Simulating the uncertain environmental impact of freight truck shifting programs

Simulating the uncertain environmental impact of freight truck shifting programs

Atmospheric Environment 214 (2019) 116847 Contents lists available at ScienceDirect Atmospheric Environment journal homepage: www.elsevier.com/locat...

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Atmospheric Environment 214 (2019) 116847

Contents lists available at ScienceDirect

Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Simulating the uncertain environmental impact of freight truck shifting programs

T

Haobing Liu, Daejin Kim* School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Drive, Atlanta, GA, 30332, USA

A R T I C LE I N FO

A B S T R A C T

Keywords: Truck shifting strategy Nighttime operation Mixing height Emissions PM2.5 concentration

Shifting freight from regular hours to nighttime has been widely used to relieve traffic congestion and reduce diesel emissions. However, there are also several cities that restrict trucks from operating at night to meet the noise regulation. Uncertainty exists in terms of the environmental impact of these truck shifting programs. On one hand, shifting trucks from peak hours can reduce the total emissions by easing congestion, while the impact of moving truck emissions to nighttime will be interacted with more stable atmospheric boundary layer and lower mixing height, increasing the nighttime concentration. On the other hand, shifting trucks from the night to day avoids the emissions with a stable atmosphere at night, while worsening traffic condition at daytime, and hence increasing the total emissions. The research presents a realistic case study and explores a comparative analysis of PM2.5 concentration originated from a hypothetical freeway corridor and concentrated at a hospital located at 200-m far away from the corridor by implementing four truck strategies applied under various scenarios with traffic demands and heavy-duty trucks (HDTs) proportions. Two meteorological conditions are also considered, with one representing coast region and unstable atmospheric boundary layer, and thus good dispersion condition, and one inland region with a relatively stable atmospheric condition and bad dispersion condition. The results indicate that shifting trucks from peak-period to off-peak period is effective to reduce both emissions and 24-hr average concentrations in scenarios of high traffic demands and high truck proportions, in which the peak period traffic is congested and thus the benefits of reducing congestion and resultant emissions are quite large by altering freight trucks from peak period. It is also observed in the scenarios with lower-traffic demand where the traffic congestion is less severe (in this case, the throughput is less than 75,000 vehicles per day on a three-lane freeway segment, and average speed is higher than 60 km/h), although shifting truck from night to day increase emissions by adding the shifted traffic demand at peak period, it decreases the average concentration by avoiding nighttime emissions. Such a benefit will be further amplified in the meteorological condition of low mixing height at night. Shifting trucks from daytime to nighttime is found to be the strategy that contributes to the highest average PM2.5 concentration, although sometimes the total emissions are minimized.

1. Introduction Traffic congestion has been sitting there for long as a major problem in urban areas all over the world, and can lead to substantial costs for both business operators and travelers (Weisbrod et al., 2003). Off-peak policies are generally accepted as an efficient way to relieve traffic congestion. For instance, congestion pricing is one of the most popular strategies to stimulate the shift in transportation activities away from a peak period, and it has been widely implemented in many cities in the world, including Singapore (Goh, 2002), London (Peters and Gordon, 2009), New York City (Zheng et al., 2014), Stockholm (Eliasson and Jonsson, 2011), Hong Kong (Noordegraaf et al., 2014), and Milan

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(Percoco, 2013). For the freight industry, the delayed or unreliable delivery services due to the congestion are estimated to cause tremendous costs in as high as 1.5% of GDP (gross domestic product) in European countries (European Commission, 2007). In the meantime, freight transport delivers services that are mainly operated by diesel engine-driven vehicles (e.g. heavy-duty trucks or cargo trains) that cause negative externalities on the environment and livability. Shifting freight trucks from peak hours to off-peak hours has been widely promoted in several cities to induce more efficient utilization of transportation infrastructure (Pierpass, 2007; Verlinde et al., 2010; Browne et al., 2014; HolguínVeras et al., 2014; LaBelle and Frève, 2015; Sánchez-Díaz et al., 2017;

Corresponding author. E-mail addresses: [email protected] (H. Liu), [email protected] (D. Kim).

https://doi.org/10.1016/j.atmosenv.2019.116847 Received 21 January 2019; Received in revised form 16 July 2019; Accepted 18 July 2019 Available online 19 July 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.

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the air pollutants to be dispersed, and leading to lower air pollutant concentrations at the ground level. On the contrary, the lower mixing height at night “compresses” the dispersion rooms, and causes higher pollutant concentrations at the ground level. This paper will focus on the environmental impact that accounts for the complex nature of the atmospheric environment and traffic operations caused by truck shifting strategies, especially the shift taking places between daytime and nighttime when the mixing heights are quite different. Such a study is becoming increasingly important, considering the growing concerns about the detrimental health impacts of diesel exhaust (USEPA, 2002) and the wide implementations of truck shifting strategies. However, analyses of truck shifting policies in the incorporation of a comprehensive assessment of environmental impacts are still limited. Up to now, there have been three previous studies identified to show that the average exhausted concentrations may increase from off-peak vehicle operations. Int Panis and Beckx (2007) highlighted the potential problems resulting from off-peak policies based on the estimated changes in pollutant concentrations resulting from the changes in truck operations. They pointed out the impact of the time of day distribution of emissions on identifying local air quality and exposure. Hu et al. (2009) conducted an experimental study in Southern California, showing that pollutant concentrations can be higher during pre-sunrise than daylight hours, despite lower total traffic volumes were observed during pre-sunrise. Another study was conducted by Sathaye et al. (2010), who revealed an interesting environmental impact of a set of truck shifting scenarios in Oakland and Livermore, California based on fundamental diagram graphic tool developed based on traffic flow theory (Daganzo and Daganzo, 1997) and CALINE4 (atmospheric dispersion modeling software) modeling results (Benson, 1984). They estimated the impact of PM2.5 concentration and human intake from shifting freight trucks to nighttime. Since the ABL is generally more stable during the nighttime than the daytime, shifting logistics operations to nighttime may increase the 24h PM2.5 concentrations. This is more likely to happen in the inland region, or locations where exhibit significant diurnal meteorological variation. Also, shifting lower-speed freight trucks from peak hours tends to decrease the likelihood of worsening air condition, since this reduces the high peak-period emissions. Following the similar idea by Sathaye et al. (2010), this study will explore the interaction between traffic operations and atmospheric dynamics under various traffic demands and truck proportion scenarios, by using simulation-based method, in which the vehicle operation and the change of vehicle engine load distribution resulting from the truck shifting strategies will be simulated based on a microscopic traffic simulation VISSIM® (PTV Group, 2018), so that the traffic dynamics can be better captured compared with “static” mathematical method such as from the fundamental diagram. The emission rate from USEPA's regulatory model MOVES (USEPA, 2015) and AERMOD (USEPA, 2018) will be used to conduct the emissions and dispersion analysis. Study sites, truck shifting scenarios, and meteorology conditions are presented in the next section. The methodology is then followed, including VISSIM® simulation, emissions modeling, dispersion modeling, and the linkage process among them. The simulated traffic operations,

Fu and Jenelius, 2018). The implementation of off-peak freight policies is generally supported by studies which indicate benefits in relieving city congestion (Browne et al., 2006; Fu and Jenelius, 2018) and reducing emissions (Yannis et al., 2006; Holguín-Veras et al., 2016). However, some of the previous studies have found that shifting freight trucks to off-peak periods may aggravate the environmental conditions due to the effect of induced trip length. One case study on logistics in the Netherlands indicates that vehicle restrictions by time windows increase pollutant emissions from deliveries, mainly due to the changes in delivery behaviors, in which round-trip lengths of retailers are restricted by vehicle capacity (Quak and de Koster, 2009). Another example in South California has shown that the peak-period truck restrictions induced the use of smaller vehicles and caused an increase in emissions (Campbell, 1995). A case study focusing on Rome's Limited Traffic Zone conducted by Holguín-Veras et al. (2013) shows that with the operating restriction of trucks for heavier laden weights, congestion and pollution became even worse since the induced trips by small trucks are less efficient than large trucks. In addition to the effects of fleet use restrictions, the dynamics of meteorology is also a critical factor that brings uncertain impacts on the environment. One of the important aspects of atmospheric physics is the concept of stability, which describes the degree of vertical mixing in the atmospheric boundary layer (ABL). A more stable ABL reduces vertical dispersion, thereby increasing pollutant concentrations. One of the primary contributors to the instability of the ABL is solar heating (Seinfeld and Pandis, 2012). Solar heating causes an increase in surface temperature and a decrease in the density of air parcels at the surface of the Earth. These parcels rise through surrounding air and cause pollutants to be dispersed to higher altitudes. Although the degree of the influence of this phenomenon may vary, the ubiquitous effect of solar heating over the Earth's land masses makes it relevant for the majority of metropolitan areas. This explains, on the other hand, that ABL is generally more stable at night than during the day primarily because of the absence of the sunlight at night. The meteorological variables for describing the atmospheric stability and hence determining atmospheric dispersion conditions include mixing height, wind direction, wind speed, and temperature (Beychok, 2005). The mixing height is the height of the vertical mixing of the air and suspended particles above the surface. Mixing height is primarily determined by the observation of the atmospheric temperature profile. A parcel of the air rising from the surface of the Earth will rise when the parcel of the air is warmer than the ambient temperature. When the parcel becomes colder than the ambient temperature, it will fall down and eventually stay steady. It is at this junction where the temperature of the parcel crosses the curve denoting the vertical environmental temperature profile that determines the mixing height. Mixing height is one of the most important parameters to characterize the dispersion potential of the ABL (Beyrich et al., 1998). The mixing height provides a “lid” representing the maximum height that the parcels can reach. The more unstable the atmosphere, the higher the mixing height is by nature. A descriptive relationship between mixing height and dispersion condition is shown in Fig. 1. With the solar heating from sunlight during the day, the mixing height is higher, allowing more rooms for

Fig. 1. Mixing height and dispersion conditions. 2

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Fig. 2. – I: Corridor and receptor Configuration; II: One-way daily traffic demands and truck proportions scenarios; III: Hourly demand distribution of LDVs (RLDVi, j ) and HDTs (RHDTi, j ) for base scenario.

aggregated to hourly based data. Light-duty vehicles (LDVs) occupies most of the on-road fleets as found in usual urban freeways. Mainly due to the commute activities, the morning peak hour of LDVs takes place on the link of East-to-West direction (Link-EW), and afternoon peak hour takes place on the link of West-to-East direction (Link-WE), from the detection data collected on the I-285 corridors. On the contrary, the peak period of heavy-duty trucks (HDTs) activities is 12:00 p.m.–15:00 p.m., indicating that the truck drivers have already been avoiding peak congestions to some extent. For each link, the hourly traffic demand is then calculated using:

estimated emissions, and estimated concentration are evaluated and discussed by comparing the base and three alternative truck shifting strategies, while considering various realistic traffic demands, truck proportions, and two meteorological conditions. The final section concludes the paper. 2. Study site 2.1. Hypothetical corridor and traffic demand The study will estimate the PM2.5 concentration at a hospital contributed by a hypothetical freeway corridor in a calendar year (CY) of 2017. The hypothetical corridor is a 2 km long, two-way freeway segments, with three lanes in each direction, as depicted in Fig. 2 (I). The hospital is located 200 m north of the corridor with an air intake located 5 m above the ground. A cross combination of traffic demand and trucks proportion scenarios are proposed to explore the effectiveness of truck shifting strategies under various traffic conditions. Fig. 2 (II) shows all the 81 scenarios to be estimated, with numbers from horizontal coordinate referring to the truck proportion scenarios ranging from 2% to 10% in 1%-interval, and vertical coordinate as one-way daily traffic demand from 72,000 to 80,000 vehicles per day in 1000-interval. For each scenario, the total daily traffic demands in two directions are set as the same. The 24-h traffic demand distributions of the two directions, as shown in Fig. 2 (III), are obtained from the traffic records of a realworld I-285 freeway segment between Chamblee Dunwoody Rd and N Peachtree Rd in Atlanta, Georgia, USA. The traffic data was collected from the Georgia Department of Transportation's NaviGAtor intelligent transportation system (GDOT, 2018). The system records traffic volumes and vehicle speeds for each lane at a 5-min resolution based on the video detection machine vision system and loop detectors, and are

VolLDVi, j = Voli × (1 − PHDT ) × RLDVi, j

(1)

VolHDTi, j = Voli × PHDT × RHDTi, j

(2)

where:

VolLDVi, j : LDVs demand for link i in hour j; i ε (Link-EW,Link-WE); j = 1,2, …,24; VolHDTi, j : HDTs demand for link i in hour j; Voli : Daily traffic demand for link i, ranging 72,000–80,000; PHDT : HDT proportion, ranging 2%–10% RLDVi, j : The proportion of LDVs for link i in hour j throughout a day 24

(Fig. 2 (III)), ∑ j = 1 RLDVi, j = 1 RHDTi, j : The proportion of HTDs for link i in hour j throughout a day 24

(Fig. 2 (III)), ∑ j = 1 RHDTi, j = 1 2.2. Meteorology scenarios The PM2.5 concentrations are quantified using 2017 hourly meteorology conditions that represent two locations in Georgia, USA, that can be retrieved from Georgia Environmental Protection Division (Georgia EPD) website (Georgia EPD, 2018). The locations where the 3

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Fig. 3. Locations of two meteorological sites.

(Fig. 4 II). In particular, the average mixing height in Valdosta-Inland is much lower than SSI-Coast region during the night as shown in Fig. 4 III. All these factors indicate that the ABL is less stable in SSI-Coast region, which implies that the air pollutants can be more easily dispersed in SSI-Coast region as compared to in Valdosta-Inland region.

meteorology data were collected are McKinnon St. Simons Island Airport at Saint Simons Island, Georgia (SSI-Coast in short, 31.155427 N, 81.386599 W), and Valdosta Regional Airport at Valdosta, Georgia (Valdosta-Inland in short, 30.787318 N, 83.276062 W). Fig. 3 lists the locations of these two meteorological sites. The meteorology of these two locations contrasts in that Saint Simons Island's is a representative of coastal regions and Valdosta's is for inland regions. Since the weather patterns are moderated by the ocean, which changes temperature more gently than the land, and as a result, diurnal temperature variations are typically smaller at coastal areas. Also, coastal regions have a greater dynamic in wind motions than inland since near-land sea water slows down the heating up or cooling down the land temperature, thereby leading to a difference in temperature between the inland and water-body. These phenomena are apparent as shown in Fig. 4. The wind rose diagrams show stronger winds at SSI-Coast region (Fig. 4 I) with the calming condition (i.e., the absence of apparent motions in the air) in only 0.4%, compared to Valdosta-Inland region where the 5% of the time is in calming condition

2.3. Truck shifting strategies Four truck operation scenarios were considered in the simulation, including the base scenario where the traffic distribution conforms to typical travel activities as shown in Fig. 2 III, and the three proposed truck shifting strategies that have been implemented in many cities. Fig. 5 shows the hourly traffic demand distributions for all the four scenarios regarding both LDVs and HDTs. The shifted trucks are assumed to be uniformly re-distributed across the other periods of the time. The detailed information for each scenario is listed: A: Base scenario (Fig. 5 A): The hourly distributions of LDV and HDT demand were constructed based on the real-world detection data

Fig. 4. Meteorology Input. I: Wind plot for SSI-Coast site; II: Wind plot for valdosta-inland site; III: 2017 annual hourly average mixing height. 4

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Fig. 5. The 24-h distribution of HDTs demand under 4 truck shifting strategies.

simulation software (PTV Group, 2018). VISSIM® simulates individual vehicle movements and interactions, and is typically used to predict onroad vehicle operating conditions. VISSIM® is able to generate secondby-second vehicle trajectories to represent the instant vehicle operations, making it a viable option for emissions analysis at the project level (Henclewood, 2012; Xu et al., 2016). In this study, VISSIM® is used for simulating the movements of individual vehicles through a whole day on the hypothetical corridors given the diverse scenarios. In this study, a VISSIM® network is built to include the hypothetical corridors (Link-EW and Link-WE). For each scenario, the generation of vehicle inputs in VISSIM® models is based on the hourly traffic demands and HDTs proportions pre-defined by equations (1) and (2). For each simulation hour, VISSIM® produces the number of vehicles present in the network through the initial link points. Fig. 6 illustrates examples of the simulated vehicles at both AM and PM peak periods with 77,000 vehicles/day of one-way traffic demand and 3% (I) and 9% (II) of HDTs proportion. As shown, Link-EW is congested in the morning peak hour with high traffic demand in western bound. On the other hand, Link-WE is congested in the afternoon peak hour. Also, more HDTs are observed in scenarios with 9% of HDTs proportion. Individual vehicle operations are sensitive to VISSIM® parameters (Hunter et al., 2017; Xu et al., 2016; Song et al., 2012; Park and Schneeberger, 2003), potentially affecting emissions results. Therefore, careful selection of each parameter value is required. The influential parameters include the maximum and desired speed/acceleration levels, vehicle head-to-head distance (known as headway), and driving behavior parameters. For those parameters, the team calibrated each of the parameter values with experience-based trial-and-error processes through multiple simulations. Driving behavior parameters and headto-head distance are calibrated and verified to ensure the traffic throughputs and corridor average speeds (or levels of service) well represent the real-world observations from the I-285 corridor. Since emissions calculation is based on instant vehicle operations, calibrating desired/instant vehicle speed and acceleration levels is of importance. Improper values for these parameters, for example, extremely high speed and acceleration, can lead to over-estimated emission results or vice-versa. The team calibrated the maximum and desired speed/accelerations by referring to pre-conducted research (Liu, 2018). Since the traffic condition is inherently dynamic, multiple simulations were conducted for each scenario to capture such variability (or uncertainty). The study performed 35 simulation runs with different random seeds for each scenario to retain a statistically significant result. It sums up to 11,340 simulation runs (4 HDTs strategies × 9 traffic demands × 9 HDTs proportions × 35 random seeds), with each model

that reflects the case where any truck restriction strategies not being implemented. B: Shifting trucks from peak to off-peak period (Fig. 5 B): To relieve congestion in peak periods, trucks operated at AM peak period, 7:00 a.m. - 11:59 a.m., on Link-EW are shifted and uniformly distributed to 12:00 p.m. - 6:59 a.m.; trucks operated at PM peak period, 15:00 p.m. - 19:59 p.m., on Link-WE are shifted and uniformly distributed to 20:00 p.m. - 14:59 p.m. A similar strategy has been implemented in Bogotá, Colombia (COE-SUFS, 2016b) and London (Allen et al., 2000). This strategy is appropriate for the cities where peakhours only occur a few hours during the day. C: Shifting trucks from daytime to nighttime (Fig. 5 C): For both link directions, trucks operated during the day, 7:00 a.m. - 19:59 p.m., are shifted and uniformly distributed to the night, 20:00 p.m. - 6:59 a.m. The strategy fits the cities with heavy traffic and thus tends to prolong heavy-congested hours throughout the entire day. The consistent restriction time periods in both directions of the corridor also seem easier to implement in terms of management feasibility and public awareness. Similar strategies have been implemented in New York City, USA (Holguín-Veras et al., 2016), Los Angeles, USA (Mani and Fischer, 2009), and São Paulo, Brazil (COE-SUFS, 2016a). D: Shifting trucks from nighttime to daytime (Fig. 5 D): For both link directions, trucks operated during the night, 20:00 p.m. - 6:59 a.m., are shifted and uniformly distributed to the day, 7:00 a.m. - 19:59 p.m. The real-world cases of this strategy include some cities across Denmark (Kolstrup et al., 2014) and Stockholm, Sweden (Fu and Jenelius, 2018), where truck operations are restricted during the night mainly due to the noise regulations. 3. Methodology The modeling procedure for this study includes a traffic simulation module, an emissions modeling module, and a dispersion modeling module. The traffic operations in response to truck shifting strategies under various traffic demand and HDTs proportion conditions are simulated using microscopic traffic simulation. The operation outputs then serve as fleet activity inputs for emissions modeling to estimate hourly PM2.5 emission rate, which is used for line source dispersion modeling for PM2.5 concentrations at the hypothetical hospital under two different meteorological settings. 3.1. VISSIM® simulation VISSIM® is a microscopic, time step and behavior-based traffic 5

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Fig. 6. Examples of the simulated VISSIM® models for peak period of base scenario “a” with 77,000 daily traffic demands.

road fleet activities than older vehicles) and the amount of on-road vehicle activities by operating mode bin to calculate a composite emission rate for each link. In this study, second-by-second vehicle operation data is available from the VISSIM® simulation. Vehicle Specific Power (VSP) is calculated first to represent second-by-second engine loads using the equation below.

simulating the second-by-second traffic operations through a whole day. Individual vehicles’ second-by-second driving information (i.e. instant driving speed/acceleration, geographical location) was extracted through the VISSIM® COM (Component Object Model) interface (PTV Group, 2010). In particular, a Python™ program is developed to efficiently automate the multiple simulation processes through the COM interface.

A B C m VSPt = ⎛ ⎞ vt + ⎛ ⎞ v 2t + ⎛ ⎞ v 3t + ⎛ ⎞ (at + g* sin θt )vt ⎝M⎠ ⎝M⎠ ⎝M⎠ ⎝M⎠

3.2. Emissions modeling based on MOVES-Matrix

(3)

where: The MOtor Vehicle Emission Simulator (MOVES) is an emission modeling system released by the US Environmental Protection Agency (USEPA) to estimate emissions for mobile sources in the United States (USEPA, 2015). In general, the states should use MOVES model for State Implementation Plan (SIP) development or conformity analysis (Vallamsundar and Lin, 2012; US EPA, 2015b). MOVES model employs a “binning” approach and present emissions as a function of Vehicle Specific Power (VSP) and operating speed (USEPA, 2015). MOVESMatrix is composed of the outputs from a tremendous number of MOVES model runs (Guensler et al., 2016). The process is constructed to run MOVES across all variables that affect output emission rates and obtain emission rates for all pollutant types from all vehicle source types, model years, on-road operating conditions under a wide range of calendar years, fuel properties, inspection and maintenance (I/M) program characteristics, and meteorology conditions. After conducting hundreds of thousands of MOVES runs, the resulting MOVES emission rate matrix (MOVES-Matrix) can be queried to obtain the exact same emission rates that would be obtained for any MOVES model run, without ever having to launch MOVES again. The emission database in MOVES-Matrix was grouped into multiple sub-matrices, with each sub-matrix storing emission rates for all vehicle source types, all source model years, all on-road operations, for one specific region (Atlanta is used in this study), calendar year (2017 in this study), month, temperature, relative humidity, fuel supply (determined by region, year and month), and I/M strategy (determined by region and year). This way, a small subset of emission rates can be extracted from the matrix based on the user's year, month, and meteorology inputs. This structure helps support analyses for any emission control strategies, given that users generally pay attention to a single temperature, humidity, and fuel condition, when exploring the impacts of strategies on traffic activity and emissions. After the sub-matrix of emission rates is identified and accessed, the emission rate processing is the same as used by MOVES in project-level modeling. MOVES-Matrix weights the emission rates from individual source types to generate the composite emission rate. The weighting combines on-road vehicle activity, as defined by the combined source type and model year distribution (newer vehicles typically account for a larger share of the on-

vt = velocity at time t (m/sec) at = acceleration at time t (m/sec2) θt = road grade as the ratio between vertical movement to horizontal distance (%) g= graviational acceleration (9.81 m/sec2) m= vehicle mass (tonnes) M= fixed mass factor for the source type (tonnes) A= rolling resistance (kW − sec/m) B= rotating resistance (kW − sec2/m2) C= aeodynamic drag (kW − sec3/m3) M= fixed mass factor (tonnes) Each second of the vehicle operations is assigned to an operating mode bin based on the calculated VSP and speed level as defined by the MOVES model. The emission rates of vehicles with the 2017 national default model year distribution are applied to the emissions modeling. LDV fleets are assumed to be a mix of passenger cars (MOVES vehicle type ID = 21; e.g., sedan, station wagon) and passenger trucks (MOVES vehicle type ID = 31; e.g., SUV, pickup truck, mini-van) with ratio of 5:4.3 as observed from infield investigation at Atlanta freeways (Xu et al., 2017). HDT fleets are modeled as combination trucks (MOVES vehicle type ID = 62). Fig. 7 presented PM2.5 emission rates of 2017 Atlanta LDVs and HDTs by operating mode bin with temperature 75 F and humidity 75%. Under the same vehicle operation conditions, the PM2.5 emission rates of HDTs are much greater than of those of LDVs. Operating mode distribution is then calculated by aggregating operating mode bin of each second for each vehicle source type in each of the two roadway links. The emission rate weighting equation for each simulation hour is as follows:

Fleet ER =

∑ ∑ ∑ ST% × MY%ST × OM%ST, MY × ERST, MY, OM ST MY OM

(4)

where: Fleet ER: composite fleet comprehensive emission rate (grams/km). ST: vehicle source type 6

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Fig. 7. MOVES PM2.5 emission rates (mg/second) for LDVs and HDTs.

sources and receptor into AERMOD elements, the coordinates for the link segments and receptor were determined as shown in Fig. 8. The connection between VISSIM, MOVES-Matrix, and AERMOD is shown in Fig. 9. The framework shown in Fig. 9 has been used in research of Xu et al. (2016), Liu et al. (2017), and Kim et al. (2019), and it is also consistent with the recommended procedures on the application of traffic-simulation for transportation conformity and the hot-spot analysis released by the US EPA (2015). Input data can be divided into dynamic and static inputs. Dynamic inputs include traffic volume, operation speed, meteorology (labeled with year, date and hour), and they change over time of the day. Static inputs include link geometry and receptors' coordinates. The modeling system will prepare sub-matrix emission data from MOVES-Matrix, based on the simulation scenario, temperature, and humidity information. With the subset of MOVESMatrix, corresponding volume, operation, and fleet composition data for each link from VISSIM® simulation are used to calculate emissions for each vehicle type and road link. The emission inventory was then converted to the emission source rates used for AERMOD modeling. The traffic volume, operation, and meteorology data are updated in each hour for emissions calculation, which amounts to a total of 272,160 emission outputs (9 vol demands × 9 truck proportions × 4 HDTs scenarios × 24 h × 35 simulation random seeds) and is then used for dispersion modeling for two meteorological conditions. The above processes are streamlined with the team's Python™ program using Georgia Tech Parallel Computing Cluster (PACE, 2017).

MY: model year OM: operating mode bin ST%: the proportion of one source type (from vehicle source type distribution input) MY%ST: the proportion of one model year by one source type (from age distribution input) OM%ST, MY: proportion of one operating mode bin by one source type and one model year (from operating mode distribution input or link input). ERST,MY,OM: emission rate (grams/km) of one vehicle source type, model year, and operating mode bin 3.3. Dispersion modeling The hourly emission outputs from equation (4) are used as the emissions source input for the dispersion model. AERMOD is a steadystate dispersion model designed for the dispersion of air pollutant emissions in the range up to 50 km from the emission source (Cimorelli et al., 1998). It incorporates air dispersion based on planetary boundary layer turbulence structure and scaling concepts, and assumes the concentration is Gaussian distributed in both the vertical and horizontal direction. AERMOD is the USEPA's recommended air dispersion models for conformity and hot-spot analysis (US EPA, 2016). AERMOD requires refined inputs for atmospheric conditions generated from AERMET, the meteorology data preprocessor for AERMOD. The meteorological inputs of the two locations for this study are downloaded from Georgia EPD (Georgia EPD, 2018). AERMOD uses Cartesian coordination system with the self-defined original point, with coordinates of the four ends of the “rectangle” determined separately for the two link segments. “AREA” method is utilized to simulate the on-road vehicle emission sources (Wu and Niemeier, 2016). By converting the link emission

4. Traffic operation and emission rates Fig. 10 shows the hourly average speeds of the simulated vehicles and corresponding standard deviations of the base scenario “A” with one-way daily traffic demands from 72,000 vehicles/day to 80,000

Fig. 8. Line source and receptors coordinates (meters) in Aermod.. 7

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Fig. 9. Modeling framework: VISSIM® traffic simulation, MOVES-Matrix emissions modeling, and AERMOD dispersion modeling.

vehicles/day and 9% of HDTs. The figure indicates that peak-hour speeds drop and the peak-hour spans expand significantly with the increase in daily total traffic demands. The results show that the AM peak-hour speeds do not drop significantly (i.e., remained at 100–120 km/h) until the traffic demands reach 76,000 vehicles/day or higher. On the other hand, the afternoon peak-hour speeds greatly drop even from traffic demand of 73,000 vehicles/day. Under extremely congested conditions, where the traffic demand reaches 80,000 vehicles/day, the hourly average speeds in peak period drop to 20–25 km/ h. The standard deviations of hourly average vehicle speeds are estimated based on the sample variance of 35 random seeds runs, and they represent the dynamics or uncertainty of hourly traffic operations under the same traffic demand inputs. The uncertainty of the hourly speeds is quite small under uncongested conditions (where vehicles are operating in desired speed on freeways) and under extremely congested conditions (where most vehicles are restricted by the traffic flow and in crawling mode). The uncertainty becomes quite larger under “transition” periods, i.e., between free-flow and congested conditions, where the addition of traffic demand can cause a significant drop in vehicle speeds (Kesting and Treiber, 2013). Aggregating the results from 35

random runs could provide robust comparison analysis on emissions and concentrations estimates. Fig. 11 shows the impact of truck shifting strategies on the hourly average vehicle speeds under the traffic demands of 74,000, 77,000 and 80,000 vehicles/day, and HDTs proportions of 3% and 9%. Note that all the scenarios were highlighted with red circles in Fig. 2 II. Shifting trucks from peak hours to off-peak hours (strategy B) increases average traffic speeds for all of the scenarios considered. Such improvements are more obvious in the scenarios with higher traffic demands and higher HDTs proportions (e.g., 80,000 vehicles/day and 9% HDTs, as shown in Fig. 11 VI), indicating that shifting trucks from peak hours can relieve traffic congestions with greater magnitudes as the total traffic demands increase. Shifting trucks from daytime to nighttime (strategy C) produces a similar, but greater impact on vehicle operations across the scenarios as compared to the strategy B. However, for the scenarios with high traffic demand and high HDTs proportion, the strategy C causes additional traffic congestions at the late-night (20:00 p.m. to 22:59 p.m.) on Link-WE, assumedly due to the addition of HDTs demands shifted from the daytime. For example, the congestion on LinkWE takes place at 20:00 p.m.–22:59 p.m. (Fig. 11 VI). In contrast,

Fig. 10. Hourly Average Speed and Standard Deviation in Scenarios of 9% HDTs and One-way Daily Traffic Demand 72,000–80,000 vehicles/dayNote error bars indicate the standard deviations from the mean. 8

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Fig. 11. Hourly average speeds by link under four truck shifting strategies.

hourly data for all four truck shifting strategies and both link directions, the relationship between emission rates and average vehicle speeds are quite concentrated. It confirms that average vehicle speed is critical in determining corridor emissions: vehicles produce higher emissions per unit distance on more congested conditions. Such relationship provides an explanation for the differences in total emissions by different truck shifting strategies. Figs. 5 and 11 together suggest that traffic conditions

shifting trucks from nighttime to daytime (strategy D) further worsen the current traffic congestions in peak hours, especially for the scenarios with higher proportions of HDTs (i.e., Fig. 11 II, IV, VI), and the trends are more obvious with more traffic demands. Fig. 12 plotted the hourly PM2.5 emission rates in micrograms per vehicle-kilometer traveled (mg/vehicle-km) versus hourly average speeds (km/h) for both LDVs and HDTs. Although the plots include the

Fig. 12. PM2.5 emission rate (mg/vehicle-km) versus average speed (km/h). 9

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Fig. 13. Average hourly distribution of emissions (I), PM2.5 average concentrations in SSI-Coast case (II), and valdosta-inland case (III), with traffic demand 77,000/ day and 9% HDTs proportion.

peak hours to non-peak hours. The emissions resulting from strategy C (Fig. 13 I: C) shows that there are no HDTs emissions produced during the daytime since all HDTs are shifted to the nighttime, while causing a significant increase in emissions at night contributed from HDTs. When compared with strategy A, strategy C also decreases emissions from LDVs because of the relief of congestion in peak periods. It is worth noticing that emissions from HDTs in strategy C reach a peak at 20:00 p.m.–21:59 p.m., as highlighted in a green circle, due to the shift of traffic congestion as shown by the hourly speeds of strategy C in Fig. 11 IV. Such transmission of emissions peak is expected to be more obvious in the scenario with higher traffic demands and higher HDTs proportions; for example, Fig. 11 VI, in which the serious congestions are induced at early night on Link-WE from strategy C. For strategy D, moving all HDTs to the daytime leads to quite high emissions during the day, due to the deterioration of traffic and increased emissions contribution of HDTs (Fig. 13 I: D). Regarding the PM2.5 concentration estimates (Fig. 13 II and III), the lower mixing height magnifies the air quality impact at night. Comparing strategy A with strategy B and C, the PM2.5 concentrations of strategy B and C appear to reduce the daytime concentrations, whereas increasing the nighttime concentrations. The trends are more obvious in the lower mixing height as the concentrations in the lower mixing height (Valdosta-Inland) are much higher than the concentrations in the higher mixing height (SSI-Coast). In particular, the nighttime concentrations appear to be very high in strategies B and C because of the lower mixing height and the increased truck operations at night. The results from strategy D are interesting in that the contributions of the daytime emissions to the daytime concentrations (Fig. 13 II: D and Fig. 13 III: D) is less than the substantial amount of emissions produced

become relieved, transferred or worse through the day, depending on total traffic demand levels and proportions of trucks. Such changes in combined with meteorology dynamics are expected to derive different concentration patterns depending on traffic demands and truck shifting strategies. A more detailed discussion will be presented in the following section. 5. Results The section addresses the PM2.5 emission and concentration results from the extensive modeling processes that considered diverse scenarios. To help explain the characteristics of PM2.5 emission and concentration over time from the hypothetical corridor, some important results depicted in figures are provided. Comparative analysis results among four truck shifting strategies under different traffic demands and HDTs proportions are discussed. 5.1. Examples of emissions and concentration analysis Fig. 13 describes the hourly emissions and concentrations contributed from the hypothetical corridors (Link-EW and Link-WE) for the scenario with 77,000 vehicles/day of one-way traffic demand and 9% of HDTs. Note that the hourly average vehicle speeds of this scenario are shown in Fig. 11 IV. Compared with the emissions distribution from the base scenario (Fig. 13 I: A), strategy B cuts peak-period emissions contributed from both LDVs and HDTs (Fig. 13 I: B), which may be attributed to the relieved peak-period congestion. Meanwhile, a great amount of emissions from HDTs are produced in the middle of the day (12:00 p.m. to 14:59 p.m.), potentially due to the shift of HDTs from 10

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Fig. 14. Mean and Standard Deviation of PM2.5 Daily Total Emissions and Average Concentration with Scenarios of HDTs: 3% and 9%, and One-way Daily Traffic Demand: 74,000, 77,000, and 80,000 vehicles/day.

Although strategy C produces lower emissions among all truck shifting strategies mainly due to the enhanced traffic conditions from the strategy (as total emission levels ranked 3 and 4 across all the scenarios considered in Fig. 14), it contributes the highest concentrations in all listed scenarios. This appears to be attributed to truck shifting from nighttime to daytime as the huge increases in the PM2.5 concentrations in strategy C are mostly from HDTs emissions as shown in Fig. 14. This is mainly because of the magnificent impact of low mixing height at night to the air pollutant concentration. Similar to strategy C, strategy B also has the benefits in relieving traffic congestion and thereby reducing total emissions. Compared to strategy C, since the implementation of strategy B requires less HDTs to be moved to the night, even though there are some additions of emissions to the nighttime, the significant relief of traffic congestion during the daytime may contribute to the significant reduction in daily total emissions, leading to a drop in average daily PM2.5 concentrations. The impacts of strategy B appear to be greater as the levels of traffic demands and HDTs proportions increase as Fig. 14 shows that the changes in total emissions from strategy A to strategy B increase as the traffic demands and HDTs proportions increase. Accordingly, the impacts of strategy B on PM2.5 concentrations are not significant in lower-traffic demands and HDTs proportion scenario. This may be due to the fact that the reduction in total emissions during the daytime from strategy B seems to be offset by the increased PM2.5 concentrations at night because of the lower mixing height and the addition of HDTs at night.

during the daytime (Fig. 13 I: D), where an exception exists at 19:00 p.m.–19:59 p.m. (where mixing height is dropping while heavy traffic is taking places on Link-WE). In this circumstance, the nighttime PM2.5 concentrations become very high in strategy C (Fig. 13 II: C and Fig. 13 III: C). The impact is especially significant with Valdosta-Inland meteorology (Fig. 13 III: C), where the average mixing height is less than 100 m at night. The impact of mixing height and the time of day emissions distributions on the air pollutant concentrations provide an interesting point of view: the truck shifting strategies that may lead to the high total emissions do not necessarily lead to high air pollutant concentrations. Fig. 14 provides a better explanation for this situation, showing that although strategy D causes the highest daily total emissions for all traffic demand scenarios, none of which strategy D leads to the highest daily PM2.5 concentrations. It is probably due to the fact that the highest emissions produced during the daytime by the addition of HDTs from strategy D can be easily dispersed during the daytime. Fig. 14 suggests that the impacts of daily total emissions and concentrations vary depending on truck shifting strategies, traffic demands, and meteorology conditions. Overall, strategy B produces less daily total emissions and PM2.5 concentrations than other strategies, somewhat varying depending on scenarios. Interestingly, strategy D appears to produce more daily total emissions than other strategies, while strategy C produces the highest PM2.5 concentrations among the strategies. In cases of 74,000 vehicles/day for both 3% and 9% of HDTs and inland meteorology condition (i.e., labeled as 74000_3_ci and 74000_9_ci in Fig. 14), because traffic demands are low and the overall traffic congestions are not severe, there are extra traffic capacity during the daytime that can accommodate shifted HDTs from the nighttime without dropping the speeds significantly, in which case strategy D produces the lowest concentration among all four HDTs strategies.

5.2. Comparison analysis This section presents a series of comparison analysis results among all the scenarios considered in this study (refer to Fig. 2 II), focusing on 11

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Fig. 15. Daily total emissions and average concentrations in base strategy “a″

eAi, j = Emissions (kg) or concentration (ug/m3) for strategy A , shown in Fig. 15

the impacts of different truck shifting strategies on the daily total emissions (kg) and average concentrations (μg/m3) under various traffic demands and meteorology conditions. The analysis is conducted by comparing the emission (kg) and concentration (μg/m3) estimates from the three alternative strategies with those from the base scenario A. The estimation results of total emissions and concentrations for the base strategy “A” under two meteorological regions are presented as gridded heatmap in Fig. 15, indicating that scenarios with more traffic demands and higher HDTs proportions produce higher emissions and concentrations, as expected. Also, applying Valdosta-Inland meteorology with lower mixing height ends up with higher concentrations than applying SSI-Coast meteorology. The changes in daily total emissions and concentrations from strategy B, C, and D as compared to strategy A are calculated using equation (5).

DX i , j =

(eX i, j − eAi, j ) eAi, j

× 100

The result differences of total emissions and concentrations, DX i, j , are presented in Fig. 16. Fig. 16 indicates that strategy B and C can reduce the total emissions as compared to the base scenario, mainly because of the congestion reliefs from shifting trucks of peak period or daytime (as addressed earlier). Specifically, in the strategy B with high traffic demand conditions 78,000–80,000 vehicles/day), shifting HDTs from peak period can reduce total emissions by 21%–47%, depending on the proportions of HDTs. In the case of strategy C, the reduction rates range from 20% to 42%. In contrast, moving HDTs from nighttime to daytime (strategy D) appears to increase the total daily emissions, which may be mainly due to the aggravated traffic congestions during the day. Especially, in the scenarios with traffic demands of 74,000 to 77,000 vehicles/day with HDTs proportion of 8% and higher, where the peak-period operating speeds are under “transition” between free-flow and congested conditions, the increase rates were higher than other scenarios. This suggests that adding more HDTs to the transition periods from strategy D may greatly deteriorate the traffic conditions during the daytime and expand the peak periods (refer to Fig. 11 II and IV), and thus significantly increases the total emissions (65%–87%) as compared to the scenario A, as shown in Fig. 16 III. For strategy B, the changes in PM2.5 concentrations vary, depending on traffic demands, HDTs proportions and mixing height as shown in Fig. 16 IV and VII. The areas of reducing the daily average PM2.5

(5)

where:

i = One − way daily traffic demand, i∈ [72000, 73000, …, 80000] (vehicles/day) j = HDTs proportion, j∈ [2, 3, …, 10] (%) shown DX i, j = Result difference compared with strategy A (%) Fig. 16 eX i, j = Emissions (kg) or concentration (ug/m3) for strategy X,

in

X∈ [B, C, D] 12

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Fig. 16. Emission and concentration differences comparing three alternative strategies b, C, and D with base scenario a

traffic demand and HDTs proportion scenarios, avoiding HDTs operations at night with lower mixing height decreases the overall PM2.5 concentrations in many of the scenarios, except for the scenarios with middle level of traffic demands and high HDTs proportions, in which as mentioned, the increase of emissions are extremely significant, reaching 65%–87% (red areas in Fig. 16 III), dominating the air quality impacts. In terms of meteorology conditions, the areas of increasing PM2.5 concentrations in Valdosta-Inland region (”+” in Fig. 16 IX) are smaller than those in SSI-Coast region (”+” in Fig. 16 VI). This suggests that in the regions with low mixing height at night such as Valdosta-Inland, strategy D is more likely to decrease PM2.5 concentrations by avoiding

concentrations (”−” in Fig. 16 IV and VII) appear to be larger in lower mixing height (Valdosta-Inland) than higher mixing height (SSI-Coast). In particular, for the strategy B in lower-traffic demands and especially higher HDTs proportions, the limited benefits of emission reductions from relieving peak period congestions is eclipsed by the addition of HDTs emissions at night, causing the rise of average PM2.5 concentration levels. Fig. 16 V and VIII indicate that by shifting all HDTs to the nighttime, strategy C increases daily average PM2.5 concentrations, despite the reduction of total emissions. Such an increase becomes more significant at Valdosta-Inland than SSI-Coast region. For Strategy D, although emissions are significantly increased in all 13

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Fig. 17. The “best” and “worst” HDTs strategies for emission and concentration.

The daily total PM2.5 emissions are closely related to traffic operating conditions. By relieving traffic congestions, the implementation of strategy B and strategy C was found to produce the most emissions reduction as shown in Fig. 17 I. In the scenarios with higher traffic demands and higher HDTs proportions, the strategy B that shifts HDTs from peak hours to non-peak hours appears to be the “best” strategy in reducing daily total emissions (Fig. 17 I), while strategy C is the “best” strategy in lower-traffic demand and lower HDTs proportion scenarios. This situation is probably because strategy C can greatly relieve the traffic conditions throughout the daytime in lower-traffic demand and lower HDTs proportion; however, with higher traffic demands and HDTs proportions, the strategy C appears to transfer traffic congestions

truck operations at night, unless the increase in daily total emissions is extremely high (e.g., reaching 50% or higher in this case study). 6. Discussion From the previous section, it was commonly seen that depending on the meteorological conditions, the strategies that minimize PM2.5 emissions do not always lead to the least PM2.5 concentrations. In this circumstance, this section will discuss what the “best” and “worst” strategies in terms of total emissions and average concentrations across all the traffic demand and HDTs proportion scenarios, as presented in Fig. 17. 14

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air quality improvement. While daytime truck shifting strategies (e.g. strategy B and C) overall produce a potential to reduce the daily total emissions, uncertain impacts of the strategies on air quality were found depending on the level of traffic volumes and meteorology conditions. The results in this study show that in the scenarios with medium to low levels of traffic volumes, where there is a capacity to accommodate extra traffic demands during the daytime, significant environmental benefits are likely to occur by shifting trucks from nighttime to daytime, especially with a greater magnitude in inland regions with low mixing height. In the scenarios with low mixing height and less congested daytime traffic conditions, shifting nighttime trucks to daytime is likely to produce a positive impact on air quality, as the strategy can significantly reduce the nighttime pollutant concentrations which are mostly attributed to emissions produced by trucks. On the contrary, in the scenarios with high mixing height and more congested daytime traffic conditions, shifting nighttime trucks to daytime is found to increase the average air pollutant concentration because of the significant increase in pollutant emissions produced during the peak periods in the daytime. The study found that the air pollutant concentrations from on-road vehicle operations are mainly determined by three factors: 1) emission factors that are affected by traffic condition and operating fleet composition, 2) temporal distribution of emissions, and 3) meteorological conditions. The comparison results among different truck shifting strategies depicted in gridded heatmap developed in this study provide visual-aided and useful information for decision-makers that can be utilized to assess environmental impacts of truck shifting policies with an understanding of underlying relationships among various determining factors, emissions, and pollutant concentrations. These findings highlight the importance of comprehensive environmental assessments for such managing vehicle operation policies as truck shifting in consideration of the complex interaction of traffic dynamics and meteorological characteristics. Future studies may be benefited by considering the health impacts of such policies on human intakes by incorporating temporal/geographical distributions of pollutant concentrations and human activities. For the future studies, the impacts of human intake of PM2.5 from different fuel sources (e.g. typically HDTs use diesel and LDVs use gasoline as their fuel sources) on human health can also be considered. In that way, the future studies can provide a better understanding of the truck shifting strategies that help the decision-makers for their decision making processes. It is important to remind the readers that this study focused on one single freeway corridor and one receptor, and based on simulation results from the linkage across traffic simulation, MOVES emission inventory, and AERMOD dispersion model. The result resolution is obviously limited by the models’ capability. Although challenging, a stronger proof is expected from real-world “before-and-after” case studies that at least include these following conditions: fluctuated mixing height, high truck proportions, the decomposed air pollution impact from traffic activity, and most importantly, the before and after observations (i.e., strategy change).

in peak periods to other time periods (in this case study, the congestion on Link-WE seems to be transferred to between 20:00 p.m. and 22:59 p.m.), which affects the daily total emissions. For the “worst” case, due to the addition of traffic congestions in the daytime, strategy D appears to produce the most daily total emissions across all traffic demand and HDTs proportion scenarios (Fig. 17 II). In terms of PM2.5 concentrations, strategy B is the best in minimizing average concentrations for the scenarios with higher traffic demands. Also, strategy B covers more of the “best” scenarios in SSICoast region (Fig. 17 III) than Valdosta-Inland region (Fig. 17 V), since the impact of emissions is more likely to become the dominant factor in the regions with high mixing height. Despite strategy D were found to be the “worst” case that produces the highest daily total emissions across all of the scenarios explored, strategy D becomes the “best” strategy in minimizing the PM2.5 concentrations in the scenarios with lower traffic demands, as it can prevent HDTs operations at night with bad dispersion meteorology conditions, while there is extra capacity for the addition of HDTs to the daytime that avoids the large deterioration of traffic conditions. Also, strategy D covers more of the “best” scenarios in Valdosta-Inland region (Fig. 17 V) than SSI-Coast (Fig. 17 III) since the lower mixing height magnifies the benefit of avoiding HDTs nighttime operations. It is worth noticing that the base scenario A was found to be the “best” in some scenarios with a middle level of traffic demands and high HDTs proportions, indicating that implementing truck shifting strategies sometimes worsen the pollutant concentrations depending on present traffic conditions. This is because shifting trucks to nighttime (in case of strategy B and C) would cause a significant rise in nighttime pollutant concentrations, and shifting trucks to daytime (in case of strategy D) would cause a significant increase of daytime emissions (Fig. 16 III). Summarizing the results presented in this study, it is found that the truck shifting strategies have a significant impact on traffic conditions, daily total emissions, and pollutant concentrations on the focused area. The impact appears to vary depending on the levels of traffic demands and HDTs proportions. As this study addressed various aspects of the impacts of different truck shifting strategies, this study is expected to help policymakers and researchers to predict the impacts of implementing truck shifting strategies in their local contexts. For example, in case of a near-freeway hospital as in this study, the concentration results for the “worst” strategies in Fig. 17 IV and VI may be of interest in that it is related to the patients’ highest level of exposure to the pollutant. 7. Conclusion This study explored the uncertain impacts of three alternative truck shifting strategies on PM2.5 emissions and concentrations, suggesting the importance of thorough air quality impact assessments to identify the potential benefits or disadvantages from truck shifting strategies. The findings in this study indicate that relieving traffic congestions by altering truck demands from congested time periods to non-congested time periods does not necessarily lead to reducing air pollutant concentrations. For example, although the traffic congestion is relieved and thus the total emissions are reduced by shifting trucks to nighttime, the stable atmospheric boundary layer during the nighttime are likely to increase the average 24-h PM2.5 concentrations. Such unintended impacts have more potential to take places in inland locations, (e.g. Valdosta-Inland, Georgia), where the mixing height at night is low, and significant diurnal meteorological variation is observed. On the other hand, altering trucks from peak periods are likely to produce more air quality benefits when the peak-period traffic is more congested, largely due to the significant reduction in emissions from the enhanced traffic conditions. Such benefits were found to be more obvious in coastal regions (e.g. SSI-Coast Georgia as in this study), where the mixing height is relatively high, and the reduction of emissions dominates the

Acknowledgment We would also like to thank Dr. Fang (Cherry) Liu and Dr. Mehmet (Memo) Belgin in Georgia Tech's PACE (The Partnership for an Advanced Computing Environment) Center for the distributed computing technical support.

Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.116847.

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Declaration of competing interest

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