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. Author manuscript; available in PMC: 2026 Jul 9.
Published in final edited form as: Atmos Pollut Res. 2026 Apr 19;17(5):102904. doi: 10.1016/j.apr.2026.102904

Trends in New York State Light-Duty Vehicle Fleet Composition: Emission Standards and Engine Technology (2013-2025)

Fariha Hoque Rimu 1, David Q Rich 1,2,3, Gavin M Lemley 4, Philip K Hopke 1,2,5
PMCID: PMC13345421  NIHMSID: NIHMS2140400  PMID: 42422862

Abstract

The United States Environmental Protection Agency (USEPA) established regulatory frameworks (Pre-Tier 2, Tier 2, and Tier 3) to address vehicle emissions. Simultaneously, a technological shift from port fuel injection (PFI) to gasoline direct injection (GDI) engines (began in 2007), has been underway. GDI has greater fuel efficiency, but potentially produces more secondary organic aerosol (SOA) under Tier 2 emissions than Tier 3. This study examined light-duty vehicle fleet transitions in New York State from 2013-2025, specifically the shift between engine technology and fleet turnover across emission tiers. Registration data from the New York State Department of Motor Vehicles were analyzed and vehicles classified into Pre-Tier 2, Tier 2, and Tier 3 based on Model Year, Vehicle Identification Number (VIN), and regulatory phase-in schedules. Manufacturer-reported statistics and VIN were used to categorize vehicles. New vehicles sold after 2007 had to meet Tier 2 standards, but only 53% of the total fleet were Tier 2 vehicles in 2013 suggesting lag time in fleet-wide penetration. Similarly, penetration of Tier 3 vehicles (introduced in 2017) was slower than expected. By 2025 only 36% of the fleet consisted of Tier 3 vehicles, and ~6% were plug-in electric or hybrid vehicles. Meanwhile, GDI technology adoption increased rapidly and grew from 5% in 2013 to 36% in 2025. The slow fleet turnover highlights substantial lag between regulatory implementation and fleet composition changes. This lag resulted in continuing higher emissions and SOA formation indicating that air quality benefits from Tier 3 implementation will take longer to be realized.

Keywords: Fleet composition, USEPA emission standards, engine technology, air quality policy

Graphical Abstract

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1. INTRODUCTION

Fine airborne particulate matter with aerodynamic diameters less than or equal to 2.5 μm (PM2.5) is the most damaging air pollutant in terms of impacts on human health (WHO, 2021). Thus, understanding the sources of PM2.5 and the population health effects of source-specific PM2.5 is important to the development of control strategies that will provide the maximum public health benefit by targeting those sources most responsible for adverse health events. Thus, multiple studies have been conducted to examine rates/risks of population health events associated with source-specific PM2.5 concentrations (Croft et al., 2020; 2024; Ebisu et al., 2018; Halonen et al., 2009; Hopke et al., 2020; Ito et al., 2006; Krall et al., 2018; Laden et al., 2000; Lin et al., 2024; Mar et al., 2000; Mar et al., 2006; Ozkaynak and Thurston, 1987; Rich et al., 2019; Thurston et al., 2005).

Among the studies focused on New York State, Croft et al. (2020; 2024), Hopke et al. (2020), Lin et al. (2024); and Rich et al. (2019) explored changes in the toxicity of source-specific PM2.5 during intervals related to changing regulations and actions designed to improve air quality and public health. However, different PM2.5 sources were found to have different toxicities per unit mass with some becoming more toxic (i.e., same mass concentration associated with a higher rate of cardiorespiratory hospitalizations) over the period of 2005 to 2019. Hopke and Hill (2021) showed that these toxicity changes with time reduced the benefits that would have been obtained had all PM2.5 been equally toxic. The PM2.5 source whose toxicity changed the most with respect to multiple health outcomes was gasoline vehicular emissions (GAS) (Rich et al. 2019; Croft et al., 2020; Hopke et al., 2020). The higher health effects from GAS continued in the period of 2017 to 2019 (Croft et al., 2024; Lin et al., 2025). The changes in toxicity were hypothesized to be derived from major changes in light-duty gasoline-fueled vehicles (engine types and emissions) driven by changes in regulations over this period.

Over the past 2 decades, there have been multiple changes to motor vehicle emission standards in the United States resulting from the implementation of various regulations. In 2004 the United States Environmental Protection Agency (USEPA) implemented Tier 2 regulations that were promulgated in 2000 (USEPA, 2000). These regulations included both direct emission standards for light-duty vehicles as well as requirements for reduced sulfur content in gasoline. Tier 2 regulations required an increasing number of new vehicles sold in each year to meet the stricter emission standards with 25% in 2004, 50% in 2005, 75% in 2006, and all new vehicles in 2007, except heavy light-duty vehicles (gross weight ≥ 6000 lb.) where 50% in 2007 and 100% in 2008 were required to be Tier 2 (USEPA, 2000).

Vehicles are placed in “bins” based on “EPA smog rating standards” such that each bin has separate emission standards. However, by 2009, the corporate fleet average emission of NOx could not exceed 0.07 g/mi. For non-methane organic gases (NMOG), CO, PM, and formaldehyde (HCHO), there were no corporate average requirements. The fuel sulfur content in gasoline in 2004 had to meet a corporate average of 120 ppm S with a maximum of 300 ppm for any gasoline sold. In 2006, the average had to be 30 ppm with a maximum of 60 ppm.

Tier 3 encompassed a second set of vehicle emission standards (USEPA, 2014), with the phase-in beginning in 2017 with 20% of new vehicles required to be Tier 3 in 2017 and 2018, increasing to 40% in 2019, 70% in 2020, and 100% in 2021. These standards required even further reductions in pollutant emissions, again based on values defined for each of 7 bins (USEPA, 2014). In Tier 3, there is a fleet average emission requirement for the sum of NOx and NMOG of 0.03 g/mi, with again, no fleet average standards for the other pollutants. Gasoline sulfur content was required to be further reduced, with a maximum S content of <10 ppm S.

In addition to emission standards, vehicles are also subject to Corporate Average Fuel Efficiency (CAFE) standards now set by the USEPA and the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA). In 1985, the passenger car CAFE standard was set at 27.5 miles per gallon (mpg) by the NHTSA and remained in place until 2010. The 2010 regulations (Fed. Reg., 2010) required increasing fleet average fuel economies so that by 2016, combined passenger cars and light duty trucks had to attain 34.1 mpg.

Beginning in 2007, manufacturers began to change engine technology from port fuel injection (PFI) to gasoline direct injection (GDI) to improve fuel economy. PFI injects gasoline into the air intake port in front of the inlet valve, such that a gasoline-air mixture enters the cylinder. Alternatively, GDI injects smaller gasoline droplets directly into the cylinder resulting in more efficient combustion and improved fuel economy. The fraction of new vehicles using GDI engines increased from 2.3% of new vehicles in 2008 to 54.6% in 2024 (USEPA, 2025).

The final major regulatory change in the 2005 to 2019 period was the reformulation of gasoline that occurred between 2010 and 2013 to reduce the concentrations of benzene, 1,3-butadiene, formaldehyde, acetaldehyde, acrolein, and naphthalene concentrations under the Mobile Source Air Toxic (MSAT) rules (USEPA, 2007). To continue to provide similar anti-knock properties as benzene, intermediate volatility organic compounds (IVOCs) were substituted (Zhao et al., 2015; 2016). However, these species can react in the atmosphere to form secondary organic aerosol (SOA) (Zhao et al., 2015; 2016). Zhao et al. (2018) then showed that GDI vehicles with only Tier 2 level controls produced more SOA than PFI vehicles with similar emission controls. Lin et al. (2024) reported increased concentrations of secondary organic carbon (SOC) from 2014 to 2019. The increased SOC was hypothesized to arise from the combination of reformulated gasoline and increasing numbers of GDI vehicles in the on-road light duty vehicle fleet. Zhao et al. (2018) also reported that higher levels of NMOG control on Tier 3 vehicles (regardless of injection system type) substantially reduced SOA formation. Thus, if gasoline reformulation and change in injection technology was responsible for increased SOC through 2019, then an increasing fraction of Tier 3 vehicles in New York State should result in decreased SOC, less toxic PM, and presumably lower rates of cardiorespiratory health events in the population.

To explore this hypothesis, a study of the changes in the makeup of the on-road fleet over time was needed to determine the relative proportions of pre-Tier 2, Tier 2 with PFI, Tier 2 with GDI, Tier 3 with PFI, Tier 3 with GDI, and plug-in electric or hybrid vehicles. The purpose of this study was to make such determinations and quantify the rate at which new, cleaner vehicles entered the on-road fleet in New York State from 2013 to 2025.

2. METHODS

2.1. Data Sources

We used registration database records from the New York State Department of Motor Vehicles (NYSDMV) from 2013 to 2025 to get the vehicle registration data (https://data.ny.gov/Transportation/Vehicle-Snowmobile-and-Boat-Registrations/w4pv-hbkt/about_data). These records encompassed data from both upstate and downstate regions and included vehicle VINs, valid registration dates, registration expiration dates, model years, and other details. To estimate the numbers of GDI and PFI vehicles in New York, we used information from the U.S. Environmental Protection Agency (USEPA, 2025) that provided manufacturer-reported fractions of GDI and PFI vehicles. The procedure for utilizing the data are outlined in Figure 1.

Figure 1.

Figure 1.

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Vehicle classification workflow for assigning emission tiers (Pre-Tier 2, Tier 2, Tier 3) and engine technologies (PFI, GDI, Other), based on model-year fractions and manufacturer reported proportions of engine technologies. Tables S1 and S2 provide the EPA emission regulations and implementation timelines as well as proportions of engine technologies used.

2.2. Total Fleet Calculation

The total numbers of registered vehicles for each year in New York State (NYS) were calculated from vehicle registration data for upstate and downstate NYS regions for 2013-2025 separately. The files were filtered for gasoline fueled light duty vehicles and accessed with the R package RODBC (R Core Team, 2025). Unique VINs for each model year were counted and aggregated across regions to determine the total number of vehicles per year, with any duplicate VINs removed.

2.3. Vehicle Tier Assignment

We classified vehicles into Pre-Tier 2, Tier 2, and Tier 3 using EPA emission regulation and implementation timelines. Beginning with the 2004 model year, Tier 2 was phased in and required all new passenger cars and light trucks weighing up to 8,500 pounds and all new passenger vehicles weighing up to 10,000 pounds, including large sport-utility vehicles, to show complete compliance by the 2009 model year (USEPA, 2000). To calculate the proportion of the vehicle fleet that were Tier 2 and Tier 3 vehicles, the fractional values of the EPA’s phase-in schedule were used (Tables S1 and S2 in supplemental material). Based on these proportions and by model year, vehicles were assigned emission tiers for all the datasets. Any vehicle that was not assigned to Tier 2 or Tier 3 was assigned as Pre-Tier 2. Thus, for this study, we defined all vehicles that were manufactured before 2004 as Pre-Tier 2. To calculate the total number of vehicles in each tier/engine type category, data was aggregated across upstate and downstate for each year.

2.4. GDI and PFI Distribution

We calculated the frequency of vehicles by emission tiers and engine type starting by grouping all vehicles into eight categories: Pre-Tier 2 PFI, Pre-Tier 2 other, Tier 2 PFI, Tier 2 GDI, Tier 2 other, Tier 3 PFI, Tier 3 GDI and Tier 3 other. Using manufacturer-reported statistics and historical adoption trends from 2008 onwards, the percentage of each category within a given model year was calculated. Proportions of GDI vehicles increased steadily over the years. Based on the available proportions for 2004-2024, we used linear extrapolation to estimate the 2025 PFI, GDI and other category proportion. These proportions were multiplied by the total number of vehicles in each tier group and model year. Proportions of the vehicle fleet that were categorized as using other engine technology were calculated as a complement of total PFI and GDI proportions. We summed the total number of vehicles across all model years to get the total number of vehicles in each group for the study period (Pre-Tier 2 PFI, Pre-Tier 2 other, Tier 2 PFI, Tier 2 GDI, Tier 2 other, Tier 3 PFI, Tier 3 GDI, and Tier 3 other). All data cleaning and analyses were performed using R (R Core Team, 2025).

2.5. Uncertainty Estimation for Engine Technology Fractions

To provide an estimate of the uncertainty in the extrapolated values of engine technology fractions, linear regression models were fitted to the historical manufacturer-reported fractions of GDI, PFI, and Other technologies in Tier 3 vehicle for 2024 and 2025. The 95th percentile prediction intervals were computed for Tier 3 vehicles by engine technology (GDI, PFI, and Other) to assess the uncertainty in the 2024 and 2025 estimates. The year 2024 was included since the 2024 file also included MY 2025 vehicles. We used separate linear models to generate the prediction intervals, demonstrating future fleet composition uncertainty rather than uncertainty in the predicted mean trend. The interval bounds of the predicted fractions were limited to the feasible range [0,1] before converting to vehicle counts to avoid implausible values.

2.6. Electric and Hybrid Vehicle Identification

Annual counts for battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs) (both considered plug-in electric vehicles; PEVs) and non-plug-in hybrid electric vehicles (HEVs) were calculated from historical annual snapshots of the NYSDMV registration database from 2013 through 2025. Each dataset was filtered to light-duty passenger vehicles and those with registration classes or fuel types not pertinent to PEVs or HEVs were omitted. All VINs were then decoded using a standalone version of the National Highway Traffic Safety Administration’s Product Information Catalog and Vehicle Listing (vPIC) database (National Center for Statistics and Analysis, 2025). Vehicles were then sorted by Electrification Level and summarized using R (R Core Team, 2025).

3. RESULTS AND DISCUSSION

3.1. Trends in Vehicle Fleet Composition (2013–2025)

The number of vehicles on the road and the composition of the total registered vehicle fleet each year from 2013 to 2025 in New York State have been fairly consistent with an annual count hovering around 10 million vehicles (Figure 2 and Tables S3 – S5). Although the total fleet size was consistent throughout the years, the distribution across regulatory emission tiers has changed substantially. The number of pre-Tier 2 vehicles has decreased significantly. This decline in Pre-Tier 2 vehicles was accompanied by a steady rise in the Tier 2 and Tier 3 vehicles. However, the penetration of Tier 2 and Tier 3 vehicles was not as quick as expected. Given this slow pace of Tier 3 adoption, it is likely that the full benefits of Tier 3 emission standards will take longer to materialize after the regulatory implementation period planned for 2017-2025.

Figure 2.

Figure 2.

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Trend plot for annual fleet size in New York State (NYS) and vehicles categorized by U.S. EPA emission tier from 2013-2025. The dark blue line represents total vehicle fleet size over the study period. The black, orange and light blue lines represent the total number of Pre-Tier 2, Tier 2 and Tier 3 vehicles per year, respectively. The numerical values that are plotted here are provided in Tables S3 to S5 for the pre-Tier 2 vehicles, Tier 2 vehicles, and Tier 3 vehicles, respectively.

Although the variation in total fleet size over the 13 years was minimal, there was a notable dip in the total fleet size in 2020. This decrease in fleet size was likely due to the COVID-19 pandemic since it reduced the need for commuting as more people were working remotely, and limited supply chain operations affected vehicle manufacturing, sales, and therefore DMV vehicle registrations (Coffin et al., 2022; Zheng et al., 2024). Major purchases, such as buying a car, were likely postponed during this time of economic uncertainty (Ansell and Mullins, 2021). However, the impact was short lived, and the fleet size rebounded in 2021, likely attributed to a return to in-person activities and a comparatively stabilized economy.

3.2. Emission Standards Transition and the Distributions of GDI and PFI Vehicles

Trends in emission standards changes are relevant to assessing the effectiveness of USEPA’s Tier 2 and Tier 3 vehicle emission standards and the increasing prevalence of gasoline direct injection (GDI) engine technology. Over the study period, a slow but steady shift from Pre-Tier 2 to Tier 2 and then to Tier 3 vehicles were observed (Figure 3 and Tables S3 – S5). Only 53% of the New York fleet consisted of Tier 2 compliant vehicles in 2013. Although Tier 2 regulations had been fully implemented for new vehicles by 2007, 46% of the total NYS vehicle fleet was still Pre-Tier 2 vehicles in 2013. This slow transition highlights a clear lag between actual NYS vehicle fleet turnover and regulatory implementation of approximately 5-7 years after full implementation of the Tier 2 standards. A similar pattern is seen for Tier 3 vehicles as well. By 2019, three years after the regulatory phase-in started in 2017, Tier 3 vehicles made up a mere 6% of the total NYS vehicle fleet.

Figure 3:

Figure 3:

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Trend plot for temporal shift in U.S. on-road fleet composition by emission standards tier, separated by port fuel injection (PFI) and gasoline direct injection (GDI) technologies, from 2013-2025. The figure illustrates the percentage share of vehicles in five emission technology categories: Pre-Tier 2 vehicles (black filled squares), Tier 2 gasoline direct injection (GDI, black open squares), Tier 2 port fuel injection (PFI, green filled triangles), Tier 3 GDI (red open triangles), and Tier 3 PFI (orange filled inverted triangles). The numerical values that are plotted here are provided in Tables S3 to S5 for the pre-Tier 2 vehicles, Tier 2 vehicles, and Tier 3 vehicles, respectively

Expectedly, the decline in Pre-Tier 2 vehicles has been steady throughout the years because of vehicle retirement, turnover, and changing emission standards. By 2025, these vehicles represented only 8% of the total NYS fleet.

Following a similar pattern, Tier 2 vehicles made up 74% of the fleet in 2019, but by 2024, this percentage dropped to 49%. This shift is a result of both the introduction of Tier 3 vehicles into the fleet and the natural attrition of older Tier 2 vehicles. Starting with model year 2017, there was a slow penetration of Tier 3 vehicles into the fleet and by 2019, Tier 3 vehicles composed <6% of the total fleet and likely resulted in the continued effects of GAS on short-term health outcomes (Croft et al., 2024; Lin et al., 2025).

In 2013, PFI vehicles dominated all the existing vehicle tier categories, comprising 46% of all Pre-Tier 2 vehicles and 48% of all Tier 2 vehicles. In contrast, Tier 2 GDI vehicles accounted for only 5% of vehicles. As the Pre-Tier 2 and Tier 2 vehicles were phased out, the proportion of Pre-Tier 2 PFI and Tier 2 PFI vehicles decreased to 8% and 31%, respectively, by 2025. Alternatively, total GDI vehicles (Tier 2 and Tier 3) started to increase, and together constituted more than one-third of the total fleet in 2025. Table S7 and Figure S2 show the estimated prediction intervals for the years 2024 and 2025. The estimated number of Tier 3 GDI vehicles for 2025 is 2,058,141 (95% prediction interval: 2,020,434 to 2,095,840) with an resulting uncertainty of ±1.8%. Tier 3 PFI vehicles are projected at 771,577 (95% prediction interval: 758,823 to 800,056), representing a ±2.7% uncertainty. The long-term linear trends in engine technology adoption are illustrated in Figure S1, which shows the historical fractions of GDI, PFI, and other engine technologies exhibiting robust, roughly linear trajectories through 2024. For GDI and other engine technologies, in particular, extrapolating these trends to 2025 yielded comparatively narrow prediction ranges that show consistent adoption patterns across the observed period.

3.3. Electric and Hybrid Vehicle Adoption

PEVs (battery (BEV) or hybrid plug-in (PHEV) electric vehicles) and HEVs (non-plug-in hybrid electric vehicles) represented a low, but steadily growing percentage of the New York State fleet during the study period. In 2013, the combined percentage of PEVs and HEVs accounted for approximately 1.3% of the fleet, increasing to 6.3% by 2025 (Table S6). HEVs, which solely rely on fuel combustion (but utilize electric generation for increased efficiency), consistently made up a decreasing majority of “electrified” vehicles over the study period, comprising about 97% of these vehicles in the fleet in 2013, and declining to around 59% by 2025 as PEV (vehicles with the capability to run with no tailpipe emissions) adoption grew. BEVs (which have no tailpipe emissions) had the highest growth of all electric vehicle types over the study period, with an average 52% growth rate per year from 2015-2025, surpassing PHEVs in 2021 (Table S6 and Figure 4). In spite of strong PEV growth in recent years, recent federal policy regressions for incentives and acceleration of PEV adoption and infrastructure are likely to slow this growth, causing vehicle tailpipe emissions reductions via this pathway to stagnate in the coming years.

Figure 4.

Figure 4.

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Trends in hybrid and electric vehicles in the fleet, 2013–2025. The figure demonstrates the percentages of four categories of hybrid and electric vehicles: battery electric vehicles (BEV, blue triangles), plug-in hybrid electric vehicles (PHEV, green squares), the total of plug-in electric vehicles (PEV = BEV + PHEV, black circles), and all non-plug-in hybrid electric vehicles (HEV, orange diamonds) in the fleet. The numerical values that are plotted here are provided in Tables S6 for the electrified vehicle types.

3.4. Implications for Air Quality and Atmospheric Chemistry

The observed changes in the fleet composition of NYS have important implications for its air quality and atmospheric chemistry. The lag time in transition from Pre-Tier 2 to Tier 2 and then Tier 3 vehicles suggests that the anticipated air quality benefits from emissions reductions from newer regulatory changes and engine technology will take longer than predicted. This is consistent with findings from previous studies that reported vehicle emission control legislation usually becomes apparent long after they are implemented (Fujita et al., 2012; McDonald et al., 2018). One of the ways that these changes would have an effect on atmospheric chemistry is the generation of secondary organic aerosol (SOA) due to the growing use of GDI technology in the NYS vehicle fleet. GDI vehicles create intermediate volatility organic compounds (IVOCs), which serve as key precursors of secondary organic aerosol (SOA), especially under the Tier 2 emission standard (Du et al., 2018). These IVOCs oxidize in the atmosphere to produce SOA, which adds to the quantifiable amount of PM2.5 measured as secondary organic carbon (SOC). SOC has a high oxidative potential and has been associated with negative health outcomes like immunosuppression and pulmonary inflammation (Eaves et al., 2019; Li et al., 2016; Tong et al., 2018). Therefore, even if the total mass of PMs stays the same, rising IVOC emissions from GDI vehicles may result in higher SOC concentrations. Although there was no difference in SOA production between GDI and PFI vehicles certified to the same emissions standard, Zhao et al. (2018) have shown that, under comparable emission control circumstances, Tier 2 GDI vehicles generate significantly more SOA precursors than their PFI counterparts. According to our analysis (Figure 3), the proportion of Tier 2 GDI vehicles increased between 2013 and 2019 (from 5% to 22% of the fleet). In order to reduce toxic emissions under the Mobile Source Air Toxic regulations (USEPA, 2007), gasoline underwent reformulation between 2010 and 2013, which likely augmented the effect. According to Zhao et al. (2015; 2016) it is possible that the potential for SOA formation, particularly in vehicles with GDI engines, increased when toluene and intermediate volatility organic compounds (IVOCs) were added to reformulated gasoline in place of benzene. Due to stricter non-methane organic gas (NMOG) regulations, it has been demonstrated that Tier 3 vehicles with either injection technique significantly lower the risk of SOA formation (Zhao et al., 2018). According to our study results, as of 2025, only 36% of the New York fleet was composed of Tier 3 vehicles.

The increased market share and the adoption of GDI vehicles by NYS residents are partly because GDI vehicles have better fuel economy than PFI vehicles and are conducive to reaching the United States Corporate Average Fuel Economy (CAFE) standards (NRC, 2015). However, previous studies reported that GDI vehicles emit higher PM mass compared to PFI vehicles (Bahreini et al., 2015; Liang et al., 2013; Saliba et al., 2017). Although some of these studies were conducted on only a few vehicles, one study with a comparatively larger fleet size reported a higher PM emission from vehicles with GDI engines (4.2 ±1.0 mg/mi, N = 15) compared to vehicles with PFI engines (2.6 ± 0.5 mg/mi, N= 30) when the vehicles were classified as low-emission vehicles (LEV2; comparable to federal Tier 2) or to the more stringent SULEV standard (Saliba et al., 2017). For the composition of PM, Elemental Carbon (EC) seemed to drive the variations between these two types of vehicles with approximately 5 times higher emissions from ultra-low-emission vehicles (ULEV) GDI vehicles compared to ULEV PFI vehicles (Saliba et al., 2017; Zimmerman et al., 2016). EC and black carbon (BC) were highly correlated and are likely indicators of combustion-derived PM and have been associated with adverse health effects (Janssen et al., 2011; Li et al., 2016). Thus, higher EC from GDIs could offset some of the health benefits expected from the emissions regulations. However, with gradually increased penetration of more stringent Tier 3 vehicles, these differences should diminish and eventually disappear.

3.5. Health Implications

The observed trends in emission standard transition and engine technology have crucial implications for observed adverse health effects associated with different components of air pollution. In previous studies, continuing or increasing adverse health effects associated with spark-ignition vehicle emissions had been found even though PM2.5 concentrations decreased (Croft et al., 2020, 2024; Hopke et al., 2020; Rich et al., 2019). According to Croft et al. (2020), who examined emergency department (ED) visits for respiratory illnesses in Rochester, New York between 2005 and 2016, increased concentrations of PM2.5 from spark-ignition/gasoline vehicle emissions (GAS) were associated with an increased rate of ED visits for influenza (Croft et al., 2019). This rate ratio was larger in the 2014–2016 period than earlier time periods, which coincided with the increased proportion of Tier 2 GDI vehicles during that period. In a follow-up study, they observed that GAS-related PM2.5 was associated with a similarly sized excess rate of ED visits for influenza in spite of the fact that the Tier 3 emission standard was implemented in 2017 (Croft et al., 2024). Similarly, Lin et al. (2024) found that PM2.5, secondary organic carbon (SOC), and primary organic carbon (POC) were associated with increased rates of asthma and chronic obstructive pulmonary disease (COPD) hospitalizations and ED visits. This result was concerning given that SOC has a high oxidative potential and the ability to compromise immunological response, increasing the risk of respiratory diseases (Wang et al., 2018).

Furthermore, both Hopke et al. (2020) and Rich et al. (2019) reported that increased GAS concentrations were associated with increased cardiovascular and respiratory hospitalizations during this period. For example, IQR increases in GAS were associated with increased excess rates of COPD hospitalizations (4.2%, 95%CI=1.1%,7.5%), cardiac arrhythmia hospitalizations (2.3%; 95% CI=0.4%, 4.2%; IQR=2.56 μg/m3) and ischemic stroke hospitalizations (3.7%; 95% CI=1.1%, 6.4%; 2.73 μg/m3). According to source-apportioned PM2.5 studies (Squizzato et al., 2018; Chen et al. 2024), GAS emissions from spark-ignition vehicles increased throughout New York urban areas between 2005 and 2019. In some areas, GAS concentrations rose by up to 0.2 μg/m3 annually, compared to decreasing trends for diesel and secondary inorganic species concentrations (Masiol et al., 2019). Findings from both these studies point towards GAS as being the toxic contributor to the observed adverse health effects.

The increasing GAS concentration trend coincides with the current findings of increased use of GDI engines, which increased from 5% of the NYS fleet in 2013 to 36% by 2025. Theoretically, the gradual phase-out of Pre-Tier 2 vehicles should minimize the overall adverse effects of light-duty vehicle emissions, but the growing use of GDI technology could have produced some countervailing effects. By increasing the emission of reactive organic gases (such as IVOCs and SVOCs), which contribute to the atmospheric formation of fine particles through photochemical aging, the increased potential for SOA formation associated with gasoline reformulation and GDI technology could compound exposure to secondary PM2.5 and partially offset the gains made in reducing primary particle emissions. Thus, these findings help explain the observed population PM/cardiorespiratory associations (Croft et al., 2020, 2024, Hopke et al., 2020; Lin et al. 2025; Rich et al., 2019) given the results of Zhao et al. (2015; 2016) showing that GDI vehicles release more IVOCs than PFI vehicles, and these substances led to more production of secondary organic aerosol (SOA). IVOC emissions from on-road GDI vehicles have a strong correlation with non-methane hydrocarbons (NMHCs), and under photooxidative conditions, they generate a substantial amount of SOA. GDI engines produce more primary particles and intermediate volatility organic compounds (IVOCs) than PFI engines during cold starts and under Tier 2 standards (Zhao et al., 2016; 2018). These findings are consistent with our findings because, despite Tier 3 implementation starting in 2017, Tier 2 vehicles dominated the fleet from 2014 to 2019 with more than half of the fleet being Tier 2. Similarly, the Tier 2 GDI fraction was also increasing during this time. The toxicity per unit PM2.5 mass was higher in 2017-2019 than in 2014-2016 suggesting that although CAFE standards changes were implemented to improve efficiency and potentially reduce emissions, the real health burden per unit mass (i.e., rate of cardiorespiratory hospitalizations associated with each IQR increase in PM concentrations) likely increased as a result of changes in fleet composition and slow penetration of Tier 3 vehicles.

By 2019, there were not yet tangible benefits from lower sulfur gasoline, the stricter NOx and NMOG restrictions of Tier 3, and/or better engine efficiency technology. Going forward, a decrease in SOA production and related health concerns may be observed given that partial zero emission vehicles (PZEV) and Tier 3 vehicles will be up to or more than 40% of the total fleet by the end of 2025 given the impact of 2026 model year vehicles added to the fleet in the second half of 2025. Future studies involving the 2023-2025 period may show expected health benefits since Tier 3 vehicles represented one-third of the fleet in 2023, and thus the less toxic emissions of Tier 3 vehicles should represent a larger fraction of NYS vehicle fleet emissions.

4. LIMITATIONS AND STRENGTHS

Our study has several limitations that should be taken into consideration while interpreting the results. First, this study utilized New York State vehicular registration data, which may not be representative of national trends or other regions with distinct economic situations, vehicle preferences, or regulatory frameworks. Additionally, we classified vehicles into different emission tiers and engine technologies based on EPA phase-in schedules and manufacturer-reported technology fractions. Although this technique is methodologically sound, it is predicated on the assumption that all manufacturers adhere to regulatory schedules and that reported rates of technology adoption appropriately reflect the composition of on-road fleets. Thus, we cannot exclude the possibility that some vehicles were misclassified. However, we expect the number of misclassifications to be minimal and non-differential. Third, for the 2025 projections, we used linear extrapolation and reported prediction intervals. However, these intervals only take into consideration statistical uncertainty in the extrapolation. They do not consider prospective policy changes, unforeseen regulatory changes, market fluctuations, economic recessions, or other outside variables that might have an impact on fleet turnover rates and vehicle purchase decisions in 2025. Finally, the availability of source-apportioned data at only a few monitoring locations limits validation against observed air quality or source-specific PM2.5 measurements. Therefore, inferences regarding the relationship between fleet composition changes and secondary organic aerosol formation, as well as health implications, are based on published literature and remain qualitative rather than quantitatively validated against statewide monitoring data.

Nevertheless, our study has a number of key strengths that should be acknowledged. This study provides a comprehensive longitudinal characterization of fleet composition changes over 13 years across an entire state. Compared to surveys or proxy methods, the utilization of real VINs and EPA classification schedules yields higher accuracy. Additionally, a significant gap in many fleet-based research studies has been addressed by differentiating between GDI and PFI vehicles using manufacturer-reported engine technology proportions. Furthermore, this analysis provides insight into the temporal lag between regulatory implementation and on-road fleet turnover by capturing a significant transition period (2013–2025) that includes three important regulatory phases (Pre-Tier 2, Tier 2, and Tier 3). Consequently, the findings thus address a critical gap in the literature about why health benefits from regulatory policies have not materialized as quickly as predicted.

5. CONCLUSIONS

These results highlight a substantial gap between the goals of Tier 3 and Tier 2 regulations and their intended improvements in air quality and health in practice. Even though Tier 3 vehicle requirements were introduced in 2017, the expected improvements in air quality and health were outweighed by a sharp increase in Tier 2 GDI vehicles and a delayed fleet transition. Increased GAS levels, higher SOA formation, and elevated IVOC emissions account for the continuation of the negative effects seen between 2014 and 2019. Faster fleet turnover and source-specific PM pollution monitoring are necessary for future advancements. Findings from this study highlight the need to take into consideration actual vehicle fleet dynamics when assessing the efficacy of regulations.

Supplementary Material

1

Highlights.

  • Multiple policy actions were made to reduce emissions from light duty vehicles.

  • Engine technology (PFI to GDI) was changed to improve fuel efficiency.

  • Gasoline was reformulated to reduce benzene exposures.

  • Effectiveness of emissions policies depends on new vehicles entering the fleet.

  • Relatively slow penetration of Tiers 2 and 3 vehicles limit control effectiveness.

Acknowledgments

We wish to thank Dr. Yunle Chen for supplying the links to the CARB information that helped make these analyses possible. The views expressed here do not necessarily reflect the views or opinions of the New York State Department of Environmental Conservation.

Funding:

This work was funded by the New York State Energy Research and Development Authority (Contract #’s 125993 and 156226) and the National Institute of Environmental Health Sciences (Grant #P30ES001247). The content is solely the responsibility of the authors and does not necessarily represent official views of the funding agencies.

Footnotes

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Declaration of competing interest: The authors state that the work presented in this study was not influenced by any known financial affiliations or personal relationships that could be deemed competitive.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability Statement:

All data were publicly available.

References

  1. Ansell R, Mullins J, 2021. COVID-19 ends longest employment expansion in CES history, causing unprecedented job losses in 2020. Mon Labor Rev., June 2021. 10.21916/MLR.2021.13. [DOI] [Google Scholar]
  2. Bahreini R, Xue J, Johnson K, Durbin T, Quiros D, Hu S, Huai T, Ayala A, Jung H, 2015. Characterizing emissions and optical properties of particulate matter from PFI and GDI light-duty gasoline vehicles. J Aerosol Sci 90, 144–153. 10.1016/j.jaerosci.2015.08.011. [DOI] [Google Scholar]
  3. California Air Resources Board (CARB), 2025. New Vehicle and Engine Certification: Executive Orders for Light-Duty Vehicles. https://ww2.arb.ca.gov/new-vehicle-and-engine-certification-executive-orders-my2025-passenger-cars-light-duty-trucks-and
  4. Chen Y, Rich DQ, Hopke PK, 2024. Changes in source specific PM2.5 from 2010 to 2019 in New York and New Jersey identified by dispersion normalized PMF. Atmos Res 304, 107353. 10.1016/j.atmosres.2024.107353. [DOI] [Google Scholar]
  5. Coffin D, Downing D, Horowitz J, Larocca G, 2022. The Roadblocks of the COVID-19 Pandemic in the U.S. Automotive Industry. U.S. International Trade Commission, Office of Industries, Working Paper ID-091. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4143474. [Google Scholar]
  6. Croft DP, Zhang W, Lin S, Thurston SW, Hopke PK, Van Wijngaarden E, Squizzato S, Masiol M, Utell MJ, Rich DQ, 2020. Associations between Source-Specific Particulate Matter and Respiratory Infections in New York State Adults. Environ Sci Technol 54, 975. 10.1021/acs.est.9B04295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Croft DP, Utell MJ, Liu H, Lin S, Hopke PK, Thurston SW, Chen Y, Rich DQ, 2024. Change in rate of healthcare encounters for respiratory infection from air pollution exposure after improved vehicle emissions standards in New York State. Air Qual Atmos Health. 10.1007/s11869-024-01505-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Du Z, Hu M, Peng J, Zhang W, Zheng J, Gu F, Qin Y, Yang Y, Li M, Wu Y, Shao M, Shuai S, 2018. Comparison of primary aerosol emission and secondary aerosol formation from gasoline direct injection and port fuel injection vehicles. Atmos Chem Phys 18, 9011–9023. 10.5194/ACP-18-9011-2018. [DOI] [Google Scholar]
  9. Eaves LA, Smeester L, Hartwell HJ, Lin YH, Arashiro M, Zhang Z, Gold A, Surratt JD, Fry RC, 2019. Isoprene-Derived Secondary Organic Aerosol Induces the Expression of MicroRNAs Associated with Inflammatory/Oxidative Stress Response in Lung Cells. Chem Res Toxicol 33, 381. 10.1021/acs.chemrestox.9B00322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Ebisu K, Malig B, Hasheminassab S, Sioutas C, Basu R, 2018. Cause-specific stillbirth and exposure to chemical constituents and sources of fine particulate matter. Environ Res 160, 358–364. 10.1016/j.envres.2017.10.015. [DOI] [PubMed] [Google Scholar]
  11. Energy Information Administration (EIA), 2021. FOTW# 1178, March 22, 2021: Gasoline Direct Injection was Installed on 55% of all Light-Duty Vehicles Produced in 2020. U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, https://www.energy.gov/eere/vehicles/articles/fotw-1178-march-22-2021-gasoline-direct-injection-was-installed-55-all-light. [Google Scholar]
  12. Energy Information Administration (EIA), 2024. FOTW #1331, February 26, 2024: Seventy-three percent of all Light-Duty Vehicles Produced in 2023 Came with Gasoline Direct Injection, U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, https://www.energy.gov/eere/vehicles/articles/fotw-1331-february-26-2024-seventy-three-percent-all-light-duty-vehicles. [Google Scholar]
  13. Federal Register, 2010. Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; Final Rule. Federal Register 75, 25324–25728. [Google Scholar]
  14. Federal Register, 2012. 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards, Federal Register 77, 62624–63200. [Google Scholar]
  15. Fujita EM, Campbell DE, Zielinska B, Chow JC, Lindhjem CE, DenBleyker A, Bishop GA, Schuchmann BG, Stedman DH, Lawson DR, 2012. Comparison of the MOVES2010a, MOBILE6.2, and EMFAC2007 mobile source emission models with on-road traffic tunnel and remote sensing measurements. J Air Waste Manage Assoc 62, 1134–1149. 10.1080/10962247.2012.699016. [DOI] [PubMed] [Google Scholar]
  16. Halonen JI, Lanki T, Yli-Tuomi T, Tiittanen P, Kulmala M, Pekkanen J 2009. Particulate air pollution and acute cardiorespiratory hospital admissions and mortality among the elderly. Epidemiology. 20, 143–153. 10.1097/EDE.0b013e31818c7237. [DOI] [PubMed] [Google Scholar]
  17. Health Effects Institute (HEI), Institute for Health Metrics and Evaluation (IMME), 2024. The State of Global Air/2024, State of Global Air, https://www.stateofglobalair.org/resources/report/state-global-air-report-2024. [Google Scholar]
  18. Hopke PK, Croft DP, Zhang W, Lin S, Masiol M, Squizzato S, Thurston SW, van Wijngaarden E, Utell MJ, Rich DQ, 2020. Changes in the hospitalization and ED visit rates for respiratory diseases associated with source-specific PM2.5 in New York State from 2005 to 2016. Environ Res 181. 10.1016/j.envres.2019.108912. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hopke PK, Hill EL, 2021. Health and charge benefits from decreasing PM2.5 concentrations in New York State: Effects of changing compositions. Atmos. Pollut. Res. 12, 47–53. 10.1016/j.apr.2021.01.018. [DOI] [Google Scholar]
  20. Ito K, Christensen WF, Eatough DJ, Henry RC, Kim E, Laden F, Lall R, Larson TV, Neas L, Hopke PK, Thurston GD, 2006. PM source apportionment and health effects: 2. An investigation of intermethod variability in associations between source-apportioned fine particle mass and daily mortality in Washington, DC. J Expo Sci Environ Epidemiol. 16, 300–310. 10.1038/sj.jea.7500464. [DOI] [PubMed] [Google Scholar]
  21. Janssen NAH, Hoek G, Simic-Lawson M, Fischer P, van Bree L, Brink H. Ten, Keuken M, Atkinson RW, Ross Anderson H, Brunekreef B, Cassee FR, 2011. Black carbon as an additional indicator of the adverse health effects of airborne particles compared with PM10 and PM2.5. Environ Health Perspect 119, 1691–1699. 10.1289/ehp.1003369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Krall JR, Mulholland JA, Russell AG, Balachandran S, Winquist A, Tolbert PE, Waller LA, Sarnat SE, 2017. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environ. Health Perspect. 125, 97–103. 10.1289/EHP2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Laden F, Neas LM, Dockery DW, Schwartz J, 2000. Association of fine particulate matter from different sources with daily mortality in six US cities. Environ Health Perspect 108, 941–947. 10.1289/ehp.00108941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Li Y, Henze DK, Jack D, Henderson BH, Kinney PL, 2016. Assessing public health burden associated with exposure to ambient black carbon in the United States. Science of The Total Environment 539, 515–525. 10.1016/j.scitotenv.2015.08.129 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Liang B, Ge Y, Tan J, Han X, Gao L, Hao L, Ye W, Dai P, 2013. Comparison of PM emissions from a gasoline direct injected (GDI) vehicle and a port fuel injected (PFI) vehicle measured by electrical low pressure impactor (ELPI) with two fuels: Gasoline and M15 methanol gasoline. J Aerosol Sci 57, 22–31. 10.1016/j.jaerosci.2012.11.008. [DOI] [Google Scholar]
  26. Lin S, Xue Y, Thandra S, Qi Q, Hopke PK, Thurston SW, Croft DP, Utell MJ, Rich DQ, 2024. PM2.5 and its components and respiratory disease healthcare encounters – Unanticipated increased exposure-response relationships in recent years after environmental policies. Environmental Pollution 360. 10.1016/j.envpol.2024.124585. [DOI] [PubMed] [Google Scholar]
  27. Lin S, Xue Y, Thandra S, Qi Q, Thurston SW, Croft DP, Utell M, Hopke PK, Rich DQ, 2025. Source specific fine particles and rates of asthma and COPD healthcare encounters pre- and post-implementation of the Tier 3 vehicle emissions control regulations. Journal of Hazardous Materials, 484. 10.1016/j.jhazmat.2024.136737. [DOI] [PubMed] [Google Scholar]
  28. Mar TF, Ito K, Koenig JQ, Larson TV, Eatough DJ, Henry RC, Kim E, Laden F, Lall R, Neas L, Stölzel M, Paatero P, Hopke PK, Thurston GD, 2006. PM source apportionment and health effects. 3. Investigation of inter-method variations in associations between estimated source contributions of PM2.5 and daily mortality in Phoenix, AZ. J Expo Sci Environ Epidemiol. 16, 311–320. 10.1038/sj.jea.7500465. [DOI] [PubMed] [Google Scholar]
  29. Masiol M, Squizzato S, Rich DQ, Hopke PK, 2019. Long-term trends (2005–2016) of source apportioned PM2.5 across New York State. Atmos Environ 201, 110–120. 10.1016/j.atmosenv.2018.12.038. [DOI] [Google Scholar]
  30. McDonald BC, McKeen SA, Cui YY, Ahmadov R, Kim SW, Frost GJ, Pollack IB, Peischl J, Ryerson TB, Holloway JS, Graus M, Warneke C, Gilman JB, De Gouw JA, Kaiser J, Keutsch FN, Hanisco TF, Wolfe GM, Trainer M, 2018. Modeling Ozone in the Eastern U.S. using a Fuel-Based Mobile Source Emissions Inventory. Environ Sci Technol 52, 7360–7370. 10.1021/acs.est.8b00778. [DOI] [PubMed] [Google Scholar]
  31. National Center for Statistics and Analysis, 2025. Product information catalog and vehicle listing (vPIC) analytical user’s manual. Report No. DOT HS 813. https://vpic.nhtsa.dot.gov
  32. National Research Council (NRC), 2015. Cost, effectiveness, and deployment of fuel economy technologies for light-duty vehicles. https://www.nationalacademies.org/projects/DEPS-BEES-11-02.
  33. Ozkaynak H, Thurston GD, 1987. Associations between 1980 U.S. mortality rates and alternative measures of airborne particle concentration. Risk Anal. 7, 449–461. 10.1111/j.1539-6924.1987.tb00482.x. [DOI] [PubMed] [Google Scholar]
  34. R Core Team, 2025. R: A language and environment for statistical computing. Version 4.3.3. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. [Google Scholar]
  35. Rich DQ, Zhang W, Shao L, Squizzato S, Thurston SW, van Wijngaarden E, Croft D, Masiol M, Hopke PK, 2019. Triggering of cardiovascular hospital admissions by source-specific fine particle concentrations in urban centers of New York State. Environ. Int 126, 387–389. 10.1016/j.envint.2019.02.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Saliba G, Saleh R, Zhao Y, Presto AA, Lambe AT, Frodin B, Sardar S, Maldonado H, Maddox C, May AA, Drozd GT, Goldstein AH, Russell LM, Hagen F, Robinson AL, 2017. Comparison of Gasoline Direct-Injection (GDI) and Port Fuel Injection (PFI) Vehicle Emissions: Emission Certification Standards, Cold-Start, Secondary Organic Aerosol Formation Potential, and Potential Climate Impacts. 10.1021/acs.est.6b06509. [DOI] [PubMed] [Google Scholar]
  37. Thurston GD, Ito K, Mar T, Christensen WF, Eatough DJ, Henry RC, Kim E, Laden F, Lall R, Larson TV, Liu H, Neas L, Pinto J, Stölzel M, Suh H, Hopke PK, 2005. Workgroup report: workshop on source apportionment of particulate matter health effects–intercomparison of results and implications. Environ. Health Perspect. 113, 1768–1774. 10.1289/ehp.7989. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Tong H, Lakey PSJ, Arangio AM, Socorro J, Shen F, Lucas K, Brune WH, Pöschl U, Shiraiwa M, 2018. Reactive Oxygen Species Formed by Secondary Organic Aerosols in Water and Surrogate Lung Fluid. Environ Sci Technol 52, 11642–11651. 10.1021/acs.est.8b03695. [DOI] [PubMed] [Google Scholar]
  39. U.S. Environmental Protection Agency (USEPA), 2000. Final Rule for Control of Air Pollution from New Motor Vehicles: Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements. https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-air-pollution-new-motor-vehicles-tier.
  40. U.S. Environmental Protection Agency (USEPA, 2007. Final Rule for Control of Hazardous Air Pollutants from Mobile Sources - 40 CFR Parts 59, 80, 85, and 86. https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-hazardous-air-pollutants-mobile.
  41. U.S. Environmental Protection Agency (USEPA), 2014. Final rule for control of air pollution from motor vehicles: tier 3 motor vehicle emission and fuel standards. https://www.epa.gov/regulations-emissions-vehicles-and-engines/final-rule-control-air-pollution-motor-vehicles-tier-3.
  42. U.S. Environmental Protection Agency (USEPA), 2019. Integrated Science Assessment for Particulate Matter (EPA/600/R-19/188), https://assessments.epa.gov/isa/document/&deid=347534.
  43. U.S. Environmental Protection Agency (USEPA), 2022. Supplement to the 2019 Integrated Science Assessment for Particulate Matter (EPA/600/R-22/028). https://assessments.epa.gov/isa/document/&deid=354490. [PubMed]
  44. U.S. Environmental Protection Agency (USEPA), 2025. Explore the Automotive Trends Data https://www.epa.gov/automotive-trends/explore-automotive-trends-data.
  45. Wang S, et al. , 2018. Chemical composition and oxidative potential of SOA. Atmos Chem Phys 18, 3987–4003. 10.5194/acp-18-3987-2018. [DOI] [Google Scholar]
  46. WHO, 2021. WHO Global Air Quality Guidelines. Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulphur Dioxide and Carbon Monoxide. World Health Organization. https://www.who.int/publications/i/item/9789240034228. [PubMed] [Google Scholar]
  47. Zhao Y, Nguyen NT, Presto AA, Hennigan CJ, May AA, Robinson AL, 2015. Intermediate volatility organic compound emissions from on-road diesel vehicles: chemical composition, emission factors, and estimated secondary organic aerosol production. Environ. Sci. Technol 49, 11516–11526. https://pubs.acs.org/doi/abs/10.1021/acs.est.5b02841. [DOI] [PubMed] [Google Scholar]
  48. Zhao Y, Nguyen NT, Presto AA, Hennigan CJ, May AA, Robinson AL, 2016. Intermediate volatility organic compound emissions from on-road gasoline vehicles and small off-road gasoline engines. Environ. Sci. Technol 50, 4554–4563. https://pubs.acs.org/doi/abs/10.1021/acs.est.5b06247. [DOI] [PubMed] [Google Scholar]
  49. Zhao Y, Lambe AT, Saleh R, Saliba G, 2018. Secondary organic aerosol production from gasoline vehicle exhaust: effects of engine technology, cold start and emission certification standard. Environ. Sci. Technol. 52, 1253–1261. https://pubs.acs.org/doi/abs/10.1021/acs.est.7b05045. [DOI] [PubMed] [Google Scholar]
  50. Zheng Y, Wang S, Liu L, Aloisi J, Zhao J, 2024. Impacts of remote work on vehicle miles traveled and transit ridership in the USA. Nature Cities 2024 1:5 1, 346–358. 10.1038/s44284-024-00057-1. [DOI] [Google Scholar]
  51. Zimmerman N, Wang JM, Jeong C-H, Wallace JS, Evans GJ, 2016. Assessing the Climate Trade-Offs of Gasoline Direct Injection Engines. Environ. Sci. Technol, 50, 8385–8392. 10.1021/acs.est.6b01800. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS2140400-supplement-1.pdf (348.6KB, pdf)

Data Availability Statement

All data were publicly available.

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