	0.01	0.01	0.01
	N2O	kg	0.04	0.04	0.04	0.04
	NH3	kg	0.08	0.09	0.09	0.09
	NMVOCs	kg	0.20	0.24	0.31	0.34

quantifies the differences in emissions between diversion plans and the reference case (before construction).

3. Results

3.1. Environmental Impact Assessment Results

Table 5. The increased environmental impact of vehicle emissions at peak hours on working days.

		Unit	Plan A	Plan B	Plan C	Plan D
Midpoint	Global warming	kg CO2 eq	$550.86\times {10}^2$	$654.38\times {10}^2$	$879.94\times {10}^2$	$938.07\times {10}^2$
	Fine particulate matter formation	kg PM2.5 eq	1.05	1.15	1.11	1.17
	Human health ozone formation	kg NOx eq	183.18	217.06	262.63	292.04
	Terrestrial ecosystems ozone formation	kg NOx eq	186.44	220.99	224.00	225.63
	Stratospheric ozone depletion	kg CFC11 eq	0.06	0.07	0.07	0.07
	Terrestrial acidification	kg SO2 eq	88.59	102.97	118.03	129.82
Endpoint	Human health	DALY	$5.20\times {10}^{-2}$	$6.17\times {10}^{-2}$	$7.97\times {10}^{-2}$	$8.81\times {10}^{-2}$
	Ecosystems	Species.year	$1.97\times {10}^{-4}$	$2.34\times {10}^{-4}$	$2.91\times {10}^{-4}$	$3.19\times {10}^{-4}$

demonstrates the environmental impact assessment results for increased vehicle emissions due to traffic congestion during the construction process at the midpoint and endpoint levels based on the ReCiPe 2016 method. The results were directly obtained through the ReCiPe 2016 method by using the Simapro software package, which is widely used for building inventory and impact assessment analysis.

Generally, different diversion plans can result in different environmental impacts. The potential impact on the global warming midpoint category, attributable to different traffic diversion plans, ranged from $550.86\times {10}^2$ kg CO₂ eq to $938.07\times {10}^2$ kg CO₂ eq. For endpoint categories, the increased vehicle emissions due to the bridge construction process mainly impacted human health and ecosystems. The potential impacts on human health and ecosystems ranged from 0.0520 DALY to 0.0881 DALY and from $1.97\times {10}^{-4}$ species.year to $3.19\times {10}^{-4}$ species.year.

Based on Figure 4, the types of vehicle emissions that dominated each midpoint category are illustrated for the four plans. CO₂ emissions contributed more than 95% of the “global warming” impact in all four diversion plans, while NO_x emissions presented a considerable impact in the “terrestrial acidification”, “ecosystems ozone formation”, “human health ozone formation” and “fine particulate matter formation” categories. The characterized results in two endpoint impact categories (human health and ecosystems) for the four plans are illustrated in Figure 5.

Accordingly, it is shown in Figure 5 that “Global warming” dominated both the “Human health” and “Ecosystems” impacts in all four plans. In the “Human health” category, more than 95% of the environmental impact was contributed by global warming. Similarly, the impact of “Global warming” was higher than 75% in the “Ecosystems” category. The proportion of the global warming impact in the ecosystems category was highest in Plan D, which might result from the combined influence of traffic speed and traffic flow.

3.2. Sensitivity Analysis

The environmental impact of increased pollutant emissions due to traffic congestion caused by traffic diversions was deduced using the ReCiPe method, considering the consequential changes in traffic speed and flow. The case study results imply that the different diversion plans could lead to different final environmental impacts (Table 5). Based on the traffic condition deterioration assessment method presented in this study, the environmental impact of pollution emissions could be determined by the comprehensive variables, including the mean cumulative mileage, traffic speed, traffic flow rate and diversion length. This study conducted a sensitivity analysis to explore the model performance and identify the main parameters affecting model output variation. To quantify the relationship between traffic flow and speed on a given road, we adopted a classical speed–flow model—Greenshields’ Speed-Flow Curve [44,48], which modelled the traffic flow as a function of maximum road capacity, traffic speed, traffic speed at maximal capacity and free-flow speed (driving speed with freedom to change lanes without influences from other vehicles).

The Sobol method was applied in this study as a global variance-based method to quantify the attributions of the individual input variables to the total variance of the output variables and their interactions [49]. Variance effect indices of the Sobol method measure the first-order effect (Si) and total-order effect (STi), indicating the independent effect of variations in the parameters and possible interactions and non-linear effects of the parameters [50]. The detailed calculation methods for the Si and STi were illustrated in the study [51]. To investigate the parameters that have the major impacts on the environment (endpoint), the current study considered seven input parameters used for inputs, as shown in

Table 6. Input parameters for Sobol sensitivity analysis.

Input Parameter	Label	Bounds
Traffic speed before construction	Speed_Before	10–55 km/h
Traffic speed during construction	Speed_During	10–55 km/h
Length of diversion route	Distance	0–10 km
Maximal traffic capacity	Volume_MAX	1000–4000 vehicle/h/ln
Traffic speed at maximal capacity	Speed_Capacity	20–40 km/h
Free-flow speed	Speed_Free	70 km/h
Mean cumulative mileage	CMileage	80,000–200,000 km

. Specifically, this analysis assumed that the speeds during construction would always be lower than before.

The Sobol analysis was conducted in the Python environment using SALib, an open-source Python library for sensitivity analysis [52]. A total of 16,000 samples were generated for the analysis, as required by the Saltilli sampler N*(2D+2), where N was 1000 and D was 7 in this study (the number of model inputs).

As shown in Figure 6, the results of first-order indices showed that the traffic speed on diverted roads during the construction process is the primary factor that influences the overall environmental impact. It results from the differences in vehicle pollutant emissions generated at lower driving speeds due to traffic congestion caused by traffic diversions. Moreover, the road distance and maximal capacity presented a less independent contribution to the variance in environmental impact. Based on the results for the total-order indices, these parameters had a non-negligible effect on interactions with other parameters, except for the mean cumulative mileage. Therefore, the change in traffic speeds due to diversions before and during the construction period can be significant contributors to the final environmental impact.

4. Discussion

During road infrastructure construction processes, traffic network re-organization contributes considerably to the urban environment, but its influences have often been underestimated in the relevant LCA studies. This study quantified the incremental environmental impact of deterioration in traffic conditions due to traffic congestion stemming from traffic diversions for urban road infrastructure construction projects. The environmental impact of pollutant emissions was assessed by an actual bridge construction project in China, revealing the nonnegligible environmental impacts caused by deterioration in traffic conditions due to traffic diversions. By conducting sensitivity analysis, the traffic speeds on diversion routes before and during the construction process were then found to contribute most to the final environmental impact. This section will discuss the influential factors that might affect the accuracy and uncertainty of assessments.

4.1. The Environmental Impact and Traffic Conditions

Transport-related activities, such as material transport, have been widely considered in environmental impact assessments for construction projects. However, less attention has been paid to the environmental impact of traffic network re-organization caused by temporary road closures. Particularly, urban traffic diversions can directly influence urban transportation efficiency and environmental quality. Based on the analysis via the ReCiPe 2016 method, different traffic diversion plans would lead to different final environmental impacts in the “Human health” and “Ecosystems” categories. Based on the results of the case study, “Global warming” dominated both the “Human health” and “Ecosystems” impacts in all four plans, while CO₂ emissions contributed more than 95% of the “global warming” impact in all four diversion plans. It is interesting to find that the CO₂ emissions were the most influential emission source among 10 vehicle emissions in this traffic diversion case. One of the reasons could be the reported strong correlation between fuel consumption and CO₂ emissions [16]. Due to the dynamic nature and complex impact on the environment, traffic networks require careful planning and flexible management to balance traffic systems for lane closures due to road infrastructure construction projects. Commonly, the more congested the roads are, the more negative the impact that traffic can have.

The environmental impact of traffic emissions during construction projects should not be omitted from the LCA-based analysis. Based on Table 5, the potential impacts on human health and ecosystems ranged from 0.0520 DALY to 0.0881 DALY and $1.97\times {10}^{-4}$ species.year to $3.19\times {10}^{-4}$ species.year, which is close to the overall environmental impact of a short-span bridge construction project. Milani et al. [53] used the ReCiPe endpoint methodology to evaluate the potential environmental impacts of short-span bridges, and the results indicated that the potential impacts on human health and ecosystems were 0.08 DALY and $1.70\times {10}^{-4}$ species.year for a short-span bridge construction project (10 m wide and 18 m long) in Brazil. Therefore, considering the environmental impact of traffic diversions can effectively increase the accuracy of LCA-based models when evaluating the environmental impact of road infrastructure construction projects. Although vehicle speeds in traffic diversions before and during bridge construction projects were suggested to be major contributors based on sensitivity analysis, the overall environmental impact also depends on road distance, maximal capacity and traffic speed at maximal capacity. For example, referring to Table 3 and Table 5, although Plan A had more congested roads than Plan C, the incremental environmental impact of Plan A was much lower than that of Plan C, potentially resulting from road F with long distances and high traffic volume. Moreover, the overall environmental impact can also be influenced by the duration of projects. Therefore, it is suggested that the more accurate traffic condition prediction models can yield improved accuracy.

Although very few studies have specifically examined the environmental impact of traffic diversion plans for work zones, the related studies have investigated the impact of traffic diversion on travel performance, highlighting the importance of accurately acquiring traffic condition data. For example, Zhou [54] developed a simulation approach to assess the effectiveness of traffic diversion plans and found that the diverted traffic volume greatly influenced delays in the traffic network. Similarly, Memarian et al. [55] proposed a traffic diversion model to optimize the overall network performance by saving traveling time in road-closure situations. However, modelling the traffic network with reliable demand and supply data was one of the main challenges. To overcome this challenge, data mining and machine learning methods are often involved in state-of-the-art studies of traffic condition predictions. Yao et al. [56] proposed a spatial-temporal dynamic network framework to predict traffic conditions and reported reasonable prediction performance for road intersection volume. Similarly, to address the dynamic spatial-temporal correlation of traffic networks, Li et al. [57] proposed a multi-sensor data correlation graph convolution network model that showed the remarkable ability to improve long-term prediction accuracy for traffic networks of various sizes. With the help of the dynamic and accurate prediction of traffic conditions, the environmental impact of traffic diversion plans can be optimized with much less uncertainty in the planning stages.

4.2. The Environmental Impact and Vehicle Types

The case study presented in Section 3 compared the environmental impact of four traffic diversion plans, so an ideal situation (all petrol-powered passenger cars) was assumed to simplify the calculation processes. However, the actual extra environmental impact may also be attributed to the types of fuels and vehicles [58]. With the energy shortage and global-warming issues, carbon neutrality has been gradually regarded as one of the top targets worldwide, resulting in the rapid development of clean-energy vehicles. As a potential substitution for conventional fuel-powered vehicles, the electric vehicle has been experiencing a rapid increase in the global stock annually [59]. China has been one of the leading countries in electric-vehicle ownership [60]. Based on official statistics from the Ministry of Public Security of China, the ratio of electric vehicle ownership to total vehicle ownership increased from 0.7% in 2017 to 2.6% in 2021, as shown in Figure 7. This proportion is expected to grow significantly in China and reach 75% by the year 2050 [61].

Moreover, Hawkins et al. [62] compared the environmental impact of conventional and electric vehicles under the use phase and found that electric vehicles powered by average European electricity presented a 10–24% reduction in global warming potential (GWP) compared with gasoline and diesel vehicles. Ma et al. [63] examined the exergy value of the environmental impact of different vehicle fuel types. They reported that the environmental impact of electric vehicles was only 55% that of conventional gasoline-type vehicles during use phases in China’s context. Meanwhile, with the development of electric vehicle technology, Wang et al. [61] predicted that electric passenger vehicle energy efficiency will dramatically improve in the next 30 years, resulting in 16.5% and 55.4% reductions in average energy consumption for the year 2030 and year 2050 scenarios, respectively. Therefore, considering the future development of electric vehicles, the environmental impact caused by traffic congestion can be further reduced in road infrastructure projects, and this may lead to a new method of evaluating environmental impact accordingly.

4.3. Limitation and Future Outlook

Building low-carbon cities and achieving carbon neutrality are among the priorities of major countries, and sustainable urban transportation systems will contribute to their realization. Schrank et al. [64] reported that road congestion in the USA resulted in a delay of 6.9 billion hours and the consumption of 3.1 billion extra gallons of fuel. Among 14 potential influencing factors, transportation structure and population size were the two main factors affecting urban transportation carbon emissions [60]. However, temporary road or bridge closures due to construction projects can unavoidably interrupt normal traffic by adding extra traffic flows to diversion routes. The results of the current study found that the various traffic diversion plans could lead to variations in the final environmental impact. Therefore, besides traffic diversion optimization in the planning phases, effective traffic management is also necessary to minimize the impact due to urban carbon emissions [65,66].

This study mainly focused on quantifying the environmental impact of vehicle emissions based on deducing their emission factors but did not include the environmental impact of incremental increases in fuel consumption, although the amount of increased fuel consumption was calculated and is listed in Table A3 in Appendix A. Moreover, traffic diversion can physically alter traffic conditions and influence travelers’ behavior patterns [67]. Boggio-Marzet et al. [68] reported that poor traffic conditions could significantly increase vehicle fuel consumption regardless of road type. The environmental impact framework proposed in the current study has not considered travelers’ driving habits and behaviors, which could be sources of potential uncertainty that influence the results of an environmental impact assessment. Future study can attempt to use historic floating car data (FCD) to analyze traffic flow patterns and travelers’ behavior [69]. Models have been developed to predict the diverted traffic flows and reveal dynamic traveling behaviors. Zhao et al. [70] developed a traffic model to effectively determine optimal traffic diversion plans using the speed–volume relationship by considering the effectiveness of work-zone schedules. Xu et al. [71] established a traffic state prediction model by considering the impact of travelers’ route-switching behavior. Results showed that the proposed model can accurately predict traffic flows and travelers’ actions based on the traffic information provisions in traffic diversions. Therefore, to achieve more accurate performance in quantifying the environmental impact of traffic diversions, this study recommends that future studies examine the uncertainties posed by traveler behaviors and traffic management practices in real-world situations.

5. Conclusions

Current LCA-based environmental impact assessments underestimate the environmental impact of deteriorating traffic conditions caused by traffic diversions during temporary road closures due, for example, to road infrastructure construction projects. This study quantified the incremental environmental impact of deterioration in traffic conditions due to congestion caused by traffic diversions for urban road infrastructure construction projects. Ten types of traffic emissions were deduced using the COPERT model as a function of average traffic speed and mean cumulative mileage. The environmental impact of pollutant emissions was assessed with the ReCiPe 2016 method for an actual bridge construction project in China. This study also conducted a sensitivity analysis to explore the model performance and identify the main parameters that affect variations in model output. The main conclusions are presented as follows.

• The proposed quantification method can effectively evaluate the environmental impact of traffic diversions due to road infrastructure construction projects, especially in the early planning stage;
• The environmental impact of increased vehicle emissions caused by traffic diversion is non-negligible and should be considered in the road infrastructure LCA analysis, and different diversion plans can lead to different final environmental impacts;
• Based on the case study, “Global warming” dominated both “Human health” and “Ecosystems” impacts, while the CO₂ emissions were the main contributor to the overall “Global Warming” impact;
• Using Sobol sensitivity analysis, we found that the traffic speeds in traffic diversions before and during the road infrastructure construction process are the major factor influencing the overall environmental impact. However, the influence of road distance, road capacity and speed at maximal capacity cannot be neglected;
• The urban planner and construction manager can use the proposed quantification method to quickly assess traffic diversion plans with lower environmental impact. However, the uncertainties, such as vehicle types, traffic behaviors, traffic speed and traffic flow, are recommended to be considered in future studies.