**3. Case study**

The study area of this research project is in the Utah state, United States, where EV technology has gained public's attention as clean transportation to alleviate the impacts on Utah's air quality. The Salt Lake and Provo/Orem metropolitan areas are part of a unique mountainous region called the Wasatch Front in the State of Utah. The State of the Air reported serious pollution in both short-term and long-term particle air pollution in this area [21].

Salt Lake and Provo/Orem metropolitan areas are listed on the top of short-term air pollution, ranked 6th and 9th, respectively. Atmospheric inversion is the major factor causing air pollution in the area [22]. During winter time, cold air is caped under warm air and traps air pollutants near the valley floor (**Figure 2**). The harmful small particles of air pollutants, such as nitrogen oxides, sulfur dioxide, carbon monoxide, and ozone accumulate in the cold air above the safety levels defined by the U.S. Environmental Protection Agency [23]. The Utah Department of Environmental Quality reported that fuel-based vehicles produced 60% of the polluting particle matter [24]. Exposure to poor air quality conditions has substantial adverse effects on human health, especially for people who have respiratory and cardiovascular conditions. A variety of health issues in Utah is found be to associated with poor air quality. Struggling against air pollution is a longtime task for the state. The Intermountain Air Quality and Health Group was established in 2014 to address the escalating evidence. Intermountain Health Care encouraged its employees to utilize public transportation which contributed to declining emissions of 3.5 million pounds [23]. The State of Utah developed several programs to combat poor air quality along the Wasatch Front, such as the statewide TravelWise program that aims to reduce air pollution by providing alternative transportation ideas. These ideas range from alternative work schedules, active transportation, carpooling, public transit, skip the trip, teleworking, trip chaining, and trip planning [25]. This program encourages employers and citizens to participate in activities that will reduce air pollution throughout the Wasatch Front. There are approximately 106 EV charging stations throughout the State of Utah [18]. Only 77 of those EV charging stations are located along the Wasatch Front where 77%

**Figure 2.** *Atmospheric inversion in Salt Lake City [22].*

of Utah's population resides [26]. To combat range anxiety and increase EV usage, a well-developed robust EV charging station infrastructure needs to be established.

This project took the Wasatch Front in Utah as a case study for its significant role of being the economic hub of the state, being the most populated area in the state, and presenting a large portion of work-related trips from nearby counties [26]. 17.2% of the workforce in Utah County and 47% in Davis County work commute from outside of the county [27]. The commute tends to increase with the growth of the population and economy.

A series of data sets, 34 GIS polygon data layers in total, that characterize the physical and urban features of the study area were collected from various government agencies. The focus was to identify GIS datasets that exhibited a high trip attraction by the driving public. Although this research project focused on locations that matter to the driving public, both positive and negative factors were taken into account. Special attention was given to environmentally sensitive areas along the Wasatch Front to prevent these areas from being identified as prime locations in the analysis. The datasets were compiled and preprocessed to GIS data format.

All data layers were given a weighted score based on their positive or negative influence on an EV charging station. The scoring system for each input demand factor was broken down into the following categories: high suitability (score of 5), moderate suitability (score of 3), low suitability (score of 1), and unsuitable (negative score). These weighted spatial input parameters can be taken into a suitability model to determine the final suitable spatial outcome.

The factor score in the master grid was then multiplied by the physical feature index score (1 for valley floor and − 1 for mountains and waterbodies) which resulted in a series of positive and negative final index demand scores. The resulting negative scores indicated that these grid squares fell among mountainous and waterbody areas that would be unsuitable for an EV charging station. Grid squares that contained positive scores were indicators of suitable valley floor locations for EV charging stations. These positive final index demand scores were further evaluated to determine a proper classification that would assist in identifying prime locations for EV charging stations. An unsupervised classification within ArcMap was initially utilized to first visualize the spatial pattern of the final index demand factor scores. A series of supervised classifications were then applied to the final index demand factor scores and viewed on the top of an aerial photograph of the study area. Visual checks were applied to areas known to have a high attraction for EV charging stations. The resulting scoring patterns in these areas were used to establish a uniform supervised classification system that was used to display high, moderate, and low suitability areas for EV charging stations throughout the study area.
