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Earth system and environmental impact studies need high quality and up-to-date estimates of atmospheric deposition. This study demonstrates the methodological benefits of multimodel ensemble and measurement-model fusion mapping approaches for atmospheric deposition focusing on 2010, a year for which several studies were conducted. Global model-only deposition assessment can be further improved by integrating new model-measurement techniques, including expanded capabilities of satellite observations of atmospheric composition. We identify research and implementation priorities for timely estimates of deposition globally as implemented by the World Meteorological Organization.

The extent to which climate change and other factors will influence building energy use and population exposures to indoor pollutants is not well understood. Here, we develop and apply nationally representative residential energy and indoor pollutant model sets to estimate energy use, indoor pollutant concentrations, and associated chronic health outcomes across the U.S. residential building stock in the mid-21st century. The models incorporate expected changes in meteorological and ambient air quality conditions associated with IPCC RCP 8.5 and assumptions for changes in housing characteristics and population movements while keeping other less predictable factors constant. Site and source energy consumption for residential space-conditioning are predicted to decrease by ∼37–43 and ∼20–31%, respectively, in the 2050s compared to those in a 2010s reference scenario. Population-average indoor concentrations of pollutants of ambient origin are expected to decrease, except for O3. Holding indoor emission factors constant, indoor concentrations of pollutants with intermittent indoor sources are expected to decrease by <5% (PM2.5) to >30% (NO2); indoor concentrations of pollutants with persistent indoor sources (e.g., volatile organic compounds (VOCs)) are predicted to increase by ∼15–45%. We estimate negligible changes in disability-adjusted life-years (DALYs) lost associated with residential indoor pollutant exposures, well within uncertainty, although the attribution among pollutants is predicted to vary.

This study integrates data from regional model simulations, reanalysis data set, radiosonde observations, lidar measurements, and satellite products to evaluate the direct radiative forcing effect of biomass burning and dust over East Asia. During March and April, we find an overall cooling effect of the dust of −5 to −9 W/m2. Biomass burning aerosols from Peninsular Southeast Asia exhibit a warming effect of 5–10 W/m2over the source area, lower than that over the downwind area of 10–20 W/m2because of significantly higher cloud coverage in South China. Dust and biomass burning aerosols are found to cool the near surface layer (0–1 km) by −0.5 and −0.3 K, respectively, and warm the upper air (1–5 km) by +0.1 and +0.2 K, respectively. In Taipei, simultaneous presences of dust and biomass burning lead to cooling of near‐surface air by −1.5 K and warming of upper air by +1 K. Biomass burning and dust are the largest natural emission sources of atmospheric aerosol. However, no solid consensus exists regarding whether biomass burning or dust contributes to a net warming or cooling effect. This study integrates data from regional climate model simulations, reanalysis data, radiosonde observations, lidar measurements, and satellite products to evaluate the climate effects of biomass burning and dust at a regional scale over East Asia. We find prominent warming effects by biomass burning in Peninsular Southeast Asia, which is more significant over the downwind area in South China than over the emission source area. Dust exhibits cooling effects, with a few exceptions over areas covered by semipersistent snow or ice. We also reveal that under conditions in which both biomass burning and dust aerosols are present, they exhibit an overall slight warming effect. Peninsular Southeast Asia biomass burning shows larger warming effect (10‐20 W/m2) over downwind region than over the source area (5‐10 W/m2)Dust shows a cooling effect of up to ‐9 W/m2with a few exceptions along north and east edges of Tibet Plateau due to snow or ice coverDust and biomass burning cool the near surface air and warm the upper air, and their radiative efficiencies decrease during transport

One unique long‐range transport event with multiple layers of aerosol plumes was observed over Taiwan during 29–31 March 2006. A synergy of ground‐based observation, remote sensing, and backward trajectory simulation collectively indicated the high‐altitude (above 3 km) plume originated from biomass burning in Southeast Asia while the midaltitude (around 0.8–2 km) plume was attributed to dust from the Gobi Desert. Aerosol optical properties measured at a low‐altitude site were characterized of abundant coarse mode particles and increased single scattering albedo as a function of increased wavelength, indicating the influence from dust particles. While at a high mountain site (elevation of ~3 km), aerosol optical depth was elevated by a factor of 3–4 compared to its background value and mainly comprised of fine particles. It was diagnosed that the high‐altitude aerosols were influenced by the transported smoke plumes but exempted from dust. Simulation of the meteorological conditions against a Taiwan‐wide meteorology network showed strong near surface temperature rise of more than 2° during this long‐range transport event as well as for the vertical temperature profiles. Both dust and biomass burning aerosol plumes via long‐range transport contributed significantly to the atmospheric warming, resulting in strong instantaneous aerosol radiative forcing of 46.0 W/m2in the atmosphere. A “double dome” warming effect mechanism was proposed that both biomass burning and dust plumes above the boundary layer could efficiently reserve the solar energy and heat the lower troposphere. One long‐range transport event with concurring dust and biomass burning aerosol layers was observed over the West Pacific regionStrong warming near the surface and at higher tropospheric altitudes was ascribed to the massive aerosol inputs from the transported plumesA “double dome” mechanism effect was proposed that dust and biomass burning plumes at high altitudes could effectively warm the atmosphere

There is significant uncertainty in the global inventory of black carbon (BC). Several recent studies have reported BC emission updates, including the Fire Emission Inventory‐northern Eurasia, anthropogenic emission in Russia, and global natural gas flaring. Compared with the inventory used by Intergovernmental Panel on Climate Change, these updates are only 10% higher on a global scale but are 3 times greater than previous estimations in Arctic (60–90°N). We applied GEOS‐Chem to examine these emission updates and evaluate their impacts on direct forcing. We found that Fire Emission Inventory‐northern Eurasia may be substantially overestimated, Russia shows no prominent influence on simulation, and natural gas flaring noticeably improves simulation performance in the Arctic. Model estimated direct forcing of BC is increased by 30% on the global scale and is 2 times higher in the Arctic through application of these emission updates. This study reveals the urgent need to improve the reliability of emission inventories in the high latitudes, especially over Eurasia. Recent black carbon (BC) emission updates suggest a substantially higher inventory than that used by Intergovernmental Panel on Climate Change. Through GEOS‐Chem modeling, we found that the Fire Emission Inventory‐northern Eurasia biomass burning emission is overestimated over northern Eurasia, likely due to employment of U.S. plants species‐based emission factors. Russian anthropogenic and natural gas flaring inventories help improve simulation performance in the Arctic. Model estimated direct forcing of BC is doubled when applying these emission updates, indicating the urgent need to further validate and improve the BC emission inventory. Recent updated black carbon (BC) emissions are applied within GEOS‐Chem model to estimate BC direct forcingFEI‐NE may overestimate fire emission over northern Eurasia, probably because of applying the U.S. plant species‐based emission factorsThe emission updates are only 10% higher on global scale but are 3 times of the inventory used by IPCC in the Arctic region, leading to substantial enhancement of direct forcing estimation, and indicating the urgent need to further validate and improve these emissions

ABSTRACTVolatile organic compounds (VOCs) evaporate and vent from a vehicle’s fuel tank to its evaporative control system when the vehicle is both driven and parked. VOCs making it past the control system are emissions. Driving and parking activity, fuel volatility, and temperature strongly affect vapor generation and the effectiveness of control technologies, and the wide variability in these factors and the sensitivity of emissions to these factors make it difficult to estimate evaporative emissions at the macro level. Established modeling methods, such as COPERT and MOVES, estimate evaporative emissions by assuming a constant in-use canister condition and consequently contain critical uncertainty when real conditions deviate from that standard condition. In this study, we have developed a new method to model canister capacity as a representative variable, and estimated emissions for all parking events based on semi-empirical functions derived from real-world activity data and laboratory measurements. As compared to chamber measurements collected during this study, the bias of the MOVES diurnal tank venting simulation ranges from −100% to 129%, while the bias for our method’s simulation is 1.4% to 8.5%. Our modeling method is compared to the COPERT and MOVES models by estimating evaporative emissions from a Euro-3/4/5 and a Tier 2 vehicle in conditions representative for Chicago, IL, and Guangzhou, China. Estimates using the COPERT and MOVES methods differ from our method by −56% to 120% and −100% to 25%, respectively. The study highlights the importance for continued modeling improvement of the anthropogenic evaporative emission inventory and for tightened regulatory standards.Implications: The COPERT and MOVES methodologies contain large uncertainties for estimating evaporative emissions, while our modeling method is developed based on chamber measurements to estimate evaporative emissions and can properly address those uncertainties. Modeling results suggested an urgent need to complete evaporative emissions inventories and also indicated that tightening evaporative emission standards is urgently needed, especially for warm areas.

ABSTRACTPollutant abatement systems are widely applied in the coal-fired power sector, and the energy consumption is considered an important part of the auxiliary power. An energy consumption analysis and assessment model of pollutant abatement systems in a power unit was developed based on the dynamic parameters and technology. The energy consumption of pollutant abatement systems in a 1000-MW coal-fired power unit that meets the ultra-low emission limits and the factors of operating parameters, including unit load and inlet concentration of pollutants, on the operating power were analyzed. The results show that the total power consumption of the pollutant abatement systems accounted for 1.27% of the gross power generation during the monitoring period. The wet flue gas desulfurization (WFGD) system consumed 67% of the rate, whereas the selective catalytic reduction (SCR) and electrostatic precipitator (ESP) systems consumed 8.9% and 24.1%, respectively. The power consumption rate of pollutant abatement systems decreased with the increase of unit load and increased with the increase of the inlet concentration of pollutants. The operation adjustment was also an effective method to increase the energy efficiency. For example, the operation adjustment of slurry circulation pumps could promote the energy-saving operation of the WFGD system.Implications: The application of pollutant abatement technologies increases the internal energy consumption of the power plant, which will lead to an increase of power generation costs. The real-time energy consumption of the different pollutant abatement systems in a typical power unit is analyzed based on the dynamic operating data. Further, the influence of different operating parameters on the operating power of the system and the possible energy-saving potential are analyzed.

The western United States has experienced increasing wildfire activities, which have negative effects on human health. Epidemiological studies on fine particulate matter (PM2.5) from wildfires are limited by the lack of accurate high‐resolution PM2.5exposure data over fire days. Satellite‐based aerosol optical depth (AOD) data can provide additional information in ground PM2.5concentrations and has been widely used in previous studies. However, the low background concentration, complex terrain, and large wildfire sources add to the challenge of estimating PM2.5concentrations in the western United States. In this study, we applied a Bayesian ensemble model that combined information from the 1 km resolution AOD products derived from the Multi‐angle Implementation of Atmospheric Correction (MAIAC) algorithm, Community Multiscale Air Quality (CMAQ) model simulations, and ground measurements to predict daily PM2.5concentrations over fire seasons (April to September) in Colorado for 2011–2014. Our model had a 10‐fold cross‐validated R2of 0.66 and root‐mean‐squared error of 2.00 μg/m3, outperformed the multistage model, especially on the fire days. Elevated PM2.5concentrations over large fire events were successfully captured. The modeling technique demonstrated in this study could support future short‐term and long‐term epidemiological studies of wildfire PM2.5. A Bayesian ensemble model was used to predict the daily PM2.5concentrations during fire seasons in ColoradoOur model successfully captured the PM2.5enhancements over large fire eventsThe data sets obtained in this study could support future epidemiological studies of wildfire PM2.5in Colorado

Wildfire emission inventories are usually applied with biome‐scale emission factors for atmospheric modeling. However, emission factors measured for different plant species vary substantially within the same biome. We apply the species‐specific emission factors and refine the Fire Emission Inventory‐northern Eurasia (FEI‐NE), and derive the wildfire black carbon emission inventory in northern Eurasia from 2002 to 2015. Our new inventory produces 61% more black carbon emissions than current estimates based on Global Fire Emission Database (GFED) and 33% less than FEI‐NE. Model simulations with different inventories are compared with ground‐based and satellite retrievals of aerosol absorption optical depth (AAOD). Compared with the Ozone Monitoring Instrument, the normalized root mean square deviation of AAOD over northern Eurasia is reduced from 1.0 under FEI‐NE to 0.95 through application of the new inventory. This study reveals the importance of applying sub‐biome‐scale emission factors for wildfire inventories development and revisiting emissions uncertainty in atmospheric modeling. A recent biomass burning emission inventory for black carbon in northern Eurasia was developed as FEI‐NE. We found that the emission factors applied for wildfires in northern Eurasia boreal forest and grassland were adopted from the prescribed fire measurements conducted in the United States for woody savannas and wetlands, respectively. By comparing with recently published local measurements in Siberia, we mapped the emission factors for plant species from the database published by U.S. Forest Service to biomass structures in northern Eurasia and rebuilt the FEI‐NE inventory as FEI‐NEsp. Modeling results obtained from GEOS‐Chem for 2011–2012 suggested that the simulation with FEI‐NEspagreed better with ground‐based and satellite observations than FEI‐NE and GFEDv4.1s. Emission factors indexed by plant species are used to replace biome‐scale factors to update fire emission inventory over northern EurasiaUpdated fire emission for black carbon over northern Eurasia is 33% lower than the original inventory FEI‐NE, but 61% higher than GFEDSimulation with the new inventory shows better reproduced ground‐based and satellite retrievals of aerosol absorption optical depth Emission factors indexed by plant species are used to replace biome‐scale factors to update fire emission inventory over northern Eurasia Updated fire emission for black carbon over northern Eurasia is 33% lower than the original inventory FEI‐NE, but 61% higher than GFED Simulation with the new inventory shows better reproduced ground‐based and satellite retrievals of aerosol absorption optical depth

The present study utilizes air quality modeling to probe the sources and characteristics of PM2.5(particles less than 2.5 micrometers in aerodynamic diameter) at the northern tip of Taiwan (CAFE station) in the early stage of the Asian haze period. Since CAFE is the first place that is influenced by the Asian haze coming from the north, this study focused on the wind field, PM2.5concentration, and PM2.5composition at CAFE. During the research period (Oct. 16, 2015, to Nov. 15, 2015), four PM2.5episodes occurred at CAFE. This study classified these four episodes into three types, according to their PM2.5sources: the long-range transport (LRT) type, the local pollution (LP) type, and the LRT/LP mix type. For the LRT type, Asian outflows prevailed in a north to northeast wind at the north of Taiwan. The proportion of NO3−in the PM2.5resolvable compositions was very small at CAFE due to evaporation during transport, whereas the relative proportion of sea salt increased due to strong winds. For the LP type, an east wind prevailed and formed a cyclone/lee vortex in northwest Taiwan. Although the background PM2.5concentrations were low (4–20 µg m−3), the cyclone transported local anthropogenic emissions northward and elevated the PM2.5levels at CAFE. For the LRT/LP mix type, an east wind also prevailed, but the background PM2.5concentrations were at an intermediate level (20–30 µg m−3) because the Asian outflows had already transported haze to the West Pacific. The combined LRT and LP increased PM2.5at CAFE. In addition, the proportions of NO3−(nitrate) for the LP and LRT/LP episodes were obviously higher than those on the days before and after. This suggests a considerable contribution on PM2.5from LP.

The Community Multi-scale Air Quality Model (CMAQ) is used to simulate the long-range transport of biomass burning (BB) pollutants from Southeast Asia (SEA) towards the Taiwan Central Mountain Range (CMR) in March and April 2010. The results show that a proportion of the BB plume was blocked and compressed at the windward side of CMR. High-altitude BB plume is shown to influence air quality on the ground via three mechanisms: (1) the subsidence in the anticyclone, (2) the downward motion in the cold surge, and (3) the vertical mixing of the boundary layer over land. Two case studies are further investigated to probe the chemical evolution of the air parcel approaching Mt. Lulin. The first case shows that the third mechanism also explained the increase in the concentrations of peroxyacyl nitrate (PAN), higher peroxyacyl nitrate (PANX), NH3, SO2, and volatile organic compounds in the BB plume when entering the land over western Taiwan. Meanwhile, the percentage of NO3−in the plume is also significantly increased. The second case reveals that valley wind transported air pollutants from the ground to the mountains. The air parcel, accompanied with considerable concentrations of PAN, PANX, SULF, and anthropogenic secondary organic aerosol, moved up Mt. Lulin. The pollutant concentrations, except for elemental carbon, in the air parcel decreased on approach to Mt. Lulin because the air parcel was mixed with a clean air.

This study investigates the air pollutant interactions and emission source contributions to ozone (O3) formation within a complex terrain. Air quality simulations using the Community Multiscale Air Quality (CMAQ) Model focused on vertical distributions of O3for the July 14–18, 2005 episode in the South Coast Air Basin (SoCAB). The Zero-Out method was applied in sensitivity tests for seven emission source categories. Elevated O3concentrations were found near the top of the planetary boundary layer (PBL, ~1200 m) and in the free troposphere (~3500 m) over the eastern SoCAB. Low O3concentrations were found near the surface at the center of the basin due to nitrogen oxide (NO) titration by fresh vehicle exhaust. Sea and land breezes, enhanced by up-slope flows (the “mountain chimney effect”) transported O3upward. Formation of O3is sensitive to the H2O2/HNO3ratio, depending on fresh vs. aged pollutant mixtures. Biogenic emissions were important contributors to O3formation, both in the SoCAB and at the top of the PBL. In contrast, the highest vehicle contributions to O3were found far from urban areas and in the lower free troposphere. Vertical cross-sectional analysis provided some insights into the O3formation and mixing processes present in the SoCAB.

This paper evaluates the PM2.5- and ozone-related mortality at present (2000s) and in the future (2050s) over the continental United States by using the Environmental Benefits Mapping and Analysis Program (BenMAP-CE). Atmospheric chemical fields are simulated by WRF/CMAQ (horizontal resolution: 12 × 12km), applying the dynamical downscaling technique from global climate-chemistry model under the Representative Concentration Pathways scenario (RCP 8.5). Future air quality results predict that the annual mean PM2.5concentration in continental U.S. decreases nationwide, especially in the Eastern U.S. and west coast. However, the ozone concentration is projected to decrease in the Eastern U.S. but increase in the Western U.S. Future mortality is evaluated under two scenarios (1) holding future population and baseline incidence rate at the present level and (2) using the projected baseline incidence rate and population in 2050. For PM2.5, the entire continental U.S. presents a decreasing trend of PM2.5-related mortality by the 2050s in Scenario (1), primarily resulting from the emissions reduction. While in Scenario (2), almost half of the continental states show a rising tendency of PM2.5-related mortality, due to the dominant influence of population growth. In particular, the highest PM2.5-related deaths and the biggest discrepancy between present and future PM2.5-related deaths both occur in California in 2050s. For the ozone-related premature mortality, the simulation shows nation-wide rising tendency in 2050s under both scenarios, mainly due to the increase of ozone concentration and population in the future. Furthermore, the uncertainty analysis shows that the confidence interval of all causes mortality is much larger than that for specific causes, probably due to the accumulated uncertainty of generating datasets and sample size. The confidence interval of ozone-related all cause premature mortality is narrower than the PM2.5-related all cause mortality, due to its smaller standard deviation of the concentration-mortality response factor.Implications: The health impact of PM2.5is more linearly proportional to the emission reductions than ozone. The reduction of anthropogenic PM2.5precursor emissions is likely to lead to the decrease of PM2.5concentrations and PM2.5related mortality. However, the future ozone concentrations could increase due to increase of the greenhouse gas emissions of methane. Thus, to reduce the impact of ozone related mortality, anthropogenic emissions including criteria pollutant and greenhouse gas (i.e. methane) need to be controlled.

An air quality monitoring network consisting of 53 stations at 9 cities over the Yangtze River Delta (YRD) simultaneously measured gaseous and particulate pollutants (SO2, NO2,CO, O3, PM2.5and PM10) during the six-month Shanghai World Expo in 2010. The regional distribution of air pollutants showed that Shanghai was a low-SO2-PM10zone over the YRD during the Expo, owing to the effective controls that were applied to power plants, industrial activities and construction works. However, Shanghai also became a high-NOx-CO-O3zone in the YRD, partly due to the large number of vehicles in Shanghai, and also the expected increase in transportation emissions due to the tremendous number of visitors during the Expo. Monthly variations in the major pollutants generally presented similar patterns, with lower values in the middle of the Expo, i.e., from July to September, and higher values in May, June and October. The magnitudes of pollutant precursor (SO2and NO2) concentrations and meteorological conditions (e.g., wind speed, directional wind from X vector, and mixing layer height) were investigated, and found to play important roles in the monthly variations. Spatial correlations of air pollutants between Shanghai and the other 8 cities revealed the impact of regional transport on air quality in Shanghai, and vice versa. Intense air pollution episodes in Shanghai were mainly related to the regional/long-range transport from inland polluted regions. The high frequency of marine winds during the Expo had a positive effect on the air quality of coastal cities, while it had a negative effect on some inland cities in the YRD.

Heavy-duty diesel vehicles were measured for noise level, whole-body vibration from the driver's seat, and in-cab air quality while the vehicles were parked with engine idling at a rest area and while they were driven. These baseline data will help similar studies determine whether new truck designs have changed these conditions for drivers. Twenty-seven trucks (model years 2006 to 2008) from four manufacturers were tested. Results showed slightly higher noise levels driving on the Interstate versus driving on the state highway. However, overall in-cab noise levels were found to be lower than occupational exposure standards. Evaluation of seating vibration used ISO guidelines. Average vibrations in the x-, y-, and z-axes of the seats were generally found to be well below European Union standard exposures for an 8-h driving day. Inferior road pavement conditions were thought to have contributed to higher vibrations in a few trucks where several instances of the vibrations exceeded the standards. For most trucks, the likely comfort reaction from the vibration magnitude of the driver's seat was “a little uncomfortable.” Air quality was determined by measuring in-cab concentrations of carbon monoxide (CO), oxides of nitrogen (NOX), and particulate matter with less than 2.5 microns aerodynamic diameter (PM2.5). Results indicated a tendency of trucks to self-pollute the cabs during periods of extended parked idling. Although overall CO and NOXconcentrations were well below occupational exposure levels, PM2.5concentrations during several parked-idling scenarios were higher than U.S. Environmental Protection Agency limits for ambient monitoring standards. During driving on public roadways, in-cab concentrations were lower than those measured during the extended parked-idling conditions.

Low-income households face long-standing challenges of energy insecurity and inequality (EII). During extreme events (e.g., disasters and pandemics) these challenges are especially severe for vulnerable populations reliant on energy for health, education, and well-being. However, many EII studies rarely incorporate the micro- and macro-perspectives of resilience and reliability of energy and internet infrastructure and social-psychological factors. To remedy this gap, we first address the impacts of extreme events on EII among vulnerable populations. Second, we evaluate the driving factors of EII and how they change during disasters. Third, we situate these inequalities within broader energy systems and pinpoint the importance of equitable infrastructure systems by examining infrastructure reliability and resilience and the role of renewable technologies. Then, we consider the factors influencing energy consumption, such as energy practices, socio-psychological factors, and internet access. Finally, we propose interdisciplinary research methods to study these issues during extreme events and provide recommendations.

The University of Tennessee and Oak Ridge National Laboratory conducted a 5-month-long air monitoring study at the Watt Road interchange on I-40 in Knoxville, Tennessee, where 20,000 heavy-duty trucks travel the Interstate each day. In addition, three large truck stops are situated at this interchange, where as many as 400 trucks idle engines at night. As a result, high levels of PM2.5have been measured near the interchange, often exceeding National Ambient Air Quality Standards. This paper presents the results of the air monitoring study, illustrating average hourly patterns of PM2.5resulting from diesel truck emissions on the Interstate and at the truck stops. Most of the PM2.5concentrations detected occurred during the night, when the largest contribution of emissions was from idling trucks rather than trucks on the Interstate. A nearby background air monitoring site was used to identify the contribution of regional PM2.5emissions, which were also a significant factor in the concentrations measured at the site. The relative contributions of regional background, local truck idling, and trucks on the Interstate to local PM2.5concentrations are presented and discussed in the paper. The results indicate the potential significance of diesel truck idling emissions to the occurrence of hot spots of high PM2.5concentrations near large truck stops, ports, or border crossings. The significance of truck idling emissions is similar to the findings of other studies.

This study examines the advantages and disadvantages of basic, intermediate, and advanced methods for visitor use forecasting where seasonality and limited data are characteristics of the estimation problem. The monthly use rates at the Milwaukee County Zoo, Wisconsin are used to illustrate the seasonal time series techniques. Forecasting methods include the Naive 1, Naive 2, single moving average (SMA) with the classical decomposition procedure, single exponential smoothing (SES), double exponential smoothing (DES), Winter's, and the seasonal autoregressive integrated moving average (SARIMA). The variation in visitor rates over the years makes the visitation trend for the Milwaukee County Zoo appealing in this empirical application. The series ranges from January 1981 through December 1999, a total of 228 months. The last 12, 24, or 60 months of those data are excluded from the original analysis, and used to evaluate the various methods. SARIMA and SMA with the classical decomposition procedure are found to be roughly equivalent in performance, as judged by modified mean absolute percentage error (MAPE) and modified root mean square percentage error (RMSPE) values of a longer estimation period with shorter period ahead forecasts. This study also finds that the SMA with classical decomposition method is more accurate than other techniques when a shorter estimation period with longer period ahead forecasts are included. While this study may not speak to all users of leisure related data, it serves as a comparative reference for those who seek guidance in deciding among a set of forecasting tools.

Heavy-duty vehicle (HDV) classifications used for modeling emissions in the MOBILE6 model have been expanded from 2 classifications in MOBILE5 to 16 classifications in MOBILE6. The new classifications are based on vehicle weight and fuel used (i.e., gasoline or diesel). The heavier vehicles have higher emissions, so it is important to use correct vehicle weight distributions. Tennessee’s HDV registration data show a distribution very similar to the national defaults, but with more vehicles in the heaviest weight category (HDV8B). More than 50% of Tennessee’s HDVs fall in the lightest vehicle category (HDV2B). The biggest difference in truck characteristics in Tennessee versus national defaults in MOBILE6 is the higher HDV fraction on Tennessee rural Interstates. Also, the ratio of single-unit trucks to trailer trucks varies considerably by facility type. The emissions of volatile organic compounds and carbon monoxide per mile of travel of gasoline-fueled single-unit trucks can be 2.5 to 5 times higher than those of heavy-duty diesel trailer trucks. The emissions of nitrogen oxides per mile of travel of diesel-fueled tractor–trailer trucks can be five times higher than those of gasoline-fueled single-unit trucks. For these reasons it is important to accurately characterize the HDV fleet. The characteristics of the Tennessee HDV fleet are compared with national defaults used in MOBILE6, and a new scheme for classifying vehicles by road type is presented.