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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Characterizing particulate polycyclic aromatic hydrocarbon emissions from diesel vehicles using a portable emissions measurement system.

Particulate polycyclic aromatic hydrocarbons (p-PAHs) emitted from diesel vehicles are of concern because of their significant health impacts. Laboratory tests, road tunnel and roadside experiments have been conducted to measure p-PAH emissions. While providing valuable information, these methods have limited capabilities of characterizing p-PAH emissions either from individual vehicles or under real-world conditions. We employed a portable emissions measurement (PEMS) to measure real-world emission factors of priority p-PAHs for diesel vehicles representative of an array of emission control technologies. The results indicated over 80% reduction in p-PAH emission factors comparing the China V and China II emission standard groups (113 µg kg-1 vs. 733 µg kg-1). The toxicity abatement in terms of Benzo[a]pyrene equivalent emissions was substantial because of the large reductions in highly toxic components. By assessing real traffic conditions, the p-PAH emission factors on freeways were lower than on local roads by 52% ± 24%. A significant correlation (R2~0.85) between the p-PAH and black carbon emissions was identified with a mass ratio of approximately 1/2000. A literature review indicated that diesel p-PAH emission factors varied widely by engine technology, measurement methods and conditions, and the molecular diagnostic ratio method for source apportionment should be used with great caution.

Characteristics of black carbon emissions from in-use light-duty passenger vehicles.

Mitigating black carbon (BC) emissions from various combustion sources has been considered an urgent policy issue to address the challenges of climate change, air pollution and health risks. Vehicles contribute considerably to total anthropogenic BC emissions and urban BC concentrations. Compared with heavy-duty diesel vehicles, there is much larger uncertainty in BC emission factors for light-duty passenger vehicles (LDPVs), in particular for gasoline LDPVs, which warrants further studies. In this study, we employed the dynamometer and the Aethalometer (AE-51) to measure second-by-second BC emissions from eight LDPVs by engine technology and driving cycle. The average BC emission factors under transient cycles (e.g., ECE-15, New European Driving Cycle, NEDC, Worldwide Harmonized Light Vehicles Test Cycle, WLTC) are 3.6-91.5 mg/km, 7.6 mg/km and 0.13-0.58 mg/km, respectively, for diesel (N = 3), gasoline direct injection (GDI) (N = 1) and gasoline port-fuel injection (PFI) engine categories (N = 4). For gasoline PFI LDPVs, the instantaneous emission profiles show a strong association of peak BC emissions with cold-start and high-speed aggressive driving. Such impacts lead to considerable BC emission contributions in cold-start periods (e.g., the first 47 s-94 s) over the entire cycle (e.g., 18-76% of the NEDC and 13-36% of the WLTC) and increased BC emission factors by 80-440% under the WLTC compared to the NEDC. For diesel BC emissions, the size distribution exhibits a typical unimodal pattern with one single peak appearing approximately from 120 to 150 nm, which is largely consistent with previous studies. Nevertheless, the average mass ratios of BC to particle mass (PM) range from 0.38 to 0.54 for three diesel samples, representing substantial impacts from both driving and engine conditions. The significant discrepancy between gasoline BC emission factors obtained from tailpipe exhaust versus ambient conditions suggest that more comparative measurements and fine-grained simulations should be designed and implemented to address this discrepancy.

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.

On-road vehicle emissions and their control in China: A review and outlook.

The large (26-fold over the past 25years) increase in the on-road vehicle fleet in China has raised sustainability concerns regarding air pollution prevention, energy conservation, and climate change mitigation. China has established integrated emission control policies and measures since the 1990s, including implementation of emission standards for new vehicles, inspection and maintenance programs for in-use vehicles, improvement in fuel quality, promotion of sustainable transportation and alternative fuel vehicles, and traffic management programs. As a result, emissions of major air pollutants from on-road vehicles in China have peaked and are now declining despite increasing vehicle population. As might be expected, progress in addressing vehicle emissions has not always been smooth and challenges such as the lack of low sulfur fuels, frauds over production conformity and in-use inspection tests, and unreliable retrofit programs have been encountered. Considering the high emission density from vehicles in East China, enhanced vehicle, fuel and transportation strategies will be required to address vehicle emissions in China. We project the total vehicle population in China to reach 400-500 million by 2030. Serious air pollution problems in many cities of China, in particular high ambient PM2.5 concentration, have led to pressure to accelerate the progress on vehicle emission reduction. A notable example is the draft China 6 emission standard released in May 2016, which contains more stringent emission limits than those in the Euro 6 regulations, and adds a real world emission testing protocol and a 48-h evaporation testing procedure including diurnal and hot soak emissions. A scenario (PC[1]) considered in this study suggests that increasingly stringent standards for vehicle emissions could mitigate total vehicle emissions of HC, CO, NOX and PM2.5 in 2030 by approximately 39%, 57%, 59% and 79%, respectively, compared with 2013 levels. With additional actions to control the future light-duty passenger vehicle population growth and use, and introduce alternative fuels and new energy vehicles, the China total vehicle emissions of HC, CO, NOX and PM2.5 in 2030 could be reduced by approximately 57%, 71%, 67% and 84%, respectively, (the PC[2] scenario) relative to 2013. This paper provides detailed policy roadmaps and technical options related to these future emission reductions for governmental stakeholders.

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.

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.