Economic and Climate Benefits of Electric Vehicles in China, the United States, and Germany.

Mass adoption of electric vehicles (EVs) is widely viewed as essential to address climate change and requires a compelling case for ownership worldwide. While the manufacturing costs and technical capabilities of EVs are similar across regions, customer needs and economic contexts vary widely. Assessments of the all-electric-range required to cover day-to-day driving demand, and the climate and economic benefits of EVs, need to account for differences in regional characteristics and individual travel patterns. To meet this need travel profiles for 1681 light-duty passenger vehicles in China, the U.S., and Germany were used to make the first consistent multiregional comparison of customer and greenhouse gas (GHG) emission benefits of EVs. We show that despite differences in fuel prices, driving patterns, and subsidies, the economic benefits/challenges of EVs are generally similar across regions. Individuals who are economically most likely to adopt EVs have GHG benefits that are substantially greater than for average drivers. Such "priority" EV customers have large (32%-63%) reductions in cradle-to-grave GHG emissions. It is shown that low battery costs (below approximately $100/kWh) and a portfolio of EV offerings are required for mass adoption of electric vehicles.

On-Road Chemical Transformation as an Important Mechanism of NO2 Formation.

Nitrogen dioxide (NO2) not only is linked to adverse effects on the respiratory system but also contributes to the formation of ground-level ozone (O3) and fine particulate matter (PM2.5). Our curbside monitoring data analysis in Detroit, MI, and Atlanta, GA, strongly suggests that a large fraction of NO2 is produced during the "tailpipe-to-road" stage. To substantiate this finding, we designed and carried out a field campaign to measure the same exhaust plumes at the tailpipe-level by a portable emissions measurement system (PEMS) and at the on-road level by an electric vehicle-based mobile platform. Furthermore, we employed a turbulent reacting flow model, CTAG, to simulate the on-road chemistry behind a single vehicle. We found that a three-reaction (NO-NO2-O3) system can largely capture the rapid NO to NO2 conversion (with time scale ≈ seconds) observed in the field studies. To distinguish the contributions from different mechanisms to near-road NO2, we clearly defined a set of NO2/NO x ratios at different plume evolution stages, namely tailpipe, on-road, curbside, near-road, and ambient background. Our findings from curbside monitoring, on-road experiments, and simulations imply the on-road oxidation of NO by ambient O3 is a significant, but so far ignored, contributor to curbside and near-road NO2.

A cost-benefit analysis of a pellet boiler with electrostatic precipitator versus conventional biomass technology: A case study of an institutional boiler in Syracuse, New York.

BACKGROUND: Biomass facilities have received increasing attention as a strategy to increase the use of renewable fuels and decrease greenhouse gas emissions from the electric generation and heating sectors, but these facilities can potentially increase local air pollution and associated health effects. Comparing the economic costs and public health benefits of alternative biomass fuel, heating technology, and pollution control technology options provides decision-makers with the necessary information to make optimal choices in a given location. METHODS: For a case study of a combined heat and power biomass facility in Syracuse, New York, we used stack testing to estimate emissions of fine particulate matter (PM2.5) for both the deployed technology (staged combustion pellet boiler with an electrostatic precipitator) and a conventional alternative (wood chip stoker boiler with a multicyclone). We used the atmospheric dispersion model AERMOD to calculate the contribution of either fuel-technology configuration to ambient primary PM2.5 in a 10km×10km region surrounding the facility, and we quantified the incremental contribution to population mortality and morbidity. We assigned economic values to health outcomes and compared the health benefits of the lower-emitting technology with the incremental costs. RESULTS: In total, the incremental annualized cost of the lower-emitting pellet boiler was $190,000 greater, driven by a greater cost of the pellet fuel and pollution control technology, offset in part by reduced fuel storage costs. PM2.5 emissions were a factor of 23 lower with the pellet boiler with electrostatic precipitator, with corresponding differences in contributions to ambient primary PM2.5 concentrations. The monetary value of the public health benefits of selecting the pellet-fired boiler technology with electrostatic precipitator was $1.7 million annually, greatly exceeding the differential costs even when accounting for uncertainties. Our analyses also showed complex spatial patterns of health benefits given non-uniform age distributions and air pollution levels. CONCLUSIONS: The incremental investment in a lower-emitting staged combustion pellet boiler with an electrostatic precipitator was well justified by the population health improvements over the conventional wood chip technology with a multicyclone, even given the focus on only primary PM2.5 within a small spatial domain. Our analytical framework could be generalized to other settings to inform optimal strategies for proposed new facilities or populations.

Demand response, behind-the-meter generation and air quality.

We investigated the implications of behind-the-meter (BTM) generation participating in demand response (DR) programs. Specifically, we evaluated the impacts of NOx emissions from BTM generators enrolled in the New York Independent System Operator (NYISO)'s reliability-based DR programs. Through analyzing the DR program enrollment data, DR event records, ozone air quality monitoring data, and emission characteristics of the generators, we found that the emissions from BTM generators very likely contribute to exceedingly high ozone concentrations in the Northeast Corridor region, and very likely account for a substantial fraction of total NOx emissions from electricity generation. In addition, a companion study showed that the emissions from BTM generators could also form near-source particulate matter (PM) hotspots. The important policy implications are that the absence of up-to-date regulations on BTM generators may offset the current efforts to reduce the emissions from peaking power plants, and that there is a need to quantify the environmental impacts of DR programs in designing sound policies related to demand-side resources. Furthermore, we proposed the concept of "Green" DR resources, referring to those that not only provide power systems reliability services, but also have verifiable environmental benefits or minimal negative environmental impacts. We argue that Green DR resources that are able to maintain resource adequacy and reduce emissions at the same time are key to achieving the cobenefits of power system reliability and protecting public health during periods with peak electricity demand.

Near-road modeling and measurement of cerium-containing particles generated by nanoparticle diesel fuel additive use.

Cerium oxide nanoparticles (nCe) are used as a fuel-borne catalyst in diesel engines to reduce particulate emissions, yet the environmental and human health impacts of the exhaust particles are not well understood. To bridge the gap between emission measurements and ambient impacts, size-resolved measurements of particle composition and mass concentration have been performed in Newcastle-upon-Tyne, United Kingdom, where buses have used an nCe additive since 2005. These observations show that the noncrustal cerium fraction thought to be associated with the use of nCe has a mass concentration ∼ 0.3 ng m(-3) with a size distribution peaking at 100-320 nm in aerodynamic diameter. Simulations with a near-roadway multicomponent sectional aerosol dynamic model predict that the use of nCe additives increases the number concentration of nuclei mode particles (<50 nm in diameter) while decreasing the total mass concentration. The near-road model predicts a downwind mass size distribution of cerium-containing particles peaking at 150 nm in aerodynamic diameter, a value similar to that measured for noncrustal cerium in Newcastle. This work shows that both the emission and atmospheric transformation of cerium-containing particles needs to be taken into account by regional modelers, exposure scientists, and policymakers when determining potential environmental and human health impacts.

Analyses of turbulent flow fields and aerosol dynamics of diesel engine exhaust inside two dilution sampling tunnels using the CTAG model.

Experimental results from laboratory emission testing have indicated that particulate emission measurements are sensitive to the dilution process of exhaust using fabricated dilution systems. In this paper, we first categorize the dilution parameters into two groups: (1) aerodynamics (e.g., mixing types, mixing enhancers, dilution ratios, residence time); and (2) mixture properties (e.g., temperature, relative humidity, particle size distributions of both raw exhaust and dilution gas). Then we employ the Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model to investigate the effects of those parameters on a set of particulate emission measurements comparing two dilution tunnels, i.e., a T-mixing lab dilution tunnel and a portable field dilution tunnel with a type of coaxial mixing. The turbulent flow fields and aerosol dynamics of particles are simulated inside two dilution tunnels. Particle size distributions under various dilution conditions predicted by CTAG are evaluated against the experimental data. It is found that in the area adjacent to the injection of exhaust, turbulence plays a crucial role in mixing the exhaust with the dilution air, and the strength of nucleation dominates the level of particle number concentrations. Further downstream, nucleation terminates and the growth of particles by condensation and coagulation continues. Sensitivity studies reveal that a potential unifying parameter for aerodynamics, i.e., the dilution rate of exhaust, plays an important role in new particle formation. The T-mixing lab tunnel tends to favor the nucleation due to a larger dilution rate of the exhaust than the coaxial mixing field tunnel. Our study indicates that numerical simulation tools can be potentially utilized to develop strategies to reduce the uncertainties associated with dilution samplings of emission sources.

Modeling spatial variations of black carbon particles in an urban highway-building environment.

Highway-building environments are prevalent in metropolitan areas. This paper presents our findings in investigating pollutant transport in a highway-building environment by combing field measurement and numerical simulations. We employ and improve the Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model to simulate the spatial variations of black carbon (BC) concentrations near highway I-87 and an urban school in the South Bronx, New York. The results of CTAG simulations are evaluated against and agree adequately with the measurements of wind speed, wind directions, and BC concentrations. Our analysis suggests that the BC concentration at the measurement point of the urban school could decrease by 43-54% if roadside buildings were absent. Furthermore, we characterize two generalized conditions in a highway-building environment, i.e., highway-building canyon and highway viaduct-building. The former refers to the canyon between solid highway embankment and roadside buildings, where the spatial profiles of BC depend on the equivalent canyon aspect ratio and flow recirculation. The latter refers to the area between a highway viaduct (i.e., elevated highway with open space underneath) and roadside buildings, where strong flow recirculation is absent and the spatial profiles of BC are determined by the relative heights of the highway and buildings. The two configurations may occur at different locations or in the same location with different wind directions when highway geometry is complex. Our study demonstrates the importance of incorporating highway-building interaction into the assessment of human exposure to near-road air pollution. It also calls for active roles of building and highway designs in mitigating near-road exposure of urban population.

Uncertainty investigation of plume-chasing method for measuring on-road NOx emission factors of heavy-duty diesel vehicles.

The plume-chasing method has shown great advantages in measuring on-road emission factors (EFs) compared with regulatory methods like dynamometer and portable emission measurement systems (PEMS). In this study, a new on-board measurement system incorporating ultrasonic anemometers and solid-state Lidar was developed to investigate the uncertainties of on-road emission factors measured by plume-chasing method due to variables such as on-road wind velocity, chasing speed, chasing distance, and turbulent kinetic energy (TKE). A series of PEMS-chasing experiments for heavy-duty diesel vehicles (HDDVs) were conducted on both highways and local roadways in Beijing, China. Our analysis demonstrated that the differences in EF estimations between concurrent plume-chasing and PEMS measurement decreased with increasing chasing speed as a result of greater vehicle-induced TKE in the wake between HDDV and the mobile platform, whereas the effect of chasing distance on EF estimations appeared insignificant within the tested distance range (12-22 m). In the case of strong crosswinds, overprediction of chasing-based EFs was observed due to convective plume mixing from surrounding vehicular sources. The findings of this study contribute greatly to interpret emission factors measured by the plume-chasing method, and also calls for a future study to develop real-time EF correction algorithms for large-scale mobile chasing measurements.

Characterizing spatial variations of city-wide elevated PM10 and PM2.5 concentrations using taxi-based mobile monitoring.

The spatial distribution of elevated particulate matter (PM) concentrations represents a public health concern due to its association with adverse health effects. In this study, a city-wide spatial variability of PM (PM10 and PM2.5) concentrations in Jinan, China is evaluated using a combination of measurements from 1700 fixed sites and taxi-based mobile monitoring (300 taxis recruited). The taxi fleet provides high spatial resolution and minimizes temporal sampling uncertainties that a single mobile platform cannot address. A big dataset of PM concentrations covering three land-use domains (roadway, community and open-field) and pollution episodes is derived from the taxi-based mobile monitoring (~3 × 107 pairs of PM10 and PM2.5). The ability of taxi-based mobile monitoring to characterize location-specific concentrations is assessed. We applied an "elevation ratio" to identify the elevated PM concentrations and quantified the ratios at 30-m road segments. Higher PM concentrations occurred during haze episode with lower elevation ratios in all land-use domains compares to non-haze episode. Different characteristics (distribution and range) of the elevation ratios are shown in different land-use domains which highlight the potential local emission hotspots and could have transformative implications for environmental management, thus, contribute to the effectiveness of pollution control strategy.

Ambient sampling of real-world residential wood combustion plumes.

Wood smoke contains large quantities of carbonaceous aerosols known to increase climate forcing and be detrimental to human health. This paper reports the findings from our ambient sampling of fresh residential wood combustion (RWC) plumes in two heating seasons (2015-2016, 2016-2017) in Upstate New York. An Aethalometer (AE33) and a pDR-1500 were employed to monitor residential wood smoke plumes in Ithaca, NY through a hybrid mobile-stationary method. Fresh wood smoke plumes were captured and characterized at 13 different RWC sources in the city, all without significant influence from other combustion sources or atmospheric aging. Wood smoke absorption Ångström exponent (AAE) was estimated using both a one-component model, AAEWB, and a two-component model, AAEBrC (assuming AAEBC = 1.0). Consistent with the recent laboratory studies, our results show that AAEs were highly variable for residential wood smoke for the same source and across different sources, with AAEWB values ranging from 1.3 to 5.0 and AAEBrC values ranging from 2.2 to 7.4. This finding challenges the use of using a single AAE wood smoke value within the range of 1 to 2.5 for source apportionment studies. Furthermore, the PM2.5/BC ratio measured using optical instruments was demonstrated to be potentially useful to characterize burning conditions. Different wood smoke sources can be distinguished by their PM2.5/BC ratio, which range between 15 and 150. This shows promise as an in-situ, cost-effective, ambient sampling-based method to characterize wood burning conditions.Implications: There are two main implications from this paper. First, the large variability in wood smoke absorption Ångström exponent (AAE) values revealed from our real-world, ambient sampling of residential wood combustion plumes indicated that it is not appropriate to use a single AAE wood smoke value for source apportionment studies. Second, the PM2.5/BC ratio has been shown to serve as a promising in-situ, cost-effective, ambient sampling-based indicator to characterize wood burning conditions. This has the potential to greatly reduce the costs of insitu wood smoke surveillance.

Designing roadside green infrastructure to mitigate traffic-related air pollution using machine learning.

Communities located in near-road environments are exposed to traffic-related air pollution (TRAP), causing adverse health effects. While roadside vegetation barriers can help mitigate TRAP, their effectiveness to reduce TRAP is influenced by site-specific conditions. To test vegetation designs using direct field measurements or high-fidelity numerical simulations is often infeasible since urban planners and local communities often lack the access and expertise to use those tools. There is a need for a fast, reliable, and easy-to-use method to evaluate vegetation barrier designs based on their capacity to mitigate TRAP. In this paper, we investigated five machine learning (ML) methods, including linear regression (LR), support vector machine (SVM), random forest (RF), XGBoost (XGB), and neural networks (NN), to predict size-resolved and locationally dependent particle concentrations downwind of various vegetation barrier designs. Data from 83 computational fluid dynamics (CFD) simulations was used to train and test the ML models. We developed downwind region-specific models to capture the complexity of this problem and enhance the overall accuracy. Our feature space was composed of variables that can be feasibly obtained such as vegetation width, height, leaf area index (LAI), particle size, leaf area density (LAD) and wind speed at different heights. RF, NN, and XGB performed well with a normalized root mean square error (NRMSE) of 6-7% and an average test R2 value >0.91, while SVM and LR had an NRMSE of approximately 13% and an average test R2 value of 0.56. Using feature selection, vegetation dimensions and particle size had the highest influence in predicting pollutant concentrations. The ML models developed can help create tools to aid local communities in developing mitigation strategies to address TRAP problems.

[Status and distribution of atmospheric ultrafine particles during Olympics Games in Beijing].

OBJECTIVE: To understand the characteristics of size and concentration of the ultrafine particles (UFPs) during the 2008 Olympics in Beijing, as well as the influencing factors for number concentration of UFPs. METHODS: Ultrafine particle monitor model 3031, manufactured by USA TSI Co., was used to monitor the daily number concentration of 20-500 nm particles, and descriptive analysis was conducted based on the data collected. RESULTS: During the Olympics, the mean value of number concentration for 20-100 nm particulate matter is 5 920.43 particles/ cm3. Particles ranging 30-50 nm account for the largest portion(23.49%) among particles of all sizes. There are two peak values on the curve of 24 hours' number concentration for 20-100 nm particles, respectively, at noon and evening. CONCLUSION: This paper indicates that the number concentration of ultrafine particles registered considerable reduction in the 2008's Olympics compared to the pre-Olympics years. And observation of the curves indicated that the number concentrations of the UFPs with bigger size are related to traffic volume, whereas for smaller size UFPs, illumination may be a more important influencing factor. Also there are significant correlations between number concentration and temperature as well as relative humidity.

Enhancing the local air quality benefits of roadside green infrastructure using low-cost, impermeable, solid structures (LISS).

Communities located in near-road environments face adverse health effects due to elevated exposures to traffic-related air pollution (TRAP). While the use of a combination of solid structures (i.e. sound walls) and vegetation barriers can be an effective TRAP mitigation tool, installing these barriers can also present challenges to local communities. Sound walls are costly, and building these structures often requires the involvement of federal, state, and local permitting agencies. In this paper, we proposed that the use of low-cost, impermeable, solid structures (LISS), e.g., an impermeable thin wooden, plastic or metal fence, combined with vegetation can provide an effective option for local communities to improve near-road air quality due to lower costs and easier implementation. We conducted Large Eddy Simulations (LES) for different design scenarios of LISS and vegetation barriers under various conditions. Our results indicate that (i) combining LISS and vegetation is more effective than either alone, (ii) combining a less dense vegetation and LISS can be as effective as a dense vegetation barrier, (iii) In certain scenarios, depending on wind speed and particle size, vegetation barriers alone might lead to elevated pollutant concentrations; however, combining LISS with vegetation can mitigate those negative impacts, (iv) placing LISS closer to the freeway and in front of the vegetation barrier enhances vertical dispersion of pollutants, and (v) increasing LISS height promotes pollutant concentration reduction. These design recommendations can be used by urban planners, developers, and local community leaders to evaluate and implement green infrastructure to mitigate TRAP.

Four-Month Changes in Air Quality during and after the COVID-19 Lockdown in Six Megacities in China.

The pandemic of coronavirus disease 2019 (COVID-19) resulted in a stringent lockdown in China to reduce the infection rate. We adopted a machine learning technique to analyze the air quality impacts of the COVID-19 lockdown from January to April 2020 for six megacities with different lockdown durations. Compared with the scenario without lockdowns, we estimated that the lockdown reduced ambient NO2 concentrations by 36-53% during the most restrictive periods, which involved Level-1 public health emergency response control actions. Several cities lifted the Level-1 control actions during February and March, and the avoided NO2 concentrations subsequently dropped below 10% in late April. Traffic analysis during the same periods in Beijing and Chengdu confirmed that traffic emission changes were a major factor in the substantial NO2 reduction, but they were also associated with increased O3 concentrations. The lockdown also reduced PM2.5 concentrations, although heavy pollution episodes occurred on certain days due to the enhanced formation of secondary aerosols in association with the increased atmospheric oxidizing capacity. We also observed that the changes in air pollution levels decreased as the lockdown was gradually eased in various cities.

Evaluating mobile monitoring of on-road emission factors by comparing concurrent PEMS measurements.

Many countries have adopted portable emissions measurement system (PEMS) testing in their latest regulations to measure real-world vehicular emissions. However, its fleetwide implementation is severely limited by the high equipment costs and lengthy setup procedures, posing a need to develop more cost-effective, efficient emission measurement methods, such as mobile chasing tests. We conducted conjoint PEMS-chasing experiments for twelve heavy-duty diesel vehicles (HDDTs) to evaluate the accuracy of mobile measurement results. Two data processing approaches were integrated to automate the calculations of fuel consumption-based emission factors of nitrogen oxides (NOX). With a total of 245 plume chasing tests conducted, and then averaged by vehicle and road types, we found that the relative errors of vehicle-specific emission factors using an algorithm developed for this project were within approximately ±20% of the PEMS results for all tested vehicles. Stochastic simulations suggested reasonable results could be obtained using fewer chasing tests per vehicle (e.g., 71% for freeways and 94% for local road, equivalent to two chase tests per vehicle). This study improves the understanding of the accuracy of the mobile chasing method, and provides a practical approach for real-time emission measurements for future scaled-up mobile chasing studies.

Near-source air quality impact of a distributed natural gas combined heat and power facility.

The wide adoption of combined heat and power (CHP) can not only improve energy efficiency, but also strengthens energy system resiliency. While CHP reduces overall emissions compared to generating the same amount of electricity and heat separately, its on-site nature also means that CHP facilities operate in populated areas, raising concerns over their near-source air quality impact. Evaluation of the near-source impact of distributed CHP is limited by emission data availability, especially in terms of particulate matter (PM). In this paper, we report on stack emission testing results of a community-scale CHP plant with two natural gas turbine units (15 MW each) from measurements conducted in both 2010 and 2015, and assess the near-source air quality impact using an integrated modeling framework using the stack test results, site-specific meteorological data and terrain profiles with buildings. The NOx removal efficiency by selective catalytic reduction (SCR) is estimated to be ∼83% according to the emission testing. The integrated framework employs AERMOD to screen air quality in a 2.7  km × 2.3  km domain from 2011 to 2015 to identify the highest ground-level concentrations (GLCs). Examining the corresponding meteorological conditions, we find that those high GLCs appeared during the stable atmospheric boundary layer with relative high wind speed. Next, the worse-case scenarios identified from the screening process are simulated using the detailed Unsteady Reynolds Averaged Navier-Stokes (URANS) model coupled with a chemistry solver. The results generally show low GLCs of primary PM2.5 for this case study. However, our analysis also suggests greater building downwash impacts with the presence of taller and denser urban structures. Therefore, the near-source impact of natural gas-fired CHP in large metropolitan areas is worthy of further investigation.

The effects of roadside vegetation characteristics on local, near-road air quality.

Roadside vegetation has been shown to impact downwind, near-road air quality, with some studies identifying reductions in air pollution concentrations and others indicating increases in pollutant levels when vegetation is present. These widely contradictory results have resulted in confusion regarding the capability of vegetative barriers to mitigate near-road air pollution, which numerous studies have associated with significant adverse human health effects. Roadside vegetation studies have investigated the impact of many different types and conditions of vegetation barriers and urban forests, including preserved, existing vegetation stands usually consisting of mixtures of trees and shrubs or plantings of individual trees. A study was conducted along a highway with differing vegetation characteristics to identify if and how the changing characteristics affected downwind air quality. The results indicated that roadside vegetation needed to be of sufficient height, thickness, and coverage to achieve downwind air pollutant reductions. A vegetation stand which was highly porous and contained large gaps within the stand structure had increased downwind pollutant concentrations. These field study results were consistent with other studies that the roadside vegetation could lead to reductions in average, downwind pollutant concentrations by as much as 50% when this vegetation was thick with no gaps or openings. However, the presence of highly porous vegetation with gaps resulted in similar or sometimes higher concentrations than measured in a clearing with no vegetation. The combination of air quality and meteorological measurements indicated that the vegetation affects downwind pollutant concentrations through attenuation of meteorological and vehicle-induced turbulence as air passes through the vegetation, enhanced mixing as portions of the traffic pollution plume are blocked and forced over the vegetation, and through particulate deposition onto leaf and branch surfaces. Computational fluid dynamic modeling highlighted that density of the vegetation barrier affects pollutant levels, with a leaf area density of 3.0 m2 m-3 or higher needed to ensure downwind pollutant reductions for airborne particulate matter. These results show that roadside bushes and trees can be preserved or planted along highways and other localized pollution sources to mitigate air quality and human health impacts near the source if the planting adheres to important characteristics of height, thickness, and density with full coverage from the ground to the top of the canopy. The results also highlight the importance of planting denser vegetation and maintaining the integrity and structure of these vegetation barriers to achieve pollution reductions and not contribute to unintended increases in downwind air pollutant concentrations.

Considerations for evaluating green infrastructure impacts in microscale and macroscale air pollution dispersion models.

Green infrastructure (GI) in urban areas may be adopted as a passive control system to reduce air pollutant concentrations. However, current dispersion models offer limited modelling options to evaluate its impact on ambient pollutant concentrations. The scope of this review revolves around the following question: how can GI be considered in readily available dispersion models to allow evaluation of its impacts on pollutant concentrations and health risk assessment? We examined the published literature on the parameterisation of deposition velocities and datasets for both particulate matter and gaseous pollutants that are required for deposition schemes. We evaluated the limitations of different air pollution dispersion models at two spatial scales - microscale (i.e. 10-500 m) and macroscale (i.e. 5-100 km) - in considering the effects of GI on air pollutant concentrations and exposure alteration. We conclude that the deposition schemes that represent GI impacts in detail are complex, resource-intensive, and involve an abundant volume of input data. An appropriate handling of GI characteristics (such as aerodynamic effect, deposition of air pollutants and surface roughness) in dispersion models is necessary for understanding the mechanism of air pollutant concentrations simulation in presence of GI at different spatial scales. The impacts of GI on air pollutant concentrations and health risk assessment (e.g., mortality, morbidity) are partly explored. The i-Tree tool with the BenMap model has been used to estimate the health outcomes of annually-averaged air pollutant removed by deposition over GI canopies at the macroscale. However, studies relating air pollution health risk assessments due to GI-related changes in short-term exposure, via pollutant concentrations redistribution at the microscale and enhanced atmospheric pollutant dilution by increased surface roughness at the macroscale, along with deposition, are rare. Suitable treatments of all physical and chemical processes in coupled dispersion-deposition models and assessments against real-world scenarios are vital for health risk assessments.

Fine-grained vehicle emission management using intelligent transportation system data.

The increasing adoption of intelligent transportation system (ITS) data in smart-city initiatives worldwide has offered unprecedented opportunities for improving transportation air quality management. In this paper, we demonstrate the effective use of ITS and other traffic data to develop a link-level and hourly-based dynamic vehicle emission inventory. Our work takes advantage of the extensive ITS infrastructure deployed in Nanjing, China (6600 km2) that offers high-resolution, multi-source traffic data of the road network. Improved than conventional emission inventories, the ITS data empower the strength of revealing significantly temporal and spatial heterogeneity of traffic dynamics that pronouncedly impacts traffic emission patterns. Four urban districts account for only 4% of the area but approximately 30%-40% of vehicular emissions (e.g., CO2 and air pollutants). Owing to the detailed resolution of road network traffic, two types of emission hotspots are captured by the dynamic emission inventory: those in the urban area dominated by urban passenger traffic, and those along outlying highway corridors reflecting inter-city freight transportation (especially in terms of NOX). Fine-grained quantification of emissions reductions from traffic restriction scenarios is explored. ITS data-driven emission management systems coupled with atmospheric models offer the potential for dynamic air quality management in the future.

Microenvironmental air quality impact of a commercial-scale biomass heating system.

Initiatives to displace petroleum and climate change mitigation have driven a recent increase in space heating with biomass combustion. However, there is ample evidence that biomass combustion emits significant quantities of health damaging pollutants. We investigated the near-source micro-environmental air quality impact of a biomass-fueled combined heat and power system equipped with an electrostatic precipitator (ESP) in Syracuse, NY. Two rooftop sampling stations with PM2.5 and CO2 analyzers were established in such that one could capture the plume while the other one served as the background for comparison depending on the wind direction. Four sonic anemometers were deployed around the stack to quantify spatially and temporally resolved local wind patterns. Fuel-based emission factors were derived based on near-source measurement. The Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model was then applied to simulate the spatial variations of primary PM2.5 without ESP. Our analysis shows that the absence of ESP could lead to an almost 7 times increase in near-source primary PM2.5 concentrations with a maximum concentration above 100 µg m-3 at the building rooftop. The above-ground "hotspots" would pose potential health risks to building occupants since particles could penetrate indoors via infiltration, natural ventilation, and fresh air intakes on the rooftop of multiple buildings. Our results demonstrated the importance of emission control for biomass combustion systems in urban area, and the need to take above-ground pollutant "hotspots" into account when permitting distributed generation. The effects of ambient wind speed and stack temperature, the suitability of airport meteorological data on micro-environmental air quality were explored, and the implications on mitigating near-source air pollution were discussed.